Climate Observations

I Moved

2011-03-29T05:47:00.001-04:00

I moved my website

Bob Tisdale

Click the link above.

Please update your favorites and blogrolls.

See ya there.

WHAT HAS BLOGGER DONE?

2011-03-28T20:02:00.003-04:00

UPDATE

Blogger has repaired the problems I was seeing, but I had been wanting to switch over to WordPress and the temporary problems provided the prompt.

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I’m not sure what blogger has done. But I can no longer write a post in MS Word, copy and paste what I’ve written into blogger, and have blogger retain the format. I lose all line breaks, paragraphs, etc. Adding illustrations in the format I’ve always used has turned into a nightmare.

Hopefully, this is a temporary problem, because right now I’m not having fun. I try html paragraph breaks and line breaks, but blogger now double spaces everything.

ERRRRRRRR!!!!!

Miscellaneous Graphs

2011-03-26T11:31:00.007-04:00

I’ve moved to WordPress. This post can now be found at Miscellaneous Graphs
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This post is simply a place for me to post graphs that I refer to or link often, or foresee the need to in the future. This way I don’t have to go searching for them.

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Annual North Pacific SST Anomalies North of 20N (HADISST) Link: http://i55.tinypic.com/30svapu.jpg
That’s for the area of the North Pacific used in the PDO. Note the shift in the late 1980s. That should correspond to a shift in the North Pacific Sea Level Pressure. It also impacted Ocean Heat Content for that area, and was discussed in North Pacific Ocean Heat Content Shift In The Late 1980s. %%%%%%%%%%%%%%%%%%%%%%%%%%%
NINO3.4 SST Anomalies With Linear Trend (HADISST) Link: http://i56.tinypic.com/2ag0u2u.jpg
There’s basically no trend.
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NINO3.4 SST Anomalies Smoothed With a 121-Month Filter (HADISST) Link: http://i43.tinypic.com/33agh3c.jpg
Yes, there’s multidecadal variations to ENSO.

ARGO-Era NODC Ocean Heat Content Data (0-700 Meters) Through December 2010

2011-03-25T09:56:00.014-04:00

I’ve moved to WordPress. This post can now be found at ARGO-Era NODC Ocean Heat Content Data (0-700 Meters) Through December 2010

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NOTE: This post contains 5 .gif animations that total 10MB. Have patience. They may take a while to load.This post is a follow-up to the recent post October to December 2010 NODC Ocean Heat Content (0-700Meters) Update and Comments. I wanted to discuss the ARGO-based period separately.

For those new to ARGO, under the heading of “What is Argo?”, the University of California, San Diego Argo webpage describes Argo as a “global array of 3,000 free-drifting profiling floats that measures the temperature and salinity of the upper 2000 m of the ocean.” The UCSD Argo website provides much more information, including an argo.avi video.

Much of the data in this post is supplied by ARGO for the upper 700 meters.

THE ARGO ERA (2003 TO PRESENT)
The NOAA NCEP webapge that presents the Global Ocean Data Assimilation System (GODAS) Input data distributions (1979-present) (Plots) allows users to plot the number of Temperature profiles at different depths for the globe, or for the Atlantic, Indian, and Pacific Oceans. An example of Global data for depths of 250 to 500 meters is shown in Figure 1. According to it, ARGO floats have been in use since the early 1990s, but they had very limited use until the late 1990s. ARGO use began to rise then, and in 2003, ARGO-based temperature readings at depth became dominant. Based on that, I’ll use January 2003 as the start month for the “ARGO-era” in this post.

http://i56.tinypic.com/1448yo3.jpg
Figure 1

Note the significant drop in samples in 2010. I have not found an explanation for this.

The NCEP GODAS Input data (Plots) webpage also allows visitors to create maps of temperature profile locations. Animation 1 is a gif animation that shows the annual data locations from 1979 to 2004. The measurements made with Expendable Bathythermographs (XBTs) are shown in red (x), the moored buoys that are parts of the TAO/ TRITON (Pacific) and PIRATA (Atlantic) projects are shown in green (+), and the blue (o) are ARGO-based measurements. Note how sparse the data is in the Southern Hemisphere prior to the early 2000s, especially south of 30S.
http://i55.tinypic.com/14ikdxs.jpg
Animation 1

Unfortunately, GODAS switched map formats in 2005 and again in 2006, so an animation that included the three map formats would be difficult to watch. The format used in 2005 is unlike those in use before or after, so I’ve excluded it in both animations. Animation 2 shows the Monthly temperature profile locations from January 2006 to December 2010. Note the decline in sampling in 2009/10, especially in the Indian Ocean. Why? Dunno.
http://i55.tinypic.com/2copg03.jpg
Animation 2

ARGO-ERA TREND VERSUS GISS PROJECTION
In past posts, when I’ve compared the NODC Global Ocean Heat Content to GISS projections, I’ve used the rate of 0.98*10^22 Joules per year for the GISS projection. This value was based on Roger Pielke Sr’s February 2009 post Update On A Comparison Of Upper Ocean Heat Content Changes With The GISS Model Predictions. The recent RealClimate posts Updates to model-data comparisons and 2010 updates to model-data comparisons have presented the projections based on Gavin Schmidt extending a linear trend of the GISS Model-ER simulations past 2003. The linear trends in both graphs are approximately 0.7*10^22 Joules per year. I’ll use this value in the comparison, but first a few more notes.

Gavin writes in the 2009 post, “Unfortunately, I don’t have the post-2003 model output handy, but the comparison between the 3-monthly data (to the end of Sep) and annual data versus the model output is still useful,” and he continues, “I have linearly extended the ensemble mean model values for the post 2003 period (using a regression from 1993-2002) to get a rough sense of where those runs could have gone.”

The only paper that I’m aware of in which GISS presented their simulations of Ocean Heat Content was Hansen et al (2005) “Earth's energy imbalance: Confirmation and implications”. Science, 308, 1431-1435, doi:10.1126/science.1110252 (PDF). In it, they only presented their data from 1993 to 2003. Refer to their Figure 2 (not illustrated in this post).

For those who might be concerned that extending the linear trend does not represent the actual model simulations, refer to Page 8 of the .pdf file GISS ModelE: MAP Objectives and Results. The graph there presents two GISS OHC Model E simulations, one with the Russell Ocean model, the other with the HYCOM Ocean model. The simulations run to 2010 for both models. Do they extend further into the future? And for those who want to attempt to duplicate that comparison of the Model-ER and Model-EH versus the early NODC OHC data, the NODC OHC data (older version) was based on the 2005 Levitus paper “The Warming Of The World Ocean: 1955 to 2003” (Manuscript). Link for the 0 - 700 meters data.

Back to the comparison of the ARGO-era OHC data and the GISS Projection: The most recent version of the NODC OHC data is linked here for 0 - 700 meters. I’ve compared it for the period of 2003-2010 to the GISS projection in Figure 2. Note that I’ve shifted the data down so that it starts at zero in 2003. The GISS projection of 0.7*10^22 Joules per year dwarfs the linear trend of the ARGO-era NODC OHC data. No surprise there.
http://i53.tinypic.com/vh5gtd.jpg
Figure 2

NOTE ABOUT THE DATA
The remainder of the data in this post was downloaded from the KNMI Climate Explorer Monthly observations webpage. The NODC OHC data there is presented in Gigajoules per square meter (GJ/m^2), not the units (10^22 Joules) provided by NODC. That’s why the scale and trends in Figures 2 and 3 are different. The NODC also provides their OHC data on a quarterly basis, but KNMI presents it as monthly data, thus allowing for comparisons to other monthly datasets. This is why the OHC data appears in 3-month tiers in Figures 3, 4 and 5.

GLOBAL AND OCEAN BASIN TRENDS
Figure 3 shows the Global NODC OHC data for the period of January 2003 to December 2010. Comparing its linear trend (0.19 GJ/m^2 per Century) to the trend of the long-term data from 1955 to 2002 shown in Figure 4 (0.52 GJ/m^2 per Century), there has been a significant flattening of the Global OHC data in recent years. And this flattening was not anticipated by the GISS models, which show a continuous rise through 2010.
http://i52.tinypic.com/2nu4vnc.jpg
Figure 3
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http://i54.tinypic.com/sxhqio.jpg
Figure 4

Of course, the oceans are not warming uniformly. Refer to Figure 5. The trends for the North Pacific and the Southern Oceans are basically flat. The only two ocean basins with major increases in OHC during the ARGO era are the South Atlantic and the Indian Oceans, while the North Atlantic, Arctic, and South Pacific Oceans show significant declines in OHC.
http://i56.tinypic.com/28qvtqu.jpg
Figure 5

Note: The coordinates for the ocean basins are:
North Atlantic = 0-75N, 78W-10E
South Atlantic = 60S-0, 70W-20E
Indian = 60S-30N, 20E-120E
North Pacific = 0-65N, 120E-90W
South Pacific = 60S-0, 120E-70W
Arctic = 65N-90N
Southern = 90S-60S

ARGO-ERA CHANGES IN NODC OHC

Figure 6 is a map that displays the change in ARGO-era OHC, from 2003 to 2010. It was created by using 2003 as the base year for anomalies, and plotting the annual OHC values for 2010. Much of the cooling in the North Atlantic has taken place at mid and lower latitudes. In the South Pacific, there was also a decline in the lower latitudes, but there appears to also have been a drop there at higher latitudes along the Antarctic Circumpolar Current (ACC).
http://i51.tinypic.com/21crset.jpg
Figure 6

Animations 3, 4 and 5 present the ARGO-era OHC data, using 12-month averages. The first cells are the average OHC from January to December 2003. These are followed by cells that show the period of February 2003 to January 2004 and so on, until the final cell that captures the average OHC from January to December 2010. The 12-month average reduces the noise and any seasonal component in the data. I’ve also included a graph of NINO3.4 SST anomalies (smoothed with a 12-month filter, and centered on the 6th month) since the effects of ENSO dominate the OHC data. The NINO3.4 SST anomaly graph infills with time. Animation 3 presents global maps.
http://i54.tinypic.com/eu4pzq.jpg
Animation 3

Animation 4 is the North Pole stereographic view. Note the warming of the western tropical North Pacific during the 2007/08 La Niña. It’s tough to miss. There also appears to be a lagged decline in the North Atlantic OHC in response to the 2007/08 La Niña. Will we see a lagged increase there next year?
http://i53.tinypic.com/2mo8fuq.jpg
Animation 4

And Animation 5 is the South Pole stereographic view. Note the persistence of the warm and cool anomalies moving southward from the equatorial Pacific in waves, and also into the South Indian Ocean. I believe those would be classified as oceanic Rossby waves.
http://i54.tinypic.com/2v9rqy0.jpg
Animation 5

CLOSING
Watching the animations, it is very obvious that ENSO and the distribution of warm and cool waters caused by ENSO are major components of Global Ocean Heat Content. Refer to ENSO Dominates NODC Ocean Heat Content (0-700 Meters) Data for further discussion and illustrations. OHC studies such as Hansen et al (2005), however, do not include ENSO in their models. They assume that Anthropogenic Greenhouse Gases have a measurable impact on Ocean Heat Content. The impacts of the failure of GISS to include ENSO and other natural variables in their analysis was illustrated and discussed in detail in Why Are OHC Observations (0-700m) Diverging From GISS Projections?

Refer also to North Pacific Ocean Heat Content Shift In The Late 1980s and North Atlantic Ocean Heat Content (0-700 Meters) Is Governed By Natural Variables.

Mid-March 2011 SST Anomaly Update

2011-03-21T19:46:00.007-04:00

I’ve moved to WordPress. This post can now be found at Mid-March 2011 SST Anomaly Update

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NINO3.4NINO3.4 SST anomalies for the week centered on March 12, 2011 show that central equatorial Pacific SST anomalies have resumed their rise from La Niña maximum after a minor pause. They’re at approximately -0.8 deg C.
http://i54.tinypic.com/2s7apfq.jpg
NINO3.4 SST Anomalies - Short-Term

GLOBAL

Weekly Global SST anomalies have been stagnant for the past few weeks, but they should resume their rise shortly in response to the end of the peak ENSO season. They are presently at +0.105 deg C.
http://i52.tinypic.com/51si68.jpg
Global SST Anomalies - Short-Term

NOTE

This weekly Reynolds OI.v2 SST dataset begins in 1990. I’ve started the graphs in 2004 to make the variations visible.

SOURCEOI.v2 SST anomaly data is available through the NOAA NOMADS system:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite

October to December 2010 NODC Ocean Heat Content (0-700Meters) Update and Comments

2011-03-17T06:24:00.023-04:00

I’ve moved to WordPress. This post can now be found at October to December 2010 NODC Ocean Heat Content (0-700Meters) Update and Comments

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INTRODUCTION

The National Oceanographic Data Center’s Ocean Heat Content (OHC) data for the depths of 0-700 meters are available through the KNMI Climate Explorer Monthly observations webpage. The NODC OHC dataset is based on the Levitus et al (2009) paper “Global ocean heat content(1955-2008) in light of recent instrumentation problems”, Geophysical Research Letters. Refer to Manuscript. It was revised in 2010 as noted in the October 18, 2010 post Update And Changes To NODC Ocean Heat Content Data. As described in the NODC’s explanation of ocean heat content (OHC) data changes, the changes result from “data additions and data quality control,” from a switch in base climatology, and from revised Expendable Bathythermograph (XBT) bias calculations.

This update includes the data through the quarter of October to December 2010. There has been an upswing in the Indian Ocean OHC data. And in the tropical Pacific, there’s been a delayed response to ENSO or a downward shift. Other than those, there are no other major changes with the latest 3 months on which to report.

GLOBAL

The Global OHC data through December 2010 is shown in Figure 1. It continues to be remarkably flat, considering the rise that took place during the 1980s and 1990s.

http://i53.tinypic.com/jrsoc3.jpg
Figure 1

In an upcoming post, I’ll present only the post-2003 data, the era when ARGO floats dominated OHC data.

A CHANGE OF COORDINATES
I’ve changed the coordinates of the Indian Ocean and South Pacific data. The coordinates I was using for the Indian Ocean (60S-30N, 20E-145E) caused too much overlap with the North Pacific and Tropical Pacific data. So I’ve shifted the coordinates so that the Indian Ocean is now represented by 60S-30N, 20E-120E. This required that I shift the South Pacific; it’s coordinates are now 60S-0, 120E-90W.

TROPICAL PACIFIC
Figure 2 illustrates the Tropical Pacific OHC data (24S-24N, 120E-90W). The major variations in tropical Pacific OHC are related to the El Niño-Southern Oscillation (ENSO). Tropical Pacific OHC drops during El Niño events and rises during La Niña events.
http://i54.tinypic.com/2vrxw1i.jpg
Figure 2

At least it should. Figure 3 compares tropical Pacific OHC to NINO3.4 SST anomalies (a commonly used ENSO proxy) where the NINO3.4 SST anomalies have been scaled and inverted (multiplied by a scaling factor of -0.15) to help show the relationship. The drop in the tropical Pacific OHC during 2010 is unusual. It should be rising (recharging) during this period. It’s impossible to tell at this time if this is a delayed response or a downward shift.
http://i52.tinypic.com/24n2m2r.jpg
Figure 3

The equatorial Pacific, on the other hand, Figure 4, is responding as one would expect.
http://i53.tinypic.com/f51snm.jpg
Figure 4

We’ll have to keep an eye on the tropical Pacific OHC data.

INDIAN OCEAN
Figure 5 illustrates the Indian Ocean OHC data. Note the sudden upswing since 2006. It’s odd when we consider the trends for most of the other ocean basins since 2003 are flat or negative. (I’ll illustrate this in an upcoming post.)
http://i54.tinypic.com/1fwilz.jpg
Figure 5

The Tropical Pacific OHC dropped and the Indian Ocean OHC rose; one might think warm water has migrated from the Tropical Pacific to the Tropical Indian Ocean. If we combine the Tropical Indian and Pacific subsets and compare it to the Tropical Pacific, Figure 6, we can see the two datasets mimic one another and that the recent drop is suppressed. It’s possible (and likely) there has been some migration of warm water from one subset to the other (likely because the current known as the Indonesian Throughflow does flow between the tropical Pacific and Indian Oceans).
http://i52.tinypic.com/1vj2f.jpg
Figure 6

In fact, this transport appears to take place in the animation of NODC OHC from 2005 to 2010, Animation 1, which was taken from the video that's included in the post The Electric Kool-Aid Ocean Heat Content Animation.
http://i53.tinypic.com/5dvryd.jpg
Animation 1

And here’s the YouTube video from that post. (The animation with music starts around the 2 minute mark, so check your volume setting if you’re at work.)

YouTube Link:http://www.youtube.com/watch?v=PUONorBCcxU

But the recent rise in Indian Ocean OHC is not limited to the tropics. Figure 7 compares Indian Ocean OHC to the OHC of the Indian Ocean South of 24S. The OHC of the mid-to-high latitudes also has the sudden surge.
http://i56.tinypic.com/9693zl.jpg
Figure 7

And yes, that rise and fall in the OHC of the Indian Ocean South of 24S during the late 1990s does look odd. In fact, if we smooth those two datasets, Figure 8, we can see how unusual that spike appears.
http://i53.tinypic.com/2pqkrpv.jpg
Figure 8

THE HEMISPHERES AND THE REST OF THE BASINS

http://i56.tinypic.com/2j49chc.jpg
(9) Northern Hemisphere
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http://i54.tinypic.com/2w67vbn.jpg
(10) Southern Hemisphere
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http://i56.tinypic.com/10cqgl5.jpg
(11) North Atlantic (0 to 75N, 78W to 10E)
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http://i53.tinypic.com/2vkfehv.jpg
(12) South Atlantic (0 to 60S, 70W to 20E)
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http://i55.tinypic.com/r02xrl.jpg
(13) North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
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http://i52.tinypic.com/2w1y0dj.jpg
(14) South Pacific (0 to 60S, 120E to 290E, where 290E=70W)
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http://i51.tinypic.com/2eb5t39.jpg
(15) Arctic Ocean (65 to 90N)
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http://i53.tinypic.com/nvcw0k.jpg
(16) Southern Ocean (60 to 90S)

SOURCE
All data used in this post is available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

The Electric Kool-Aid Ocean Heat Content Animation

2011-03-15T06:27:00.010-04:00

I’ve moved to WordPress. This post can now be found at The Electric Kool-Aid Ocean Heat Content Animation

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(With Apologies To Tom Wolfe For The Post Title)

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UPDATE (Added Preview)

This animation has turned out but better than past attempts to animate NODC Ocean Heat Content data. The following is a preview .gif that limits the time period to 2003 to 2010, keeping the file size to about 2MB.
http://i53.tinypic.com/5dvryd.jpg
Preview (2003-2010)

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The post includes a YouTube link to the video titled “NODC Ocean Heat Content Animation 1990-2010”. The video presents animated maps of the National Oceanographic Data Center (NODC) Ocean Heat Content (OHC) data from 1990 to 2010. The data in each map has been “smoothed” with a 12-month filter to minimize noise and any seasonal component, much like a 12-month running-average filter smoothes noisy data in a graph.

Why “Electric Kool-Aid” in the title of this post? When my GIF Movie Gear software first previewed the animation, it reminded me of a background animation from a 1960s-era movie—looking like a flattened lava lamp. With that in mind, the music then seemed to fit. The music starts with the animation, not with the introductory comments, so don’t start turning up the volume, wondering where it is.

The video includes a statement about North Atlantic Ocean Heat Content (OHC) that some will want me to document. I suggested that viewers keep an eye out for “The Sea Level Pressure-Caused ‘Switch’ In High Latitude North Atlantic OHC In The Late 1990s.” This was illustrated and discussed in the post North Atlantic Ocean Heat Content (0-700 Meters) Is Governed By Natural Variables. That post also includes a link to Lozier et al (2008) “The Spatial Pattern and Mechanisms of Heat-Content Change in the North Atlantic”, provided again here:
http://www.sciencemag.org/cgi/content/abstract/319/5864/800?rss=1

The paper that describes the NODC OHC dataset is Levitus et al (2009) “Global Ocean Heat Content(1955-2008) in Light of Recent Instrumentation Problems”, Geophysical Research Letters. Link to Manuscript. And the 2010 changes were discussed in the NODC’s Explanation of Ocean Heat Content (OHC) Data Changes.

The maps were created using the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

Additional discussions of the effects of natural variables on OHC:
1. ENSO Dominates NODC Ocean Heat Content (0-700 Meters) Data

2. North Pacific Ocean Heat Content Shift In The Late 1980s

There are two pulses in the animation, one around the year 1996, the other about 2000. I believe they result from the sudden appearance of positive anomalies in the Arctic Ocean, which appear at the same time as the year markers (red dots) in the upper left-hand corner of the maps.

Enough preliminaries, here’s the video:

YouTube Link:
http://www.youtube.com/watch?v=PUONorBCcxU

RSS MSU TLT Anomalies February 2011 Update and A Look At Version 3.3

2011-03-11T12:01:00.014-05:00

I’ve moved to WordPress. This post can now be found at RSS MSU TLT Anomalies February 2011 Update and A Look At Version 3.3

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FEBRUARY 2011 UPDATERSS TLT anomalies continue to drop in response to the 2010/2011 La Niña. RSS MSU TLT anomalies are now at 0.051 deg C, Figure 1.
http://i54.tinypic.com/qn4qvm.jpg
Figure 1

RECENT VERSION UPDATE

RSS recently updated their MSU Lower Troposphere Temperature (TLT) anomaly data with a new version, v3.3. This was discussed last month at Watts Up With That? in the post RSS global temp drops, version change adjusts cooler post 1998. At that time, RSS had not described the changes. They now have at their website on their data description webpage.
http://www.remss.com/msu/msu_data_description.html

Refer to the Version Notes. Here’s what RSS has to say:
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Version Notes
RSS Version 3.3 Channel TLT, TMT, TTS, and TLS – January, 2011
Change from 3.2 to 3.3:

* Additional satellites are now included in the merge. Version 3.2 only used data from one AMSU instrument, NOAA-15. For TLT, TMT, and TLS, Version 3.3 includes data from the AMSU instruments on NOAA-15, AQUA, NOAA-18, and METOP-A. AMSU channel 7 exhibits unexplained drifts in METOP-A, so for TTS, data from METOP-A is not used.

* Comparisons with other AMSU satellites are now used to detemine [sic] the AMSU merging coefficients.

* When merging MSU and AMSU together, the data for each generation of satellites is weighted by the number of satellites with valid data for that month. This has the effect of de-emphasizing MSU data after the advent of the AQUA satellite in June 2002. Since the 2002-2004 period is when there is an unexplained warming drift in MSU channel 2 data from NOAA-14 relative to AMSU data, this change has the effect of lowering the overall warming in TMT and TLT during the post 2002 period.

* The changes also result in a reduction of sampling noise and “orbital striping” for periods when data from more satellites is used.

* Data from NOAA-16 is not used because all 3 channels show unexplained drift throughout it’s [sic] lifetime. NOAA-17 was only operational for a short period of time, thus it’s [sic] data is of little use for climate studies. We plan to begin including data from NOAA-19 after 3 years of operation.
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VERSION COMPARISON
Figure 2 compares the anomaly data and linear trends of the new RSS TLT Version 3.3 to the obsolete Version 3.2. The update lowered the linear trend since 1979 from approximately 1.6 deg C to 1.5 deg C per Century, Figure 2.
http://i52.tinypic.com/16c8lcw.jpg
Figure 2

The difference between the two datasets is shown in Figure 3.
http://i53.tinypic.com/258uqzb.jpg
Figure 3

Figure 4 is a .gif animation that compares the2010 anomaly maps for the new and old versions when using 1979-1980 as the base years. Basically both maps are showing the change in TLT anomalies from the average of the years 1979 and 1980 to the year 2010. The patterns for both datasets are similar, but there are minor changes in the variations.
http://i54.tinypic.com/261bxja.jpg
Figure 4

COMPARISON TO UAH MSU TLT DATA
The linear trends of the RSS version 3.3 and the most recent version of UAH TLT anomaly data (v5.4) are basically the same: 1.47 versus 1.44 deg C per Century. Refer to Figure 5. Note that I’ve switched to KNMI climate Explorer as the source for both datasets, so that I could limit the UAH latitudes to those used by RSS, 70S-82.5N.
http://i51.tinypic.com/2d0xmds.jpg
Figure 5

Figure 6 shows the difference between the two datasets.
http://i52.tinypic.com/iz263m.jpg
Figure 6

And Figure 7 is a gif animation similar to Figure 4, but this compares RSS (v3.3) to UAH (v5.4) TLT anomaly data.
http://i56.tinypic.com/14w3s6w.jpg
Figure 7

THE ENSO-INDUCED STEP CHANGES
I illustrated and discussed the ENSO-induced rises in the RSS MSU TLT anomalies for the data north of 20N in the post RSS MSU TLT Time-Latitude Plots... Show Climate Responses That Cannot Be Easily Illustrated With Time-Series Graphs Alone. I further discussed the likely cause for the upward steps in the post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures.

Figure 8 illustrates Volcano-adjusted RSS TLT anomalies north of 20N in “raw” form and smoothed with a 13-month running-average filter. Also included are the period average temperature anomalies of -0.187 for 1979 to 1987, -0.016 for 1988 to 1997, and 0.268 for 1998 to present.
http://i54.tinypic.com/5c0svt.jpg
Figure 8

I adjusted the data for the linear effects of the two major volcanic eruptions, El Chichon and Mount Pinatubo. To determine the scaling factor for the volcanic aerosol proxy, I used a linear regression software tool (Analyse-it for Excel) with global RSS TLT anomalies (v3.3) as the dependent variable and GISS Stratospheric Aerosol Optical Thickness data (ASCII data) as the independent variable. The scaling factor determined was 2.9.

And in Figure 9 the “raw” data has been deleted to help show the ENSO-induced upward steps in this dataset. So the revisions have not changed these to any great extent, so I won't go back and update the earlier posts.
http://i52.tinypic.com/15p4uia.jpg
Figure 9

SOURCES
The following are links to the data use to create Figures 1, 2, and 3.
RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_2.txt
RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_3.txt

All other data were downloaded, and the maps were created, using the KNMI Climate Explorer Monthly observations webpage.

(Many thanks to Dr. Geert Jan van Oldenborgh of KNMI for the quick update to RSS TLT version 3.3.)

February 2011 SST Anomaly Update

2011-03-07T10:52:00.023-05:00

I’ve moved to WordPress. This post can now be found at February 2011 SST Anomaly Update

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MONTHLY SST ANOMALY MAP

The map of Global OI.v2 SST anomalies for February 2011 downloaded from the NOMADS website is shown below.

http://i51.tinypic.com/14dkbar.jpg
February 2011 SST Anomalies Map (Global SST Anomaly = +0.098 deg C)

MONTHLY OVERVIEW
Monthly NINO3.4 SST anomalies have risen from their ENSO season low, heralding the start of the end of this La Niña. The Monthly NINO3.4 SST Anomaly is -1.24 deg C.

The SST anomalies in most ocean basins rose this month. This is likely as response to the ebbing of the La Niña. The Arctic, North Atlantic, and East Indian-West Pacific are the exceptions; the SST anomalies there rose. The result was no change in Northern Hemisphere SST anomalies, and an increase in Southern Hemisphere data, for an increase in global SST anomalies (+0.031 deg C). They are presently at +0.098 deg C.
http://i54.tinypic.com/2v2basl.jpg
(1) Global
Monthly Change = +0.031 deg C
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http://i52.tinypic.com/34pfck5.jpg
(2) NINO3.4 SST Anomaly
Monthly Change = +0.349 deg C

THE EAST PACIFIC VERSUS THE REST OF THE WORLD
As noted in the post Sea Surface Temperature Anomalies – East Pacific Versus The Rest Of The World, I have added these two datasets to the monthly updates. Both datasets have been adjusted for the impacts of volcanic aerosols, and both are smoothed with 13-month running-average filters to reduce the seasonal noise. The global oceans were divided into these two subsets to illustrate two facts. First, the linear trend of the volcano-adjusted East Pacific (90S-90N, 180-80W) SST anomalies since the start of the Reynolds OI.v2 dataset is basically flat, with a linear trend of only 0.08 deg C per Century.
http://i51.tinypic.com/1ik0w6.jpg
(3) Volcano-Adjusted East Pacific (90S-90N, 180-80W)

And second, the volcano-adjusted SST anomalies for the Rest of the World (90S-90N, 80W-180) rise in very clear steps, in response to the significant 1986/87/88 and 1997/98 El Niño/La Niña events. It also appears as though the SST anomalies of this dataset are making another shift in response to the most recent ENSO event.
http://i51.tinypic.com/ofr67s.jpg
(4) Volcano-Adjusted Rest of the World (90S-90N, 80W-180)

EAST INDIAN-WEST PACIFIC
The SST anomalies in the East Indian and West Pacific took a major nose dive this month.

I’ve added this dataset in an attempt to draw attention to what appears to be the upward steps in response to significant El Niño events that are followed by La Niña events.
http://i54.tinypic.com/dpd4k3.jpg
(5) East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = -0.005 deg C

Further information on the upward “step changes” that result from strong El Niño events, refer to my posts from a year ago Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2

And for the discussions of the processes that cause the rise, refer to More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Niña Events Recharge The Heat Released By El Niño Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents -AND- More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Niño & La Niña Events

The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.

NOTE ABOUT THE DATA
The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from December 1981 to February 2011.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES http://i52.tinypic.com/2mx0qw3.jpg
(6) Northern Hemisphere
Monthly Change = 0.000 deg C
#####
http://i52.tinypic.com/34taetz.jpg
(7) Southern Hemisphere
Monthly Change = +0.055 deg C
#####
http://i53.tinypic.com/13ygpia.jpg
(8) North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = -0.119 deg C
#####
http://i55.tinypic.com/2hncbqq.jpg
(9) South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = +0.210 deg C

Note: I discussed the upward shift in the South Atlantic SST anomalies in the post The 2009/10 Warming Of The South Atlantic. It does not appear as though the South Atlantic will return to the level it was at before that surge, and where it had been since the late 1980s. That is, it appears to have made an upward step and continues to rise. Why? Dunno---yet.

#####
http://i55.tinypic.com/f2u903.jpg
(10) North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = +0.042 Deg C
#####
http://i55.tinypic.com/2zfo4f4.jpg
(11) South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = +0.014 deg C
#####
http://i51.tinypic.com/jj5k04.jpg
(12) Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = +0.027 deg C
#####
http://i54.tinypic.com/2vsrtyx.jpg
(13) Arctic Ocean (65 to 90N)
Monthly Change = -0.019 deg C
#####
http://i56.tinypic.com/2lvbu5x.jpg
(14) Southern Ocean (60 to 90S)
Monthly Change = +0.003 deg C

WEEKLY SST ANOMALIES
The weekly NINO3.4 SST anomaly data portray OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.26 deg C.
http://i54.tinypic.com/2vttl49.jpg
(15) Weekly NINO3.4 (5S-5N, 170W-120W)

The weekly global SST anomalies are at +0.115 deg C.
http://i51.tinypic.com/33axt9t.jpg
(16) Weekly Global

SOURCE
The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

Sea Surface Temperature Anomalies – East Pacific Versus The Rest Of The World

2011-03-03T18:43:00.012-05:00

I’ve moved to WordPress. This post can now be found at Sea Surface Temperature Anomalies – East Pacific Versus The Rest Of The World

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I will be adding two volcano-adjusted SST anomaly subsets to the monthly updates starting with the February 2011 update. They are the East Pacific, which mimics NINO3.4 SST anomalies, and the Rest of the World, which rises in very clear steps during significant ENSO events.

You may want to bookmark the LINKS TO SST ANOMALY UPDATES for future reference.

INTRODUCTION

I’ve written numerous posts about the upward steps in the Sea Surface Temperature (SST) anomalies of the East Indian-West Pacific Oceans (60S-65, 80E-180). Many of them are linked at the end if this post under the heading of “Further Discussions”. In an upcoming post, which is a continuation of my Comments On Tamino’s AMO Post, I’ll also be illustrating and discussing similar upward steps in the Atlantic Multidecadal Oscillation (AMO) data, which are detrended North Atlantic SST data (0-70N, 80W-0). Refer to Figure 1, which is a .gif animation. As illustrated, ENSO- and Volcano-adjusted AMO data rise in steps during the transitions from El Niño to La Niña, but between the upward steps, they mimic the inverted NINO3.4 SST anomalies.
http://i56.tinypic.com/2repfo0.jpg
Figure 1

Similar upward steps can also be illustrated if we divide the global oceans into two subsets so that one of them contains both the North Atlantic and the East Indian-West Pacific datasets. The two subsets, Figure 2, are the East Pacific Ocean (90S-90N, 180W-80W) and the Rest Of The World (90S-90N, 80W-180E), the latter of the two containing the North Atlantic and East Indian-West Pacific datasets. As we shall see, the East Pacific SST anomalies mimic the variations of ENSO proxies, with little trend, while the SST anomalies of the Rest of the World rise in very clear steps that coincide with the 1986/87/88 and 1997/98 El Niño events.
http://i53.tinypic.com/141qcyd.jpg
Figure 2

ACCOUNTING FOR THE IMPACTS OF VOLCANIC ERUPTIONS
I’ll be removing the linear effects of the two major volcanic eruptions, El Chichon and Mount Pinatubo, from the two SST datasets. To determine the scaling factor for the volcanic aerosol proxy, I used a linear regression software tool (Analyse-it for Excel) with global SST anomalies as the dependent variable and GISS Stratospheric Aerosol Optical Thickness data (ASCII data) as the independent variable. The scaling factor determined was 1.431. This equals a global SST anomaly impact of approximately 0.2 deg C for the 1991 Mount Pinatubo eruption. Refer to Figure 3. I’ll use that scaling factor for the East Pacific and Rest of the World datasets.
http://i55.tinypic.com/33acpwp.jpg
Figure 3

EAST PACIFIC
As shown in Figure 4, the Volcano-adjusted East Pacific SST anomalies vary in concert with the scaled NINO3.4 SST anomalies. There are periodic divergences, but the variations in the East Pacific SST anomalies mimic the commonly used ENSO proxy.
http://i53.tinypic.com/2zi4ig2.jpg
Figure 4

The linear trend for the East Pacific SST anomalies since November 1981 is basically flat, at only 0.08 deg C per Century. Refer to Figure 5.
http://i56.tinypic.com/1565ldz.jpg
Figure 5

This obviously means the rise in the global SST anomalies since the start if this satellite-based SST dataset must occur outside of the East Pacific.

REST OF THE WORLD

Figure 6 compares the SST anomalies and the linear trends of the East Pacific and the Rest Of the World. Since November 1981, the SST anomalies of the Rest of the World (90S-90N, 80W-180) have risen at a rate of approximately 1.01 deg C per Century, while the trend of the East Pacific SST anomalies is only 0.08 deg C per Century.
http://i55.tinypic.com/2mxh9pf.jpg
Figure 6

The East Pacific data used in this post represent approximately 33% of the global ocean surface area. (The percentage is based on the NCEP/DOE Reanalysis-2 “Land Mask” data available through the KNMI Climate Explorer.) So we can place the two datasets in perspective by scaling the East Pacific data by a factor of 0.5. Refer to Figure 7. Notice how flat the Rest of the World SST anomalies are after the 1997/98 upward shift. There are some minor fluctuations, but the Rest of the World SST anomalies are essentially flat until 2009/10. Backing up in time, the same could be said for the period from 1987/88 and 1997/98; the volcano-adjusted Rest of the World data also have not risen over that period.
http://i55.tinypic.com/15hgy0j.jpg
Figure 7

Adding period-average values to the Rest Of The World SST anomalies, Figure 8, makes the upward steps stand out even more. It will be interesting to see where the “July 2009 to Present” SST anomaly average settles out, if it does before the next significant El Niño drives them higher.
http://i51.tinypic.com/1zmf3ev.jpg
Figure 8

CLOSING NOTE

In a recent discussion at another blog (I believe it was a discussion of the adjusted AMO data in Figure 1.), an AGW proponent noted that upward shifts in SST anomalies did not disprove the hypothesis of anthropogenic global warming.

As illustrated in this post, the SST anomalies of the East Pacific Ocean, or approximately 33% of the surface area of the global oceans, have risen very little since 1982. And between upward shifts, the trends of the SST anomalies for the rest of the world (67% of the global ocean surface area) remain flat. What processes could cause anthropogenic global warming to work only during the significant El Niño events of 1986/87/88, 1997/98 and 2009/10?

SOURCES
SST anomaly data is available through the NOAA NOMADS website:
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite

The GISS Global Stratospheric Aerosol Optical Thickness data is available here:
http://data.giss.nasa.gov/modelforce/strataer/tau_line.txt

FURTHER INFORMATION

My first detailed posts on the multiyear aftereffects of ENSO events are:
Can El Nino Events Explain All of the Global Warming Since 1976? – Part 1
And:
Can El Nino Events Explain All of the Global Warming Since 1976? – Part 2
And:
Supplement To “Can El Nino Events Explain All Of The Warming Since 1976?”
And:
Supplement 2 To “Can El Nino Events Explain All Of The Warming Since 1976?”

And for those who like visual aids, refer to the two videos included in:
La Niña Is Not The Opposite Of El Niño – The Videos.

The impacts of these El Nino events on the North Atlantic are discussed in:
There Are Also El Nino-Induced Step Changes In The North Atlantic
And:
Atlantic Meridional Overturning Circulation Data

As noted earlier, I’ve also written a rebuttal post to Tamino’s AMO Post. I hope to have a new post on the North Atlantic posted in a few weeks.

The posts related to the effects of ENSO on Ocean Heat Content are here:
ENSO Dominates NODC Ocean Heat Content (0-700 Meters) Data
And:
North Atlantic Ocean Heat Content (0-700 Meters) Is Governed By Natural Variables

More detailed technical discussions can be found here:
More Detail On The Multiyear Aftereffects Of ENSO – Part 1 – El Nino Events Warm The Oceans
And:
More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents.
And:
More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Nino & La Nina Events

PRELIMINARY February 2011 SST Anomaly Update

2011-02-28T10:44:00.006-05:00

I’ve moved to WordPress. This post can now be found at PRELIMINARY February 2011 SST Anomaly Update

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The February 2011 Reynolds OI.v2 Sea Surface Temperature (SST) data through the NOAA NOMADS website won’t be official until March 7th. Refer to the schedule on the NOAA Optimum Interpolation Sea Surface Temperature Analysis Frequently Asked Questions webpage. The following are the preliminary Global and NINO3.4 SST anomalies for February 2011 that the NOMADS website prepares based on incomplete data for the month. I’ve also included the weekly data through February 23, 2011, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the variations can be seen.

PRELIMINARY MONTHLY DATA

Monthly NINO3.4 SST anomalies reached their seasonal low in January and began the rebound in February. Based on the preliminary data they’re at -1.28 deg C.

http://i53.tinypic.com/11949xj.jpg
Monthly NINO3.4 SST Anomalies
######################

Monthly Global SST anomalies, according to the preliminary data, have rebounded after their drop last month. The preliminary global SST anomaly is +0.093 deg C.
http://i51.tinypic.com/9argaa.jpg
Monthly Global SST Anomalies
######################

WEEKLY DATA

The weekly NINO3.4 SST anomalies for the week centered on February 23, 2011 are -1.25 deg C.
http://i56.tinypic.com/1zftnpl.jpg
Weekly NINO3.4 SST Anomalies
######################

Weekly Global SST Anomalies are presently at +0.11 deg C.
http://i55.tinypic.com/e87gv6.jpg
Weekly Global SST Anomalies
######################

SOURCES
SST anomaly data is available through the NOAA NOMADS website:
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite=

Request for Assistance In Assessing an Important Sea Level Study

2011-02-24T10:09:00.007-05:00

I’ve moved to WordPress. This post can now be found at Request for Assistance In Assessing an Important Sea Level Study

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Guest Post by John Droz, Jr.

Friends:

I am asking for help from oceanographers and/or others who have experience with sea level measurements.

I am a physicist (energy expert) who has been involved with several environmental issues over the last thirty years.

I am a traditional scientist in that I am a strong advocate of subjecting hypothesis for solutions to our environmental issues to the Scientific Method. In other words, I would expect that proposed solutions have a comprehensive, independent, transparent and empirical based assessment. (Unfortunately, this now seems to be the minority view among scientists.)

I have written extensively on energy issues, and have given free presentations in some ten states. This is online at EnergyPresentation.Info. There are also several slides about AGW.
------------------------
Anyway, the case at hand is that I was recently asked by my local representatives for some scientific assistance.

The brief story is that North Carolina is attempting to be the first state in the nation to impose rather comprehensive and consequential (i.e. expensive) rules and regulations on its coastal communities. This is based on projected substantially increased sea levels, due to the assumed effects of AGW.

But it’s worse than that. The basis for these changes is a 2010 NC Sea Level Assessment Report (http://tinyurl.com/69nzem8).

I have been told that the US federal government funded this study. The stated intention was that they would like that this study be used by the rest of the coastal states (plus the federal government) as a basis for new rules and regulations. If this came about as planned, there would clearly be worldwide implications to this simple report.

As such, it is my view, that it is imperative to get it right.

In my reading of the report, the key assumptions are that:
1 - the IPCC sea level rise projections (15± inches by 2100) are the minimum expected, and
2 - that Rahmstorf (2007: http://tinyurl.com/3bhuzd), is a credible source to use as a high end (55± inches by 2100).

To give the appearance of being reasonable, the report authors (13 esteemed scientists) selected a value near the middle of these numbers: 39± inches by 2100.

Figure 2 (page 11) and the accompanying text in the report shows and explains this.

This is not my area of expertise, so I can not make a technical critique of Rahmstorf's work, or the referenced Church & White (2006) report. If anyone can provide some scientific evidence, pro or con, regarding these documents, it would be greatly appreciated.

Again, what happens about this in NC will likely be a precursor to other coastal states (and countries), so this is an international big deal.

Feel free to email me directly at "aaprjohn@northnet.org".

THANK YOU!

john droz, jr.
physicist & environmental advocate
Morehead City, NC

The Recent Drop In The Sea Surface Temperatures Of U.S. Coastal Waters

2011-02-20T10:39:00.007-05:00

I’ve moved to WordPress. This post can now be found at The Recent Drop In The Sea Surface Temperatures Of U.S. Coastal Waters

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This post illustrates the recent drop in the SST anomalies of the U.S. coastal waters. We’ll represent this subset with the coordinates of 20N-50N, 130W-65W. I’ve used those coordinates in at least one earlier post about the SST Anomalies of U.S. “Coastal” Waters. Figure 1 is the December 2010 Reynolds OI.v2 SST anomaly map with those coordinates highlighted. The cooling appears to be an exaggerated response to the 2010/11 La Niña.
http://i51.tinypic.com/ny902e.jpg
Figure 1

Figure 2 is the Reynolds OI.v2-based time-series graph for this subset. After the two-month flattening in September and October 2010, at approximately -0.24 deg C, the SST anomalies dropped more than 0.65 deg C by January 2011. The January 2011 reading for the U.S. Coast Waters is the lowest on record for the satellite-based Reynolds Oi.v2 SST dataset.
http://i55.tinypic.com/2hqe9hu.jpg
Figure 2

Note: This is not a post about global sea surface temperature (SST) anomalies, nor is it a post about the SST anomalies of the individual ocean basins. For those refer to the January 2011 SST Anomaly Update.

Figures 3, 4, and 5 show the long-term SST anomalies for the U.S. Coastal Waters using the HADISST, ERSST.v3b, and HADSST2 datasets. They provide a different perspective on the magnitude of the recent drop. For the HADISST and ERSST.v3b datasets, one has to go back to the April 1971 to find similar SST anomalies, and back to July 1933 with HADSST2 data.
http://i53.tinypic.com/339o96f.jpg
Figure 3
###################
http://i54.tinypic.com/30w5e6v.jpg
Figure 4
###################
http://i56.tinypic.com/29e54s6.jpg
Figure 5

The weekly data for this subset, Figure 6, show that the SST anomalies are rebounding. I’ll add this dataset to the monthly update for a few months to assure it rebounds fully.
http://i52.tinypic.com/11lmsuw.jpg
Figure 6

SOURCE
The map and all data presented in this post are available through the KNMI Climate Explorer:http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

GISS Has Removed ERSST.v3b As An Option On Its Map-Making Webpage

2011-02-17T10:09:00.003-05:00

I’ve moved to WordPress. This post can now be found at GISS Has Removed ERSST.v3b As An Option On Its Map-Making Webpage

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I had noted the addition of ERSST.v3b SST dataset to the GISS Global Maps webpage almost a year ago in the February 25, 2010 post WHEN DID GISS ADD ERSST.v3b DATA TO THEIR MAP-MAKING WEB PAGE? In a follow-up post a month later, GISS Acknowledges Addition of ERSST.v3b Data To Their GISTEMP Options, I provided a link to the draft of Hansen et al (2010) which explained the reason for the inclusion of the ERSST.v3b data on its website, even though GISS uses a combination of HADISST and Reynolds OI.v2 SST data in their GISS Land-Ocean Temperature Index (LOTI) dataset. Basically, GISS was evaluating the two SST datasets (and awaiting additional analysis), but they would continue to use the combined HADISST/Reynolds OI.v2 data “unless and until verifications show ERSST to be superior.”

Hansen et al (2010) was published on December 10, 2010, “Global surface temperature change”. [Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345] (PDF). Then, approximately two months later, GISS removed ERSST.v3b SST data as an option on its map-making webpage. See Figure 1.

http://i56.tinypic.com/10cj3bs.jpg
Figure 1

This of course raises a number of questions. Does it mean that “verifications” did not “show ERSST to be superior”? If so, in what publication was this presented? Or was the webpage simply updated incorrectly and the update failed to include the ERSST.v3b option in the drop-down menu?

NINO3.4 SST Anomalies Have Started Their La Niña Rebound

2011-02-14T10:41:00.008-05:00

I’ve moved to WordPress. This post can now be found at NINO3.4 SST Anomalies Have Started Their La Niña Rebound

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Figure 1 shows weekly NINO3.4 SST anomalies from January 7, 2004 through February 9, 2011. The central equatorial Pacific SST anomalies have risen significantly in the last week.
http://i51.tinypic.com/epnha9.jpg
Figure 1

Figure 2 compares the SST anomalies for the transitions from El Niño to La Niña events, during the years of 1988/89, 1998/99, 2007/08, and 2010 through February 9, 2011. At first glance, it appears this rebound started early, but the rebound from the 1988/89 La Niña actually started rising from its minimum a few weeks earlier.
http://i51.tinypic.com/14x2mmr.jpg
Figure 2

And since we’re looking at weekly data, Figure 3 shows the Global SST anomalies from January 7, 2004 through February 9, 2011.
http://i56.tinypic.com/213403k.jpg
Figure 3

SOURCE
The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

January 2011 SST Anomaly Update

2011-02-07T11:46:00.017-05:00

I’ve moved to WordPress. This post can now be found at January 2011 SST Anomaly Update

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MONTHLY SST ANOMALY MAP

The map of Global OI.v2 SST anomalies for January 2011 downloaded from the NOMADS website is shown below. There was a mix of variations in ocean basin SST anomalies this month. The Arctic, South Atlantic, and South Pacific rose; the rest fell. The result was a decrease in global SST anomalies (-0.033 deg C). They are presently at +0.067 deg C.

Note the pattern in the Pacific. It is not a typical La Niña pattern. Note also the elevated anomalies in mid-latitude South Atlantic and in the South Pacific, in what could be described as an extension of the South Pacific Convergence Zone (SPCZ).

http://i54.tinypic.com/33wm8ly.jpg
January 2011 SST Anomalies Map (Global SST Anomaly = +0.067 deg C)

MONTHLY OVERVIEW

Monthly NINO3.4 SST anomalies are still varying at or near the seasonal low for this La Niña. The Monthly NINO3.4 SST Anomaly is -1.59 deg C.

The drop in Northern Hemisphere this month (-0. 060 deg C) is significantly larger than the Southern Hemisphere (-0.011 deg C). Globally, there was a healthy decline (-0.033 deg C).
http://i53.tinypic.com/ic7pzd.jpg
Global
Monthly Change = -0.033 deg C
############
http://i52.tinypic.com/11lhg86.jpg
NINO3.4 SST Anomaly
Monthly Change = -0.063 deg C

EAST INDIAN-WEST PACIFIC
The SST anomalies in the East Indian and West Pacific took a major nose dive this month.

I’ve added this dataset in an attempt to draw attention to what appears to be the upward steps in response to significant El Niño events that are followed by La Niña events.
http://i51.tinypic.com/rsv7nn.jpg
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = -0.171 deg C

Further information on the upward “step changes” that result from strong El Niño events, refer to my posts from a year ago Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2

And for the discussions of the processes that cause the rise, refer to More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Niña Events Recharge The Heat Released By El Niño Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents -AND- More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Niño & La Niña Events

The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.

NOTE ABOUT THE DATA
The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from December 1981 to January 2011.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES http://i55.tinypic.com/2utgoav.jpg
Northern Hemisphere
Monthly Change = -0.060 deg C
#####
http://i53.tinypic.com/2a9y73a.jpg
Southern Hemisphere
Monthly Change = -0.011 deg C
#####
http://i55.tinypic.com/f06irn.jpg
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = -0.002 deg C
#####
http://i51.tinypic.com/11ke0dz.jpg
South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = +0.054 deg C

Note: I discussed the upward shift in the South Atlantic SST anomalies in the post The 2009/10 Warming Of The South Atlantic. It does not appear as though the South Atlantic will return to the level it was at before that surge. That is, it appears to have made an upward step.

#####
http://i53.tinypic.com/r6wyms.jpg
North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = -0.053 Deg C
#####
http://i53.tinypic.com/v6rucg.jpg
South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = +0.021 deg C
#####
http://i51.tinypic.com/wcnthv.jpg
Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = -0.135 deg C
#####
http://i55.tinypic.com/35jvx37.jpg
Arctic Ocean (65 to 90N)
Monthly Change = +0.013 deg C
#####
http://i55.tinypic.com/2vb0y0x.jpg
Southern Ocean (60 to 90S)
Monthly Change = -0.046 deg C

WEEKLY SST ANOMALIES

The weekly NINO3.4 SST anomaly data portray OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.49 deg C.
http://i56.tinypic.com/nby7wl.jpg
Weekly NINO3.4 (5S-5N, 170W-120W)
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The weekly global SST anomalies are at +0.080 deg C.
http://i53.tinypic.com/al6zis.jpg
Weekly Global

SOURCE
The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

Comments On Tamino’s AMO Post

2011-02-03T08:02:00.012-05:00

I’ve moved to WordPress. This post can now be found at Comments On Tamino’s AMO Post

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UPDATE (3-16-11): Refer to the update at the end of the post.

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Tamino’s AMO post is a response to my post Removing The Effects of Natural Variables - Multiple Linear Regression-Based or “Eyeballed” Scaling Factors (hereinafter referred to as the “Removing” post). Tamino took exception to my inclusion of the AMO as one of the datasets used to explain the rise in GISS Land-Ocean Temperature Index (60S-60N) during the satellite era. Please read Tamino’s AMO post before continuing.

My “Removing” post, as discussed in its opening paragraph, was the second in a series follow-ups to the earlier post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases? (hereinafter referred to as the “Can Most” post). The first follow-up was Notes On Polar Amplification.

And for those new to the Atlantic Multidecadal Oscillation (AMO) refer to the post An Introduction To ENSO, AMO, and PDO -- Part 2.

THE REAL CLIMATE DESCRIPTION OF THE AMO

Tamino wrote in his post, “Bob Tisdale (and others) simply can’t wrap their brains around the fact that global warming is the cause, not the effect, of much of the changes in N.Atl SST anomaly. Therefore global warming is the cause, not the effect, of much of the variation in the AMO.”

My AMO posts typically include the RealClimate description of the Atlantic Multidecadal Oscillation (“AMO”), but I failed to include it in “Removing” post. RealClimate states, “A multidecadal (50-80 year timescale) pattern of North Atlantic ocean-atmosphere variability whose existence has been argued for based on statistical analyses of observational and proxy climate data, and coupled Atmosphere-Ocean General Circulation Model (“AOGCM”) simulations. This pattern is believed to describe some of the observed early 20th century (1920s-1930s) high-latitude Northern Hemisphere warming and some, but not all, of the high-latitude warming observed in the late 20th century. The term was introduced in a summary by Kerr (2000) of a study by Delworth and Mann (2000).”

Tamino’s opinion contradicts the opinions of his associates at RealClimate, or at least the opinion of the author of the RealClimate AMO webpage. RealClimate describes the AMO as being responsible for some, but not all, of the warming, but Tamino states it’s the other way around, that the global warming signal is the cause of the AMO variability.

Tamino’s RealClimate associates must be among “the others” who “simply can’t wrap their brains around the fact that global warming is the cause, not the effect, of much of the changes in N.Atl SST anomaly.”

A NOTE ABOUT THE SST DATASET USED IN THIS POST
GISS uses two SST anomaly datasets in its Land-Ocean Temperature (LOTI) product: HADISST from January 1880 to November 1981 and Reynolds OI.v2 from December 1981 to present. There is little difference between the HADISST and Reynolds OI.v2 data for the North Atlantic during the satellite era, as shown in Figure 1. So my use of HADISST data in the short-term will not influence the results of this post.

http://i52.tinypic.com/fxyvwp.jpg
Figure 1

However, there is a significant difference between the long-term Kaplan North Atlantic SST data used by the ESRL (and Tamino) and the HADISST data used by GISS. Refer to Figure 2. Keep in mind my use of the ESRL data was only for the AMO index in the short term, not the long-term SST data used by Tamino. (Note: I confirmed via email that the ESRL uses the coordinates of 0-70N and 80W-0 for its AMO data.)
http://i51.tinypic.com/28atkzb.jpg
Figure 2

And the difference does impact Tamino’s post. He uses the wrong North Atlantic SST anomaly dataset when he subtracts global temperatures from it. That is, assuming Tamino did not switch to the HADISST version of the North Atlantic, he biased the results in his last graph by the difference in the trends of the HADISST data (used by GISS) and the Kaplan data (used by ESRL) shown in Figure 2.

ON THE NONLINEARITY OF THE WARMING SIGNAL

The natural multidecadal variability of the North Atlantic SST anomalies is significantly greater than that of the Global (90S-90N) SST anomalies. This is very apparent if we compare detrended North Atlantic SST anomalies (AMO) to detrended Global SST data, Figure 3. The data have been smoothed with a 121-month running-average filter.
http://i54.tinypic.com/xnuvbq.jpg
Figure 3

Tamino opens his post with a discussion of the how the AMO is calculated by detrending North Atlantic SST anomalies, and he notes that the Wikipedia definition warns about the nonlinearity of the actual warming signal. The nonlinearity of the detrended global SST signal is shown clearly in my Figure 5 above. Based on his presentation, Tamino concludes, “Variations in the forced signal do leak into the AMO definition.”

Let’s compare the short-term linear trends of the North Atlantic SST anomalies to the trends of the other ocean basins. This is a general discussion of the AMO, so I’ve left in the Arctic and Southern Ocean data. Keep in mind that my “Removing” and “Can Most” posts only dealt with the period starting in 1982, which is the satellite era for SST data. As shown in the spaghetti graph, Figure 4, the SST anomaly linear trend of the North Atlantic is significantly higher than all other SST basins. The linear trend of the Arctic Ocean SST anomalies comes in second, in part because those two datasets overlap and due to the influence of the North Atlantic on the Arctic Ocean. Regardless, the North Atlantic linear trend is almost twice that of the Arctic Ocean. The North Atlantic trend is more than 3 times higher than the trends of the North Pacific and Indian Oceans and more than 5 times higher than the trends of the South Atlantic and South Pacific. And of course, the Southern Ocean linear trend is negative. (Note: The impact of the Southern Ocean cooling is so substantial that the trend is basically flat for all HADISST anomaly data south of 40S, or about 35% of the global oceans, since 1982.)
http://i56.tinypic.com/vo0ck0.jpg
Figure 4

This difference in linear trends can also be seen in the comparison of North Atlantic SST anomalies and the SST anomalies for the rest of the world. To determine the rest-of-the-world data (identified as “Global Without No Atlantic” in Figure 5), I approximated the North Atlantic surface area as a percentage of the global oceans. The Atlantic represents approximately 30% of the surface area of the global oceans. I assumed the North Atlantic made up half of that, or 15%, before scaling the North Atlantic data and subtracting it from the global data for Figure 5. The linear trend of the North Atlantic SST anomalies is more than 5 times greater than the average of the other ocean basins.
http://i53.tinypic.com/ml1jz9.jpg
Figure 5

In fact, the contribution of the North Atlantic is so great, without it, the global trend drops by 45%, Figure 6.
http://i56.tinypic.com/2zhei46.jpg
Figure 6

Tamino did not suggest how to account for the global warming signal in his AMO post, unless the last graph in which he subtracts global GISS LAND-Ocean Temperature Index data from North Atlantic SEA Surface Temperature data is his recommendation. But he did make a suggestion on his earlier How Fast is Earth Warming? thread. He wrote in response to a January 23, 2011 at 4:42 pm comment, “It might be interesting to correlate AMO to short-term global temperature fluctuations, if AMO is detrended nonlinearly, or if only the modern era (1975 to present) is detrended separately. But then: the denialists' claim disappears.”

To account for the nonlinear signal, Trenberth and Shea (2006) proposed subtracting the global (60S-60N) SST data from the North Atlantic in “Atlantic hurricanes and natural variability in 2005”. But the North Atlantic represents a major portion (almost 50%) of the recent rise in global SST anomalies (90S-90N) since 1982, Figure 6. Therefore, Trenberth and Shea are suggesting the subtraction of a dataset with a strong North Atlantic signal from the North Atlantic SST data itself. Why not subtract the SST anomalies of the rest of the world from the North Atlantic? It’s the additional variability of the North Atlantic, above and beyond the rest of the world, that’s of interest, not a signal that’s been suppressed by itself.

The reason that method hasn’t been suggested becomes obvious when one compares that dataset to the AMO data based on detrended North Atlantic SST anomalies. Refer to Figure 7. (The “Rest of the World” data is calculated the same as the “Global Without North Atlantic” from Figures 5 & 6.) Note how the curves mimic one another from 1905 to the early 1980s. They diverge from time to time, but the curves are similar. But note how VERY similar the two curves are after 1982. That’s the period of the AMO data used in my “Removing” post.
http://i53.tinypic.com/2v1ukg5.jpg
Figure 7

Let’s look at the satellite-era portion (1982 to present) of those two datasets, Figure 8. The trends are basically the same, and the year-to-year variability of the two signals mimic one another with small divergences and lags. Based on Figure 10, the “Variations in the forced signal do leak into the AMO definition,” as Tamino notes, but they have had little impact on the results of my “Removing” post.
http://i54.tinypic.com/kdpe7c.jpg
Figure 8

THE DIFFERENCE BETWEEN THE KAPLAN AND HADISST NORTH ATLANTIC SST ANOMALIES
The Kaplan and HADISST versions of the North Atlantic SST anomalies were illustrated together in Figure 2. There was a significant difference in their linear trends. For Figure 9, I subtracted the HADISST version of the North Atlantic SST anomalies from the Kaplan SST anomalies used by ESRL (and Tamino for his last graph). Note the similarities between Figure 9 and Tamino’s final graph in his AMO post.
http://i53.tinypic.com/2806uqx.jpg
Figure 9

TAMINO’S FINAL COMPARISONS

Tamino’s post included a comparison graph of Global (90S-90N) GISS LOTI and the North Atlantic SST anomalies he created from the data on the ESRL AMO webpage. The last illustration was a graph of the difference. While I can’t find fault is his not knowing there was a shift in the Kaplan North Atlantic SST data, I can find fault in his using the wrong SST dataset. GISS does not use Kaplan SST.

There is little difference between the HADISST and Reynolds OI.v2 versions of the North Atlantic SST data, as shown in Figure 1. To assure the following comparisons were correct, for the following graphs I spliced those two North Atlantic SST anomaly datasets using the method described by GISS in Step 4 on their current analysis webpage. Had Tamino used the HADISST/Reynolds OI.v2-based GISS SST anomalies for the North Atlantic in his comparison, Figure 10, the difference between it and the Global GISS LOTI data would have maintained the appearance of the AMO.
http://i53.tinypic.com/2hofas0.jpg
Figure 10

And had Tamino detrended both datasets and smoothed them with 121-month filters, Figure 11, he would have noted that the multidecadal variability of the North Atlantic far exceeds that of the Global GISS LOFTI data—even with the additional land surface temperature variability in the LOTI data—even with the exaggeration of polar amplification in the LOTI data—even with the bias caused by GISS’s deletion of polar sea surface temperature data in the LOTI data.
http://i52.tinypic.com/3149sm9.jpg
Figure 11

I’ll respond to his comments about “eyeballing” in another post.

SOURCES
With the exception of the ESRL North Atlantic SST data (linked numerous times in the post), all data are available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

UPDATE 3-16-11
In this post, one method used to determine the AMO was to calculate it as the difference between the North Atlantic and the Rest of the World SST anomalies. I had used 15% as the scaling factor of the North Atlantic. The 15% was 1/2 of the 30% represented by the surface area of the Atlantic (North and South) when compared to the rest of the world, BUT this excluded the Arctic and Southern Oceans. That is, using the surface areas (Source Wikipedia) of the Atlantic (106.4 million sq. km), Indian (73.5 million sq. km) and Pacific (165.2 million sq. km) Oceans, the Atlantic represents about 30% of the surface area of those ocean basins. Half of that is obviously 15%. Excluding the Arctic and Southern Oceans seemed appropriate for the ballpark number since GISS deletes most of the Southern and Arctic Oceans, and the original post in the series was Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases? However, if the Southern and Arctic Oceans are included, the North Atlantic surface area ballpark percentage would drop to around 12%. If you include only the surface area of the coordinates used in the post, that scaling factor for the North Atlantic would drop to around 11.5%.

I will clarify this in another post and illustrate the minimal effect this has on the AMO when the AMO is calculated as the difference between the North Atlantic and Rest of the World SST anomalies.

Ultimately, this has no impact on the conclusion of this post, which was that Tamino had used the wrong SST dataset in his post. To illustrate that fact, I subtracted the Global Land+Ocean Temperature anomalies from the North Atlantic SST anomalies, not the AMO.

PRELIMINARY January 2011 SST Anomaly Update

2011-02-01T11:41:00.009-05:00

I’ve moved to WordPress. This post can now be found at PRELIMINARY January 2011 SST Anomaly Update

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Sorry for the delay. Snow (lots of it) and family obligations have kept me from posting and responding to comments on posts for a few days. And yes, I'll be responding to Tamino.

The January 2011 Reynolds OI.v2 Sea Surface Temperature (SST) data through the NOAA NOMADS website won’t be official until February 7th. Refer to the schedule on the NOAA Optimum Interpolation Sea Surface Temperature Analysis Frequently Asked Questions webpage. The following are the preliminary Global and NINO3.4 SST anomalies for January 2011 that the NOMADS website prepares based on incomplete data for the month. I’ve also included the weekly data through January 26, 2011, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the variations can be seen.

PRELIMINARY MONTHLY DATA
Monthly NINO3.4 SST anomalies have taken another drop. Based on the preliminary data they’re at -1.6 deg C.

http://i51.tinypic.com/2rqoadc.jpg
Monthly NINO3.4 SST Anomalies
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Monthly Global SST anomalies, according to the preliminary data, have dropped a healthy 0.04 deg C. The preliminary global SST anomaly is 0.055 deg C.
http://i54.tinypic.com/qqs77l.jpg
Monthly Global SST Anomalies
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A NOTE ABOUT THE YEAR-TO-YEAR VARIABILITY
The following is a repeat of a discussion from the past updates

As noted in the November 2010 SST Anomaly Update, the global SST anomalies do not appear as though they will drop to the level they had reached during the 2007/08 La Niña, even if one were to account for the differences in NINO3.4 SST anomalies. This of course will be raised by alarmists as additional proof of anthropogenic global warming.

But the reason the global SST anomalies have warmed in that time is due primarily to the fact that the East Indian and West Pacific Oceans (about 25% of the surface area of the global oceans) can warm in response to both El Niño and La Niña events. Refer to Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2, and the video included in La Niña Is Not The Opposite Of El Niño – The Videos. In addition, the North Atlantic also remains at elevated levels during La Niña events in response to the ENSO-related warming of the Kuroshio-Oyashio Extension. This was discussed and illustrated in the recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures.

Keep in mind, the warm water released from below the surface of the Pacific Warm Pool doesn’t simply vanish at the end of the El Niño.

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WEEKLY DATA
The weekly NINO3.4 SST anomaly data are continuing to cycle up and down at what appears to be the low end of the 2010/11 La Niña. They are at -1.68 deg C.
http://i53.tinypic.com/2edoxo6.jpg
Weekly NINO3.4 SST Anomalies
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Weekly Global SST Anomalies have dropped slightly, and it appears they also might have reached the seasonal low. Will they drop more? They are presently at +0.069 deg C.
http://i55.tinypic.com/jidk6c.jpg
Weekly Global SST Anomalies
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SOURCES
SST anomaly data is available through the NOAA NOMADS website:
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite=

Removing The Effects of Natural Variables - Multiple Linear Regression-Based or “Eyeballed” Scaling Factors

2011-01-28T05:59:00.027-05:00

I’ve moved to WordPress. This post can now be found at Removing The Effects of Natural Variables – Multiple Linear Regression-Based or “Eyeballed” Scaling Factors

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This is the second of a series of follow-up posts to Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?. The first follow-up was Notes On Polar Amplification.

This post discusses the impacts on global temperatures of a natural mode of Sea Surface Temperature variability: the El Niño-Southern Oscillation (ENSO). If this subject is new to you, refer to the post An Introduction To ENSO, AMO, and PDO – Part 1.

INTRODUCTION
My post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases? was cross posted at Watts Up With That? under the same title (Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?) In a step-by-step process, that post illustrated how I was removing the linear effects of El Niño and La Niña events and of volcanic eruptions from the global temperature record during the satellite era. I then went on to explain and provide links to more detailed explanations of the secondary effects of the ENSO process and how they impact the multidecadal trend.

There were a few comments on the WUWT thread about my use of “eyeballed” scaling factors. They wondered what I meant by “eyeballed”, expressed concern about a scaling factor that was not based on statistics, and suggested using EXCEL to determine the scaling factors.

“EYEBALLED” SCALING FACTORS
The scaling and lag I used in comparisons of the global temperatures and the ENSO and Volcano proxies were established by the visual appearance of the two variables, using the larger(est) event (the 1997/98 El Nino, and the 1991 Mount Pinatubo eruption) as reference. “Eyeballed” simply referred to the visual comparison of the impacted variable (global temperature) and the impacting variables (ENSO and Volcanic eruptions). Refer to Figure 1, which is also Figure 1 from Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?. Note how the NINO3.4 data has been scaled (multiplied by a factor of 0.16) and lagged (moved back in time 3 months) so that the rises of the two datasets are about the same during the evolution of the 1997/98 El Niño. Notice also how the response of the global temperature data to the lesser ENSO events after 2000 doesn’t always align with the NINO3.4 SST anomalies.

http://i53.tinypic.com/deuxdz.jpg
Figure 1

The data indicates that the larger events (such as the 1997/98 El Nino) are strong enough to overcome the noise that can mask the global response to lesser events. In other words, I used the larger 1997/98 ENSO event as reference because the response to it was clearest. Statistical methods such as linear (or multiple linear) regression rely on the relationship between two (or more) datasets over the term of the data. Any additional noise in the data during the smaller ENSO events (and during the lesser volcanic eruptions) may bias the results.

If we could determine the cause or causes of that additional noise, then adding those variables to a multiple linear regression analysis would be helpful.

MULTIPLE LINEAR REGRESSION

Analyse-it for Excel is a statistical add-on package for EXCEL. (It has a 30-day free trial period). One of its features is multiple linear regression. Their Multiple linear regression webpage provides a general description of this feature: “Linear regression, or Multiple Linear regression when more than one predictor is used, determines the linear relationship between a response (Y/dependent) variable and one or more predictor (X/independent) variables. The least-squares method is used to minimize the vertical distance between the response and the fitted linear line.”

The response variable discussed in this post is global temperature (represented by GISS LOTI from 60S-60N) and the predictor variables are ENSO (represented by NINO3.4 SST anomalies) and volcanic eruptions (represented by GISS Stratospheric Aerosol Optical Thickness data: ASCII data). Figure 2 shows the differences in the variability of those three datasets.
http://i52.tinypic.com/b7yplu.jpg
Figure 2

According to the EXCEL multiple regression software, the scaling factor to best fit the wiggles of the ENSO data to those in the global temperature data is 0.07262, and for the Aerosol Optical Thickness data it’s -3.191.

Note: The scaling factors determined by the regression software in this post are based on the “raw” data. I’ve used a 13-month running-average filter in the graphs after the fact to reduce the visual effects of seasonal variations and other noise.

In Figure 3, I’ve illustrated the multiple linear regression-estimated relationships between the GISS LOTI data, the NINO3.4 SST anomalies (ENSO), and the GISS Stratospheric Optical Thickness (Volcano) data. The scaling for the Volcano proxy data appears too large, while the scaling for the ENSO proxy appears too small. The global temperature anomaly (60S-60N) response to the 1997/98 El Niño almost doubled the rise in the scaled NINO3.4 SST anomalies.
http://i54.tinypic.com/2zdsltk.jpg
Figure 3

Figure 4 illustrates the result when we subtract scaled ENSO and Volcano proxy data from the Global Temperature data. That dataset is supposed to represent global temperature data that has been adjusted for ENSO and volcanic eruptions. I’ve also included the scaled NINO3.4 SST anomalies to show that much of the ENSO signal remains in the data. Note also how the response in global temperature lags the ENSO proxy.
http://i51.tinypic.com/20ffatc.jpg
Figure 4

Multiple linear regression does not appear to do a good job of removing the impacts of ENSO.

THREE-MONTH LAG

Referring back to Figure 1, we can see that the global temperature response during the 1997/98 El Niño lagged the NINO3.4 SST anomalies by 3 months. Let’s also look at the Volcano proxy. Figure 5 shows it and the ENSO-adjusted GISS Land-Ocean Temperature Index (LOTI) data from Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?. (Figure 5 was Figure 2 in that post.) In order to align the leading edge of the global temperature response to the 1991 eruption of Mount Pinatubo (the larger and clearer response), I had to lag the Volcano proxy 3 months.
http://i51.tinypic.com/8vyd54.jpg
Figure 5

If we shift the NINO3.4 SST anomalies and Aerosol Optical Thickness data three months, the EXCEL Analyse-It software, of course, calculates different scaling factors: 0.08739 for the ENSO proxy and -3.495 for the Volcano proxy. The ENSO- and Volcano-adjusted data using these updated scaling factors (based on a 3-month lag) is shown in Figure 6. Again, much of the ENSO signal remains.
http://i53.tinypic.com/2q1ib7t.jpg
Figure 6

Once again, multiple linear regression appears to have done a poor job of removing the effects of ENSO.

MODEL-PREPARED SCALING FACTORS

In a 2009 paper, Thompson et al created models of global temperature responses to ENSO and Volcanic eruptions. The paper was “Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights”. Link : http://www.atmos.colostate.edu/ao/ThompsonPapers/ThompsonWallaceJonesKennedy_JClimate2009.pdf

On page 2 they provided an overview of their methods: “The impacts of ENSO and volcanic eruptions on global-mean temperature are estimated using a simple thermodynamic model of the global atmospheric-oceanic mixed layer response to anomalous heating. In the case of ENSO, the heating is assumed to be proportional to the sea surface temperature anomalies over the eastern Pacific; in the case of volcanic eruptions, the heating is assumed to be proportional to the stratospheric aerosol loading.”
The same method was used in its companion paper Fyfe et al (2010), “Comparing Variability and Trends in Observed and Modelled Global-Mean SurfaceTemperature.”
http://www.atmos.colostate.edu/ao/ThompsonPapers/FyfeGillettThompson_GRL2010.pdf

Thompson et al provided a link to their “Global Mean”, “ENSO fit”, and “Volcano fit” data. Link to Data. Figure 7 shows the three Thompson et al datasets. Note: Thompson et al examined the data from 1900 to March 2009. Since we’re only looking at the change in temperature during the satellite era (dictated by the second, more recent of the two SST datasets used by GISS) I had to shift their global mean data down 0.25 degrees C to align it with the other datasets.
http://i53.tinypic.com/286zdpf.jpg
Figure 7

And Figure 8 shows what Thompson et al refer to as the “ENSO/Volcano Residual Global Mean” temperature anomalies after the ENSO and Volcano proxy signals have been removed. And, again, I’ve included the “ENSO fit” data to show how poorly it approximated the “Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature”. The response of their adjusted Global Mean Temperature to the 1997/98 El Niño is greater than their “ESNO fit” data, and this is after the effects of ENSO have supposedly been removed.
http://i51.tinypic.com/2z82jo8.jpg
Figure 8

In summary, linear regression and models prepared for climate studies appear to do a poor job of removing the linear effects of ENSO. If the secondary effects of ENSO were also included in the multiple linear regression, would the results be better?

These secondary effects are easy to see if we look at…

THE RESULTS USING THE EYEBALLED METHOD

As noted earlier, for the “eyeballed” method, I keyed the scaling of the ENSO and Volcano proxies visually off the leading edges of the 1997/98 El Niño and the 1991 eruption of Mount Pinatubo. Refer to Figure 9. In this example, I’ve reduced the scaling on the Volcano proxy data, so that its impact is approximately 0.2 deg C for the Mount Pinatubo eruption.
http://i55.tinypic.com/9iy1xd.jpg
Figure 9

The result when the ENSO and volcano signals are removed is shown in Figure 10. Also shown is the scaled ENSO proxy as a reference. The rises that occur after the 1986/87/88 and the 1997/98 El Niño events make it appear as though there is another lagged ENSO-related signal.
http://i54.tinypic.com/351gxds.jpg
Figure 10

And to see this signal, we invert the scaled NINO3.4 SST anomalies. Refer to Figure 11. That is, we multiply the NINO3.4 SST anomalies by a negative number (-0.1 scaling factor). The inverted ENSO data has not been lagged. But it has been shifted down 0.05 deg C to align it with the 1987/88 upward shift in the ENSO- and Volcano-adjusted global data. The two datasets diverge slightly at times between 1989 and 1996, but the adjusted global temperatures follow the inverted NINO3.4 SST anomalies reasonably well. Then there is a significant divergence during the evolution of the 1997/98 El Niño. The adjusted global temperature anomalies do not drop at that time proportionately to the El Niño. (And there is no reason global temperatures should drop a large amount. The majority of the warm water that fuels an El Niño comes from below the surface of the Pacific Warm Pool.)
http://i56.tinypic.com/2da0dx4.jpg
Figure 11

If we shift the NINO3.4 SST anomalies up 0.21 deg C, Figure 12, we can see that the adjusted global temperatures rise in response to the transition from El Niño to La Niña in 1998 and then, once again, they follow the general variations in the inverted NINO3.4 SST anomalies, but running in and out of synch with it.
http://i54.tinypic.com/msi0br.jpg
Figure 12

In other words, the vast majority of the rise in ENSO- and Volcano-Adjusted GISS Land-Ocean Temperature Index data could be explained by one or more detrended Sea Surface Temperature dataset(s) that mimicked inverted NINO3.4 SST anomalies with upward shifts, similar to what’s illustrated in Figure 13.
http://i54.tinypic.com/19y44y.jpg
Figure 13

I qualified the above statement with “detrended”. At a few alarmist blogs, I received a few negative comments about my Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases? post because I had used the trends in the SST subsets to explain the trend in global temperature. I had explained in the post why the upward trends were associated with the process of ENSO, but I received the complaints regardless. Those bloggers, of course, failed to read the explanation (It starts under the heading of “La Niña Events Are Not The Opposite Of El Niño Events”) or had elected to misrepresent my post. But in order to overcome this objection in this post, I’ll use detrended SST anomalies for the following illustrations. The same and other bloggers also complained about the minimal sizes of the Kuroshio-Oyashio Extension and South Pacific Convergence Zone (SPCZ) Extension SST subsets I used in the “Can Most” post. Those areas were used because they had the strongest warming signals during a La Niña. But since the objections exist, I’ll use SST datasets that represent larger portions of the global oceans.

There are two detrended sea surface temperature subsets covering significant portions of the global oceans that have the same upward changes in temperature during those two El Niño to La Niña transitions shown in Figure 13. But if we had relied on the scaling factors suggested by the multiple linear regression, or if we had used a model similar to the one created by Thompson et al, would we have noticed the relationship?

EAST INDIAN-WEST PACIFIC SST ANOMALIES
The first of the SST subsets is the East Indian-West Pacific Ocean. The coordinates of this dataset are 60S-60N, 80E-180E. I’ve highlighted those coordinates in the following map. Figure 14 is a correlation map of annual (January to December) East Indian-West Pacific SST anomalies and annual GISS LOTI data for 1982 to 2010. Much of the Northern Hemisphere land surface temperature anomalies vary with the East Indian-West Pacific SST anomalies. That is, when the East Indian-West Pacific SST anomalies rise in those upward ENSO-induced steps, so do those areas highly correlated with it.
http://i53.tinypic.com/14kz9s4.jpg
Figure 14

I have discussed the East Indian-West Pacific dataset in many posts over the past two years, so I do not intend to repeat the discussion here. Those posts started with Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2. The most detailed explanation of why the East Indian-West Pacific SST anomalies shift upwards as a response to those two ENSO events is provided in my series of posts:
More Detail On The Multiyear Aftereffects Of ENSO – Part 1 – El Nino Events Warm The Oceans
And:More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents.
And:
More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Nino & La Nina Events

And for those who like visual aids, refer to the two videos included in La Niña Is Not The Opposite Of El Niño – The Videos.

Again, to overcome one of the complaints, I need to detrend the East Indian-West Pacific data. Detrending is said to eliminate the “global warming signal”. So I employed the same method used for the Atlantic Multidecadal Oscillation data. HADISST is the long-term Sea Surface Temperature anomaly dataset used by GISS in their LOTI product. The long-term HADISST (1870-2010) East Indian-West Pacific SST anomalies had a linear trend of 0.44 deg C per Century, and that’s slightly higher than the global SST anomaly trend of 0.39 deg C per century. To detrend it, I subtracted the linear trend values for each month from the East Indian-West Pacific SST data.

And as shown in Figure 15, the detrended East Indian-West Pacific SST anomalies could easily explain much of the rise in the ENSO- and Volcano-Adjusted GISS LOTI data since 1982. The similarities between the adjusted global temperature data and the East Indian-West Pacific SST anomaly data are remarkable.
http://i54.tinypic.com/2nalcab.jpg
Figure 15

As discussed and illustrated in the linked posts, the upward shifts in the East Indian-West Pacific SST anomalies are secondary effects of the warm water that was leftover from the 1986/87/88 and 1997/98 El Niño events and the results of the La Niña process itself. Basically, the ENSO processes cause the SST for this part of globe to rise in response to both El Niño and La Niña events. And the effects are cumulative if a La Niña follows an El Niño. The cumulative effect can be seen in the following animation. It shows a series of maps of 12-month average global SST anomalies, and it runs from the start of the 1997/98 El Niño to the end of 1998/99/00/01 La Niña. To its right is a graph of scaled NINO3.4 SST anomalies and the SST anomalies of the East Indian-West Pacific Oceans. The data in the graph have been smoothed with a 12-month running-average filter to match the maps.
http://i51.tinypic.com/2qiwscz.jpg
Animation 1

And for reference, Animation 2 includes the Sea Surface Temperature Anomalies of the rest of the oceans (East Pacific, Atlantic, and West Indian), and this dataset includes the North Atlantic with its Atlantic Multidecadal Oscillation.
http://i51.tinypic.com/b46lph.jpg
Animation 2

The next variable is widely known, but it is often overlooked.

THE ATLANTIC MULTIDECADAL OSCILLATION (AMO)
The second dataset that matches the upward steps in the adjusted GISS LOTI data is the Atlantic Multidecadal Oscillation or AMO data. For those new to the AMO, refer to the post An Introduction To ENSO, AMO, and PDO -- Part 2.

The AMO data used here is detrended North Atlantic SST anomaly data. Again, as noted in the Wikipedia Atlantic Multidecadal Oscillation webpage, “detrending is intended to remove the influence of greenhouse gas-induced global warming from the analysis.” The data is available through the NOAA Earth System Research Laboratory (ESRL) Atlantic Multidecadal Oscillation webpage (the AMO unsmooth, long: Standard PSD Format data).

Figure 16 is a correlation map of annual (January to December) Atlantic Multidecadal Oscillation (AMO) data and annual GISS LOTI data for 1982 to 2010. Like the East Indian-West Pacific SST anomalies, much of the northern hemisphere varies in temperature with the AMO. Again, this means that much of the northern hemisphere surface temperatures rise with the upward steps in the AMO data.
http://i55.tinypic.com/opsbv9.jpg
Figure 16

And for those interested, Figure 17 is a GISS LOTI trend map created at the GISS Global Mapswebpage. It illustrates the rise in surface temperature anomalies from 1982 to 2010. Note the similarities between it and the correlation maps in Figures 14 and 16. For the most part, the regions where the AMO and the East Indian-West Pacific SST anomalies correlate with the Global GISS LOTI data are also where most of the rises in surface temperature occurred. There are a few areas with differences, but the maps agree quite well.
http://i56.tinypic.com/2lar62e.jpg
Figure 17

The AMO data is compared to the adjusted GISS LOTI data in Figure 18. Again, note the agreement between the AMO data and the adjusted GISS LOTI data.
http://i55.tinypic.com/eu0sk0.jpg
Figure 18

And if we compare the ENSO- and Volcano-adjusted GISS LOTI data to both detrended SST-based datasets, Figure 19, we can see that it wouldn’t require a lot of effort to explain most of the global warming from 1982 to 2010 using the AMO and detrended East Indian-West Pacific SST anomaly datasets.
http://i53.tinypic.com/2gt9mic.jpg
Figure 19

Let’s add the AMO and the detrended East Indian-West Pacific SST anomalies to the multiple regression analysis and see how that impacts the results.

MULTIPLE LINEAR REGRESSION WITH ENSO, VOLCANO, AMO, AND EAST INDIAN-WEST PACIFIC SST DATA

The NINO3.4 SST anomalies, the mean optical thickness data, the AMO data, and the detrended East Indian-West Pacific (EIWP) SST anomalies were all entered into the EXCEL multiple linear regression software as predictors with the GISS LOTI data as the response variable. EXCEL determined the scaling factors listed in parentheses in Figure 20. The GISS LOTI data illustrated has been adjusted by those four scaled variables. I’ve also included the scaled NINO3.4 SST anomalies as reference. The low frequency variations in the adjusted GISS data mimic the ENSO proxy before the 1997/98 El Niño. They show little relationship from 1998 to 2007, and then they appear to mimic one another again.
http://i51.tinypic.com/f4g3ns.jpg
Figure 20

Figure 21 compares the unadjusted GISS LOTI data (60S-60N) to the data that has been adjusted by the four factors. The trend of the adjusted data is approximately 27% of the unadjusted GISS LOTI data. In other words, approximately 73% of the rise in global (60S-60N) surface temperature could be natural.
http://i51.tinypic.com/dpvhhd.jpg
Figure 21

And for the sake of discussion, I had EXCEL perform the regression analysis again, but I used “raw” East Indian-West Pacific SST anomalies instead of the detrended data. As one would expect, the multiple regression software created different scaling factors, which are listed in Figure 22. It compares the resulting adjusted global (60S-60N) temperature anomalies to the unadjusted GISS LOTI data. In this instance, the trend of the adjusted data is approximately 18% of the unadjusted data, which is similar to the result I reached using the “eyeball” method and different natural factors in Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?.
http://i51.tinypic.com/2s60p40.jpg
Figure 22

Would the eyeball method with the AMO and East Indian-West Pacific adjustments reduce the global temperature trend by similar amounts? We’ll have to look at that in another post. This one is long enough.

THANKS TO…
…Michael D Smith for suggesting EXCEL to weed out lags, scaling factors, etc. Without his suggestion, I would not have looked for the Analyse-it for Excel. I think I’m going to buy it when the trial period is done.

SOURCES
The GISS LOTI, Reynolds OI.v2 SST (for the NINO3.4 SST anomalies), and HADISST (for the detrended East Indian-West Pacific SST anomalies) data used in this post are available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

The Stratospheric Aerosol Optical Thickness data is available from GISS:
http://data.giss.nasa.gov/modelforce/strataer/tau_line.txt

And the Atlantic Multidecadal Oscillation data is available from NOAA ESRL:
http://www.esrl.noaa.gov/psd/data/correlation/amon.us.long.data

Mid-January 2011 SST Anomaly Update

2011-01-25T04:40:00.005-05:00

I’ve moved to WordPress. This post can now be found at Mid-January 2011 SST Anomaly Update

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This mid-month update only includes the shorter-term NINO3.4 and global SST anomaly graphs; that is, the ones from January 2004 to present. Both the NINO3.4 and Global SST anomalies have dropped.

As noted in the November 2010 SST Anomaly Update, the global SST anomalies do not appear as though they will drop to the level they had reached during the 2007/08 La Niña, even if one were to account for the differences in NINO3.4 SST anomalies. This of course will be raised by alarmists as additional proof of anthropogenic global warming.

But the reason the global SST anomalies have warmed in that time is due primarily to the fact that the East Indian and West Pacific Oceans (about 25% of the surface area of the global oceans) can warm in response to both El Niño and La Niña events. Refer to Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2, and the video included in La Niña Is Not The Opposite Of El Niño – The Videos.

Keep in mind, the warm water released from below the surface of the Pacific Warm Pool doesn’t simply vanish at the end of the El Niño.

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NINO3.4
NINO3.4 SST anomalies for the week centered on January 12, 2011 show that central equatorial Pacific SST anomalies have dropped in the past two weeks. They cycled back down to near their earlier low for this La Niña season. They’re at approximately -1.8 deg C.

http://i53.tinypic.com/zxp5l0.jpg
NINO3.4 SST Anomalies - Short-Term

GLOBAL
Weekly Global SST anomalies have dropped to a new seasonal low, but they are far from the low reached during the 2007/08 La Niña. They are presently at +0.04 deg C.
http://i51.tinypic.com/2choryb.jpg
Global SST Anomalies - Short-Term

SOURCE
OI.v2 SST anomaly data is available through the NOAA NOMADS system:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite

Notes On Polar Amplification

2011-01-15T16:52:00.006-05:00

I’ve moved to WordPress. This post can now be found at Notes On Polar Amplification

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This post shows, based on GISS LOTI data, there is nothing unusual about the Polar Amplification taking place during the current warming period and also shows Polar Amplification exaggerates the cooling during periods when global temperatures decline.

This post is a follow-up to my post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?

BACKGROUND
I used GISS Land-Ocean Temperature Index (LOTI) data for the latitudes of 60S-60N in my post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases? Even though I explained why I had excluded the polar data, the fact that I had deleted it was not well received by some around the blogosphere. Why did I exclude the polar data?

First, GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data. GISS then extends land surface data out over the Arctic and Southern Oceans. Since land surface temperatures warm more than sea surface temperatures during warming periods (and cool more during cooling periods), replacing sea surface temperature data with land-based data as GISS does creates a bias during seasons of sea ice melt.

Second: As I wrote in that post, Keep in mind that the Arctic is amplifying the effects of the rise in temperature at lower latitudes. This is the basis of the concept of polar amplification. If the vast majority of the change in temperature at the lower latitudes is natural, the same would hold true for the Arctic.

My simple explanation of Polar Amplification received complaints as well, but it does not differ significantly from other definitions.

For example, the Wikipedia definition of Polar amplification : “Polar amplification is defined by International Arctic Science Committee on page 23 of the Arctic Climate Impact Assessment ‘Polar amplification (greater temperature increases in the Arctic compared to the earth as a whole) is a result of the collective effect of these feedbacks and other processes.’ It does not apply to the Antarctic, because the Southern Ocean acts as a heat sink. It is common to see it stated that ‘Climate models generally predict amplified warming in polar regions’, e.g. Doran et al. However, climate models predict amplified warming for the Arctic but only modest warming for Antarctica.”

So let’s take a look at the GISS data and see if my description of Polar Amplification is confirmed or contradicted by it.

THE GISS LOTI DATA INDICATES POLAR AMPLIFICATION EXAGGERATES TEMPERATURE INCREASES AND DECREASES
Figure 1 shows Annual (January to December) GISS LOTI data (1880 to 2010) that has been detrended. (Source: Global-mean monthly, seasonal, and annual means webpage.) Figure 1 serves as a reference for the multidecadal periods of warming and cooling used in this post. Based on the detrended annual GISS LOTI data, the recent warming period began when global temperature anomalies rose from their minimum in 1976. The cooling era runs from its maximum in 1944 to the 1976 minimum. And the early 20th Century warming period starts at the minimum in 1917 and ends in 1944. (Later, I explain why I used detrended data in Figure 1.)

http://i53.tinypic.com/349easy.jpg
Figure 1

The GISS website allows users to create two different types of maps at their Global Maps webpage:(1) global temperature anomalies, and (2) “trends,” which are the changes in surface temperature over user-defined periods (based on local linear trends).

GISS also creates “Zone Mean” data in their Surface Temperature Analysis output page. If you were to scroll down to the plot below the GISS map, you’d find a graph with “Latitude” as the x-axis and “Zonal Mean” as the y-axis. When “trend” maps are created using the GISS map-making feature, GISS calculates the average surface temperature change for every 2-degree latitude band from pole to pole, where the temperature change data is based on the local linear trends for the period selected. The data for those graphs is also available toward the bottom of the page.

Figure 2 shows the average Change in Annual Surface Temperature Anomalies per latitude band for the periods of 1944 to 1976 (the 20th Century cooling period) and 1976 to 2010 (the current warming period), according to the GISS data. Arctic temperatures dropped more than the rest of the hemisphere or globe during the cooling period. The reverse holds true during the current warming period. Figure 2 illustrates that, since the early 1940s, Polar Amplification exaggerates the multidecadal change in global temperature when temperatures rise and when they cool.
http://i54.tinypic.com/ruzbxh.jpg
Figure 2

THE DATA FOR THE EARLIER WARMING PERIOD REVEALS THE CURRENT ARCTIC AMPIFICATION IS NOT UNUSUAL
Referring to Figure 1 again, Global Temperatures warmed from 1917 to 2010. In Figure 3, I’ve added the Zonal Mean data for the period of 1917 to 1944 to the graph. The change in Arctic surface temperature anomalies during the early warming period is comparable to the current warming period. One could conclude that the Polar Amplification during the current period is not unusual. It’s a natural response to warming.
http://i51.tinypic.com/2v8j1gg.jpg
Figure 3

ON THE USE OF DETRENDED GISS LOTI DATA IN FIGURE 1
Figure 4 shows the GISS LOTI data without the detrending. I used the detrended GISS LOTI data because it was difficult to determine when the early warming period started in the “raw” data, Figure 4. Was it 1907 or 1917? But based on the detrended data, the minimum occurred in 1917.
http://i55.tinypic.com/25h00pj.jpg
Figure 4

If we were to use 1907 as the start year for the early 20th Century warming period, it would make a significant difference in the Arctic warming. The GISS Zonal Mean data with that start year indicates the Arctic warmed much more during the earlier warming period than the current warming period. Refer to Figure 5. (Wouldn’t want someone to accuse me of cherry-picking the start year, would I? So I used the 1917 start date).
http://i55.tinypic.com/2e67o1w.jpg
Figure 5

CLOSING NOTES
This post illustrated that Polar Amplification not only causes the Arctic to exaggerate a rise in global surface temperature, it also causes the Arctic to amplify a decrease in global surface temperature. It additionally showed the current warming of the Arctic is not unusual.

Someone might try to argue the Arctic data is very sparse during the early warming period, and the lack of data prevents a true comparison of the two warming periods. Realistically, one needs to consider the fact that most of the Arctic data presented by GISS in all periods is make-believe data. During seasons with sea ice, GISS uses 1200km radius smoothing to infill the vast majority of the Arctic data. That part of the GISS 1200km radius smoothing process for the Arctic is logical.

BUT (big but) sea ice melts every year, some years more than others. During seasons of sea ice melt, the fact that GISS deletes the SST data makes a significant difference, because SST varies less and has a lower trend than land surface data. This biases the Arctic (and Southern Ocean) data. GISS also deletes the SST data so they can extend the land data over open waters; that is, if they were to include the sea surface temperature data when it’s available, GISS could not extend the land data over the open ocean to reach the pole.

The Arctic amplifies the lower latitude trends when the globe is warming and when it is cooling. The Arctic should amplify any multidecadal warming or cooling signal regardless of source. If a major portion of the current warming period is due to natural variability, as suggested in the post Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?, then I believe my statement in that post was correct. And that statement was, If the vast majority of the change in temperature at the lower latitudes is natural, the same would hold true for the Arctic.

hidethedecline.eu Hides A Post

2011-01-12T18:57:00.007-05:00

I’ve moved to WordPress. This post can now be found at hidethedecline.eu Hides A Post

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UPDATE (January 21, 2011): On the HideTheDecline post Where should we expect UHI in temperature data 1979-2009?, I have added a request of Frank for his future posts:

Title: Hopefully My Final Reply To Frank

Frank, with respect to your ongoing efforts along this line research, please do you and me a favor.

Please do not refer to or link my posts, please do not refer to me by name, and please do not link to or use my graphs in your posts. If you adhere to my request, I will have no need to return to your website and find error with what you've written and presented.

XXXXXXXXXXXXXXXXX

UPDATE (January 16, 2011): Frank Lansner has again hidden the second of his posts in which he attempts to use TLT anomalies to identify Urban Heat Island (UHI) effect. The link…
http://hidethedecline.eu/pages/posts/discussion-of-the-article-uah-reveals-urban-heat-210.php
…in my January 13, 2011 update (below) brings you to a page with “Sorry, no active content to display”.

It appears that Frank Lansner of HideTheDecline has now merged the follow-up post with the original UAH reveals UrbAn Heat post. And the comment thread from the second post, which held my criticisms of the posts and his replies, has now been turned into a linked Word file…
http://hidethedecline.eu/media/UAHUHI/bobsdiscussion.doc
…with fonts so small the comments are difficult to read without changing their size or your screen zoom. The returns and spacing between paragraphs have disappeared so that they are hard to follow.

Frank has now written a third post in the series called Where should we expect UHI in temperature data 1979-2009? He continues to subscribe to the error-filled line of thought that the difference between TLT and surface temperatures can be used to identify areas of UHI effect, Figure 2. He attempts to confirm this by using population growth maps, Figure 6. But he misses the point once again. UHI exists in areas that he has identified as “No UHI”, but there is limited to no UHI effect in other areas that he identifies as “UHI”.

My final comment to Frank (refer to above linked Word file) was titled "End Of Discussion - Goodbye Frank".

The comment reads:

Frank replied, "So basically, your result says that the UAH-Grounbased difference is from oceans..."

Wrong. Read what I wrote again. I wrote that the trend in TLT anomalies over the North Atlantic was higher than the SST trend. I did not say “the UAH-Grounbased difference is from oceans.” You misrepresent what I write repeatedly. Every chance you get, you redirect the discussion, stating that I have said one thing, when, in fact, I have said the opposite and presented data to confirm it.

What about my comment about inland Africa, Frank? There, I showed you that the GISS trend was considerably higher than the TLT trend:
http://i43.tinypic.com/if1oh5.png

I used that as an example that UHI effect is not the only reason for the difference between land surface data and TLT. There’s not a lot of UHI in the Sahara desert, Frank. At WUWT I provided you with a link to the post that graph came from. Here it is again:
http://bobtisdale.blogspot.com/2009/06/part-2-of-comparison-of-gistemp-and-uah.html

In that post, there are many graphs of many areas of the globe that disagree with your initial premise that UHI is responsible for the difference between TLT anomalies and surface temperature anomalies.

In my latest reply to you at my blog, I wrote, All you are doing is rehashing discussions we've already had on the thread at WUWT. I've already presented to you in multiple ways why what you had written there was wrong. My rewriting it another way apparently doesn't help because you become defensive and you argue.

And what are you doing in your latest reply on 14th January, 2011 at 14:44:54?

You being defensive and you are arguing.

And in my last comment at my blog, I suggested, Reread what I discussed with you on that thread. Attempt to understand what people who disagreed with you were saying to you. Don't become defensive. Don't become argumentative. Steven Mosher suggested ways for you redo all of your graphs. Have you tried it to see if it presents different results?

Have you tried that? Apparently not.

In your latest reply here, you wrote, “You get the opposite result compared to me, so it’s a little early to conclude anything much for now.”

Any of your readers, those who are interested in determining whether you or I are right, can reproduce the graphs I had presented to you, Frank. They can see for themselves I have presented reality and that your original post was flawed.

Since you, Frank, are still arguing and still misrepresenting what I have written, I cannot continue this discussion.

Your readers will understand why I have chosen to end this conversation. You might try to spin it, but your readers will understand.

Goodbye, Frank.
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UPDATE January 13, 2011: The post at HideTheDecline has reappeared.
http://hidethedecline.eu/pages/posts/discussion-of-the-article-uah-reveals-urban-heat-210.php

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Bloggers have comments deleted all the time. Most don’t like it because of the time and effort that goes into writing it. I just had something happen that’s a little bit different. Not only has my comment disappeared, so has the entire post. The title of the blog post was “List of claimed ‘errors’ mostly received from Bob Tisdale in the comments on WUWT”, and for some reason it caught my eye. It ran at the blog HideTheDecline.

Someone at HideTheDecline must have been displeased with me.

I didn’t mind having my name in the title of the post. I didn’t mind having my arguments misrepresented. But I did not like having my comment on that HideTheDecline thread deleted.

BACKGROUND
I’ve prepared a number of posts that compare TLT and surface temperature data, and if I find a blog post or comment that contradict what I’ve found, I leave a reply, explaining where I disagree. I disagreed with much of the UAH and UHI post by Frank Lansner that was cross-posted at WUWT. Apparently Frank was not satisfied with the lack of progress of his arguments at WUWT UAH and UHI, so on December 26, 2010, he prepared a post with my name in the title. Some sort of payback? Who knows?

I discovered Frank’s post at hidethedecline on January 8, 2011, and replied in a comment that was name- and date-stamped with “By Unknown on 8th January, 2011 at 01:35:38”. My comment remained on that hidethedecline thread for a couple of days without reply from Frank. When I went back to check for a reply today, January 12, 2011, I discovered not only was my comment gone, but so was the entire post. It vanished. No explanation. Someone had deleted that entire HideTheDecline post.

THE POST REMAINS IN GOOGLE CACHE
The google cache version of Frank’s post (without comments) is here: Cached. And here’s a screen cap of the cache page:

http://i54.tinypic.com/15z2y6h.jpg

MY COMMENT
The title of my comment was “If You Had Performed a Reasonable Analysis...” and it read:

If YOU had performed a reasonable analysis, Frank, you would have discovered that the vast majority of the difference between TLT and surface data exists in only one area of the globe. All areas would be impacted by UHI if it had a significant effect, but they are not. And that fact alone contradicts your claims that UHI is the primary reason for the difference between surface and TLT data.

If you had researched it a little more, you would have discovered that much of the difference between TLT and surface data occurs over the North Atlantic. One wouldn't think that there is a lot of UHI effect on the North Atlantic, Frank.
http://bobtisdale.blogspot.com/2010/10/on-differences-between-surface-and-tlt.html

If you had performed a reasonable analysis, Frank, you would have discovered that GISS land surface data for much of the United States has a lower trend than the TLT data:
http://i40.tinypic.com/nget8k.png

If you had performed a reasonable analysis, Frank, you would have discovered that there is little difference between GISS land surface data and TLT data for much of Europe:
http://i40.tinypic.com/k6ija.png

If UHI was such a dominant factor, why are they so similar, Frank?

Everyone knows the major difference between GISS and TLT data is how GISS treats the Arctic data.
http://bobtisdale.blogspot.com/2010/05/giss-deletes-arctic-and-southern-ocean.html

THE COMMENT STILL RESIDES IN GOOGLE
Since the Google cache of the post does not contain any comments, someone might think I’ve fabricated it. So I Googled the opening of my comment in quotes “If YOU had performed a reasonable analysis, Frank, you would have discovered” and Google returned with the link shown in the following screen cap.
http://i51.tinypic.com/2vdqlbo.jpg
I clicked on the link, but my comment did not exist.

I JUST SEEMS A LITTLE ODD
It’s odd that a blogger would take the time to write a post, then delete the entire post because he didn’t like a comment on the thread, but still leave the original erroneous post UAH reveals UrbAn Heat.

I’m not sure what to make of it.

Please don’t delete another of my comments, Frank Lansner.

December 2010 SST Anomaly Update

2011-01-11T20:18:00.018-05:00

I’ve moved to WordPress. This post can now be found at December 2010 SST Anomaly Update

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MONTHLY SST ANOMALY MAPThe map of Global OI.v2 SST anomalies for December 2010 downloaded from the NOMADS website is shown below. There was a mix of variations in ocean basin SST anomalies this month. Some rose, some fell. The result was a very minor rise in global SST anomalies (+0.002 deg C). They are presently at +0.099 dg C.
http://i53.tinypic.com/2r6yufb.jpg
December 2010 SST Anomalies Map (Global SST Anomaly = +0.099 deg C)

MONTHLY OVERVIEW

Monthly NINO3.4 SST anomalies continue to vary at or near the seasonal low for this La Niña. The Monthly NINO3.4 SST Anomaly is -1.53 deg C.

The drop in Northern Hemisphere this month (-0. 019 deg C) was countered by an equal increase in the Southern Hemisphere, so globally, as noted before, rose slightly (+0.002 deg C).
http://i54.tinypic.com/23mv9sp.jpg
Global
Monthly Change = +0.002 deg C
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http://i51.tinypic.com/2ivn3n8.jpg
NINO3.4 SST Anomaly
Monthly Change = -0.070 deg C

EAST INDIAN-WEST PACIFIC

The SST anomalies in the East Indian and West Pacific cycled back up this month.

I’ve added this dataset in an attempt to draw attention to what appears to be the upward steps in response to significant El Niño events that are followed by La Niña events.
http://i55.tinypic.com/2udz7yh.jpg
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = +0.060 deg C

Further information on the upward “step changes” that result from strong El Niño events, refer to my posts from a year ago Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2

And for the discussions of the processes that cause the rise, refer to More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Niña Events Recharge The Heat Released By El Niño Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents -AND- More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Niño & La Niña Events

The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.

NOTE ABOUT THE DATA
The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from December 1981 to December 2010.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES http://i52.tinypic.com/2ymfwok.jpg
Northern Hemisphere
Monthly Change = -0.019 deg C
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http://i54.tinypic.com/f3xddu.jpg
Southern Hemisphere
Monthly Change = +0.019 deg C
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http://i55.tinypic.com/199vlc.jpg
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = -0.123 deg C
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http://i56.tinypic.com/sbkhw8.jpg
South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = +0.057 deg C

Note: I discussed the upward shift in the South Atlantic SST anomalies in the post The 2009/10 Warming Of The South Atlantic. Will the South Atlantic return to the level it was at before that surge or will it remain at a new plateau?

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http://i52.tinypic.com/wvv706.jpg
North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = +0.106 Deg C
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http://i53.tinypic.com/nxq06p.jpg
South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = +0.067 deg C
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http://i56.tinypic.com/55hyzt.jpg
Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = -0.025 deg C
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http://i54.tinypic.com/rrlbom.jpg
Arctic Ocean (65 to 90N)
Monthly Change = -0.139 deg C
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http://i56.tinypic.com/amwg8k.jpg
Southern Ocean (60 to 90S)
Monthly Change = -0.080 deg C

WEEKLY SST ANOMALIES
The weekly NINO3.4 SST anomaly data portray OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.48 deg C.
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Weekly NINO3.4 (5S-5N, 170W-120W)

The weekly global SST anomalies are at +0.082 deg C.
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Weekly Global

A NOTE ABOUT THE YEAR-TO-YEAR CHANGES
The following is a repeat of a discussion from earlier updates.

As noted in the November 2010 SST Anomaly Update, the global SST anomalies do not appear as though they will drop to the level they had reached during the 2007/08 La Niña, even if one were to account for the differences in NINO3.4 SST anomalies. This of course will be raised by alarmists as additional proof of anthropogenic global warming.

But the reason the global SST anomalies have warmed in that time is due primarily to the fact that the East Indian and West Pacific Oceans (about 25% of the surface area of the global oceans) can warm in response to both El Niño and La Niña events. Refer to Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2, and the video included in La Niña Is Not The Opposite Of El Niño – The Videos. In addition, the North Atlantic also remains at elevated levels during La Niña events in response to the ENSO-related warming of the Kuroshio-Oyashio Extension. This was discussed and illustrated in the recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures.

Keep in mind, the warm water released from below the surface of the Pacific Warm Pool doesn’t simply vanish at the end of the El Niño.

SOURCE
The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?

2011-01-09T20:28:00.042-05:00

I’ve moved to WordPress. This post can now be found at Can Most Of The Rise In The Satellite-Era Surface Temperatures Be Explained Without Anthropogenic Greenhouse Gases?

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In this post, I divide the globe (60S-60N) into two subsets and remove the linear effects of ENSO and volcanic eruptions from GISS Land-Ocean Temperature Index data since 1982. This is done using common methods. I further adjust the data to account for secondary ENSO-related processes. The Sea Surface Temperature subsets used for these adjustments are identified. The processes are briefly discussed, supported by links to past posts, and the data are presented that support the existence of these secondary effects. An additional volcanic aerosol refinement that increases the global trend is made. The bottom line is, the GISS LOTI and Reynolds OI.v2 SST data indicates that natural variables could be responsible for approximately 85% of the rise in global surface temperature since 1982. I’ll be the first to point out that I qualified my last sentence with the word “could”. This post illustrates a story presented by the data, nothing more. But this basic evaluation indicates these secondary effects of ENSO require further research.

This post continues with the two-year series of posts that basically illustrate that the effects of El Niño-Southern Oscillation (ENSO) cannot be accounted for using a single index like a commonly used SST-based dataset such as NINO3.4, or CTI, or MEI. These indices represent only the sea surface temperature of the central and eastern equatorial Pacific (that’s modified in the case of the MEI). They do not represent the process of ENSO. They do not account for the warm water that is returned to the western Pacific and redistributed during the La Niña. This post provides further evidence of those effects.

This post is long but I elected not to divide it in two. It’s 6,000 words or 13 single-spaced pages in length. It includes 32 Figures, a gif animation, and a video. So there’s a lot to digest. I tried to anticipate questions and answer them.

REMOVING THE LINEAR EFFECTS OF ENSO AND VOLCANIC AEROSOLS HELP TO SHOW THE TIMING OF THE WARMING
Many papers and blog posts that attempt to prove the existence of anthropogenic global warming remove the obvious linear effects of El Niño-Southern Oscillation (ENSO) events and of stratospheric aerosols discharged by explosive volcanic eruptions. An example is Thompson et al (2009) “Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights”… http://www.atmos.colostate.edu/ao/ThompsonPapers/ThompsonWallaceJonesKennedy_JClimate2009.pdf
…and its companion paper Fyfe et al (2010), “Comparing Variability and Trends in Observed and Modelled Global-Mean Surface Temperature.”
http://www.atmos.colostate.edu/ao/ThompsonPapers/FyfeGillettThompson_GRL2010.pdf

Let’s run through the process using GISS Land-Ocean Temperature (LOTI) data. That’s their global temperature anomaly dataset with the 1200km radius smoothing. A known problem with that dataset is that GISS Deletes Arctic And Southern Ocean Sea Surface Temperature (SST) Data. Since that creates a bias, we’ll delete the GISS LOTI data where they extend land surface data (with its higher variability) out over the oceans. That is, we’ll confine the data used in this post to 60S-60N.

Someone is bound to complain that I’ve deleted the Arctic data from the GISS LOTI data and that the Arctic is warming much faster than lower latitudes. Keep in mind that the Arctic is amplifying the effects of the rise in temperature at lower latitudes. This is the basis of the concept of polar amplification. If the vast majority of the change in temperature at the lower latitudes is natural, the same would hold true for the Arctic. Regardless, these latitudes were also chosen because the effects I want to illustrate with this post are relatively easy to display using them.

Back to the data: since GISS switches sources for their Sea Surface Temperature data from HADISST to Reynolds OI.v2 data in December 1981, we’ll look at the LOTI data starting in 1982. Smith and Reynolds (2004) Improved Extended Reconstruction of SST (1854-1997)] states the following about the OI.v2 SST data: “Although the NOAA OI analysis contains some noise due to its use of different data types and bias corrections for satellite data, it is dominated by satellite data and gives a good estimate of the truth.”

The truth is a nice place to start.

And we’ll smooth the monthly data with a 13-month running-average filter to lessen noise and season variations.

Figure 1 shows monthly GISS LOTI data (60S-60N), from March 1982 to November 2010, compared to NINO3.4 SST anomalies. The NINO3.4 data represent the Sea Surface Temperature of a region in the central equatorial Pacific bound by the coordinates of 5S-5N, 170W-120W. NINO3.4 SST anomalies are a commonly used proxy for the strength and frequency of El Niño and La Niña events, also known as ENSO. (And for those new to ENSO, refer to An Introduction To ENSO, AMO, and PDO – Part 1.) Note also that the NINO3.4 data has been scaled (multiplied by a factor of 0.16) so that the rises of the two datasets are about the same during the evolution of the 1997/98 El Niño. The NINO3.4 SST anomalies have also been shifted down 0.01 deg C and moved back in time by 3 months (lagged) to align the leading edges of the two datasets at that time. (The data in the graph starts in March 1982 because of the 3-month lag in the NINO3.4 data.) I chose the 1997/98 El Niño because that event wasn’t opposed by a volcanic eruption and it was large enough to overwhelm the background noise. As you can see, the wiggles of lesser El Niño events after 2000 don’t match as well.

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Figure 1

Many of the large year-to-year changes in global temperatures are removed when we subtract the scaled NINO3.4 data from the GISS Global (60S-60N) LOTI data. Refer to the “olive drab” curve in Figure 2. Since the NINO3.4 data has a negative trend since 1982, we increase the trend in the GISS LOTI data by subtracting it. Also note how the ENSO-adjusted GISS LOTI data has “flattened” after 1998. Without the volcano-related dip and rebound starting in 1991, the period from 1988 to 1998 would also be relatively flat. It appears as though the ENSO-adjusted GISS LOTI rose in two steps since 1982. Let’s remove the cooling effects of the El Chichon and Mount Pinatubo eruptions to see if that holds true. We’ll use a GISS dataset that represents Stratospheric Aerosols. (ASCII data) Like the ENSO Proxy, we’ll scale the data and lag it. The estimated range of the impact of Mount Pinatubo on Global Temperatures varies from 0.2 to 0.5 deg C, depending on the study, so we’ll use approximately 0.35 deg C to account for its effect. Visually, that scaling appears right.
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Figure 2

Figure 3 illustrates the GISS Land-Ocean Temperature Index (LOTI) anomaly data with the linear effects of ENSO events and the effects of large volcanic eruptions removed. Also illustrated is the linear trend. I’ve included the linear trend line to illustrate the effect the straight line has on the appearance of the data. The trend line gives the misleading impression that there has been a constant but noisy rise in global temperatures.
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Figure 3

During the discussion of Figure 2, I noted that the data appeared to flatten after 1998. The upward steps in the data can be illustrated if we present the period average temperature anomalies for the three periods of 1982 to 1987, 1988 to 1997, and 1998 to 2010.
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Figure 4

WHAT COINCIDES WITH THE UPWARD STEPS?
The timings of those upward steps coincide with the transitions from the large El Niño to La Niña events that took place in 1988 and 1998. This can be seen in Figure 5, which includes the adjusted GISS LOTI data. The other dataset is scaled NINO3.4 SST anomalies that have been inverted (multiplied by a negative number). Figure 5 is a gif animation, and in it, the NINO3.4 data shifts up and down. That was done to show how precisely the upward steps in the adjusted GISS data coincide with ENSO transitions. The adjusted GISS data trails the NINO3.4 data by a month or two. And the scales are correct for both upward steps.
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Figure 5

But how can ENSO be impacting global temperature data if we’ve subtracted the scaled NINO3.4 anomaly data from the Global (60S-60N) GISS LOTI anomalies?

LA NIÑA EVENTS ARE NOT THE OPPOSITE OF EL NIÑO EVENTS
The assumption made when we removed the linear effects of ENSO (discussion of Figure 1) was that La Niña events were the opposite of El Niño events. But they are not. (This is the same incorrect assumption made by papers like Thompson et al 2009). This post is very long and to adequately describe how La Niña events are not the opposite of El Niño events would make it much longer. So it will be best to provide links to earlier detailed discussions on this topic.

Refer to:
More Detail On The Multiyear Aftereffects Of ENSO – Part 1 – El Nino Events Warm The Oceans
And:
More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents.
And:
More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Nino & La Nina Events

I provide a relatively brief description in the following section.

WHY ENSO INDICES LIKE NINO3.4 SST ANOMALIES DO NOT ACCOUNT FOR THE PROCESS OF ENSO
ENSO is a process, and ENSO indices such as NINO3.4 SST anomalies, the Cold Tongue Index (CTI), or the Multivariate ENSO Index (MEI) do not account for that process.

El Niño description: A reduction in the strength of the Pacific trade winds triggers an El Niño. A number of interrelated events then take place. Huge amounts of warm water from the surface and, more importantly, from below the surface of the western tropical Pacific (the Pacific Warm Pool) slosh east during an El Niño and are spread across the surface of the central and eastern equatorial Pacific. The increased area of warm water on the surface allows the tropical Pacific Ocean to discharge more heat than normal into the atmosphere through evaporation. That, combined with the change in location of the convection, cause drastic changes in global atmospheric circulation patterns. As a result, global temperatures vary. And most parts of the globe outside of the central and eastern tropical Pacific warm during an El Niño. The changes in atmospheric circulation work their way eastward--over the Americas, the Atlantic, Europe and Africa, the Indian Ocean and Asia. Eventually, the changes reach the western Pacific, but by that time, the El Niño is transitioning to a La Niña.

Refer again to the NINO3.4 SST anomaly data in Figure 1. A La Niña event, based on the temperature values on a graph, appears to be an El Niño of the opposite sign, and for some regional responses in temperature and precipitation that is true. But as noted before the use of NINO3.4 SST anomalies or other ENSO indices does not capture the fact that ENSO is a process. Those indices fail to account for the relocation and redistribution of huge amounts of warm water.

In the description of the El Niño, I noted that huge amounts of warm water from the surface and below the surface of the West Pacific Warm Pool had sloshed east during an El Niño. What happens to all of that warm water from below the surface of the Pacific Warm Pool that had been spread across the surface of the central and eastern tropical Pacific during the El Niño? Before the El Niño, it was below the surface and not included in the measured global surface temperature anomalies. During the El Niño, some of the warm water that had been below the surface is now on the surface of the central and eastern tropical Pacific and included in the measured global temperature. In response, surface temperatures there rose. The ENSO index captures that part of the process and only that part.

But during the La Niña, what happens to the warm water? It wasn’t all “used up” by the El Niño. And what happens to all of the subsurface warm water that had shifted east during the El Niño and had remained below the surface. It doesn’t simply disappear during the La Niña. Answering those questions explains why La Niña events are not the opposite of El Niño events, and why an ENSO index does not capture the aftereffects of an ENSO event.

The leftover warm water returns to the western Pacific. This is accomplished in a few ways. One is through a phenomenon called a slow-moving Rossby Wave. This can be seen in Video 1. It illustrates global Sea Level Residuals from January 1998 to June 2001 and captures the 1998/99/00/01 La Niña in its entirety. The video was taken from the JPL video “tpglobal.mpeg”. The slow moving Rossby wave is shown as the westward moving band of elevated sea level at about 10N. Watch the effect it has on western Pacific Sea Level Residuals when it reaches there.

Video 1Link to Video 1:
http://www.youtube.com/watch?v=MF5vZErQ6HM

The second way that the leftover warm water is carried to the Western Pacific is through a strengthening of the trade winds. During a La Niña event, trade winds strengthen above their “normal” levels and the ocean currents carry the warm water back to the west and then poleward.

Animation 1 is taken from the videos in the post La Niña Is Not The Opposite Of El Niño – The Videos. It presents the 1997/98 El Niño followed by the 1998 through 2001 La Niña. Each map represents the average Sea Surface Temperature anomalies for a 12-month period and is followed by the next 12-month period in sequence. Using 12-month averages eliminates the seasonal and weather noise. The effect is similar to smoothing data in a time-series graph with a 12-month running-average filter.

There are a number of things to note in Animation 1. First, the El Niño and La Niña events cause changes in the sea surface temperatures in the central and eastern tropical Pacific. The NINO3.4 SST anomalies used in this post are a measure of that variation in the central equatorial Pacific, and only that variation. Second, during the El Niño, note how the sea surface temperatures warm first in the Atlantic, then in the Indian Ocean, and then in the western Pacific. The warming is caused by changes in atmospheric circulation. And by the time these changes in atmospheric circulation make their way east to the western Pacific and it starts to warm there, the El Niño is transitioning to La Niña. Third, note how the sea surface temperature anomalies in the Western Pacific (and East Indian Ocean) continue to rise as the La Niña event strengthens. Fourth, note how the SST anomalies remain elevated in the East Indian and West Pacific Oceans during the entire term of the 1998/99/00/01 La Niña.
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Animation 1

The Sea Surface Temperatures of the East Indian and West Pacific Oceans remain elevated during the La Nina because the stronger trade winds reduce cloud cover. The reduction in cloud cover allows more Shortwave Radiation (visible light) to provide additional warming of the tropical Pacific waters east of the Pacific Warm Pool. The ocean currents carry this sunlight-warmed water to the west and then poleward.

DIVIDING THE GLOBE IN TWO HELPS IDENTIFY THE REASONS FOR THE UPWARD STEPS IN THE GISS LOTI DATA
To help illustrate the reasons for the upward shifts in the ENSO- and Volcano-adjusted GISS LOTI data (Figure 4), let’s divide the data into two subsets split at 20N. Refer to Figure 6.

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Figure 6

First we’ll look at the Northern Hemisphere GISS LOTI anomaly data, north of 20N. It has a relatively high linear trend since 1982, about 2.8 deg C/Century. Part is due to the additional variability of the North Atlantic. To compound that, these latitudes have a relatively high land surface area, and land surface temperatures vary much more than sea surface temperatures. The land surface area of the Northern Hemisphere latitudes of 20N-60N is about 45% of the total surface area, but the land surface in the tropical and Southern Hemisphere latitudes of 60S-20N is only 17%.
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Figure 7

The dataset shown in Figure 7 has not been adjusted for ENSO or volcanic eruptions. Let’s correct first for ENSO, then for the volcanic eruptions, using the same methods we did for the Global (60S-60N) data. Figures 8 and 9 illustrate the interim steps and the required scaling factors, and Figure 10 illustrates the result.
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Figure 8
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Figure 9
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Figure 10

The Northern Hemisphere data still has a relatively high trend, approximately 2.2 deg C/Century. But what causes the additional variability if we’ve removed the effects of ENSO and volcanic eruptions? The additional variations are often described as noise, but they have sources.

THE KUROSHIO-OYASHIO EXTENSION HOLDS THE ANSWER
There is a strong ENSO-related warming of the Kuroshio-Oyashio Extension that occurs during La Niña events. This was discussed and illustrated in my recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures. That secondary warming can be used to explain a major portion of the year-to-year variability in Northern Hemisphere land and sea surface temperature. And, along with ENSO, it helps to explain nearly all of the variations in the Northern Hemisphere (20N-60N) GISS LOTI data, including the rising trend. Figure 11 illustrates the location of the KOE dataset used in this post (30N-45N, 150E-150W).
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Figure 11

The GISS LOTI anomalies for much of the Northern Hemisphere warm (cool) when the Kuroshio-Oyashio Extension SST anomalies warm (cool). This can be seen in the correlation map of annual (January to December) Kuroshio-Oyashio Extension SST anomalies and annual Northern Hemisphere (0-90N) GISS LOTI data, Figure 12. Also note the correlation with the North Atlantic.
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Figure 12

As mentioned above, the secondary warming of the Kuroshio-Oyashio Extension was discussed in detail in my recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures. A quick description of the process: During a La Niña event, leftover warm water from the El Niño is returned to the Western Pacific and spun poleward by the North and South Pacific gyres. Much of that warm water finds its way to the Kuroshio-Oyashio Extension, where it apparently impacts atmospheric circulation.

The agreement between the variations in KOE SST anomalies and the adjusted Northern Hemisphere GISS LOTI anomalies is shown in Figure 13. I find that match quite remarkable. The additional spike (highlighted in blue) in the KOE data that starts in 1990 is out of place. It will make itself known later in this post. The other thing to note is the scaling factor required to align the two datasets in Figure 13. The scaling factor of 0.7 is very high. We’ll discuss this later in the post.
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Figure 13

Some might think the agreement between those datasets is a lucky coincidence. Of course, the agreement between the adjusted LOTI data and the unadjusted KOE data in Figure 13 is based solely on the lags and scaling factors I used. But the scaling and lags were established logically. Eyeballing the data, the scaling factors appear to be correct. And as we shall see, using the same methods, the results are very similar for the data that covers the Tropics and Southern Hemisphere.

LET’S LOOK AT THE TROPICS AND SOUTHERN HEMISPHERE
The Southern Hemisphere and Tropics dataset includes the GISS LOTI data from 60S-20N, Figure 14. This subset has a relatively low trend, approximately 1 deg C/Century. Some of this is related to the amount of continental land mass. For these latitudes, land represents only about 17% of the surface area. The Southern Ocean (90S-60S), which is outside of the latitudes portrayed in the post, also impacts the Southern Hemisphere data. And since the Southern Ocean SST anomaly trend over this period is negative, its interaction with the Southern Hemisphere oceans lowers the trend of the dataset.
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Figure 14

And again, using the same methods, we’ll adjust for ENSO, then volcanic eruptions, Figures 15 and 16, and present the results, Figure 17. Refer to Figures 15 and 16 for the scaling factors.
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Figure 15
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Figure 16
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Figure 17

As shown in Figure 17, removing the effects of the volcanoes has once again lowered the trend, and removing the ENSO data reduced the year-to-year variations.

Now we need a dataset for these latitudes to illustrate the secondary warming due to the leftover warm water from El Niño events and use it to account for the adjusted GISS LOTI data for the latitudes of 60S-20N.

THE SOUTH PACFIC CONVERGENCE ZONE (SPCZ) EXTENSION SST ANOMALY DATA AND CORRELATION MAP ARE REVEALING
The KOE was used in the discussion of the Northern Hemisphere data, so it seems logical that a similar area exists in the South Pacific. And for this discussion, we’ll designate that area as the South Pacific Convergence Zone (SPCZ) Extension. The SPCZ Extension data will be the SST anomalies of the area east of Australia (35S-20S, 160E-150W). As shown in Figure 18, it had a relatively high SST anomaly at the peak of the 1998/99 portion of the 1998 through 2001 La Niña.
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Figure 18

The SST anomalies for SPCZ Extension are shown in Figure 19.
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Figure 19

Like the KOE Extension data, the SST anomalies of the SPCZ Extension warm greatly during transitions from El Niño to La Niña events and appear to shift upward at those times. Refer to Figure 20.
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Figure 20

Creating the correlation map of annual (January to December) SPCZ Extension SST anomalies and annual Tropical and Southern Hemisphere (90S-20N) GISS LOTI data was eye-opening. It appears the SPCZ data is a good proxy for those areas in the western tropical Pacific and southwest Pacific that warm during La Niña events. It would also appear to show the effects those western Pacific areas have on the rest of the globe. As we can see in Figure 21, when the SPCZ Extension warms (cools) many areas throughout the tropics and Southern Hemisphere warm (cool). But as illustrated in Figure 20, the warming that occurs during La Niña events is not counteracted by the cooling during El Niño events. This causes the data to rise in steps during the La Niña events.
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Figure 21

Does the correlation map indicate that the upward shifts in the SPCZ Extension data also exist in the tropical and Southern Hemisphere GISS LOTI data? My understanding of correlation maps is that they emphasize the larger events in the data, and if we refer again to Figure 20, the larger events are those that occur during these upward shifts. We can also confirm this by comparing the respective time-series graphs.

Figure 22 illustrates the adjusted GISS LOTI data for the Tropics and Southern Hemisphere north of 60S. Also shown are scaled (0.25) SPCZ Extension SST anomalies. There are minor divergences from time to time, but in general the two curves agree surprisingly well.
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Figure 22

WHAT’S THE BOTTOM LINE?
What do the curves and linear trends of the adjusted GISS LOTI data look like if the KOE and SPCZ Extension data are removed? And what happens when you combine the two results to form a global dataset with all of the adjustments? Let’s take a look. The Northern Hemisphere GISS LOTI data (20N-60N) that’s been adjusted for ENSO and volcanic aerosols and the KOE SST anomalies is shown in Figure 23. Recall the divergence circled in blue in Figure 13; that’s the cause of the significant additional dip in 1990. Other than that, this was not a bad first attempt with scaling factors. But notice how small the trend is, 0.13 deg C/Century. If that dip was removed, the trend would be even lower.
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Figure 23

The Tropical and Southern Hemisphere GISS LOTI data (60S-20N) with the ENSO, Volcano, and SPCZ Extension adjustments is shown in Figure 24. The trend is basically flat. This dataset appears noisy, but look at the temperature scale. The range is only one-quarter of one used in Figure 23.
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Figure 24

We can combine the Northern Hemisphere data (20N-60N) with the Tropical and Southern Hemisphere data (60S-20N) using a weighted average. (The latitudes of 20N-60N represent approximately 29% of the surface area between 60S-60N.) Figure 25 shows the result. The linear trend is basically flat at 0.06 deg C/Century. The saw-tooth pattern is interesting, but…
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Figure 25

Due to the timing, the saw-tooth pattern appears to indicate that there was a lagged (repeated) volcano signal in the data. Refer to Figure 26. The reason I say repeated is that originally when the volcanic signal was removed, the Aerosol Optical Depth data was lagged 3 months and the leading edges of the data aligned well in Figures 9 and 16. The volcano signals in Figures 25 and 26, assuming those spikes are volcano signals, are lagged 9 months. The additional signal may also simply mean the Sato Mean Optical Thickness data doesn’t account perfectly for the decay of the volcano signal and that an additional adjustment is required.
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Figure 26

So let’s make the secondary volcano correction, refer to Figure 27. That will raise the linear trend of the adjusted GISS LOTI data.
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Figure 27

After all of the adjustments are made, there is a small trend, about 0.24 deg C/Century. Compared to the original, unadjusted data, Figure 28, the trend of the adjusted data is only about 15% of the original GISS LOTI data for 60S-60N.
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Figure 28

This makes perfect sense since there is little to no evidence of an anthropogenic global warming effect on global Ocean Heat Content (OHC) data. All one needs to do is divide the global oceans into tropical and extratropical subsets per ocean basin. Then it’s relatively easy to determine that ENSO, changes in Sea Level Pressure, and AMO/AMOC are responsible for that vast majority of the rise in OHC since 1955. Refer to:
A. ENSO Dominates NODC Ocean Heat Content (0-700 Meters) Data
B. North Pacific Ocean Heat Content Shift In The Late 1980s
C. North Atlantic Ocean Heat Content (0-700 Meters) Is Governed By Natural Variables

SHOULDN’T THE KUROSHIO-OYASHIO EXTENSION AND SPCZ EXTENSION DATA BE DETRENDED?
In this post and in The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures, we illustrated that the Kuroshio-Oyashio Extension and South Pacific Convergence Zone Extension SST anomalies rise in steps during La Niña events. Since those upward steps are clearly responses to ENSO, there should be no need to detrend those datasets.

A NOTE ABOUT THE ATLANTIC MULTIDECADAL OSCILLATION

There is a natural variable I did not account for in this post, and it is the Atlantic Multidecadal Oscillation, or AMO. I did not remove its impacts on the Northern Hemisphere data. For those new to the AMO, refer to An Introduction To ENSO, AMO, and PDO -- Part 2.

As noted in that post, RealClimate defines the Atlantic Multidecadal Oscillation (“AMO”) as, “A multidecadal (50-80 year timescale) pattern of North Atlantic ocean-atmosphere variability whose existence has been argued for based on statistical analyses of observational and proxy climate data, and coupled Atmosphere-Ocean General Circulation Model (“AOGCM”) simulations. This pattern is believed to describe some of the observed early 20th century (1920s-1930s) high-latitude Northern Hemisphere warming and some, but not all, of the high-latitude warming observed in the late 20th century. The term was introduced in a summary by Kerr (2000) of a study by Delworth and Mann (2000).”

I could have accounted for the AMO before removing the impacts of ENSO and the volcanic eruptions. But I chose to leave it in so that I could include the impact of the KOE on the North Atlantic.

As shown in Figure 29, the trend of the North Atlantic SST anomalies between 20N-60N is 70% higher than the North Pacific SST anomalies trend. By accounting for that additional “some, but not all” trend from the AMO, the scaling factor required to align the KOE dataset with the North Hemisphere data would drop.
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Figure 29

THE KOE SCALING IS TOO HIGH
The scaling factor for the Kuroshio-Oyashio Extension data in Figure 13 was 0.7. To some, it would not seem likely that the secondary warming of the KOE could raise temperatures for the Northern Hemisphere (20N-60N) that high, especially when one considers the multiplier for the SPCZ Extension was 0.25 in Figure 22.

First: Let’s consider the known effects of an El Niño event. When surface temperatures around the globe warm in response to an El Niño, most of those areas warm due to changes in atmospheric circulation. That is, they do not rise because the heat released into the atmosphere is warming the land and sea surfaces. The following is an example I often use. During an El Niño, the tropical North Atlantic warms even though it is separated from the Pacific by the Americas. The tropical North Atlantic warms during the El Niño because the El Niño causes a weakening of the North Atlantic trade winds. With the decrease in Atlantic trade wind strength there is less evaporation, and if there is less evaporation, sea surface temperatures rise. There is also less upwelling of cool water from below the surface when the trade winds weaken. This also causes sea surface temperatures to rise.

Therefore, it is through teleconnections or atmospheric bridges, not the direct transfer of heat, that the KOE would impact the areas of the Northern Hemisphere.

Second: There is a second western boundary current extension in the Northern Hemisphere, and it is the Gulf Stream Extension in the North Atlantic. For this quick discussion, we’ll define the Gulf Stream Extension by the coordinates of 35N-45N, 75W-30W. The map in Figure 30 is a correlation map and it shows that when the Gulf Stream Extension warms (cools) there are many parts of the Northern Hemisphere that warm (cool). And note that the eastern tropical Pacific is negatively correlated, indicating that these areas warm during La Niña events.
http://i52.tinypic.com/x1alur.jpg
Figure 30

Scroll back up to Animation 1. It also shows the parallel warming of the Gulf Stream Extension with the KOE.

But do the SST anomalies of the Gulf Stream Extension cool during El Niño events? As shown in Figure 31, the SST anomaly variations of the Gulf Stream Extension and the Kuroshio-Oyashio Extension are very similar. Both datasets can warm significantly during La Niña events but they do not drop proportionally during El Niño events. In an earlier linked post, I described the process that causes the KOE to warm, but I have not found a paper that describes the warming of the Gulf Stream Extension at those times. Why does the Gulf Stream Extension respond differently to El Niño and La Niña events? Like the KOE, is the warm water created during an El Niño also carried north by the Gulf Stream during the following La Niña? Do the changes in atmospheric circulation caused by the La Niña add to the warming? During the La Niña, does an increase in the strength of the North Atlantic trade winds also reduce cloud cover over the tropical North Atlantic? Does the warm water created by the decrease in cloud cover and resulting increase in sunlight then get transported to the Gulf Stream Extension? There are too many unanswered questions for me to use the Gulf Stream Extension data in this post.
http://i52.tinypic.com/4g6i9w.jpg
Figure 31

But, the parallel warming of the KOE and the Gulf Stream Extension during the transitions from El Niño to La Niña events would help to reduce the KOE scaling factor required to explain the step changes in the adjusted GISS LOTI data.

WHAT ABOUT SOLAR?
If we scale sunspot numbers so that the variations from solar minimum to maximum represent about a 0.1 deg change in temperature, and if we lag the sunspot data 6 years, it compares well visually with the adjusted GISS LOTI data. Refer to Figure 32. Someone with additional data processing tools could duplicate the steps taken in this post and confirm how well the two curves align.
http://i51.tinypic.com/23jsjo1.jpg
Figure 32

WHAT FUELS THE EL NIÑO EVENTS?
The warm water created during the previous La Niña(s) via the increase in Downward Shortwave Radiation (visible light) fuels El Niño events. This was discussed in More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND... ...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents.

CAN THE THIS TYPE OF EVALUATION BE EXTENDED BACK IN TIME?
I would not expect that what was presented in this post could be extended back in time. The Pacific climate shifted in 1976/77. In the abstract of Trenberth et al (2002), they write, “The 1976/1977 climate shift and the effects of two major volcanic eruptions in the past 2 decades are reflected in different evolution of ENSO events. At the surface, for 1979–1998 the warming in the central equatorial Pacific develops from the west and progresses eastward, while for 1950–1978 the anomalous warming begins along the coast of South America and spreads westward. The eastern Pacific south of the equator warms 4–8 months later for 1979–1998 but cools from 1950 to 1978.”

The way ENSO events interacted with the Kuroshio-Oyashsio Extension and the SPCZ Extension also appear different before and after 1979 in the correlation and regression analyses presented in that paper. Link to Trenberth et al (2002):
http://www.cgd.ucar.edu/cas/papers/2000JD000298.pdf

SOURCES

Most of the data used in this post are available through the KNMI Climate Explorer Monthly observations webpage. GISS LOTI is identified there in the second field under “Temperature” as “1880-now anomalies: GISS”, with the “1200km” radius smoothing. The Reynolds OI.v2 is listed under SST as “1982-now: 1° Reynolds OI v2 SST”. The coordinates used are identified in the text and/or on the graphs.

And if you want to attempt to duplicate my results but have never used the KNMI Climate Explorer, refer to the post Very Basic Introduction To The KNMI Climate Explorer for a place to start.

The dataset used to simulate the impacts of the volcanic eruptions is available through GISS:
http://data.giss.nasa.gov/modelforce/strataer/tau_line.txt

The Sunspot data is available through the KNMI Climate Explorer Monthly climate indices webpage. Refer to the Sunspots (1749-now, SIDC) field under the heading of “Sun”.

CLOSING REMARKS

This was a very basic attempt to approximate the effects of natural variables on global temperatures, using scaling and lags that were eye-balled. Sometimes basic things work well, and in this case, they appear to have done that. The similarities between the adjusted GISS LOTI datasets and the respective KOE and SPCZ Extension data were remarkable. While those similarities and the correlation maps do not prove the KOE and SPCZ Extension SST anomalies cause those addition rises in surface temperature, they imply that natural factors are causing the upward steps in global temperatures illustrated in Figure 4.

After some preliminary discussions, I divided the global (60S-60N) GISS LOTI data into two sections. The linear impacts of ENSO and volcanic eruptions were then removed from those subsets. The processes that cause the Sea Surface Temperatures in two parts of the Pacific to warm greatly during La Niña events were discussed. The unadjusted SST anomalies of the KOE and the SPCZ Extension were then compared to their respective adjusted GISS LOTI anomalies. The related curves were surprisingly similar. After removing the impacts of the KOE and the SPCZ Extension from the related GISS LOTI data, the linear trends dropped significantly. When the two GISS LOTI datasets were again combined, we had removed approximately 85% of what some consider to be the “anthropogenic global warming signal.”

This post differs from studies such as Thompson et al (2009). Thompson et al assumed that the ENSO proxy accounts for all of the processes within the Pacific that take place during ENSO events. In reality, NINO3.4 SST anomalies (or the CTI SST anomalies they used) can only account for the linear responses to the changes in equatorial Pacific SST anomalies. NINO3.4 SST anomalies cannot be assumed to account for the ENSO processes that take place within the Pacific or the aftereffects of those processes. What I presented in this post was a simple way to view those aftereffects within the Pacific and the global responses to them.

In short, I presented a story told by the GISS Land-Ocean Temperature Index and Reynolds OI.v2 SST data between the latitudes of 60S to 60N.

Very Basic Introduction To The KNMI Climate Explorer

2010-12-30T08:42:00.031-05:00

I’ve moved to WordPress. This post can now be found at Very Basic Introduction To The KNMI Climate Explorer

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UPDATE (January 2, 2010): I just received a reply to an email, and the always-helpful Geert Jan van Oldenborgh of KNMI suggested that I replot Figure 15 using the “gridbox” option instead of “shaded”. The “gridbox” option provides a better indication of the data location. The “shaded” option interpolates, it does not extrapolate, “so isolated (rows) of values are not drawn.”

UPDATE 2 (January 5, 2010): Fixed the link to the Monthly observations webpage at KNMI Climate Explorer.

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Dr. Geert Jan van Oldenborgh of the Royal Netherlands Meteorological Institute (KNMI) created and maintains (in addition to all of his research endeavors) the web application called the KNMI Climate Explorer. It allows users to perform statistical analysis of climate data. There are a multitude of datasets and analyses available, and any attempt on my part to describe what is available would not do justice to the efforts that have gone into the tool. Many of the datasets are updated monthly. Some are not. And for some datasets, the source may not update the data for a month or two. HADISST always lags the other SST datasets by a month. You can even investigate some of the climate model outputs used for the IPCC AR4.

Readers here know that I use the KNMI Climate Explorer to prepare the vast majority of my posts and my comments in blogs. I use it for data, correlation maps, anomaly maps for comparisons and animations, etc.

Begin with the Climate Explorer: Starting point and explore. Refer also to the note and link on that page that reads, “Some restrictions are in force, notably the possibility to define your own indices, to upload data into the Climate Explorer and to handle large datasets. If you want to use these features please log in or register.”

VERY BASIC INTRODUCTION
So you want to see, for example, if there is any evidence of the 1976/77 Pacific Climate Shift in tropical Pacific Sea Surface Temperature (SST) anomaly data. The KNMI Climate Explorer is a coordinate-based system, so you’ll need to know the coordinates of that area. Based on a map (Figure 1) you elect to use 20S-20N for the latitudes and 120E-90W for the longitudes.

http://i53.tinypic.com/20zsndd.jpg
Figure 1

Open the Monthly observations webpage (Figure 2). There are combined land plus sea surface temperature datasets at the top. Scrolling down, there are datasets for many other variables: land surface temperature, sea surface temperature, marine air temperature, lower troposphere temperature, precipitation, etc. The “more information” buttons (i) to the right of each are links to the web pages of the sources.
http://i53.tinypic.com/245ftih.jpg
Figure 2

Looking at the available SST datasets, Figure 3, you decide to use HADISST. It’s a good choice for a long-term SST dataset, because it is spatially complete (they infilled all of the missing data) and the raw data is reinserted after the infilling. Click on HADISST, and hit enter.
http://i55.tinypic.com/2ezl4p4.jpg
Figure 3

That brings you to the “Field” page, Figure 4. Enter the coordinates you’ve selected: 20S-20N, 120E-90W. There are different ways they could be entered. Some will give you the correct results. Others will not. And there are other ways to investigate the dataset, so it is best to standardize on a method that will work elsewhere.

For example, there is a map-making tool “Plot this field” under the right-hand menu heading of “Investigate this field”. There, Climate Explorer requires you to enter the southern latitude of the area you’re investigating in the left-hand field and the northern latitude in the right-hand one. If you don’t, it won’t produce a map. Latitudes south of the equator are input as negative numbers. That is, if you were looking for the SST data for the latitudes of 70S-40S, you’d enter -70 in the left-hand field and -40 in the right. Likewise, west longitudes are input as negative numbers. That is, 70W is -70.

The western longitude for the area you’re examining is entered in the left-hand field. And the eastern longitude of the area is entered in the right. If you enter them in the reverse order, you’ll get data but it’s not what you want. By inputting the longitudes in reverse order the data would represent the SST of all longitudes except the tropical Pacific.

Crossing the dateline in the map-making webpage also requires that you enter a longitude in the left-hand field that is lower than the right-hand field. That is, you cannot enter 120 (for 120E) in the left-hand field and -90 (for 90W) in the right-hand one, because Climate Explorer will not produce a map. You have to use 120 (for 120E) and 270 (for 90W) or you can enter -240 (for 120E) and -90 (for 90W). If that explanation was confusing, click on “Plot this field” and produce maps for the area outlined in red in Figure 1, using those two examples.

You’ll note in Figure 4 how I’ve entered the coordinates of 20S-20N, 120E-90W. Click on “Make time series”.
http://i53.tinypic.com/343kpeg.jpg
Figure 4

For Figure 5, I’ve changed the zoom on the screen so that all three graphs on the “Time series” page are visible. The top graph presents the data in raw form. For HADISST, the values are the raw SST data. (For datasets such as GISTEMP or CRUTEMP or HADSST2 that are not presented in absolute form, the upper graph will present anomalies). The middle graph is the climatology data used to create the anomalies. And the bottom graph illustrates the anomaly data.
http://i53.tinypic.com/2j4ualy.jpg
Figure 5

Click on the “raw data” link above the top map. The Raw HADISST SST data for the selected coordinates are presented in tabular form, Figure 6. The first column is the year, obviously, and the other twelve columns are the monthly SST data, starting with January in the second column.
http://i54.tinypic.com/if94r8.jpg
Figure 6

Go back to the “Time series” page and click on the “Raw data” link above the bottom (anomaly) graph. The anomaly data is presented in two columns, Figure 7. The first is the month in numerical form and the second is the SST anomaly data for that month. The data is provided as a mix that includes values in scientific notation. (Caution: Do not delete the E-01, or E-02, etc., after the value. Your spreadsheet understands that it’s scientific notation and will accommodate it.)
http://i51.tinypic.com/qn1vls.jpg
Figure 7

Go back to the “Time series” page and scroll down to the “select years” fields under the heading of “Manipulate this times series,” Figure 8. Let’s say you want to examine the data starting in 1950. Fill in both fields and click on “Select”.
http://i52.tinypic.com/xo0ap0.jpg
Figure 8

The KNMI Climate Explorer default base years for anomalies are the entire term of the data you’ve selected. In this example, they would be 1950 through 2010. I’ve highlighted where that’s shown on the anomaly graph in Figure 9. But let’s say you want to use a 30-year period as the base years--for example 1951-1980 like GISS. Enter them in the “Redisplay the anomalies using the years” fields directly below the anomaly graph, and click “Select.”

http://i54.tinypic.com/op0lqq.jpg
Figure 9

There will only be two graphs on the page: climatology and anomalies. Again, I’ve highlighted the base years in Figure 10.
http://i53.tinypic.com/2ia4ccn.jpg
Figure 10

Click on “Raw data” above the anomaly map and Climate Explorer presents the monthly data, Figure 11. “Select all”, then copy and paste to a spreadsheet. And if you’d like to know how to get the numerical months and monthly data into separate columns in EXCEL, refer to Converting txt Data Into Columns In EXCEL.
http://i52.tinypic.com/33cxces.jpg
Figure 11

A FEW NOTES

Figure 12 shows that there is an extra month added to the end the data. The actual final month for the data in Figure 12 is October 2010, but there is a duplicate of the October value listed in November with the note “# repeat last y to get nice gnuplot plot”. I haven’t asked Geert Jan why the extra month is there, but I’ve assumed that it exists to advise you that the download is complete. Note: Don’t use the data for the extra month in your spreadsheet.
http://i51.tinypic.com/24gogef.jpg
Figure 12

There are datasets that are spatially incomplete. For example, HADSST2 (the other Hadley Centre SST dataset) was corrected for known biases, but it is not infilled. (The Hadley Centre is updating it again and correcting for additional biases. HADSST3 should be available sometime in 2011.) Let’s say you wanted to duplicate the work of Thompson et al when they were investigating the 1945 discontinuity in global HADSST2 SST data. One of the subsets they used was an ENSO index called the CTI (Cold Tongue Index), which represents the SST anomalies of the coordinates 6S-6N, 180-90W. So you select HADSST2, enter those coordinates, then limit the time period to 1940 to 1950. The anomaly curve, Figure 13, shows that there are large gaps in the data for the CTI during that period.
http://i56.tinypic.com/2nvb9s7.jpg
Figure 13

Click on “Raw data” above the graph. There's lots of missing data, Figure 14. Climate Explorer identifies missing data with “# repeat last y to get nice gnuplot plot”. The repeated value is handy if only one month is missing. Then the repeated value can be used to fill in the gap. Or if you like, you can delete the repeated data. But there are gaps much longer than one month in the CTI data during this period.
http://i52.tinypic.com/jau9le.jpg
Figure 14

If you were to make a map of one of the missing months (November 1940), Figure 15, you’d note that there is data, not a lot of it, but there is data in the CTI region.
http://i52.tinypic.com/35jxt8i.jpg

“You can retrieve that data to also reduce the gaps. Back up to the “Field” page, which is where the coordinates are entered, Figure 16. There is a field there identified as “Demand at least: ___ % valid points in this region”. The default is 30%. Click on the “help” (i) button and KNMI explains its use:
"Percentage valid points
“The area average is only considered valid when at least this many valid points are included. Enter a smaller number to get more valid data in the resulting time series, but the quality of these data will be lower. A higher number gives fewer but higher-quality data points.”

So you can reduce the gaps in the data by entering a number in the “Demand at least:” field as low as zero (0), but the quality of the data drops.
http://i51.tinypic.com/e8s3dz.jpg
Figure 16

CLOSING NOTES
I use only a tiny fraction of the capabilities of KNMI Climate Explorer. (I try to write my posts for non-technical people, and sometimes I succeed, so I don’t need all of its tools.) Therefore, I will not be able to answer many of your questions. You’ll find it has capabilities that interest you that I haven’t yet found.

Very Important: Research the dataset you intend to use. For example, a large part of the ISCCP cloud amount data is incomplete over the Indian Ocean before 1998, and, if memory serves me well, that dataset was influenced by volcanic aerosols of the El Chichon and Mount Pinatubo eruptions. If you understand where the pitfalls are in the data, it saves embarrassment after the fact.

Oops, almost forgot. If you missed it, there was evidence of the 1976/77 Pacific Climate shift in the tropical Pacific SST data. Refer again to Figure 9 or 10.

I have found Climate Explorer extremely educational and I thank Geert Jan for it.

PRELIMINARY December 2010 SST Anomaly Update

2010-12-29T08:55:00.006-05:00

I’ve moved to WordPress. This post can now be found at PRELIMINARY December 2010 SST Anomaly Update

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Sorry for the delay. The December 2010 Reynolds OI.v2 Sea Surface Temperature (SST) data through the NOAA NOMADS website won’t be official until January 10th. Refer to the schedule on the NOAA Optimum Interpolation Sea Surface Temperature Analysis Frequently Asked Questions webpage. The following are the preliminary Global and NINO3.4 SST anomalies for December 2010 that the NOMADS website prepares based on incomplete data for the month. I’ve also included the weekly data through December 24, 2010, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the variations can be seen.

PRELIMINARY MONTHLY DATA
Monthly NINO3.4 SST anomalies had stopped their decline last month and had risen slightly. The preliminary December data shows they dropped slightly, but nothing to indicate there will be a significant further decline. Presently they’re at -1.52 deg C.
http://i52.tinypic.com/flbuww.jpg
Monthly NINO3.4 SST Anomalies
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Monthly Global SST anomalies, according to the preliminary data, have dropped another 0.01 deg C. The preliminary global SST anomaly is 0.085 deg C.
http://i52.tinypic.com/25hmk9t.jpg
Monthly Global SST Anomalies
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A NOTE ABOUT THE YEAR-TO-YEAR VARIABILITY
The following is a repeat of a discussion from the Mid-December 2010 SST Anomaly Update.

As noted in the November 2010 SST Anomaly Update, the global SST anomalies do not appear as though they will drop to the level they had reached during the 2007/08 La Niña, even if one were to account for the differences in NINO3.4 SST anomalies. This of course will be raised by alarmists as additional proof of anthropogenic global warming.

But the reason the global SST anomalies have warmed in that time is due primarily to the fact that the East Indian and West Pacific Oceans (about 25% of the surface area of the global oceans) can warm in response to both El Niño and La Niña events. Refer to Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2, and the video included in La Niña Is Not The Opposite Of El Niño – The Videos. In addition, the North Atlantic also remains at elevated levels during La Niña events in response to the ENSO-related warming of the Kuroshio-Oyashio Extension. This was discussed and illustrated in the recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures.

Keep in mind, the warm water released from below the surface of the Pacific Warm Pool doesn’t simply vanish at the end of the El Niño.

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WEEKLY DATA
The weekly NINO3.4 SST anomaly data are cycling up and down at what appears to be the low end of the 2010/11 La Niña. They are at -1.73 deg C.
http://i54.tinypic.com/saxonp.jpg
Weekly NINO3.4 SST Anomalies
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Weekly Global SST Anomalies have dropped slightly, and it appears they also might have reached the seasonal low. It’s impossible to tell if they will they drop more? They are presently at +0.089 deg C.
http://i56.tinypic.com/2pq7uyb.jpg
Weekly Global SST Anomalies
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SOURCES
SST anomaly data is available through the NOAA NOMADS website:
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite=

Links To NODC Ocean Heat Content Posts

2010-12-24T09:10:00.008-05:00

I’ve moved to WordPress. This post can now be found at Links To NODC Ocean Heat Content Posts

Happy Holidays

2010-12-23T05:30:00.003-05:00

I’ve moved to WordPress. This post can now be found at Happy Holidays

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Thanks to all who read and comment on my posts here and at WattsUpWithThat. I, like many people, will be spending most of my time with family over the next few days (until December 27th), so if comment moderation seems to take a little longer than normal, you'll understand.

Enjoy the holidays.

Regards

Bob Tisdale

TAO Project Sea Air And Sea Surface Temperature Data

2010-12-22T06:43:00.010-05:00

I’ve moved to WordPress. This post can now be found at TAO Project Sea Air And Sea Surface Temperature Data

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This is brief introduction to the TAO Project Sea Air and Sea Surface Temperature data that’s available through the KNMI Climate Explorer.

The Monthly observations webpage of the KNMI Climate Explorer includes Sea Air Temperature and Sea Surface Temperature data from the NOAA Pacific Marine Environmental Laboratory (PMEL) Tropical Atmosphere Ocean (TAO) project. Refer to the TOA Project Home webpage. A Flash player overview of the TAO project is provided here: The TAO Story.

If you were to download the data from the KNMI Climate Explorer for the full area covered (8S-9N, 137E-95W), you’d note that data starts as early as 1980. But, like all datasets, the timing of partial and complete coverage needs to be understood. There may be TOA Project data available as far back as 1980, but it is very sparse in early years. The installation of the buoys was not completed until 1994. As an initial reference, Animation 1 shows the locations of available TOA Project sea air and sea surface temperature data for Januaries starting in 1989. It shows how sparse the coverage was of the tropical Pacific prior to 1994. So caution should be exercised when using TAO project data before 1994. And as you will note, there can be months after 1994 when data from individual buoys is not available, leaving incomplete coverage.
http://i52.tinypic.com/23k3zwx.jpg
Animation 1

Keeping that in mind, Figure 1 compares Sea Surface and Sea Air Temperature data (not anomalies) for the NINO3.4 region (5S-5N, 170W-120W) of the central equatorial Pacific starting in 1995. As one would expect, monthly NINO3.4 SST is higher than NINO3.4 Sea Air Temperature.
http://i54.tinypic.com/ve6a78.jpg
Figure 1

If we subtract the NINO3.4 Sea Air Temperature from the NINO3.4 Sea Surface Temperature, Figure 2, the difference appears to be a noisy ENSO dataset. And it clearly illustrates that the monthly SST data stays above the monthly Sea Air temperature for the NINO3.4 region. The average monthly NINO3.4 Sea Surface Temperature is approximately 0.55 deg C warmer than the average Sea Air Temperature. Referring back to the animation, the sharp drop in 2008 could be caused by the loss of data in that area.
http://i56.tinypic.com/2z5uq9s.jpg
Figure 2

Smoothing the data with a 13-month running average filter to reduce the noise, the difference compares well to scaled NINO3.4 SST anomalies, Figure 3.
http://i54.tinypic.com/5m05jc.jpg
Figure 3

The TAO project Sea Surface and Sea Air Temperatures for the entire dataset (8S-9N, 137E-95W) are illustrated in Figure 4. SST is clearly higher then SAT on a monthly basis.
http://i55.tinypic.com/sv5dmq.jpg
Figure 4

The difference is shown in Figure 5. Since 1995, the average monthly equatorial Pacific Sea Surface Temperature has been approximately 0.82 deg C higher than Sea Air Temperature.
http://i52.tinypic.com/2ypkpsl.jpg
Figure 5

And as one would expect, the variations in the difference between the TAO Project Sea Air and Sea Surface Temperatures is a function of ENSO. Refer to Figure 6, which compares the difference to scaled and ranged NINO3.4 SST anomalies.
http://i53.tinypic.com/291na4x.jpg
Figure 6

SOURCE
The TAO Project data used in this post is available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

Hmmm. My Comment Got Deleted At Tamino’s “Not So” Open Mind

2010-12-20T10:28:00.004-05:00

I’ve moved to WordPress. This post can now be found at Hmmm. My Comment Got Deleted At Tamino’s “Not So” Open Mind

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Occasionally I will run a google blog search of my name to see who’s writing what about one of my posts. And when someone misses a point or misrepresents something, I reply. This morning I found that a commenter at Tamino's Open Mind had referred to one. It was in the thread of Tamino’s post Odd Man Out, by blogger Same Ordinary Fool at December 17, 2010 at 9:51 pm.

I copied my reply before it was deleted during moderation. It follows.

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Bob Tisdale December 20, 2010 at 10:09 am Reply

Your comment is awaiting moderation.

Same Ordinary Fool : About Anthony’s title “’Tisdale K.O.e’s GISS’s latest ‘warmest-year nonsense’”, you wrote, “Presumably the title refers to what was also Steve Goddard’s favorite objection, and what is only briefly mentioned here:
“‘GISS deletes SST data from areas with seasonal sea ice and extends land surface data out over the oceans (Arctic and Southern) with its 1200km radius smoothing.’”

The title was a play on words. My post was about the Kiroshio-Oyashio Extension, a.k.a. KOE.

Also, Steve Goddard’s objection was the GISS 1200km radius smoothing in general. I wrote the post about the GISS deletion of SST data:
http://bobtisdale.blogspot.com/2010/05/giss-deletes-arctic-and-southern-ocean.html
WUWT co-post is here:
http://wattsupwiththat.com/2010/05/31/giss-deletes-arctic-and-southern-ocean-sea-surface-temperature-data/

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I wonder what it was Tamino didn’t like in that reply. It’s the truth. I wrote the post about GISS Deleting Arctic And Southern Ocean Sea Surface Temperature Data. And GISS does delete it. The Arctic and Southern Ocean SST data exists in the source Reynolds OI.v2 SST data, but parts of it are not present in the GISS LOTI product.

Anthony Watts cross posted my post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures, and came up with the play-on-words title of Tisdale K.O.e’s GISS’s latest “warmest-year nonsense”.

And Steve Goddard at his new blog Real Science has continued with his posts about GISS. Apparently, Steve does not like anything about the GISS surface temperature products.

Mid-December 2010 SST Anomaly Update

2010-12-20T08:18:00.004-05:00

I’ve moved to WordPress. This post can now be found at Mid-December 2010 SST Anomaly Update

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This mid-month update only includes the shorter-term NINO3.4 and global SST anomaly graphs; that is, the ones from January 2004 to present. There’s not much happening, other than both datasets appear to have reached their seasonal lows for this La Niña.

As noted in the November 2010 SST Anomaly Update, the global SST anomalies do not appear as though they will drop to the level they had reached during the 2007/08 La Niña, even if one were to account for the differences in NINO3.4 SST anomalies. This of course will be misrepresented by some people as additional proof of anthropogenic global warming.

But the reason the global SST anomalies have warmed in that time is due primarily to the fact that the East Indian and West Pacific Oceans (about 25% of the surface area of the global oceans) can warm in response to both El Niño and La Niña events. Refer to Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2, and the video included in La Niña Is Not The Opposite Of El Niño – The Videos. In addition, the North Atlantic also remains at elevated levels during La Niña events in response to the ENSO-related warming of the Kuroshio-Oyashio Extension. This was discussed and illustrated in the recent post The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures.

Keep in mind, the warm water released from below the surface of the Pacific Warm Pool doesn’t simply vanish at the end of the El Niño.

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NINO3.4
NINO3.4 SST anomalies for the week centered on December 15, 2010 show that central equatorial Pacific SST anomalies have risen slightly in the past two weeks after a small dip. In other words, they’ve apparently reached the low end of this La Niña and they’re simply varying slightly at the seasonal La Niña level. They’re at approximately -1.4 deg C.
http://i56.tinypic.com/rbn0js.jpg
NINO3.4 SST Anomalies - Short-Term

GLOBAL

Weekly Global SST anomalies may have reached their seasonal low. They are presently at +0.1 deg C.
http://i55.tinypic.com/16bhhz7.jpg
Global SST Anomalies - Short-Term

SOURCE

OI.v2 SST anomaly data is available through the NOAA NOMADS system:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite

The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures

2010-12-08T06:39:00.024-05:00

I’ve moved to WordPress. This post can now be found at The ENSO-Related Variations In Kuroshio-Oyashio Extension (KOE) SST Anomalies And Their Impact On Northern Hemisphere Temperatures

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OVERVIEWThis post provides brief background information about the Kuroshio-Oyashio Extension (KOE), and discusses the relationship between NINO3.4 SST anomalies and the SST anomalies of the KOE following major El Niño events. Using correlation maps the post also illustrates the possible impacts of the KOE Sea Surface Temperature (SST) anomalies on North Atlantic SST anomalies, Combined Land and Ocean Surface Temperature anomalies, and Lower Troposphere Temperature anomalies.

INTRODUCTION

The Kuroshio Current and Oyashio Current are located in the western North Pacific Ocean. The Kuroshio Current is the western boundary current of the North Pacific Subtropical Gyre. Its counterpart in the North Atlantic Ocean is the well-known Gulf Stream. The Kuroshio Current carries warm tropical waters northward from the North Equatorial Current to the east coast of Japan. The East Kamchatka Current and the Oyashio Current are the western boundary currents of the Western Subarctic Gyre. The East Kamchatka Current is renamed the Oyashio Current south of the Bussol Strait (which is located about half way between Hokkaido and the Kamchatka Peninsula). They carry cold subarctic waters south to the east coast of Japan. The Kuroshio and Oyashio currents meet and form the North Pacific Current that runs from west to east across the North Pacific at mid latitudes. The Qiu, (2001) paper Kuroshio and Oyashio Currents. In Encyclopedia of Ocean Sciences, (Academic Press, pp. 1413-1425) provides a detailed but easily readable description of the two currents. Figure 1, from Qiu (2001), illustrates the general locations and paths of the Kuroshio and Oyashio Currents.

http://i51.tinypic.com/15zs014.jpg
Figure 1

As noted above, the Kuroshio and Oyashio Currents collide East of Japan and form the western portion of the North Pacific Current. These waters are often referred to as the Kuroshio-Oyashio Extension or the KOE. For the purpose of this post, I’ve used the coordinates of 30N-45N, 150E-150W for the Kuroshio-Oyashio Extension, Figure 2.
http://i52.tinypic.com/14twvox.jpg
Figure 2

CORRELATION WITH NORTHERN HEMISPHERE TEMPERATURES

Sea Surface Temperature (SST) anomalies for much of the North Atlantic warm (cool) when the Kuroshio-Oyashio Extension SST anomalies warm (cool). This can be seen in the correlation map of annual (January to December) Kuroshio-Oyashio Extension SST anomalies and annual North Atlantic SST anomalies, Figure 3.
http://i52.tinypic.com/fjj23r.jpg
Figure 3

And, as shown in Figures 4 (RSS) and 5 (UAH), annual TLT anomalies for much of the Northern Hemisphere correlate with the annual SST anomalies of the Kuroshio-Oyashio Extension.
http://i52.tinypic.com/6gd98k.jpg
Figure 4
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http://i53.tinypic.com/2qsx7j8.jpg
Figure 5

The same thing holds true for combined land plus sea surface temperature datasets such as the GISS Land-Ocean Temperature Index (LOTI) data for the Northern Hemisphere, Figure 6. Much of the Northern Hemisphere GISS LOTI data warms (cools) as KOE SST anomalies warm (cool). (Also note the differences in the North Atlantic correlations in Figures 3 and 6. They’re based on the same SST dataset, so why are there differences? GISS deletes SST data from areas with seasonal sea ice and extends land surface data out over the oceans with its 1200km radius smoothing. Refer to GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data.)
http://i54.tinypic.com/303llxg.jpg
Figure 6

WHEN DOES THE KOE WARM?
As we’ve seen in past posts, the East Indian and West Pacific Oceans warm in response to El Niño events and then during the subsequent La Nina events. As part of the East Indian-West Pacific subset, the Kuroshio-Oyashio Extension warms significantly during La Niña events. Animation 1 is taken from the videos in the post La Niña Is Not The Opposite Of El Niño – The Videos. It presents the 1997/98 El Niño followed by the 1998 through 2001 La Niña. Each map represents the average SST anomalies for a 12-month period and is followed by the next 12-month period in sequence. Using 12-month averages eliminates the seasonal and weather noise. The effect is similar to smoothing data in a time-series graph with a 12-month running-average filter. Note how the Kuroshio-Oyashio Extension warms significantly during the La Niña event and how the warming persists for the entire term of the La Niña.
http://i53.tinypic.com/etb58j.jpg
Animation 1

Note in Animation 1 that the SST anomalies of the Kuroshio-Oyashio Extension were cool during the 1997/98 El Niño. The KOE actually started with depressed SST anomalies, and they did not drop significantly during the 1997/98 El Niño. Refer to Figure 7. On the other hand, the KOE SST anomalies did rise significantly during the transition from the El Niño to the La Niña in 1998. The other major El Niño event that wasn’t impacted by the aerosols of an explosive volcanic eruption was the 1986/87/88 event. The SST anomalies of the Kuroshio-Oyashio Extension cooled during the 1986/87/88 El Niño, but also rose significantly during the 1988/89 La Nina. We’ll take a closer look at that event later in the post.
http://i53.tinypic.com/2qa1onl.jpg
Figure 7

This response of the Kuroshio-Oyashio Extension to El Niño and La Niña events is easier to see if the NINO3.4 SST anomalies are inverted, Figure 8. That is, the Kuroshio-Oyashio Extension warms much more during the 1998/99/00/01 La Niña event than it cools during the 1997/98 El Niño. But could the significant drop in the Kuroshio-Oyashio Extension during the 1986/87/88 El Niño impact the global response to that El Niño? Again, we’ll examine that later in the post.
http://i52.tinypic.com/wjvow.jpg
Figure 8

WHY DOES THE KOE WARM DURING LA NIÑA EVENTS?

Let’s start with the El Niño. During an El Niño event, a significant volume of warm water from the west Pacific Warm Pool travels east to the central and eastern equatorial Pacific, where it releases heat primarily through evaporation. And most of the warm water from the Pacific Warm Pool water comes from below the surface. There is “leftover” warm water when the La Niña forms, and a portion of this leftover warm water is returned to the western tropical Pacific at approximately 10 deg N latitude. Video 1 illustrates global Sea Level Residuals from January 1998 to June 2001. It captures the 1998/99/00/01 La Niña in its entirety. The video was taken from the JPL video “tpglobal.mpeg”. The phenomenon shown carrying warm waters from east to west in the tropical Pacific at approximately 10 deg N is called a slow-moving Rossby Wave.

Video 1
Link to Video 1:
http://www.youtube.com/watch?v=MF5vZErQ6HM

Unfortunately, the video “tpglobal.mpeg” is no longer available at the JPL VIDEOS web page, but for those who would like to watch the entire video, I uploaded it to YouTube as Sea Surface Height Animation 1992 to 2002 - JPL Video tpglobal.mpg.

In Video 1, the warm “leftover” warm water from the 1997/98 El Niño is clearly carried as far west as the Philippines. Shortly thereafter Kuroshio-Oyashio Extension sea level residuals rise and remain elevated for the duration of the La Niña.

In addition, there are other factors that add to and maintain the elevated SST anomalies in the Kuroshio-Oyashio Extension during the La Niña. As shown in Animation 1 (the gif animation, not the video), Sea Surface Temperature anomalies outside of the tropical Pacific rise in response to the El Niño. The changes occur first in the Atlantic, then Indian, and finally the west Pacific. Sea Surface Temperature anomalies rise as changes in atmospheric circulation caused by the El Niño make their way eastward around the globe to the western Pacific. Then, during the La Niña, the opposite occurs for much of the globe. But in the tropical Pacific, the trade winds strengthen and the North and South Equatorial Currents return warm “leftover” surface waters from the El Niño to the west. So the western Pacific is warmed cumulatively by the El Niño and then by the La Niña. In the northwest Pacific, the Kuroshio Current carries the leftover warm water up to the Kuroshio-Oyashio Extension.

Additionally, the increased strength of the trade winds during the La Niña also reduces cloud cover over the tropical Pacific, which increases the amount of Downward Shortwave Radiation (visible light) there. The increased Downward Shortwave Radiation warms the tropical Pacific. The warmed water is carried to the west by the Equatorial Currents and the North Pacific Gyre spins the warmed water up to the Kuroshio-Oyashio Extension.

WHY IS THIS IMPORTANT?

In the post “RSS MSU TLT Time-Latitude Plots...Show Climate Responses That Cannot Be Easily Illustrated With Time-Series Graphs Alone”, I illustrated that the RSS Lower Troposphere Temperature (TLT) anomalies of Southern Hemisphere and of the Tropics (70S-20N) followed the basic variations in NINO3.4 SST anomalies, Figure 9. This is how one would expect TLT anomalies to respond to El Niño and La Niña events. El Niño events cause the TLT anomalies to rise because they release more heat than normal to the atmosphere, and La Niña events cause TLT anomalies to fall because the tropical Pacific is releasing less heat than normal.
http://i54.tinypic.com/r9h0d5.jpg
Figure 9

But the TLT anomalies of the Northern Hemisphere north of 20N, Figure 10, appear to rise in a step after the 1997/98 El Niño. That is, there is very little response to the 1998 through 2001 La Niña. It appears as though a secondary source of heat is maintaining the Northern Hemisphere TLT anomalies at elevated levels.
http://i53.tinypic.com/11lsb6e.jpg
Figure 10

A similar upward step can be seen in the GISS Land-Ocean Temperature anomaly index (LOTI) for the latitudes of 20N-65N, Figure 11. (North of 65N the GISS data is biased by their deleting Sea Surface Temperature data and replacing it with land surface data with a higher trend. Again, refer to GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data.)
http://i53.tinypic.com/34qr5t2.jpg
Figure 11

And a similar upward step is visible in the North Atlantic SST anomaly data, Figure 12.
http://i56.tinypic.com/1zewmqq.jpg
Figure 12

The North Atlantic SST anomalies, the Lower Troposphere Temperature( TLT) anomalies of the Northern Hemisphere north of 20N, and the Northern Hemisphere Land-Ocean Temperature anomalies (20N-65N) all rise in response to the 1997/98 El Niño, but fail to respond fully to the 1998/99/00/01 La Niña. The similarity of the curves can be seen in Figure 13.
http://i54.tinypic.com/200v0j5.jpg
Figure 13

The correlation maps in Figures 3 through 6 show that a portion of the warming of the Northern Hemisphere north of 20N should be a response to the elevated Kuroshio-Oyashio SST anomalies during the 1998 through 2001 La Niña. To further illustrate this relationship, Figure 14 compares the KOE SST anomalies (not scaled) to the three datasets shown in Figure 13. I did not scale the Kuroshio-Oyashio SST anomalies because I wanted to illustrate the differences in the magnitudes of the variations. The variations in Kuroshio-Oyashio SST anomalies are clearly far greater than the variations of the other three datasets in Figure 14. In fact, the KOE SST anomaly variations are about 40% to 50% of the variations in NINO3.4 SST anomalies (refer back to Figures 7 and 8).
http://i56.tinypic.com/29e0pvp.jpg
Figure 14

Figure 15 presents the same datasets as Figure 14, but in Figure 15, the Kuroshio-Oyashio Extension SST anomalies have been scaled. Keep in mind that the three Northern Hemisphere temperature anomaly datasets rise first in response to the El Niño.
http://i54.tinypic.com/25hl2tz.jpg
Figure 15

It appears the warming of the Kuroshio-Oyashio Extension during the 1998/99/00/01 La Niña and its interaction with the other datasets could explain a portion of the trend in Northern Hemisphere SST anomalies, TLT anomalies, and Land-Ocean temperature anomalies since 1995. The warming of the Kuroshio-Oyashio Extension during that La Niña counteracts the normal cooling effects of the La Niña and prevents the temperature anomalies for the three datasets shown in Figures 13, 14, and 15 from responding fully to the La Niña.

THE 1986/87/88 EL NIÑO & 1988/89 LA NIÑA

There is a similar effect during the 1988/89 La Niña. That is, Northern Hemisphere temperature anomalies do not drop as one would expect during a La Niña. But the response during the 1986/87/88 El Niño may help to confirm the impact of the Kuroshio-Oyashio Extension on Northern Hemisphere temperatures.

Figure 16 compares scaled NINO3.4 SST anomalies for the period of 1985 through 1994 to the same datasets used in Figures 13: North Atlantic SST anomalies, the Lower Troposphere Temperature (TLT) anomalies of the Northern Hemisphere north of 20N, and the GISS Northern Hemisphere Land-Ocean Temperature anomalies (20N-65N). Once again, the Northern Hemisphere datasets rise in response to the El Niño event, but don’t drop in response to the La Niña. Note also that the North Atlantic SST anomalies lag the NINO3.4 SST by more than 6 months during the ramp-up phase, but the lag in the Northern Hemisphere TLT and Surface Temperature datasets is excessive, about 18 months. Why?
http://i53.tinypic.com/iqx3te.jpg
Figure 16

Could the dip in the Kuroshio-Oyashio Extension SST anomalies during the 1986/87/88 El Niño have counteracted their responses to the El Niño? Refer to Figure 17. It compares Kuroshio-Oyashio Extension SST anomalies (not scaled) to the North Atlantic and Northern Hemisphere datasets. The drop in KOE SST anomalies is significant in 1986/87/88.
http://i51.tinypic.com/2cwjs6c.jpg
Figure 17

And in Figure 18, the Kiroshio-Oyashio SST anomalies have been scaled. The North Atlantic SST anomalies rise in response to the 1986/87/88 El Niño as noted earlier. The timing of the rises in the KOE data and the GISS LOTI data are very similar. But the rise in the TLT anomalies north of 20N precedes the rise in the KOE data. If the dip in KOE SST anomalies were the only factor preventing the TLT anomalies from rising in response to the El Niño, shouldn’t we expect the TLT anomalies to lag the rise in the KOE data? Or are the TLT anomalies responding to the rise in North Atlantic SST anomalies?
http://i52.tinypic.com/2r5xdl3.jpg
Figure 18

If we replace the RSS TLT data with TLT data from UAH, Figure 19, the lag decreases between the North Atlantic SST anomalies and the TLT anomalies north of 20N.
http://i52.tinypic.com/2wqbui9.jpg
Figure 19

CLOSING
An El Niño event releases vast amounts of warm water from below the surface of the west Pacific Warm Pool. But the end of an El Niño event does not mean all of that warm water suddenly disappears. The warm water sloshes back to the western tropical Pacific during the La Niña. And some of that warm water is spun up into the Kuoshio-Oyashio Extension where it continues to release heat.

Kuroshio-Oyashio Extension SST anomalies rose significantly during the La Niña events of 1988/89 and 1998/99/00/01. These warmings appear to have counteracted the effects of those La Niña events on North Atlantic SST anomalies, and on Lower Troposphere Temperature anomalies north of 20N, and on combined Land-Ocean temperature anomalies of the Northern Hemisphere between the latitudes of 20N-65N. During the 1997/98 El Niño, the drop in Kuroshio-Oyashio Extension SST anomalies was very small and the KOE does not appear to have had a noticeable impact on the effects of that El Niño. On the other hand, the Kuroshio-Oyashio Extension SST anomalies did drop significantly during the 1986/87/88 El Niño and they appear to have suppressed the effects of that El Niño on Northern Hemisphere temperature anomalies. But why did the Kuroshio-Oyashio Extension SST anomalies drop significantly during the 1986/87/88 El Niño but not during the 1997/98 El Niño? Differences in Sea Level Pressure?

SOURCE
Data for graphs are available through, and the correlation and anomaly maps were downloaded from, the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

November 2010 SST Anomaly Update

2010-12-06T15:54:00.017-05:00

I’ve moved to WordPress. This post can now be found at November 2010 SST Anomaly Update

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MONTHLY SST ANOMALY MAP
The map of Global OI.v2 SST anomalies for November 2010 downloaded from the NOMADS website is shown below. With the exception of the South Atlantic, all ocean basins showed a decline in SST anomalies in November.

http://i56.tinypic.com/2r71xsi.jpg
November 2010 SST Anomalies Map (Global SST Anomaly = +0.097 deg C)

MONTHLY OVERVIEW

Monthly NINO3.4 SST anomalies MAY have reached their seasonal low value. The Monthly NINO3.4 SST Anomaly is -1.46 deg C.

Global SST anomalies dropped in both hemispheres this month for a total decline of about 0.041 deg C. Over the past two months, global SST anomalies have declined almost 0.1 deg C.
http://i56.tinypic.com/6hnudt.jpg
Global
Monthly Change = -0.041 deg C
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http://i55.tinypic.com/2rzp4ax.jpg
NINO3.4 SST Anomaly
Monthly Change = +0.13 deg C

EAST INDIAN-WEST PACIFIC
The SST anomalies in the East Indian and West Pacific made a major decline this month.

I’ve added this dataset in an attempt to draw attention to what appears to be the upward steps in response to significant El Niño events that are followed by La Niña events.
http://i56.tinypic.com/2qnvzix.jpg
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = -0.126 deg C

Further information on the upward “step changes” that result from strong El Niño events, refer to my posts from a year ago Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2

And for the discussions of the processes that cause the rise, refer to More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Niña Events Recharge The Heat Released By El Niño Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents -AND- More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Niño & La Niña Events

The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.

NOTE ABOUT THE DATA
The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from November 1981 to November 2010.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES http://i51.tinypic.com/20u78l4.jpg
Northern Hemisphere
Monthly Change = -0.073 deg C
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http://i52.tinypic.com/208abko.jpg
Southern Hemisphere
Monthly Change = -0.016 deg C
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http://i52.tinypic.com/2vs1bpv.jpg
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = -0.069 deg C
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http://i54.tinypic.com/25zmatu.jpg
South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = +0.170 deg C

Note: I discussed the upward shift in the South Atlantic SST anomalies in the post The 2009/10 Warming Of The South Atlantic. Will the South Atlantic return to the level it was at before that surge or will it remain at a new plateau?

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http://i54.tinypic.com/2mwrngw.jpg
North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = -0.103 Deg C
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http://i55.tinypic.com/2i21zeu.jpg
South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = -0.097 deg C
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http://i54.tinypic.com/23w3ehv.jpg
Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = -0.023 deg C
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http://i54.tinypic.com/2cprsih.jpg
Arctic Ocean (65 to 90N)
Monthly Change = -0.238 deg C
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http://i55.tinypic.com/156rcav.jpg
Southern Ocean (60 to 90S)
Monthly Change = -0.051 deg C

WEEKLY SST ANOMALIES
The weekly NINO3.4 SST anomaly data illustrate OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.32 deg C. That’s an increase of about 0.5 deg C since the minimum of about a month ago.
http://i53.tinypic.com/33v0uxg.jpg
Weekly NINO3.4 (5S-5N, 170W-120W)

The weekly global SST anomalies are at +0.071 deg C. They do not appear as though they will drop to the level they had reached during the 2007/08 La Niña, even if one were to account for the differences in NINO3.4 SST anomalies. This of course will be raised as additional proof the global oceans are warming. But the reason the global SST anomalies have warmed in that time is due primarily to the fact that the East Indian and West Pacific Oceans warm in response to both El Niño and La Niña events. Keep in mind, the warm water released from below the surface of the Pacific Warm Pool doesn’t simply vanish at the end of the El Niño. Also, the unusual rise in South Atlantic SST anomalies has added to the trend.
http://i54.tinypic.com/2j0e6p4.jpg
Weekly Global

SOURCE
The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

Long-Term ONI-Like Table Of El Niño and La Niña Events

2010-11-30T18:51:00.003-05:00

I’ve moved to WordPress. This post can now be found at Long-Term ONI-Like Table Of El Niño and La Niña Events

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The following tables were prepared to provide a reference of El Niño and La Niña events since 1900, using HADISST-based SST anomalies for the NINO3.4 region. They are based on the same definition of warm and cool ENSO events that NOAA uses for its Oceanic NINO Index (ONI). That is, all data are first smoothed with a 3-month running-average filter. An El Niño event is highlighted in red and identified with an “EN” in the far-right column if the 3-month average of NINO3.4 SST anomalies remained equal to or above 0.5 deg C for 5 consecutive 3-month periods. Likewise, a La Niña event is represented by blue and identified with a “LN” if the 3-month average of NINO3.4 SST anomalies remained equal to or below -0.5 deg C for 5 consecutive 3-month periods. And the same base years were used as the ONI Index, 1971 to 2000.

Since ENSO events typically peak in boreal winter, the second to last column reflects the two years of event evolution and decay. ENSO events typically develop during one year and decay the next—except, obviously, when an El Niño or La Niña event spans multiple years. The response of global temperatures lags the tropical Pacific and would typically be seen in the year of the decay. The same thing holds true for La Niña events. The two-year designation in the table should help avoid the confusion over an “El Niño year” or a “La Niña year” because it identifies the evolution and decay years.

Due to the length of the table, I had to break it into two parts.
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http://i56.tinypic.com/i3850k.jpg
Long-Term ONI-Like Table Of El Niño and La Niña Events – 1900 to 1949
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http://i56.tinypic.com/npmbkg.jpg
Long-Term ONI-Like Table Of El Niño and La Niña Events – 1950 to 2010

The table allows for quick comparisons. Example:

Global sea surface temperature anomalies rose from 1910 to 1944, dropped from 1945 to 1975, and rose again from 1976 to present. Keying off the development year, during the period from 1910 to 1944, there were 10 El Niño events and 6 La Niña events. (For this simple comparison, if an El Niño or La Niña event extends from one winter to the next, it would be considered two events.) From 1945 to 1975, there were only 7 El Niño events, compared to 11 La Niña events. And from 1976 to present, El Niño events dominated again. There were 12 El Niño events but only 8 La Niña events.

NOTES
The tables have been formatted similar to the ONI Index, but that doesn’t mean one should subscribe to the notion that global temperatures only respond to the 3-month average of NINO3.4 SST anomalies if they are beyond the threshold used in the tables.

SOURCE
HADISST NINO3.4 SST anomalies are available through the KNMI Climate Explorer Monthly climate indices webpage.

PRELIMINARY November 2010 SST Anomaly Update

2010-11-29T10:19:00.008-05:00

I’ve moved to WordPress. This post can now be found at PRELIMINARY November 2010 SST Anomaly Update

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The November 2010 SST data through the NOAA NOMADS website won’t be official until December 6. Refer to the schedule on the NOAA Optimum Interpolation Sea Surface Temperature Analysis Frequently Asked Questions webpage. The following are the preliminary Global and NINO3.4 SST anomalies for November 2010 presented by the NOMADS website. I’ve also included the weekly data through November 24, 2010, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the variations can be seen

PRELIMINARY MONTHLY DATA
Based on the preliminary data, monthly NINO3.4 SST anomalies have stopped their decline and have risen slightly. Presently they’re at -1.42 deg C.

http://i51.tinypic.com/9hmo3t.jpg
Monthly NINO3.4 SST Anomalies
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Monthly Global SST anomalies, according to the preliminary data, have dropped another 0.04 deg C. The preliminary global SST anomaly is 0.1 deg C. As noted last month, with the step up in the South Atlantic and its effect on the North Atlantic, it will be interesting to see how much global SST anomalies will decline in response to the La Niña. Refer to the post The 2009/10 Warming Of The South Atlantic. Combine that with the typical rise in the East Indian and West Pacific Oceans that occur when El Niño events are followed by La Niña events, and one should not expect the global SST anomalies to drop to the level they reached in December 2007.
http://oi51.tinypic.com/11qiaee.jpg
Monthly Global SST Anomalies
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WEEKLY DATA
The weekly NINO3.4 SST anomaly data are dropping again after their minor rebound. They are at -1.6 deg C.
http://i56.tinypic.com/21kwmrn.jpg
Weekly NINO3.4 SST Anomalies
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Weekly Global SST Anomalies have stopped their rebound, and they are presently at +0.08 deg C.
http://i51.tinypic.com/b4dwyd.jpg
Weekly Global SST Anomalies
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SOURCES
SST anomaly data is available through the NOAA NOMADS website:
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite=

Does Hadley Centre Sea Surface Temperature Data (HADSST2) Underestimate Recent Warming?

2010-11-26T18:46:00.006-05:00

I’ve moved to WordPress. This post can now be found at Does Hadley Centre Sea Surface Temperature Data (HADSST2) Underestimate Recent Warming?

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In advance of the UN negotiations next week in Cancun, the press and blogs today have included numerous elaborations on the UK Met Office press release Scientific evidence is Met Office focus at Cancun. The Australian article “Global temperature rises may be underestimated due to errors, Met Office study says” by Ben Webster includes the following statement, “The long-term rate of global warming was about 0.16C a decade in the 1970s, 1980s and 1990s but it slowed in the past 10 years to between 0.05C and 0.13C, depending on which of three major temperature records is used. The Met Office said that changes in the way ocean temperatures were measured had resulted in an under-estimate of about 0.03C in recent years.”

But what the Met Office fails to mention is that the dataset being discussed in the press release, their HADSST2 data, which is the sea surface temperature dataset used in their HADCRUT3 and HADCRUT3v global temperature products, is biased upwards by almost 0.12 deg C after 1998 due to a change in source data in 1998. I’ve illustrated and discussed this bias in two previous posts: Met Office Prediction: “Climate could warm to record levels in 2010” and The Step Change in HADSST Data After the 1997/98 El Nino.

The new source Sea Surface Temperature data was not fully consistent with the source dataset the Hadley Centre used prior to 1998. So when they merged the two datasets, the Hadley Centre failed to account for the inconsistency and created an upward bias in their HADSST2 data. This bias is easily seen when the other Hadley Centre sea surface temperature dataset, HADISST, is subtracted from the HADSST2 data, Figure 1. Note that the HADISST has relied primarily on satellite-based measurements since 1982, but the HADSST2 data is based on buoy and ship readings. The upward step is approximately 0.12 deg C. The bias created by the change in measurement methods over the past decade that was reported on in The Australian would only offset a portion of that shift.

http://i56.tinypic.com/308fjar.jpg
Figure 1

Hopefully, when the Hadley Centre finally releases its updated Sea Surface Temperature dataset (HADSST3) they will eliminate the upward step. And for those interested, here’s a link to a Met Office Scientific Advisory Committee (MOSAC) publication, “Climate monitoring and attribution,” that provides an overview of the upcoming HADSST3 and HADISST2 datasets. Refer to page 3 under the heading of “3. Progress in development of marine datasets.”
http://research.metoffice.gov.uk/research/nwp/publications/mosac/MOSAC_15.10.pdf

SOURCE
The HADSST2 and HADISST data used in this post are available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

Guan and Nigam 2008 And 2009

2010-11-24T04:47:00.002-05:00

I’ve moved to WordPress. This post can now be found at Guan and Nigam 2008 And 2009

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The following two papers by Guan and Nigam are the most in depth and informative analyses of Sea Surface Temperature (SST) for the 20th Century that I’ve run across to date.

The first in chronological order is Guan and Nigam, 2008: Pacific sea surface temperatures in the twentieth century: An evolution-centric analysis of variability and trend. J. Climate, 21(12), 2790-2809:
http://www.atmos.umd.edu/~bguan/download/index.php?Guan&Nigam_2008.pdf

And the second is Guan and Nigam, 2009: Analysis of Atlantic SST variability factoring inter-basin links and the secular trend: clarified structure of the Atlantic Multidecadal Oscillation. J. Climate, 22(15), 4228-4240
http://www.atmos.umd.edu/~bguan/download/index.php?Guan&Nigam_2009.pdf

I am presenting them without further introduction. I am still studying both papers, and I will not be able to answer many of your questions. But I am interested in your impressions on what you believe to be the major points of the papers, so I won’t attempt to influence your conclusions by stating mine.

Mid-November 2010 SST Anomaly Update

2010-11-24T04:36:00.003-05:00

I’ve moved to WordPress. This post can now be found at Mid-November 2010 SST Anomaly Update

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I’ve shortened this edition of the mid-month update by including only the shorter-term NINO3.4 and global SST anomaly graphs; that is, the ones from January 2004 to present.

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NINO3.4
NINO3.4 SST anomalies for the week centered on November 17, 2010 show that central equatorial Pacific SST anomalies have dropped slightly in the past two weeks after a small rise. They’re at -1.5 deg C.
http://i56.tinypic.com/es3eyq.jpg
NINO3.4 SST Anomalies - Short-Term

GLOBAL
Weekly Global SST anomalies are continuing their decline. But like weekly responses to past La Niña events, there are major steps down with lesser steps up.
http://i53.tinypic.com/2wbxe7a.jpg
Global SST Anomalies - Short-Term

SOURCE
OI.v2 SST anomaly data is available through the NOAA NOMADS system:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite

Multidecadal Changes In Sea Surface Temperature

2010-11-17T05:23:00.033-05:00

I’ve moved to WordPress. This post can now be found at Multidecadal Changes In Sea Surface Temperature

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Longer Title: Do Multidecadal Changes In The Strength And Frequency Of El Niño and La Niña Events Cause Global Sea Surface Temperature Anomalies To Rise And Fall Over Multidecadal Periods?

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UPDATE (November 19, 2010): I’ve added a clarification about the running total of scaled NINO3.4 SST anomalies and its implications. I changed a paragraph after Figure 13, and added a discussion under the heading of “What Does The Running Total Imply?”

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OVERVIEW

This post presents evidence that multidecadal variations in the strength and frequency of El Niño and La Niña events are responsible for the multidecadal changes in Global Sea Surface Temperature (SST) anomalies. It compares running 31-year averages of NINO3.4 SST anomalies (a widely used proxy for the frequency and magnitude of ENSO events) to the 31-year changes in global sea surface temperature anomalies. Also presented is a video that animates the maps of the changes in Global Sea Surface Temperature anomalies over 31-year periods, (maps that are available through the GISS Map-Making web page). That is, the animation begins with the map of the changes in annual SST anomalies from1880 to 1910, and it is followed by maps of the changes from 1881 to 1911, from 1882 to 1912, etc., through 1979 to 2009. The animation of the maps shows two multidecadal periods, both containing what appears to be a persistent El Niño event, one in the early 1900s and one in the late 1900s to present, and between those two epochs, there appears to be a persistent La Niña event.

INTRODUCTION
A long-term (1880 to 2009) graph of Global Surface Temperature anomalies or Global Sea Surface Temperature (SST) anomalies (Figure 1) often initiates blog discussions about the causes of the visible 60-year cycle. The SST anomalies rise from early-1910s to the early-1940s, drop from the early 1940s to the mid-1970s, then rise from the mid-1970s to present. Natural variables like the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO) are cited as the causes for these variations.

http://i51.tinypic.com/119z1ux.jpg
Figure 1

Note: HADISST data was used for the long-term SST anomaly graphs in this post. The exception is the GISS SST data, which is a combination of HADISST data before the satellite era and Reynolds OI.v2 SST data from December 1981 to present.

THE PDO CANNOT BE THE CAUSE
The SST anomalies of the North Pacific region used to calculate the PDO are inversely related to the PDO over decadal periods. This was shown in the post An Inverse Relationship Between The PDO And North Pacific SST Anomaly Residuals. This means that the SST anomalies of the North Pacific contribute to the rise in global SST anomalies during decadal periods when the PDO is negative and suppress the rise in global SST anomalies when the PDO is positive. The PDO, therefore, cannot be the cause of the multidecadal rises and falls in global SST anomalies. That leaves the AMO or another variable.

MULTIDECADAL CHANGES IN GLOBAL SST ANOMALIES
If we subtract the annual global SST anomalies in 1880 from the value in 1910, the difference is the change in global SST anomalies over that 31-year span. Using this same simple calculation for the remaining years of the dataset provides a curve that exaggerates the variations in global SST anomalies. This dataset is identified as the “Running Change (31-Year) In Global SST Anomalies” in Figure 2. The data have been centered on the 16th year.
http://i55.tinypic.com/2cndnq1.jpg
Figure 2

Why 31 years? A span of 31 years was used because it is approximately one-half the apparent cycle in the datasets, and it should capture the maximum trough-to-peak and peak-to-trough changes that occur as part of the 60-year cycle. Using 31 years also allows the data to be centered on the 16th year, with 15 years before and after.

The curve of the “Running Change (31-Year) In Global SST Anomalies” is very similar to the curve of annual NINO3.4 SST anomalies that have been smoothed with a 31-year filter. Refer to Figure 3. (NINO3.4 SST anomalies are commonly used to illustrate the frequency and magnitude of El Niño and La Niña events. For readers new to the topic of El Niño and La Niña events, refer to the post An Introduction To ENSO, AMO, and PDO – Part 1.) Both datasets are centered on the 16th year. Considering how sparse the SST measurements are for the early source data, the match is actually remarkable at that time.
http://i55.tinypic.com/zmgv9l.jpg
Figure 3

Let’s take a closer look at that relationship. The purple curve represents the running 31-year average of annual NINO3.4 SST anomalies, and it shows that, for example, at its peak in 1926, the frequency and magnitude of the El Niño events from 1911 to 1941 were far greater than the frequency and magnitude of La Niña events. The blue curve, on the other hand, portrays the change in global SST anomalies based on a 31-year span, and it shows, at its peak in 1926 that global SST anomalies rose more from 1911 to 1941 than it did during the other 31-year periods in the early 20th century. Skip ahead a few decades to 1960. Both curves reached a low point about then. At 1960, the purple curve indicates the frequency and magnitude of La Niña events from 1945 to 1975 outweighed El Niño events. And over the same period of 1945 to 1975, annual global SST anomalies dropped the greatest amount. Afterwards, the frequency and magnitudes of El Niño events increased (and/or the frequency and magnitude of La Niña events decreased) and the multidecadal changes in global SST anomalies started to rise, eventually reaching their peak around 1991 (the period of 1976 to 2006).

Since Global SST anomalies respond to changes in NINO3.4 SST anomalies, this relationship implies that the strengths and frequencies of El Niño and La Niña events over multidecadal periods cause the multidecadal rises and falls in global sea surface temperatures. In other words, its shows that global sea surface temperatures rose from 1910 to the early 1940s and from the mid-1970s to present because El Niño events dominated ENSO during those periods, and it shows that global sea surface temperatures dropped from the early 1940s to the mid 1970s because La Niña events dominated ENSO.

This apparent relationship contradicts the opinion presented by some climate studies that ENSO is only noise, that ENSO is only responsible for the major year-to-year wiggles in the global SST anomaly curve. Refer back to Figure 1. Examples of these studies are Thompson et al (2009) “Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights” and Trenberth et al (2002) “Evolution of El Nino–Southern Oscillation and global atmospheric surface temperatures".

Link (with paywall) to Thompson et al (2009):
http://journals.ametsoc.org/doi/abs/10.1175/2009JCLI3089.1

Link to Trenberth et al (2002):
http://www.cgd.ucar.edu/cas/papers/2000JD000298.pdf

Keep in mind, when climate studies such as Thompson et al (2009) and Trenberth et al (2002)attempt to account for El Niño and La Niña events in the global surface temperature record they scale an ENSO proxy, like NINO3.4 SST anomalies, and subtract it from the Global dataset, removing the major wiggles. They then assume the difference, which is a smoother rising curve, is caused by anthropogenic greenhouse gases.

The relationship in Figure 3 (that the multidecadal variations in strength and frequency of ENSO events are responsible for the rises and falls in global sea surface temperature) also contradicts the basic premise behind the hypothesis of anthropogenic global warming, which assumes that the rise in global sea surface temperatures since 1975 could only be caused the increase in anthropogenic greenhouse gases.

The first question that comes to mind: shouldn’t a multidecadal rise in Sea Surface Temperatures require an increase in radiative forcing? The answer is no, and I’ll discuss this later in the post. Back to Figure 3.

Once more, the relationship in Figure 3 illustrates that multidecadal variations in the frequency and magnitude of El Niño and La Niña events cause the multidecadal changes in SST anomalies. But how do I verify that this is the case, and how do I illustrate it for those without science backgrounds? Again, for those who need to brush up on El Niño and La Nina events, refer to the post An Introduction To ENSO, AMO, and PDO – Part 1.

THE ANIMATION OF MULTIDECADAL CHANGES IN SST ANOMALIES
The Goddard Institute of Space Studies (GISS) Global Map-Making webpage allows users to create maps of global SST anomalies and maps of the changes in global SST anomalies (based on local linear trends) over user-specified time intervals. Figure 4 is a sample map of the changes in annual SST anomalies for the 31-year period from 1906 to 1936. In the upper right-hand corner is a value that represents the change in annual SST anomalies over that time span. GISS describes the value as, “Temperature change of a specified mean period over a specified time interval based on local linear trends.” And as far as I can tell, these local linear trends are weighted by latitude. I downloaded the GISS maps of the changes in annual global SST anomalies, starting with the interval of 1880 to 1910 and ending with the interval of 1979 to 2009, with the intent of animating the maps, but the data they presented was also helpful.
http://i56.tinypic.com/21ou8lg.jpg
Figure 4

Figure 5 shows the curve presented by the GISS Multidecadal (31-year span) Changes In Global SST anomalies for all those maps, with the data centered on the 16th year. Comparing it to the “Running Change (31-Year) In Global SST Anomalies” data previously calculated, Figure 6, illustrates the similarities between the two curves. The GISS data from the maps presents a much smoother curve.
http://i53.tinypic.com/14j50et.jpg
Figure 5
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http://i51.tinypic.com/9hq83s.jpg
Figure 6

And if we compare the curve of the GISS Multidecadal (31-year span) Changes In Global SST anomalies from those maps to the NINO3.4 SST anomalies smoothed with a 31-month filter, Figure 7, we can see that the multidecadal changes in Global SST anomalies lag the variations in strengths and magnitudes of ENSO events. The lag prior to 1920 appears excessive, but keep in mind that the early source SST measurements are very sparse. The fact that there are similarities in the curves in those early decades says much about the methods used by researchers to infill all of that missing data.
http://i54.tinypic.com/9gvyh0.jpg
Figure 7

THE VIDEO
The animations are presented in two formats in the YouTube video titled “Multidecadal Changes In Global SST Anomalies”. The first format is as presented by GISS, with the Pacific Ocean split at the dateline. That is, the maps are centered on the Atlantic. Refer back to Figure 4. The second format is with the maps rearranged so that the major ocean basins are complete. Those maps are centered on the Pacific. With the maps centered on the Pacific, the animation shows what appear to be two (noisy) multidecadal El Niño events separated by a multidecadal La Niña event.

As noted in the video, the long-term El Niño and La Niña events appear in the patterns, not necessarily along the central and eastern equatorial Pacific. For those not familiar with the SST anomaly patterns associated with ENSO, refer to Figure 8. It is Figure 8 from Trenberth et al (2002) “Evolution of El Nino–Southern Oscillation and global atmospheric surface temperatures". Link to Trenberth et al (2002) was provided earlier.
Figure 8 shows where Sea Surface Temperatures warm and cool during the evolution (the negative lags) of an ENSO event, at the peak of an ENSO event (zero lag), and during the decay of ENSO events (the positive lags). The reds indicate areas that are positively correlated with ENSO events, and the blues are areas that are negatively correlated. That is, the red areas warm during an El Niño and the blues are the areas of that cool during an El Niño. During a La Niña event, the reds indicate areas that cool, and the blues indicate areas that warm.
http://i55.tinypic.com/z1f6o.jpg
Figure 8

And for those wondering why the ENSO events don’t always appear along the equatorial Pacific in the animated maps, keep in mind that the maps are showing the multidecadal changes in SST anomalies based on linear trends. The long-term linear trend of the equatorial Pacific SST anomalies are incredibly flat, meaning there is little trend. Refer to Figure 9, which shows the annual NINO3.4 SST anomalies and linear trend from 1900 to 2009.
http://i56.tinypic.com/2ag0u2u.jpg
Figure 9
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http://www.youtube.com/watch?v=O_QopFYSyGE
Video 1

And here’s a link to a stand-alone version of the video. The only difference is that the following version includes a detailed introduction, discussion, and conclusion, which are presented in this post. It’s about 5 minutes longer.
http://www.youtube.com/watch?v=SMKA_uG3zK0
Link To Stand-Alone Version Of Video

DOES THE VIDEO AND DATA PRESENT MORE THAN MULTIDECADAL VARIABILITY IN GLOBAL SST ANOMALIES?
Yes. This has actually been stated a number of times, but the following explanation may be helpful.

One of the arguments presented during discussions of multidecadal variations in global SST anomalies is that the Atlantic Multidecadal Oscillation (AMO) is detrended and that it strengthens or counteracts the basic long-term rise in global SST anomalies. However, the data associated with the GISS maps used in the video are based on linear trends. And Figure 7 shows that the Global SST anomalies rose from 1910 to 1944 and from 1976 to 2009 because El Niño events dominated, and dropped from 1945 to 1975 because La Niña events dominated.

That is, the animation of the GISS maps and the data GISS provides with those maps show that the trends in global sea surface temperature are driven by the multidecadal variations in the strengths and magnitudes of El Niño and La Niña events. The “GISS Multidecadal (31-year span) Changes In Global SST anomaly” data peaked in 1931 at 0.39 deg C. Refer back to Figure 5. That is, from 1916 to 1946, global SST anomalies rose 0.39 deg C (based on local linear trends). That equals a linear trend of 0.13 deg C per decade. And the “GISS Multidecadal (31-year span) Changes In Global SST anomaly” data peaked in 1989 at 0.41 deg C, and that equals a trend of 0.137 deg C per decade from 1974 to 2004. Let’s look at the “Raw” Global SST anomaly data. The linear trends of the “Raw” Global SST Anomalies for the same periods, Figure 10, are approximately 0.12 deg C per decade. Again, the peaks in the “GISS Multidecadal (31-year span) Changes In Global SST anomaly” data represent the periods with the greatest linear trends, and, as shown in Figure 7, they lag the peaks of the multidecadal variations in NINO3.4 SST anomalies.
http://i56.tinypic.com/343r903.jpg
Figure 10

Note: The highest trend in the later epoch of the GISS-based “change data” is about 5% higher than the highest trend in the earlier warming period. And that’s not unreasonable considering the early period was so poorly sampled. Again, the similarities in trends between the two epochs speaks highly of the methods used by the researchers to infill the data

A NOTE ABOUT THE NORTH ATLANTIC
Oceanic processes such as Atlantic Meridional Overturning Circulation (AMOC) and Thermohaline Circulation (THC) are normally cited as the cause of the additional multidecadal variability of North Atlantic SST anomalies. This additional variability is presented in an index called the Atlantic Multidecadal Oscillation or AMO. The AMO data are simply North Atlantic SST anomalies that have been detrended. As discussed in the post An Introduction To ENSO, AMO, and PDO -- Part 2, the NOAA Earth System Research Laboratory (ESRL) Atlantic Multidecadal Oscillation webpage refers readers to the Wikipedia Atlantic Multidecadal Oscillation webpage for further discussion. And Wikipedia’s description includes the statement, “While there is some support for this mode in models and in historical observations, controversy exists with regard to its amplitude…” The phrase “some support” does not project or instill a high level of confidence.

Early in this post we prepared a dataset that illustrated the “Running Change (31-Year) In Global SST Anomalies” by subtracting the annual SST anomalies of a given year from the SST anomalies 30 years later and repeating this each year for the term of 1880 to 2009. We can prepare the “Running Change (31-Year) In North Atlantic SST Anomalies” using the same simple method. Those two datasets (based on global and North Atlantic SST anomalies) are shown in Figure 11. The “Running Change (31-Year) In North Atlantic SST Anomalies” dataset appears simply to be an exaggerated version of the “Running Change (31-Year) In Global SST Anomalies”.
http://i52.tinypic.com/72yjwj.jpg
Figure 11

And comparing the “Running Change (31-Year) In North Atlantic SST Anomalies” to the NINO3.4 SST anomalies smoothed with a 31-year filter, Figure 12, shows that the NINO3.4 SST anomalies lead the multidecadal changes in North Atlantic SST anomalies.
http://i54.tinypic.com/besbvb.jpg
Figure 12

Putting Figures 11 and 12 into other words, the AMO appears to simply be the North Atlantic exaggerating the cumulative effects of the variations in the frequency and magnitude of ENSO. During epochs when El Niño events dominate, the SST anomalies of the North Atlantic rise more than the SST anomalies of the other ocean basins, and when La Niña events dominate, the North Atlantic SST anomalies drop more than the SST anomalies for the rest of the globe.

Why? The South Atlantic (not a typo) is the only ocean basin where heat is transported toward the equator (and into the North Atlantic). So warmer-than-normal surface waters in the South Atlantic created by the changes in atmospheric circulation during an El Niño should be transported northward into the North Atlantic (and vice versa for a La Niña). This effect seems to be visible in the animation of Atlantic SST anomalies from September 23, 2009 to November 3, 2010, Animation 1. (Note: By the start of the animation, September 2009, the 2009/10 El Niño was well underway.) Unfortunately, there is a seasonal component in those SST anomaly maps, and it’s difficult to determine whether the seasonal component is enhancing or inhibiting the appearance of northward migration of warm waters. Rephrased as a question, is the seasonal component in the SST anomalies creating (or detracting from) an illusion that makes it appear that the warm SST anomalies are migrating from the South Atlantic to the North Atlantic?
http://i55.tinypic.com/jzbdqe.jpg
Animation 1

The northward migration of warm waters from the South Atlantic to the North Atlantic also appears to be present in the following animation of the correlation of NINO3.4 SST anomalies with Atlantic SST anomalies at time lags that vary from 0 to 12 months, Animation 2. Again the correlation maps show areas that warm (red) or cool (blue) in response to an El Niño and the positive lags represent the number of months following the peak of the El Niño. Three month average NINO3.4 and Atlantic SST anomalies were used.
http://i52.tinypic.com/2gtai6d.jpg
Animation 2

Another reason the North Atlantic exaggerates the effects of ENSO is because the North Atlantic is open to the Arctic Ocean. El Niño events cause increases in seasonal Arctic sea ice melt during the following summer. It would also seem logical that El Niño events would increase the seasonal Greenland glacial melt as well. Refer again to Animation 2. Starting around the 9-month lag, positive correlations (warm waters during an El Niño) migrate south from the southern tip of Greenland, and starting around the 4-month lag from the Davis Strait, along the west coast of Greenland. Is that from glacial ice melt in Greenland and Arctic sea ice melt, with the melt caused by the El Niño? They're correlated with NINO3.4 SST anomalies.

Regardless of the cause, in the North Atlantic, there are significant positive correlations with NINO3.4 SST anomalies 12 months after the peak of the ENSO event, and for at least 6 months after the ENSO event has ended. And this means that the El Niño event is responsible for the persistent warming (or cooling for a La Niña event) in the North Atlantic.

MYTH: EL NIÑO EVENTS ARE COUNTERACTED BY LA NIÑA EVENTS
One of the common misunderstandings about ENSO is that La Niña events are assumed to balance out the effects of El Niño events.

The fact: correlations between NINO3.4 SST anomalies and global sea surface temperatures are basically the same for El Niño and La Niña events; that is, El Niño and La Niña events have similar effects on regional sea surface temperatures; they are simply the opposite sign.

But that does not mean the effects of the El Niño event will be counteracted by the La Niña event that follows. First problem with that logic: La Niña events do not follow every El Niño event. That’s plainly visible in instrument temperature record. Refer to the Oceanic Niño Index (ONI) (ERSST.v3b) table. Also an El Niño event may be followed by a La Niña event that lasts for up to three years. And sometimes there are multiyear El Niño events, like the 1986/87/88 El Niño.

The easiest way the show that La Niña events do not counteract El Niño events is by creating a running total of annual NINO3.4 SST anomalies. If La Niña events counteracted El Niño events, a Running Total would return to zero with each El Niño-La Niña cycle. Refer to the Wikipedia webpage on Running total. The running total of NINO3.4 SST anomalies (to paraphrase the Wikipedia description) is the summation of NINO3.4 SST anomalies which is updated each year when the value of a new annual NINO3.4 SST anomaly is added to the sequence, simply by adding the annual value of the NINO3.4 SST anomaly to the running total each year. I’ve scaled the NINO3.4 SST anomalies by a factor of 0.06 before calculating the running total for the comparison graph in Figure 13.
http://i53.tinypic.com/29fcjl2.jpg
Figure 13

And what the Running Total shows is that El Niño and La Niña events do not tend to cancel out one another. There are periods (from 1910s to the 1940s and from the mid 1970s to present) when El Niño events dominated, and a period when La Niña events dominated (from the mid-1940s to the mid-1970s). And with the scaling factor, the running total does a good job of reproducing the global SST anomaly curve. Global temperature anomalies can also be reproduced using monthly NINO3.4 SST anomaly data. This was illustrated and discussed in detail in the post Reproducing Global Temperature Anomalies With Natural Forcings.

UPDATE- The original paragraph has been crossed out and the updated version follows.

Figure 13 implies that 6% of each El Niño and La Niña event remains within the global surface temperature record and that it is this cumulative effect of ENSO events that raises and lowers global Sea Surface Temperatures.

Figure 13 appears to imply that 6% of each El Niño and La Niña event remains within the global surface temperature record and that it is this cumulative effect of ENSO events that raises and lowers global Sea Surface Temperatures. Let’s examine that later in the post.

So that’s two ways, using sea surface temperature data, that the multidecadal rises and falls in global sea surface temperatures appear to be responses to the frequency and magnitude of El Niño and La Niña events.

HOW COULD THE OCEANS WARM WITHOUT AN INCREASE IN RADIATIVE FORCING?

Someone is bound to ask, how could the global Sea Surface Temperatures rise over multidecadal periods without an increase in radiative forcing? The answer is rather simple, but it requires a basic understanding of why and how, outside of the central and eastern tropical Pacific, sea surface temperatures rise and fall in response to ENSO events. Refer back to Figure 8, which includes the correlation maps from Trenberth et al (2002), and note that there are areas of the global oceans outside of the central and eastern equatorial Pacific that warm and cool in response to ENSO events. During an El Niño event, the warming outside of the eastern and central equatorial Pacific is greater than the cooling, and global SST anomalies rise.

But why do global SST anomalies rise outside of the eastern and central tropical Pacific during an El Niño event?

There are changes in atmospheric circulation associated with ENSO events, and these changes in atmospheric circulation cause changes in processes that impact surface temperatures. Let’s look at the tropical North Atlantic as an example. Tropical North Atlantic SST anomalies rise during an El Niño event because the trade winds there weaken and there is less evaporation. This is discussed in detail in the paper Wang (2005), "ENSO, Atlantic Climate Variability, And The Walker And Hadley Circulation." Wang (2005) link:
http://www.aoml.noaa.gov/phod/docs/Wang_Hadley_Camera.pdf

Reworded, the reduction in trade wind strength due to the El Niño causes less evaporation, and since there is less evaporation, tropical North Atlantic sea surface temperatures rise. The weaker trade winds also draw less cool water from below the surface. So there are two effects that cause the Sea Surface Temperatures of the tropical North Atlantic to rise during El Niño events. And, of course, the opposite would hold true during La Niña events.

Again for example, during multidecadal periods when El Niño events dominate, the tropical North Atlantic trade winds would be on average weaker than “normal”, there would be less evaporation, less cool subsurface waters would be drawn to the surface, and tropical North Atlantic sea surface temperatures would rise. The western currents of the North Atlantic gyre would spin the warmer water northward. Some of the warm water would be subducted by Atlantic Meridional Overturning Circulation/Thermohaline Circulation, some would be carried by ocean currents into the Arctic Ocean where it would melt sea ice, and the remainder would be spun southward by the North Atlantic gyre toward the tropics so it could be warmed more by the effects of the slower-than-normal trade winds. Similar processes in the tropical South Atlantic also contribute to the warming of the North Atlantic, since ocean currents carry the warmer-than-normal surface waters from the South Atlantic to the North Atlantic.

Refer again to the correlation maps in Figure 8. Those are snapshots of monthly SST anomaly correlations. If those patterns were to persist for three decades due to a prolonged low-intensity El Niño event, global SST anomalies would rise. And the opposite would hold true for a prolonged La Niña event.

Let’s look at the average NINO3.4 SST anomalies during the three epochs of 1910 to 1944, 1945 to 1975, and 1976 to 2009. As shown in Figure 14, the average NINO3.4 SST anomalies were approximately +0.15 deg C from 1910 to 1944; then from 1945 to 1975, they were approximately -0.06 deg C; and from 1976 to 2009, the NINO3.4 SST anomalies were approximately 0.2 deg C. This is a very simple way to show that El Niño events dominated the two periods from 1910 to 1945 and from 1976 to 2009 and that La Niña events dominated from 1945 to 1975.
http://i56.tinypic.com/zxmsg8.jpg
Figure 14

Figure 15 compares annual Global SST anomalies to the average NINO3.4 SST anomalies for those three periods. Global SST anomalies rose from 1910 to 1944 because El Niño events dominated, and because the SST anomaly patterns (caused by the changes in atmospheric circulation) associated with El Niño events persisted. Because La Niña events dominated from 1945 to 1975, and because the SST anomaly patterns associated with La Niña events persisted, Global SST anomalies dropped. And Global SST anomalies rose again from 1976 to 2009 because El Niño events dominated, and because the SST anomaly patterns associated with El Niño events persisted.
http://i55.tinypic.com/33cwt4j.jpg
Figure 15

The fact that the rise in global Sea Surface Temperature anomalies since the early 1900s can be recreated without an increase in radiative forcing implies a number of things, one being that anthropogenic greenhouse gases do nothing more than cause a little more evaporation from the global oceans.

UPDATE – The following discussion (What Does The Running Total Imply?) has been added.

WHAT DOES THE RUNNING TOTAL IMPLY?
Earlier I wrote, Figure 13 [which was the comparison graph of global SST anomalies versus the running total of scaled NINO3.4 SST anomalies] appears to imply that 6% of each El Niño and La Niña event remains within the global surface temperature record and that it is this cumulative effect of ENSO events that raises and lowers global Sea Surface Temperatures. But is that really the case?

Keep in mind that the running total is a simple way to show the rise in global SST anomalies can be explained by the oceans integrating the effects of ENSO. It does not, of course, explain or encompass many interrelated ENSO-induced processes taking place in each of the ocean basins. Each El Niño and La Niña event is different and the global SST anomalies responses to them are different. For example, the South Atlantic SST anomalies remained relatively flat for almost 20 years, but then there was an unusual warming Of The South Atlantic during 2009/2010. Why? I have not found a paper that explains why South Atlantic SST anomalies can and do remain flat, let alone why there was the unusual rise. In this post, the gif animation of NINO3.4 SST anomaly correlation with North Atlantic SST anomalies, Animation 2, showed that the response of the North Atlantic can persist far longer than the El Niño or La Niña, but if I understand correctly, this type of analysis will emphasize the stronger events. What happens during lesser ENSO events? And there’s the East Indian and West Pacific Ocean. In January 2009, I began illustrating and discussing how the East Indian and West Pacific Oceans (60S-65N, 80E-180 or about 25% of the global ocean surface area) can and does warm in response to El Niño AND La Niña events. The first posts on this cumulative effect were Can El Nino Events Explain All of the Global Warming Since 1976? – Part 1, and Can El Nino Events Explain All of the Global Warming Since 1976? – Part 2. And the most recent post was La Niña Is Not The Opposite Of El Niño – The Videos. The Eastern Pacific Ocean is, of course, dominated by the ENSO signal along the equator. However, because of the North and South Pacific gyres, the East Pacific also influences and is influenced by the West Pacific, which can warm during El Niño and La Niña events. And there’s the Indian Ocean with its own internal variability, represented in part by the Indian Ocean Dipole (IOD). The decadal variability of the IOD has been found to enhance and suppress ENSO, and, one would assume, vice versa.

HOW MUCH OF THE RISE IN GLOBAL TEMPERATURES OVER THE 20TH CENTURY COULD BE EXPLAINED BY THE GLOBAL OCEANS INTEGRATING ENSO?
As shown in Figure 13 and as discussed in detail in the post Reproducing Global Temperature Anomalies With Natural Forcings, virtually all of the rise in global surface temperatures from the early 1900s to present times can be reproduced using NINO3.4 SST anomaly data. The scaled running total of NINO3.4 SST anomalies establishes the base curve and would represent the integration of ENSO outside of the eastern and central equatorial Pacific. Scaled NINO3.4 SST anomalies are overlaid on that curve to represent the direct effects of ENSO on the eastern and central equatorial Pacific. Add to that scaled monthly sunspot data to introduce the 0.1 deg C variations is surface temperature resulting from the solar cycle and add scaled monthly Stratospheric Aerosol Optical Depth data for dips and rebounds due to volcanic eruptions, and global surface temperature anomalies can be reproduced quite well. Refer to Figure 16, which is Figure 8 from the post Reproducing Global Temperature Anomalies With Natural Forcings.
http://i42.tinypic.com/2zqufzp.jpg
Figure 16

Basically, that was the entire point of this post. One of the mainstays of the anthropogenic global warming hypothesis is that there are no natural factors that could explain all of the global warming since 1975. But this post has shown that ALL of the rise in global sea surface temperatures since 1900 can be explained by the oceans integrating the effects of ENSO.

CLOSING
This post presented graphs and animations that showed Global SST anomalies rose and fell over the past 100 years in response to the dominant ENSO phase; that is, Global SST anomalies rose over multidecadal periods when and because El Niño events prevailed and they fell over multidecadal periods when and because La Niña events dominated. Basically, it showed that the oceans outside of the central and eastern tropical Pacific integrate the impacts of ENSO, and that it would only require the oceans to accumulate 6% of the annual ENSO signal (Figure13) in order to explain most of the rise in global SST anomalies since 1910. And the post provided an initial explanation as to why and how the global oceans could rise and fall without additional radiative forcings. It also showed that the Atlantic Multidecadal Oscillation (AMO) appears to be an exaggerated response to the dominant multidecadal phase of ENSO. Hopefully, it also dispelled the incorrect assumption that La Niña events tend to cancel out El Niño events.

SOURCES
The HADISST data used in this post is available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

The maps used in the video are available from the GISS map-making webpage:
http://data.giss.nasa.gov/gistemp/maps/

October 2010 SST Anomaly Update

2010-11-08T18:40:00.021-05:00

I’ve moved to WordPress. This post can now be found at October 2010 SST Anomaly Update

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MONTHLY SST ANOMALY MAPThe map of Global OI.v2 SST anomalies for October 2010 downloaded from the NOAA NOMADS website is shown below. The peak of the seasonal elevated SST anomalies in the North Atlantic appears to have passed. They were enhanced this season by the Arctic sea ice melt associated with the last winter’s El Niño and with the 2009/10 upward shift in South Atlantic SST anomalies. The central equatorial Pacific SST anomalies are continuing their decline, and the Pacific is showing a classic La Niña pattern: Eastern tropical Pacific SST anomalies are depressed while the Kuroshio Extension in the northwest Pacific and the South Pacific Convergence Zone east of Australia have elevated SST anomalies.
http://i53.tinypic.com/30d9mhg.jpg
October 2010 SST Anomalies Map (Global SST Anomaly = +0.19 deg C)

A NOTE ABOUT GLOBAL SST ANOMALIES

Global SST anomalies, based on the weekly Reynolds OI.v2 data, have made a rebound and taken another drop over the past month. This can be seen in the graph of weekly Global SST anomalies. The present value is about where it was a month ago, 0.095 deg C.
http://i53.tinypic.com/23721s.jpg
Weekly Global SST Anomalies

The next graph is a comparison of the global SST anomalies for 2010 compared to 1988, 1998, and 2007. Those were other transition years from El Niño to La Niña. In the graph, the data has been shifted so that the first weeks were all zeroed. The current transition is well within the range of past transitions from El Niño to La Niña.
http://i55.tinypic.com/ih72bs.jpg
Comparison Of Transitions From El Niño To La Nina

MONTHLY OVERVIEW
Monthly NINO3.4 SST anomalies are well below the -0.5 deg C threshold of a La Niña. The Monthly NINO3.4 SST Anomaly is -1.59 deg C. Weekly data has rebounded a bit and is now at -1.3 deg C.

Global SST anomalies took a good drop this month, -0.056 deg C. The decline in the Northern Hemisphere (-0.14 deg C) was partly offset by the increase in the Southern Hemisphere (+0.009 deg C).
http://i51.tinypic.com/2u5xetl.jpg
Global
Monthly Change = -0.056 deg C
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http://i54.tinypic.com/rm8okm.jpg
NINO3.4 SST Anomaly
Monthly Change = -0.02 deg C

EAST INDIAN-WEST PACIFIC
The SST anomalies in the East Indian and West Pacific made a slight rise this month. Will they continue to rise, noticeably, in response to the La Niña as they have in the past?

I’ve added this dataset in an attempt to draw attention to what appears to be the upward step responses. Using the 1986/87/88 and 1997/98 El Niño events as references, East Indian-West Pacific SST Anomalies peak about 7 to 9 months after the peak of the NINO3.4 SST anomalies, so we shouldn’t expect any visible sign of a step change for almost 18 to 24 months. We’ll just have to watch and see.
http://i56.tinypic.com/27xgqi8.jpg
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = -0.007 deg C

Further information on the upward “step changes” that result from strong El Niño events, refer to my posts from a year ago Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2

And for the discussions of the processes that cause the rise, refer to More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Niña Events Recharge The Heat Released By El Niño Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents -AND- More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Niño & La Niña Events

The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.

NOTE ABOUT THE DATA

The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from November 1981 to October 2010.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES http://i51.tinypic.com/2ntctg8.jpg
Northern Hemisphere
Monthly Change = -0.139 deg C
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http://i51.tinypic.com/11snx8n.jpg
Southern Hemisphere
Monthly Change = +0.009 deg C
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http://i52.tinypic.com/2j10gvl.jpg
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = -0.087 deg C
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http://i51.tinypic.com/24lqvls.jpg
South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = +0.127 deg C

Note: I discussed the upward shift in the South Atlantic SST anomalies in the post The 2009/10 Warming Of The South Atlantic.

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http://i54.tinypic.com/24azzb8.jpg
North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = -0.172 Deg C
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http://i53.tinypic.com/10nrn7o.jpg
South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = -0.082 deg C
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http://i52.tinypic.com/2hqulg6.jpg
Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = +0.019 deg C
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http://i55.tinypic.com/bgsbcg.jpg
Arctic Ocean (65 to 90N)
Monthly Change = -0.075 deg C
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http://i54.tinypic.com/zn8roi.jpg
Southern Ocean (60 to 90S)
Monthly Change = -0.031 deg C

WEEKLY NINO3.4 SST ANOMALIES
The weekly NINO3.4 SST anomaly data illustrate OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.32 deg C. That’s an increase of about 0.5 deg C since the minimum of about a month ago.
http://i56.tinypic.com/radbnk.jpg
Weekly NINO3.4 (5S-5N, 170W-120W)

SOURCE
The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

PRELIMINARY October 2010 SST Anomaly Update

2010-11-01T10:11:00.008-04:00

I’ve moved to WordPress. This post can now be found at PRELIMINARY October 2010 SST Anomaly Update

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The October 2010 SST data through the NOAA NOMADS website won’t be official until October 8. Refer to the schedule on the NOAA Optimum Interpolation Sea Surface Temperature Analysis Frequently Asked Questions webpage. The following are the preliminary Global and NINO3.4 SST anomalies for October 2010 presented by the NOMADS website. I’ve also included the weekly data through October 27, 2010, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the wiggles are visible.

PRELIMINARY MONTHLY DATA

Based on the preliminary data, monthly NINO3.4 SST anomalies are continuing to drop, but the rate has slowed. Presently they’re at -1.62 deg C.
http://i55.tinypic.com/wa0xv5.jpg
Monthly NINO3.4 SST Anomalies
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Monthly Global SST anomalies, according to the preliminary data, have dropped considerably. The preliminary global SST anomaly is 0.15 deg C. As noted last month, with the step up in the South Atlantic and its effect on the North Atlantic, it will be interesting to see how much global SST anomalies will decline in response to the La Niña. Refer to the post The 2009/10 Warming Of The South Atlantic
http://i51.tinypic.com/20pzmux.jpg
Monthly Global SST Anomalies
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WEEKLY DATA
The weekly NINO3.4 SST anomaly data have risen again over the past week. They are at -1.4 deg C.
http://i53.tinypic.com/4t3hax.jpg
Weekly NINO3.4 SST Anomalies
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Weekly Global SST Anomalies have increased again slightly, approximately 0.02 deg C.
http://i55.tinypic.com/2n6rfdi.jpg
Weekly Global SST Anomalies
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SOURCES
SST anomaly data is available through the NOAA NOMADS website:
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite=