Preliminary September 2009 SST Anomalies

I’ve moved to WordPress.  This post can now be found at Preliminary September 2009 SST Anomalies

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The “official” September 2009 OI.v2 SST anomaly data will not be published online until October 5, 2009. These are the preliminary results NOMADS presented today by NOMADS for September 2009.

NINO3.4 SST anomalies remain in the 0.8 to 0.9 deg C range where they have lingered for the past few months. Northern and Southern Hemisphere and Global SST anomalies have all dropped approximately 0.03 deg C.
http://i36.tinypic.com/2jbmfq8.png
Preliminary NINO3.4 SST Anomalies Through September 2009
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Preliminary Global SST Anomalies Through September 2009
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Preliminary Southern Hemisphere Anomalies Through September 2009
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Preliminary Northern Hemisphere Anomalies Through September 2009
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http://i35.tinypic.com/2zrqb8h.png
Map of Preliminary September 2009 Global SST Anomalies

SOURCE

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

A Discontinuity In 1945 Or A Missing ENSO Event?

I’ve moved to WordPress.  This post can now be found at A Discontinuity In 1945 Or A Missing ENSO Event?

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UPDATE – September 28, 2009

The post has been updated to include brief discussions of the other datasets in which the 1945 discontinuity appear.

OVERVIEW

For those still concerned about the upcoming adjustments to the Hadley Centre’s SST data, and because the methods used by Thomspon et al as discussed in Thompson et al (2009) - High-Tech Wiggle Matching Helps Illustrate El Nino-Induced Step Changes left significant ENSO residuals, I investigated the possibility that the discontinuity revealed in Thompson et al (2008) was the result of an unrecorded El Nino event in 1943/44 or 1944/45. It appears as though global temperatures are responding to an El Nino event in 1943 and 1944, with sharp rises and falls in Sea Surface Temperature (SST) and Land Surface Temperature (LST). This could then be the cause of the severe drop in 1945 (the discontinuity) if the global temperatures were then responding to the subsequent unrecorded La Nina event. But, using the Southern Oscillation Index (SOI) as a second reference, there is no evidence of an ENSO event during those years. But this assumes that the measurement of sea level pressure in Darwin and Tahiti was not impacted by the war years.

Keep in mind, though, that the discontinuity also exists in COADS and Hadley Centre’s Marine Air Temperature data (as does the appearance of an El Nino beforehand) and in the COADS Cloud Cover and Wind Speed data.

INTRODUCTION

The Hadley Centre is rewriting the Global SST record to reflect the findings of Thompson et al (2008). In the 2008 paper “A large discontinuity in the mid-twentieth century in observed global-mean surface temperature”, Thompson et al write, “The discontinuity in global-mean surface temperatures in late 1945 is evident in the unfiltered global-mean time series, but its prominence and unique character are highlighted by the removal of the ENSO and COWL-related variability (Fig. 2).” They continue in the next paragraph, “The step in late 1945 does not appear to be related to any known physical phenomenon. No substantial volcanic eruptions were reported at the time, and the nuclear explosions over Hiroshima and Nagasaki are estimated to have had little effect on global-mean temperatures…” The discontinuity is illustrated in Figure 1.
http://i35.tinypic.com/28r2ow3.png
Figure 1

Thompson et al (2009) “Identifying signatures of natural climate variability in time series of global-mean surface temperature: Methodology and Insights” reinforces the 1945 discontinuity and provide advanced notice of the Hadley Centre’s intent. They write, “THE SST DATA CORRECTED FOR INSTRUMENT CHANGES IN THE MID 20TH CENTURY ARE EXPECTED TO BECOME AVAILABLE IN 2009, and it will be interesting to see how the corrections affect the time history of global-mean temperatures, particularly in the middle part of the century.” [Emphasis added.]

But Thompson et al (2009) acknowledged that there was also a drop in land surface temperatures at that time. They wrote, “It is worth noting that the land data also exhibit a small drop in the mean ~1945, albeit much smaller than that found in the residual SST time series (Figure 10).”

A simultaneous drop in the land data? This leads to the question…

IS THERE A MISSING ENSO EVENT IN THE SST RECORD?

Thompson et al base their claim of a 1945 discontinuity in the Global SST dataset on the fact that, after they eliminated the effects of ENSO and COWL, there was no corresponding volcanic eruption to cause the drop in temperature. This assumes the volcano records and the SST records for the NINO regions are correct. It is difficult to imagine that an explosive volcanic eruption that would have lowered global temperatures 0.3 deg C could be missed, but can the same be said about ENSO events? Also, the methods used by Thompson et al (2008) to extract the ENSO signal from the global temperature record are similar to those in Thompson et al (2009), but as illustrated in Thompson et al (2009) - High-Tech Wiggle Matching Helps Illustrate El Nino-Induced Step Changes, they left significant ENSO residuals in their adjusted data. With this in mind, I resorted to raw HADISST Global and NINO3.4 SST anomaly data and to raw CRUTEM3 Land Surface Temperature anomaly data for this post.

Figure 2 illustrates the global SST anomalies from January 1935 to December 1974 for three datasets: HADISST, ERSST.v2, and ERSST.v3b. The data has been smoothed with a 13-month filter to remove the rough edges. Note how the discontinuity now appears to be a gradual decrease from late 1944 to late 1946. Scaled NINO3.4 SST anomalies have also been provided for comparison. All three global SST datasets appear as if they’re responding to an El Nino event when none exists in the NINO3.4 SST anomaly data. Also note that there is also little agreement between NINO3.4 SST anomalies and the global SST datasets between 1943 and 1950, while global SST anomalies do appear to respond to NINO3.4 SST anomalies at other times.
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Figure 2

In Figure 3, HADISST Global SST anomaly data from January 1935 to December 1974 are compared to CRUTEM3 Land Surface Temperature data. Again, the data have been smoothed with a 13-month filter. And again, scaled HADISST NINO3.4 SST anomalies are provided for comparison. Global land surface temperatures also appear to be responding to an El Nino event around that time. But looking at other ENSO events, global land surface temperatures do exaggerate some ENSO events but not others. And looking at the land surface temperature data for 1938/39, there was also a similar anomalous rise and fall without an El Nino event.
http://i37.tinypic.com/292tohw.png
Figure 3

THE SOI INDICATES NO EL NINO EVENT IN 1943 OR 1944

Figure 4 is a comparison chart of NINO3.4 SST anomalies and inverted and scaled Southern Oscillation Index (SOI) data, the surface pressure component of ENSO. The SOI represents the sea level pressure difference between Tahiti and Darwin Australia, and would, therefore, not be impacted by the transition between SST measurement types. This assumes that the sea level pressure measurements during those war years were accurate. However, if there were shifts in sea level pressure measurements during those years as there were in SST, the anomalous rises and falls in global SST and LST data may reflect El Nino events.
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Figure 4

CLOSING

Seeing how poorly global SST anomalies track NINO3.4 SST anomalies in the 1940s, the upcoming modifications by the Hadley Centre could be a good thing. Maybe they’ll try to clean up the data from 1910 to 1940 next.
Will the Hadley Centre attempt to flatten out the appearance of a 1943/44 and/or 1944/45 El Nino in the Global SST data. That rise and fall also appears in the COADS and MOHMAT Marine Air Temperature datasets, and, as illustrated above, in the CRUTEM3 LST data. Global Ocean Cloud Cover and Wind Speed datasets also indicate anomalous variations during the war years, and cloud cover and wind speed are major drivers of SST. Refer to my posts The Large 1945 SST Discontinuity Also Appears in Cloud Cover and Marine Air Temperature Data and Part 2 of The Large SST Discontinuity Also Appears in Cloud Cover and Marine Air Temperature Data.

SOURCE

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

Thompson et al (2009) - High-Tech Wiggle Matching Helps Illustrate El Nino-Induced Step Changes

I’ve moved to WordPress.  This post can now be found at Thompson et al (2009) – High-Tech Wiggle Matching Helps Illustrate El Nino-Induced Step Changes

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INTRODUCTION

In “Identifying signatures of natural climate variability in time series of global-mean surface temperature: Methodology and Insights”, Thompson et al (2009) remove the effects of three natural variables from the Global Surface Temperature record (January 1900 to March 2009). Those three natural variables are El Nino-Southern Oscillation, stratospheric aerosols emitted by explosive volcanic eruptions, and “variations in the advection of marine air masses over the high latitude continents during winter”, which they condense to “dynamically induced variability” or Tdyn in the paper. Thompson et al use “a series of novel methodologies to identify and filter out of the unsmoothed monthly-mean time series of global-mean land and ocean temperatures the variance associated with ENSO, dynamically-induced atmospheric variability, and volcanic eruptions.”

Thompson et al (2009) Link:
http://ams.allenpress.com/perlserv/?request=get-abstract&doi=10.1175%2F2009JCLI3089.1

Preprint Version:

http://www.atmos.colostate.edu/ao/ThompsonPapers/TWJK_JClimate2009_revised.pdf

Thompson et al (2009) also provided a link to five of the datasets they used and created while preparing the paper. The webpage is identified as “Data for Thompson, Wallace, Jones, Kennedy”:
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/

OVERVIEW

This post briefly discusses the data made available by Thompson et al (2009), it illustrates the ENSO and volcanic aerosol residuals that remained in the global temperature anomaly data after the effects of ENSO, volcanic aerosols, and dynamically induced variability were said to be removed, and it illustrates the El Nino-induced step changes that resulted from the significant El Nino events that occurred since 1976.

The post does not discuss the erroneous assumption made by Thompson et al (2009), which is that the relationship between ENSO and global temperature is linear. It is not. The non-linear relationship between ENSO and global temperatures was discussed in the following three posts, which all cover the same subject, fundamentally, though there are differences in the presentation:
1. The Relationship Between ENSO And Global Surface Temperature Is Not Linear
2. Multiple Wrongs Don’t Make A Right, Especially When It Comes To Determining The Impacts Of ENSO
3. Regression Analyses Do Not Capture The Multiyear Aftereffects Of Significant El Nino Events.”

The data furnished by Thompson et al actually reinforces the fact that the global temperature response to El Nino events is not linear.

THOMPSON ET AL (2009) DATA

INITIAL NOTE: The title block of the graphs in this post use the nomenclature from the “Data for Thompson, Wallace, Jones, Kennedy” webpage linked above.

Figure 1 illustrates the residual global temperature time-series data after the effects of ENSO, volcanic aerosols, and dynamically induced variability were removed. It is identified throughout this post as “Tdyn/ENSO/Volcano residual global mean”. ENSO continues to make its presence known in the “Tdyn/ENSO/Volcano residual global mean” data in Figure 1, indicating that Thompson et al failed to remove all of the effects. Note the spike from the 1997/98 El Nino and the dip due to the 2007/08 La Nina. Both are reduced in magnitude, but they are still quite visible. There are other El Nino event residuals in the data, as will be illustrated later.


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Figure 1

Figure 2 is a comparative graph of the raw “Tdyn/ENSO/Volcano residual global mean” data and Global Surface Temperature anomalies (listed as “Global mean” on the “Data for Thompson, Wallace, Jones, Kennedy” webpage linked above). Thompson et al (2009) identifies the “Global mean” data as HadCRUT3, which is the Hadley Centre’s combined land surface temperature and SST data.
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Figure 2

Smoothing both datasets with 13-month filters, Figure 3, helps to highlight the ENSO residuals left in the “Tdyn/ENSO/Volcano residual global mean” data. It also illustrates how well the methods used by Thompson et al (2009) appear to have removed the effects of volcanic aerosols. Note the differences in the datasets immediately following the 1982 and 1991 volcanic eruptions of El Chichon and Mount Pinatubo.
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Figure 3

Thompson et al (2009) uses Cold Tongue Index data [5S-5N, 180-90W] as the base for its “ENSO fit” data, Figure 4. Link to “ENSO fit” data:
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/TGlobe1900March2009_ENSOfit

NOTE: The methods used by Thompson et al (2009) to create the “ENSO fit” (“Volcano fit” and “Dynamic fit”) datasets will not be discussed in this post. Refer to the paper for further information.
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Figure 4

Figure 5 is comparative graph of scaled HADSST Cold Tongue Index data (downloaded through the KNMI Climate Explorer) and the “ENSO fit” data. The model used by Thompson et al (2009) exaggerated the Cold Tongue Index data in some months and suppressed it in others.
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Figure 5

A time-series graph of the “Volcano fit” data is presented in Figure 6. Link to “Volcano fit” data:
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/TGlobe1900March2009_VOLCANOfit
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Figure 6
The data source for volcanic aerosols in Thompson et al (2009) is the Sato Stratospheric aerosol optical depth data available though GISS:
http://data.giss.nasa.gov/modelforce/strataer/
Specifically:
http://data.giss.nasa.gov/modelforce/strataer/tau_line.txt

Figure 7 compares the “Volcano fit” data and the inverted and scaled Sato Mean Optical Thickness data. There are minor differences in the month-to-month variations between the source data and the model output.
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Figure 7
The “Dynamic fit” dataset, Figure 8, is unique to Thompson et al (2009). As noted above, it accounts for the “variations in the advection of marine air masses over the high latitude continents during winter.” They provide a detailed description of the dataset starting on page 9 of the paper. Link to “Dynamic fit” data:
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/TGlobe1900March2009_TDYNfit http://i38.tinypic.com/amfxup.png
Figure 8

Figure 9 is a comparative graph of the “ENSO fit”, “Volcano fit”, and “Dynamic fit” datasets for those who are interested in seeing their relative magnitudes. The data have been smoothed with 13-month running average filters.
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Figure 9

DETRENDED “Tdyn/ENSO/Volcano residual global mean” DATA

To provide an alternate view of the “Tdyn/ENSO/Volcano residual global mean” data, I’ve excluded the first 11 years of data, a short period of declining temperatures, and divided the remainder into three epochs, Figure 10: January 1912 to December 1943 (period of temperature increase), January 1944 to December 1975 (period of flat to declining temperatures), and January 1976 to March 2009 (period of temperature increase). I then detrended the “Tdyn/ENSO/Volcano residual global mean” data during those periods and compared them to the “ENSO fit” and “Volcano fit” datasets, the two major climate variables, to illustrate how much of the ENSO signal remained after the effects of the three variables were removed.
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Figure 10

Figure 11 covers the period of Jan 1976 to March 2009. It compares detrended “Tdyn/ENSO/Volcano residual global mean” data to “ENSO fit” and “Volcano fit” data. There appears to be very little of the 1982 volcanic eruption left in the “Tdyn/ENSO/Volcano residual global mean” data, while the some of the 1991 eruption remains.
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Figure 11

In Figure 12, I’ve deleted the “Volcano fit” data, leaving a comparison of detrended “Tdyn/ENSO/Volcano residual global mean” and “ENSO fit” data for the period of January 1976 to March 2009. As illustrated there are very large ENSO residuals remaining in the detrended “Tdyn/ENSO/Volcano residual global mean”. Note how the lag varies with each ENSO event. It is apparent that Thompson et al failed to capture and remove a significant portion of ENSO during this period.

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Figure 12

Figure 13 illustrates the detrended Thompson et al (2009) “Tdyn/ENSO/Volcano residual global mean”, the “ENSO fit” and the “Volcano fit” data for January 1944 to December 1975. The major drop in detrended “Tdyn/ENSO/Volcano residual global mean” from 1943 to 1947 represents the discontinuity in the HadCRUT3 data that was first discussed in Thompson et al (2008) “A large discontinuity in the mid-twentieth century in observed global-mean surface temperature,” Nature, 453, 646–650, doi:10.1038/nature06982.Link:
http://www.nature.com/nature/journal/v453/n7195/abs/nature06982.html
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Figure 13

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SIDE NOTE

The Hadley Centre appears to be using both papers to justify changes they are making to the HADSST dataset. In the concluding remarks of Thompson et al (2009), they write, “THE SST DATA CORRECTED FOR INSTRUMENT CHANGES IN THE MID 20TH CENTURY ARE EXPECTED TO BECOME AVAILABLE IN 2009, and it will be interesting to see how the corrections affect the time history of global-mean temperatures, particularly in the middle part of the century.” [Emphasis added.]

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Figure 14 compares detrended “Tdyn/ENSO/Volcano residual global mean” and “ENSO fit” data from January 1944 to December 1975 to illustrate, again, that there are sizable residual ENSO effects in the dataset. Note that during this period there is little to no lag between the “ENSO fit” and the detrended “Tdyn/ENSO/Volcano residual global mean” data, while there were considerable lags between the two datasets from 1976 to present. Is this a function of the magnitude of the ENSO events, where there are longer lags with larger ENSO events?
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Figure 14

Figures 15 and 16 are the comparative graphs of detrended “Tdyn/ENSO/Volcano residual global mean” and “ENSO fit” data from January 1912 to December 1943. Figure 15 includes the “Volcano fit” data; Figure 16 does not. Note how there is little agreement between the multiyear variations of the ENSO data and the detrended “Tdyn/ENSO/Volcano residual global mean” data.
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Figure 15
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Figure 16

The lack of agreement between the two datasets during this period is likely the result of the uncertainties in the datasets, especially the Cold Tongue Index data. Figures 17 and 18 illustrate the number of SST observations for the Cold Tongue region from 1845 to 1991 and from 1900 to 1950. Note how few observations were made in the early part of the Thompson et al (2009) data compared to more recent numbers. Also note that the number of observations between 1900 and 1950 was influenced by the opening of the Panama Canal in 1914 and by the two World Wars. JISAO link:
http://jisao.washington.edu/data/cti/
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Figure 17
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Figure 18

Would the model used by Thompson et al (2009) have better determined the relationship between ENSO and global temperature during the last two epochs had they excluded the data before 1943? In other words, did the uncertainties in the Global Surface Temperature and Cold Tongue Index data prior to 1943 skew their model so that it failed to identify the true relationship between the datasets in later years. Figures 19, 20, and 21 are comparative graphs of detrended “Tdyn/ENSO/Volcano residual global mean” data and detrended “Global mean” data, which is the unadjusted global surface temperature data, for the three periods. The model used by Thompson et al (2009) appears to have removed little of the effects of ENSO.
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Figure 19
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Figure 20
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Figure 21

A CLOSER LOOK AT THE RESIDUAL DATA FROM 1976 TO PRESENT REVEALS STEP CHANGES DUE TO SIGNIFICANT EL NINO EVENTS

Like many climate bloggers I have removed the linear effects of ENSO and volcanic aerosols from a number of different TLT and surface temperature datasets. While doing so, I’ve noted a curious effect in the data since 1976 but I’ve been hesitant to post the results because of the possibility of claims that I’d somehow manipulated the data to create the effect. Since the data was created by Thompson et al (2009) there should be no way for others to accuse me of misrepresenting the data. They may not agree with my results or how I segmented the data, but that is something else entirely. For them, in the closing, I've provided links to my posts that illustrate El Nino-induced step changes in TLT and SST data. Additionally, I would anticipate that someone will note that El Nino (and La Nina) events are not official ENSO events unless NINO3.4 SST anomalies equal or rise above (or fall below) 0.5 deg C. For that someone, global temperatures do not respond only to variations in eastern equatorial Pacific SST anomalies when the ENSO event is official; they respond to the entire ENSO signal.

Figure 22 is a comparison graph of the “ENSO fit” and “Tdyn/ENSO/Volcano residual global mean” data from 1976 to present. Neither dataset has been smoothed. What struck me was, after the initial warming from 1976 to early in 1982, the majority of the rises in the “Tdyn/ENSO/Volcano residual global mean” data occurred during the significant El Nino events of 1982/83, 1986/87/88 and 1997/98. I’ve highlighted the months when the “ENSO fit” data crosses zero for those El Nino events.
http://i37.tinypic.com/2nqxgm1.png
Figure 22

If the “Tdyn/ENSO/Volcano residual global mean” data during those El Nino events is eliminated, Figure 23, something else emerges. Note how there appears to be little to no rise in the “Tdyn/ENSO/Volcano residual global mean” data after the 1997/98 El Nino. The data looks flat. Also note how little the “Tdyn/ENSO/Volcano residual global mean” data rose between the 1986/87/88 and 1997/98 El Nino events. There’s the decline in the data between the 1982/83 and 1986/87/88 El Nino events. Last thing to note, the most substantial rise in “Tdyn/ENSO/Volcano residual global mean” data occurred from 1976 to early in 1982.
http://i34.tinypic.com/s2zhvs.png
Figure 23

UPDATE (September 26, 2009): At the suggestion of blogger H.R. on the version of this post at WattsUpWithThat…
A look at the Thompson et al paper – hi tech wiggle matching and removal of natural variables
…I have revised the following papragraph.

We’ll ignore the anomalous period with the negative trend from April 1984 to August 1986 because it is dominated solely by one La Nina event. If we then compare the trends of periods between significant El Nino events, for those periods longer than ~2 years, that is from January 1976 to March 1982 (0.41 deg C/decade), from August 1988 to May 1997 (0.1 deg C/decade), and from November 1998 to March 2009 (0.01 deg C/decade), the trends of the “Tdyn/ENSO/Volcano residual global mean” data have decreased with time. In other words, it has not been accelerating; it has been decelerating. This can be seen quite clearly after the trends of the “non-significant El Nino periods” are added to the illustration. Refer to Figure 24.

http://i33.tinypic.com/2i7o3dj.png
Figure 24

In Figure 25, keying off the trend lines, I’ve listed the rises in the “Tdyn/ENSO/Volcano residual global mean” data that occurred during the significant El Nino events of 1982/83, 1986/87/88, and 1997/98. It appears that most of the rise in “Tdyn/ENSO/Volcano residual global mean” data after early 1982 was the direct result of those El Nino events.
http://i38.tinypic.com/10nt7w3.png
Figure 25

CLOSING

Step changes in TLT anomalies and SST anomalies that resulted from significant El Nino events are discussed in detail in:
RSS MSU TLT Time-Latitude Plots...
Can El Nino Events Explain All of the Global Warming Since 1976? – Part 1
Can El Nino Events Explain All of the Global Warming Since 1976? – Part 2

Mid-September 2009 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at Mid-September 2009 SST Anomaly Update

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A GIF animation of the OI.v2 SST anomaly maps for the Weeks Centered On August 19 and September 16, 2009 shows that the elevated SST anomalies in the mid and high latitudes of the Northern Hemisphere have reduced in magnitude. The tropical Pacific SST anomalies are still elevated, but there are no apparent indications of a strengthening El Nino. The tropical Atlantic is still not showing any areas of exceptionally warm SST anomalies.
http://i33.tinypic.com/21o7z42.gif
SST Anomaly Map

Global SST anomalies are still elevated, but they have dropped 0.08 deg C in the last 3 weeks.
http://i37.tinypic.com/122en2b.png
Global SST Anomalies

NINO3.4 SST anomalies for the week centered on September 16, 2009 are still well into El Nino territory, but they have been cycling near the same value for a few months.
http://i37.tinypic.com/142vrzn.png
NINO3.4 SST Anomalies

And the SST anomalies in both the Southern and Northern Hemispheres are decreasing.
http://i33.tinypic.com/akfl6q.png
Southern Hemisphere SST Anomalies
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http://i38.tinypic.com/epljwl.png
Northern Hemisphere SST Anomalies

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

Record Sea Surface Temperatures Are Only In NOAA ERSST.v3b Dataset

I’ve moved to WordPress.  This post can now be found at Record Sea Surface Temperatures Are Only In NOAA ERSST.v3b Dataset

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The NOAA press release claims the August Global Sea Surface Temperature (SST) was the warmest on record.
http://www.noaanews.noaa.gov/stories2009/20090916_globalstats.html

The record ERSST.v3b SST for August can be seen in Figure 1.
http://i32.tinypic.com/2jaiydh.png
Figure 1

And of course SST anomalies, Figure 2, were also at record levels in August 2009.
http://i28.tinypic.com/ive0y1.png
Figure 2

RECORD NOT CONFIRMED BY NOAA SATELLITE SST DATA

August 2009 SST, Figure 3, and SST anomalies, Figure 4, for the NOAA satellite-based OI.v2 SST dataset were not records. NOAA writes about the Optimum Interpolation (OI.v2) data, “The optimum interpolation (OI) sea surface temperature (SST) analysis is produced weekly on a one-degree grid. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, THE SATELLITE DATA IS ADJUSTED FOR BIASES using the method of Reynolds (1988) and Reynolds and Marsico (1993).” [Emphasis added.]
http://www.cdc.noaa.gov/data/gridded/data.noaa.oisst.v2.html
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Figure 3
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http://i31.tinypic.com/ajp9ap.png
Figure 4

NOAA does not use satellite data in its ERSST.v3b SST dataset. However, when NOAA originally released the ERSST.v3b dataset in 2008, they included satellite data to supplement the buoy- and ship-based data. This was discussed in my post “Recent Differences Between GISS and NCDC SST Anomaly Data And A Look At The Multiple NCDC SST Datasets” and repeated here:

In “Improvements to NOAA’s Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006)”, Smith et al note the use of satellite data for ERSST.v3 data in their abstract, “Beginning in 1985, improvements are due to the inclusion of bias-adjusted satellite data.” That’s a positive description. They further proclaim, “Of the improvements, the two that have the greatest influence on global averages are better tuning of the reconstruction method and inclusion of bias adjusted satellite data since 1985.” In fact there is a whole subsection in the paper about the satellite adjustments.

But the satellite data was removed because it was felt the satellite data caused a downward bias. Reynolds, Smith, and Liu write in a November 14, 2008 attachment to their main ERSST.v3b webpage, “In the ERSST version 3 on this web page WE HAVE REMOVED SATELLITE DATA from ERSST and the merged product. The addition of satellite data caused problems for many of our users. Although, the satellite data were corrected with respect to the in situ data as described in reprint, there was a residual cold bias that remained as shown in Figure 4 there. The bias was strongest in the middle and high latitude Southern Hemisphere where in situ data are sparse. THE RESIDAL BIAS LED TO A MODEST DECREASE IN THE GLOBAL WARMING TREND AND MODIFIED GLOBAL ANNUAL TEMPERATURE RANKINGS.” [Emphasis added.]
The link for that quote is here:http://www.ncdc.noaa.gov/oa/climate/research/sst/papers/merged-product-v3.pdf

Note that the “merged product” referenced above is their ERSST.v3b-based land plus sea surface temperature data.

RECORD NOT CONFIRMED BY ANOTHER SHIP- AND BUOY-BASED SST ANOMALY DATASET

The Hadley Centre’s HADSST2 does not show record SST anomalies for July, August, or for the Summer of 2009. Far from it. Refer to Figure 5. The Hadley Centre uses different techniques to smooth and infill missing data. The differences between the Hadley Centre and NOAA methodologies are explained in the NOAA paper about the ERSST.v3b data, “Improvements to NOAA’s Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006)”.
http://i27.tinypic.com/kbuets.png
Figure 5

CLOSING

It appears that the methods used by NOAA to calculate Global SST in their ERSST.v3b dataset and the removal of the satellite data from those calculations created an upward bias.

SOURCES

NOAA’s ERSST.v3b SST anomaly data is available here:
ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo/aravg.mon.ocean.90S.90N.asc

NOAA’s ERSST.v3b SST data was downloaded from the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

NOAA’s OI.v2 SST and SST anomaly data is available through their NOMADS website:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite=

THE HADSST2 SST anomaly data is listed in the second column in the following webpage. The other columns list the uncertainty ranges for measurement and grid box sampling, for coverage, for bias, and for the combination of those uncertainties:
http://hadobs.metoffice.com/hadsst2/diagnostics/global/nh+sh/monthly

UPDATE

While doing a visual check of the sources against the graphs, I noticed a difference between the SST anomaly data presented by NOAA for the same dataset. I’m noting it in case someone else spot checks the graphs. The Monthly Global Ocean Temperature Anomalies (degrees C) uses 1901 to 2000 as base years, but the ERSST.v3b data uses 1971 to 2000. Confirmation here:
http://www.ncdc.noaa.gov/oa/climate/research/sst/ersstv3.php

For those who want to split hairs, the difference in the base years changes the rankings of SST anomalies, Figure 6. But it has no impact on the SST data rankings.
http://i30.tinypic.com/5y6xcx.png
Figure 6

El Nino Events Are Not Getting Stronger

I’ve moved to WordPress.  This post can now be found at El Nino Events Are Not Getting Stronger

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The Texas A&M press release in the WattsUpWithThat post “Possible Linkage between the 1918 El Niño and the 1918 flu pandemic ?” stated that “some researchers” continued to believe that global warming was causing stronger El Nino events. Link to press release:
http://dmc-news.tamu.edu/templates/?a=8028&z=15

Quote from it: “Giese adds, ‘The most commonly used indicator of El Niño is the ocean temperature anomaly in the central Pacific Ocean. By that standard, the 1918-19 El Niño is as strong as the events in 1982-83 and 1997-98, considered to be two of the strongest events on record, causing some researchers to conclude that El Niño has been getting stronger because of global warming. Since the 1918-19 El Niño occurred before significant warming from greenhouse gasses, it makes it difficult to argue that El Niños have been getting stronger.”

HOWEVER

Not to discount the work by Giese et al: a quick look at a graph of NINO3.4 SST anomalies that includes the 30 years before 1900, Figure 1, reveals that there were two comparably sized “Super” El Nino events in 1877/78 and 1888/89.
http://i25.tinypic.com/259v9si.png
Figure 1

Link to the preprint version of Giese et al (2009) “The 1918/1919 El Niño”:
http://www.cdc.noaa.gov/people/gilbert.p.compo/Gieseetal2009.pdf

SOURCE

HADISST Anomaly data is available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

Supplement To ENSO Is A Major Component Of Sea Level Rise

I’ve moved to WordPress.  This post can now be found at Supplement To ENSO Is A Major Component Of Sea Level Rise

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In my post ENSO Is A Major Component Of Sea Level Rise, I illustrated that ENSO is the cause of the pattern of Sea Level Trends illustrated in Figure 1. The IPCC confirms this in their discussion of regional variations in the rate of Sea Level Change.
http://i30.tinypic.com/4jrrr6.png
Figure 1

The following is that IPCC discussion on the rate of sea level change. The illustrations referenced by the IPCC follow the quote. Refer to page 416 of the IPCC AR4, or page 32 of 48 of AR4, Chapter 5.
http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter5.pdf

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“5.5.4 Interpretation of Regional Variations in the Rate of Sea Level Change

“Sea level observations show that whatever the time span considered, rates of sea level change display considerable regional variability (see Sections 5.5.2.2 and 5.5.2.3). A number of processes can cause regional sea level variations.

“5.5.4.1 Steric Sea Level Changes

“Like the sea level trends observed by satellite altimetry (see Section 5.5.2.3), the global distribution of thermosteric sea level trends is not spatially uniform. This is illustrated by Figure 5.15b and Figure 5.16b, which show the geographical distribution of thermosteric sea level trends over two different periods, 1993 to 2003 and 1955 to 2003 respectively (updated from Lombard et al., 2005). Some regions experienced sea level rise while others experienced a fall, often with rates that are several times the global mean. However, the patterns of thermosteric sea level rise over the approximately 50-year period are different from those seen in the 1990s. This occurs because the spatial patterns, like the global average, are also subject to decadal variability. In other words, variability on different time scales may have different characteristic patterns.

“An EOF analysis of gridded thermosteric sea level time series since 1955 (updated from Lombard et al., 2005) displays a spatial pattern that is similar to the spatial distribution of thermosteric sea level trends over the same time span (compare Figure 5.20 with Figure 5.16b). In addition, the first principal component is negatively correlated with the Southern Oscillation Index. Thus, it appears that ENSO-related ocean variability accounts for the largest fraction of variance in spatial patterns of thermosteric sea level. Similarly, decadal thermosteric sea level in the North Pacific and North Atlantic appears strongly influenced by the PDO and NAO respectively.

“For the recent years (1993–2003), the geographic distribution of observed sea level trends (Figure 5.15a) shows correlation with the spatial patterns of thermosteric sea level change (Figure 5.15b). This suggests that at least part of the nonuniform pattern of sea level rise observed in the altimeter data over the past decade can be attributed to changes in the ocean’s thermal structure, which is itself driven by surface heating effects and ocean circulation. Note that the steric changes due to salinity changes have not been included in these figures due to insufficient salinity data in parts of the World Ocean.”

http://i32.tinypic.com/2zow7zm.png
Figure 5.15
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http://i31.tinypic.com/2kn1xl.png
Figure 5.16
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http://i25.tinypic.com/mayl8j.png
Figure 5.20

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Note that the negative correlation between the Southern Oscillation Index (SOI) and the first principal component of thermosteric sea level time series means that the first principal component would correlate directly (not negatively) with NINO3.4 and Cold Tongue Index SST anomalies.

Also, by using the trend maps, the IPCC fails to present the magnitude of the ENSO component in those sea level trends. I plotted the data in ENSO Is A Major Component Of Sea Level Rise. Figures 2 and 3 illustrate the Sea Level variations (total, not just thermosteric) for the Cold Tongue Index area of the eastern tropical Pacific (6S-6N, 180W-90W) and of the Pacific Warm Pool in the western tropical Pacific (10S-20N, 110E-175E).
http://i25.tinypic.com/24oohzo.png
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Figure 2
http://i28.tinypic.com/15yykj5.png
Figure 3