GISS Acknowledges Addition of ERSST.v3b Data To Their GISTEMP “Options”

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I had noted the addition of ERSST.v3b SST dataset to the GISTEMP map-making webpage back in the February 25, 2010 post WHEN DID GISS ADD ERSST.v3b DATA TO THEIR MAP-MAKING WEB PAGE? This month, GISS posted a link to the .pdf draft of a journal article "Current GISS Global Surface Temperature Analysis" on their GISS Surface Air Temperature Analysis webpage. GISS compares their HADISST/OI.v2 based data to the ERSST.v3b data in the draft of the paper. At the end of the Supplementary Material, page 32, they write, “Until improved assessments of the alternative SST data sets exist, the GISS global analysis will be made available for both HadISST1 and ERSST, in both cases with these longterm data sets concatenated with OISST for 1982-present. HadISST1+OISST will continue to be our standard product unless and until verifications show ERSST to be superior.”

And in the paragraph before that one, GISS acknowledges the impacts on short- and long-term trends, “The standard GISS global analysis uses the concatenated HadISST1+OISST data set, as described in the main text. Any of the alternative ocean data sets that we have described here would yield slightly greater global warming, both in recent decades and on the century time scale.” The differences in trends are illustrated in my post WHEN DID GISS ADD ERSST.v3b DATA TO THEIR MAP-MAKING WEB PAGE?

(BTW, I hate words like “concatenate”, which basically means link or splice. Why not use a word that the vast majority of people will understand, so they won’t have to spend time checking a dictionary?)

Which Has The Greater Impact On Global SST Anomalies, The Rise In The SST Anomalies Of The Arctic Ocean Or The Decrease In The Southern Ocean?

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This post illustrates an observation I’ve made a number of times while investigating other subjects, so I thought I’d post it. It shows the relative strengths of the polar SST anomalies.

Using the NCDC’s Optimum Interpolation (OI.v2) SST Anomaly dataset, which begins in November 1981, the Arctic Ocean shows a significant positive linear trend. Refer to Figure 1. Over the same period, the Southern Ocean shows a negative linear trend that’s much less significant. The latitudes I’ve used for the Arctic Ocean are 65N-90N, while the Southern Ocean latitudes are 90S-60S.
http://i43.tinypic.com/2e4m62r.png
Figure 1

According to Wikipedia, the area of the Arctic Ocean is 14,056,000 sq. km. The Southern Ocean, on the other hand, has an area of 20,327,000 sq. km or about 45% larger than the Arctic Ocean. Sea ice also varies in the polar oceans, as is well known.

Taking those factors into consideration, which plays a greater role in global SST anomaly trends, the rise in the Arctic Ocean SST anomalies, or the drop in the Southern Ocean SST anomalies?

The easiest way to illustrate it is to compare global SST anomalies with and without the polar oceans. (Or someone other than me could go through all of the calculations to factor in area, monthly sea ice extent, etc., using polar ocean data.) If the rise in the SST anomalies of the Arctic Ocean is having the greater impact on global SST anomalies than the drop in the Southern Ocean data, then the Global SST anomaly dataset including the polar oceans would have a higher trend than the a global dataset without the polar oceans. Refer to Figure 2. The opposite takes place.
http://i44.tinypic.com/10ngtnr.png
Figure 2

While the two linear trends are close, the drop in the Southern Ocean SST anomalies has the greater effect. That is, adding the polar oceans to the data caused the global SST anomaly trend to decrease.

Someone will note that the latitudes of the Arctic and Southern Ocean datasets are not the same. The Southern Ocean is generally defined as the ocean south of 60S. But the Arctic Ocean borders aren’t defined by one latitude. Does this skew the results? Since the NOAA NOMADS system requires latitudes and longitudes as inputs, I can’t customize the Arctic Ocean borders. But I can extend the latitudinal range to 60N-90N. Same results. The linear trend of the dataset without the polar data has the higher linear trend, though the gap is closing.
http://i40.tinypic.com/15nrj9f.png
Figure 3

SOURCE
SST anomaly data is available through the NOAA NOMADS website:http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

PRELIMINARY March 2010 SST Anomaly Update

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

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The official March 2010 SST data through the NOAA NOMADS website won’t be official until Monday April 5th. 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 March 2010. I’ve also included the weekly data through March 24, 2010.

Based on the preliminary data, monthly NINO3.4 SST anomalies are continuing their decline, though the decline slowed last month. They’ve dropped ~0.13 deg C over the past month.
http://i39.tinypic.com/35kr69c.png
Monthly NINO3.4 SST Anomalies

Monthly Global SST anomalies, according to the preliminary data, increased 0.008 deg C since February.
http://i43.tinypic.com/kd8xhy.png
Monthly Global SST Anomalies

The weekly NINO3.4 SST anomaly data, with the most recent value centered on March 24, 2010, shows it may have ended the period of slower decline that has existed for the past month and a half. That little wiggle at the end is actually a drop of 0.12 deg C in one week.
http://i39.tinypic.com/1znblm8.png
Weekly NINO3.4 SST Anomalies


Weekly Global SST Anomalies are still elevated and have taken a minor upward swing yet again, though it’s difficult to see in the graph. It would be nice if the NINO3.4 SST anomalies continued their decline, so that the global data would start to drop again--eventually.
http://i42.tinypic.com/ioq13r.png
Weekly Global SST Anomalies

SOURCE

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

The Inverted ENSO Signal In The SST Residuals Of The East Indian And West Pacific Ocean

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OVERVIEW

This post illustrates an inverted ENSO signals contained within the Global SST anomaly dataset, using SST anomaly residuals for the East Indian and West Pacific Oceans. SST anomaly residuals in this post are defined as the SST anomalies for specific areas of the globe minus global SST anomalies.

This inverted ENSO signal contained within the East Indian and West Pacific Ocean dataset confirms the dipole effect between the Eastern and Western Tropical Pacific that extends into the East Indian Ocean that was discussed in two other posts about the multiyear aftereffects of ENSO events:
More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND...
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

EAST INDIAN-WEST PACIFIC SST RESIDUALS SHOW AN INVERTED ENSO SIGNAL

Figure 1 compares Global SST anomalies and the SST anomalies for the East Indian-West Pacific Ocean, a dataset with the coordinates of 60S-65N, 80E-180. It’s well understood that the Global dataset includes an ENSO component. Global SST anomalies increase when Eastern and Central Tropical Pacific SST anomalies rise in response to an El Nino, and Global SST anomalies fall when a La Nina causes Eastern and Central Tropical Pacific SST anomalies to drop. The East Indian and West Pacific SST anomalies in Figure 1 follow the basic rises and falls of the Global dataset, but there is additional variability, indicating the East Indian and West Pacific SST anomalies are impacted by something other than the “normal” ENSO signal.

http://i44.tinypic.com/28r35hv.png
Figure 1

In Figure 2, the Global SST anomalies (with its ENSO component) have been subtracted from the East Indian and West Pacific SST anomalies, leaving the East Indian and West Pacific SST anomaly residual. Again, this residual illustrates the difference between Global SST anomalies and the East Indian-West Pacific SST anomalies. Recognize the curve?
http://i43.tinypic.com/25jfg9t.png
Figure 2

In Figure 3, I’ve scaled the NINO3.4 SST anomaly data and inverted it by multiplying it by a factor of -0.15. As illustrated, the curves of the East Indian and West Pacific SST anomaly residuals and the inverted and scaled NINO3.4 SST anomalies are remarkably similar. They correlate well for the entire term of the data, but they do diverge slightly at times.
http://i39.tinypic.com/27ybwyc.png
Figure 3

THE CAUSE OF THIS OPPOSING EFFECT

A much-simplified version: during a significant El Nino, warm water from the Western Pacific Warm Pool (to depths of 300 meters) sloshes to the east and spreads across the surface of the central and eastern tropical Pacific. Warm water that was below the surface of the Pacific Warm Pool and excluded from the Sea SURFACE Temperature measurement is now included, raising SST anomalies in the central and eastern tropical Pacific.

A number of things happen during the La Nina that follows. Trade winds increase in the tropical Pacific, and with strengthened Equatorial Currents, the warmer-than-normal water in the central and eastern tropical Pacific is carried back to the western tropical Pacific. Some of the warm water helps to recharge the warm water in the Pacific Warm Pool, and some of it is carried by ocean currents north into the Northwest Pacific, and some of it is carried south by ocean currents into the Southwest Pacific, and some of it is carried into the eastern tropical Indian Ocean by a current called the Indonesian Throughflow. The stronger-than-normal trade winds during the La Nina also cause a decrease in cloud cover in the tropical Pacific, which causes an increase in Downward Shortwave Radiation (visible light). This additional Downward Shortwave Radiation warms the surface and subsurface waters of the tropical Pacific, and the trade winds and ocean currents carry the warm water to the west, where it is transported into the Northwest and Southwest Pacific and into the eastern tropical Indian Ocean, as described above, by ocean currents.

The result is a visible dipole (seesaw-like) effect between the central and eastern tropical Pacific and the western Pacific as an ENSO event goes from El Nino to La Nina. Refer to Figure 4.
http://i48.tinypic.com/xc6s0l.gif
Figure 4

THE “REST OF THE WORLD” SST ANOMALY RESIDUALS

Someone was bound to ask, What does the rest of the world oceans look like? So I’ve added this section to the post.

Figure 5 is a comparison graph of global SST anomalies and the SST anomalies for the area between the latitudes of 60S and 65N that is not included in the East Indian and West Pacific Ocean dataset. The coordinates for the “Rest of the World” SST anomaly data are 60S-65N, 180-80E. As illustrated, the variations in the two datasets mimic one another, with the “Rest of the World” subset varying more than the global data.
http://i44.tinypic.com/2nc3nnl.png
Figure 5

The same process was used to create the residuals. That is, the Global SST anomalies (which have a strong ENSO component) are subtracted from the “Rest of the World” SST anomalies. The residual illustrates how that dataset differs from the global data. The result was not unexpected. It shows that the “Rest of the World” data has yet another, but smaller, ENSO component. In Figure 6, I’ve scaled NINO3.4 SST anomalies by a factor of 0.05 for comparison to the “Rest of the World” SST anomaly residual.
http://i43.tinypic.com/x0pnc6.png
Figure 6

KEEP IN MIND THIS POST WAS ABOUT SST ANOMALY RESIDUALS

Again, the intent of this post was to provide another means of illustrating the east to west Pacific SST dipole effect, by showing the inverted signal within the residuals of the East Indian-West Pacific dataset. Recall that the SST residuals contain the positive trend of the global SST anomaly curve. This changes perspective. Also, if we look at the SST anomaly data of East Indian through the Eastern Pacific Oceans, it is clear that the positive ENSO signal dominates. Comparing it to scaled NINO3.4 SST anomalies, Figure 7, there is little lag, at least on the leading side of most of the major variations. This appears to indicate that the opposing East Indian and West Pacific dataset only suppresses the global response to the primary ENSO signal being produced in the Central and Eastern Tropical Pacific. Then, when looking for a secondary ENSO signal in the global dataset, it should be a lagged positive signal.
http://i39.tinypic.com/rr4j14.png
Figure 7

SOURCE
The data used in this post is available through the NOAA NOMADS website:
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

Absolute Land Surface Temperature Dataset

I’ve moved to WordPress.  This post can now be found at Absolute Land Surface Temperature Dataset

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On the GISSTemp +0.71C: Slightly higher than January. thread at Lucia's The Blackboard, I replied to a comment with, “There’s simply no gridded absolute land surface temperature data that I’ve found.” I received an email a few days later, advising me the KNMI Climate Explorer did, in fact, include an absolute Land Surface Temperature dataset, which is a merger of GHCN and CAMS station data. Refer to Figure 1. It’s identified on the KNMI Climate Explorer webpage as the CPC GHCN/CAMS t2m analysis, and it’s presented in the Fan and Dool (2007) paper “A global monthly land surface air temperature analysis for 1948-present.”
ftp://ftp.cpc.ncep.noaa.gov/wd51yf/GHCN_CAMS/cpc_globalT.pdf
http://i41.tinypic.com/1zzw37.png
Figure 1

THINGS TO CONSIDER
In addition to the obvious difference (absolute temperature versus anomalies), there are also some other things to consider when using this dataset. The abstract of Fan and Dool (2007) includes, “The study also reveals that there are clear biases between the observed surface air temperature and the existing Reanalysis data sets, and they vary in space and seasons.”

On page 4, line 16 of the paper, they caution, “The readers are advised that the resulting temperature data set to be described in this paper was NOT constructed first and foremost for climate change studies. While the GHCN component of the data has gone through most quality checks one would like to see, the CAMS component of the data (much more numerous than GHCN over the last few years) is less strictly quality controlled.”

HOW SIGNIFICANT ARE THE BIASES?
That will depend on how you define significant. Figures 2 through 4 are comparison graphs with linear trends of the GHCN+CAMS land surface air temperature anomalies, identified as GHCN-CAMS T2m, and the three major land surface temperature products: CRUTEM3, GISTEMP (1200km smoothing), and NCDC. In all three instances, the linear trend from 1948 to present of the GHCN-CAMS T2m anomalies exceeds linear trends of the more commonly used datasets.
http://i40.tinypic.com/11hxq87.png
Figure 2
http://i39.tinypic.com/sb2fqt.png
Figure 3
http://i41.tinypic.com/34jek92.png
Figure 4

THE DIVERGENCES INCREASE IN RECENT YEARS

Figures 5 through 7 illustrate the differences between the GHCN-CAMS T2m anomalies and those of CRUTEM3, GISTEMP (1200km smoothing), and NCDC datasets.
http://i42.tinypic.com/1zleo09.png
Figure 5
http://i43.tinypic.com/s658gk.png
Figure 6
http://i41.tinypic.com/2bo5qt.png
Figure 7

STATION LOCATION AND DENSITY

Figure 8 is Figure 2 from Fan and Dool (2007), showing the locations and number of surface stations per grid. Refer to the text at the bottom of the illustration for the description.
http://i39.tinypic.com/30sfvbl.png
Figure 8

ANNUAL MAXIMUM, AVERAGE, AND MINIMUM

And for those interested, the annual maximum, minimums and averages of the GHCN-CAMS T2m data from 1948 through 2009 are shown in Figure 9, as are their linear trends.
http://i43.tinypic.com/25qr8yo.png
Figure 9

Thanks for the heads-up, Geert Jan.

SOURCE

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

Mid-March 2010 SST Anomaly Update

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

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NINO3.4 and Global SST anomalies are still stalled at the levels they’ve been at for the last six weeks.

NINO3.4 SST anomalies for the week centered on March 17, 2010 show that central equatorial Pacific SST anomalies have remained relatively level for the past month and a half. Presently they’re at 1.19 deg C.
http://i44.tinypic.com/30lpapt.png
NINO3.4 SST Anomalies

The delay in the decline is likely the response to another Kelvin wave. Refer to the .gif animation of the subsurface temperature anomalies for the equatorial Pacific.
http://i44.tinypic.com/sy9qiv.gif
Equatorial Pacific Subsurface Temperature Anomalies

Weekly Global SST anomalies are still elevated, but they are lower than the peak for this El Nino. Like the NINO3.4 SST anomalies, the Global SST anomalies appear content to cycle where they are now. There’s still no indication that there will be a lagged rise.
http://i40.tinypic.com/2csi785.png
Global SST Anomalies

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

The .gif animation of the subsurface temperature anomalies for the equatorial Pacific is available from the NOAA Climate Prediction Center:
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_update/wkxzteq.shtml

Is There A 60-Year Pacific Decadal Oscillation Cycle?

I’ve moved to WordPress.  This post can now be found at Is There A 60-Year Pacific Decadal Oscillation Cycle?

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INTRODUCTIONClimate bloggers often refer to a 60-year cycle in the Pacific Decadal Oscillation (PDO). I assume they derive the 60-year period from the graph of the PDO, Figure 1, from the JISAO "Pacific Decadal Oscillation (PDO)" web page. As illustrated the PDO data changes from positive to negative about 1945, reaches into positive values again about 1975, then appears to “switch” back to negative in the 2000s. Remarkably, this 60-year period is referred to even though the data before 1945 does not support a continuation before then. And referring to Paleoclimatological studies of the PDO, there is also no evidence of a persistent 60-year PDO cycle.
http://i44.tinypic.com/2eyb1xs.png
Figure 1

PACIFIC DECADAL OSCILLATION RECONSTRUCTIONS
The NOAA Paleoclimatology Climate Reconstructions webpage includes 5 PDO reconstructions under the heading of Atmospheric Circulation Patterns. Figure 2 is a comparison graph of the data available from those studies. Note: All of the paleoclimatological PDO data in this post have been smoothed with a 30-year running-average filter to highlight the low frequency variability.
http://i40.tinypic.com/2vjbj91.png
Figure 2

The longer-term dataset from MacDonald and Case (2005) skews the graph, so I’ve started the comparison in 1700 in Figure 3. There does not appear to be a persistent 60-year cycle in any of the PDO reconstruction datasets. If fact, there appears to be little agreement between the reconstructions prior to the early 1900s, but the datasets were, of course, based on different proxies and from different continents.
http://i40.tinypic.com/14c6zpg.png
Figure 3

GRAPHS, DATA LINKS, AND DATA CITATIONS OF INDIVIDUAL STUDIES
MacDonald and Case (2005)
Data Link:
ftp://ftp.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/pdo-macdonald2005.txt

DATA CITATION: MacDonald, G.M., and R.A. Case. 2006.
Pacific Decadal Oscillation Reconstruction for the Past Millennium.
IGBP PAGES/World Data Center for Paleoclimatology
Data Contribution Series # 2006-023.
NOAA/NCDC Paleoclimatology Program, Boulder CO, USA.

ORIGINAL REFERENCE: MacDonald, G.M., and R.A. Case. 2005.
Variations in the Pacific Decadal Oscillation over the past millennium.
Geophys. Res. Lett., 32, L08703, doi:10.1029/2005GL022478.
http://i43.tinypic.com/e0jpxj.png
Figure 4
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D'Arrigo and Wilson (2006)
Data Link:
ftp://ftp.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/pdo-darrigo2006.txt

DATA CITATION: D'Arrigo, R. and R. Wilson. 2006.
Spring Pacific Decadal Oscillation Index Reconstruction.
IGBP PAGES/World Data Center for Paleoclimatology
Data Contribution Series # 2006-095.
NOAA/NCDC Paleoclimatology Program, Boulder CO, USA.

ORIGINAL REFERENCE: D'Arrigo, R. and R. Wilson. 2006.
On the Asian Expression of the PDO.
International Journal of Climatology. 26: 1607-1617.
http://i42.tinypic.com/9uoy34.png
Figure 5
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Biondi et al (2001)
Data Link:
ftp://ftp.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/pdo.txt

DATA CITATION: Biondi, F. et al., 2001,
Pacific Decadal Oscillation Reconstruction.
International Tree-Ring Data Bank.
IGBP PAGES/World Data Center for Paleoclimatology
Data Contribution Series #2001-001.
NOAA/NGDC Paleoclimatology Program, Boulder CO, USA.

ORIGINAL REFERENCE: Biondi, F., A. Gershunov, and D.R. Cayan, 2001,
North Pacific decadal climate variability since AD 1661,
Journal of Climate, Volume 14, Number 1, January 2001.
http://i44.tinypic.com/riyk3a.png
Figure 6
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Shen et al (2006)
Data Link:
ftp://ftp.ncdc.noaa.gov/pub/data/paleo/historical/pacific/pdo-shen2006.txt

DATA CITATION: Shen, C., et al. 2006.
Pacific Decadal Oscillation Reconstruction.
IGBP PAGES/World Data Center for Paleoclimatology
Data Contribution Series # 2006-045.
NOAA/NCDC Paleoclimatology Program, Boulder CO, USA.

ORIGINAL REFERENCE:
Shen, C., W.-C. Wang, W. Gong, and Z. Hao. 2006.
A Pacific Decadal Oscillation record since 1470 AD reconstructed from proxy data of summer rainfall over eastern China.
Geophysical Research Letters, vol. 33, L03702, February 2006.
doi:10.1029/2005GL024804.
http://i43.tinypic.com/2j16iwx.png
Figure 7

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D'Arrigo et al (2005)
Data Link:
ftp://ftp.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/pdo-darrigo2001.txt

DATA CITATION: D'Arrigo, R., et al. 2005.
Pacific Decadal Oscillation Reconstruction.
IGBP PAGES/World Data Center for Paleoclimatology
Data Contribution Series # 2005-020.
NOAA/NGDC Paleoclimatology Program, Boulder CO, USA.


ORIGINAL REFERENCE: D'Arrigo, R., R. Villalba, and G. Wiles. 2001.
Tree-ring estimates of Pacific decadal climate variability.
Climate Dynamics, Volume 18, Numbers 3-4, pp. 219-224, December 2001.
http://i39.tinypic.com/2rxzig0.png
Figure 8

ADDITIONAL PDO POSTS
Misunderstandings about the PDO – REVISED

Revisiting “Misunderstandings About The PDO – Revised”

Absolute RSS MSU TLT Data

I’ve moved to WordPress.  This post can now be found at Absolute RSS MSU TLT Data

If you haven’t noticed, KNMI has added RSS MSU TLT data to their Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

In absolute form, the Global TLT data of course includes seasonal variations, as shown in Figure 1.

http://i40.tinypic.com/141qvra.png
Figure 1

And for those who are interested in things like these, Figure 2 shows the Annual Global TLT Minimums, Maximums, and Means and their trends. The linear trend of the annual maximums is higher than the minimums. And curiously, the trends for the annual means and minimums are the same.

http://i40.tinypic.com/260g26f.png
Figure 2

And there’s always Global TLT anomalies and linear trend, Figure 3.

http://i41.tinypic.com/2qan2wj.png
Figure 3

At present, the RSS TLT data at KNMI is lagging a few months.#################

February 2010 SST Anomaly Update

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

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MONTHLY SST ANOMALY MAP

The map of Global OI.v2 SST anomalies for February 2010 downloaded from the NOMADS website is shown below.
http://i49.tinypic.com/23m4e1g.png
February 2010 SST Anomalies Map (Global SST Anomaly = +0.285 deg C)

MONTHLY OVERVIEW

Global SST anomalies continued to drop in small monthly increments. They dropped 0.008 deg C between January and February. SST Anomalies in both the Southern and Northern Hemispheres declined. The equatorial Pacific remains in El Nino conditions (Monthly NINO3.4 SST Anomaly = +1.24 deg C and Weekly NINO3.4 SST Anomaly = +1.14 deg C), but SST anomalies there are dropping. Monthly NINO3.4 SST anomalies dropped 0.27 in February, and weekly data NINO3.4 SST anomalies have dropped (-0.8 deg C) from their peak over the past ten weeks. The Indian Ocean and the East Indian-West Pacific Ocean datasets both show significant rises this month.
http://i46.tinypic.com/17degl.png
Global
Monthly Change = -0.008 deg C
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http://i49.tinypic.com/143gopv.png
NINO3.4 SST Anomaly
Monthly Change = -0.31 deg C

EAST INDIAN-WEST PACIFIC

The East Indian and West Pacific SST Anomalies are showing an increase. I’ve added this dataset in an attempt to draw attention to the atypical response. Using the 1986/87/88 and 1997/98 El Nino 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. I’ve also revised the blocked question in the illustration to include “& 2010/11 La Nina”, since the rise would actually occur during, and be caused in part by, the La Nina event.
http://i50.tinypic.com/2yyz68z.png
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = +0.181 deg C

Further information on the upward “step changes” that result from strong El Nino events, refer to my posts from a year ago 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 for the discussions of the processes that cause the rise, refer to 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

NOTE ABOUT THE DATA

The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from November 1981 to February 2009.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES
http://i45.tinypic.com/2uiu6n9.png
Northern Hemisphere
Monthly Change = -0.010 deg C
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http://i45.tinypic.com/aadk5i.png
Southern Hemisphere
Monthly Change = -0.006 deg C
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http://i48.tinypic.com/358ciud.png
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = +0.063 deg C
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http://i49.tinypic.com/25rozk2.png
South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = -0.117 deg C
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http://i46.tinypic.com/2zoxms0.png
North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = -0.043 Deg C
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http://i45.tinypic.com/294ihow.png
South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = -0.158 deg C
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http://i49.tinypic.com/2njzrzc.png
Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = +0.203 deg C
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http://i47.tinypic.com/eb9ajc.png
Arctic Ocean (65 to 90N)
Monthly Change = -0.019 deg C
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http://i49.tinypic.com/jt6zvn.png
Southern Ocean (60 to 90S)
Monthly Change = +0.086 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 +1.14 deg C, down 0.08 Deg C from a peak of 1.94 Deg C ten weeks ago.
http://i50.tinypic.com/ma9e0z.png
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