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Sunday, July 25, 2010


The following are links to the Monthly SST Anomaly Updates, the Preliminary Monthly SST Anomaly Updates, and the Mid-Month SST Anomaly Updates since January 2010. These updates present time-series graphs of monthly and weekly Reynolds (OI.v2) Sea Surface Temperature (SST) anomaly data from the NOAA NOMADS system.

For earlier updates, use the search feature in the upper left-hand corner of this webpage.


The monthly update posts include NINO3.4, global, hemispheric, and individual ocean basin SST anomalies. Also included are the weekly NINO3.4 SST anomalies.
January 2011 SST Anomaly Update
December 2010 SST Anomaly Update
November 2010 SST Anomaly Update
October 2010 SST Anomaly Update
September 2010 SST Anomaly Update
August 2010 SST Anomaly Update
July 2010 SST Anomaly Update
June 2010 SST Anomaly Update
May 2010 SST Anomaly Update
April 2010 SST Anomaly Update
March 2010 SST Anomaly Update
February 2010 SST Anomaly Update
January 2010 SST Anomaly Update


The NOAA NOMADS System creates monthly data before the "official" release date. The preliminary data appears to be based on less than a full month of data. These posts present those values for global and NINO3.4 SST anomalies, and the weekly NINO3.4 and global SST anomalies.
PRELIMINARY February 2011 SST Anomaly Update
PRELIMINARY January 2011 SST Anomaly Update
PRELIMINARY December 2010 SST Anomaly Update
PRELIMINARY November 2010 SST Anomaly Update
PRELIMINARY October 2010 SST Anomaly Update
PRELIMINARY September 2010 SST Anomaly Update
PRELIMINARY August 2010 SST Anomaly Update
No Preliminary SST Data For July 2010---Yet
PRELIMINARY June 2010 SST Anomaly Update
PRELIMINARY May 2010 SST Anomaly Update
VERY PRELIMINARY April 2010 SST Anomaly Update
PRELIMINARY March 2010 SST Anomaly Update
PRELIMINARY February 2010 SST Anomaly Update
PRELIMINARY January 2010 SST Anomaly Update


The mid-month updates present global and NINO3.4 SST anomalies based on weekly Reynolds (OI.v2) SST anomaly data.

Provided Instead Of Mid-February 2011 Update: NINO3.4 SST Anomalies Have Started Their La Niña Rebound
Mid-January 2011 SST Anomaly Update
Mid-December 2010 SST Anomaly Update
Mid-November 2010 SST Anomaly Update
Mid-October 2010 SST Anomaly Update
Mid-September 2010 SST Anomaly Update
Mid-August 2010 SST Anomaly Update
Provided Instead of Mid-June: La Niña Update – Week Of July 7, 2010
Mid-May 2010 SST Anomaly Update
Mid-April 2010 SST Anomaly Update
Mid-March 2010 SST Anomaly Update
Mid-February 2010 SST Anomaly Update
Mid-January 2010 SST Anomaly Update

Thursday, July 22, 2010

Land Surface Temperature Contribution To Non-Polar Temperatures

I’ve moved to WordPress.  This post can now be found at Land Surface Temperature Contribution To Non-Polar Temperatures
INTRODUCTIONFrank Lansner’s post Did GISS discover 30% more land in the Northern Hemisphere? at Jo Nova’s blog created a recent stir. Watts Up With That ran a similar post by Frank, Tipping point at GISS? Land and sea weight out of balance. Both posts spawned rebuttals/explanations, including Zeke Hausfather’s post The GISTemp Land Fraction at Lucia's The Blackboard and my post Notes On The GISTEMP Ratio Of Land To Sea Surface Temperature Data, which Anthony Watts co-posted as GISS land and sea ratios revisited, on the same day that he ran Frank Lansner’s post.

Basically, Frank Lansner’s post contends that GISS has increased the ratio of land to sea surface data with time, from zero percent early in the 20th century to near 70% in recent years. With a close examination of the graph that was being presented as a reference, Frank’s land surface data looked unusual, and I believe Frank’s observations are skewed by his choice of base years, and possibly by his smoothing method. I discussed this with him in a detailed comment that I posted at WattsUpWithThat and Jo Nova’s website. Refer to:

There are lingering beliefs that there’s something unusual about the way GISS handles land surface data. In an effort to dispel those misunderstandings, the land surface data contribution to combined land and sea surface temperature data will be illustrated in this post, using a very simple method. Sea surface temperature data will be subtracted from GISS, Hadley Centre, and NCDC combined (land and sea) surface temperature products. The remainders, which are the contributions of the land surface temperature data to the combined products, will be compared. Personally, I was surprised with the results. But first, we need to eliminate the effects of known differences between the GISS and the other two global temperature datasets.

GISS treats the polar regions differently than the Hadley Centre and NCDC. GISS has better land surface temperature data coverage than the Hadley Centre and NCDC in the Arctic and Antarctic. And the Hadley Centre and NCDC include Arctic and Southern Ocean sea surface temperature data as seasonal sea ice melts, while GISS deletes sea surface temperature from areas where there is seasonal sea ice. The treatment of polar data by GISS was discussed in GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data, which was also co-posted at WattsUpWithThat, GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data. So, due to those differences, this post will only examine the global temperature data between the latitudes of 60S and 60N. These latitudes represent approximately 85% of the surface area of the globe.

Note: It is also known there is little to no Antarctic land surface temperature data prior to the 1950s. But this can’t explain the results Frank Lansner was reporting.

GISS and Hadley Centre combined (land and sea) surface temperature anomaly data were downloaded from the KNMI Climate Explorer:

The KNMI Climate Explorer was also the source of Hadley Centre sea surface temperature (SST) anomaly data (HADSST2) used in its combined product. It also served as the source of the two SST components of the GISS combined product, HADISST from January 1880 to November 1981 and Reynolds (OI.v2) from December 1981 to present. The method employed to merge the two SST datasets used in the GISS product is discussed under Step 4 of the GISS current analysis webpage. The base years (1982 to 1992) used for splicing are different than those presented by the KNMI Climate Explorer for the GISS combined product, so I shifted the merged SST anomaly data to account for this.

I wanted to compare NCDC data in this post also, but it is not available through the KNMI Climate Explorer, and since the NCDC does not break down its standard combined surface temperature product into the desired latitude band (60S-60N) on its Global Surface Temperature Anomalies page, I used a second source. The NCDC also has SST, LST, and combined temperature anomaly data available through its ERSST Version 3/3b webpage, and it is available in multiple latitude bands, including 60S-60N. Scroll down to their link ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo under the heading of “ASCII Time series Tables”.

Figure 1 compares the combined global (land and sea) surface temperature anomalies of the standard NCDC product and the data available through the ERSST.v3b webpage. The trends are identical at 0.057 deg C/decade. The difference appears to be caused by the use of different base years. The standard NCDC product uses 1901 to 2000 for base years while the data available through the ERSST.v3b webpage appears to be based on the NCDC climatology of 1971 to 2000. So this post uses the NCDC SST and combined (LST and SST) data that’s available through the ERSST.v3b webpage:
Figure 1

Links to the data presented in Figure 1:
“Standard” NCDC Global combined product:
NCDC Global combined product through ERSST.v3b webpage:

Note: There are two different series of data available through the ERSST.v3b webpage. Those ending with .gv3.asc are recent additions, and since they have a slightly different trend and I have not found any mention of them in any other webpage, I have not used them in this post.


Figures 2 through 4 are comparison graphs of GISS, Hadley Centre, and NCDC SST and Combined (SST&LST) datasets for the latitudes of 60S-60N. All data has been smoothed with a 13-month running-average filter. They are being provided as references.
Figure 2
Figure 3
Figure 4


As discussed in the overview, to determine the contribution of land surface temperature anomaly data, the SST anomaly data was subtracted from the combined data. Before subtraction, the SST data was scaled by a factor of 0.755 to represent the ratio of ocean to land between the latitudes of 60S-60N. The remainders for each dataset are shown in Figures 5 through 7. Note that these graphs represent the remainder of subtraction, not the actual land surface temperature anomalies. To convert them back to land surface anomalies, they would have to be scaled.
Figure 5
Figure 6
Figure 7


Figure 8 compares the remainders resulting from the subtraction of the scaled SST data from the combined (LST&SST) GISS, Hadley Centre, and NCDC products. As shown in Figures 5 through 7, the land surface residuals are noisy, so for this comparison, the data was smoothed with a 37-month running-average filter. While there are slight differences in the yearly and decadal variations in Figure 8, the linear trends for the three datasets are basically the same, differing only 0.001 deg C/decade.
Figure 8

This suggests, for the latitudes of 60S-60N, the differences between the combined products from GISS, Hadley Centre, and NCDC result from differences between the SST data they employ. Refer to An Overview Of Sea Surface Temperature Datasets Used In Global Temperature Products. And the most significant differences in SST anomalies occur before 1940. From 1880 to 1940, the SST anomaly data used by the Hadley Centre and NCDC have significant dips and rebounds, as shown in Figure 3 and 4, while the dip and rebound is much less pronounced in the SST data used by GISS, Figure 2.

The Hadley Centre and NCDC land surface temperature anomaly datasets represent continental land masses only, and on a global basis, both of those datasets exclude Antarctica. The land surface data presented by GISS, on the other hand, includes continental land mass data plus much more. First, looking at Figure 9, continental land mass data is extended out over the oceans in the GISS land surface temperature product with 1200km radius smoothing. (Figure 9 is a trend map available through the GISS Global Maps webpage, and it shows the regional changes in temperature anomaly from 1880 to 2009. I’ve cropped the map to show the latitudes, 60S-60N, used in this post.)
Figure 9

Second, GISS also includes data from island surface stations and from “Ship stations,” and these values are also extended out over the oceans. This GISS dataset is a carryover from the methods developed by GISS back in the 1980s, when SST datasets were incomplete. They were attempting to simulate global temperature anomalies without using SST data. This is explained further in a WUWT comment from Zeke Hausfather to Frank Lansner in which Zeke quotes from a correspondence from Dr. Reto Ruedy of GISS:

In part it reads, “The curve NCDC and most likely you are computing shows the mean temperature over the land area (which covers about 1/3 of the globe, a large part of it located in the Northern hemisphere).

“None of our graphs represents that quantity. We could obtain it by creating a series of maps, then averaging just over the land areas (similar to what we do to get the US graph).”

It continues, “Since our interest is in the total energy contained in the atmosphere which correlates well with the global mean surface temperature, all our graphs display estimates for the global mean, the ones based on station data only as well as the ones based on a combination of station and ship and satellite data. Obviously, the latter is the more realistic estimate and we keep the first one mostly for the following historical reason:

“When we started out in the 1980s analyzing available temperature data, historic ocean temperature data were not yet available and we did the best we could with station data. As soon as ocean data compilations became available, we used them to refine our estimates (calling it LOTI). But we kept the earlier estimates also, mostly for sentimental reasons; they are rarely if ever mentioned in our discussions (see also the ‘note’ in the ‘Table’ section of our main web site).”

And continuing this post, the “‘note’ in the ‘Table’ section of [the GISTEMP] main web site” reads, “Note: LOTI provides a more realistic representation of the global mean trends than dTs below; it slightly underestimates warming or cooling trends, since the much larger heat capacity of water compared to air causes a slower and diminished reaction to changes; dTs on the other hand overestimates trends, since it disregards most of the dampening effects of the oceans that cover about two thirds of the earth's surface.

And again, LOTI represents the GISTEMP combined land and sea surface data and the dTs represents the land surface data.


The GISS Global-mean monthly, seasonal, and annual means dataset does not represent continental land mass temperature anomalies as many believe. It, therefore, cannot be employed in analyses like the one Frank Lansner is attempting to perform.

Dr. Ruedy’s statement that GISS could create a continental land temperature anomaly dataset similar to NCDC “by creating a series of maps, then averaging just over the land areas,” is another way of saying they could create it by masking the areas where the land surface data extends out over the oceans. And this was noted in the post Notes On The GISTEMP Ratio Of Land To Sea Surface Temperature Data.

Then there’s the similarity in the linear trends of the land surface contributions for the three combined datasets, Figure 8. It confirms the findings of the independent researchers who are creating land surface temperature anomaly datasets: the results are pretty much the same as the GISS, Hadley Centre, and NCDC data.

Saturday, July 17, 2010

Notes On The GISTEMP Ratio Of Land To Sea Surface Temperature Data

I’ve moved to WordPress.  This post can now be found at Notes On The GISTEMP Ratio Of Land To Sea Surface Temperature Data
Over the past few days there has been some blogosphere buzz about the apparent ratio of Land Surface Temperature (LST) Data used in the GISTEMP combined land and sea surface temperature data with 1200km smoothing. I’ll provide a few comparison graphs to explain.

Figure 1 includes a time series graph of the Hadley Centre’s HADCRUT3 Combined Surface Temperature product, from January 1982 to April 2010. Also included is the weighted average of the two datasets that make up the HADCRUT3 data, with the weighting of 27% LST [CRUTEM3] and 73% Sea Surface Temperature (SST) [HADSST2]. Those weightings were required to match the linear trend of the weighted average to the linear trend of the HADCRUT3 combined product. The weighting makes sense, since the global oceans represent about 70% of the surface area of the globe.
Figure 1

Figures 2 and 3 provide similar comparison graphs. Figure 2 shows the NCDC combined surface temperature product and the weighted average of its LST and SST components. To align the linear trends, the weighting required for the components of the NCDC product was also 27% LST data and 73% SST data. Figure 3 shows the GISTEMP product with 250km radius smoothing. For this GISTEMP product, the weighting required for the components was 28.5% LST data and 71.5% SST data. Again, the relationships of the SST and LST data make sense.
Figure 2
Figure 3

Here’s the curiosity. It appears in the GISTEMP Product with 1200km radius smoothing when we apply the same component weighting (28.5% LST data and 71.5% SST data) that we had used on the other GISTEMP combined product. The weighted average of the components of the GISTEMP combined product with 1200km radius smoothing has a significantly lower trend than the actual GISTEMP data. This can be seen in Figure 4.
Figure 4

In order to achieve the same linear trend as the GISTEMP combined product with 1200km radius smoothing, the components have to be weighted with 67% LST data and 33% SST data, almost reversing the ratio of the areas of global oceans and continental land masses.
Figure 5

Figure 6 is a map illustrating the GISTEMP LST data (trends) from 1982 to 2009. Note how the GISTEMP LST data extends out over the oceans. This is not the case for their combined product, because GISS masks the LST data over the oceans in its combined product. So in order to properly create a weighted average of GISTEMP land and sea surface temperature data with 1200km radius smoothing, the land surface data where it extends out over the oceans would first need to be masked.

Figure 6

Much of the divergence between GISTEMP and the Hadley Centre and NCDC combined surface temperature products is likely caused by the fact that GISS deletes SST data in the Southern and Arctic Oceans and replaces it with LST data, which has a significantly higher linear trend than the SST data it replaces. This was discussed in the post GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data.

It can also appear that GISS extends LST data out over the oceans in areas other than those with seasonal sea ice. In fact, I made this mistake in a comment at Lucia's The Blackboard this morning. Refer to my Comment#49191 at the bottom of her post NOAA: Hottest June in Record. This illusion can be seen in the following .gif animation of GISTEMP trend maps for the period of 1982 to 2009. The April trend is presented in Figure 7. Note how, in the highlighted area of the North Atlantic, there are differences between the SST trend and the trend of the GISTEMP combined product with 1200km radius smoothing. The Faroe Islands are located between Scotland and Iceland, and GISS uses station data there, so that explains the differences in that area. But what of the area of the North Atlantic west of Ireland and south of Iceland, with the approximate coordinates of 50N-60N, 20W-15W? There aren’t any islands there with weather stations.

Figure 7

In its GISTEMP LST products, GISS also includes surface station data identified as Ship followed by a letter; that is, “Ship J”, “Ship R”, etc. Refer to Figure 8. These can be found using the station locator feature on the GISTEMP Station Data webpage.
Figure 8

Here’s a link to the webpage presented in Figure 8.

I have found little to no information on these GHCN “ship stations”. Are they presenting SST or Nighttime Marine Air Temperature? Dunno. They may have served a purpose when GISS first prepared their GISTEMP product due to the sparseness of SST data in the early SST datasets, but now, these “ship stations” only add an unknown bias to well-documented optimally interpolated SST data. (And if they don’t add a bias either way, then there’s really no reason to have them. All they do is add confusion.)

Monday, July 12, 2010

La Niña Update – Week Of July 7, 2010

I’ve moved to WordPress.  This post can now be found at La Niña Update – Week Of July 7, 2010
Reynolds (OI.v2) SST anomaly data shows all four NINO regions well into La Niña territories.
Map of NINO Regions

The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).

Tuesday, July 6, 2010

June 2010 SST Anomaly Update

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

The map of Global OI.v2 SST anomalies for June 2010 downloaded from the NOMADS website is shown below. The central equatorial Pacific SST anomalies have decreased to the threshold of a La Niña.
June 2010 SST Anomalies Map (Global SST Anomaly = +0.26 deg C)


Monthly NINO3.4 SST anomalies have reached the threshold of a La Niña, which is -0.5 deg C. The central tropical Pacific (Monthly NINO3.4) SST Anomaly is -0.5 deg C, while weekly data has fallen well into La Niña ranges (-0.62 deg C).

Global SST anomalies have hesitated this month in their decline, but a glimpse at the weekly global data I posted last week reveals that the decline will continue next month. Refer to PRELIMINARY June 2010 SST Anomaly Update . The reason global SST anomalies stalled in their response to the drop in NINO3.4 SST anomalies is the decline in the Southern Hemisphere (-0.031 deg C) was less than the rise in the Northern Hemisphere (+0.049 deg C). Both the North Atlantic and North Pacific SST anomalies rose, with the larger rise in the North Pacific.
Monthly Change = +0.004 deg C
NINO3.4 SST Anomaly
Monthly Change = -0.41 deg C


The SST anomalies in the East Indian and West Pacific continue their decrease also. Will they rise, noticeably, in response to a 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.
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = +0.109 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.


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

Northern Hemisphere
Monthly Change = +0.049 deg C
Southern Hemisphere
Monthly Change = -0.031 deg C
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = +0.027 deg C
South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = +0.004 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.

North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = +0.089 Deg C
South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = -0.031 deg C
Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = -0.041 deg C
Arctic Ocean (65 to 90N)
Monthly Change = +0.030 deg C
Southern Ocean (60 to 90S)
Monthly Change = -0.067 deg C


The weekly NINO3.4 SST anomaly data illustrate OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -0.62 deg C.
Weekly NINO3.4 (5S-5N, 170W-120W)


The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).

Monday, July 5, 2010

An Overview Of Sea Surface Temperature Datasets Used In Global Temperature Products

I’ve moved to WordPress.  This post can now be found at An Overview Of Sea Surface Temperature Datasets Used In Global Temperature Products
I was asked by one of the independent researchers who are reconstructing global temperature data for an overview of Sea Surface Temperature (SST) datasets. I’ve limited the discussion to those SST datasets used in the Hadley Centre, GISS, and NCDC global temperature products.

The Hadley Centre uses HADSST2 data in its HADCRUT data. GISS uses HADISST from January 1880 to November 1981 and Reynolds (OI.v2) from December 1981 to present for their combined GISTEMP dataset. And the NCDC has used its ERSST.v3b data in its global temperature product since July 2009.


Long-term Sea Surface Temperature (SST) datasets are based on the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) data, which is divided into 2 degree grids. ICOADS link:

Sea Surface Temperature (SST) before the advent of buoys and satellites was measured by ships. The majority of ships traversing the oceans kept to shipping lanes. This limited the spatial coverage of SST measurements, especially in the South Pacific, which continues to have limited measurements in recent decades. Figure 1 is a collection of COADS SST data coverage maps available from the National Center for Atmospheric Research (NCAR). And here are the links to the full-size individual maps:

NCAR describes the maps as “Percentage of non-missing data in each time period is plotted. The minimum number of observations needed per month per grid box was 1.” Non-missing data is an interesting but descriptive explanation. It definitely gets the point across that missing data is the norm in many areas. Each of the maps illustrates the monthly SST coverage for a 20-year period, starting in 1861 and ending in 1997. Zero to 10% coverage is in white, while complete coverage (90 to 100%) is shown in gold. Even the last period from 1981 to 1997 shows major gaps in the data for the South Pacific.

http://i48.tinypic.com/2i7spwy.pngFigure 1

The SST data suppliers take the long-term ICOADS data, make corrections for the transitions from one measuring method to another (non-insulated buckets, insulated buckets, ship intakes, buoys, satellites) and infill missing data using methods that vary from one dataset to another.

The Hadley Centre takes two approaches to infill the missing data. The simplest is the approach they use for their HADSST2 data, which is available monthly from 1850 to present. The HADSST2 data is presented in 5 degree grids, so basically, the Hadley Centre has taken the ICOADS data that’s in 2 degree grids and expanded its coverage by converting it to 5 degree grids. But for the HADSST2 data, that’s as far as the Hadley Centre takes the infilling. There are large gaps in the coverage of HADSST2 data even to current times. Refer to Figure 2, which shows two maps of HADSST2 SST anomaly data. Cell a represents January 1900 and Cell b shows March 2010. Missing data is in white. While coverage has improved greatly in recent years, there are still large areas of the Southern Hemisphere where SST measurements are missing.
Figure 2

In one respect, the Hadley Centre provides the most realistic presentation of SST with their HADSST2 data, since they only present data in grids where measurements exist. But HADSST2 data has a curious upward bias after 1998. The Hadley Centre changed SST data suppliers in 1998. The merging of the two datasets appears to have created an upward shift in the HADSST2 data that appears in no other SST dataset. This can be seen in comparisons to other datasets; that is, when other global SST datasets are subtracted from HADSST2 global data. An example is shown in Figure 3. There are other examples in my post Met Office Prediction: “Climate could warm to record levels in 2010”.
Figure 3

The HADSST2 dataset is described in Raynor et al (2006) Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: the HadSST2 data set.

A link to the application required to download raw HADSST2 data from the Met Office can be found at:

Note that the Met Office restricts to whom they make data available. See:

Under the heading of Restricted Data Access, they write, “The online application for access to the Met Office SST data includes the Met Office Agreement to be electronically accepted. Please note that the Met Office data sets are available for bona fide academic research only (sorry no undergraduates), on a per person per project basis (i.e. all members on a same project who will be using the data must individually apply for access to the data). If you wish to access the Met Office data for commercial or personal purposes, please contact the Met Office directly.”


The second of the Hadley Centre SST datasets is HADISST, which is “globally complete” monthly from January 1871 to present. Refer to Figure 4.
Figure 4

HADISST uses satellite data from 1982 to present that is supplemented by buoy and ship readings. Prior to 1982, they basically use ICOADS data and infill missing SST data using what they describe as Reduced Space Optimum Interpolation (RSOI) in Raynor et al (2003):

They write in Raynor et al (2003): “These historical data sets all use data reconstruction techniques based on empirical orthogonal functions (EOFs), which are used to capture the major modes of SST variability and are then projected onto the available gridded SST observations to form quasi-globally complete fields.” And they continue, “In HadISST1, broad-scale fields of SST are reconstructed using one of these EOF-based techniques, reduced space optimal interpolation (RSOI). RSOI is described by Kaplan et al. [1997], who show that it is more reliable than EOF projection, which was used in GISST and by Smith et al. [1996]. We adapt RSOI into a two-stage process: first reconstructing the global pattern of long-term change and then the residual interannual variability. This results in a better representation of trends than does a single application of RSOI as used by Kaplan et al. [1998, 2003]. Also, we augment the reconstructions by blending with quality improved in situ SST to recapture local variance lost in the broad-scale RSOI.”

The last sentence is important as it MAY separate HADISST data from the long-term NCDC dataset that follows. After manufacturing data to create the appropriate patterns of SST and SST variability, the Hadley Centre reinserts (blends back in) the actual readings.

A link to the referenced Kaplan et al (1997) “Reduced Space Approach To The Optimal Analysis Of Historical Marine Observations: Accomplishments, Difficulties, And Prospects”:

Note: I wrote above that the Hadley Centre “basically use(s) ICOADS data.” In their description of HADISST data…
…the Hadley Centre states, “The SST data are taken from the Met Office Marine Data Bank (MDB), which from 1982 onwards also includes data received through the Global Telecommunications System (GTS). In order to enhance data coverage, monthly median SSTs for 1871-1995 from the Comprehensive Ocean-Atmosphere Data Set (COADS) (now ICOADS) were also used where there were no MDB data.”

But since the Met Office MDB data is a part of ICOADS data, “they basically use ICOADS data.” Refer to Woodruff (2001) “COADS Updates Including Newly Digitized Data and the Blend with the UK Meteorological Office Marine Data Bank.”

The Hadley Centre also requires an application be submitted prior to their release of their HADISST data, and the same restrictions apply:


ERSST.v3b is a product of the NOAA National Climatic Data Center (NCDC). It is also globally complete over the term of the dataset, from January 1854 to present. Refer to the January 1900 and March 2010 SST anomaly maps, Figure 5.
Figure 5

There have been a number of versions of the NCDC Extended Reconstruction Sea Surface Temperature (ERSST) dataset since it was first released in 2003. Version 1 is presented in Smith and Reynolds (2003) “Extended Reconstruction of Global Sea Surface Temperatures Based on COADS Data (1854-1997).” Version 2 appeared a year later and was accompanied by Smith and Reynolds (2004) “Improved Extended Reconstruction of SST (1854-1997).” Those papers provide detailed descriptions of the methods used to reconstruct SST data for the early ERSST datasets and the improvements made with the new version.

Version 3 was released early in 2008 and was accompanied by the Smith et al (2008) paper “Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006)”. Much of the improvements in the ERSST.v3 data were due to the inclusion of satellite data starting in 1985. In the Appendix, Smith et al (2008) write, “The ERSST.v3 is improved by explicitly including bias-adjusted satellite infrared SST estimates. In ERSST.v2 and ERSST.v3, information from satellites is indirectly included because the HF analyses are based on modes computed from the Reynolds et al. (2002) analysis, which includes the satellite data. In ERSST.v3 the Pathfinder infrared SST estimates are introduced in the analysis by combining those SST data with ship and buoy data. Satellite SSTs are bias adjusted relative to the ship and buoy data as previously discussed. The SST estimates from satellite, ships, and buoys are merged using a weighted sum of the different inputs, with weights inversely proportional to the noise estimate for each type (see section 2d). The merged SSTs are used in the ERSST.v3 analysis. In ERSST.v2 only in situ SSTs are used. The greatest influence of the satellite data is to produce greater variability south of 45S beginning in 1985. In most other regions the influence of satellite data is small because of generally sufficient in situ monthly sampling in the recent period.”

In fact, a major portion of the SST portion of Smith et al (2008) discussed the lengths taken to include satellite data and benefits of including it. Refer to Section 2d.

The NCDC made the ERSST.v3 data available in a number of latitude bands on monthly and annual bases through a webpage linked to their ERSST Version 3/3b webpage. (The link to the ERSST.v3 data is no longer operational). I used it in a few early posts, including ERSST.v3 Version of Southern Ocean SST Anomaly. Then, after only a few months, the NCDC stopped updating the new ERSST.v3 data (last updated April 2008). About 6 months later, the data in numerous latitude bands was made available again through the ERSST Version 3/3b webpage, and the data was identified as Version 3b. NCDC provided an explanation about the switch to version 3b. Reynolds, Smith and Liu write in the third paragraph, “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 residual bias led to a modest decrease in the global warming trend and modified global annual temperature rankings.”

What is curious, though, is that there were differences between the ERSST.v3 and .v3b versions before 1985, Figure 6, which is from my post Unheralded Changes in ERSST.v3 Data.
Figure 6

There was no explanation provided for the changes to the earlier data.

Gridded ERSST.v3b data is available in two formats, ASCII and NetCDF.
ASCII format: Monthly ERSST. See also readme fileNetCDF format: Monthly NetCDF ERSST.

More on the removal of satellite data in ERSST.v3b later in this post.

Reynolds (OI.v2) [USED BY GISS FOR 1982 TO PRESENT]
The Reynolds (OI.v2) Optimum Interpolation SST dataset is also a product of NOAA. It is globally complete and has been available since November 1981. Refer to Figure 7 for the March 2010 SST anomaly map for this dataset.
Figure 7

This in situ and satellite-based SST dataset is described in NCEP Environmental Modeling Center NOAA Optimum Interpolation Sea Surface Temperature Analysis webpage as, “The optimum interpolation (OI) sea surface temperature (SST) analysis is produced weekly on a one-degree grid. The analysis uses in situ and satellite SSTs plus SSTs 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). A description of the OI analysis can be found in Reynolds and Smith (1994). The bias correction improves the large scale accuracy of the OI.”

There is a long list of reference papers at the bottom of the NOAA Optimum Interpolation Sea Surface Temperature Analysis webpage.

Gridded data is available from the NOAA National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) in weekly and monthly formats.


Note: HADSST2 data, as far as I can tell, is only available in anomaly form. The other datasets are presented as Sea Surface Temperature. NOAA/NCDC also provides climatologies for calculating anomalies. Refer to:
Reynolds, R. W. and T. M. Smith, 1995: A high resolution global sea surface temperature climatology. J. Climate, 8, 1571-1583.
Smith, T. M. and R. W. Reynolds, 1998: A high resolution global sea surface temperature climatology for the 1961-90 base period. J. Climate, 11, 3320-3323.


Figure 8 is a comparison graph of global SST anomalies for the four long-term SST datasets discussed above: ICOADS, HADSST2, HADISST, and ERSST.v3b. It is provided to show the magnitude of the difference between the ICOADS data and the other datasets. These differences represent the corrections made to account for changes in measurement methods and represent the additional impacts of data treatment: infilling, interpolation, etc.
Figure 8

In Figure 9, the ICOADS data has been eliminated from the comparison. The HADISST variability is much less than the HADSST2 and ERSST.v3b datasets. And the HADSST2 has the greatest year-to-year variability, which makes sense, since the HADSST2 has fewer filtering adjustments and it is the least spatially complete. Note the dip and rebound from the 1880s to the 1940s is suppressed in the HADISST data. The HADISST SST anomaly in 1878 is approximately the same as the 1976 value, but the HADSST2 and ERSST.v3b readings are significantly higher in 1878 than they are in 1976.
Figure 9

The short-term Reynolds (OI.v2) data has been added to the comparison in Figure 10, and the term of the graph has been shortened accordingly. Again, the HADSST2 data has the greatest month-to-month variability. The impact (upward shift) of the Hadley Centre’s switch of SST suppliers for the HADSST2 data is also visible.
Figure 10

In Figure 11, the datasets have been smoothed with a 13-month running-average filter.
Figure 11


Figures 12 through 15 are maps of the changes in SST anomalies during the two cooling and two warming epochs that occurred between 1880 and 2005. The period was broken down into four epochs: 1880 to 1910, 1910 to 1945, 1945 to 1975, and 1975 to 2005. To create the maps of the changes in SST anomalies, the first year of an epoch was used as the base year for anomalies and the SST anomalies for the the last year of the epoch were then plotted. Note how the patterns north of 30S for the cooling epoch from 1945 to 1975 (Figure 14) are basically the opposite of the warming epoch of 1975 to 2005 (Figure 15). There are also some similarities in the opposing patterns for the earlier cooling and warming epochs, Figures 12 and 13. Note also how much greater the changes are in ERSST.v3b data than the HADISST data in Figures 12 and 13.
Figure 12
Figure 13
Figure 14
Figure 15

The ERSST.v3b and HADISST SST anomalies for the Southern Ocean (90S-60S) are shown in Figure 16. There is little to no variability in the HADISST data prior to 1962, while the ERSST.v3b data shows considerable variation. As shown in Figure 1, there is little to no data in the Southern Hemisphere in early decades. Is the ERSST.v3b data in this portion of the globe a result of the reconstruction methods employed by the NCDC? Or is the curve based on another reconstruction? On a thread at WattsUpWithThat, an observant blogger noted the similarity between the curve of the ERSST.v3b Southern Ocean SST anomaly data and, what my memory says was, a curve of an Antarctic sea ice reconstruction for the same period. He provided a link to the paper. I believe the post was from 2008 or 2009, but, unfortunately, I have not been able to find that comment or the paper.
Figure 16

If I do find it, or if the blogger who had linked the Antarctic sea ice paper at WUWT reads this post and links it in the comments below, I will post the graph in an update.

Earlier during the discussion of the HADISST data, I noted that after manufacturing data to create the appropriate patterns of SST and SST variability, the Hadley Centre reinserted (blended back in) the actual readings. I also noted that this step may separate HADISST from the NCDC ERSST.v3b data.

I can find no mention of a similar step in the paper about ERSST.v3b data, Smith et al (2008) “Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006)”. And since the addition of satellite data was such a major portion of Smith et al (2008),it is difficult to determine what calculations and methods remain. There is mention of fitting the reconstructed SST data to in situ data in ERSST.v1 in Smith and Reynolds (2004) Improved Extended Reconstruction of SST (1854-1997). In Smith and Reynolds (2004), they note, “…the ERSST.v1 analysis is always computed by a fit to in situ data, even in the period with satellite data. The U.K. Met Office computed their own analysis (Rayner et al. 2003) using a similar technique but using all available data, both in situ and satellite.” But again, there is no mention of a similar step in the ERSST.v3b data.

The paper for the ERSST.v2 version [Smith and Reynolds (2004) Improved Extended Reconstruction of SST (1854-1997)] notes the perceived value of satellite data. On page 10, they state, “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.”

Figure 17 is a comparison graph of HADSST2, HADISST, ERSST.v3b, and Reynolds (OI.v2) data for the South Pacific and Southern Ocean (90S-0, 145E-70W), from January 1982 to April 2010. The data have been smoothed with a 13-month filter. This area was chosen because the ICOADS data, upon which the ERSST.v3b and HADSST2 data are based, is incomplete even in current times. Refer back to Figure 1. As discussed earlier, the HADSST2 data is biased upwards by the switch in source data in 1998. With the data shift, the HADSST2 data has the highest trend. HADISST contains satellite data, and the Reynolds (OI.v2) data “is dominated by satellite data and gives a good estimate of the truth.” And since the ERSST.v3b data does not include satellite data, it appears it is biased upward, creating a trend that is approximately 50% to 80% higher than the Reynolds (OI.v2) and HADISST data, respectively, for this area of the globe.
Figure 17

Data and maps used in the post unless otherwise noted are available through the KNMI Climate Explorer:


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Comment Policy, SST Posts, and Notes

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