Guest post by Bob Tisdale
There are numerous blog posts and discussions about how the GISS global temperature anomaly product GISTEMP differs from the Hadley Centre and NCDC datasets.
The repeated reasons presented for this are, GISS uses 1200km radius smoothing to fill in the areas of the globe with sparse surface temperature readings, and the area this has the greatest impact is the Arctic. Typically, a map or comparison of global temperature anomaly maps is included, similar to Figure 1. The top two maps were cropped from Figure 3 in the Real Climate post “2009 temperatures by Jim Hansen”. I added the third.
The bottom map was created at the GISS Global Maps webpage. It’s a map of the GISTEMP Global Temperature Anomaly product with 250km radius smoothing for the calendar year 2005, the same year as the top two maps. I did not include a temperature scale because the bottom map was provided to allow a visual comparison of the spatial coverage of the HadCRUT product and the GISTEMP product with 250km radius smoothing. Examine the Arctic and the ocean surrounding Antarctica, the Southern Ocean. Notice a difference? In 2005, the HadCRUT data had better coverage of the Arctic and Southern Oceans than the GISTEMP dataset with 250km radius smoothing. What’s missing in the GISTEMP product? There’s no sea surface temperature data.
http://i45.tinypic.com/htsgeq.jpg
Figure 1
GISS DELETES POLAR SEA SURFACE TEMPERATURE DATA
The general regions where GISS deletes Sea Surface Temperature data are shown in Figure 2. Three areas are highlighted: two cover the Arctic Ocean, and a third surrounds Antarctica. The specific locations are clarified in the following. GISS then uses their 1200km radius smoothing to replace the sea surface data with land data.
http://i48.tinypic.com/33adj86.jpg
Figure 2
Tilo Reber in his recent “Diverging views” post at Watts Up With That? noted that the GISS Current Analysis webpage includes the following statement:
“Areas covered occasionally by sea ice are masked using a time-independent mask.”
This means that vast regions of Sea Surface Temperature (SST) anomaly data in the Arctic Ocean and Southern Ocean are deleted from the GISTEMP record. GISS does not delete all of the Arctic and Southern Ocean SST anomaly data, just the data from the areas where the annual sea ice melt occurs, and those are good portions of them.
I have looked for but have not found an explanation for this exclusion of Sea Surface Temperature data in the papers provided on the GISTEMP references page.
THE AREA OF THE ARCTIC OCEAN WHERE GISS DELETES SST DATA
Figure 3 shows four Arctic (North Pole Stereographic, 65N-90N) maps prepared using the map-making feature of the KNMI Climate Explorer. The maps illustrate temperature anomalies and sea ice cover for the month of September, 2005. The calendar year 2005 was chosen because it was used in the RealClimate post by Jim Hansen, and September is shown because the minimum Arctic sea ice coverage occurs then. The contour levels on the temperature maps were established to reveal the Sea Surface Temperature anomalies. Cell (a) shows the Sea Ice Cover using the Reynolds (OI.v2) Sea Ice Concentration data.
The data for the Sea Ice Cover map has been scaled so that zero sea ice is represented by grey. In the other cells, areas with no data are represented by white. Cell (b) illustrates the SST anomalies presented by the Reynolds (OI.v2) Sea Surface Temperature anomaly data. GISS has used the Reynolds (OI.v2) SST data since December 1981. It’s easy to see that SST anomaly data covers the vast majority of Arctic Ocean basin, wherever the drop in sea ice permits. Most of the data in these areas, however, are excluded by GISS in its GISTEMP product. This can be seen in Cell (c), which shows the GISTEMP surface temperature anomalies with 250km radius smoothing. The only SST anomaly data used by GISS exists north of the North Atlantic and north of Scandinavia.
The rest of the SST data has been deleted.
The colored cells that appear over oceans (for example, north of Siberia and west of northwestern Greenland) in Cell (c) are land surface data extending over the Arctic Ocean by the GISS 250km radius smoothing. And provided as a reference, Cell (d) presents the GISTEMP “combined” land plus sea surface temperature anomalies with 1200km radius smoothing, which is the standard global temperature anomaly product from GISS. Much of the Arctic Ocean in Cell (d) is colored red, indicating temperature anomalies greater than 1 deg C, while Cell (b) show considerably less area with elevated Sea Surface Temperature anomalies.
http://i46.tinypic.com/dpygcj.jpg
Figure 3
Basically, GISS excludes Arctic Ocean SST data from 65N to 90N and, for round numbers, from 40E to 40W. This is a good portion of the Arctic Ocean. Of course, the impact would be seasonal and would depend on the seasonal drop in sea ice extent or cover. The sea ice extent or cover has to decrease annually in order for sea surface temperature to be measured.
I’ll use the above-listed coordinates for the examples that follow, but keep in mind that they do not include areas of sea ice in the Northern Hemisphere south of 65N where sea surface temperature data are also deleted by GISS. These additional areas are highlighted in Figure 4. They include the Bering Sea, Hudson Bay, Baffin Bay and the Davis Strait between Greenland and Canada, and the Sea of Okhotsk to the southwest of the Kamchatka Peninsula.
http://i50.tinypic.com/28j9u6u.jpg
Figure 4
Note: GISS uses Hadley Centre HADISST data as its source of Sea Surface Temperature (SST) data from January 1880 to November 1981 and NCDC Reynolds (OI.v2) data from December 1981 to present. To eliminate the need to switch between or merge SST datasets, this post only examines the period from 1982 to present. And to assure the graphics presented in Figures 3 and 6 are not biased by differences in base years of the GISTEMP data and the Reynolds (OI.v2) SST data, the latter of which has only been available since November 1981, I’ve used the period of 1982 to 2009 as base years for all anomaly data.
WHY WOULD DELETING SEA SURFACE TEMPEATURE DATA AND REPLACING IT WITH LAND SURFACE DATA BE IMPORTANT?
Land Surface Temperature variations are much greater than Sea Surface Temperature variations. Refer to Figure 5. Since January 1982, the trend in GISTEMP Arctic Land Surface Temperature Anomalies (65N-90N, 40E-40W) with 250km radius smoothing is approximately 8 times higher than the Sea Surface Temperature anomaly trend for the same area.
The Arctic Ocean SST anomaly linear trend is 0.082 deg C/ decade, while the linear trend for the land surface temperature anomalies is 0.68 deg C/decade. And as a reference, the “combined” GISTEMP Arctic temperature anomaly trend for that area is 9 times the SST anomaly trend.
http://i46.tinypic.com/1zpheme.jpg
Figure 5
By deleting the Sea Surface Temperature anomaly data, GISS relies on the dataset with the greater month-to-month variation and the much higher temperature anomaly trend for its depictions of Arctic temperature anomalies. This obviously biases the Arctic “combined” temperature anomalies in this area.
GISS DELETES SEA SURFACE TEMPERATURE DATA IN THE SOUTHERN HEMISPHERE, TOO
Figure 6 shows four maps of Antarctica and the Southern Ocean (South Pole Stereographic, 90S-60S). It is similar to Figure 8. Cell (b) illustrates the SST anomalies presented by the Reynolds (OI.v2) Sea Surface Temperature anomaly data. SST anomaly data covers most of the Southern Ocean, but GISS deletes a substantial portion of it, as shown in Cell (c). The only SST anomaly data exists toward some northern portions of the Southern Ocean. These are areas not “covered occasionally by sea ice”.
http://i50.tinypic.com/aensly.jpg
Figure 6
Figure 7 illustrates the following temperature anomalies for the latitude band from 75S-60S:
-Sea Surface Temperature, and
-Land Surface temperature of the GISTEMP product with 250km radius smoothing, and
-Combined Land and Sea Surface of the GISTEMP product with 1200km radius smoothing, the GISTEMP standard product.
The variability of the Antarctic land surface temperature anomaly data is much greater than the Southern Ocean sea surface temperature data. The linear trend of the sea surface temperature anomalies are negative while the land surface temperature data has a significant positive trend, so deleting the major portions of the Southern Ocean sea surface temperature data as shown in Cell (c) of Figure 6 and replacing it with land surface temperature data raises temperature anomalies for the region during periods of sea ice melt.
Note that the combined GISTEMP product has a lower trend than the land only data. Part of this decrease in trend results because the latitude band used in this comparison still includes portions of sea surface temperature data that is not excluded by GISS (because it doesn’t change to sea ice in those areas).
http://i45.tinypic.com/im6q29.jpg
Figure 7
ZONAL MEAN GRAPHS REINFORCE THE REASON FOR THE GISS DIVERGENCE
When you create a map at the GISS Global Maps webpage, two graphics appear. The top one is the map, examples of which are illustrated in Figure 1, and the bottom is a Zonal Mean graph. The Zonal Mean graph presents the average temperature anomalies for latitudes, starting near the South Pole at 89S and ending near the North Pole at 89N. Figure 8 is a sample. It illustrates the changes (rises and falls) in Zonal Mean temperature anomalies from 1982 to 2009 of the GISTEMP combined land and sea surface temperature product with 1200km radius smoothing. The greatest change in the zonal mean temperature anomalies occurs at the North Pole, the Arctic. This is caused by a phenomenon called Polar Amplification.
http://i48.tinypic.com/spd4li.jpg
Figure 8
To produce a graph similar to the GISS plot of the changes in Zonal Mean Temperature Anomalies, I determined the linear trends of the GISTEMP combined product (1200km radius smoothing) in 5 degree latitude increments from 90S-90N, for the years 1982 to 2009, then multiplied the decadal trends by 2.8 decades. I repeated the process for HADCRUT data. Refer to Figure 9.
The two datasets are similar between the latitudes of 50S-50N, but then diverge toward the poles. As noted numerous times in this post, GISS deletes sea surface temperature data at higher latitudes (poleward of approximately 50S and 50N), and replaces it with land surface data.
http://i47.tinypic.com/2uzfc6r.jpg
Figure 9
Figure 10 shows the differences between the changes in GISTEMP and HADCRUT Zonal Mean Temperature Anomalies. This better illustrates the divergence at latitudes where GISS deletes Sea Surface Temperature data and replaces it with land surface temperature anomaly data, that latter of which naturally has higher linear trends during this period.
http://i45.tinypic.com/xnsp40.jpg
Figure 10
SOURCE
Maps and data of sea ice cover and temperature anomalies are available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

Steve Mosher: To illustrate my investigations of the subject at hand mentioned in my earlier reply, here’s a sample graph of Reynolds SST anomalies and the anomalies of the two GISTEMP combined LST+SST (250km and 1200km) datasets for the grid 75N-77N, 45E-47E. Note that there are only two datasets plotted. Reason: There is no data for the GISTEMP combined LST+SST (250km) dataset The output from the KNMI Climate Explorer read, “No valid data. Please check your choices on the previous page.”
To me that means that the GISTEMP combined LST+SST (1200km) data for that cell is only land surface temperature data that has been extended out over the ocean.
http://i48.tinypic.com/30c60ww.jpg
And here’s a plot of the Reynolds sea ice cover (presented as percentage). There has been sea ice in that cell, so GISS masked (deleted) the SST data.
http://i49.tinypic.com/11bja1l.jpg
There is no need to merge water temp with air temp in the arctic. We have over 30 years of of air temp records and that should suffice. From the above link:
“The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979–1997 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979–1997. “
Hi Anna V-
“It is the heat content that is important. The poles can show huge anomalies in the winter, even without extending land temperatures. The winds do that anyway. Even if one had thermometers every 100m^2 there would be little meaning in the value for the energy in the area. It is the skin surface that radiates as T^4 and that is either the ice temperature or the water temperature.”
Good point!
John Finn wrote, “If there is a warming bias it will only occur when teh arctic is warm. ”
It will occur anytime sea ice declines and there is SST data available to be included in the GISTEMP product.
@davidmhoffer says:
May 31, 2010 at 9:54 pm
“There are a few odd years in there but the whopper is 1917 which has annual temp anomalies ranging to -6 in the arctic.”
How does that compare with the other cold winters last century; 1940, 1947 and 1963.
http://climexp.knmi.nl/data/tcet.dat
Whether or not there was a SSW will make a big difference;
http://www.appmath.columbia.edu/ssws/index.php
DR wrote, “Apparently GISS is making assumptions about polar amplification due to ‘global warming’, but where is the justification for such speculation? Ah yes, climate models.”
Polar amplification is a natural effect and occurs whenever climate changes. If the Northern Hemisphere is warming, the higher latitudes warm more than lower latitudes, and when the Northern Hemisphere is cooling, higher latitudes cool more. No need for climate models on that one.
Regards
Nick Stokes says:
May 31, 2010 at 8:11 pm
Well, the obvious reason for not plotting SST data in areas covered by ice is that SST is not then measured. I presume your complaint is about areas that have SST measurements in summer only, but are iced in winter. How do you think they should deal with regions with only summer SST readings when compiling an annual average plot?
___________________________________________________________________________
The way any true scientist would. You use the actual real life measured data and you leave it BLANK when you can not measure it with an explanatory note. Also you do not go ,b.removing,/b. gobs of data (weather stations) and substitute a 1200km interpolation function for the real available data. This causesThe Bolivia Effect
As far as I am concerned this type of messing with the data makes the entire data set completely useless and that is before you get into the temperature data error Also note as you read that article standard error is a cop-out which deflates the error! SE is the SD divided by the square root of the number of measurements, so it is much smaller than sd when the significant of the measurements are judged.
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Of course, nobody should take any notice of these kinds of blog posts. No self-respecting climate scientist would do so. After all, they are written by amateurs and they are not in the peer-reviewed literature. Science is a closed shop, Bob – surely you realize that by now!
I added an update to the version at my blog that clarifies what is being illustrated:
http://bobtisdale.blogspot.com/2010/05/giss-deletes-arctic-and-southern-ocean.html
It reads:
There appears to be some confusion on the WattsUpWithThat version of my post GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data about what this post illustrates. I prepared a graph for this post but chose not to use it since it appeared redundant to me. It should clarify what is being presented. It is a comparison graph of GISTEMP Arctic Surface Temperature anomalies for the grid 65N-90N, 40E-40W, which is a major portion of Arctic Ocean as shown above in Figure 4. One dataset is the combined land plus sea surface data; the other is the land-only data. The two datasets are identical. If you subtract one from the other, the difference is 0.0 (zero) for all months. This indicates that there is no Sea Surface Temperature data in the combined product in this grid.
http://i48.tinypic.com/34o3hjq.jpg
Update Figure 1
Does the Sea Surface Temperature data exist for this area? Yes. It is illustrated above as the green curve in Figure 5.
To me, this indicates that GISS deleted the sea surface temperature data for this portion of the Arctic.
END UPDATE
REPLY: Thanks Bob, but doesn’t that “WattsUpWithThat version of my post” sort of imply that WUWT made a mistake? It is your post verbatim at your invitation to repost it. I made no changes to it. – Anthony
“Crafted story” data is better than real data, when you are trying to prove a hoax.
Why not let everyone have the actual data to make up their own story with? With a open plot — Supply the actual software, open the procedures, the analysis, no more guessing games, just the facts. Then we can at least argue from the same known point …
What happened with searching for the truth? And is science ever settled?
Nick Stokes says:{May 31, 2010 at 8:11 pm}
” How do you think they should deal with regions with only summer SST readings when compiling an annual average plot?”
Perhaps a good reason to view “annual averages” as meaningless.
Anthony: Sorry. I missed a prepositional phrase in there. Thanks for picking it up. The confusion is in the comments, not in the WUWT version of the post. I’ve changed the initial sentence to read: There appears to be some confusion in the comments in the WattsUpWithThat thread of my post…
Better?
There is another way of looking at this. Here is a map of the Arctic sea surface temperatures at the warmest they have ever gotten in the modern era, September 15th, 2007.
http://www7320.nrlssc.navy.mil/GLBhycom1-12/navo/arcticsst/nowcast/sst2007091918_2007091500_903_arcticsst.001.gif
In the 75N circle, the sea surface temperatures are mostly -2.0C and slightly lower, the area which is still covered in ice .
In some areas in the Arctic basin, however, sea surface temperatures got as high +6.0C.
Generally, this map points to a physical limit to how high GISS can make the anomalies, at least in September. The 1200 km smoothing algorithm should show ZERO anomaly for the areas which are still ice covered in September because the sea surface temperatures should be close to -2.0C and slightly lower.
If you look at the GISS monthly zonal anomaly map – from 2006 to today – station temperatures only – 1200 km smoothing -, they have temperatures in the far north averaging about +3C (and as high as +9.2C) above normal in September which is entirely unphysical. ALL the ice would have melted in September over the last 4 years.
http://data.giss.nasa.gov/work/modelEt/time_series/work/tmp.4_observedTs_1_2006_2010_1951_1980-0/map.gif
The same map using SST numbers from HADISST and Reynolds have much lower anomalies (closer to the reality of the ice melt) but it is cut-off at 80N and 65S.
http://data.giss.nasa.gov/work/modelEt/time_series/work/tmp.4_observdSST_1_2006_2010_1951_1980-0/map.gif
There is a problem here.
Anto wrote, “Of course, nobody should take any notice of these kinds of blog posts. No self-respecting climate scientist would do so. After all, they are written by amateurs and they are not in the peer-reviewed literature. Science is a closed shop, Bob – surely you realize that by now!”
But there are those who have read this post and understand the significance of the bias created by deleting SST data in areas of seasonal sea ice. It now offers them a source during discussions with other bloggers who claim that what GISS does, does not create a bias, when in fact it does create a bias.
Regards
Bob Tisdale says:
June 1, 2010 at 3:50 am
John Finn wrote, “If there is a warming bias it will only occur when teh arctic is warm. ”
It will occur anytime sea ice declines and there is SST data available to be included in the GISTEMP product.
Which like I said will have the reverse effect when the arctic cools and sea ice increases . If there are cycles ati npaly as everyone on this blog seems to think then it will all come out in the wash. GISS exhibits a big increase (~2 deg) in arctic temperatures between 1910 and 1940 and a big drop ( >1 deg) between 1940 and 1970. The global decadal trends between 1982 and 2009 for GISS, HadCrut and UAH respectively are ~0.18, ~0.17 and ~0.16. RSS is ~0.18. While your observations and analysis are interesting I think they need to be put in perspective.
climatepatrol: But if you create a weighted average of GISTEMP global land surface temperature anomalies with 1200km radius smoothing (30%) and Reynolds (OI.v2) global SST anomalies (70%) including the SST data where they delete it, the weighted average of the two datasets used in the combined GISTEMP product also flattens over the past decade:
http://i49.tinypic.com/11j22h0.jpg
In other words, the “weighted average” of the sources does not equal the end product with GISTEMP but it does with HADCRUT and NCDC combined data.
@davidmhoffer says:
May 31, 2010 at 9:54 pm
“There are a few odd years in there but the whopper is 1917 which has annual temp anomalies ranging to -6 in the arctic.”
How does that compare with the other cold winters last century; 1940, 1947 and 1963.>>
Even more odd. land/ocean(noaa)
1940 +1.0
1947 +1.8
1963 -1.1
Part of the confusion I think is from what they are measuring. If you start in 1881 the top 5 degrees N latitude is mostly grey meaning no data. The first data appears in 1911 with a stripe from about 45w to 80e. In 1933 it turns into 45w to 160e. in 1939 it is 45w to 180e and in 1947 it is 170w to 180e.
When I run 1881 to 1946, 45w to 180 w is entirely grey, no data
When I run 1947, 45w to 180w shows +1 to + 2 in that section
When I run 1881 to 1947, it shows EXACTLY the same thing in that section.
1947 alone blends nicely into the areas to the south. 1881-1947 stands out like a sore thumb. So it seems clear that it is taking the 1947 data for that section and using it for the entire 1881 to 1947 time period.
Same thing with the block from 80e to 160e. It is grey until 1933. If you run 1881 to 1932 it is grey. If you run 1882 to 1933 it is filled in, my assumption again being that they have used a single year of data to cover the 1882 to 1933 period.
How all this relates to the gisstemp zonal mean for 64n to 90n is beyond me.
Mike Bryant says: “Someone needs to go over to GISS and break all the red crayons…”
They’d still need some red crayons, but not as many for coloring in the Arctic and Southern Oceans during seasonal sea ice decline. They could always use what they’ve got left over and mix them in with some yellow crayons.
Steven mosher says: “Carrick, From memory ( dont trust but verify) I think the way the GISS program is going to work is as follows. The 1 degree SST bins are going to be mapped into the 8000 equal area subgrids. if there is an SST present you will get a SST value. if ICE is present you get NA. Then the entire subgrid structure is averaged. So its not entirely clear that only land values will contribute to the value. IF your grid goes: land:ice:land”
From the update that I referred to above, here’s a comparison graph of GISTEMP Arctic Surface Temperature anomalies for the grid 65N-90N, 40E-40W. One dataset is the combined land plus sea surface data; the other is the land-only data. The two datasets are identical. If you subtract one from the other, the difference is 0.0 (zero) for all months. This indicates that there is no Sea Surface Temperature data in the combined product in this grid, and that’s a chunk of the Arctic Ocean.
http://i48.tinypic.com/34o3hjq.jpg
The standard GISTEMP combined product is not land plus sea surface data for the coordinates 65N-90N, 40E-40W; it is land surface data only.
HadSST Arctic data: no net warming since 40ties
http://climexp.knmi.nl/data/ihadsst2_0-360E_66-90N_na.png
Antarctic data: cooling since 80ties:
http://climexp.knmi.nl/data/ihadsst2_0-360E_-70–90N_na.png
By not accounting for polar regions, GISS diverges from HadCRUT more and more.
http://www.woodfortrees.org/plot/gistemp/from:1998/plot/hadcrut3vgl/from:1998
“Man made” warming accounts for 0.3 deg C since 1998. J. Hansen is a movie archetype of a mad scientist.
Bob,
WRT 65-90N 40e-40w. cool. I can probably ( give me a few days ) go pull all the historical data for the SSTs in that zone and the land temps in that zone. Can we work with a narrower slice in long? thats a big chuck. Currently it will have to be HADISST only till I find a good source of asscii for the reynolds product.
Bob, check out the thread on LUCIA WRT GISS mystery. Contact Zeke. He’s a got a GISS ocean data set
John Finn wrote: “Which like I said will have the reverse effect when the arctic cools and sea ice increases . If there are cycles ati npaly as everyone on this blog seems to think then it will all come out in the wash.”
But as long as global temperatures rise and polar amplification causes the Arctic to warm at a higher rate, GISTEMP will exaggerate the rise in global temperature by deleting sea surface temperature in areas of seasonal sea ice decline and extending land surface data over the ocean.
You concluded you comment with, “The global decadal trends between 1982 and 2009 for GISS, HadCrut and UAH respectively are ~0.18, ~0.17 and ~0.16. RSS is ~0.18. While your observations and analysis are interesting I think they need to be put in perspective.”
They are in perspective. This post was about biases created by GISS in their GISTEMP product, not about HADCRUT or the two TLT datasets.
Also, if you’re not aware, the Hadley Centre created an upward bias in their HADSST2 dataset in 1998, when they spliced two SST datasets together. The HADSST2 data represents about 70% of the HADCRUT3 product. I covered it in this post about the Met Office’s prediction about 2010:
http://bobtisdale.blogspot.com/2009/12/met-office-prediction-climate-could.html
Regards
Bill Illis: I can’t get your GISS links to work.