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.
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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.
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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.
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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.
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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.
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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”.
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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).
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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.
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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.
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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.
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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
What tangled data webs they weave, with data crafted to deceive…
No one has the right to delete ANY data. If you need to look at the past or see mistakes then the data would be useful to show.
But this is deliberate tampering with data and deleting the evidence!
Another excellent article by Bob Tisdale. Much appreciated.
And I agree wholeheartedly with Joe Lalonde:
“No one has the right to delete ANY data.”
The cost of data storage is so minuscule today that there is no excuse whatever for deleting taxpayer-funded data. What gives them the right?!
The data was deleted because GISS has something to hide.
Things really have gotten to the point where nothing any official body cobbles up regarding SST or any other climate indicator seems even worth discussing. Hansen’s latest exercise in GISS/NASA subterfuge reminds one of J.B. Rhine’s parapsychology “experiments” in the 1930s, which Martin Gardner revealed to have omitted negative results because they “skewed the data” (!).
[snip]
Too bad they can not delete Greenland Ice Temperature Data:
http://www.globalwarmingskeptics.info/forums/thread-188-post-5233.html#pid5233
A cooling trend of about 3300 years.
Thanks, Anthony. The Golden Three Shell Game lead-in photo was apropos.
How odd. I started playing arounf with the Global Maps from the link in the article. I tried to run NH cold season from the unadjusted data and was surprised to get an error saying that the unadjusted data is only available to 1999. Interesting. Unadjusted data which takes zero work is unavailable after 1999 but adjusted data which takes considerable work is.
I ran it from 1881 (despite what it says, 1880 doesn’t work) and was surprised to see how tiny the anomalies are that result. 0.2 degrees from top of graph to bottom. And yes, a clear downward trend from 15S to 75N after which….rocket ship.
Good Golly, Miss Molly.
Do you find this jolly?
Each rock o’erturned reveals a snake
And there’s a forest of trees to shake.
=========================
Meanwhilst, the forest fires in Quebec are casting a hazy shade over New England of a type that Britain is likely enjoying from Iceland. Today, our Channel 9 weatherman said that temps across the state were about 4 degrees lower today than they were predicted to be because of this smoke overcast.
Of course, you couldn’t get him to acknowledge that this is a factor in climate change predictions, that the Clean Air Act can easily explain any actual warming we’ve experienced since 1972.
“The data was deleted because GISS has something to hide.”
The data is NOT deleted. The other day I spent a few hours writing some R code to download the data that GISS uses from the source. As Bob notes GISS uses two sources. I will discuss the first source 1880-2003, HADISST. The data gives you monthly figures for the entire globe from before 1880 up to 2003. The data is in 1 degree bins. If you look at the source data, you will find that the product contains one of 3 numbers: a value for those bins that are land, or a value for SST, or a value for ICE when there is ICE. The same is true I believe for the reynolds product.
The question is How does GISS use this information in the calculation of their final product. Given a grid or sub grid that is sometimes ICE ( a value of -1000 in the source data) and sometimes a valid SST, how do GISS process this? That is an answerable question. Personally, I would like to see the GISS approach explained in more detail. They very well may disregard grids that are occassionaly covered by ICE or may use land figures in interpolating over these positions. But they didnt delete the data.
For example: Taking the data from SOURCE, you have the following figures for
grid box 4546 (around lat 82) for the months jan through december: -1000 means it is covered by ICE, -180 is -1.8C.
jan feb mar apr may june
1881 -180 -1000 -180 -180 -1000 -180 52 189 87 123 64 -180
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?
Can somebody do a sanity check on something I’ve stumbled across? I believe that GISS is based on adjusted NOAA data. Plot 12-month running mean of GISS versus 12-month running mean of NOAA anomalies. The respective URLS for the data are…
Notice that GISS is almost always lower than NOAA. However, since November 2009, the GISS 12-month running mean has pulled ahead of NOAA, and is now leaving a lot of daylight. There might be an explanation for the GISS/Hadley delta, and easily one for GISS versus UAH and RSS. But I can’t see the justification for what’s happening now versus NOAA.
I’ve also plotted the 12-month running means for Hadley/GISS/RSS/UAH/NOAA. GISS is THE ONLY ONE of the 5 whose 12-month mean to April exceeds the 1998 super El Nino peak. In other words, GISS is the outlier, against Hadley/RSS/UAH/NOAA.
In case you’re wonder why I’m pounding away on the 12-month mean, it’s because the 12-month mean to the end of December 2010 will constitute the 2010 annual mean temperature.
First we had a questionable procedural problem with CRU, GISS, et.al. :
1) deleting non-conforming weather stations by the thousands
Then we had ethical problems:
2) adding data (interpolating) into huge 1000 sq km voids, while pretending otherwise.
Now, we have a moral problem:
3) deleting data that does not follow the scripts.
How low can they go?
How about:
4) measuring CO2 levels on top of an active volcano system.
#4 is ignorant and more.
It does look damning, but I can see why water which is sometimes covered in ice is difficult to measure.
I think the best thing to do in these circumstances is to NOT graph the data. To (presumably intentionally) spread temperature measurements that are pretty obviously not representative over this area, especially when those spots are so very few and so very far apart, is disingenuous at best.
We need proper data. The current measurements, and indeed, their sources, are obviously invalid and deeply (politically) tainted.
Bob Tisdale…. You ROCK!!!!
Someone needs to go over to GISS and break all the red crayons…
Mike
Here’s an idea. If you don’t have data over a large area, don’t include that area in your less-than-global temperature analysis. The crime here is one that I have heard too often in AGW science: we didn’t have reliable data so we just assumed what ever data we wanted. GISS does it at the poles. They did it to ‘prove’ that hurricanes are getting stronger, the Amazon is really susceptible to drought, the Himalayan glaciers are all melting, diseases are spreading due to warming, animals are increasingly going extinct because of AGW and there was no Medieval Warm Period. All of these false claims have been ‘supported’ by data manipulation that is nearly criminal.
If you want good science these days, you have to go to your local elementary school science fair. The professionals are becoming a joke.
One thing that strikes me about the ice extent curves is that the shoulder values, May and Nov, are remarkably consistent year on year.
I took the daily differences from the average over the whole data period, then extracted the four month averages for the winter and summer extremes for each year. From these, I created the following plots:
a) summer diff versus preceding winter diff;
b) winter diff versus preceding summer diff;
c) summer diff versus preceding summer diff;
These gave me R2 values of .17, .01, .15 respectively. (Indeed, (b) was a slight negative correlation.) This may make physical sense. Each winter, the arctic ocean freezes right over, and the freezing beyond that may be far more a function of what’s going on in the Nth Atlantic etc. then what’s going on around the pole. So very low correlation with preceding summer minimum. OTOH, the summer melt will depend both on ice thickness and on any buffer ice hanging around the channels to the other oceans.
So finally I plotted summer versus sum of preceding winter and the summer before that. Sure enough, R2 went up to 0.32.
Examining figure 10, it appears to me that the over temperature anomalies are in the areas not actually covered by real recordings. Therefor if you “0” all areas that have no thermometer recordings there would be no warming. Actually there would be cooling.
Is this due to stupidity or is this due to fraud? One more piece of evidence to go into a very large basket.
Look at the south pole! no warming! because they have a weather station recording actual temperatures. WUWT
Nick Stokes:
Either way it’s problematic, especially if Bob is correct about them using data interpolated from land.
Sounds to me like it needs a rethink.
Thanks Bob for this outstanding post and expertise! Giss not only deletes SST that is mostly covered with sea ice but (your quote)
Nick is right. After downloading the source data and looking at it, the approach one should use is not immediately clear. take the example line of data I produced from the source. Suppose we have three adjacent grid squares: land:sea:sea.
suppose further that the land is at 0C and the SST is -1.8C and -1.8C. when the water is open. Now in some months this water is frozen. The SST file does not report a temperature for this. It reports a dummy value of -1000. So, using the example above, suppose the values are then 0c; ICE; -1.8C. Does anyone have a clear idea of how to average over these? As Nick suggests this is a interesting problem. CRU appears to handle the problem one way. GISS uses a different approach. The DIFFERENCE between GISS and CRU is not some nefarious plot. The difference is caused by a analytical choice on how to handle these cases. The difference in results is an uncertainty in the estimate of global temperature. two different ways of handling the same problem. That’s the sum total of this.
Terms such as ‘extrapolate’ and ‘interpolate’ have completely different meanings so should be used in the proper context. It appears in this instance GISS is extrapolating temperatures, not interpolating.
As far as parsing the word ‘delete’, as Bob’s chart clearly shows, GISS most definitely “deleted” data, which is a legitimate usage of the word in the strictest definition. If the data was there before, but not after, it was in fact deleted. “Delete” may seem accusatory, but data “manipulation” is another term many view as a dirty word, which again is nothing of the sort.
Apparently GISS is making assumptions about polar amplification due to “global warming”, but where is the justification for such speculation? Ah yes, climate models.
Thanks Bob for this outstanding post and expertise! Giss not only deletes SST that is mostly covered with sea ice but (your quote)
If there is anything I could add, it’s how Giss gloated in their recent paper
. Now, it is worse than (I) expected. “Rapidly” means +0.17°C global trend since 1997 vs. no trend at all at HadCRUT3!
Thank you Bob, fantastic analysis. A copy or email link of this one article would awaken any scientist in the physical sciences realm save climatologists. You don’t have to be a climatologist to see exactly what is going on and what they are doing to manipulate the data. Got an update global list handy of every physical science scientist on this globe? 🙂 Wish I had one to give you.
Kind of sad how physics, chemistry, and most other sciences are getting values closer and closer to reality, more and more digits of accuracy, while one single man in climatology, Jim Hansen, has deceived science to accept that accurate numbers should not be sought, and in fact, after coerced a paper through publication that says it’s OK to throw most of the real world data away. His NASA division will just assume all of these values at will by picking where the surviving stations reside. If that’s not fraud I don’t know what is.
Why o’ why is not science at an uproar over that one single deception? I’m ashamed of science lately! Not an honest bone left or you would hear the shouting from the rafters everywhere, not just in the few honest blogs. Enough, my blood pressure’s rising.
It would be enlightening if for each blank 250km area in the GISS map you took the HadCRUT value at that cell, if it exists, and fill it in, HadCRUT data is better than no data, right. Then do the Hansen trick of 1200 kilometer extrapolation. You just know the two maps would always look like night and day.