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
Am I running this Global Map thing right? I ran adjusted NH cold season land only for a bunch of ranges. 1910 to 1920 showed an arctic anomaly of -5 degrees. No, not -0.5, MINUS FIVE DEGREES. Ran it with NOAA ocean added in, same result. I’ve played around with zonal data a lot and that didn’t look right so I started running individual years. There are a few odd years in there but the whopper is 1917 which has annual temp anomalies ranging to -6 in the arctic. You can run warm season and cold season and I got -3 and -11 (!!) which averages to about -6. I then interpolated from the annual graph and got:
~ 60N -0.5
~ 85N -6.0
average ~ -2.75
Zoom over to gisstemp:
http://data.giss.nasa.gov/gistemp/tabledata/ZonAnn.Ts.txt
They show 64N to 90N at -1.19.
They show 44N to 64N at about -0.5 which is a pretty good match for Global Map.
But 60+ on Global Map just looks totaly out of whack. Its late, I’m interpolating, maybe I see what I want to see but this just looks wrong to me. I even looked at seasonal which unfortunately is only broken down by hemisphere, but it doesn’t show anything super unusual that year though winter was a bit cooler. 1912 also appears to have some huge anomalies in the arctic.
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
then you will average a land value over that grid that is ice. Plenty of issues but there is not a clear analytic choice in my mind. Unless somebody wants to estimate an air temp over ice. suggestions? I know Zeke is working on this as chad got him the data.
2010-Jun-01 3:00 Water temp at N:88°44′, W:042°43′ is -2.0
Air temp is -28.2
The info is there if you look hard enough. Giss just doesn’t look at all.
Try drifting buoy 25593
Maybe GIZZ needs to hook up with these guys-
http://iabp.apl.washington.edu/
25593 says air temp is -28.2
25595 says water temp is -2.0 and it is located at N:88°43′, W:42°58′.
2010-Jun-01 3:00
It would appear that data has not been “deleted” from the record and lost for all eternity, but rather that data has been substituted in the polar regions. See
Steven mosher says:
May 31, 2010 at 8:11 pm
Still, one has to wonder why the polar regions are uniformly a large positive anomaly.
Today’s high at Point Barrow was a whopping 34F, low 30F.
Nick Stokes and Steve Mosher,
Yes, I agree that nothing nefarious is going on but this problem is inherent in any “global mean” temperature measurement regimen.
Absent a nearly ubiquitous network of thermometers placed at equidistant arbitrarily high concentrations in all three spacial dimensions in the entire atmosphere and ocean, and monitored at an arbitrarily high temporal frequency, the idea of a “global mean” temperature is of very limited utility.
This is like the UHI effect, change the data to show colder long ago, because they can’t increase the current data because everyone knows if it’s sweltering now.
There’s been simply too many changes with no or little explanation why, and as they say perception is everything. So if you act like a crook, everyone thinks you’re a crook.
Thanks Bob for yet another excellent example of why global temperature anomalies have little value in determining what is happing to climate. Without good data no conclusions can be reached and it’s time climate scientists went back to the drawing board with the CAGW hypothesis.
In this situation the null hypothesis of natural climate change has to take precedence.
carrick
GISS builds one SST record from two sources. The first source HADISST has NO SST where there is ice. The cell is either
A: land, in which case there is no temperature value in the SST dataset
B: Open sea, in which case there is a temp value
C: ICE, in which case there is NO temperature value.
Reynolds, appears to be different> here is there format:
lon = 180.5 lat = 89.5 sst = -1.8 ice = 100 tagls= 1.
lon = 180.5 lat = 88.5 sst = -1.8 ice = 96 tagls= 1.
lon = 180.5 lat = 87.5 sst = -1.7 ice = 96 tagls= 1.
lon = 180.5 lat = 86.5 sst = -1.7 ice = 94 tagls= 1.
The HADISST has no comparable ice concentration value. Its binary. Further,
HADISST has no SST value where there is ICE. GISS merge these two datasets. The first question is how do GISS decide to do this. Arguably, one can adopt the hadisst approach. where there is ice, there is no SST. Merging hadisst and reynolds thus would have the effect of not using the reynolds SSTs where there is ice or partial ice.
It appears that is what they do. what that means of course is that when you average the temperatures at the south pole you are using the land stations that ring anarctica to provided estimates of the air temp over that ice.
lets take a look at panel C. You will see the land stations. march 2005
lets pick scott base
http://www.antarctica.ac.uk/met/READER/surface/Scott_Base.All.temperature.html
The temperature of the land station (-21C) is used to extrapolate over those areas covered by Ice.. areas where the SST is arguably -1.8C
So, what value would you suggest that people use when an ocean area is covered by ice? The value of the SST under the ice? -1.8C. no value? or the value of a land station 100s of km away ( like -21C)
Those are your choices, can u think of others. neat problem.
Steven mosher says:
May 31, 2010 at 9:37 pm
“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.”
The difference is not an “uncertainty”, but a GISS warming bias. They use data with a 8-fold higher warming trend to interpolate sea surface data.
This is an important posting by Bob Tisdale, as it proofs very easily and understandable a signifant warming bias in GISS data, particularly the prominent polar regions.
Sera says:
May 31, 2010 at 10:47 pm
2010-Jun-01 3:00 Water temp at N:88°44′, W:042°43′ is -2.0
Air temp is -28.2
The info is there if you look hard enough. Giss just doesn’t look at all.”
GISS is well aware of this data. here is the problem. For one of your datasets you have no historical value for the SST or the air temp where there is ice. For more current data you have ice concentration and STT for every cell, but no air temp above Ice or above water. And finally you have the latest data ( the bouys) where you have the SST value and the air temp. Having a consistent treatment over all three datasets is historically problematic. it requires the dreaded modeling word.
Carrick. put this one in your thought locker
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
Clearly the minds at GISS think the large amount of ice melt that goes on in these areas creates a kind of climate buffer. Melting a large amount of ice will actually remove a lot of heat energy from the local climate and work to minimize any observed increases in temperature. If the world actually was warming, the presence of large bodies of ice would actually maintain a stable local climate, and the best indicator of warming would be in increased rates of ice melt rather than increased temperatures.
That being said, we are talking about a closed system here. The GISS needn’t offset for climate buffers like melting sea ice. We are interested in the workings of the climate as a whole, and therefore I think offsetting a localized temperature buffer is disingenuous at best. Any buffer that does exist should be understood, not covered up.
Nick Stokes wrote, “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.”
I believe I note in the post that the impact is seasonal.
You asked, “How do you think they should deal with regions with only summer SST readings when compiling an annual average plot?”
How do I think GISS should deal with areas with seasonal sea ice? Not sure what you feel would be so difficult. Use SST data when it’s available. They could extend land surface data out onto ice when there is no SST data. But if the land surface data can’t extend to its full 1200km because there’s open ocean in the way, they could then use the Reynolds SST climatology or another climatology for the remaining sea ice areas.
Steven Mosher;
So, what value would you suggest that people use when an ocean area is covered by ice? The value of the SST under the ice? -1.8C. no value? or the value of a land station 100s of km away ( like -21C)>>
So what value to we put on surface temps on land that are covered with snow?
Para-climatology?
Steve Mosher: You wrote, “The DIFFERENCE between GISS and CRU is not some nefarious plot.”
I never said it was a nefarious plot. I noted that SST data is apparently not used when it is available in areas with seasonal sea ice. GISS chooses to use the dataset with the higher trend (land) and that choice biases their version of global temperature. If the SST data is available, why not use it?
Regarding your earlier comment, you wrote, “Taking the data from SOURCE, you have the following figures for grid box 4546 (around lat 82) for the months jan through december…”
82N is far enough north to be considered permanent sea ice. What does the source reveal for a cell from “Areas covered occasionally by sea ice…” that “…are masked using a time-independent mask”, for example, for the cell with the coordinates 75N, 45E?
My interpretation (assumption) of the two sentences in their Current Analysis webpage…
http://data.giss.nasa.gov/gistemp/sources/gistemp.html
“Areas covered occasionally by sea ice are masked using a time-independent mask. The Reynolds climatology is included, since it also may be used to find that mask.”
…is that they monitor SST and the climatology in areas with what has been permanent sea ice and if the two differ, it means they have an SST reading. If there’s a SST reading, the cell gets put into the permanent “time-independent mask”. But again, that’s an assumption.
Regardless, SST data in areas with occasional sea ice appear to be permanently masked. That is, they are not used in (deleted from) the analysis of global temperature. And as noted above, GISS chooses to use the dataset with the higher trend (land) and that choice biases their version of global temperature.
All right, we already know that GISS uses less instead of more information for assembling its global temperature dataset. But how could they explain the deletion of available Arctic SST data??
They try to explain their ‘divergence problem’ with differences in Arctic coverage, by stating that they have more data than all the others, including CRU and NCDC. In fact, they have less data and they are even deleting a significant part of it – maybe in order to get the desired results.
I’ve rarely seen such hipocrisy from a high-profile institution. Hansen is going to get some tough questions.
Steven mosher says:
So, what value would you suggest that people use when an ocean area is covered by ice? The value of the SST under the ice? -1.8C. no value? or the value of a land station 100s of km away ( like -21C)
Those are your choices, can u think of others. neat problem.
Yes. Acknowledge that temperature anomalies have a meaning only locally and cannot be integrated into a global map from which a global anomaly is made and then used to define whether and how much the planet is heating.
This was displayed to me graphically by a recent weather here in Athens, Greece: a hot wind came, 27C, with a lot of clouds, which means no sun to heat the building. There is very little energy content in the air so the house remained a cool 23C just by shutting the windows. Next day with the sun full force hitting the walls the inside and outside temperature balanced, no matter the closed windows.
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.
Interpolating coastal land temperature anomalies over the Arctic basin should be regarded as a serious flaw. The variance of anomalies are at least 3 times larger over land surface than over open water or sea ice.
Furthermore, in case of a reduction in sea ice coverage the coastal areas can experience very high temperature anomalies because of the missing cooling factor. The local anomalies on the coastline could be abnormally high compared to the Central Arctic basin and the Siberian or North American interior. These skyrocketing temperatures are measured by numerous Arctic stations, including Barrow (Alaska), Vrangel Island (Northern Siberia), etc. while nobody measures the actual air temperature in the Central Arctic Ocean.
Steve Mosher, You wrote, “Merging hadisst and reynolds thus would have the effect of not using the reynolds SSTs where there is ice or partial ice. It appears that is what they do.”
What they appear to do based on my review of the maps of their outputs through KNMI is not use Reynolds SST where there has been ice or has been partial ice. That is, Reynolds SST data is masked (deleted) if there was ever ice in the grid.
You asked carrick, “So, what value would you suggest that people use when an ocean area is covered by ice? The value of the SST under the ice? -1.8C. no value? or the value of a land station 100s of km away ( like -21C).”
The GISS 1200km smoothing seems logical for infilling temperatures for areas with sparse surface readings, though it’s a subject of much debate. And in areas with sea ice, extending land data out over sea ice, when it is sea ice, also seems logical. But, during periods when SST data is available, it should be used. Masking SST data and extending land surface data in its place biases the data in those areas, and that’s what this post was about.
This is an artificially created problem.
On land they don’t stop measuring the surface temperature in the Winter. Doesn’t matter if it’s a bare-land Winter, a snow-covered Winter, or an ice-covered Winter. They just measure the temperature of the air at the surface, business as usual. They don’t stick thermometers into the ground under your driveway. If I go ice-fishing on a lake in Manitoba or Wyoming, I know exactly what the water temperature will be at the bottom of the hole I drill. It will be 0°C. But is that what gets listed in the temperature records? No. What gets listed is the air temperature. What do they do in the Summer? Same thing. For these purposes, nobody cares that the water surface is at 0°C, or the dirt in the back yard is at 10°C, or that the core of the planet might be 6000°C, nor should they.
On the ocean, however, they get schizoid for some reason. When there’s no ice, they measure the Sea Surface Temperature. When there is ice, they appear to be flummoxed. Why? Beats me. If they really want to know the SST, i.e. the temperature of the liquid surface under the ice, there’s no mystery about that. It’s pretty much the same value anywhere on the planet where sea ice forms, namely -1.8°C. It could be a little higher than that if the ice formed very slowly and squeezed out a bit of salt while freezing. Unless it is abnormally salty, however, and froze very quickly, it will not be colder than -1.8°C, nor warmer than 0°C, no matter how much salt was squeezed out. That’s a pretty tight window, and it could be tightened a lot more with a few measurements.
OK then, so what happens if I go ice-fishing offshore in Nunavut or Alaska? Do I care that the water at the bottom of my fishing hole is always -1.8°C? Not really, especially if I’m freezing my buns off because the air temperature is -50°C. And if they’re interested in getting a set of values for the temperature of the surface of the Earth, they should be using values for the surface of the Earth, regardless of whether it’s covered with grass, ice, or peanut butter.
There’s no real conceptual problem here. If you want to know the temperature of the liquid surface of the sea, it’s either whatever it is, or it’s -1.8°C. If you want to know the temperature of the air above the surface of the sea, it’s simply whatever it is.
One last point about this 1200km thing: I live at 45°N. One entire time zone here is 1180km across, or 20km shy of GISS’s ‘reach out and touch me distance’. The temperature across town can be several degrees up or down from where I am sitting. It’s pretty random. At highway speeds, i would have to drive non-stop for 12 hours to visit my friends who live one time zone away. What are the odds that their temperature right now is the same as mine, or anywhere close to it? Abject nonsense.
/dr.bill
Manfred says:
June 1, 2010 at 12:26 am
Steven mosher says:
May 31, 2010 at 9:37 pm
“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.”
The difference is not an “uncertainty”, but a GISS warming bias. They use data with a 8-fold higher warming trend to interpolate sea surface data.
This is an important posting by Bob Tisdale, as it proofs very easily and understandable a signifant warming bias in GISS data, particularly the prominent polar regions.
If there is a warming bias it will only occur when teh arctic is warm. If, as everyone seems to think, we are in a cycle (solar, PDO whatever) which has or is about to change then the GISS extrapolkation will introduce a cooling bias and we should see the large drop in arctic temperatures similar to what happened between 1940 and 1970. It’s a case of swings and roundabouts.
However, I still think GISS is closer to the mark than Hadcrut. Hadcrut anomalies have been about the same as the satellite anomalies recently which is ridiculous when you consider the different base periods used. I can understand the attraction of Hadcrut at the moment. UAH NoPol anomalies are ~2 deg above the 1979-98 mean and RSS clearly shows the arctic region is ‘warmer’ than normal.
http://www.remss.com/msu/msu_data_monthly.html?channel=tlt
Figure 8 http://i48.tinypic.com/spd4li.jpg
GISTEMP shows a temperature increase of about 2.6°c between 1982 and 2009, which is equal to a trend of 1.0°c per decade. No one measured this trend directly, because as I mentioned it before, there are no permanent temperature recording sites within about 1000km of the North Pole.
The northernmost meteorological stations are located at a latitude of about 80N – Alert (Nunavut), Nord Ads (Greenland), Ostrov Vize (a Russian island). The 1982-2009 trend is 0.7°c per decade in this latitude band which is probably amplified by sea ice reduction.
It is mathematically impossible to get a warming trend of 1.0°c per decade (or anything higher than 0.7) in the Central Arctic basin with spatial interpolation. It is only possible by extrapolating the zonal mean values shown on Figure 8.