>Part 1 of Comparison of GISTEMP and UAH MSU TLT Anomalies

>CORRECTION: Many thanks to bloggers timetochooseagain and Mark Nodine for catching the errors in the Arctic and North American Plus trends that I had listed in the text. They found them in the WattsUpWithThat version of this post.

I had listed the annual values but identified them as decadal. These errors have been corrected.

I originally started this comparison by looking at the differences between OI.v2 SST and the UAH MSU Lower Troposphere Temperature (TLT) anomalies for the same ocean segments. Since OI.v2 SST data has been used by GISS for their GISTEMP product since December 1981, I decided to add another comparison: GISS Land Surface Temperature (LST) versus UAH MSU TLT for the same continental land segments. Then I added one last comparison, which is the subject of this post.

Note 1: The data illustrated in the following graphs are as I downloaded them from the KNMI Climate Explorer website. I made no effort to offset either dataset in the comparative graphs so that the two curves rested on one another. The graphs will show that GISTEMP anomalies are higher than UAH MSU TLT anomalies. This is a function of base years. Focus on the trends and the shapes of the curves, not the location of the curves.

Figure 1 is a comparison of Global GISS Surface Temperature (GISTEMP) and UAH MSU Lower Troposphere Temperature (TLT) anomalies. Both datasets have been smoothed with 12-month running-average filters.
Figure 1

Similar graphs always create speculative comments about the basis for the differences between GISS and UAH data. In this post, I’ve segmented the globe, Figure 2, to locate the areas with the largest differences, in an effort to narrow the possible reasons for those divergences. The coordinates used are listed on the graphs. I’ve plotted the data and added the linear trends, but I have not speculated about the causes for the differences in the data for the smaller global areas.
Figure 2

Note 2: GISTEMP data through the KNMI Climate Explorer is available with 250 km and 1200km smoothing. The graphs in the post use the 1200 km smoothing, which is the smoothing presented by GISS in their GISTEMP product. Figure 2, however, is the May 2009 GISS Global Temperature Anomaly map with 250km smoothing. Grey areas indicate locations with no data. These are the areas infilled by the 1200 km smoothing.

Note 3: Also keep in mind that the MSU TLT data reaches to 82.5N and 70S. The approximate locations of those latitudes are shown in Figure 2. UAH also fills in the polar data. On the other hand, MSU data has better global coverage in other areas where surface station data is lacking.

Note 4: And for the last note before looking at graphs and EXCEL-calculated trends, keep in mind that GISTEMP and UAH MSU TLT represent datasets made up of different variables. GISTEMP is composed of Sea Surface Temperature (SST) and of Land Surface Temperature data based on surface station readings. The UAH MSU TLT data represents the temperature of the lower troposphere.


Figure 3 illustrates GISTEMP and UAH MSU TLT anomalies for the Arctic, 65N to 90N. The GISTEMP linear trend for the period is 0.595 deg C/decade while the UAH MSUTLT data has a linear trend of 0.461 deg C/decade. Note how the GISS data exaggerates (or the UAH MSU data suppresses) the variations, especially after mid-2004.
Figure 3

The North America Plus datasets, Figure 4, also include the Eastern North Pacific and the majority of the North Atlantic. The trends are significantly lower than the Arctic datasets, as would be expected. The linear trend for the UAH MSU TLT data (0.185 deg C/decade) is greater than the trend for the GISTEMP data (0.159 deg C/decade).
Figure 4

The South America Plus datasets, Figure 5, also show a UAH MSU TLT linear trend (0.063 deg C/decade) that is higher than the GISTEMP trend (0.05 deg C/decade). These datasets also include major portions of the eastern South Pacific and western South Atlantic. Both linear trends are again significantly lower than the North American Plus datasets. Note the dominance of the ENSO signal in the South American Plus data.
Figure 5

The Europe Plus datasets show the highest trends of those examined in this post. This should be due to the impact of the North Atlantic on Europe. As illustrated and discussed in my post “Putting The Short-Term Trend Of North Atlantic SST Anomalies Into Perspective”, the linear trend of the North Atlantic SST anomalies is more than 2.5 times the dataset with the next highest trend. The GISTEMP trend (0.429 deg C/decade) for the Europe Plus dataset is slightly higher than the UAH MSU trend (0.379 deg C/decade).
Figure 6

The difference in linear trends is greatest in the Africa Plus datasets, Figure 7. The GISTEMP linear trend at 0.194 deg C/decade is more than twice the linear trend of 0.093 deg C/decade for the UAH MSU data.
Figure 7

For the Asia Plus subsets, Figure 8, the GISTEMP linear trend (0.256 deg C/decade) is also higher than the UAH MSU linear trend (0.179 deg C/decade). The Asia Plus datasets have the second highest linear trends of the areas illustrated in this post.
Figure 8

The comparison of the Australia Plus datasets, Figure 9, illustrates another occasion when the GISTEMP linear trend (0.076 deg C/decade) is less than the USH MSU linear trend (0.096 deg C/decade).
Figure 9

The first thing that stands out in the comparison of Antarctic datasets is the difference in the signs of the linear trends. The GISTEMP data show a positive trend of 0.048 deg C/decade, while the UAH MSU data show a negative trend, -0.091 deg C/decade.
Figure 10

The Antarctic datasets are also the noisiest of those illustrated in this post. But the real curiosity is the timing of the mid-to-late 1990s spike in the GISTEMP Antarctic data. At first glance, it appears to be a result of the 1997/98 El Nino. But the spike is more than a year early. In Figure 11, scaled NINO3.4 SST anomalies have been added to the comparative graph of Antarctic Plus GISTEMP and UAH MSU TLT data. The spike in the GISTEMP Antarctic data is not a response to the 1997/98 El Nino.
Figure 11

Figure 12 illustrates the GISTEMP Surface Temperature and the two components of it: GISTEMP Land Surface Temperature, and OI.v2 SST data for the Southern Ocean. The source of the anomalous spike in the mid-1990s is the GISTEMP Land Surface Temperature data, not the SST data.
Figure 12

Note 5: The GISTEMP Surface Temperature data from 90S to 60S is clearly dominated by the GISTEMP Land Surface Temperature data, though the surface area of the Southern Ocean (20.3 million sq km) is greater than the land mass of Antarctica (14.0 million sq km). This appears to be a function of Southern Hemisphere sea ice area, which can vary from 1.5 to 16.5 million sq km over the course of a year. During the winter, sea ice area increases. The land surface area then becomes greater than the sea surface area, making it the dominant dataset.


I do not recall any discussions of a 1996 spike in the GISTEMP Antarctic surface temperature data. I have double-checked to assure I downloaded the data correctly. However, I have not tried to confirm whether or not the 1996 spike occurs in the individual Antarctic surface station data available from GISS:

A gif animation of the annual GISTEMP maps, Figure 13, does show elevated Antarctic surface temperatures in 1996.
Figure 13


THE GISTEMP Surface Temperature, GISTEMP Land Surface Temperature, UAH MSU TLT, and OI.v2 SST data are available through the KNMI Climate Explorer website:http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in GISS, Lower Troposphere Temperature. Bookmark the permalink.

8 Responses to >Part 1 of Comparison of GISTEMP and UAH MSU TLT Anomalies

  1. BoulderFreak says:

    >The reason the GISS anomalies are higher than the UAH-MSU data is that the GISS anomalies are relative to the 1950-1980 period and the MSU anomalies are relative to the 1979-2009 period.That is why the mean of your GISS temperatures are above zero for the 1979-2009 period and the MSU temperatures are centered on zero.See the discussion of "Anomalies and Absolute Temperatures" on the GISS website:Hereand MSU data isHere

  2. Bob Tisdale says:

    >BoulderFreak: You wrote, "The reason the GISS anomalies are higher than the UAH-MSU data is that the GISS anomalies are relative to the 1950-1980 period and the MSU anomalies are relative to the 1979-2009 period."Agreed. I explained that in my Note 1 in the post. It read:Note 1: The data illustrated in the following graphs are as I downloaded them from the KNMI Climate Explorer website. I made no effort to offset either dataset in the comparative graphs so that the two curves rested on one another. The graphs will show that GISTEMP anomalies are higher than UAH MSU TLT anomalies. THIS IS A FUNCTION OF BASE YEARS. [Caps added.]

  3. Anonymous says:

    >Bob How do you get this information off the KNMI site? I have visited it and cannot see how it is done. Is the raw data even available.The reason I ask is I like to do my own playing around with data. At present I am looking at the RSS/UAH data and eliminating the EL Nino bump and looking at the trends from 1979 to Oct 1997 and Nov 1998 to present. I would like to do that for the regions that you have charted.Always wondered if it was possible to break the globe up and see what UAH/RSS give for the different regions.By eyeballing your charts, it seems to me that the time before the El Nino of 1997/8 some of the trends are insignificant or negative (Australia, Africa, South America, Arctic even the Globe (.036 C/Dec UAH.It sure seems to me that the El Nino of 1997/8 had and continues to have, a significant effect on the trend of the satellite data. It will be interesting to see in the next 5/10 years what happens to those trends. Will the temps basically go back to what they were 1979 to 1998?You stuff is fascinating. Love the site.

  4. Bob Tisdale says:

    >Anonymous, just so happens I wrote up something in a WUWT comment thread last week. That was specific to a conversation, so I’ve quickly revised it as follows.Go to:http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhereScroll down and select the dataset you’re interested in, then scroll back up and click on “Select Field”. On the next page, there are fields for Latitude and Longitude. Enter the coordinates for the data. Also enter a zero in the “Demand at least” field. That will get you all the data, without filtering from KNMI. Click on “Make Time Series.” On the next page, for anomalies, scroll down to the third graph. It reads “Anomalies with respect to the above annual cycle”. On that same line, click on “raw data.” That next page is the raw monthly anomaly data. You’ll have to convert it to a form usable by your spreadsheet.

  5. BarryW says:

    >Could the spike in the Antarctic be a precursor or trigger to the El Nino events?

  6. Bob Tisdale says:

    >BarryW: You asked, "Could the spike in the Antarctic be a precursor or trigger to the El Nino events?"It would be great to be able to find a precursor, but we'd have to identify the cause of that spike in the GISS Antarctic temperatures and that's tough to do with the sparse data. Also, that spike doesn't appear in the SST or the TLT data. Why?

  7. Anonymous says:

    >Hi BobI'm trying to duplicate your results but I am having difficulty getting exactly the same.Did you use some convention for entering Lat Long?For example for Africa if I enter 0 65 Lat; 50 -17 Long I get a different result than if I enter0 65 Lat; -17 50 in the respective boxes.ThanksSteve H

  8. Bob Tisdale says:

    >Steve H: For the latitudes it's Southern value first. For the longitudes its Western boundry of the area first. 0 & 65 for Latitudes and -17 & 50 as the Longitudes as inputs should get you Northern Africa and Europe through Scandinavia. By switching the order of the longitudes you should get everything at those latitudes except for the Northern Africa and European data. That is, with 0 & 65 as the Latitudes and 50 & -17 as Longitudes, you'd get all of the Northern Hemisphere to the Arctic Circle minus the area for Northern Africa through Europe.

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