I had begun calculations for a post on urban warming, but as usual, I got sidetracked on something else. This is that sidetrack. There’s much interest in the differences in the RSS and UAH temperature data sets. We’ve seen plenty of graphs showing the difference in the spatial averages of both data sets. But I think we can learn more by looking at the differences between the gridded data. I took the difference between UAH and RSS from January 1979 – Sept 2009 and calculated the trend at each grid point.
As you can see, there is strong differential warming of RSS relative to UAH in the tropics. This probably explains why Santer’s analysis of tropical tropospheric amplification went well with RSS and not so much with UAH. Outside of the tropics, the differential warming is in the opposite direction but dependent on hemisphere with UAH in the lead in the North. Approaching the North Pole, the differential warming becomes unmistakably larger and larger. However, approaching the South Pole, the warming rises, then at about 50° S, it starts to drop. I remember reading that RSS’s correction of diurnal drift is latitude dependent, but I couldn’t find the paper or website where I found it. If anyone knows if this is true, I’d appreciate a reference. Here’s the zonal trend.
Now let’s look at how each satellite record compares to the three surface temperature records. To make the comparison, I had to calculate the monthly mean anomaly and subtract it from the entire gridded data set. This is where I ran into some problems. GISS has the best consistent coverage of the three data sets. As you know, GISS produces two anomaly series. One that interpolates anomalies out as far as 250 km and another up to 1200 km. I used the 1200 km series for this analysis. HadCRUT and NCDC however don’t interpolate to fill in missing data. This presents something of a problem when trying to rebaseline the data. If I have a series of January anomalies corresponding to my new baseperiod, the easiest situation is where all the anomalies area real-valued. What does one do when not all of the anomalies area real valued? By computing a mean and ignoring missing values, it could bias the results in unpredictable ways. That wasn’t an issue I was interested in dealing with. So I resorted to only computing the mean when all values are defined. This resulted in a serious loss of coverage for HadCRU and NCDC.
The period of analysis is Jan 1979 – Dec 2008. I chose this period because a) it makes the programming easier and b) the NCDC gridded data only goes out to April 2009. Here’s RSS and UAH minus GISS.
Both data sets show relatively similar patterns of warming but RSS shows stronger more widespread warming in the tropics. Here’s the trend by latitude.
Oct 26- CORRECTION: John N-G discovered an error with the zonal trend calculation. See his comment below. I’ve updated the zonal trend graphs. You can see the three original versions here, here and here.)
Next up is HadCRUT.
It’s not apparent from these two plots, but there is a significant difference in warming in the tropics as the latitude trend shows.
Oct 26, UPDATE: I’ve truncated the x-limits of the graph because of extreme values at high latitudes in the South. These values shouldn’t be taken seriously because there are few grid boxes with real-valued trends in this region, so it’s not really a valid spatial average.
Moving on to NCDC,
The pattern of warming for both data sets is almost identical. So identical I thought I may have mistakenly processed the same data set twice. I checked and that wasn’t the case. The latitude trends confirm this.
This plot raises some questions. There is a strong difference between RSS and UAH anomalies. So then why does this difference almost completely disappear relative to NCDC? It’s almost like RSS and UAH suddenly became similar. Strange.
UPDATE! Oct 21, 2009
JeffID suggested that I repeat the trend calculation for the period before and after RSS/UAH were using the same satellites. I recalculated the trend map for the periods Jan 1979 – Dec 1991 and Jan 1992 – Sept 2009. Here are the results.
The differences are like night and day. Before 1992, the trends were similar as what I found previously. But after 1992, there is strong differential warming over South America, Africa (with some cold spots) and Australia. I’ll leave it to you discuss the specifics.