Space.com has an interesting article about recent research by Santer et al. (2018, Science 361, 245) into how the seasonal cycle of temperature has changed. It’s a topic we’ve looked at recently for surface temperature in the USA. Santer et al. study it in the troposphere (both TLT, the lower troposphere temperature, and TMT, mid-troposphere temperature), and look for patterns over the whole globe.
Result: a human-caused signal is now evident.
They use satellite data for temperature in the troposphere, from three sources: RSS (Remote Sensing Systems), UAH (Univ. Alabama at Huntsville), and STAR (NOAA Center for Satellite Applications and Research). They also investigate how different versions of those data sets affect the results, by using both current and former releases of their data. With these, they determine the trends in annual mean temperature over space and time (referred to as TAM(x,t)), and the size of the annual cycle of temperature (TAC(x,t)). They do the same for the results from a suite of computer simulations of climate.
Then comes the heart of the study: they undertake fingerprint analysis by checking how well the patterns we’ve observed with satellites match the patterns found in computer model simulations.
The most interesting aspect is probably the geographic pattern of trends in the annual cycle. Computer models (a large multi-model ensemble) show considerable variety around the globe, illustrated thus for TMT:
The most pronounced feature is an increase in the size of the annual cycle since 1979 (when satellite observations begin) in mid-latitude regions of both hemispheres, most pronounced in the northern hemisphere (as is well shown in the diagram above). Other features include a decrease in the size of the annual cycle in the Arctic (also visible in the diagram).
The use of a multi-model ensemble tends strongly to “smooth out” the kind of fluctuations that happen from place to place; hence we can’t expect the observed pattern to match such an ensemble mean. But if the overall patterns of observations (the “big picture”) matches well enough with the overall patterns of the computer simulations, that’s powerful evidence that the models are getting the big picture right, and that features which the models say are due to human influences really are due to human influence, in the real world.
And so they do.
Here’s the observed pattern of the rate of change of the annual cycle using RSS data (red for bigger seasonal cycle, blue for smaller):
Although there’s a lot of geographic variation which the model ensemble doesn’t show, the “big picture” patterns are there and quite pronounced: the seasonal cycle has gotten bigger in the mid-latitudes of both hemispheres, more so in the northern hemisphere, while the opposite has happened in polar regions, most strongly in the Arctic. It’s a pronounced fingerprint forecast by computer models, which has now been observed and verified in actual observations. And, the models reproduce this pattern not because of natural variation, but because of human-caused climate change.
Naturally I was curious whether or not similar behavior is shown in surface temperature (as well as mid-troposphere temperature), so I used gridded data from NASA to compare the mid-latitude band from latitudes 40°N to 60°N, to the Arctic latitude band from 70°N to 90°N. I computed the trend rates for each month of the year from 1979 (when the satellite data begin) to the present by two methods: least squares regression and Theil-Sen regression. Here are the monthly trend rates (least squares in red, Theil-Sen in blue):
The size of the annual cycle is roughly the difference between July’s temperature and January’s. According to the NASA data, in the 40°N to 60°N latitude band this difference has been increasing by 0.12 °C per decade, while in the 70°N to 90°N latitude band is has been decreasing by 0.68 °C per decade. So yes, the pattern (at least in the northern hemisphere) of mid-latitude seasonal cycle increase and high-latitude seasonal cycle decrease is present in surface temperature data also.
There’s a great deal more to this research, and I’m impressed with the depth and rigor the authors apply to their study of the data. I’ll leave further exploration to interested readers, but emphasize again the overall conclusion, which is well summed up in their closing paragraph:
Across the most recent versions of observational TMT datasets, structural uncertainty in the geographical pattern of trends appears to be smaller for annual cycle amplitude than for the annual mean (Fig. 2, A to F). This is advantageous for detection and attribution studies. Furthermore, we note that the annual cycle of tropospheric temperature is not routinely used in model evaluation. It is highly unlikely, therefore, that the positive fingerprint identification results obtained here for the annual cycle could be due to model tuning. The best explanation for these results is that basic physics and basic physical mechanisms are driving the large-scale changes in TAC(x,t). For tropospheric temperature, a human-caused signal is now evident in the seasonal cycle itself.
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