Why Haven’t the Tropics Warmed Much? A Tantalizing Piece of Evidence
The radiative resistance to global temperature change is what limits the temperature change in response to radiative forcing from (say) increasing CO2, or the sun suddenly deciding to pump out a 1 percent more sunlight.
If the climate system sheds only a little extra energy with warming, it warms even more until radiative energy balance is restored. If it sheds a lot of energy, then very little warming is required to restore global energy balance. This is the climate sensitivity holy grail, and it will determine just how much warming results from increasing CO2 in the atmosphere.
John Christy and I are preparing a paper based upon Dept. of Energy-sponsored research explaining why the tropical troposphere hasn’t warmed as much in nature as in climate models. (The discrepancy exists for surface temperature trends; for both RSS and UAH tropical tropospheric trends; as well as for global reanalysis datasets). Danny Braswell and I did a lot of research on this subject about 5-10 years ago, and published several papers.
Without going into the gory details of why it is so difficult to measure “feedbacks” (how strong the climate system radiatively resists a temperature change in response to radiative forcing), I’m going to present one graph of new results from our work that suggests where the problem with the models might be.
The plot I will show is based upon month-to-month variations in area-averaged tropical (30N-30S) tropospheric temperatures. When those temperature changes are the largest, we expect to see the clearest signal of radiative resistance (negative “feedback”) which, by definition, is a response to that temperature change. In contrast, if the month-to-month temperature change was zero, any change in radiative flux would result in an infinite feedback parameter, which is clearly unphysical.
So, let’s focus on the biggest observed temperature changes. If we take the 10% of the 224 months of detrended CERES satellite radiative flux data (March 2000 through October 2018) which have the LARGEST month-to-month temperature changes (warming and cooling) in detrended UAH LT data, and compare them, we get the following plot of diagnosed feedback parameter (flux change divided by temperature change) versus average absolute temperature change. Also included in the plot are the results computed in the same manner from 19 different CMIP5 climate models, where I have used the model surface to 500 mb geopotential thickness converted to temperature to approximate the UAH LT product.