Wet and Dry Tropical/Subtropical Regions Show Opposite Sensitivities to Observed Greenhouse Gas Warming

Different surfaces are expected to respond differently to greenhouse gas-induced surface warming. For example the average warming rate over global land is stronger than that over the global ocean. Large scale difference in surface water availability, and hence difference in evaporative fluxes in a warmer world, has been proposed as one of the causes for this difference in responses. A similar mechanism may operate over land, distinguishing dry and wet regions as characterized by climatological soil moisture. One would expect a negative correlation between climatological soil moisture and greenhouse gas-induced surface temperature trends. It is this correlation we test in various tropical regions (between 30S to 30N), specifically comparing dry with wet regions.

We utilize both reanalyses and observations to understand regional responses to greenhouse gas warming differentiated by climatological soil moisture within 30 degrees of latitude of the equator. There is a marked distinction between dry regions such as the Sahara, the Saudi Arabian desert and the western Australian desert, and wet regions such as the Amazon rainforest, the Congo Basin rainforest and the maritime continent with respect to their response to greenhouse gas warming over the past 4 decades. Wet and dry regions exhibit opposite sensitivities of surface temperature trends to soil moisture. Whereas a negative correlation between the climatological soil moisture and surface temperature trend would reflect the expected negative dependence of the temperature trend on evaporative fraction, this process occurs predominantly only in the dry regions. Wet regions, in contrast, display positive sensitivity of surface temperature trends to regional soil moisture variations, particularly during the wet season, indicating the predominant mediating role of the overlying atmosphere’s response to warming. Our study shows that different regions have different sensitivites to greenhouse gas warming. Apart from the analysis of the robustness of this signal, results explaining these correlations will be presented.