J11.3
Dynamic and soil moisture feedbacks in regional climate change over the central U.S

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Thursday, 27 January 2011: 11:30 AM
Dynamic and soil moisture feedbacks in regional climate change over the central U.S
612 (Washington State Convention Center)
Zaitao Pan, St. Louis Univ., St. Louis, MO

Climate system is complex and involves many intertwined interactive processes. Consequently, signs and magnitudes of climate change reflect many positive and negative feedbacks combined. The positive snow cover effect and negative aerosol (direct) forcing are two feedback examples of the climate system. On regional scale, local climate change also reflects remote forcing and telecommunications. Diagnosing individual climate change feedbacks will improve understanding in climate dynamics and shed light on eventual decomposing climate change into natural and anthropogenic components. This study identifies and examines three feedback processes related to the climate change in the central U.S.

The first process examined is the baroclinicity feedbacks where the horizontal gradient in surface warming increase thermal winds and baroclinic instability that further interact with climate change. The second feedback is soil moisture feedback. The future climate change causes soil moisture to change, which alters the soil heat capacity and thus feedback on near-surface temperature change. The last process is the boundary-layer (BL) depth/low-level jet (LLJ) feedback, as stronger surface warming and thus higher BL height upstream would induce stronger LLJ and moisture transport downstream, resulting in more convectivness and likely cooling effect. Adopting the IPCC AR4 GCMs' projected surface warming and soil moisture change, we used WRF model to evaluate these feedbacks under both the past and future climates. The preliminary results show that the magnitudes of the surface temperature change attributable to these feedbacks can reach +/- 1-2K, reinforcing/compensating regional climate change. This presentation will report a series of modeling results and discuss the implications to the climate change attribution.