Rong Fu

Rainfall variability and changes over tropical and subtropical land contribute to global carbon-climate feedbacks and food security in the 21st century.  As an extreme of such variability, drought is one of the costliest and least understood of natural disasters in US and worldwide, as evident by the ongoing droughts in California and South America.  Although control from oceanic variability dominates explained rainfall variability over much of the tropical and subtropical land, it often does not explain enough of the total rainfall climate variability to provide adequate seasonal to decadal predictions, especially over the interior of continents.   Soil moisture memory is considered as another significant source of predictability.  However, it tends to be too weak and short in climate models to provide adequate drought prediction.  In this presentation, I will report on our research in exploring the processes responsible for rainfall variability beyond that which can be explained by oceanic influences. I am especially interested in extreme droughts and increases of dry season length over US Great Plains and Amazon, and in identifying the potential sources of the models’ uncertainty in predicting or projecting droughts over these two regions.  This climate information is applied to support regional water resource decisions. 

 

The main results include a) Seasonal changes and the coupling between soil moisture anomalies, clouds and rainfall and large-scale circulation are central in initiating and sustaining persistent drought memory; b) The interplay between vegetation response, biomass burning aerosols and extra tropical synoptic systems could further lengthen drought memory; c) the drought memory provided by such land-atmospheric feedbacks can provide improved predictability for droughts; d) Inadequate cloud/radiative response to drought may be an important but overlooked source of uncertainty, in addition to uncertainties of the land surface and precipitation processes; I will also briefly discuss challenges and future directions in predictive understanding of the rainfall variability in a changing climate.