Statistical exploration of processes controlling soil moisture in present and future climates

 

Authors

Scott Matthew Collis — Argonne National Laboratory

Category

General Topics

Description

Potential changes in soil moisture may have significant impacts on society, as soil moisture direct influences agriculture. Soil moisture is also recognized as a critical factor in simulating past, current and future climates in climate models as it is the moisture source of evapotranspiration over land. Climate model projections generally show a reduction in soil moisture in future warmer climate conditions. However, this predicted large-scale drought is based on the incomplete knowledge of soil moisture dynamics and soil moisture-atmosphere interaction. Understanding of soil moisture dynamics is hampered by the lack of observation data extending long enough temporarily. The existing mechanisms used in climate models to describe the controlling factors of soil moisture dynamics and simulated interactions between the land and the atmosphere, are far from being perfect. This scale of potential adverse impacts from predicted soil moisture changes means that checking the underlying physics is a critical priority for the climate community. To date, processes in climate models have not been rigorously evaluated against observations. We report here on preliminary work from a new collaboration funded by a University of Chicago / Argonne National Laboratory Strategic Collaboration Initiative grant, to study controls on soil moisture at The Southern Great Plains ARM site. SGP is a near-unique site for this exercise given its many years of simultaneous measurements of soil moisture and moisture fluxes. The goal of our work is to use this unique observational resource to statistically explore the drivers of soil moisture in observation and to compare our findings against the state-of-the-art GCMs. We will characterize and understand variations in soil moisture and moisture fluxes and their relationship to forcing variables (e.g. temperature, precipitation, wind speed, and relative humidity), with an overall goal of understanding whether models capture the processes controlling soil moisture with sufficient fidelity.