Evaluating soil moisture feedback on convective triggering
Williams, Ian N. - Lawrence Berkeley National Laboratory
Area of research:
General Circulation and Single Column Models/Parameterizations
Journal Reference:Williams I. 2018. "Evaluating soil moisture feedback on convective triggering: Roles of convective and land‐model parameterizations." Journal of Geophysical Research: Atmospheres, 124(1), 10.1029/2018JD029326.
The sign of the feedback of soil moisture on convective triggering varies with tropospheric state, challenging its quantification. Using model experiments to classify tropospheric states, this research identified positive and negative feedbacks in observations and climate models, and explored modeled feedback sensitivity to convective and land-surface parameterizations.
Soil moisture-precipitation feedbacks are not well represented in current Earth system models, and can affect drought and extreme precipitation projections. Here, feedback mechanisms were identified using a combined model and observational approach more powerful than correlation metrics or simplified boundary-layer models used in the past. The results help inform model development by demonstrating the importance of convective inhibition (CIN) in determining the sign of the feedback.
To explore the atmospheric and land-surface influences on the initiation (triggering) of daytime deep convection, single-column model experiments were performed using the NCAR Community Earth System Model (CESM1.2) over the U.S. Southern Great Plains (SGP). The results indicate that the positive and negative feedback mechanisms found in earlier studies (using simplified boundary-layer models) are robust to large-scale forcing and interactions between boundary-layer turbulence, cloud dynamics, and radiation. However, systematic responses of convective triggering to soil moisture emerged only after switching from a CAPE-based to a CIN-based convective parameterization. This suggests that the choice of convective mass-flux closure largely determines the sensitivity of modeled clouds to land-surface state. Errors in land-model parameterizations of evapotranspiration also affect the probability of deep convection, with vegetation (transpiration) playing an important role in linking soil moisture to surface energy partitioning and clouds. Parameterizations that permit the triggering feedback mechanism better predict the statistics of daytime shallow and deep convection, with respect to observations from the Atmospheric Radiation Measurement (ARM) observatory in the SGP. The results illustrate how errors in the representation of evapotranspiration in land-surface models can propagate through the chain of coupled land-atmosphere processes to affect convective clouds.