Enhancing Sub-Seasonal Soil Moisture Forecasts through Land Initialization

Abstract We assess the relative contributions of land, atmosphere, and oceanic initializations to the forecast skill of root zone soil moisture (SM) utilizing the Community Earth System Model version 2 Sub-seasonal climate forecast experiments (CESM2-SubX). Using eight sensitivity experiments, we disentangle the individual impacts of these three components and their interactions on the forecast skill, quantified using the anomaly correlation coefficient. The SubX experiment, in which land states are realistically initialized while atmosphere and ocean remain in their climatological states, contributes 91 ± 3% of the total sub-seasonal forecast skill across varying soil moisture conditions during summer and winter seasons. Most SM predictability stems from soil moisture memory effect. Additionally, land-atmosphere coupling contributes 50% of the land-driven soil moisture predictability. A comparative analysis of CESM2-SubX SM forecast skills against two other SubX climate models highlights the potential for enhancing soil moisture forecast accuracy by improving the representation of soil moisture to precipitation feedback.