Deducing Land-Atmosphere Coupling Regimes from SMAP Soil Moisture

Abstract. In recent years, there has been a growing recognition of the significance of Land-Atmosphere (L-A) interactions and feedback mechanisms and their importance for weather and climate prediction. Soil moisture plays a critical role in mediating L-A interactions; therefore, this research assesses the impact of different soil moisture datasets on the classification and distribution of L-A coupling regimes. Using SMAP Level 3 (SMAPL3) and SMAP Level 4 (SMAPL4) soil moisture data, we examine the persistence of dry and wet coupling regimes over two decades (2003–2022), exploring how soil moisture influences coupling classification. An inherent challenge in assessing the significance of soil moisture in L-A coupling classification lies in the need for consistent and unbiased observations of the atmospheric state, represented through metrics such as Convective Triggering Potential (CTP), offering insights into atmospheric stability and Humidity Index (HI), which quantifies moisture within the atmosphere. The study utilizes a Triple Collocation-based merging process to address this issue and combines three reanalysis datasets for CTP and HI. Despite significant correlated errors within the individual reanalysis datasets, the merged product demonstrates enhanced performance, showcasing increased accuracy in capturing atmospheric conditions. When combined with the merged CTP and HI for coupling classification, a higher lag-correlation between soil moisture and the CTP-HI metrics contribute to the persist coupling behaviour, potentially suggesting that temporal consistency is a leading factor. SMAPL4 demonstrates stronger persistence of the wet and dry coupling regimes as compared to SMAPL3. The stronger persistence is partially due to the higher observation count, though it may partially be linked to the unique characteristics of the SMAPL4's assimilation process. This suggests that SMAPL4's approach may offer a robust approximation when assessing land-atmosphere interactions, highlighting the inherent differences between SMAPL3 and SMAPL4 datasets. These findings lay the groundwork for understanding the sensitivity of drought evolution to soil moisture variations by gaining insight into the quantification of coupling strength, thereby providing critical insights for future drought modelling and prediction efforts.