Improving Surface Soil Moisture Simulation in FGOALS-g3 over Southeastern China: The Role of Soil Texture

Accurate soil moisture simulation is essential for understanding regional hydroclimate variability and improving climate predictions. We evaluate the performance of the FGOALS-g3 model under the Atmospheric Model Intercomparison Project (AMIP) configuration in simulating surface soil moisture (SSM) over southeastern China during 1980–2014. Compared to the ERA5 and ESA-CCI reference datasets, the model exhibits a dry bias in its spatial distribution, seasonal cycle, and interannual variability. Replacing the model’s default soil texture with the Global Soil Dataset for Earth System Modeling (GSDE) significantly reduces this bias. GSDE promotes finer soil texture, reducing sand content by 41.25% and slightly increasing clay. This textural shift directly modifies hydraulic properties (such as increasing soil water suction and decreasing hydraulic conductivity), thereby enhancing the soil’s water retention capacity and leading to more accurate SSM simulations. However, SSM improvements have only a limited effect on latent heat flux and negligible impacts on precipitation. Although the weak response in precipitation is consistent with observational evidence of weak land–atmosphere coupling, the model incorrectly simulates strong coupling, indicating systematic biases in interaction mechanisms. These findings highlight that merely refining soil texture data is insufficient; more accurate physical parameterizations of land-atmosphere processes are essential for realistic hydroclimate simulations.