Evaluation of land–atmosphere coupling processes and climatological bias in the UFS Global Coupled Model

Near-surface meteorological variables generally improve, especially surface air temperature, and the land-atmosphere coupling metrics better represent the observed covariance between sur-face soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model's ability to simulate the different balances of coupled processes between water-limited and energy-limited regions.

Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land-atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales.