Extending MPAS-NoahMP model capability beyond weather timescale: assessing land-atmosphere interactions for future subseasonal-to-seasonal applications

Subseasonal-to-seasonal (S2S) forecasts are critically important for anticipating socio-economic impacts but remain a significant scientific challenge. Reliable forecasts beyond a two-week timeframe require both accurate initial conditions and realistic representations of coupled Earth system processes. In particular, Earth's land surface provides a key source of predictability for S2S predictions through land-atmosphere interactions. Here, we present a 25-year global 60-km simulation with the Model for Prediction Across Scales (MPAS) coupled with Noah-MP land surface model, with ERA-5 sea surface temperature and sea ice boundary conditions. This study evaluates the model's climatology, systematic biases, and its ability to represent land-atmosphere interactions, all of which are necessary for future S2S applications. MPAS-NoahMP well captures annual surface temperature in the tropics and midlatitudes, while winter temperature in North America and Asia is sensitive to sea ice thickness and snowpack thermal properties. The global precipitation pattern is also well captured by MPAS-NoahMP, with slight dry biases in the tropics over the Eastern Pacific, Amazon, and Africa Congo, and wet biases in the Indian ocean and Maritime continent. The model accurately simulates spatial patterns of land-atmosphere coupling indices and hotspots, including winter snow-temperature coupling and summer soil moisture-latent heat flux coupling. Furthermore, MPAS-NoahMP reasonably captures the major modes of tropical variability, including the El Nino and Southern Oscillation and the Madden Julian Oscillation. This study is the first to establish a long-term MPAS-NoahMP reference dataset for understanding model biases and assessing land-atmosphere interactions, providing a foundation for extending the coupled MPAS-NoahMP system to future S2S applications.