Coupling Noah-MP to the NCEP Climate Forecast​​ System (CFS) to improve land-atmosphere interactions at seasonal forecast timescales

The goal of this effort is to improve the NCEP Climate Forecast System (CFS) forecast skill by enhancing the representation of soil-hydrology-vegetation interactions through the use of the new community Noah-MP (Multiple-Parameterization) land surface model (LSM). Numerous studies have illustrated the substantial influence of land-atmosphere interactions on seasonal-to interannual prediction. Soil moisture memory has been identified as a key in determining seasonal predictability in climate forecast systems. Improving soil-moisture related processes (e.g., evaporation, runoff, and groundwater) is important for potentially enhancing seasonal predictability of temperature and precipitation.

The Noah LSM used in the CFS has been shown to produce low soil moisture with too high seasonal amplitude, which raises significant concerns about the residence times of soil moisture and reduces credibility of coupling with the atmosphere.​ To address these concerns, ​the Noah-MP​ model​, which has been well tested and verified in seasonal regional climate simulations, is coupled to CFS​. ​A​ number of model enhancements have been made to the Noah-MP including added parameterization options for water-table physics, for canopy radiation and turbulence, modifications to snow schemes, and new soil-moisture stress functions in the photosynthesis model among others. Those enhancements are important to accurately represent land-atmosphere interactions and soil moisture memory in ​seasonal models. ​Our results show that using Noah-MP improves the seasonal forecast skill of ​near-surface temperature and moisture, and precipitation over important land-atmosphere coupling regions, including the central United States.​