Experimental Subseasonal Prediction of Surface Air Temperature and Precipitation

Subseasonal prediction is one of the frontier areas that are under explored. This also remains a great challenge for current dynamic model predictions. In this study, we use the GFDL’s global model fvGFS (the Finite Volume Cubed Sphere (FV3) dynamical core with GFS physics) coupled to a slab ocean model, and/or another GFDL fully coupled model to investigate the subseasonal prediction with respect to surface air temperature and precipitation. Several regions with relatively high prediction skill are emphasized, and the associated predictability sources, such as SST anomaly, MJO and some other internal modes, are examined. We also discuss the potential predictability (the upper limit of prediction skill) of surface air temperature and precipitation.