2-Tiered Seasonal Climate Prediction over North America with CCSM4

Seasonal climate prediction skill is influenced by slowly varying surface fields such as sea surface temperature (SST) and soil moisture. North American precipitation on seasonal time-scales is influenced by remote sea-surface temperature anomalies (SSTA) such as those associated with the El Niño Southern Oscillation, and coupling between precipitation and soil moisture. Climate predictions using CFSR observational estimates for the ocean, land, and atmosphere as initial conditions were recently completed with the Community Climate System Model version 4 (CCSM4). These predictions and hindcasts for 1982-present are part of the North American Multi-Model Ensemble System (NMME). The present study utilizes the hindcast output from these fully coupled CCSM4 hindcasts as boundary forcing in the atmospheric general circulation model (AGCM) component of CCSM4, the Community Atmosphere Model version 4 (CAM4) coupled to the Community Land Model version 4 (CLM4). In addition, a “potential predictability” experiment is completed using observed SST as boundary forcing. In the AGCM experiment(s), initialization of land and atmosphere domains from observational estimates is used that is identical to initialization in fully coupled CCSM4 hindcasts, effectively creating a 2-tiered seasonal forecast system. 10-member ensemble predictions for a period of 1982-2009 in the CCSM4-AGCM are generated using initialization every December and May, predicting up to 6 months of precipitation over North America. These experiments are used to determine any differences in overall hindcast skill due to slight changes in SST, but also due to ocean-land-atmosphere versus land-atmosphere coupled systems within CCSM4. Skill comparisons, using both deterministic and probabilistic assessment, are drawn between fully coupled CCSM4 hindcasts and CCSM4-AGCM hindcast experiments. This work documents the relative roles of local atmosphere-land interaction vs. remote SSTA forcing in North American Rainfall seasonal predictability.