Significance of Improved Initialization in Climate Models for Subseasonal-to-Seasonal Precipitation Prediction
Scientists at Lawrence Livermore National Laboratory within the Atmospheric, Earth, and Energy Division, along with collaborators from scientists from Pacific Northwest National Laboratory, University of California Los Angeles, and Tsinghua University, examined the impact of initial conditions on subseasonal-to-seasonal (S2S) precipitation prediction in two climate models, including E3SMv1. The focus is to reveal the importance of the nudging approach to generating more realistic initial conditions for S2S precipitation predictions.
This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction.
They found that the nudging approach helps generate more realistic initial conditions and large-scale wave patterns in climate models. Simulations with nudged initial conditions are able to capture the impact of the springtime land temperature anomaly over the Tibetan Plateau on the summertime precipitation predictions, as observation showed. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere.