Physical Processes Responsible for Model-Related Forecast Uncertainty for Extreme Cold Events over Southern China Revealed by C-NFSVs-Based Ensemble

Prediction of 2 m temperature during extreme cold events remains challenging because it is obtained diagnostically from near-surface atmospheric states and depends on various physical parameterization processes, thereby introducing multiple sources of forecast uncertainty. Our previous study has demonstrated that the Combined Nonlinear Forcing Singular Vectors (C-NFSVs) based ensemble forecasts can effectively characterize forecast uncertainty and recognize the dominant sources. However, the specific dynamical and physical parameterization processes associated with model-related forecast uncertainty remain unclear. In this study, we investigate the sensitivity of model-related 2 m temperature forecast uncertainty within the Weather Research and Forecasting (WRF) model to different dynamical and physical parameterization processes during extreme cold events over southern China. Based on the good reliability of C-NFSVs-based ensemble forecasts, the sensitivities of different temperature tendency terms to model perturbations were diagnosed by comparing the ensemble spreads from experiments using full C-NFSVs and those using only the initial component of C-NFSVs. The results indicate that, for the extreme cold events examined over southern China, the vertical advection and Planetary boundary layer (PBL) parameterization terms in the WRF model exhibit the strongest sensitivities to model perturbations, suggesting their close association with model-related 2 m temperature forecast uncertainty. This sensitivity may be associated with the roles of these processes in regulating the vertical redistribution of heat and the evolution of lower-tropospheric thermal structures. These findings provide new insights into the processes associated with model-related forecast uncertainty of 2 m temperature. Furthermore, they highlight the need for further investigations of vertical advection and PBL-related processes in the WRF model. Such investigations are expected to improve the understanding of their roles in forecast uncertainty and evaluate their potential implications for improving 2 m temperature forecasts during extreme cold events.