A New Ensemble‐Based Approach to Correct the Systematic Ocean Temperature Bias of CAS‐ESM‐C to Improve Its Simulation and Data Assimilation Abilities
Mengjiao Du, Fei Zheng, Jiang Zhu, Renping Lin, Haipeng Yang, Quanliang Chen
Abstract
Abstract Over the past several decades, many efforts have been devoted to increasing the simulation performance of climate models, but significant biases remain that hinder the performance of coupled systems. Hence, bias correction is regarded not only as a useful tool for improving climate simulations but also as an important step before data assimilation, which depends on the hypothesis of unbiasedness. In this study, using sea temperature climatological data, a new ensemble‐based approach is proposed for correcting the biases of the sea temperature in CAS‐ESM‐C. Through analyzing the results of the proposed bias correction method with various intensities and time windows, its performance in suppressing the simulation biases of ocean fields is evaluated. The simulation biases of atmospheric variables are also reduced via air‐sea interactions, which will improve the ocean simulation performance. Additional benefits can be realized by applying the bias correction method. For example, a superior simulation of climate variabilities in a coupled model, such as ENSO (El Niño‐Southern Oscillation), is realized due to the improvement of climatological fields. The ability to assimilate various ocean observations is also significantly improved with a better background mean state.