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.

中文翻译：

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一种基于整体的新方法来纠正CAS-ESM-C的系统海洋温度偏差，以改善其模拟和数据同化能力

在过去的几十年中，为提高气候模型的仿真性能做出了许多努力，但是仍然存在严重的偏差，阻碍了耦合系统的性能。因此，偏差校正不仅被视为改善气候模拟的有用工具，而且还被视为数据同化之前的重要步骤，这取决于无偏差的假设。在这项研究中，利用海水气候数据，提出了一种基于整体的新方法来校正CAS-ESM-C中的海水温度偏差。通过分析提出的具有不同强度和时间窗口的偏差校正方法的结果，评估了其在抑制海洋模拟偏差方面的性能。大气-海洋相互作用也减少了大气变量的模拟偏差，这将改善海洋模拟性能。通过应用偏差校正方法可以实现其他好处。例如，由于气候场的改善，可以在耦合模型（例如ENSO（厄尔尼诺-南方涛动））中对气候变化进行出色的模拟。具有更好的背景均值状态，同化各种海洋观测资料的能力也得到了显着提高。