Assessment and correction of BCC_CSM's performance in capturing leading modes of summer precipitation over North Asia

Abstract

This article examines the ability of Beijing Climate Center Climate System Model (BCC_CSM) in demonstrating the prediction accuracy and the leading modes of the summer precipitation over North Asia (NA). A dynamic‐statistic combined approach for improving the prediction accuracy and the prediction of the leading modes of the summer precipitation over NA is proposed. Our results show that the BCC_CSM can capture part of the spatial anomaly features of the first two leading modes of NA summer precipitation. Moreover, BCC_CSM regains relationships such that the first and second mode of the empirical orthogonal function (EOF1 and EOF2) of NA summer precipitation, respectively, corresponds to the development of the El Niño and La Niña conditions in the tropical East Pacific. Nevertheless, BCC_CSM exhibits limited prediction skill over most part of NA and presents a deficiency in reproducing the EOF1's and EOF2's spatial pattern over central NA and EOF2's interannual variability. This can be attributed as the possible reasons why the model is unable to capture the correct relationships among the basic climate elements over the central NA, lacks in its ability to reproduce a consistent zonal atmospheric pattern over NA, and has bias in predicting the relevant Sea Surface Temperature (SST) modes over the tropical Pacific and Indian Ocean regions. Based on the proposed dynamic‐statistic combined correction approach, compared with the leading modes of BCC_CSM's original prediction, anomaly correlation coefficients of corrected EOF1/EOF2 with the tropical Indian Ocean SST are improved from 0.18/0.36 to 0.51/0.62. Hence, the proposed correction approach suggests that the BCC_CSM's prediction skill for the summer precipitation prediction over NA and its ability to capture the dominant modes could be certainly improved by choosing proper historical analogue information.

The inter‐decadal change in anomalous summertime water vapour transport modes over the tropical Indian Ocean–western Pacific in the mid‐1980s

“…Chen and Huang () and Gong et al . () noted that the tripole distribution of summer precipitation in eastern China was mainly influenced by the dipole pattern of anomalous water vapour transport over East Asia and the northwestern Pacific. Zhou and Yu () found that the anomalous water vapour sources of the two typical modes of anomalous summer precipitation over China are remarkably different.…”

Influential Climate Teleconnections for Spatiotemporal Precipitation Variability in the Lancang‐Mekong River Basin From 1952 to 2015

“…EOF analysis includes three components: eigenvectors (EOFs representing Spatial Patterns), principal components (PCs, i.e., corresponding time coefficients), and eigenvalues (Hannachi et al, 2007). Numerous previous studies have comprehensively described and reviewed the EOF and its application for climate variability and change assessment (e.g., Fujinami et al, 2016; Gong et al, 2018; Jiang et al, 2014; Yao et al, 2015). Therefore, this study used the EOF to simplify the interpretation of interannual and intra‐annual precipitation variability across the LMRB in the space‐time domain.…”

Two deep learning-based bias-correction pathways improve summer precipitation prediction over China

“…However, due to the simplification of physical processes, the semi-empirical model of physical parameterization and the low-resolution grid, the predictions of climate models are often vulnerable to systematic errors [9][10][11]. Although many state-of-the-art dynamical models can provide skillful predictions of large-scale features of atmospheric variables [12], they still suffer from a limited capability in predicting summer rainfall [13,14]. Therefore, it is of great importance to reduce the biases of the predictions of the dynamical models in order to improve their prediction skills.…”