Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Abstract

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method.

Near-Future Projection of Sea Surface Winds in Northwest Pacific Ocean Based on a CMIP6 Multi-Model Ensemble

“…The Multi-Model Mean (MMM) emerges as a powerful tool, exhibiting significant model performance, with higher correlations and lower RMSD compared to the individual model (Figure 1). The results show that MMM could reduce the individual model biases, as also shown in other studies [36][37][38]. Notably, IPSL-CM6A-LR, MIROC6, and ACCES-CM2 exhibit outstanding individual skill, reinforcing the utility of MMM.…”

Evaluation of Extreme Climate Indices over the Three Northeastern Provinces of China Based on CMIP6 Models Outputs

“…For the extreme precipitation indices, GCMs underestimated RX1day, RX5day, CDD and SDII, and similar situations occurred in the United States [38] and Thailand [39]. However, our study found that R10mm was overestimated, which is different from the results of Lei et al [40]. The complexity of the climate system and the representation of the physical processes carried out by GCMs can lead to difficulties for models in accurately simulating extreme climate events [18].…”

Rainfall Erosivity Characteristics during 1961–2100 in the Loess Plateau, China

“…A multi-model ensemble can reduce the uncertainty of multi-model simulation and restore real data. Existing research has confirmed that the results of multi-model ensembles are better than those of single models [58]. In this study, out of the 27 models available, 10 models demonstrating superior simulation capabilities were chosen, and the future climate prediction data of the Loess Plateau were obtained by a multi-model ensemble method.…”