Future changes in precipitation extremes across China based on CMIP6 models
Abstract
A comparison assessment of model capabilities in simulating precipitation extremes across China was first implemented by using 30 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and using 36 CMIP6 models. The results indicate that the multi-model median ensembles (MME) of both the CMIP5 and CMIP6 models can reasonably reproduce the climate means for the period from 1986 to 2005, and the biases are lower in most CMIP6 models compared to the CMIP5 models, especially over southern China. To provide further comparisons, 14 CMIP6 models are selected and compared with their predecessors in CMIP5. The results show that the CMIP6 models generally exhibit superior skill in simulating the extreme precipitation indices over China. The model spreads for most of the extreme indices in the CMIP6 version are also smaller. Additionally, the MMEs of the two CMIPs outperform individual models. However, some CMIP6 models also exhibit weaker skill levels in simulating some particular indices compared with those in CMIP5, which merits further investigation. The results from seven reanalyses further show large uncertainties for these indices; therefore, care should be taken in comparison with reanalyses. For future changes in precipitation extremes, total wet day precipitation (PRCPTOT), maximum 5-day precipitation (RX5day) and very heavy precipitation days (R20mm) are projected to clearly increase across China over the coming century under the shared socioeconomic pathway (SSP) 2-4.5 and SSP5-8.5 scenarios. However, the dry condition index of CDD exhibits a decreasing tendency in the future, which implies that the dry conditions induced by precipitation anomalies will be mitigated. However, large uncertainties are still observed for future changes, which are primarily sourced from inter-imodel and scenario variabilities, especially for the projected changes at the end of the 21st century.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.A25H1772X