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Nicolas Freychet, Huang-Hsiung Hsu, Chia Chou, and Chi-Hua Wu

Abstract

Change in extreme events in climate projections is a major concern. If the frequency of dry events is expected to increase in a warmer climate (thus, the overall number of wet days will decrease), heavy and extreme precipitation are also expected to increase because of a shift of the precipitation spectrum. However, the forecasts exhibit numerous uncertainties.

This study focuses on the Asian region, separated into the following three subregions: the East Asian region, the Indian region, and western North Pacific region, where the summer monsoon can bring heavy rainfall. Particularly emphasized herein is the reliability of the projection, using data from a large ensemble of 30 models from phase 5 of the Coupled Model Intercomparison Project. The scattering of the ensemble enables obtaining an optimal estimate of the uncertainties, and it is used to compute the correlation between projected changes of extreme events and circulation changes.

The results show clear spatial and temporal variations in the confidence of changes, with results being more reliable during the wet season (i.e., the summer monsoon). The ensemble predicts changes in atmospheric circulation with favorable confidence, especially in the low-level moisture flux convergence (MFC). However, the correlation between this mean change and the modification of extreme events is nonsignificant. Also analyzed herein are the correlation and change of MFC exclusively during these events. The horizontal MFC exerts a nonnegligible influence on the change in the intensity of extremes. However, it is mostly the change in vertical circulation and moisture advection that is correlated with the change in frequency and intensity of extreme events.

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Andrew D. King, Reto Knutti, Peter Uhe, Daniel M. Mitchell, Sophie C. Lewis, Julie M. Arblaster, and Nicolas Freychet

Abstract

Given the Paris Agreement it is imperative there is greater understanding of the consequences of limiting global warming to the target 1.5° and 2°C levels above preindustrial conditions. It is challenging to quantify changes across a small increment of global warming, so a pattern-scaling approach may be considered. Here we investigate the validity of such an approach by comprehensively examining how well local temperatures and warming trends in a 1.5°C world predict local temperatures at global warming of 2°C. Ensembles of transient coupled climate simulations from multiple models under different scenarios were compared and individual model responses were analyzed. For many places, the multimodel forced response of seasonal-average temperatures is approximately linear with global warming between 1.5° and 2°C. However, individual model results vary and large contributions from nonlinear changes in unforced variability or the forced response cannot be ruled out. In some regions, such as East Asia, models simulate substantially greater warming than is expected from linear scaling. Examining East Asia during boreal summer, we find that increased warming in the simulated 2°C world relative to scaling up from 1.5°C is related to reduced anthropogenic aerosol emissions. Our findings suggest that, where forcings other than those due to greenhouse gas emissions change, the warming experienced in a 1.5°C world is a poor predictor for local climate at 2°C of global warming. In addition to the analysis of the linearity in the forced climate change signal, we find that natural variability remains a substantial contribution to uncertainty at these low-warming targets.

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Simon F. B. Tett, Alexander Falk, Megan Rogers, Fiona Spuler, Calum Turner, Joshua Wainwright, Oscar Dimdore-Miles, Sam Knight, Nicolas Freychet, Michael J. Mineter, and Caroline E. R. Lehmann
Open access
Cheng Qian, Jun Wang, Siyan Dong, Hong Yin, Claire Burke, Andrew Ciavarella, Buwen Dong, Nicolas Freychet, Fraser C. Lott, and Simon F. B. Tett
Open access
Liwen Ren, Dongqian Wang, Ning An, Shuoyi Ding, Kai Yang, Nicolas Freychet, Simon F. B. Tett, Buwen Dong, and Fraser C. Lott
Free access
Zhiyuan Hu, Haiyan Li, Jiawei Liu, Shaobo Qiao, Dongqian Wang, Nicolas Freychet, Simon F. B. Tett, Buwen Dong, Fraser C. Lott, Qingxiang Li, and Wenjie Dong
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