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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.

Full access
Simon F. B. Tett
,
Jonathan M. Gregory
,
Nicolas Freychet
,
Coralia Cartis
,
Michael J. Mineter
, and
Lindon Roberts

Abstract

Uncertainty in climate projections is large as shown by the likely uncertainty ranges in equilibrium climate sensitivity (ECS) of 2.5–4 K and in the transient climate response (TCR) of 1.4–2.2 K. Uncertainty in model projections could arise from the way in which unresolved processes are represented, the parameter values used, or the targets for model calibration. We show that, in two climate model ensembles that were objectively calibrated to minimize differences from observed large-scale atmospheric climatology, uncertainties in ECS and TCR are about 2–6 times smaller than in the CMIP5 or CMIP6 multimodel ensemble. We also find that projected uncertainties in surface temperature, precipitation, and annual extremes are relatively small. Residual uncertainty largely arises from unconstrained sea ice feedbacks. The more than 20-year-old HadAM3 standard model configuration simulates observed hemispheric-scale observations and preindustrial surface temperatures about as well as the median CMIP5 and CMIP6 ensembles while the optimized configurations simulate these better than almost all the CMIP5 and CMIP6 models. Hemispheric-scale observations and preindustrial temperatures are not systematically better simulated in CMIP6 than in CMIP5 although the CMIP6 ensemble seems to better simulate patterns of large-scale observations than the CMIP5 ensemble and the optimized HadAM3 configurations. Our results suggest that most CMIP models could be improved in their simulation of large-scale observations by systematic calibration. However, the uncertainty in climate projections (for a given scenario) likely largely arises from the choice of parameterization schemes for unresolved processes (“structural uncertainty”), with different tuning targets another possible contributor.

Significance Statement

Climate models represent unresolved phenomena controlled by uncertain parameters. Changes in these parameters impact how well a climate model simulates current climate and its climate projections. Multiple calibrations of a single climate model, using an objective method, to large-scale atmospheric observations are performed. These models produce very similar climate projections at both global and regional scales. An analysis that combines uncertainties in observations with simulated sensitivity to observations and climate response also has small uncertainty showing that, for this model, current observations constrain climate projections. Recently developed climate models have a broad range of abilities to simulate large-scale climate with only some improvement in their ability to simulate this despite a decade of model development.

Restricted access
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.

Full 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
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
Full access
Hongyong Yu
,
Xiaojing Yu
,
Ziwei Zhou
,
Yu Wang
,
Yingxin Li
,
Nergui Nanding
,
Nicolas Freychet
,
Buwen Dong
,
Dongqian Wang
,
Fraser C. Lott
,
Simon F. B. Tett
, and
Sarah Sparrow
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
Full 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
Open access