Assessment of Indices of Temperature Extremes Simulated by Multiple CMIP5 Models over China

Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 （CMIP5） models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity （absolute indices and threshold indices）, duration （duration indices）, and frequency （percentile indices） of extreme temperature. The overall performance of each model is summarized by a ＂portrait＂ diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986-2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961-2005.

Future Changes in Extreme High Temperature over China at 1.5℃-5℃ Global Warming Based on CMIP6 Simulations

Extreme high temperature(EHT)events are among the most impact-related consequences related to climate change,especially for China,a nation with a large population that is vulnerable to the climate warming.Based on the latest Coupled Model Intercomparison Project Phase 6(CMIP6),this study assesses future EHT changes across China at five specific global warming thresholds(1.5℃-5℃).The results indicate that global mean temperature will increase by 1.5℃/2℃ before 2030/2050 relative to pre-industrial levels(1861-1900)under three future scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5),and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5.Under SSP5-8.5,global warming will eventually exceed 5℃ by 2100,while under SSP1-2.6,it will stabilize around 2℃ after 2050.In China,most of the areas where warming exceeds global average levels will be located in Tibet and northern China(Northwest China,North China and Northeast China),covering 50%-70%of the country.Furthermore,about 0.19-0.44 billion people(accounting for 16%-41%of the national population)will experience warming above the global average.Compared to present-day(1995-2014),the warmest day(TXx)will increase most notably in northern China,while the number of warm days(TX90p)and warm spell duration indicator(WSDI)will increase most profoundly in southern China.For example,relative to the present-day,TXx will increase by 1℃-5℃ in northern China,and TX90p(WSDI)will increase by 25-150(10-80)days in southern China at 1.5℃-5℃ global warming.Compared to 2℃-5℃,limiting global warming to 1.5℃ will help avoid about 36%-87%of the EHT increases in China.

Uncertainty in Projection of Climate Extremes:A Comparison of CMIP5 and CMIP6

Climate projections by global climate models(GCMs)are subject to considerable and multi-source uncertainties.This study aims to compare the uncertainty in projection of precipitation and temperature extremes between Coupled Model Intercomparison Project(CMIP)phase 5(CMIP5)and phase 6(CMIP6),using 24 GCMs forced by 3 emission scenarios in each phase of CMIP.In this study,the total uncertainty(T)of climate projections is decomposed into the greenhouse gas emission scenario uncertainty(S,mean inter-scenario variance of the signals over all the models),GCM uncertainty(M,mean inter-model variance of signals over all emission scenarios),and internal climate variability uncertainty(V,variance in noises over all models,emission scenarios,and projection lead times);namely,T=S+M+V.The results of analysis demonstrate that the magnitudes of S,M,and T present similarly increasing trends over the 21 st century.The magnitudes of S,M,V,and T in CMIP6 are 0.94-0.96,1.38-2.07,1.04-1.69,and 1.20-1.93 times as high as those in CMIP5.Both CMIP5 and CMIP6 exhibit similar spatial variation patterns of uncertainties and similar ranks of contributions from different sources of uncertainties.The uncertainty for precipitation is lower in midlatitudes and parts of the equatorial region,but higher in low latitudes and the polar region.The uncertainty for temperature is higher over land areas than oceans,and higher in the Northern Hemisphere than the Southern Hemisphere.For precipitation,T is mainly determined by M and V in the early 21 st century,by M and S at the end of the 21 st century;and the turning point will appear in the 2070 s.For temperature,T is dominated by M in the early 21 st century,and by S at the end of the 21 st century,with the turning point occuring in the 2060 s.The relative contributions of S to T in CMIP6(12.5%-14.3%for precipitation and 31.6%-36.2%for temperature)are lower than those in CMIP5(15.1%-17.5%for precipitation and 38.6%-43.8%for temperature).By contrast,the relative contributions of M in CMIP6(50.6%-59.8%for precipitation and 59.4%-60.3%for temperature)are higher than those in CMIP5(47.5%-57.9%for precipitation and 51.7%-53.6%for temperature).The higher magnitude and relative contributions of M in CMIP6 indicate larger difference among projections of various GCMs.Therefore,more GCMs are needed to ensure the robustness of climate projections.

CMIP6和CMIP5模式对中国极端温度的模拟能力的评估

本文评估了CMIP5和CMIP6模式对中国在1979-2005年期间的极端温度的模拟性能。结果表明:CMIP6模式可以很好地再现年最大日最高气温,年最小日最低气温和霜冻日数的空间分布特征。对于年最大日最高气温,持续暖日日数和暖昼,CMIP6模式间的不确定性相对于CMIP5模式有所降低。并且,CMIP6模式也能表现出观测到的极端温度的趋势。然而,CMIP6模式再现暖昼和冷夜的能力仍然不足,特别是在青藏高原上存在明显的冷偏差或者暖偏差对于某些指数。单个CMIP6或CMIP5模式的模拟能力就不同的指数而有所不同,某些模式的模拟能力较为突出。对于一些来自同一机构不同版本的模式,改进的CMIP6模式与CMIP5模式的模拟结果没有明显差异。多模式中位数平均的模拟效果优于大多数单个模式。