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.

Correlation analysis of Normalized Different Vegetation Index(NDVI)difference series and climate variables in the Xilingole steppe,China from 1983 to 1999

There is a crucial need in the study of global change to understand how terrestrial ecosystems respond to the climate system.It has been demonstrated by many researches that Normalized Different Vegetation Index(NDVI)time series from remotely sensed data,which provide effective information of vegetation conditions on a large scale with highly temporal resolution,have a good relation with meteorological factors.However,few of these studies have taken the cumulative property of NDVI time series into account.In this study,NDVI difference series were proposed to replace the original NDVI time series with NDVI difference series to reappraise the relationship between NDVI and meteorological factors.As a proxy of the vegetation growing process,NDVI difference represents net primary productivity of vegetation at a certain time interval under an environment controlled by certain climatic conditions and other factors.This data replacement is helpful to eliminate the cumulative effect that exist in original NDVI time series,and thus is more appropriate to understand how climate system affects vegetation growth in a short time scale.By using the correlation analysis method,we studied the relationship between NOAA/AVHRR ten-day NDVI difference series and corresponding meteorological data from 1983 to 1999 from 11 meteorological stations located in the Xilingole steppe in Inner Mongolia.The results show that:(1)meteorological factors are found to be more significantly correlation with NDVI difference at the biomass-rising phase than that at the falling phase;(2)the relationship between NDVI difference and climate variables varies with vegetation types and vegetation communities.In a typical steppe dominated by Leymus chinensis,temperature has higher correlation with NDVI difference than precipitation does,and in a typical steppe dominated by Stipa krylovii,the correlation between temperature and NDVI difference is lower than that between precipitation and NDVI difference.In a typical steppe dominated by Stipa grandis,there is no significant difference between the two correlations.Precipitation is the key factor influencing vegetation growth in a desert steppe,and temperature has poor correlation with NDVI dif-ference;(3)the response of NDVI difference to precipitation is fast and almost simultaneous both in a typical steppe and desert steppe,however,mean temperature exhibits a time-lag effect especially in the desert steppe and some typical steppe dominated by Stipa krylovii;(4)the relationship between NDVI difference and temperature is becoming stronger with global warming.