A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

Climate warming profoundly affects hydrological changes,agricultural production,and human society.Arid and semi-arid areas of China are currently displaying a marked trend of warming and wetting.The Chinese Tianshan Mountains(CTM)have a high climate sensitivity,rendering the region particularly vulnerable to the effects of climate warming.In this study,we used monthly average temperature and monthly precipitation data from the CN05.1 gridded dataset(1961-2014)and 24 global climate models(GCMs)of the Coupled Model Intercomparison Project Phase 6(CMIP6)to assess the applicability of the CMIP6 GCMs in the CTM at the regional scale.Based on this,we conducted a systematic review of the interannual trends,dry-wet transitions(based on the standardized precipitation index(SPI)),and spatial distribution patterns of climate change in the CTM during 1961-2014.We further projected future temperature and precipitation changes over three terms(near-term(2021-2040),mid-term(2041-2060),and long-term(2081-2100))relative to the historical period(1961-2014)under four shared socio-economic pathway(SSP)scenarios(i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).It was found that the CTM had experienced significant warming and wetting from 1961 to 2014,and will also experience warming in the future(2021-2100).Substantial warming in 1997 was captured by both the CN05.1 derived from interpolating meteorological station data and the multi-model ensemble(MME)from the CMIP6 GCMs.The MME simulation results indicated an apparent wetting in 2008,which occurred later than the wetting observed from the CN05.1 in 1989.The GCMs generally underestimated spring temperature and overestimated both winter temperature and spring precipitation in the CTM.Warming and wetting are more rapid in the northern part of the CTM.By the end of the 21st century,all the four SSP scenarios project warmer and wetter conditions in the CTM with multiple dry-wet transitions.However,the rise in precipitation fails to counterbalance the drought induced by escalating temperature in the future,so the nature of the drought in the CTM will not change at all.Additionally,the projected summer precipitation shows negative correlation with the radiative forcing.This study holds practical implications for the awareness of climate change and subsequent research in the CTM.

Differences between CMIP6 and CMIP5 Models in Simulating Climate over China and the East Asian Monsoon

We compare the ability of coupled global climate models from the phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6,respectively)in simulating the temperature and precipitation climatology and interannual variability over China for the period 1961–2005 and the climatological East Asian monsoon for the period 1979–2005.All 92 models are able to simulate the geographical distribution of the above variables reasonably well.Compared with earlier CMIP5 models,current CMIP6 models have nationally weaker cold biases,a similar nationwide overestimation of precipitation and a weaker underestimation of the southeast–northwest precipitation gradient,a comparable overestimation of the spatial variability of the interannual variability,and a similar underestimation of the strength of winter monsoon over northern Asia.Pairwise comparison indicates that models have improved from CMIP5 to CMIP6 for climatological temperature and precipitation and winter monsoon but display little improvement for the interannual temperature and precipitation variability and summer monsoon.The ability of models relates to their horizontal resolutions in certain aspects.Both the multi-model arithmetic mean and median display similar skills and outperform most of the individual models in all considered aspects.

An Introduction to the Integrated Climate Model of the Center for Monsoon System Research and Its Simulated Influence of El Ni?no on East Asian–Western North Pacific Climate

This study introduces a new global climate model--the Integrated Climate Model （ICM）--developed for the seasonal prediction of East Asian-western North Pacific （EA-WNP） climate by the Center for Monsoon System Research at the Institute of Atmospheric Physics （CMSR, IAP）, Chinese Academy of Sciences. ICM integrates ECHAM5 and NEMO2.3 as its atmospheric and oceanic components, respectively, using OASIS3 as the coupler. The simulation skill of ICM is evaluated here, including the simulated climatology, interannual variation, and the influence of E1 Nifio as one of the most important factors on EA-WNP climate. ICM successfully reproduces the distribution of sea surface temperature （SST） and precipitation without climate shift, the seasonal cycle of equatorial Pacific SST, and the precipitation and circulation of East Asian summer monsoon. The most prominent biases of ICM are the excessive cold tongue and unrealistic westward phase propagation of equatorial Pacific SST. The main interannual variation of the tropical Pacific SST and EA-WNP climate E1 Nifio and the East Asia-Pacific Pattern--are also well simulated in ICM, with realistic spatial pattern and period. The simulated E1 Nifio has significant impact on EA-WNP climate, as in other models. The assessment shows ICM should be a reliable model for the seasonal prediction of EA-WNP climate.

Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models

The Southern Ocean is covered by a large amount of clouds with high cloud albedo.However,as reported by previous climate model intercomparison projects,underestimated cloudiness and overestimated absorption of solar radiation(ASR)over the Southern Ocean lead to substantial biases in climate sensitivity.The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models.We employ 10-year satellite observations to evaluate cloud radiative effect(CRE)and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud,radiation,and aerosol.The simulated longwave,shortwave,and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations.Total cloud fraction(CF)is also reasonably simulated in CMIP6,but the comparison of liquid cloud fraction(LCF)reveals marked biases in spatial pattern and seasonal variations.The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macroand micro-physical properties,including liquid water path(LWP),cloud optical depth(COD),and cloud effective radius,as well as aerosol optical depth(AOD).However,the large underestimation of both LWP and cloud effective radius(regional means~20%and 11%,respectively)results in relatively smaller bias in COD,and the impacts of the biases in COD and LCF also cancel out with each other,leaving CRE and ASR reasonably predicted in CMIP6.An error estimation framework is employed,and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE.Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations,while the modeled CRE is too sensitive to LWP and COD.The relationships between cloud effective radius,LWP,and COD are also analyzed to explore the possible uncertainty sources in different models.Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR)is of urgent demand for the local economic and societal development.This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously SINTEX-F).The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands,initialized from 1 March.In addition,the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature(SST)anomalies,but the individual members show great discrepancies,indicating large uncertainty in the forecasts.Furthermore,the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982-2020,albeit with underestimated magnitudes.The Weather Forecast and Research(WRF)downscaling hindcast experiments with a finer resolution of 30 km,which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method,display improved predictions of the extreme summer precipitation anomalies to some extent.However,the performance of the downscaling predictions is highly dependent on the global model forecast skill,suggesting that further improvements on both the global and regional climate models are needed.

Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset

Investigation of the climate change effects on drought is required to develop management strategies for minimizing adverse social and economic impacts.Therefore,studying the future meteorological drought conditions at a local scale is vital.In this study,we assessed the efficiency of seven downscaled Global Climate Models(GCMs)provided by the NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP),and investigated the impacts of climate change on future meteorological drought using Standard Precipitation Index(SPI)in the Karoun River Basin(KRB)of southwestern Iran under two Representative Concentration Pathway(RCP)emission scenarios,i.e.,RCP4.5 and RCP8.5.The results demonstrated that SPI estimated based on the Meteorological Research Institute Coupled Global Climate Model version 3(MRI-CGCM3)is consistent with the one estimated by synoptic stations during the historical period(1990-2005).The root mean square error(RMSE)value is less than 0.75 in 77%of the synoptic stations.GCMs have high uncertainty in most synoptic stations except those located in the plain.Using the average of a few GCMs to improve performance and reduce uncertainty is suggested by the results.The results revealed that with the areas affected by wetness decreasing in the KRB,drought frequency in the North KRB is likely to increase at the end of the 21st century under RCP4.5 and RCP8.5 scenarios.At the seasonal scale,the decreasing trend for SPI in spring,summer,and winter shows a drought tendency in this region.The climate-induced drought hazard can have vast consequences,especially in agriculture and rural livelihoods.Accordingly,an increasing trend in drought during the growing seasons under RCP scenarios is vital for water managers and farmers to adopt strategies to reduce the damages.The results of this study are of great value for formulating sustainable water resources management plans affected by climate change.

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.

Simulated perturbation in the sea-to-air flux of dimethylsulfi de and the impact on polar climate

Marine biogenic emission of dimethylsulfi de(DMS)has been well recognized as the main natural source of reduced sulfur to the remote marine atmosphere and has the potential to aff ect climate,especially in the polar regions.We used a global climate model(GCM)to investigate the impact on atmospheric chemistry from a change to the contemporary DMS fl ux to that which has been projected for the late 21 st century.The perturbed simulation corresponded to conditions that pertained to a tripling of equivalent CO 2,which was estimated to occur by year 2090 based on current worst-case greenhouse gas emission scenarios.The changes in zonal mean DMS fl ux were applied to 50°S–70°S Antarctic(ANT)and 65°N–80°N Arctic(ARC)regions.The results indicate that there are clearly diff erent impacts after perturbation in the southern and northern polar regions.Most quantities related to the sulfur cycle show a higher increase in ANT.However,most sulfur compounds have higher peaks in ARC.The perturbation in DMS fl ux leads to an increase of atmospheric DMS of about 45%in ANT and 33.6%in ARC.The sulfur dioxide(SO 2)vertical integral increases around 43%in ANT and 7.5%in ARC.Sulfate(SO 4)vertical integral increases by 17%in ANT and increases around 6%in ARC.Sulfur emissions increases by 21%in ANT and increases by 9.7%in ARC.However,oxidation of DMS by OH increases by 38.2%in ARC and by 15.17%in ANT.Aerosol optical depth(AOD)increases by 4%in the ARC and by 17.5%in the ANT,and increases by 22.8%in austral summer.The importance of the perturbation of the biogenic source to future aerosol burden in polar regions leads to a cooling in surface temperature of 1 K in the ANT and 0.8 K in the ARC.Generally,polar regions in the Antarctic Ocean will have a higher off setting eff ect on warming after DMS fl ux perturbation.

Validation of the effects of temperature simulated by multi-model ensemble and prediction of mean temperature changes for the next three decades in China

Using series of daily average temperature observations over the period of 1961-1999 of 701 meteorological stations in China, and simulated results of 20 global climate models （such as BCCR_BCM2.0, CGCM3T47） during the same period as the observation, we validate and analyze the simulated results of the models by using three factor statistical method, achieve the results of mul- ti-model ensemble, test and verify the results of multi-model ensemble by using the observation data during the period of 1991-1999. Finally, we analyze changes of the annual mean temperature result of multi-mode ensemble prediction for the period of 2011-2040 under the emission scenarios A2, A1B and B 1. Analyzed results show that： （1） Global climate models can repro- duce Chinese regional spatial distribution of annual mean temperature, especially in low latitudes and eastern China. （2） With the factor of the trend of annual mean temperature changes in reference period, there is an obvious bias between the model and the observation. （3） Testing the result of multi-model ensemble during the period of 1991-1999, we can simulate the trend of temper- ature increase. Compared to observation, the result of different weighing multi-model ensemble prediction is better than the same weighing ensemble. （4） For the period of 20ll-2040, the growth of the annual mean temperature in China, which results from multi-mode ensemble prediction, is above 1℃. In the spatial distribution of annual mean temperature, under the emission scenarios of A2, A1B and B 1, the trend of growth in South China region is the smallest, the increment is less than or equals to 0.8℃; the trends in the northwestern region and south of the Qinghai-Tibet Plateau are the largest, the increment is more than 1℃.

Projection of precipitation extremes over South Asia from CMIP6 GCMs

Extreme precipitation events are one of the most dangerous hydrometeorological disasters,often resulting in significant human and socio-economic losses worldwide.It is therefore important to use current global climate models to project future changes in precipitation extremes.The present study aims to assess the future changes in precipitation extremes over South Asia from the Coupled Model Intercomparison Project Phase 6(CMIP6)Global Climate Models(GCMs).The results were derived using the modified Mann-Kendall test,Sen's slope estimator,student's t-test,and probability density function approach.Eight extreme precipitation indices were assessed,including wet days(RR1mm),heavy precipitation days(RR10mm),very heavy precipitation days(RR20mm),severe precipitation days(RR50mm),consecutive wet days(CWD),consecutive dry days(CDD),maximum 5-day precipitation amount(RX5day),and simple daily intensity index(SDII).The future changes were estimated in two time periods for the 21^(st) century(i.e.,near future(NF;2021-2060)and far future(FF;2061-2100))under two Shared Socioeconomic Pathway(SSP)scenarios(SSP2-4.5 and SSP5-8.5).The results suggest increases in the frequency and intensity of extreme precipitation indices under the SSP5-8.5 scenario towards the end of the 21^(st) century(2061-2100).Moreover,from the results of multimodel ensemble means(MMEMs),extreme precipitation indices of RR1mm,RR10mm,RR20mm,CWD,and SDII demonstrate remarkable increases in the FF period under the SSP5-8.5 scenario.The spatial distribution of extreme precipitation indices shows intensification over the eastern part of South Asia compared to the western part.The probability density function of extreme precipitation indices suggests a frequent(intense)occurrence of precipitation extremes in the FF period under the SSP5-8.5 scenario,with values up to 35.00 d for RR1mm and 25.00-35.00 d for CWD.The potential impacts of heavy precipitation can pose serious challenges to the study area regarding flooding,soil erosion,water resource management,food security,and agriculture development.

Seasonal Forecasts of Precipitation during the First Rainy Season in South China Based on NUIST-CFS1.0

Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy season(FRS,i.e.,April–June)over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously known as SINTEX-F).The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general.But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index.NUIST-CFS1.0 captures the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the FRS.By examining the correlations between sea surface temperature(SST)and FRS precipitation and the Philippines anticyclone,we find that the model reasonably captures SST-associated precipitation and circulation anomalies,which partly explains the predictability of FRS precipitation.A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events.Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts.These results help improve the understanding and forecasts for FRS precipitation in South China.

Abrupt Summer Warming and Changes in Temperature Extremes over Northeast Asia Since the Mid-1990s: Drivers and Physical Processes

This study investigated the drivers and physical processes for the abrupt decadal summer surface warming and increases in hot temperature extremes that occurred over Northeast Asia in the mid-1990s. Observations indicate an abrupt increase in summer mean surface air temperature （SAT） over Northeast Asia since the mid-1990s. Accompanying this abrupt surface wanning, significant changes in some temperature extremes, characterized by increases in summer mean daily maximum temperature （Tmax）, daily minimum temperature （Train）, annual hottest day temperature （TXx）, and annual warmest night temperature （TNx） were observed. There were also increases in the frequency of summer days （SU） and tropical nights （TR）. Atmospheric general circulation model experiments forced by changes in sea surface temperature （SST）/sea ice extent （SIE）, anthropogenic greenhouse gas （GHG） concentrations, and anthropogenic aerosol （AA） forcing, relative to the period 1964- 93, reproduced the general patterns of observed summer mean SAT changes and associated changes in temperature extremes, although the abrupt decrease in precipitation since the mid-1990s was not simulated. Additional model experiments with different forcings indicated that changes in SST/SIE explained 76% of the area-averaged summer mean surface warming signal over Northeast Asia, while the direct impact of changes in GHG and AA explained the remaining 24% of the surface warming signal. Analysis of physical processes indicated that the direct impact of the changes in AA （through aerosol- radiation and aerosol-cloud interactions）, mainly related to the reduction of AA precursor emissions over Europe, played a dominant role in the increase in TXx and a similarly important role as SST/SIE changes in the increase in the frequency of SU over Northeast Asia via AA-induced coupled atmosphere-land surface and cloud feedbacks, rather than through a direct impact of AA changes on cloud condensation nuclei. The modelling results also imply that the abrupt summer surface warming and increases in hot temperature extremes over Northeast Asia since the mid-1990s will probably sustain in the next few decades as GHG concentrations continue to increase and AA precursor emissions over both North America and Europe continue to decrease.

Role of Microphysical Parameterizations with Droplet Relative Dispersion in IAP AGCM 4.1

Previous studies have shown that accurate descriptions of the cloud droplet effective radius （Re） and the autoconversion process of cloud droplets to raindrops （At） can effectively improve simulated clouds and surface precipitation, and reduce the uncertainty of aerosol indirect effects in GCMs. In this paper, we implement cloud microphysical schemes including two-moment Ar and Re considering relative dispersion of the cloud droplet size distribution into version 4.1 of the Institute of Atmospheric Physics＇s atmospheric GCM （IAP AGCM 4.1）, which is the atmospheric component of the Chinese Academy of Sciences＇ Earth System Model. Analysis of the effects of different schemes shows that the newly implemented schemes can improve both the simulated shortwave and longwave cloud radiative forcings, as compared to the standard scheme, in lAP AGCM 4.1. The new schemes also effectively enhance the large-scale precipitation, especially over low latitudes, although the influences of total precipitation are insignificant for different schemes. Further studies show that similar results can be found with the Community Atmosphere Model, version 5.1.

Empirical assessment of the role of the Sun in climate change using balanced multi-proxy solar records

The role of the Sun in climate change is hotly debated.Some studies suggest its impact is significant,while others suggest it is minimal.The Intergovernmental Panel on Climate Change(IPCC)supports the latter view and suggests that nearly 100%of the observed surface warming from 1850–1900 to 2020 is due to anthropogenic emissions.However,the IPCC’s conclusions are based solely on computer simulations made with global climate models(GCMs)forced with a total solar irradiance(TSI)record showing a low multi-decadal and secular variability.The same models also assume that the Sun affects the climate system only through radiative forcing–such as TSI–even though the climate could also be affected by other solar processes.In this paper I propose three“balanced”multi-proxy models of total solar activity(TSA)that consider all main solar proxies proposed in scientific literature.Their optimal signature on global and sea surface temperature records is assessed together with those produced by the anthropogenic and volcanic radiative forcing functions adopted by the CMIP6 GCMs.This is done by using a basic energy balance model calibrated with a differential multi-linear regression methodology,which allows the climate system to respond to the solar input differently than to radiative forcings alone,and to evaluate the climate’s characteristic time-response as well.The proposed methodology reproduces the results of the CMIP6 GCMs when their original forcing functions are applied under similar physical conditions,indicating that,in such a scenario,the likely range of the equilibrium climate sensitivity(ECS)could be 1.4℃to 2.8℃,with a mean of 2.1℃(using the HadCRUT5 temperature record),which is compatible with the low-ECS CMIP6 GCM group.However,if the proposed solar records are used as TSA proxies and the climatic sensitivity to them is allowed to differ from the climatic sensitivity to radiative forcings,a much greater solar impact on climate change is found,along with a significantly reduced radiative effect.In this case,the ECS is found to be 0.9–1.8℃,with a mean of around 1.3℃.Lower ECS ranges(up to 20%)are found using HadSST4,HadCRUT4,and HadSST3.The result also suggests that at least about 80%of the solar influence on the climate may not be induced by TSI forcing alone,but rather by other Sun-climate processes(e.g.,by a solar magnetic modulation of cosmic ray and other particle fluxes,and/or others),which must be thoroughly investigated and physically understood before trustworthy GCMs can be created.This result explains why empirical studies often found that the solar contribution to climate changes throughout the Holocene has been significant,whereas GCM-based studies,which only adopt radiative forcings,suggest that the Sun plays a relatively modest role.

Understanding the cold biases of CMIP5 models over China with weather regimes

Most models in the fifth phase of the Coupled Model Intercomparison Project(CMIP5)underestimate the surface air temperature over China in both winter and summer.Understanding the weather regime in association with the simulated temperature variability is of high interest to get insight into those biases.Based on the weather regime method,we investigated the contributions of large-scale dynamics and non-dynamical processes to temperature biases and inter-model spread.The weather regimes associated with the observational temperature patterns were obtained through a/t-means clustering algorithm applied to daily 500 hPa geopotential height anomalies.Here we identified the clustering number of weather regimes using the classifiability and reproducibility indices which can provide the optimal clustering number to obtain objective clustering.Both indices suggested the weather regimes in East Asia can be classified as four clusters in winter(December—January—February)and three in summer(June—July—August).The results indicated that the first and second weather regimes were related to the cold temperature anomalies in China during winter,and the three weather regimes could not effectively classify the temperature patterns during summer.The ensemble mean of 23 CMIP5 models overestimated the occurrence frequencies of the second weather regime,which corresponds to a weaker high latitude westerly jet over East Asia during winter.The 500 hPa geopotential height anomalies and the inter-model spread over the Tibetan Plateau may be associated with the limited ability of the CMIP5 models in simulating the thermal effects of plateau in summer.We also found that the non-dynamical processes had major contribution to the ensemble-mean biases,and the large-scale dynamics played a minor role.The non-dynamical processes substantially affected the inter-model spread,especially over the Tibetan Plateau and the Sichuan Basin,during both winter and summer.The results suggested that improving the simulation of regional processes may help to improve model performance.The use of multi-model mean is recommended since it performs better than most of individual models.