Regional Climate Change and Uncertainty Analysis based on Four Regional Climate Model Simulations over China

Four sets of climate change simulations at grid spacing of 50 km were conducted over East Asia with two regional climate models driven at the lateral boundaries by two global models for the period 1981–2050. The focus of the study was on the ensemble projection of climate change in the mid-21 st century(2031–50) over China. Validation of each simulation and the ensemble average showed good performances of the models overall, as well as advantages of the ensemble in reproducing present day(1981–2000) December–February(DJF), June–August(JJA), and annual(ANN) mean temperature and precipitation. Significant warming was projected for the mid-21 st century, with larger values of temperature increase found in the northern part of China and in the cold seasons. The ensemble average changes of precipitation in DJF, JJA, and ANN were determined, and the uncertainties of the projected changes analyzed based on the consistencies of the simulations. It was concluded that the largest uncertainties in precipitation projection are in eastern China during the summer season(monsoon precipitation).

Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging

Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21^（st） century for the Tianshan and northern Kunlun Mountains（TKM） based on the general circulation model（GCM） simulation ensemble from the coupled model intercomparison project phase 5（CMIP5） under the representative concentration pathway（RCP） lower emission scenario RCP4.5 and higher emission scenario RCP8.5 using the Bayesian model averaging（BMA） technique. Results show that（1） BMA significantly outperformed the simple ensemble analysis and BMA mean matches all the three observed climate variables;（2） at the end of the 21^（st） century（2070–2099） under RCP8.5, compared to the control period（1976–2005）, annual mean temperature and mean annual precipitation will rise considerably by 4.8°C and 5.2%, respectively, while mean annual snowfall will dramatically decrease by 26.5%;（3） precipitation will increase in the northern Tianshan region while decrease in the Amu Darya Basin. Snowfall will significantly decrease in the western TKM. Mean annual snowfall fraction will also decrease from 0.56 of 1976–2005 to 0.42 of 2070–2099 under RCP8.5; and（4） snowfall shows a high sensitivity to temperature in autumn and spring while a low sensitivity in winter, with the highest sensitivity values occurring at the edge areas of TKM. The projections mean that flood risk will increase and solid water storage will decrease.

Monitoring the little fire ant,Wasmannia auropunctata(Roger 1863),in the early stage of its invasion in China:Predicting its geographical distribution pattern under climate change

Invasive alien ants(IAAs)are among the most aggressive,competitive,and widespread invasive alien species(IAS)worldwide.Wasmannia auropunctata,the greatest IAAs threat in the Pacific region and listed in“100 of the world’s worst IAS”,has established itself in many countries and on islands worldwide.Wild populations of W.auropunctata were recently reported in southeastern China,representing a tremendous potential threat to China’s agricultural,economic,environmental,public health,and social well-being.Estimating the potential geographical distribution(PGD)of W.auropunctata in China can illustrate areas that may potentially face invasion risk.Therefore,based on the global distribution records of W.auropunctata and bioclimatic variables,we predicted the geographical distribution pattern of W.auropunctata in China under the effects of climate change using an ensemble model(EM).Our findings showed that artificial neural network(ANN),flexible discriminant analysis(FDA),gradient boosting model(GBM),Random Forest(RF)were more accurate than categorical regression tree analysis(CTA),generalized linear model(GLM),maximum entropy model(MaxEnt)and surface distance envelope(SRE).The mean TSS values of ANN,FDA,GBM,and RF were 0.820,0.810,0.843,and 0.857,respectively,and the mean AUC values were 0.946,0.954,0.968,and 0.979,respectively.The mean TSS and AUC values of EM were 0.882 and 0.972,respectively,indicating that the prediction results with EM were more reliable than those with the single model.The PGD of W.auropunctata in China is mainly located in southern China under current and future climate change.Under climate change,the PGD of W.auropunctata in China will expand to higher-latitude areas.The annual temperature range(bio7)and mean temperature of the warmest quarter(bio10)were the most significant variables affecting the PGD of W.auropunctata in China.The PGD of W.auropunctata in China was mainly attributed to temperature variables,such as the annual temperature range(bio7)and the mean temperature of the warmest quarter(bio10).The populations of W.auropunctata in southern China have broad potential invasion areas.Developing strategies for the early warning,monitoring,prevention,and control of W.auropunctata in southern China requires more attention.

Prediction of forest fire occurrence in China under climate change scenarios

Climate change has an impact on forest fire patterns.In the context of global warming,it is important to study the possible effects of climate change on forest fires,carbon emission reductions,carbon sink effects,forest fire management,and sustainable development of forest ecosystems.This study is based on MODIS active fire data from 2001-2020 and the influence of climate,topography,vegetation,and social factors were integrated.Temperature and precipitation information from different scenarios of the BCC-CSM2-MR climate model were used as future climate data.Under climate change scenarios of a sustainable low development path and a high conventional development path,the extreme gradient boosting model predicted the spatial distribution of forest fire occurrence in China in the 2030s(2021-2040),2050s(2041-2060),2070s(2061-2080),and2090s(2081-2100).Probability maps were generated and tested using ROC curves.The results show that:(1)the area under the ROC curve of training data(70%)and validation data(30%)were 0.8465 and 0.8171,respectively,indicating that the model can reasonably predict the occurrence of forest fire in the study area;(2)temperature,elevation,and precipitation were strongly correlated with fire occurrence,while land type,slope,distance from settlements and roads,and slope direction were less strongly correlated;and,(3)based on future climate change scenarios,the probability of forest fire occurrence will tend to shift from the south to the center of the country.Compared with the current climate(2001-2020),the occurrence of forest fires in 2021-2040,2041-2060,2061-2080,and 2081-2100 will increase significantly in Henan Province(Luoyang,Nanyang,S anmenxia),Shaanxi Province(Shangluo,Ankang),Sichuan Province(Mianyang,Guangyuan,Ganzi),Tibet Autonomous Region(Shannan,Linzhi,Changdu),Liaoning Province(Liaoyang,Fushun,Dandong).

Changes in Extreme Events as Simulated by a High-Resolution Regional Climate Model for the Next 20-30 Years over China

In this paper, the changes in temperature and precipitation extremes over the next 20-30 years (2021-2050) in relative to the present day (1986-2005) under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario are analyzed based on a high-resolution climate change simulation performed by a regional climate model (the Abdus Salam International Center for Theoretical Physics (ICTP) RegCM3). The extreme indices of summer days (SU), frost days (FD), and growing season length (GSL) for temperature and simple daily intensity index (SDII), number of days with precipitation ≥10 mm d-1 (R10), and consecutive dry days (CDD) for precipitation are used as the indicators of the extremes. The results show that the indices simulated by RegCM3 in the present day show good agreement with the observed. A general increase in SU, a decrease in FD, and an increase in GSL are found to occur in the next 20-30 years over China. A general increase in SDII, an increase in R10 over western China, and a decrease in R10 in north, northeast, and central China are simulated by the model. Changes in CDD are characterized by a decrease in the north and an increase in the south and the Tibetan Plateau.

Notes of Numerical Simulation of Summer Rainfall in China with a Regional Climate Model REMO

Regional climate models are major tools for regional climate simulation and their output are mostly used for climate impact studies. Notes are reported from a series of numerical simulations of summer rainfall in China with a regional climate model. Domain sizes and running modes are major foci. The results reveal that the model in forecast mode driven by ＂perfect＂ boundaries could reasonably represent the inter-annual differences： heavy rainfall along the Yangtze River in 1998 and dry conditions in 1997. Model simulation in climate mode differs to a greater extent from observation than that in forecast mode. This may be due to the fact that in climate mode it departs further from the driving fields and relies more on internal model dynamical processes. A smaller domain in climate mode outperforms a larger one. Further development of model parameterizations including dynamic vegetation are encouraged in future studies.

Modeling the impacts of climate change on China's agriculture

The impacts of climate change on China's agriculture are measured based on Ricardian model. By using county-level cross-sectional data on agricultural net revenue, climate, and other economic and geographical data for 1275 agriculture-dominated counties in the period of 1985-1991, we find that both higher temperature and more precipitation will have overall positive impact on China's agriculture. However, the impacts vary seasonally and regionally. Higher temperature in all seasons except spring increases agricultural net revenue while more precipitation is beneficial in winter but is harmful in summer. Applying the model to five climate scenarios in the 2020s and 2050s shows that the North, the Northeast, the Northwest, and the Qinghai-Tibet Plateau would always benefit from climate change while the South and the Southwest may be negatively affected. For the East and the Central China, most scenarios show that they may benefit from climate change. In conclusion, climate change would be beneficial to the whole China.

Simulating responses of Northeastern China forests to potential climate change

A simulation study on the responses of forests in Northeastern China to possible climate change was done by running NEWCOP, a computer model of forest stands “gap” dynamics with a set of parameters of 24 tree species. Based on the simulation, climate change will continue to make coniferous trees less and less and deciduous trees more and more. By the end of 100a transient process and 100a equilibrium climate period, forest biomass is reduced by a total of 6,531 million t dry material for the whole region of NE China. There is only a small area in the north on which there stands more biomass than without climate change. Korean pine will be first tree species which decrease by the most amount. In the northern part of NE China, oak forest will cover much more area with climate change and the larch forest may cover less area than it does at present. In the middle part areas, coniferous and broad-leaved mixed forest will remain, but the portion of deciduous species in composition of forest will increase. In the southem part areas, Korean pine will become companion tree species and its distribution area will greatly decrease.

PREDICTION AND UNCERTAINTY OF CLIMATE CHANGE IN CHINA DURING 21ST CENTURY UNDER RCPS

Based on integrated simulations of 26 global climate models provided by the Coupled Model Intercomparison Project(CMIP), this study predicts changes in temperature and precipitation across China in the 21 st century under different representative concentration pathways(RCPs), and analyzes uncertainties of the predictions using Taylor diagrams. Results show that increases of average annual temperature in China using three RCPs(RCP2.6, RCP4.5,RCP8.5) are 1.87 ℃, 2.88 ℃ and 5.51 ℃, respectively. Increases in average annual precipitation are 0.124, 0.214, and 0.323 mm/day, respectively. The increased temperature and precipitation in the 21 st century are mainly contributed by the Tibetan Plateau and Northeast China. Uncertainty analysis shows that most CMIP5 models could predict temperature well, but had a relatively large deviation in predicting precipitation in China in the 21 st century. Deviation analysis shows that more than 80% of the area of China had stronger signals than noise for temperature prediction;however, the area proportion that had meaningful signals for precipitation prediction was less than 20%. Thus, the multi-model ensemble was more reliable in predicting temperature than precipitation because of large uncertainties of precipitation.

Comparative Assessment of Impacts of Future Climate Change on Runoff in Upper Daqinghe Basin,China

Assessing runoff changes is of great importance especially its responses to the projected future climate change on local scale basins because such analyses are generally done on global and regional scales which may lead to generalized conclusions rather than specific ones.Climate change affected the runoff variation in the past in the upper Daqinghe Basin,however,the climate was mainly considered uncertain and still needs further studies,especially its future impacts on runoff for better water resources management and planning.Integrated with a set of climate simulations,a daily conceptual hydrological model(MIKE11-NAM)was applied to assess the impact of climate change on runoff conditions in the Daomaguan,Fuping and Zijingguan basins in the upper Daqinghe Basin.Historical hydrological data(2008–2017)were used to evaluate the applicability of the MIKE11-NAM model.After bias correction,future projected climate change and its impacts on runoff(2025–2054)were analysed and compared to the baseline period(1985–2014)under three shared social economic pathways(SSP1-2.6,SSP2-4.5,and SSP5-8.5)scenarios from Coupled Model Intercomparison Project Phase 6(CMIP6)simulations.The MIKE-11 NAM model was applicable in all three Basins,with both R^(2)and Nash-Sutcliffe Efficiency coefficients greater than 0.6 at daily scale for both calibration(2009–2011)and validation(2012–2017)periods,respectively.Although uncertainties remain,temperature and precipitation are projected to increase compared to the baseline where higher increases in precipitation and temperature are projected to occur under SSP2-4.5 and SSP5-8.5 scenarios,respectively in all the basins.Precipitation changes will range between 12%–19%whereas temperature change will be 2.0℃–2.5℃ under the SSP2-4.5 and SSP5-8.5 scenarios,respectively.In addition,higher warming is projected to occur in colder months than in warmer months.Overall,the runoff of these three basins is projected to respond to projected climate changes differently because runoff is projected to only increase in the Fuping basin under SSP2-4.5 whereas decreases in both Daomaguan and Zijingguan Basins under all scenarios.This study’s findings could be important when setting mitigation strategies for climate change and water resources management.

Climate Change and Ecological Projects Jointly Promote Vegetation Restoration in Three-River Source Region of China

As the source of the Yellow River,Yangtze River,and Lancang River,the Three-River Source Region(TRSR)in China is very important to China’s ecological security.In recent decades,TRSR’s ecosystem has degraded because of climate change and human disturbances.Therefore,a range of ecological projects were initiated by Chinese government around 2000 to curb further degradation.Current research shows that the vegetation of the TRSR has been initially restored over the past two decades,but the respective contribution of ecological projects and climate change in vegetation restoration has not been clarified.Here,we used the Moderate Resolution Imaging Spectroradiometer(MODIS)Enhanced Vegetation Index(EVI)to assess the spatial-temporal variations in vegetation and explore the impact of climate and human actions on vegetation in TRSR during 2001–2018.The results showed that about 26.02%of the TRSR had a significant increase in EVI over the 18 yr,with an increasing rate of 0.010/10 yr(P<0.05),and EVI significantly decreased in only 3.23%of the TRSR.Residual trend analysis indicated vegetation restoration was jointly promoted by climate and human actions,and the promotion of human actions was greater compared with that of climate,with relative contributions of 59.07%and40.93%,respectively.However,the degradation of vegetation was mainly caused by human actions,with a relative contribution of71.19%.Partial correlation analysis showed that vegetation was greatly affected by temperature(r=0.62,P<0.05)due to the relatively sufficient moisture but lower temperature in TRSR.Furthermore,the establishment of nature reserves and the implementation of the Ecological Protection and Restoration Program(EPRP)improved vegetation,and the first stage EPRP had a better effect on vegetation restoration than the second stage.Our findings identify the driving factors of vegetation change and lay the foundation for subsequent effective management.

Using ORYZA2000 to model cold rice yield response to climate change in the Heilongjiang province, China

Rice(Oryza sativa L.) is one of the most important staple crops in China. Increasing atmospheric greenhouse gas concentrations and associated climate change may greatly affect rice production. We assessed the potential impacts of climate change on cold rice production in the Heilongjiang province, one of China's most important rice production regions. Data for a baseline period(1961–1990) and the period 2010–2050 in A2 and B2 scenarios were used as input to drive the rice model ORYZA2000 with and without accounting for the effects of increasing atmospheric CO2 concentration. The results indicate that mean,maximum, and minimum temperature during the rice growing season, in the future period considered, would increase by 1.8 °C under the A2 scenario and by 2.2 °C under the B2 scenario compared with those in the baseline. The rate of change in average maximum and minimum temperatures would increase by 0.6 °C per 10-year period under the A2 scenario and by 0.4 °C per 10-year period under the B2 scenario. Precipitation would increase slightly in the rice growing season over the next 40 years. The rice growing season would be shortened and the yield would increase in most areas in the Heilongjiang province. Without accounting for CO2 effect, the rice growing season in the period 2010–2050 would be shortened by 4.7 and 5.8 days,and rice yields would increase by 11.9% and 7.9%, under the A2 and B2 scenarios, respectively.Areas with simulated rice yield increases greater than 30.0% were in the Xiaoxing'an Mountain region. The simulation indicated a decrease in yield of less than 15% in the southwestern Songnen Plain. The rate of change in simulated rice yield was 5.0% and 2.5% per 10 years under the A2 and B2 scenarios, respectively. When CO2 effect was accounted for, rice yield increased by 44.5% and 31.3% under the A2 and B2 scenarios, respectively. The areas of increasing yield were sharply expanded. The area of decreasing yield in the western region of Songnen Plains disappeared when increasing CO2 concentration was considered. The stability of rice yield would increase from 2010 to 2050. Overall, the simulation indicates that rice production will be affected positively by climate change in the next 40 years in the Heilongjiang province, China.

Potential risks and challenges of climate change in the arid region of northwestern China

In the arid region of northwestern China(ARNC),water resources are the most critical factor restricting socioeconomic development and influencing the stability of the area’s ecological systems.The region’s complex water system and unique hydrological cycle show distinctive characteristics.Moreover,the intensified hydrological cycle and extreme climatic and hydrological events resulting from global warming have led to increased uncertainty around water resources as well as heightened conflict between water supply and water demand.All of these factors are exerting growing pressures on the socioeconomic development and vulnerable ecological environment in the region.This research evaluates the impacts of climate change on water resources,hydrological processes,agricultural system,and desert ecosystems in the ARNC,and addresses some associated risks and challenges specific to this area.The temperature is rising at a rate of 0.31
C per decade during 1961–2017 and hydrological processes are being significantly influenced by changes in glaciers,snow cover,and precipitation form,especially in the rivers recharged primarily by melt water.Ecosystems are also largely influenced by climate change,with the Normalized Difference Vegetation Index(NDVI)of natural vegetation exhibited an increasing trend prior to 1998,and then reversed in Xinjiang while the Hexi Corridor of Gansu showed the opposite trends.Furthermore,the desert-oasis transition zone showed a reduction in area due to the warming trend and the recent rapid expansion of irrigated area.Both the warming and intensified drought are threatening agriculture security.The present study could shed light on sustainable development in this region under climate change and provides scientific basis to the construction of the“Silk Road Economic Belt”.

Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models

This study assessed the regional climate models (RCMs) employed in the Coordinated Regional climate Downscaling Experiment (CORDEX) South Asia framework to investigate the qualitative aspects of future change in seasonal mean near surface air temperature and precipitation over the Hindu Kush Himalayan (HKH) region. These RCMs downscaled a subset of atmosphere ocean coupled global climate models (AOGCMs) in the Coupled Model Intercomparison Project phase 5 (CMIP5) to higher 50 km spatial resolution over a large domain covering South Asia for two representation concentration pathways (RCP4.5 and RCP8.5) future scenarios. The analysis specifically examined and evaluated multi-model and multi-scenario climate change projections over the hilly sub-regions within HKH for the near-future (2036e2065) and far-future (2066e2095) periods. The downscaled multi-RCMs provide relatively better confidence than their driving AOGCMs in projecting the magnitude of seasonal warming for the hilly sub-region within the Karakoram and northwestern Himalaya, with higher projected change of 5.4
C during winter than of 4.9
C during summer monsoon season by the end of 21st century under the high-end emissions (RCP8.5) scenario. There is less agreement among these RCMs on the magnitude of the projected warming over the other sub-regions within HKH for both seasons, particularly associated with higher RCM uncertainty for the hilly sub-region within the central Himalaya. The downscaled multi-RCMs show good consensus and low RCM uncertainty in projecting that the summer monsoon precipitation will intensify by about 22% in the hilly subregion within the southeastern Himalaya and Tibetan Plateau for the far-future period under the RCP8.5 scenario. There is low confidence in the projected changes in the summer monsoon and winter season precipitation over the central Himalaya and in the Karakoram and northwestern Himalaya due to poor consensus and moderate to high RCM uncertainty among the downscaled multi-RCMs. Finally, the RCM related uncertainty is found to be large for the projected changes in seasonal temperature and precipitation over the hilly sub-regions within HKH by the end of this century, suggesting that improving the regional processes and feedbacks in RCMs are essential for narrowing the uncertainty, and for providing more reliable regional climate change projections suitable for impact assessments in HKH region.

East China Summer Rainfall during ENSO Decaying Years Simulated by a Regional Climate Model

The performance of the Climate version of the Regional Eta-coordinate Model (CREM), a regional climate model developed by State Key Laboratory of Nu- merical modeling for Atmospheric Science and Geophysical Fluid Dynamics/Institute of Atmospheric Physics (LASG/IAP), in simulating rainfall anomalies during the ENSO decaying summers from 1982 to 2002 was evaluated. The added value of rainfall simulation relative to reanalysis data and the sources of model bias were studied. Results showed that the model simulated rainfall anomalies moderately well. The model did well at capturing the above-normal rainfall along the Yangtze River valley (YRV) during El Nio decaying summers and the below and above-normal rainfall centers along the YRV and the Huaihe River valley (HRV), respectively, during La Nia decaying summers. These features were not evident in rainfall products derived from the reanalysis, indicating that rainfall simulation did add value. The main limitations of the model were that the simulated rainfall anomalies along the YRV were far stronger and weaker in magnitude than the observations during El Nio decaying summers and La Nia decaying summers, respectively. The stronger magnitude above-normal rainfall during El Nio decaying summers was due to a stronger northward transport of water vapor in the lower troposphere, mostly from moisture advection. An artificial, above-normal rainfall center was seen in the region north to 35°N, which was associated with stronger northward water vapor transport. Both lower tropospheric circulation bias and a wetter model atmosphere contributed to the bias caused by water vapor transport. There was a stronger southward water vapor transport from the southern boundary of the model during La Nia decaying summers;less remaining water vapor caused anomalously weaker rainfall in the model as compared to observations.

MODELING THE EFFECTS OF ANTHROPOGENIC SULFATE IN CLIMATE CHANGE BY USING A REGIONAL CLIMATE MODEL

Effects of aerosol with focus on the direct climate effect of anthropogenic sulfate aerosol under 2×CO2 condition were investigated by introducing aerosol distribution into the latest version of RegCM2. Two experiments, first run (2×CO2 + 0 aerosol concentration) and second run (2×CO2 + aerosol distribution), were made for 5 years respectively. Preliminary analysis shows that the direct climate effect of aerosol might cause a decrease of surface air temperature. The decrease might be larger in winter and in South China. The regional-averaged monthly precipitation might also decrease in most of the months due to the effect. The annual mean change of precipitation might be a decrease in East and an increase in West China. But the changes of both temperature and precipitation simulated were much smaller as compared to the greenhouse effect.

Detection,Causes and Projection of Climate Change over China:An Overview of Recent Progress

This article summarizes the main results and findings of studies conducted by Chinese scientists in the past five years. It is shown that observed climate change in China bears a strong similarity with the global average. The country-averaged annual mean surface air temperature has increased by 1.1℃ over the past 50 years and 0.5-0.8℃ over the past 100 years, slightly higher than the global temperature increase for the same periods. Northern China and winter have experienced the greatest increases in surface air temperature. Although no significant trend has been found in country-averaged annual precipitation, interdecadal variability and obvious trends on regional scales are detectable, with northwestern China and the mid and lower Yangtze River basin having undergone an obvious increase, and North China a severe drought. Some analyses show that frequency and magnitude of extreme weather and climate events have also undergone significant changes in the past 50 years or so. Studies of the causes of regional climate change through the use of climate models and consideration of various forcings, show that the warming of the last 50 years could possibly be attributed to an increased atmospheric concentration of greenhouse gases, while the temperature change of the first half of the 20th century may be due to solar activity, volcanic eruptions and sea surface temperature change. A significant decline in sunshine duration and solar radiation at the surface in eastern China has been attributed to the increased emission of pollutants. Projections of future climate by models of the NCC （National Climate Center, China Meteorological Administration） and the IAP （Institute of Atmospheric Physics, Chinese Academy of Sciences）, as well as 40 models developed overseas, indicate a potential significant warming in China in the 21st century, with the largest warming set to occur in winter months and in northern China. Under varied emission scenarios, the country-averaged annual mean temperature is projected to increase by 1.5 2.1℃ by 2020, 2.3 3.3℃ by 2050, and by 3.9-6.0℃ by 2100, in comparison to the 30-year average of 1961-1990. Most models project a 10% 12% increase in annual precipitation in China by 2100, with the trend being particularly evident in Northeast and Northwest China, but with parts of central China probably undergoing a drying trend. Large uncertainty exists in the projection of precipitation, and further studies are needed. Furthermore, anthropogenic climate change will probably lead to a weaker winter monsoon and a stronger summer monsoon in eastern Asia.

Recent Progress in Studies of Climate Change in China

An overview of basic research on climate change in recent years in China is presented. In the past 100 years in China, average annual mean surface air temperature （SAT） has increased at a rate ranging from 0.03℃ （10 yr）-1 to 0.12℃ （10 yr）-1. This warming is more evident in northern China and is more significant in winter and spring. In the past 50 years in China, at least 27% of the average annual warming has been caused by urbanization. Overall, no significant trends have been detected in annual and/or summer precipitation in China on a whole for the past 100 years or 50 years. Both increases and decreases in frequencies of major extreme climate events have been observed for the past 50 years. The frequencies of extreme temperature events have generally displayed a consistent pattern of change across the country, while the frequencies of extreme precipitation events have shown only regionally and seasonally significant trends. The frequency of tropical cyclone landfall decreased slightly, but the frequency of sand/dust storms decreased significantly. Proxy records indicate that the annual mean SAT in the past a few decades is the highest in the past 400-500 years in China, but it may not have exceeded the highest level of the Medieval Warm Period （1000 1300 AD）. Proxy records also indicate that droughts and floods in eastern China have been characterized by continuously abnormal rainfall periods, with the frequencies of extreme droughts and floods in the 20th century most likely being near the average levels of the past 2000 years. The attribution studies suggest that increasing greenhouse gas （GHG） concentrations in the atmosphere are likely to be a main factor for the observed surface warming nationwide. The Yangtze River and Huaihe River basins underwent a cooling trend in summer over the past 50 years, which might have been caused by increased aerosol concentrations and cloud cover. However, natural climate variability might have been a main driver for the mean and extreme precipitation variations observed over the past century. Climate models generally perform well in simulating the variations of annual mean SAT in China. They have also been used to project future changes in SAT under varied GHG emission scenarios. Large uncertainties have remained in these model-based projections, however, especially for the projected trends of regional precipitation and extreme climate events.

Advance in Application of Regional Climate Models in China

Regional climate models have become the powerful tools for simulating regional climate and its change process and have been widely used in China. Using regional climate models, some research results have been obtained on the following aspects: 1) the numerical simulation of East Asian monsoon climate, including exceptional monsoon precipitation, summer precipitation distribution, East Asian circulation, multi-year climate average condition, summer rain belt and so on; 2) the simulation of arid climate of the western China, including thermal effect of the Qing- hai-Tibet Plateau, the plateau precipitation in the Qilian Mountains; and the impacts of greenhouse effects (CO2 dou- bling) upon climate in the western China; and 3) the simulation of the climate effect of underlying surface changes, in- cluding the effect of soil on climate formation, the influence of terrain on precipitation, the effect of regional soil deg- radation on regional climate, the effect of various underlying surfaces on regional climate, the effect of land-sea con- trast on the climate formulation, the influence of snow cover over the plateau regions on the regional climate, the effect of vegetation changes on the regional climate, etc. In the process of application of regional climate models, the prefer- ences of the models are improved so that better simulation results are gotten. At last, some suggestions are made about the application of regional climate models in regional climate research in the future.

Impact of climate change on maize yield in China from 1979 to 2016

Climate change severely impacts agricultural production, which jeopardizes food security. China is the second largest maize producer in the world and also the largest consumer of maize. Analyzing the impact of climate change on maize yields can provide effective guidance to national and international economics and politics. Panel models are unable to determine the group-wise heteroscedasticity, cross-sectional correlation and autocorrelation of datasets, therefore we adopted the feasible generalized least square(FGLS) model to evaluate the impact of climate change on maize yields in China from 1979–2016 and got the following results:(1) During the 1979–2016 period, increases in temperature negatively impacted the maize yield of China. For every 1℃ increase in temperature, the maize yield was reduced by 5.19 kg 667 m^–2(1.7%). Precipitation increased only marginally during this time, and therefore its impact on the maize yield was negligible. For every 1 mm increase in precipitation, the maize yield increased by an insignificant amount of 0.043 kg 667 m^–2(0.014%).(2) The impacts of climate change on maize yield differ spatially, with more significant impacts experienced in southern China. In this region, a 1℃ increase in temperature resulted in a 7.49 kg 667 m^–2 decrease in the maize yield, while the impact of temperature on the maize yield in northern China was insignificant. For every 1 mm increase in precipitation, the maize yield increased by 0.013 kg 667 m^–2 in southern China and 0.066 kg 667 m^–2 in northern China.(3) The resilience of the maize crop to climate change is strong. The marginal effect of temperature in both southern and northern China during the 1990–2016 period was smaller than that for the 1979–2016 period.