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The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments. This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature (SAT), precipitation, droughts, and surface wind speed, based on studies published since 2018. The reviewed evidence indicates that human activities, including greenhouse gas and anthropogenic aerosol emissions, land use and cover change, urbanization, and anthropogenic heat release, have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China. The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region. Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events, the anthropogenic precipitation signal over China is relatively obscure. Moreover, human activities have also contributed to a decline in surface wind speed, weakening of monsoon precipitation, and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades. This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation. Additionally, a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research. [ABSTRACT FROM AUTHOR]

Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening (TsHARP) method and a binary linear model are compared to downscale the original daytime FengYun 2F (FY-2F) land surface temperature (LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land (SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error (RMSE) of the LST downscaled from the binary linear model is 1.30°C compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78°C. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37°C in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37°C and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data. [ABSTRACT FROM AUTHOR]

Analyses of the variation characteristics for aridity index (AI) can further enhance the understanding of climate change and have effect on hydrology and agriculture. In this paper, based on the data of 283 standard meteorological stations, the temporal-spatial variations and the influences of climate factors on AI were investigated and the relationship between AI and two climate indices (the Arctic Oscillation (AO); El Nino-Southern Oscillation (ENSO)) were also assessed in northern China (NC) during the period from 1961 to 2012. The results revealed that the annual mean AI decreased at the rate of −0.031 per decade in the past 52 years and the trend was statistically significant at the 0.01 level. The Mann-Kendall (M-K) test presented that the percentages of stations with positive trends and negative trends for AI were 10 and 81.9 % (22.6 % statistically significant), respectively. Spatially, in the western part of 100° E, the extremely dry area declined and the climate tended to become wet obviously. In the eastern part of 100° E, dry area moved toward the east and the south, which resulted in the enhancement of semiarid area and the shrinkage of subhumid area. The contributions of sunshine duration and precipitation to the decline of AI are more than those of other meteorological variables in NC. Moreover, the average temperature has risen significantly and AI decreased in NC, which indicated the existence of 'paradox.' Relationship between climate indices (AO and ENSO) and AI demonstrated that the influence of ENSO on AI overweight the AO on AI in NC. [ABSTRACT FROM AUTHOR]

The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble. [ABSTRACT FROM AUTHOR]

We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China (NEC) by employing climate and crop models. Based on historical data, diurnal temperature change exhibited a distinct negative relationship with maize yield, whereas minimum temperature correlated positively to rice yield. Corresponding to the evaluated climate change derived from coupled climate models included in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the Representative Concentration Pathway 4.5 scenario (RCP4.5), the projected maize yield changes for three future periods [2010-39 (period 1), 2040-69 (period 2), and 2070-99 (period 3)] relative to the mean yield in the baseline period (1976-2005) were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The uncertainties in the crop response are discussed by considering the uncertainties obtained from both the climate and the crop models. The range of impact of the uncertainty became markedly wider when integrating these two sources of uncertainty. The probabilistic assessments of the evaluated change showed maize yield to be relatively stable from period 1 to period 3, while the rice yield showed an increasing trend over time. The results presented in this paper suggest a tendency of the yields of maize and rice in NEC to increase (but with great uncertainty) against the background of global warming, which may offer some valuable guidance to government policymakers. [ABSTRACT FROM AUTHOR]

The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930s. Experimental operational climate forecasts have been performed since the late 1950s, based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program (WCRP) was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China. [ABSTRACT FROM AUTHOR]

This paper provides a review of paleoclimate modeling activities in China. Rather than attempt to cover all topics, we have chosen a few climatic intervals and events judged to be particularly informative to the international community. In historical climate simulations, changes in solar radiation and volcanic activity explain most parts of reconstructions over the last millennium prior to the industrial era, while atmospheric greenhouse gas concentrations play the most important role in the 20th century warming over China. There is a considerable model-data mismatch in the annual and boreal winter temperature change over China during the mid-Holocene [6000 years before present (ka BP)], while coupled models with an interactive ocean generally perform better than atmospheric models. For the Last Glacial Maximum (21 ka BP), climate models successfully reproduce the surface cooling trend over China but fail to reproduce its magnitude, with a better performance for coupled models. At that time, reconstructed vegetation and western Pacific sea surface temperatures could have significantly affected the East Asian climate, and environmental conditions on the Qinghai-Tibetan Plateau were most likely very different to the present day. During the late Marine Isotope Stage 3 (30-40 ka BP), orbital forcing and Northern Hemisphere glaciation, as well as vegetation change in China, were likely responsible for East Asian climate change. On the tectonic scale, the Qinghai-Tibetan Plateau uplift, the Tethys Sea retreat, and the South China Sea expansion played important roles in the formation of the East Asian monsoon-dominant environment pattern during the late Cenozoic. [ABSTRACT FROM AUTHOR]

This paper describes 2007/2008 inter-annual changes in runoff from the Zhadang Glacier located on the northern slope of Nyainqêntanglha Range, Tibet, and analyzes their causes. Precipitation increased by 17.9% in summer months of 2008 compared with the same period in 2007, drainage basin runoff decreased by 33.3%, and glacial meltwater decreased by 53.8%. Change in positive accumulated air temperature explained approximately half of the inter-annual difference in glacial meltwater using a degree-day model. This suggests that the glacier is extremely sensitive to changes in air temperature. Energy balance analysis showed that change in glacier surface albedo, considered to be caused by difference in precipitation form, resulted in the large inter-annual difference in glacial meltwater. It was shown statistically that precipitation form in the summer months of 2007 was mainly rainfall which comprised 71.5% of total precipitation, while during the same period in 2008 rainfall accounted for 30.7%, with the majority of precipitation falling as snow. Precipitation form should be considered an independent factor when analyzing glacier sensitivity to climate change or forecasting the runoff from certain glaciers. [ABSTRACT FROM AUTHOR]

This paper measures the economic impacts of climate change on China's agriculture based on the 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, we find that under most climate change scenarios both higher temperature and more precipitation would have an overall positive impact on China's agriculture. However, the impacts vary seasonally and regionally. Autumn effect is the most positive, but spring effect is the most negative. Applying the model to five climate scenarios in the year 2050 shows that the East, the Central part, the South, the northern part of the Northeast, and the Plateau would benefit from climate change, but the Southwest, the Northwest and the southern part of the Northeast may be negatively affected. In the North, most scenarios show that they may benefit from climate change. In summary, all of China would benefit from climate change in most scenarios. [ABSTRACT FROM AUTHOR]