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In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information. [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

Impacts of climate change on agriculture are a major concern worldwide, but uncertainties of climate models and emission scenarios may hamper efforts to adapt to climate change. In this paper, a probabilistic approach is used to estimate the uncertainties and simulate impacts of global warming on wheat production and water use in the main wheat cultivation regions of China, with a global mean temperature (GMT) increase scale relative to 1961-90 values. From output of 20 climate scenarios of the Intergovernmental Panel on Climate Change Data Distribution Centre, median values of projected changes in monthly mean climate variables for representative stations are adapted. These are used to drive the Crop Environment Resource Synthesis (CERES)-Wheat model to simulate wheat production and water use under baseline and global warming scenarios, with and without consideration of carbon dioxide (CO2) fertilization effects. Results show that, because of temperature increase, projected wheat-growing periods for GMT changes of 1°, 2°, and 3°C would shorten, with averaged median values of 3.94%, 6.90%, and 9.67%, respectively. There is a high probability of decreasing (increasing) changes in yield and water-use efficiency under higher temperature scenarios without (with) consideration of CO2 fertilization effects. Elevated CO2 concentration generally compensates for the negative effects of warming temperatures on production. Moreover, positive effects of elevated CO2 concentration on grain yield increase with warming temperatures. The findings could be critical for climate-change-driven agricultural production that ensures global food security. [ABSTRACT FROM AUTHOR]

Water vapor plays a key role in the global hydrologic cycle and in climatic change. However, the distribution and variability of water vapor in the troposphere are not understood well—in particular, in China with the complex Tibetan Plateau and the influence of the Asian and Pacific monsoons. In this paper, continuous global positioning system (GPS) observations for 2004–07 in China are used to produce precipitable water vapor (PWV) measurements; these measurements constitute the first investigation of PWV distribution and variability over China. It has been found that the stronger water vapor values are in southeastern China and the lower water vapor values are in northwestern China. These distributions are mainly affected by the latitude, topographical features, the season, and the monsoon. Water vapor variations over China are mainly dominated by seasonal variations. The strong seasonal cycles are in summer with maximum water vapor and in winter with minimum water vapor. The PWV in southeastern China has an annual amplitude of about 15 mm, much larger than in northwestern China at about 4 mm, and meanwhile the time of peak water vapor content is one month earlier than in other regions, probably because of the known rainy season (mei-yu). In addition, significant diurnal variations of water vapor are found over all GPS stations, with a mean amplitude of about 0.7 mm, and the peak value occurs around noon or midnight, depending on geographic location and topographical features. The semidiurnal cycle is weaker, with a mean amplitude of about 0.3 mm, and the first peak PWV value appears around noon. [ABSTRACT FROM AUTHOR]

Under the intensification of global warming, the characteristics of the Three Rivers source region (TRSR; i.e., headwaters of the Yellow River, the Yangtze River, and the Lancang River) in China were diagnosed in the summer season from 1979 to 2015 using observations and reanalysis data. The diagnoses indicate that summer precipitation decreased from 1979 to 2002 [by 9.01 mm day−1 (10 yr)−1; p < 0.05 by Student's t test] and increased significantly after 2002 [by 5.52 mm day−1 (10 yr)−1]. This abrupt change year (2002) was further confirmed by the cumulative anomaly method, the moving t-test method, and the Yamamoto method. By compositing the thermodynamics before and after the abrupt change year (2002), the results reveal that increased water vapor and more substantial lower-level convergence were present over the TRSR during 2003–15. This marked interdecadal variability in the TRSR summer precipitation responded to the interdecadal position and intensity of the large-scale forcing East Asian westerly jet (EAWJ), which is significantly modulated by the low-frequency variability associated with Southern Oscillation index. The connection between the interannual TRSR precipitation and the location and intensity of EAWJ was also explored. The position index of the EAWJ is negatively (with correlation coefficient R of −0.446; p < 0.05 by Student's t test) correlated with the precipitation over the TRSR, implying that southward and northward years of EAWJ are respectively associated with intensifying and weakening the TRSR summer precipitation, whereas the intensity of EAWJ is insignificantly correlated with the TRSR summer precipitation. [ABSTRACT FROM AUTHOR]

It is argued that the occurrence of cold events decreases under the background of global warming. However, from the mid-1990s to the early 2010s, northern China experienced a period of increasing occurrence of low temperature extremes (LTE). Factors responsible for this increase of LTE are investigated in this analysis. The results show that the interdecadal variation of the winter mean temperature over mid- and high-latitude Eurasia acts as an important thermal background. It is characterized by two dominant modes, the "consistent cooling" pattern and the "warm high-latitude Eurasia and cold midlatitude Eurasia" pattern, from the mid-1990s to the early 2010s. The two patterns jointly provide a cooling background for the increase of LTE in northern China. Meanwhile, though the interdecadal variation of the Arctic Oscillation (AO), Ural blocking (UB), and Siberian high (SH) are all highly correlated with the occurrence of LTE in northern China, the AO is found to play a dominant role. On one hand, the AO directly affects the occurrence of LTE because of its dynamic structure; on the other hand, it takes an indirect effect by affecting the intensity of UB and SH. Further analyses show that the winter temperature in mid- and high-latitude Eurasia and the AO are independent factors that influence the increase of LTE in northern China from the mid-1990s to the early 2010s. [ABSTRACT FROM AUTHOR]

Based on daily meteorological observation data in South China (SC) from 1967 to 2018, the spatiotemporal characteristics of the precipitation in SC over the past 52 years were studied. Only 8% of the stations showed a significant increase in annual rainfall, and there was no significant negative trend at any weather stations at a confidence level of 90%. Monthly rainfall showed the most significant decreasing and increasing trends in April and November, respectively. During the entire flooding season from April to September, the monthly rainfall at the weather stations in the coastal areas showed almost no significant change. The annual rainfall gradually decreased toward the inland area with the central and coastal areas of Guangdong Province as the high-value rainfall center. By using the empirical orthogonal function decomposition method, it was found that the two main monthly rainfall modes had strong annual signals. The first modal spatial distribution was basically consistent with the average annual rainfall distribution. Based on the environmental background analysis, it was found that during the flooding season the main water vapor to SC was transported by the East Asian summer monsoon and the Indian summer monsoon. In late autumn and winter, the prevailing wind from northeastern China could not bring much water vapor to SC and led to little precipitation in these two seasons. The spatial distribution of precipitation in SC during summer was more consistent with the moisture flux divergence distribution of the bottom layer from 925 to 1000 hPa rather than that of the layer from 700 to 1000 hPa. [ABSTRACT FROM AUTHOR]

Haze days induced by aerosol pollution in North and East China have posed a persistent and growing problem over the past few decades. These events are particularly threatening to densely populated cities such as Beijing. While the sources of this pollution are predominantly anthropogenic, natural climate variations may also play a role in allowing for atmospheric conditions conducive to formation of severe haze episodes over populated areas. Here, an investigation is conducted into the effects of changes in global dynamics and emissions on air quality in China's polluted regions using 35 simulations developed from the Community Earth Systems Model Large Ensemble (CESM LENS) run over the period 1920–2100. It is shown that internal variability significantly modulates aerosol optical depth (AOD) over China; it takes roughly a decade for the forced response to balance the effects from internal variability even in China's most polluted regions. Random forest regressions are used to accurately model (R2 > 0.9) wintertime AOD using just climate oscillations, the month of the year, and emissions. How different phases of each oscillation affect aerosol loading is projected using these regressions. AOD responses are identified for each oscillation, with particularly strong responses from El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO). As ENSO can be projected a few months in advance and improvements in linear inverse modeling (LIM) may yield a similar predictability for the PDO, results of this study offer opportunities to improve the predictability of China's severe wintertime haze events and to inform policy options that could mitigate subsequent health impacts. [ABSTRACT FROM AUTHOR]

Winter (December-February) haze days in the North China Plain (WHDNCP) have recently dramatically increased. In addition to human activities, climate change and variability also contributed to the severe situation and supported the possibility of seasonal predictions. In this study, using the generalized additive model (GAM), the sea surface temperature around the Alaska Gulf and sea ice area of the Beaufort Sea were selected as the predictors to establish a statistical prediction model (SPM). The difference between the current and previous year of WHDNCP (WDY) was predicted first and was then added to the observation of the previous year to obtain the final predicted WHDNCP. For WDY prediction, the root-mean-square error of the SPM using GAM was 3.01 days. In addition to the annual variation, the tropospheric biennial oscillation features and the dramatically increasing trend after 2010 were both captured successfully. Furthermore, for the final predicted WHDNCP anomalies, the long-term trend and turning points were simulated well, and the percentage of the same mathematical sign was 91.7%. Independent prediction tests were performed for 2014 and 2015, and the forecast bias was 0.86 and 0.19 days, respectively. To assess the predictive ability, recycling independent tests (including real-time hindcasts for the period 2005-15) were also applied, and the percentage of the same sign was 100%. [ABSTRACT FROM AUTHOR]

Management of water resources may benefit from seasonal precipitation forecasts, but for obtaining high enough resolution, dynamical downscaling is necessary. This study investigated the downscaling capability of the Weather Research and Forecasting (WRF) Model ARW, version 3.5, on seasonal precipitation forecasts for the Hanjiang basin in China during 2001-09, which was the water source of the middle route of the South-to-North Water Diversion Project (SNWDP). The WRF Model is forced by the National Centers for Environmental Prediction Operational Climate Forecast System, version 2 (CFSv2), and it performs at a high horizontal resolution of 10 km with four selected convection schemes. The National Oceanic and Atmospheric Administration's Climate Prediction Center global daily precipitation data were employed to evaluate the WRF Model on multiple scales. On average, when large biases were removed, the WRF Model slightly outperformed the CFSv2 in all seasons, especially summer. In particular, the Kain-Fritsch convective scheme performed best in summer, whereas little difference was found in winter. The WRF Model showed similar results in monthly precipitation, but no time-dependent characteristics were observed for all months. The spatial anomaly correlation coefficient showed greater uncertainty than the bias and the temporal correlation coefficient. In addition, the performance of the WRF Model showed considerable regional variations. The upper basin always showed better agreement with observations than did the middle and lower parts of the basin. A comparison of the forecast and observed daily precipitation revealed that the WRF Model can provide more accurate extreme precipitation information than the CFSv2. [ABSTRACT FROM AUTHOR]

The impact of climate change on rice productivity in China remains highly uncertain because of uncertainties from climate change scenarios, parameterizations of biophysical processes, and extreme temperature stress in crop models. Here, the Model to Capture the Crop-Weather Relationship over a Large Area (MCWLA)-Rice crop model was developed by parameterizing the process-based general crop model MCWLA for rice crop. Bayesian probability inversion and a Markov chain Monte Carlo technique were then applied to MCWLA-Rice to analyze uncertainties in parameter estimations and to optimize parameters. Ensemble hindcasts showed that MCWLA-Rice could capture the interannual variability of the detrended historical yield series fairly well, especially over a large area. A superensemble-based probabilistic projection system (SuperEPPS) coupled to MCWLA-Rice was developed and applied to project the probabilistic changes of rice productivity and water use in eastern China under scenarios of future climate change. Results showed that across most cells in the study region, relative to 1961-90 levels, the rice yield would change on average by 7.5%-17.5% (from −10.4% to 3.0%), 0.0%-25.0% (from −26.7% to 2.1%), and from −10.0% to 25.0% (from −39.2% to −6.4%) during the 2020s, 2050s, and 2080s, respectively, in response to climate change, with (without) consideration of CO2 fertilization effects. The rice photosynthesis rate, biomass, and yield would increase as a result of increases in mean temperature, solar radiation, and CO2 concentration, although the rice development rate could accelerate particularly after the heading stage. Meanwhile, the risk of high-temperature stress on rice productivity would also increase notably with climate change. The effects of extreme temperature stress on rice productivity were explicitly parameterized and addressed in the study. [ABSTRACT FROM AUTHOR]

The twentieth century was bracketed by two high-profile events on Mount Everest: the 1924 Mallory and Irvine disappearance and the 1996 Into Thin Air storm. During both events, fatalities occurred high on the mountain during deteriorating weather conditions. Although there have been dramatic improvements in knowledge of the mountain and in the technology used on it, it is shown that an unappreciated change that has also occurred, as a result of warming in the region, is an increase in barometric pressure. A rare and unique set of meteorological data collected at various elevations on the mountain during the 1924 British Everest expedition as well as modern datasets are used to compare and contrast conditions during the two storms and the two climbing seasons. It is shown that both storms were associated with weather systems known locally as western disturbances that resulted in summit barometric pressure drops sufficient to have exacerbated altitude-induced hypoxia. It is further shown that the Mallory and Irvine attempt occurred later in the season than typically is the case now and that this was most likely the result of a concurrent El Niño event. Despite the trend of increasing barometric pressure, the pressure drop associated with storms in the region should remain a concern for those who venture to extreme altitudes. The authors therefore argue that success and failure on Everest and other Himalayan peaks requires knowledge of the variability and trends in both the weather and climate. [ABSTRACT FROM AUTHOR]

Annual and seasonal trends of precipitable water (PW) and relative humidity (RH) at 850, 700, and 500 hPa are studied using the data from 106 radiosonde stations over China during the period 1979-2005. Analysis shows evidence of an increase in PW associated with the slight warming observed in the lower to midtroposphere over China. The northern part of China shows a significant upward trend of PW in summer, and drying of the atmosphere in winter is found in most regions over China. Annual and seasonal trends in RH at the 850-, 700-, and 500-hPa levels show no significant trends in most regions in China except for Xinjiang, which shows an upward trend, and central China, where there was a downward trend in RH at 500 hPa. It is found that changes in PW are coincident with the warming of the surface and the lower to midtroposphere. The RH in the lower to midtroposphere in most regions over China has remained steady during the most recent 30 years, as might be expected given the increasing of PW and the warming above the surface. The long-term trend of precipitation over China may be linked to the trends of PW and RH at the lower level and midlevel. [ABSTRACT FROM AUTHOR]

Solar radiation is one of the most important factors affecting climate and the environment. Routine measurements of irradiance are valuable for climate change research because of long time series and areal coverage. In this study, a set of quality assessment (QA) algorithms is used to test the quality of daily solar global, direct, and diffuse radiation measurements taken at 122 observatories in China during 1957–2000. The QA algorithms include a physical threshold test (QA1), a global radiation sunshine duration test (QA2), and a standard deviation test applied to time series of annually averaged solar global radiation (QA3). The results show that the percentages of global, direct, and diffuse solar radiation data that fail to pass QA1 are 3.07%, 0.01%, and 2.52%, respectively; the percentages of global solar radiation data that fail to pass the QA2 and QA3 are 0.77% and 0.49%, respectively. The method implemented by the Global Energy Balance Archive is also applied to check the data quality of solar radiation in China. Of the 84 stations with a time series longer that 20 yr, suspect data at 35 of the sites were found. Based on data that passed the QA tests, trends in ground solar radiation and the effect of the data quality assessment on the trends are analyzed. There is a decrease in ground solar global and direct radiation in China over the years under study. Although the quality assessment process has significant effects on the data from individual stations and/or time periods, it does not affect the long-term trends in the data. [ABSTRACT FROM AUTHOR]