Search

In this paper, we describe and analyze two datasets entitled “Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021” and “Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”. These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia (CA), six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and their changes in the near-term future (2031–50) are assessed relative to the reference period (1986–2005). The quantile mapping (QM) method is applied to correct the model data before calculating the indicators. Results show the QM method largely reduces the biases in all the indicators. Growing season length (GSL, day), summer days (SU, day), warm spell duration index (WSDI, day), and tropical nights (TR, day) are projected to significantly increase over CA, and frost days (FD, day) are projected to decrease. However, changes in biologically effective degree days (BEDD, °C) are spatially heterogeneous. The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s−1, considerably smaller than the result (1.29 m s−1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments.

Urban areas are experiencing strongly increasing hot temperature extremes. However, these urban heat events have seldom been the focus of traditional detection and attribution analysis designed for regional to global changes. Here we show that compound (day–night sustained) hot extremes are more dangerous than solely daytime or nighttime heat, especially to female and older urban residents. Urban compound hot extremes across eastern China have increased by 1.76 days per decade from 1961 to 2014 with fingerprints of urban expansion and anthropogenic emissions detected by a stepwise detection and attribution method. Their attributable fractions are estimated as 0.51 (urbanization), 1.63 (greenhouse gases) and −0.54 (other anthropogenic forcings) days per decade. Future emissions and urbanization would make these compound events two to five times more frequent (2090s versus 2010s), leading to a threefold-to-sixfold growth in urban population exposure. Our findings call for tailored adaptation planning against rapidly growing health threats from compound heat in cities.

ABSTRACTFor the area of Eurasia concentrated with developing countries (referred to here by the abbreviation DPEA), mainly located in Asia and Eastern Europe, this work presents datasets of gridded meteorological drought events and country-based drought risk by combining multiple drought indices and socio-economic data. A basic gridded dataset of the drought events during 1950–2015 is extracted from three drought indices: the self-calibrating Palmer Drought Severity Index, the Standardized Precipitation Index, and the Standardized Precipitation Evapotranspiration Index. The three drought indices generally show consistent characteristics of drought events in DPEA. A second-level dataset (a drought risk dataset) is then produced as the product of drought hazard, exposure and vulnerability during 2000–2015. For drought exposure, the indicators of population and livestock density, agricultural land and water stress are chosen, while drought vulnerability composites multiple social, economic and infrastructural factors. Drought hazard tends to concentrate at the southern rim of Eurasia. Relatively large differences in drought exposure exist between different countries, but for drought vulnerability the differences are small. After considering the socio-economic components in risk assessment, most countries in West, South-Central and South Asia have the highest drought risk in DPEA. The datasets of drought events and risks are available at http://www.dx.doi.org/10.11922/sciencedb.898.

Surface relative humidity (RH) is a key element for weather and climate monitoring and research. However, RH is not as commonly applied in studying climate change, partly because the observation series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system. A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017, ChinaRHv1.0, has been developed. Most (685 or 91.82% of the total) station time series were inhomogeneous with one or more break points. The major breakpoints occurred in the early 2000s for many stations, especially in the humid and semi-humid zones, due to the implementation of automated observation across the country. The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records, for most of the biased station series. There are more break points detected by using the MASH (Multiple Analysis of Series for Homogenization) method, with biases mainly around −0.5% and 0.5%. These inhomogeneous biases are adjusted with reference to the most recent observations for each station. Based on the adjusted observations, the regional mean RH series of China shows little long-term trend during 1960–2017 [0.006% (10 yr)−1], contrasting with a false decreasing trend [−0.414% (10 yr)−1] in the raw data. It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China, including the jump in the early 2000s, raising a caveat for its application in studying climate change in the region.

A set of homogenized monthly mean surface air temperature (SAT) series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization (MASH) method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924–2016 at all the stations, ranging from 0.48 to 3.57°C (100 yr)−1, with a regional mean trend of 1.65°C (100 yr)−1; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5°C (100 yr)−1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data are available online at https://doi.org/www.dx.doi.org/10.11922/sciencedb.516.

The observation minus reanalysis (OMR) method is widely used to investigate the impact of urbanization and land use changes on regional climate. Based on homogenized temperature observations and the National Centers for Environmental Prediction/Department of Energy Atmospheric Model Intercomparison Project II reanalysis surface temperatures, we estimate the trend in diurnal temperature range (DTR) using the OMR method to be −0.324 ± 0.063 °C per decade for the period of 1980–2009 in eastern China. However, we find that reanalysis data produce a positive trend in downward surface solar radiation, while the observations do not, because the reanalysis data significantly overestimate the negative trend in total cloud coverage in the region. Moreover, the reanalysis considerably overestimates the negative trend in the local precipitation. These systematic biases cause an overestimation of the warming rate in daily maximum temperature in the reanalysis and therefore lead to the exaggerated effect of urbanization on the DTR via the OMR analysis. After adjusting the systematic bias in the trend of daily maximum temperature in the reanalysis data, the regional mean OMR trend in the DTR is estimated to be −0.065 °C per decade, which becomes much smaller than previous OMR calculations, and is more consistent with the previous estimates based on the comparison of urban and rural stations. These results not only indicate the methodological problem of the OMR method in many studies of urbanization effects but also hint at important aspects for improving the current reanalysis system. Urbanization impacts on the diurnal temperature range tend to be exaggerated by the “observation minus reanalysis” methodSystematic biases in the trends of surface solar radiation and other relevant variables for eastern China exist in the reanalysis dataThe bias‐adjusted regional mean “observation minus reanalysis” trend in diurnal temperature range is estimated to be −0.065 °C per decade

In this study, we conducted nested high‐resolution simulations using the Weather Research and Forecasting model coupled with a single‐layer urban canopy model to investigate the impact of extensive urbanization on regional precipitation over the Beijing‐Tianjin‐Hebei region in China. The results showed that extensive urbanization decreased precipitation considerably over and downwind of Beijing city. The prevalence of impermeable urban land inhibits local evaporation that feeds moisture into the overlying atmosphere, decreasing relative humidity and atmospheric instability. The dynamic precipitation recycling model was employed to estimate the precipitation that originates from local surface evaporation and large‐scale advection of moisture. Results showed that about 11% of the urbanization‐induced decrease in total precipitation over the Greater Beijing Region and its surroundings was contributed by the decrease in local recycled precipitation, while the other part (89%) was due to decreasing large‐scale advected precipitation. Results suggest that the low evaporation from urban land surfaces not only reduces the supply of water vapor for local recycled precipitation directly but also decreases the convective available potential energy and hence the conversion efficiency of atmospheric moisture into rainfall. The urbanization‐induced variations in local recycled precipitation were found to be correlated with the net atmospheric moisture flux on a monthly time scale. Numerical simulations show that extensive urbanization decreased precipitation considerably over and downwind of Beijing city. The prevalence of impermeable urban land inhibits local evaporation that feeds moisture into the overlying atmosphere, decreasing relative humidity and atmospheric instability. The low evaporation from urban land surface not only reduced the supply of water vapor for local recycled precipitation directly but also decreased the convective available potential energy and hence the conversion efficiency of atmospheric water vapor into precipitation. About 11% of the urbanization‐induced decrease in total precipitation over the Greater Beijing Region and its surroundings was contributed by the decrease in local recycled precipitation, while the other part (89%) was due to decreasing large‐scale advected precipitation. The impact of extensive urbanization on regional precipitation should be notable and deserve further studies, especially for the arid and semiarid regions. Extensive urbanization decreased precipitation considerably over and downwind of Beijing cityDecrease in large‐scale advected precipitation contributed to ~89% of urban‐induced rainfall reduction over the GBR and its surroundingsUrban‐induced changes in local recycled precipitation were correlated with net atmospheric moisture flux on a monthly time scale

The variation of the vegetation growing season in the Three-Rivers Headwater Region of the Tibetan Plateau has recently become a controversial topic. One issue is that the estimated local trend in the start of the vegetation growing season (SOS) based on remote sensing data is easily affected by outliers because this data series is short. In this study, we determine that the spring minimum temperature is the most influential factor for SOS. The significant negative linear relationship between the two variables in the region is evaluated using Moderate Resolution Imaging Spectroradiometer–Normalized Difference Vegetation Index data for 2000–13. We then reconstruct the SOS time series based on the temperature data for 1960–2013. The regional mean SOS shows an advancing trend of 1.42 d (10 yr)−1during 1960–2013, with the SOS occurring on the 160th and 151st days in 1960 and 2013, respectively. The advancing trend enhances to 6.04 d (10 yr)−1during the past 14 years. The spatiotemporal variations of the reconstructed SOS data are similar to those deduced from remote sensing data during the past 14 years. The latter exhibit an even larger regional mean trend of SOS [7.98 d (10 yr−1)] during 2000–13. The Arctic Oscillation is found to have significantly influenced the changing SOS, especially for the eastern part of the region, during 2000–13.

The WRF model with chemistry (WRF-Chem) was employed to simulate the impacts of anthropogenic aerosols on summer precipitation over the Beijing–Tianjin–Hebei urban agglomeration in China. With the aid of a high-resolution gridded inventory of anthropogenic emissions of trace gases and aerosols, we conducted relatively long-term regional simulations, considering direct, semi-direct and indirect effects of the aerosols. Comparing the results of sensitivity experiments with and without emissions, it was found that anthropogenic aerosols tended to enhance summer precipitation over the metropolitan areas. Domain-averaged rainfall was increased throughout the day, except for the time around noon. Aerosols shifted the precipitation probability distribution from light or moderate to extreme rain. Further analysis showed that the anthropogenic aerosol radiative forcing had a cooling effect at the land surface, but a warming effect in the atmosphere. However, enhanced convective strength and updrafts accompanied by water vapor increases and cyclone-like wind shear anomalies were found in the urban areas. These responses may originate from cloud microphysical effects of aerosols on convection, which were identified as the primary cause for the summer rainfall enhancement.

In this study, we investigated how different degrees of urbanization affect local and regional rainfall using high‐resolution simulations based on the Weather Research and Forecasting Model. The extreme rainfall event of 21 July 2012 in Beijing was simulated for three representative urban land use distributions (no urbanization, early urbanization level of 1980, and recent urbanization level of 2009). Results suggest that urban modification of rainfall is potentially sensitive to urban land use condition. Rainfall was increased significantly over the downwind Beijing metropolis because of the effects of early urbanization; however, recent conditions of high urban development caused no significant increase. Further comparative analysis revealed that positive urban thermodynamical effects (i.e., urban warming, increased sensible heat transportation, and enhanced convergence and vertical motions) play major roles in urban modification of rainfall during the early urbanization stage. However, after cities expand to a certain extent (i.e., urban agglomeration), the regional moisture depression induced by the prevalence of impervious urban land has an effect on atmospheric instability energy, which might negate the city's positive impact on regional rainfall. Additional results from regional climate simulations for 10 Julys confirm this supposition. Given the explosive urban population growth and increasing demand for freshwater in cities, the potential negative effects of the urban environment on precipitation are worth investigation, particularly in rapidly developing countries and regions. Effect of urbanization level on local and regional rainfall was simulatedRainfall was increased due to the effects of early urbanizationExtensive urbanization negates the city's positive impact on regional rainfall

Daily precipitation series at 15 stations in the Beijing metropolitan region (BMR) during 1960–2012 were homogenized using the multiple analysis of series for homogenization method, with additional adjustments based on analysis of empirical cumulative density function (ECDF) regarding climate extremes. The cumulative density functions of daily precipitation series, the trends of annual and seasonal precipitation, and summer extreme events during 1960–2012 in the original and final adjusted series at Beijing station were comparatively analyzed to show the necessity and efficiency of the new method. Results indicate that the ECDF adjustments can improve the homogeneity of high-order moments of daily series and the estimation of climate trends in extremes. The linear trends of the regional-mean annual and seasonal (spring, summer, autumn, and winter) precipitation series are −10.16, 4.97, −20.04, 5.02, and −0.11 mm (10 yr)−1, respectively. The trends over the BMR increase consistently for spring/autumn and decrease for the whole year/summer; however, the trends for winter decrease in southern parts and increase in northern parts. Urbanization affects local trends of precipitation amount, frequency, and intensity and their geographical patterns. For the urban-influenced sites, urbanization tends to slow down the magnitude of decrease in the precipitation and extreme amount series by approximately −10.4% and −6.0%, respectively; enhance the magnitude of decrease in precipitation frequency series by approximately 5.7%; reduce that of extremes by approximately −8.9%; and promote the decreasing trends in the summer intensity series of both precipitation and extremes by approximately 6.8% and 51.5%, respectively.

It is well-known that global warming due to anthropogenic atmospheric greenhouse effects advanced the start of the vegetation growing season (SOS) across the globe during the 20th century. Projections of further changes in the SOS for the 21st century under certain emissions scenarios (Representative Concentration Pathways, RCPs) are useful for improving understanding of the consequences of global warming. In this study, we first evaluate a linear relationship between the SOS (defined using the normalized difference vegetation index) and the April temperature for most land areas of the Northern Hemisphere for 1982–2008. Based on this relationship and the ensemble projection of April temperature under RCPs from the latest state-of-the-art global coupled climate models, we show the possible changes in the SOS for most of the land areas of the Northern Hemisphere during the 21st century. By around 2040–59, the SOS will have advanced by −4.7 days under RCP2.6, −8.4 days under RCP4.5, and −10.1 days under RCP8.5, relative to 1985–2004. By 2080–99, it will have advanced by −4.3 days under RCP2.6, −11.3 days under RCP4.5, and −21.6 days under RCP8.5. The geographic pattern of SOS advance is considerably dependent on that of the temperature sensitivity of the SOS. The larger the temperature sensitivity, the larger the date-shift-rate of the SOS.

Long-term meteorological observation series are fundamental for reflecting climate changes. However, almost all meteorological stations inevitably undergo relocation or changes in observation instruments, rules, and methods, which can result in systematic biases in the observation series for corresponding periods. Homogenization is a technique for adjusting these biases in order to assess the true trends in the time series. In recent years, homogenization has shifted its focus from the adjustments to climate mean status to the adjustments to information about climate extremes or extreme weather. Using case analyses of ideal and actual climate series, here we demonstrate the basic idea of homogenization, introduce new understanding obtained from recent studies of homogenization of climate series in China, and raise issues for further studies in this field, especially with regards to climate extremes, uncertainty of the statistical adjustments, and biased physical relationships among different climate variables due to adjustments in single variable series.

We simulated the impact of anthropogenic heat release (AHR) on the regional climate in three vast city agglomerations in China using the Weather Research and Forecasting model with nested high-resolution modeling. Based on energy consumption and high-quality land use data, we designed two scenarios to represent no-AHR and current-AHR conditions. By comparing the results of the two numerical experiments, changes of surface air temperature and precipitation due to AHR were quantified and analyzed. We concluded that AHR increases the temperature in these urbanized areas by about 0.5°C—1°C, and this increase is more pronounced in winter than in other seasons. The inclusion of AHR enhances the convergence of water vapor over urbanized areas. Together with the warming of the lower troposphere and the enhancement of ascending motions caused by AHR, the average convective available potential energy in urbanized areas is increased. Rainfall amounts in summer over urbanized areas are likely to increase and regional precipitation patterns to be altered to some extent.

Daily precipitation and temperature records at 13 stations for the period 1960–2008 were analyzed to identify climatic change and possible effects of urbanization on low-temperature precipitation [LTP, precipitation of ⩾ 0.1 mm d−1occurring under a daily minimum temperature (Tmin) of ⩽ 0°C] in the greater Beijing region (BJR), where a rapid process of urbanization has taken place over the last few decades. The paper provides a climatological overview of LTP in BJR. LTP contributes 61.7% to the total amount of precipitation in BJR in the cold season (November–March). There is a slight increasing trend [1.22 mm (10 yr)−1] in the amount of total precipitation for the cold season during 1960-2008. In contrast, the amount of LTP decreases by 0.6 mm (10 yr)−1. The warming rate of Tmin in BJR is 0.66°C (10 yr)−1. Correspondingly, the frequency of LTP decreases with increasing Tmin by −0.67 times per °C. The seasonal frequency and amount of LTP in southeast BJR (mostly urban sites) are 17%–20% less than those in the northwestern (rural and montane sites). The intensity of LTP for the urban sites and northeastern BJR exhibited significant enhancing trends [0.18 and 0.15 mm d−1(10 yr)−1, respectively]. The frequency of slight LTP (<0.2 mm d−1) significantly decreased throughout BJR [by about −15.74% (10 yr)−1in the urban area and northeast BJR], while the contribution of the two heaviest LTP events to total LTP amount significantly increased by 3.2% (10 yr)−1.

The observation minus reanalysis (OMR) method is widely used to investigate the impact of urbanization and land use change on climate. Here we present the OMR trends for the periods of 1979–1998 and 1989–2008 in eastern China, which appear inconsistent for the regions experiencing rapid urbanization during recent decades. Using Ensemble Empirical Mode Decomposition, we extract the secular trend and multidecadal variability (MDV) from the temperature observations at stations and the corresponding reanalysis data for the last century and find that, in general, MDV in the reanalysis data is weaker than that in the station observations. This systematic difference considerably modulates the magnitude of the OMR trends during different periods, leading to inconsistent estimates of the impact of urbanization. After MDV adjustment, the OMR trends for Beijing and Shanghai are consistent for the different periods, about 0.04°C–0.1°C/decade, much smaller than some previous estimates. We caution those using OMR methods to estimate the effect of urbanization and also for those using reanalysis data for a limited period in studies of this kind. MDV in the reanalysis data is different from that in the station observationsMDV discrepancy leads to inconsistent estimates of the impact of urbanizationMany published studies based on the OMR approach may have exaggerated the effect

Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960–2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downscale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross-validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downscaling to Hadley Centre Coupled Model Version 3 (HadCM3) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of the Fifth Coupled Model Inter-comparison Project (CMIP5). The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5°C, and the 10th of daily minimum temperatures will increase by about 2°C during the period 2011–35 relative to 1980–99.

Instrumental and proxy data series from historical archives are compared over the period of the Maunder Minimum (AD 1645-1715) for western Europe (central England, Paris and Switzerland) and China. Winters became colder and drier over the late seventeenth century in western Europe. In China, however, winters were coldest around AD 1650 and became warmer and stormier over the subsequent decades. A significant warming of winters is observed around AD 1700, which, in Paris, corresponds to an increase in zonal wind frequency and a decrease in dust frequency in China. Summers during the late seventeenth century were rather dry in China and moderately wet in western Europe. These trends reversed around AD 1700, when summers became wetter in China and somewhat drier in western Europe. An approach to analysing seasonal anomalies is outlined based on a synoptic interpretation of historical weather charts derived from multi-proxy mapping.

Prominent slowdown in near‐surface wind speed (NSWS) has been observed in the Beijing‐Tianjin‐Hebei (BTH) region of China, which has important implications for regional haze mitigation and wind energy production. Other than large‐scale climatic variation, this decline is suspected of being linked to the impact of urbanization. Based on nested high‐resolution simulations using the Weather Research and Forecasting model, we assessed quantitatively the respective roles of urban development and anthropogenic heat (AH) in regional NSWSchange over the BTH region from a climatological perspective. The contribution of urbanization to station average annual NSWSover 1980–2018 was estimated to be approximately −0.37 m s−1, with the largest decline exceeding −1.0 m s−1in some highly urbanized areas. The effect of urban development on NSWSwas found partly offset by the AH effect. Urbanization‐induced NSWSdecrease was most remarkable in spring and early in the night. Urbanization impacts on NSWSwere more remarkable when regional prevailing winds were northeasterly and northwesterly. Further analysis showed that the simulated urbanization‐induced decreases in NSWSwere highly correlated with the background regional NSWSlevels. Our simulation results indicated that urbanization was an important but not a dominant cause of the observed regional wind stilling, even in such a rapidly urbanizing region, although it had remarkable influence on local NSWSin highly urbanized areas. The contribution of urbanization to station average annual near‐surface wind speed over 1980–2018 was estimated to be about −0.37 m s−1Urbanization‐induced decrease in near‐surface wind speed was most remarkable in spring and early in the nightThe effect of urban development on annual near‐surface wind speed was found partly offset by the effect of anthropogenic heat