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Central Asia (referred to as CA) is one of the climate change hot spots due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments in this region. In this study, a high-resolution (9 km) climate projection dataset over CA (the HCPD-CA dataset) is derived from dynamically downscaled results based on multiple bias-corrected global climate models and contains four geostatic variables and 10 meteorological elements that are widely used to drive ecological and hydrological models. The reference and future periods are 1986–2005 and 2031–2050, respectively. The carbon emission scenario is Representative Concentration Pathway (RCP) 4.5. The evaluation shows that the data product has good quality in describing the climatology of all the elements in CA despite some systematic biases, which ensures the suitability of the dataset for future research. Main features of projected climate changes over CA in the near-term future are strong warming (annual mean temperature increasing by 1.62–2.02 ∘ C) and a significant increase in downward shortwave and longwave flux at the surface, with minor changes in other elements (e.g., precipitation, relative humidity at 2 m, and wind speed at 10 m). The HCPD-CA dataset presented here serves as a scientific basis for assessing the potential impacts of projected climate changes over CA on many sectors, especially on ecological and hydrological systems. It has the DOI 10.11888/Meteoro.tpdc.271759 (Qiu, 2021). [ABSTRACT FROM AUTHOR]

Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9 km-resolution regional climate simulations in CA for the reference (1986–2005) and future (2031–2050) periods. The regional climate model (RCM) and GCM simulated daily temperature and precipitation are evaluated and the results show that both the bias-correction technique and dynamical downscaling method obtain numerous added values in reproducing the historical climate in CA, respect to the original GCMs. The former contributes more to reducing the biases of the climatology and the latter contributes more to capturing the spatial pattern. The RCM simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual mean temperature in a range of 1.63–2.01 ℃, relative to the reference period. Pronounced warming is detected north of ~ 45° N in CA from autumn to spring, which can be explained by the snow-albedo feedback. Enhanced warming projected in many mountains in the world is not found in CA, which is consistent with the study based on the reanalysis datasets during the past. Heatwave day frequency, number and maximum duration are expected to become more severe by 2031–2050. Changes in precipitation and Standard Precipitation Index (SPI) shows a wetter condition in CA in the coming decades. However, a fairer assessment of the wet/dry change with Standard Precipitation Evapotranspiration Index (SPEI) which takes into account of both precipitation and potential evapotranspiration reveals a drier condition. The climate change projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA. [ABSTRACT FROM AUTHOR]

Central Asia (referred to as CA) is one of the climate change Hot-Spots due to the fragile ecosystems, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments in ecological and hydrological systems. In this study, a high-resolution (9 km) climate projection dataset over CA (the HCPD-CA dataset) is derived from dynamically downscaled results based on multiple bias-corrected global climate models, and contains ten meteorological elements that are widely used to drive ecological and hydrological models. The reference and future periods are 1986-2005 and 2031-2050, respectively. The carbon emission scenario is Representative Concentration Pathway (RCP) 4.5. The results show the data product has good quality in describing the climatology of all the elements in CA, which ensures the suitability of the dataset for future research. The main feature of projected climate changes in CA in the near-term future is strong warming (annual mean temperature increasing by 1.62-2.02) and significant increase in downward shortwave and longwave flux at surface, with minor changes in other elements (e. g., precipitation, relative humidity at 2 m, and wind speed at 10 m). The HCPD-CA dataset presented here serves as a scientific basis for assessing the impacts of climate change over CA on many sectors, especially on ecological and hydrological systems. It is publicly available at http://data.tpdc.ac.cn/en/disallow/24c7467c-44a6-44ab-bbcf-e6e346dd41d0/(Qiu, 2021). [ABSTRACT FROM AUTHOR]

Long‐term changes in daily high‐temperature extremes in the summers since the late 19th century in Shanghai and their relationships with decadal or multi‐decadal climatic oscillations have been investigated via non‐stationary statistical modelling based on parametric and nonparametric frameworks. Compared to stationary modelling with the assumption of an unchanging climate, time‐varying non‐stationary models based on the generalized extreme value (GEV) result in much more reasonable estimations of the temperature extremes for the present climate in terms of their return levels and return periods. However, nonparametric modelling with cubic spline smoothing reproduced even better multi‐decadal variability for different periods. The nonparametric method also reduces the biases of estimation, especially in the heavy tail of temperature extremes in parametric GEV models. Additionally, non‐stationary models with large‐scale climate indices indicate clear influences of the large‐scale climate indices, especially the last winter's strong signals from the tropical Pacific Ocean and Indian Ocean. The significant influences of multi‐decadal variabilities from the northern Atlantic in the parametric GEV model are probably also explained by spline terms in the nonparametric method. Further application to other areas in China indicates that the developed nonparametric model can fit their extremes correctly but notable biases also remain in the estimation of distributions and heavy tails. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

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 http://www.dx.doi.org/10.11922/sciencedb.516. [ABSTRACT FROM AUTHOR]

ABSTRACT: The geographical extent, magnitude, and uncertainty of global climate change have been widely discussed and have critical policy implications at both global and local scales. In this study, a new analysis of annual mean global land surface air temperature since 1880 was generated, which has greater coverage and lower uncertainty than previous distributions. The Biased Sentinel Hospitals Areal Disease Estimation (BSHADE) method, used in this study, makes a best linear unbiased estimation (BLUE) when a sample is small and biased to a spatially heterogeneous population. For the period of 1901–2010, the warming trend was found to be 0.109 °C decade−1 with 95% confidence intervals between 0.081 °C and 0.137 °C. Additionally, warming exhibited different spatial patterns in different periods. In the early 20th century (1923–1950), warming occurred mainly in the mid‐high latitudes of the Northern Hemisphere, whereas in the most recent decades (1977–2014), warming was more spatially extensive across the global land surface. Compared with other common methods, the difference in results appears in the areas with few stations and in the early years, when stations had sparse coverage and were unevenly distributed. Validation, which was performed using real data that simulated the historic situation, showed a smaller error in the BSHADE estimate than in other methods. This study produced a new database with greater coverage and less uncertainty that will improve the understanding of climate dynamics on the Earth since 1880, especially in isolated areas and early periods, and will benefit the assessment of climate‐change‐related issues, such as the effects of human activities. [ABSTRACT FROM AUTHOR]

On 21-22 July 2012, torrential rains hit North China, with the daily precipitation record at Beijing station reaching 160.6 mm; this event is named the Beijing 7-21 case. This paper assesses the likelihood of the occurrence of local torrential rains, such as the Beijing 7-21 case, from the perspective of climate variability. In particular, the influence of the Pacific Decadal Oscillation (PDO) is assessed. There were five extreme events, with daily precipitation records equal to or larger than 160.6 mm, at Beijing station during the period 1951-2012; all of these events happened during negative phases of the PDO. The present analysis indicates that precipitation events more extreme than the Beijing 7-21 case should happen more than once per decade during negative phases of the PDO, but only about once every four decades during positive PDO phases. The negative phase of the PDO is found to be associated with a much greater probability of daily records of southerly winds in North China during summer. Strong southerly summer monsoons are deemed favorable for increasing the occurrence of local extreme rainfall over North China. [ABSTRACT FROM AUTHOR]

ABSTRACT An 'alternative multi-model ensemble mean' ( AMME) method was developed for the near-term projection of regional climate change by taking into account the capacity of currently available climate models in simulating specific timescale components. These components included a climatological mean (Mean), an amplitude-frequency modulated annual cycle ( MAC), multi-decadal variability ( MDV), a secular trend ( ST), and short-term variability ( SV). The latter four components were extracted adaptively by the ensemble empirical mode decomposition filter from the climate series. For each component, a reconstructed simulation was determined from ensemble of a limited number of model simulations that could reproduce the component in the observation relatively well. An AMME simulation was obtained by combining the five components. The new method was illustrated to construct an AMME simulation of the monthly near-surface temperature series for the training period 1902-1990 in eastern China and was applied to the validation period 1991-2004. For the eastern China average, the best performance arose from MPI-ESM-MR for Mean, IPSL-CM5A-LR for MAC, ACCESS1.3 for MDV, GFDL-ESM2M for ST, and GISS-E2-H- CC for SV. Serving as a novel tool for producing reasonable near-term future climate change scenarios by utilizing currently available model simulations, the AMME exhibited a better performance in reproducing both past and near-term 'future' climate than conventional multi-model ensemble means and weighted average schemes. [ABSTRACT FROM AUTHOR]

ABSTRACT A new homogenized temperature data set called the China Homogenized Historical Temperature Dataset ( CHHTD-V1.0) has been developed, and it includes daily and monthly mean temperature series from 2419 national stations distributed throughout mainland China for the period from 1951 to the present. The inhomogeneities in individual station series were detected using a penalized maximum t-test ( PMT) that accounted for the first-order autocorrelation. Detailed metadata information was applied to validate the changepoints caused by changes in local observation systems. Comparative analyses suggested that the quantile-matching ( QM) adjustments that accounted for high-order discontinuities led to more reasonable results than the MEAN adjustments for the daily temperature series. Therefore, the QM method was applied to adjust the discontinuities caused by non-climate changes such as changing of observing site, instrumentation and observation environments. The physical consistency among the daily maximum, mean and minimum temperatures ( Tmax, Tm and Tmin) was also checked for each station. Based on the new homogenized data set, linear trends in the annual and seasonal temperature series from 1960 to 2014 were calculated. In comparison with the original data set, the homogenized data set improves the geographical consistency of the long-term climate trends over the region. The updated nationwide mean warming rate reached 0.22 °C per 10 years for the Tmax, 0.27 °C per 10 years for the Tm and 0.38 °C per 10 years for the Tmin from 1960 to 2014, which are considerably larger than the previous estimates that were based on the more frequently used networks of a few hundred stations in China. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Global warming exerts a lengthening effect on the growing season, with observational evidences emerging from different regions over the world. However, the difficulty for a global overview of this effect for the last century arises from limited availability of the long-term daily observations. In this study, we find a good linear relationship between the start (end) date of local growing season (LGS) and the monthly mean temperature in April (October) using the global gridded daily temperature dataset for 1960-1999. Using homogenized daily temperature records from nine stations where the time series go back to the beginning of the twentieth century, we find that the rate of change in the start (end) date of the LGS for per degree warming in April (October) mean temperature keeps nearly constant throughout the time. This enables us to study LGS changes during the last century using global gridded monthly mean temperature data. The results show that during the period 1901-2009, averaged over the observation areas, the LGS length has increased by a rate of 0.89 days decade, mainly due to an earlier start (−0.58 days decade). This is smaller than those estimates for the late half of the twentieth century, because of multidecadal climate variability (MDV). A MDV component of the LGS index series is extracted by using Ensemble Empirical Mode Decomposition method. The MDV exhibits significant positive correlation with the Atlantic Multi-decadal Oscillation (AMO) over most of the Northern Hemisphere lands, but negative in parts of North America and Western Asia for start date of LGS. These are explained by analyzing differences in atmospheric circulation expressed by sea level pressure departures between the warm and cool phases of AMO. It is suggested that MDV in association with AMO accelerates the lengthening of LGS in Northern Hemisphere by 53 % for the period 1980-2009. [ABSTRACT FROM AUTHOR]

As one of the largest arid and semi-arid regions in the world, central Asia (CA) is very sensitive to changes in regional climate. However, because of the poor continuity of daily observational precipitation records in CA, the spatial and temporal variations of extreme precipitation in recent decades remain unclear. Considering their good spatial and temporal continuity, gridded data, such as Climate Prediction Center (CPC) global precipitation, and reanalysis data, such as ERA-Interim (ERA), are helpful for exploring the spatial–temporal variations of extreme precipitation. This study evaluates how well CPC and ERA can represent observed precipitation extremes by comparing the differences in eight extreme precipitation indices and observation data at 84 meteorological stations. The results indicate that the CPC (except for 1979–1981) is more suitable for depicting changes in precipitation extremes. Based on the CPC data for the period 1982–2020, we found that seven indices of precipitation extremes, including consecutive wet days (CWD), max1-day precipitation amount (Rx1day), max5-day precipitation amount (Rx5day), number of heavy precipitation days (R10), very wet days (R95p), annual total precipitation in wet days (PRCPTOT), and simple precipitation intensity index (SDII) have increased by 0.2 d/10a, 0.9 mm/10a, 1.8 mm/10a, 0.3 d/10, 8.4 mm/10a, 14.3 mm/10a and 0.1 mm/d/10a, respectively, and the consecutive dry days (CDDs) have decreased by −3.10 d/10a. It is notable that CDDs decreased significantly in the north of Xinjiang (XJ) but increased in Kyrgyzstan (KG), Tajikistan (TI), and eastern Turkmenistan (TX). The other indices increased clearly in the west of XJ, north of Kazakhstan (KZ), and east of KG but decreased in the south of KG, TI, and parts of XJ. For most indices, the largest change occurred in spring, the main season of precipitation in CA. Therefore, the large-scale atmospheric circulation in April is analyzed to contrast between the most and least precipitation years for the region. A typical circulation pattern in April for those extremely wet years includes an abnormal low-pressure center at 850 hpa to the east of the Caspian Sea, which enhances the southerly winds from the Indian Ocean and hence the transportation of water vapor required for precipitation into CA. This abnormal circulation pattern occurred more frequently after 2001 than before, thus partly explaining the recent increasing trends of precipitation extremes in CA. [ABSTRACT FROM AUTHOR]