High-resolution climate projection dataset in Central Asia (1986-2005 and 2031-2050)

Central Asia (referred to as 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 of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments. We applied three bias-corrected global climate models (GCMs) to conduct 9-km resolution dynamical downscaling in CA. A high-resolution climate projection dataset over CA (the HCPD-CA dataset) is derived from the downscaled results, which contains four static variables and ten meteorological elements that are widely used to drive ecological and hydrological models. The static variables are terrain height (HGT, m), land use category (LU_INDEX, 21 categories), land mask (LANDMASK, 1 for land and 0 for water), and soil category (ISLTYP, 16 categories). The meteorological elements are daily precipitation (PREC, mm/day), daily mean/maximum/minimum temperature at 2m (T2MEAN/T2MAX/T2MIN, K), daily mean relative humidity at 2m (RH2MEAN, %), daily mean eastward and northward wind at 10m (U10MEAN/V10MEAN, m/s), daily mean downward shortwave/longwave flux at surface (SWD/LWD, W/m2), and daily mean surface pressure (PSFC, Pa). The reference and future periods are 1986-2005 and 2031-2050, respectively. The carbon emission scenario is RCP4.5. 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. 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.

中亚地区（以下简称中亚，CA）因其脆弱的生态系统、频发的自然灾害、紧张的水资源状况以及加速的冰川融化，而成为对气候变化最为敏感的区域之一。这凸显了对于高分辨率气候预测数据集的迫切需求，此类数据集可应用于脆弱性、影响以及适应性评估。本研究采用三种偏差校正的全球气候模型（GCMs）在中亚地区进行了9公里分辨率的动力降尺度。由降尺度结果衍生出的高分辨率气候预测数据集（HCPD-CA数据集）覆盖了中亚地区，其中包含四个静态变量和十个广泛用于驱动生态和水资源模型的气象要素。静态变量包括地形高度（HGT，米）、土地利用类别（LU_INDEX，21个类别）、土地掩膜（LANDMASK，陆地为1，水域为0）以及土壤类别（ISLTYP，16个类别）。气象要素包括每日降水量（PREC，毫米/天）、每日2米处的平均/最大/最小温度（T2MEAN/T2MAX/T2MIN，开尔文）、每日2米处的平均相对湿度（RH2MEAN，百分比）、每日10米处的平均东西风和南北风（U10MEAN/V10MEAN，米/秒）、每日地表的平均短波/长波辐射通量（SWD/LWD，瓦特/平方米）以及每日地表的平均气压（PSFC，帕斯卡）。参考期和未来期分别为1986-2005年和2031-2050年，碳排放情景为RCP4.5。结果显示，该数据产品在描述中亚所有要素的气候学特征方面具有优良的品质，确保了数据集适用于未来的研究。中亚近期未来预测的气候变化主要特征为强烈增温（年平均温度上升1.62-2.02℃）以及地表向下短波和长波辐射通量的显著增加，其他要素变化较小。本研究所提出的HCPD-CA数据集为评估气候变化对中亚多领域的影响提供了科学依据，特别是在生态和水资源系统方面。