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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.

File naming and required software

The names of the files containing the static variables follow the order: [dataset name]_[variable name].nc. For example, the file name, HCPD-CA_ISLTYP.nc, represents the soil category in the HCPD-CA dataset. The names of the files containing the meteorological elements follow the order: [dataset name]_[experiment name]_[element name]_[year].[time frequency].nc. For example, the file name, HCPD-CA_WRF_CCSM_COR_T2MAX_2004.mon.nc, represents the monthly mean T2MAX of 2004 from the experiment WRF_CCSM_COR in the HCPD-CA dataset.
The Climate Data Operators (CDO, https://code.mpimet.mpg.de/projects/cdo), Python modules (like netCDF4, Xarray, and Numpy), and NCAR Command Languages (NCL, https://www.ncl.ucar.edu/) are recommended to do operations on the netCDF files.

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Qiu, Y. (2021). High-resolution climate projection dataset in Central Asia (1986-2005 and 2031-2050). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.271759. CSTR: 18406.11.Meteoro.tpdc.271759. (Download the reference： RIS | Bibtex )

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

References literature

1.Qiu, Y., Feng, J., Yan, Z., and Wang, J. (2022). HCPD-CA: high-resolution climate projection dataset in central Asia. Earth Syst. Sci. Data, 14, 2195–2208, https://doi.org/10.5194/essd-14-2195-2022. (View Details )

2.Qiu, Y., Feng, J., & Yan, Z. et al. (2021). High-resolution dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs. Clim Dyn. https://doi.org/10.1007/s00382-021-05934-2 (View Details )

Support Program

Pan-Third Pole Environment Study for a Green Silk Road-A CAS Strategic Priority A Program (No:XDA20000000)

The General Project of the National Natural Science Foundation of China (No:41875134)

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)

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