Abstract

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).

Highlights

  • It is publicly available at http://data.tpdc.ac.cn/en/disallow/24c7467c-44a6-44ab-bbcf-Central Asia has complex terrain and diverse climates and is among the most vulnerable regions to climate change due to fragile ecosystems (Zhang et al, 2016;Seddon et al, 2016;Gessner et al, 2013), strained water resources (Frenken, 2013), and accelerated glacier melting (Narama et al., 2010;Sorg et al, 2012), which underscores the need to achieve high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments in ecological and hydrological systems

  • Data climate models (GCMs) can describe the response of the global circulation to large-scale forcing, such as greenhouse gases and solar radiation (Giorgi, 2019). Their horizontal resolutions are too coarse to account for the effects of local-scale forcing and processes, such as complex topography, land cover distribution, and dynamical processes occurring at the mesoscale (Giorgi et al, 2016;Qiu et al, 2017;Torma et al, 2015)

  • The Regional climate models (RCMs) simulations with the bias-corrected GCMs (MPI, CCSM, and Had) as the driving data are referred to as WRF_MPI_COR, WRF_CCSM_COR, and WRF_Had_COR, respectively (“COR” means using the bias-correction technique)

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Summary

Introduction

It is publicly available at http://data.tpdc.ac.cn/en/disallow/24c7467c-44a6-44ab-bbcf-Central Asia (referred to as CA, Fig. 1a) has complex terrain and diverse climates and is among the most vulnerable regions to climate change due to fragile ecosystems (Zhang et al, 2016;Seddon et al, 2016;Gessner et al, 2013), strained water resources (Frenken, 2013), and accelerated glacier melting (Narama et al., 2010;Sorg et al, 2012), which underscores the need to achieve high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments in ecological and hydrological systems. Data climate models (GCMs) can describe the response of the global circulation to large-scale forcing, such as greenhouse gases and solar radiation (Giorgi, 2019). Their horizontal resolutions are too coarse to account for the effects of local-scale forcing and processes, such as complex topography, land cover distribution, and dynamical processes occurring at the mesoscale (Giorgi et al, 2016;Qiu et al, 2017;Torma et al, 2015)

Methods
Results
Conclusion

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