HCPD-CA: high-resolution climate projection dataset in central Asia

Abstract

Abstract. 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 https://doi.org/10.11888/Meteoro.tpdc.271759 (Qiu, 2021).