Abstract
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.
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Availability of data and material
The downscaled results produced in this study will be available at National Tibetan Plateau/Third Pole Environment Data Center (http://data.tpdc.ac.cn/en/).
Code availability
The code used to correct the climatology of the global climate models is available at https://rda.ucar.edu/datasets/ds316.1/#!software. The python project used to calculate SPI and SPEI is available at https://github.com/monocongo/climate_indices.
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Acknowledgements
This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grand no. XDA20020201) and the General Project of the National Natural Science Foundation of China (Grand no. 41875134). In addition, we thank two anonymous reviewers for their helpful comments.
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This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grand no. XDA20020201) and the General Project of the National Natural Science Foundation of China (Grand no. 41875134).
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All the authors except ZL made contributions to the conception or design of the work. YQ did the analyses and drafted the work and others except ZL revised it. ZL contributed to the collection of stations’ data.
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Qiu, Y., Feng, J., Yan, Z. et al. High-resolution dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs. Clim Dyn 58, 777–791 (2022). https://doi.org/10.1007/s00382-021-05934-2
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DOI: https://doi.org/10.1007/s00382-021-05934-2