Abstract
To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia (CA), six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and their changes in the near-term future (2031–50) are assessed relative to the reference period (1986–2005). The quantile mapping (QM) method is applied to correct the model data before calculating the indicators. Results show the QM method largely reduces the biases in all the indicators. Growing season length (GSL, day), summer days (SU, day), warm spell duration index (WSDI, day), and tropical nights (TR, day) are projected to significantly increase over CA, and frost days (FD, day) are projected to decrease. However, changes in biologically effective degree days (BEDD, °C) are spatially heterogeneous. The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.
摘要
为了理解未来的气候变化对中亚农业的可能影响, 本研究基于CMIP5的3个全球气候模式的9千米分辨率动力降尺度结果, 计算了6个农业气候指数, 并评估了它们在未来的变化(2031-2050 vs 1986-2005). 在计算这些指数之前, 我们使用分位数映射法订正了模式数据. 结果显示分位数映射法大大减小了各个指数的偏差. 生长季长度、 夏日天数、 热浪天数和热夜天数在整个中亚将显著增加. 然而, 有效积温的未来变化具有空间异质性. 这个高分辨率的中亚农业气候指数预估数据集可被用于评估未来气候变化给中亚农业带来的风险, 对制定适应和减缓措施有参考价值.
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Acknowledgements
This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA20020201) and the General Project of the National Natural Science Foundation of China (Grant No. 41875134). The work was carried out at National Supercomputer Center in Tianjin, and this research was supported by TianHe Qingsuo Project-special fund project in the field of climate, meteorology, and ocean. The produced dataset is provided by the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).
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Qiu, Y., Feng, J., Yan, Z. et al. High-resolution Projection Dataset of Agroclimatic Indicators over Central Asia. Adv. Atmos. Sci. 39, 1734–1745 (2022). https://doi.org/10.1007/s00376-022-2008-3
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DOI: https://doi.org/10.1007/s00376-022-2008-3