基于CMIP6气候模式降水和气温的两种降尺度模型研究以黄河兰州以上为例

Study on two downscaling models for precipitation and temperature based on the CMIP6 climate model — a case study above Lanzhou on the Yellow River

  • 摘要: 获取高分辨率气温和降水数据是流域气候变化及洪旱灾害风险评估的关键基础工作。基于黄河兰州以上流域16个气象站实测气温、降水资料及NCEP再分析数据,筛选最优网格及大气环流因子,建立SDSM(Statistical Downscaling Model)统计降尺度模型及随机森林(Random Forest, RF)降尺度模型,并利用站点实测数据作精度评估。在此基础上,选用CMIP6中MRI-ESM2-0气候模式的优选大气环流因子进一步验证两种降尺度模型结果的可靠性。结果表明:(1)率定期(1971—2004年),基于筛选的NCEP再分析数据构建降尺度模型,随机森林模型优于SDSM模型,模拟日均气温纳什效率系数达到0.97,降水模拟在月尺度上纳什效率系数超过0.64。(2)验证期(2005—2014年)采用MRI-ESM2-0模式的优选大气环流因子,相对于SDSM模型,随机森林模型气温降尺度的绝对误差 E_A\leqslant 0.5 ℃占比提升4.3%, E_A\geqslant 1.0 ℃占比降低9.1%,月降水量模拟 E_A\leqslant 2.0 mm比例提升6.7%, E_A\geqslant 10.0 mm比例降低15.8%,且在降水充沛及稀少月份表现均优于SDSM模型。(3)总体而言,两种降尺度模型在黄河兰州以上流域具有一定的适用性,日气温模拟效果优于降水,随机森林模型模拟精度相对更高。研究结果可为统计降尺度方法在区域上的选择和未来气候变化研究提供参考。

     

    Abstract: The acquisition of high-resolution temperature and precipitation data is crucial for climate change and flood and drought risk assessments in watersheds. Using measured temperature and precipitation data from 16 meteorological stations above Lanzhou in the Yellow River Basin and NCEP reanalysis data, the optimal grid and atmospheric circulation factors were selected to establish the SDSM statistical downscaling model and the Random Forest (RF) downscaling model. Accuracy evaluations were conducted using station-measured data. Based on this, the preferred atmospheric circulation factors from the MRI-ESM2-0 climate model in CMIP6 were used to further verify the reliability of the two downscaling model results. The results show that: ① During the calibration period (1971–2004), the downscaling model was constructed based on the screened NCEP reanalysis data. The Random Forest model outperforms the SDSM model, with the R^2 and Nash-Sutcliffe efficiency coefficient for daily temperature simulation reaching 0.97, and the R^2 and Nash-Sutcliffe efficiency coefficient for precipitation simulation exceeding 0.64 at the monthly scale. ② During the validation period (2005–2014), using the preferred atmospheric circulation factors of the MRI-ESM2-0 model, compared to the SDSM model, the proportion of absolute error ( E_A ) in the Random Forest model's temperature downscaling where E_A\leqslant 0.5 ℃ increased by 4.3%, while the proportion where E_A\geqslant 1.0 ℃ decreased by 9.1%. The proportion of monthly precipitation simulations where E_A\leqslant 2.0 mm increased by 6.7%, while the proportion where E_A\geqslant 10.0 mm decreased by 15.8%. The Random Forest model also performed better than the SDSM model in both wet and dry months. ③ Overall, both downscaling models are applicable above Lanzhou in the Yellow River Basin, with daily temperature simulation being more effective than precipitation simulation, and the Random Forest model demonstrating relatively higher simulation accuracy. The study results provide a reference for selecting statistical downscaling methods in the region and future climate change research.

     

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