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An extended linear scaling method for downscaling temperature and its implication in the Jhelum River basin, Pakistan, and India, using CMIP5 GCMs

机译：使用CMIP5 GCM在Jhelum流域，巴基斯坦和印度进行降温的扩展线性缩放方法及其应用

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摘要

In this study, the linear scaling method used for the downscaling of temperature was extended from monthly scaling factors to daily scaling factors (SFs) to improve the daily variations in the corrected temperature. In the original linear scaling (OLS), mean monthly SFs are used to correct the future data, but mean daily SFs are used to correct the future data in the extended linear scaling (ELS) method. The proposed method was evaluated in the Jhelum River basin for the period 1986-2000, using the observed maximum temperature (Tmax) and minimum temperature (Tmin) of 18 climate stations and the simulated Tmax and Tmin of five global climate models (GCMs) (GFDL-ESM2G, NorESM1-ME, HadGEM2-ES, MIROC5, and CanESM2), and the method was also compared with OLS to observe the improvement. Before the evaluation of ELS, these GCMs were also evaluated using their raw data against the observed data for the same period (1986-2000). Four statistical indicators, i.e., error in mean, error in standard deviation, root mean square error, and correlation coefficient, were used for the evaluation process. The evaluation results with GCMs' raw data showed that GFDL-ESM2G and MIROC5 performed better than other GCMs according to all the indicators but with unsatisfactory results that confine their direct application in the basin. Nevertheless, after the correction with ELS, a noticeable improvement was observed in all the indicators except correlation coefficient because this method only adjusts (corrects) the magnitude. It was also noticed that the daily variations of the observed data were better captured by the corrected data with ELS than OLS. Finally, the ELS method was applied for the downscaling of five GCMs' Tmax and Tmin for the period of 2041-2070 under RCP8.5 in the Jhelum basin. The results showed that the basin would face hotter climate in the future relative to the present climate, which may result in increasing water requirements in public, industrial, and agriculture sectors; change in the hydrological cycle and monsoon pattern; and lack of glaciers in the basin.
机译：在这项研究中，用于降温的线性缩放方法已从每月缩放因子扩展为每日缩放因子（SF），以改善校正后的温度的每日变化。在原始线性缩放（OLS）中，使用扩展线性缩放（ELS）方法将平均每月SF用于校正未来数据，但将平均每日SF用于校正未来数据。使用观测到的18个气候站的最高温度（Tmax）和最低温度（Tmin）以及五个全球气候模型（GCM）的模拟Tmax和Tmin（1986-2000年）在耶鲁姆河流域评估了该方法。 GFDL-ESM2G，NorESM1-ME，HadGEM2-ES，MIROC5和CanESM2），并将该方法与OLS进行比较以观察其改进。在评估ELS之前，还使用原始数据对照同期（1986-2000年）的观测数据对这些GCM进行了评估。评估过程使用了四个统计指标，即均值误差，标准偏差误差，均方根误差和相关系数。利用GCM原始数据进行的评估结果表明，根据所有指标，GFDL-ESM2G和MIROC5的性能均优于其他GCM，但结果不理想，限制了它们在盆地中的直接应用。然而，用ELS校正后，除相关系数外，所有指标均观察到明显改善，因为该方法仅调整（校正）幅度。还注意到用ELS校正的数据比OLS更好地捕获了观测数据的每日变化。最后，ELS方法被应用于Jhelum盆地在RCP8.5下5个GCM的Tmax和Tmin在2041-2070期间的缩减。结果表明，与当前气候相比，流域未来将面临更热的气候，这可能导致公共，工业和农业部门的用水需求增加；水文循环和季风模式的变化；流域内缺乏冰川。

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  • 来源
    《Theoretical and applied climatology》 |2017年第4期|725-734|共10页
  • 作者

    Mahmood Rashid; Jia Shaofeng;

  • 作者单位

    Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China;

    Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China;

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