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Evaluating the long-term changes in temperature over the low- latitude plateau in China using a statistical downscaling method

机译:使用统计降尺度方法评估中国低纬高原地区的长期温度变化

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摘要

A statistical downscaling method (SDM) has been established through multiple stepwise regressions of predictor principal components using the ERA-Interim reanalysis data and the meteorological data collected from 115 stations in the low-latitude plateau in China from 1981 to 2015. The skill of the SDM was checked by comparing the results of the different predictor combinations and the different time lengths used to construct the SDM. In addition, compared to the historical simulation of the coupled Max Planck Institute Earth System Model (MPI-ESM-LR), better performance can be achieved by using the ERA-Interim data to construct the SDM in the low-latitude plateau. The long-term changes in temperature from 1981 to 2015 in the ERA-Interim reanalysis data are calibrated by the SDM over the low-latitude plateau of China. Furthermore, the SDM is projected into the simulation results of the MPI-ESM-LR model to construct a suitable SDM (ERA-SDM), and then the ERA-SDM is implemented to evaluate the future temperature changes in the low-latitude plateau during the period of 2018-2100 using the simulation results of the MPI-ESM-LR model under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The results showed that an increase in temperature of 0.3 degrees C decade(-1) was found from 1981 to 2015, in which the fastest increase of 0.4 degrees C decade(-1) occurred in winter and the slowest increase of 0.2 degrees C decade(-1) occurred in autumn. Most models in CMIP5 failed to simulate the long-term changes in temperature over the last 30years in the low-latitude plateau region, and the temperature in the low-latitude plateau was underestimated by 2.4 degrees C using the 22 models. The SDM improved the annual and seasonal temperature characteristics and inter-annual and seasonal changes simulated by the MPI-ESM-LR. The future temperature predictions by the ERA-SDM indicated that the fastest temperature increase of 0.271 degrees C decade(-1) was found in spring under the RCP8.5 scenario. A faster rate of temperature increase was found in the northern part of the low-latitude plateau than in the southern part under the RCP8.5 scenario.
机译:通过使用ERA-Interim再分析数据和1981年至2015年从中国低纬高原的115个站点收集的气象数据,对预测因子的主要成分进行多次逐步回归,建立了统计缩减方法(SDM)。通过比较不同预测变量组合的结果和用于构建SDM的不同时间长度来检查SDM。另外,与马克斯·普朗克研究所地球系统耦合模型(MPI-ESM-LR)的历史模拟相比,通过使用ERA-Interim数据在低纬度高原构建SDM,可以获得更好的性能。 1981年至2015年ERA-Interim再分析数据中的长期温度变化是由SDM在中国低纬高原上进行校准的。此外,将SDM投影到MPI-ESM-LR模型的模拟结果中以构建合适的SDM(ERA-SDM),然后实施ERA-SDM来评估未来低纬高原的温度变化。分别使用MCP-ESM-LR模型在RCP2.6,RCP4.5和RCP8.5方案下的模拟结果得出的2018-2100年期间。结果表明,从1981年到2015年,温度升高了0.3摄氏度十年(-1),其中冬季升高了0.4摄氏度十年(-1)最快,而升高了0.2摄氏度十年最慢(-1)发生在秋天。 CMIP5中的大多数模型都无法模拟低纬高原地区过去30年的长期温度变化,使用22个模型时,低纬高原的温度被低了2.4摄氏度。 SDM改善了MPI-ESM-LR模拟的年度和季节温度特征以及年度和季节变化。 ERA-SDM对未来的温度预测表明,在RCP8.5情景下,春季发现的温度升高最快,为0.271摄氏度十进位(-1)。在RCP8.5情景下,低纬高原北部的温度升高速度快于南部。

著录项

  • 来源
    《Climate dynamics》 |2019年第8期|4269-4292|共24页
  • 作者单位

    Yunnan Univ, Dept Atmospher Sci, Key Lab Atmospher Environm & Proc Boundary Layer, Kunming 650091, Yunnan, Peoples R China;

    Yunnan Univ, Dept Atmospher Sci, Key Lab Atmospher Environm & Proc Boundary Layer, Kunming 650091, Yunnan, Peoples R China;

    Yunnan Univ, Dept Atmospher Sci, Key Lab Atmospher Environm & Proc Boundary Layer, Kunming 650091, Yunnan, Peoples R China;

    Chinese Acad Sci, Inst Atmospher Phys, CAS Key Lab Reg Climate Environm Temperate East A, Beijing 100029, Peoples R China;

    Yu Xi Meteorol Bur, Yuxi 653100, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Low-latitude plateau; Temperature; Statistical downscaling model; CMIP5;

    机译:低纬高原温度降尺度模型CMIP5;

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