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Statistical-dynamical downscaling of present day and future precipitation regimes in the Aksu river catchment in Central Asia

机译:中亚阿克苏河流域当前和未来降水状况的统计动态降尺度

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摘要

A statistical-dynamical downscaling (SDD) approach is applied to determine present day and future high-resolution rainfall distributions in the catchment of the river Aksu at the southern slopes of the Tienshan Mountains, Central Asia. First, a circulation weather type (CWT) classification is employed to define typical lower atmospheric flow regimes from ERA-40 reanalysis data. Selected representatives of each CWT are dynamically downscaled with the regional climate model COSMO-CLM 4.8 at a horizontal grid resolution of 0.0625°, using the ERA-40 reanalysis data as boundary conditions. Finally, the simulated representatives are recombined to obtain a high-resolution rainfall climatology for present day climate. The methodology is also applied to ensemble simulations of three different scenarios of the global climate model ECHAM5/MPI-0M1 to derive projections of rainfall changes until 2100. Comparisons of downscaled seasonal and annual rainfall with observational data suggest that the statistical-dynamical approach is appropriate to capture the observed present-day precipitation climatology over the low lands and the first elevations of the Tienshan Mountains. On the other hand, a strong bias is found at higher altitudes, where precipitation is clearly underestimated by SDD. The application of SDD to the ECHAM5/MPI-OM1 ensemble reveals that precipitation changes by the end of the 21st century depend on the season. While for autumn an increase of seasonal precipitation is found for all simulations, a decrease in precipitation is obtained during winter for most parts of the Aksu catchment The spread between different ECHAM5/MPI-OM1 ensemble members is strongest in spring, where trends of opposite sign are found. The largest changes in rainfall are simulated for the summer season, which also shows the most pronounced spatial heterogeneity. Most ECHAM5/MPI-OM1 realizations indicate a decrease of annual precipitation over large parts of the Tienshan, and an increase restricted to the southeast of the study area. These results provide a good basis for downscaling present-day and future rainfall distributions for hydrological purposes.
机译:应用统计-动态降尺度(SDD)方法来确定中亚天山山脉南坡的阿克苏河集水区目前和将来的高分辨率降雨分布。首先,采用循环天气类型(CWT)分类来根据ERA-40再分析数据定义典型的较低大气流态。使用ERA-40重新分析数据作为边界条件,使用区域气候模型COSMO-CLM 4.8在水平网格分辨率为0.0625°的情况下,将每个CWT的选定代表动态缩小比例。最后,将模拟代表重新组合以获得当今气候的高分辨率降雨气候。该方法还适用于全球气候模型ECHAM5 / MPI-0M1的三种不同情况的整体模拟,以得出2100年前的降雨变化预测。按比例缩小的季节性和年度降雨与观测数据的比较表明,采用统计-动力学方法是合适的捕获天山低地和第一海拔的现今降水气候。另一方面,在较高的海拔高度会发现强烈的偏差,而SDD明显低估了降水。 SDD在ECHAM5 / MPI-OM1系综中的应用表明,到21世纪末,降水变化取决于季节。在所有模拟中,秋季均发现秋季降水增加,而在阿克苏流域的大部分地区,冬季降水减少。春季,不同ECHAM5 / MPI-OM1集合体之间的传播最强,而相反的趋势是被发现。模拟了夏季最大的降雨变化,这也显示了最明显的空间异质性。大多数ECHAM5 / MPI-OM1的实现表明,天山大部分地区的年降水量减少,而仅限于研究区域的东南部增加。这些结果为缩小当前和未来的水文降雨分布提供了良好的基础。

著录项

  • 来源
    《Global and planetary change》 |2013年第8期|36-49|共14页
  • 作者单位

    Institute of Geography and Geology, University of Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany ,Institute for Geophysics and Meteorology, University of Cologne, Kerpener Str. 13, 50923 Cologne, Germany;

    Institute for Geophysics and Meteorology, University of Cologne, Kerpener Str. 13, 50923 Cologne, Germany ,Department of Meteorology, University of Reading, RG6 RBB Reading, United Kingdom;

    Institute of Geography and Geology, University of Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany;

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

    precipitation; Tienshan; Central Asia; statistical-dynamical downscaling; COSMO-CLM; climate change;

    机译:沉淀;天山;中亚;统计动态降尺度;COSMO-CLM;气候变化;

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