Generating downscaled weather data from a suite of climate models for agricultural modelling applications

被引：159

作者：

Jones, Peter G. ^{[2
]}

Thornton, Philip K. ^{[1
]}

机构：

[1] ILRI, Agr & Food Secur CCAFS, CGIAR Res Program Climate Change, Nairobi 00100, Kenya

[2] Waen Associates, Dolgellau LL40 1TS, Gwynedd, Wales

来源：

AGRICULTURAL SYSTEMS | 2013年 / 114卷

关键词：

Markov models; Climate change; Stochastic generation; Downscaling; DSSAT; SIMULATION;

D O I：

10.1016/j.agsy.2012.08.002

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

We describe a generalised downscaling and data generation method that takes the outputs of a General Circulation Model and allows the stochastic generation of daily weather data that are to some extent characteristic of future climatologies. Such data can then be used to drive any agricultural model that requires daily (or otherwise aggregated) weather data. The method uses an amalgamation of unintelligent empirical downscaling, climate typing and weather generation. We outline a web-based software tool (http://gismap.ciat.cgiar.org/MarkSimGCM) to do this for a subset of the climate models and scenario runs carried out for the 2007 Fourth Assessment Report of the Intergovernmental Panel on Climate Change. We briefly assess the tool and comment on its use and limitations. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：1 / 5

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