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Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US

机译:评估美国东南部模拟小麦和玉米产量下调气候数据的保真度

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摘要

Crop models are one of the most commonly used tools to assess the impact of climate variability and change on crop production. However, before the impact of projected climate changes on crop production can be addressed, a necessary first step is the assessment of the inherent uncertainty and limitations of the forcing data used in these crop models. In this paper, we evaluate the simulated crop production using separate crop models for maize (summer crop) and wheat (winter crop) over six different locations in the Southeastern United States forced with multiple sources of actual and simulated weather data. The paper compares the crop production simulated by a crop model for maize and wheat during a historical period, using daily weather data from three sources: station observations, dynamically downscaled global reanalysis, and dynamically downscaled historical climate model simulations from two global circulation models (GCMs). The same regional climate model is used to downscale the global reanalysis and both global circulation models' historical simulation. The average simulated yield derived from bias-corrected downscaled reanalysis or bias-corrected downscaled GCMs were, in most cases, not statistically different from observations. Statistical differences of the average yields, generated from observed or down-scaled GCM weather, were found in some locations under rainfed and irrigated scenarios, and more frequently in winter (wheat) than in summer (maize). The inter-annual variance of simulated crop yield using GCM downscaled data was frequently overestimated, especially in summer. An analysis of the bias-corrected climate data showed that despite the agreement between the modeled and the observed means of temperatures, solar radiation, and precipitation, their intra-seasonal variances were often significantly different from observations. Therefore, due to this high intra-seasonal variability, a cautious approach is required when using climate model data for historical yield analysis and future climate change impact assessments.
机译:作物模型是评估气候变化和变化对作物生产影响的最常用工具之一。但是,在可以解决气候变化对作物生产的影响之前,必要的第一步是评估这些作物模型中使用的强迫数据的固有不确定性和局限性。在本文中,我们使用多个实际和模拟天气数据源,使用美国东南部六个不同地点的玉米(夏季作物)和小麦(冬季作物)的单独作物模型评估模拟作物的产量。本文使用三个来源的每日天气数据,比较了历史时期玉米和小麦作物模型模拟的作物产量:站点观测,动态缩减的全球再分析和动态缩减的历史气候模型(来自两个全球循环模型(GCM)) )。相同的区域气候模型用于缩减全球再分析和两个全球环流模型的历史模拟。在大多数情况下,从偏差校正后的缩小比例重新分析或偏差校正后的缩小比例的GCM得出的平均模拟产量与观察值在统计上没有差异。在雨水和灌溉条件下的某些地方发现了由观测到的或按比例缩小的GCM天气产生的平均单产的统计差异,冬季(小麦)比夏季(玉米)更为频繁。使用GCM缩减数据模拟作物产量的年际变化经常被高估,尤其是在夏季。对经偏差校正的气候数据进行的分析表明,尽管模拟和观测到的温度,太阳辐射和降水量的平均值一致,但它们的季节内变化通常与观测值显着不同。因此,由于季节内的高度可变性,在将气候模型数据用于历史产量分析和未来气候变化影响评估时,需要采取谨慎的方法。

著录项

  • 来源
    《Regional Environmental Change》 |2013年第1期|S101-S110|共10页
  • 作者单位

    Department of Agricultural and Biological Engineering,University of Florida, Gainesville, FL 32611, USA;

    Center for Ocean-Atmospheric Prediction Studies,College of Arts and Sciences, Florida State University,Tallahassee, FL 32306, USA;

    Agronomy and Soils, Auburn University, Auburn,AL 36849, USA;

    Department of Agricultural and Biological Engineering,University of Florida, Gainesville, FL 32611, USA;

    Department of Agricultural and Biological Engineering,University of Florida, Gainesville, FL 32611, USA;

    Center for Ocean-Atmospheric Prediction Studies,College of Arts and Sciences, Florida State University,Tallahassee, FL 32306, USA,Earth, Ocean and Atmospheric Sci and Center for Ocean-Atmospheric Prediction Studies, College of Artsand Sciences, Florida State University, Tallahassee,FL 32306,USA,Florida Climate Institute, Florida State University, Tallahassee,FL 32306, USA;

    Department of Crop Science, North Carolina State University,Raleigh, NC 27695, USA;

    Department of Geological Sciences and WK Kellogg Biological Station, Michigan State University, East Lansing, MI 48824,USA;

    Florida Climate Institute, University of Florida, Gainesville,FL 32611, USA;

    Department of Agricultural and Biological Engineering,University of Florida, Gainesville, FL 32611, USA;

    Center for Ocean-Atmospheric Prediction Studies,College of Arts and Sciences, Florida State University,Tallahassee, FL 32306, USA;

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

    Crop simulation models; Climate variability; Global circulation models; Reanalysis; Wheat; Maize;

    机译:作物模拟模型;气候多变性;全球流通模式;重新分析;小麦;玉米;

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