Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe
Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe
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DOI：
10.1016/j.agrformet.2012.04.007
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发表时间：
2013-03-15
影响因子：
6.2
通讯作者：
Challinor, Andrew J.
中科院分区：
文献类型：
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作者：
Hawkins, Ed;Osborne, Thomas M.;Challinor, Andrew J.
对未来作物产量进行预测需要仔细考虑大气-海洋全球气候模式（AOGCM）模拟的适当使用。在这里，我们描述和展示了多种方法“校准”气候预测使用一个合奏的AOGCM模拟在一个“完美的兄弟姐妹”的框架。至关重要的是，这种类型的分析评估了每种校准方法对未来气候进行可靠估计的能力，而这仅仅使用历史观测是不可能的。这类方法可更广泛地用于评估作物建模的校准方法。评估的校准方法包括常用的“delta”（变化因子）和“nudging”（偏差校正）方法。我们专注于欧洲夏季的日最高温度为这个理想化的案例研究，但该方法可以推广到其他变量和其他地区。校准方法，这是相对容易实现适当的观测，产生更强大的预测未来每日最高温度和热应力比使用原始模型输出。选择使用哪种校准方法可能取决于具体情况，但在我们的示例中，变化因子方法往往表现最好。最后，我们表明，由于选择的校准方法的不确定性是一个显着的贡献者，在未来的气候预测的影响研究的总不确定性。我们的结论是，利用各种校准方法输出的范围广泛的AOGCM是必不可少的气候数据，将确保强大和可靠的作物产量预测。(c)2012爱思唯尔有限公司版权所有。
Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for 'calibrating' climate projections using an ensemble of AOGCM simulations in a 'perfect sibling' framework. Crucially, this type of analysis assesses the ability of each calibration methodology to produce reliable estimates of future climate, which is not possible just using historical observations. This type of approach could be more widely adopted for assessing calibration methodologies for crop modelling. The calibration methods assessed include the commonly used 'delta' (change factor) and 'nudging' (bias correction) approaches. We focus on daily maximum temperature in summer over Europe for this idealised case study, but the methods can be generalised to other variables and other regions. The calibration methods, which are relatively easy to implement given appropriate observations, produce more robust projections of future daily maximum temperatures and heat stress than using raw model output. The choice over which calibration method to use will likely depend on the situation, but change factor approaches tend to perform best in our examples. Finally, we demonstrate that the uncertainty due to the choice of calibration methodology is a significant contributor to the total uncertainty in future climate projections for impact studies. We conclude that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections. (c) 2012 Elsevier B.V. All rights reserved.