A Statistical Analysis of Three Ensembles of Crop Model Responses to Temperature and CO2 Concentration-猫眼课题宝

DOI：

10.1016/j.agrformet.2015.09.013

发表时间：

2015-10

期刊：

Agricultural and Forest Meteorology

影响因子：

6.2

通讯作者：

中科院分区：

农林科学1区

文献类型：

作者：

D. Makowski;S. Asseng;F. Ewert;S. Bassu;J. Durand;Tao Li;P. Martre;M. Adam;P. Aggarwal;Carlos Angulo;C. Baron;B. Basso;P. Bertuzzi;C. Biernath;H. Boogaard;K. Boote;B. Bouman;S. Bregaglio;N. Brisson;Samuel Buis;D. Cammarano;A. Challinor;R. Confalonieri;J. G. Conijn;M. Corbeels;D. Deryng;G. Sanctis;J. Doltra;T. Fumoto;D. Gaydon;S. Gayler;R. Goldberg;R. Grant;P. Grassini;J. Hatfield;T. Hasegawa;L. Heng;S. Hoek;J. Hooker;L. Hunt;J. Ingwersen;R. Izaurralde;R. Jongschaap;James W. Jones;R. A. Kemanian;K. Kersebaum;Soo-Hyung Kim;J. Lizaso;M. Marcaida;C. Müller;H. Nakagawa;S. Kumar;C. Nendel;G. O'Leary;J. Olesen;Philippe Oriol;T. Osborne;T. Palosuo;M. V. Pravia;E. Priesack;D. Ripoche;C. Rosenzweig;A. Ruane;F. Ruget;F. Sau;M. Semenov;I. Shcherbak;Balwinder Singh;U. Singh;H. K. Soo;P. Steduto;C. Stöckle;Pierre Stratonovitch;T. Streck;I. Supit;Liang Tang;F. Tao;E. Teixeira;P. Thorburn;D. Timlin;M. Travasso;R. Rötter;K. Waha;D. Wallach;J. White;P. Wilkens;Jimmy R. Williams;J. Wolf;X. Yin;H. Yoshida;Zi-Yu Zhang;Yan Zhu

基于过程的作物模式集越来越多地用于模拟温度和/或降水变化情景下的作物生长，这些变化对应于不同的大气二氧化碳浓度预估。这种方法生成了包含数千个模拟作物产量数据的大型数据集。这些数据集可能提供新的信息，但由于其结构的复杂性，很难以有用的方式总结它们。一个相关的问题是，比较作物和将结果插入到最初未包括在模拟协议中的其他气候情景中并不直接。在这里，我们证明了基于随机系数回归的统计模型能够模拟基于过程的作物模型的集合。所提出的统计模型的一个重要优点是，它们可以在温度水平和二氧化碳浓度水平之间进行插值，因此可以用于计算导致产量损失或产量增加的温度和[CO2]阈值，而无需重新运行原始的复杂作物模型。我们的方法用19个玉米模型、26个小麦模型和13个水稻模型模拟的3个产量数据集来说明。对这些数据集拟合了几个统计模型，然后用于分析产量对[CO2]和温度响应的变异性。基于我们的研究结果，我们表明，在考虑的地点，小麦的[CO2]增加可能超过温度升高2°C的负面影响。与小麦相比，玉米所需的[二氧化碳]增长水平要高得多，而水稻则处于中等水平。对于所有作物，模拟气候变化影响的不确定性随着温度的升高而增加，而不是随着[CO2]的升高而增加。

Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 °C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].

A Statistical Analysis of Three Ensembles of Crop Model Responses to Temperature and CO2 Concentration

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