文献类型： 外文期刊

作者： Li, Linchao ¹ ; Wang, Bin ³ ; Feng, Puyu ⁴ ; Jagermeyr, Jonas ⁵ ; Asseng, Senthold ⁸ ; Mueller, Christoph ⁷ ; Macadam, Ian ⁹ ; Liu, De Li ³ ; Waters, Cathy ¹¹ ; Zhang, Yajie ² ; He, Qinsi ¹² ; Shi, Yu ¹ ; Chen, Shang ¹³ ; Guo, Xiaowei ¹⁵ ; Li, Yi ¹⁶ ; He, Jianqiang ¹⁶ ; Feng, Hao ¹ ; Yang, Guijun ¹⁷ ; Tian, Hanqin ¹⁸ ; Yu, Qiang ¹ ;

作者机构： 1.Northwest A&F Univ, Coll Soil & Water Conservat Sci & Engn, Yangling 712100, Peoples R China

2.Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Peoples R China

3.Wagga Wagga Agr Inst, NSW Dept Primary Ind, Wagga Wagga, NSW 2650, Australia

4.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China

5.NASA, Goddard Inst Space Studies, New York, NY 10025 USA

6.Columbia Univ, Climate Sch, New York, NY 10025 USA

7.Leibniz Assoc, Potsdam Inst Climate Impact Res PIK, D-14412 Potsdam, Germany

8.Tech Univ Munich, Dept Life Sci Engn, D-85354 Freising Weihenstephan, Germany

9.CSIRO Climate Innovat, Canberra, Australia

10.Univ New South Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia

11.NSW Dept Primary Ind, Dubbo, NSW 2830, Australia

12.Univ Technol Sydney, Fac Sci, Sch Life Sci, POB 123, Broadway, NSW 2007, Australia

13.Nanjing Univ Informat Sci & Technol, Key Lab Meteorol Disasters, Minist Educ, Nanjing 210044, Peoples R China

14.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Nanjing 210044, Jiangsu, Peoples R China

15.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining, Peoples R China

16.Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling 712100, Peoples R China

17.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

18.Boston Coll, Schiller Inst Integrated Sci & Soc, Dept Earth & Environm Sci, Chestnut Hill, MA 02467 USA

期刊名称：COMMUNICATIONS EARTH & ENVIRONMENT （ 影响因子：7.9； 五年影响因子：7.9 ）

ISSN：

年卷期： 2023 年 4 卷 1 期

页码：

收录情况： SCI

摘要： Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications. A random selection of six global crop grid models and ten global climate models is sufficient to determine the uncertainty of a model ensemble, but the contribution of each crop model to this uncertainty varies by region and crop type, according to a cluster analysis of future crop yield projections.