Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14473, Potsdam, Germany.
University of Chicago and ANL Computation Institute, Chicago, IL, 60637, USA.
Sci Data. 2019 May 8;6(1):50. doi: 10.1038/s41597-019-0023-8.
The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives.
农业模型互操作和改进项目（AgMIP）的全球网格化作物模型比较（GGCMI）第 1 阶段数据集提供了一个前所未有的大型作物模型模拟数据集，涵盖了全球无冰陆地表面。该数据集由空间分辨率为 0.5 度经纬度的年度数据字段组成。14 个作物建模小组提供了多达 11 个历史输入数据集的输出，这些数据集涵盖了 1901 年至 2012 年，以及多达 3 个不同的管理协调水平。每个小组提交了多达 15 种不同作物和多达 14 个输出变量的数据。所有模拟均在所有土地上进行，无论是否存在作物或灌溉系统，均仅在纯雨养和近乎完美灌溉条件下进行。随着 GGCMI 第 1 阶段数据集的发布，我们旨在促进对作物模型性能、生产力与环境影响之间的潜在关系以及如何进一步改进全球网格化作物模型框架的进一步分析和理解。我们描述了数据集的特征和各个模型设置的说明。
