用深度学习模拟全球变化分析模型，提速千倍同时保持高精度。
Emulating the Global Change Analysis Model with Deep Learning
- 用神经网络训练出可微分的代理模型，替代原复杂模型运行。
- 预测2.25万项输出，中位R²达0.998，敏感性分析R²为0.812。
- 适合需要快速大规模情景分析的研究者，如气候政策评估。
全球变化分析模型（GCAM）模拟人类与地球系统的复杂互动，为土地、水和能源部门在不同未来情景下的协同演化提供洞见。理解这些多部门系统的敏感性和驱动因素，有助于更全面把握特定结果的实现路径。然而，耦合的人类-地球系统相互作用使GCAM模拟成本高昂，难以开展大规模集合实验以探索参数与输出的不确定性。若能构建一个具备相似预测能力但效率更高的可微分代理模型，将极大提升情景发现与分析能力，减少对原始模型的调用次数。本研究以现有大规模集合为基础，训练神经网络，覆盖风能与太阳能等能源来源贡献度的不同输入组合，并通过插值扩展输入范围及输出维度，成功预测了跨时间、部门与区域的22,528项输出。结果显示，代理模型预测的中位R²为0.998，输入-输出敏感性分析的R²为0.812。
原文摘要 · Abstract (English)
The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water, and energy sectors under different future scenarios. Understanding the sensitivities and drivers of this multisectoral system can lead to more robust understanding of the different pathways to particular outcomes. The interactions and complexity of the coupled human-Earth systems make GCAM simulations costly to run at scale - a requirement for large ensemble experiments which explore uncertainty in model parameters and outputs. A differentiable emulator with similar predictive power, but greater efficiency, could provide novel scenario discovery and analysis of GCAM and its outputs, requiring fewer runs of GCAM. As a first use case, we train a neural network on an existing large ensemble that explores a range of GCAM inputs related to different relative contributions of energy production sources, with a focus on wind and solar. We complement this existing ensemble with interpolated input values and a wider selection of outputs, predicting 22,528 GCAM outputs across time, sectors, and regions. We report a median $R^2$ score of 0.998 for the emulator's predictions and an $R^2$ score of 0.812 for its input-output sensitivity.
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