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.

翻译：全球变化分析模型（GCAM）模拟耦合地球系统与人类系统间的复杂相互作用，为不同未来情景下土地、水资源及能源部门的协同演化提供重要见解。理解这一多部门系统的敏感性与驱动因素，有助于更深入地认识达成特定结果的不同路径。人地耦合系统的交互作用与复杂性使得GCAM的大规模仿真计算成本高昂——而这正是探索模型参数与输出不确定性所需的大规模集合实验的必要条件。若构建具备相似预测能力但更高计算效率的可微分仿真器，则可在减少GCAM运行次数的前提下，为GCAM及其输出结果提供新颖的情景发现与分析方案。作为首个应用案例，我们在现有大型集合数据上训练神经网络，该集合涵盖了与能源生产来源（重点关注风能与太阳能）不同相对贡献相关的GCAM输入参数范围。我们通过插值输入参数和更广泛的输出指标对该集合进行扩展，实现了跨时间、跨部门、跨区域的22,528项GCAM输出预测。仿真器预测结果的中位$R^2$得分为0.998，其输入输出敏感性的$R^2$得分为0.812。