这篇论文告诉你，与其堆模型复杂度，不如优化训练数据——用同一个气候模型设计动态丰富的情景，能让仿真模型比用六个标准路径表现更好。
该论文发现机器学习气候仿真模型的预测能力受限于训练数据的结构多样性不足。作者提出一种通过可微简单气候模型(SCM)优化训练情景的方法，使仿真模型能泛化到训练数据中未出现的新情景。实验表明，使用单个优化情景训练的仿真模型，其技能优于使用6个标准ScenarioMIP路径训练的模型。即使训练数据更小，优化后的模型也能成功分离不同气候强迫因子（如温室气体与气溶胶）的物理行为。用SCM优化的情景驱动中等复杂度气候模型时，产生的训练数据比直接使用ScenarioMIP输出更有效。
Optimal scenario design for climate emulation
As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emulators), we show that the low structural diversity in existing scenarios commonly used to generate training data places a ceiling on predictive skill. Here, we examine whether training datasets themselves can be optimized to improve generalization. We introduce a method to create datasets that produce emulators capable of generalizing to new, structurally different scenarios absent from the training data. We use a differentiable Simple Climate Model (SCM) to calculate the sensitivity of emulator loss to perturbations in the training data, iteratively updating the training data to maximize emulator skill. For an SCM, training on one scenario optimized in this fashion outperforms an emulator trained on six standard ScenarioMIP pathways. We achieve this higher predictive skill despite training on a smaller dataset, finding that our emulator successfully isolates distinct physical behaviors of different climate forcing agents (e.g., greenhouse gases vs. aerosols) without single-forcing runs. We then demonstrate that scenarios optimized using an SCM, when used to drive an intermediate-complexity climate model, produce a training dataset that yields a more skillful emulator than training on ScenarioMIP outputs. Our results suggest that, in the compute-constrained environment of running full-scale climate models, generating a small number of dynamically rich scenarios provides greater marginal value for emulation and characterizing system responses than expanding the suite of traditional emissions pathways.