用物理约束生成模型将格陵兰冰盖气候数据分辨率提升32倍，速度快且精度高。
Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
- 基于一致性模型，结合地形与太阳辐射条件进行高分辨率下采样。
- 在测试集上表面质量平衡误差仅6.31 mmWE，温度误差0.1 K，优于传统插值法。
- 可直接处理地球系统模型输出，适合冰盖模拟与海平面上升预测。
准确、高分辨率的格陵兰冰盖表面质量平衡（SMB）和地表温度预测对理解未来海平面上升至关重要，但现有方法或计算成本过高，或空间分辨率不足。本文提出一种基于一致性模型（CM）的物理约束生成建模框架，可将低分辨率SMB与地表温度场下采样至最高32倍（从160公里降至5公里网格间距），仅需少数采样步骤。该模型在区域气候模型MARv3.12的月度输出上训练，并以冰盖地形和日照条件为条件输入。推理过程中强制执行守恒约束，确保粗分辨率上的总和近似保留，且无需重训练即可稳健泛化至极端气候状态。测试集结果显示，模型在表面质量平衡上的连续排名概率得分（CRPS）为6.31 mmWE，地表温度误差为0.1 K，优于基于插值的下采样方法。结合空间功率谱分析，证明了模型能忠实再现多尺度变异性。进一步将偏移校正后的NorESM2地球系统模型输出作为输入，验证了该模型直接下采样地球系统模型场的潜力。本方法提供快速、真实的高分辨率气候强迫数据，可无缝集成至地球系统与冰盖模型流程中，显著提升对格陵兰未来海平面上升贡献的预测能力，亦适用于其他冰川与冰盖。
原文摘要 · Abstract (English)
Accurate, high-resolution projections of the Greenland ice sheet's surface mass balance (SMB) and surface temperature are essential for understanding future sea-level rise, yet current approaches are either computationally demanding or limited to coarse spatial scales. Here, we introduce a novel physics-constrained generative modeling framework based on a consistency model (CM) to downscale low-resolution SMB and surface temperature fields by a factor of up to 32 (from 160 km to 5 km grid spacing) in a few sampling steps. The CM is trained on monthly outputs of the regional climate model MARv3.12 and conditioned on ice-sheet topography and insolation. By enforcing a hard conservation constraint during inference, we ensure approximate preservation of SMB and temperature sums on the coarse spatial scale as well as robust generalization to extreme climate states without retraining. On the test set, our constrained CM achieves a continued ranked probability score of 6.31 mmWE for the SMB and 0.1 K for the surface temperature, outperforming interpolation-based downscaling. Together with spatial power-spectral analysis, we demonstrate that the CM faithfully reproduces variability across spatial scales. We further apply bias-corrected outputs of the NorESM2 Earth System Model as inputs to our CM, to demonstrate the potential of our model to directly downscale ESM fields. Our approach delivers realistic, high-resolution climate forcing for ice-sheet simulations with fast inference and can be readily integrated into Earth-system and ice-sheet model workflows to improve projections of the future contribution to sea-level rise from Greenland and potentially other ice sheets and glaciers too.
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