发表机构
Courant Institute School of Mathematics, Computing, and Data Science, New York University（纽约大学柯朗数学科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用条件扩散模型学习多模型集合中上层海洋氧气分布，仅凭温度和盐度生成氧气场，缩小了氧气含量估算的不确定性，并发现热带太平洋氧气最小带偏差可能小于预期。
AI 中文摘要
溶解氧对海洋生态系统和生物地球化学循环至关重要。然而，地球系统模型（ESMs）在模拟当代和未来海洋氧气储量方面存在差异。为缩小海洋氧气含量估算的不确定性，我们在多模型ESM集合的输出上训练了一个条件生成扩散模型，以学习在给定温度和盐度等物理输入变量条件下上层海洋氧气的条件分布。该生成模型在大西洋和南大洋具有相当高的技能，并能生成训练数据中未见条件下的逼真氧气样本。我们使用观测数据集验证了该模型，并仅以温度和盐度作为条件输入，为没有氧气数据的模型生成氧气场。这种物理条件外推表明，在考虑更广泛的物理海洋状态时，热带太平洋氧气最小带中的模型偏差可能比目前假设的要小。我们的方法为表示概率性多模型气候分布提供了一种补充途径。
英文摘要
Dissolved oxygen is important for the ocean's ecosystems and biogeochemical cycles. Yet, Earth System Models (ESMs) vary in their simulations of the present-day and future ocean oxygen inventory. To narrow down the uncertainty in estimates of ocean oxygen content, we train a conditional generative diffusion model on outputs of a multi-model ESM ensemble to learn the conditional distribution of upper-ocean oxygen given physical input variables such as temperature and salinity. This generative model has considerable skill in the Atlantic and Southern Oceans, and can generate realistic oxygen samples under conditions not seen in the training data. We validate this model using observational datasets, and use it to generate oxygen fields for models without oxygen data using only temperature and salinity as conditioning inputs. This physics-conditioned extrapolation suggests that model biases in the tropical Pacific Oxygen Minimum Zone may be smaller than currently assumed when considering a larger set of physical ocean states. Our approach provides a complementary way to represent probabilistic multi-model climate distributions.
