发表机构
National Institute of Oceanography and Applied Geophysics (OGS); Scuola Internazionale Superiore di Studi Avanzati (SISSA); Columbia University; Lamont-Doherty Earth Observatory; New York City College of Technology（海洋与应用地球物理学国家研究所; 高级国际研究学院; 哥伦比亚大学; 拉蒙特-多尔蒂地球观测站; 纽约市立技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于数据流形的通用框架，对比表层海洋碳在SOCAT观测与GOBMs中的表征，发现GOBMs未完全捕捉观测数据空间复杂性，提出新指标以构建更准确的加权估计集合。
AI 中文摘要
我们提出了一个通用框架，用于基于数据流形的本征维度和可微信息不平衡性，量化复杂地球物理数据集的信息内容与表征质量。我们利用该框架推导并对比了表层海洋碳在观测数据集SOCAT、全球海洋生物地球化学模型（GOBMs）中的最优表征，评估了从现有数据中可提取信息的稳健性。研究发现，在最常用的特征集范围内，GOBMs未完全捕捉到SOCAT观测数据空间的复杂性，但GOBMs学习到的变量排序及相对重要性基本正确；南大洋等部分区域的海洋碳学习表征准确性较低，且近二十年来未出现显著提升；最后，我们展示了最优表征可用于提升基于距离的机器学习模型的性能，并以海洋碳为例进行验证，同时提出了两个新的指标，用于对比模型与观测数据，以构建更准确的加权估计集合。
英文摘要
We introduce a general framework for quantifying the information content and representation quality of complex geophysical datasets based on the intrinsic dimension and differentiable information imbalance of data manifolds. We use it to derive and compare optimal representations of surface ocean carbon in the SOCAT database of observations and in global ocean biogeochemistry models (GOBMs) and to assess the robustness of the information we can extract from existing data. We find that within the most widely used feature set, the complexity of the data space of SOCAT observations is not fully captured by GOBMs, but the ranking and relative importance of variables learned through GOBMs are substantially correct. We observe that the learned representation of ocean carbon is less accurate in some regions, including the Southern Ocean, but doesn't appear to have evolved significantly over the last two decades. Finally, we show how the optimal representations can be used to improve the skill of distance-based machine learning models and demonstrate it for ocean carbon, and we propose two new metrics to compare models and observations that can be used to build more accurate weighted ensembles of estimates.
CommentsMain text: 20 pages, 10 figures
