How well is surface ocean carbon represented in observations and ocean models?

2026-08-31

AI Summary

研究通过引入数据流形的内在维度和可微信息不平衡框架，评估并优化了SOCAT观测和全球海洋生物地球化学模型中表层海洋碳的表示。

Abstract

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.

Problem

• surface ocean carbon
• representation quality
• information content
• global ocean biogeochemistry models (GOBMs)
• SOCAT

Innovation

• intrinsic dimension
• differentiable information imbalance
• surface ocean carbon
• global ocean biogeochemistry models
• machine learning models