通过对垂直时空依赖性建模改进全球海洋热含量估计
Improved Global Ocean Heat Content Estimation by Modeling Vertical Spatio-Temporal Dependence
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中文总结 AI 辅助
研究利用Argo数据绘制海洋热含量，针对以往方法不足，提出用双变量局部平稳高斯过程和条件模拟联合绘制两压力层并考虑相关性的改进方法，降低了全球海洋热含量异常不确定性，对分析其统计显著性至关重要。
中文摘要 AI 辅助
可靠估计海洋热含量（OHC）的不确定性对于理解和监测地球气候演变至关重要，因为海洋储存了气候系统中大部分因地球能量失衡积累的能量。本文利用2004年至2022年的Argo剖面浮标数据绘制OHC。以往研究因水柱深处观测少，将海洋至少划分为两个压力层分别绘制，求和时不确定性估计复杂。本文针对两个压力层情况，提出用双变量局部平稳高斯过程和条件模拟联合绘制两部分并考虑相关性的改进绘制和不确定性量化方法。结果表明，与不考虑依赖性分别绘制两层相比，该方法改进了OHC异常绘制，全球OHC异常不确定性降低达15%。这些估计的不确定性对分析区域和全球尺度OHC异常的统计显著性至关重要，文中通过几个气候学案例研究进行了论证。
英文摘要
Estimating ocean heat content (OHC) with reliable uncertainties is critical for understanding and monitoring the evolution of Earth's climate, as the ocean has stored most of the energy accumulated in the climate system due to Earth Energy Imbalance. Here, we use Argo profiling float data from 2004-2022 to map OHC. As fewer Argo observations are available deeper in the water column, previous studies have partitioned the ocean into at least two pressure layers and mapped each separately, which complicates the estimation of uncertainties when the maps are summed to get the total OHC. In this work, we consider the case of two pressure layers and propose an improved mapping and uncertainty quantification method using bivariate locally stationary Gaussian processes and conditional simulations to map the two sections jointly while accounting for the correlation between them. We find that modeling this correlation results in improved OHC anomaly mapping and up to a 15 percent reduction of global OHC anomaly uncertainties in comparison to mapping the two layers separately without accounting for their dependence. These estimated uncertainties are essential to analyze the statistical significance of OHC anomalies on both regional and global scales, which we demonstrate using several climatological case studies.
