A Prototype for Remote Monitoring of Ocean Heat Content Anomalies
A Prototype for Remote Monitoring of Ocean Heat Content Anomalies
复制标题
海洋热含量异常远程监测原型
DOI：
10.1175/jtech-d-21-0037.1
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发表时间：
2022
影响因子：
2.2
通讯作者：
Tyler, Robert H.
中科院分区：
文献类型：
--
作者：
Trossman, David S.;Tyler, Robert H.
为了克服在整个海洋上观测海洋热含量（OHC）的挑战，我们提出了一种新的方法，该方法利用了丰富的卫星数据，包括来自现代卫星地磁调查（如Swarm）的数据。该方法考虑了传统的原位（温度和压力）以及卫星（测高和重力）数据与海洋电导率（深度综合电导率）的估计，这可能是从磁观测（卫星，陆地，海底，海洋和机载磁强计）的一种新的组合。为了证明所提出的方法的潜在好处，我们对海洋状态估计的模型输出进行采样，以反映现有的观测结果，并在这些样本上训练机器学习算法[广义加性模型（GAM）]。然后，我们计算OHC无处不在使用信息可能来自各种全球卫星覆盖范围，包括磁观测，以衡量GAM的拟合优度在全球范围内。列入OHC在上面2000米的Argo-like浮子和电导数据的原位观测，每个减少的均方根误差的数量级。重新训练GAM与最近的船基水文数据达到一个较小的RMSE在极地海洋比训练GAM只有一次所有可用的历史船基水文数据;相反的是真实的其他地方。GAM更准确地计算OHC异常在整个水柱比低于2000米，可以检测全球OHC异常在多年的时间尺度，即使考虑假设的测量误差。我们的方法可以补充现有的方法和它的准确性可以通过仔细的船为基础的运动planning.Significance StatementThe的目的是证明海洋热含量（OHC）异常的远程监测方法的实际实施的潜力。要做到这一点，我们从再分析产品的采样数据，主要是因为缺乏低于2000米深度的观测，可用于验证和事实，即来自卫星磁力测量的全深度综合海水电导率数据产品尚未提供。我们评估多个因素相关的OHC异常估计的准确性，并发现，即使假设的测量误差，我们的方法可以用来监测OHC异常多年的时间尺度。
To overcome challenges with observing ocean heat content (OHC) over the entire ocean, we propose a novel approach that exploits the abundance of satellite data, including data from modern satellite geomagnetic surveys such as Swarm. The method considers a novel combination of conventional in situ (temperature and pressure) as well as satellite (altimetry and gravimetry) data with estimates of ocean electrical conductance (depth-integrated conductivity), which can potentially be obtained from magnetic observations (by satellite, land, seafloor, ocean, and airborne magnetometers). To demonstrate the potential benefit of the proposed method, we sample model output of an ocean state estimate to reflect existing observations and train a machine learning algorithm [Generalized Additive Model (GAM)] on these samples. We then calculate OHC everywhere using information potentially derivable from various global satellite coverage—including magnetic observations—to gauge the GAM’s goodness of fit on a global scale. Inclusion of in situ observations of OHC in the upper 2000 m from Argo-like floats and conductance data each reduce the root-mean-square error by an order of magnitude. Retraining the GAM with recent ship-based hydrographic data attains a smaller RMSE in polar oceans than training the GAM only once on all available historical ship-based hydrographic data; the opposite is true elsewhere. The GAM more accurately calculates OHC anomalies throughout the water column than below 2000 m and can detect global OHC anomalies over multiyear time scales, even when considering hypothetical measurement errors. Our method could complement existing methods and its accuracy could be improved through careful ship-based campaign planning.Significance StatementThe purpose of this manuscript is to demonstrate the potential for practical implementation of a remote monitoring method for ocean heat content (OHC) anomalies. To do this, we sample data from a reanalysis product primarily because of the dearth of observations below 2000 m depth that can be used for validation and the fact that full-depth-integrated electrical seawater conductivity data products derived from satellite magnetometry are not yet available. We evaluate multiple factors related to the accuracy of OHC anomaly estimation and find that, even with hypothetical measurement errors, our method can be used to monitor OHC anomalies on multiyear time scales.