The accurate quantification of changes in the heat content of the world's oceans is crucial for our understanding of the effects of increasing greenhouse gas concentrations. The Argo program, consisting of Lagrangian floats that measure vertical temperature profiles throughout the global ocean, has provided a wealth of data from which to estimate ocean heat content. However, creating a globally consistent statistical model for ocean heat content remains challenging due to the need for a globally valid covariance model that can capture complex nonstationarity. In this paper, we develop a hierarchical Bayesian Gaussian process model that uses kernel convolutions with cylindrical distances to allow for spatial non-stationarity in all model parameters while using a Vecchia process to remain computationally feasible for large spatial datasets. Our approach can produce valid credible intervals for globally integrated quantities that would not be possible using previous approaches. These advantages are demonstrated through the application of the model to Argo data, yielding credible intervals for the spatially varying trend in ocean heat content that accounts for both the uncertainty induced from interpolation and from estimating the mean field and other parameters. Through cross-validation, we show that our model out-performs an out-of-the-box approach as well as other simpler models. The code for performing this analysis is provided as the R package BayesianOHC.
翻译:准确量化世界海洋热量含量的变化对于我们了解温室气体浓度增加的影响至关重要。 Argo 方案由测量全球海洋垂直温度剖面的拉格朗加浮体组成,它提供了大量数据,用以估计海洋热量。然而,建立一个全球一致的海洋热含量统计模型仍然具有挑战性,因为需要一个全球有效的共变模型,能够捕捉复杂的非静止性。在本文件中,我们开发了一个等级级的巴伊西亚高斯进程模型,利用圆柱形距离的内核变动模型,允许所有模型参数的空间非静止性,同时使用Vecchia进程,保持大型空间数据集的计算可行性。我们的方法可以为全球综合数量产生可靠的间隔,而使用以前的方法是不可能的。这些优势表现在将模型应用于Argo数据中,为海洋热含量空间差异的间隔提供了可靠的间隔,其中既包括来自内部的不确定性,也包括来自平均场和其他参数的估算。通过交叉数值分析,我们用Bay-C的模型来进行模型外置模版分析,我们作为Bay-BAR-R-BAR-R-BAR-S-For-Axxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx