The world ocean plays a key role in redistributing heat in the climate system and hence in regulating Earth's climate. Yet statistical analysis of ocean heat transport suffers from partially incomplete large-scale data intertwined with complex spatio-temporal dynamics, as well as from potential model misspecification. We present a comprehensive spatio-temporal statistical framework tailored to interpolating the global ocean heat transport using in-situ Argo profiling float measurements. We formalize the statistical challenges using latent local Gaussian process regression accompanied by a two-stage fitting procedure. We introduce an approximate Expectation-Maximization algorithm to jointly estimate both the mean field and the covariance parameters, and refine the potentially under-specified mean field model with a debiasing procedure. This approach provides data-driven global ocean heat transport fields that vary in both space and time and can provide insights into crucial dynamical phenomena, such as El Ni{\~n}o \& La Ni{\~n}a, as well as the global climatological mean heat transport field, which by itself is of scientific interest. The proposed framework and the Argo-based estimates are thoroughly validated with state-of-the-art multimission satellite products and shown to yield realistic subsurface ocean heat transport estimates.
翻译：世界海洋在气候系统的热量再分配方面发挥着关键作用,因此在调节地球气候方面也是如此。然而,海洋热迁移的统计分析却受到部分不完整的大规模数据的影响,这些数据与复杂的时空空间动态交织在一起,而且可能存在模型分辨错误。我们提出了一个全面的时空统计框架,专门用于利用静地Argo剖面浮标测量,在全球海洋热运输中进行相互交错。我们利用潜伏的当地高山进程回归,并辅之以一个两阶段的适当程序,正式确定统计挑战。我们采用了一种大致的预期-最大化算法,以联合估计中位场和共变差参数,并用偏差程序改进可能未定的中位场模型。这个方法提供了在空间和时间上各不相同的数据驱动的全球海洋热运输领域,可以提供关键动态现象的洞察力,例如El Ni ⁇ n}o ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ 以及全球气候平均热运输领域,这本身就是科学感兴趣的。拟议的框架和阿戈基的热运输估计是现实的,并展示了卫星的海平流数据。
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