Tropical cyclones (TCs), driven by heat exchange between the air and sea,
pose a substantial risk to many communities around the world. Accurate
characterization of the subsurface ocean thermal response to TC passage is
crucial for accurate TC intensity forecasts and for an understanding of the
role that TCs play in the global climate system. However, that characterization
is complicated by the high-noise ocean environment, correlations inherent in
spatio-temporal data, relative scarcity of in situ observations, and the
entanglement of the TC-induced signal with seasonal signals. We present a
general methodological framework that addresses these difficulties, integrating
existing techniques in seasonal mean field estimation, Gaussian process
modeling, and nonparametric regression into a functional ANOVA model.
Importantly, we improve upon past work by properly handling seasonality,
providing rigorous uncertainty quantification, and treating time as a
continuous variable, rather than producing estimates that are binned in time.
This functional ANOVA model is estimated using in situ subsurface temperature
profiles from the Argo fleet of autonomous floats through a multi-step
procedure, which (1) characterizes the upper ocean seasonal shift during the TC
season; (2) models the variability in the temperature observations; (3) fits a
thin plate spline using the variability estimates to account for
heteroskedasticity and correlation between the observations. This spline fit
reveals the ocean thermal response to TC passage. Through this framework, we
obtain new scientific insights into the interaction between TCs and the ocean
on a global scale, including a three-dimensional characterization of the
near-surface and subsurface cooling along the TC storm track and the
mixing-induced subsurface warming on the track's right side.Comment: 33 pages, 14 figures; supplement and code at
https://github.com/huisaddison/tc-ocean-method