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\author[1]{Sangmin Song}%
\author[2,3]{Paige D Lavin}%
\author[1]{Alison R Gray}%
\author[4]{Zachary Nachod}%
\author[5]{Dhruv Balwada}%
\author[6]{Lilian A Dove}%
\author[7]{Andrew F Thompson}%
\affil[1]{School of Oceanography, University of Washington}%
\affil[2]{Cooperative Institute for Satellite Earth System Studies/Earth System Science Interdisciplinary Center (CISESS/ESSIC), University of Maryland}%
\affil[3]{NOAA/NESDIS Center for Satellite Applications and Research}%
\affil[4]{University of Hawai'i at Mānoa}%
\affil[5]{Lamont-Doherty Earth Observatory, Columbia University}%
\affil[6]{Brown University}%
\affil[7]{Environmental Science and Engineering, California Institute of Technology}%


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  \date{June 01, 2024}


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\begin{abstract}
Nutrient cycling in the ocean is mediated by physical mixing processes
that span diverse spatial and temporal scales. New biogeochemical
profiling floats (BGC-Argo) have begun to observe nutrient distributions
globally, but their 10-day cycling period limits the types of processes
they can capture. Small-scale dynamics, occurring on
\(O\)(1) day and \(O\)(1) km, remain
particularly difficult to observe in-situ. Here, we show that random
forest regression (RFR) can recover high-frequency information by
leveraging the sampling strategies of multiple ocean profilers. Our RFR
is trained, validated, and tested on BGC-Argo and shipboard data to
within \textasciitilde{}3\% accuracy, then applied to observations from
two rapid-sampling Seagliders deployed during the Southern Ocean Glider
Observations of the Submesoscale (SOGOS) experiment in 2019. This
approach generates novel nitrate distributions at 50 times the
horizontal resolution of the original float data. Using the
high-resolution RFR outputs, we identify signatures of nutrient
injection into the mixed layer that coincide with enhanced stirring in a
turbulent region downstream of the Southwest Indian Ridge. Relating
these intermittent transport events to biological time series suggests
that small-scale stirring mediates additional nutrient drawdown and
primary production in this region. In our exploration of high-frequency
nitrate variability in the Southern Ocean, RFR extends the capabilities
of the BCG-Argo array and allows for deeper understanding of
biogeochemical cycling at a more comprehensive set of scales. As a
flexible approach that can be generalized to suit other multi-platform
observing systems, RFR presents new opportunities to maximize value from
existing datasets.%
\end{abstract}%



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\subsection*{Significance Statement}

We use a regional random forest regression (RFR) to leverage data from multiple ocean observing instruments that offer different advantages. In our study of the Southern Ocean, we use RFR to produce new nutrient maps at 50 times higher resolution than previously possible. By estimating small-scale information, RFR reveals interactions between physical and biological processes during rapid mixing events that are normally difficult to observe. These short-lived interactions appear to be important in determining local nutrient content and therefore biological activity in this important ocean basin. Similar machine learning approaches that can be applied regionally will extend possibilities for how we use data from increasingly advanced ocean platforms.

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