Random Forest Regression Using Autonomous In Situ Ocean Observations: Inferring Small-Scale Nutrient Variability during a Southern Ocean Field Experiment

Abstract Small-scale ocean processes are known to modulate nutrient availability in the upper ocean, yet nutrient variability at scales of and has remained challenging to characterize from observations. Here, we show that small-scale nutrient information can be inferred from the 2019 Southern Ocean Glider Observations of Submesoscales (SOGOS) field experiment using random forest regression (RFR). In this experiment, a Biogeochemical-Argo float with coarse-resolution nutrient-sensing capabilities was co-deployed with two rapid-sampling Seagliders to autonomously observe a turbulent region by the Southwest Indian Ridge over ∼80 days in austral winter. Since the Seagliders did not measure nitrate directly, we used RFR to estimate nitrate from temperature, salinity, pressure, and oxygen data. The RFR was locally trained using nearby ship-based and Biogeochemical-Argo float observations to capture region-specific relationships between these variables. When tested on nitrate data from the SOGOS float, our RFR reproduced these measurements with high fidelity (95% of absolute errors ≤ 0.72 μ mol kg −1 ) and outperformed existing, widely used global nutrient estimation routines trained primarily on ship-based data. Applying the RFR to the Seaglider observations then generated novel nitrate distributions extending to 1000 m with a horizontal resolution of ∼1.5 km and 2–3 h compared to ∼75 km and 5 days for the SOGOS float. Our analysis of high-resolution machine learning estimates of Seaglider nitrate, coupled with Seaglider observations of other properties, suggests that vigorous small-scale stirring enhances upper-ocean nutrient availability in this region and highlights the importance of oceanic turbulence in mediating biological productivity. Significance Statement Rapid fluctuations in ocean nutrient content can be intense but difficult to observe directly. Here, we infer high-frequency nutrient information from a Southern Ocean field experiment by using regression to leverage multiple types of instruments. We utilize local nitrate measurements from two observing platforms (ship and autonomous Argo floats) to train a random forest regression that will estimate unknown nitrate concentrations along the tracks of different autonomous instruments with significantly faster sampling rates (Seagliders). Our targeted application produces novel nitrate estimates at high enough resolution to observe strong, small-scale nutrient stirring as the Seagliders traverse a turbulent oceanic region. Increased observational coverage of the ocean, combined with machine learning approaches, can thus enable ocean analyses at increasingly fine scales.