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

   https://doi.org/10.1175/AIES-D-24-0048.1  

   Song, Sangmin; Lavin, Paige D; Gray, Alison R; Nachod, Zachary; Balwada, Dhruv; Dove, Lilian A; Thompson, Andrew F (April 2026, Artificial Intelligence for the Earth Systems)

   Abstract Small-scale ocean processes are known to modulate nutrient availability in the upper ocean, yet nutrient variability at scales ofandhas 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 ≤ 0.72μmol 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 StatementRapid 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. 

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2. Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

   https://doi.org/10.1175/AIES-D-22-0038.1  

   Weirich-Benet, Elizabeth; Pyrina, Maria; Jiménez-Esteve, Bernat; Fraenkel, Ernest; Cohen, Judah; Domeisen, Daniela I. (April 2023, Artificial Intelligence for the Earth Systems)

   Abstract Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves. Significance Statement Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves. 

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3. The Role of EDA in Developing Robust Machine Learning Models for Lithology and Penetration Rate Prediction from MWD Data

   https://doi.org/10.3390/mining6010019  

   Addy, Jesse; Anafo, Ishmael; Westman, Erik (March 2026, Mining)

   Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R2 = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments. 

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4. GEOTRACES: Ironing Out the Details of the Oceanic Iron Sources?

   https://doi.org/10.5670/oceanog.2024.416  

   Conway, Tim; Middag, Rob; Schlitzer, Reiner (January 2024, Oceanography)

   Dissolved iron (dFe) is an essential micronutrient for phytoplankton, with vanishingly low oceanic dissolved concentrations (pico- to nanomoles per kg) known to limit growth—and thus influence primary productivity and carbon cycling—over much of the surface ocean. However, because of the considerable challenges associated with contamination-free sample collection and accurate analysis of such low dFe concentrations, the first reliable dFe measurements came only in the 1980s. Further, by 2003, despite several decades of research, there were only ~25 full-depth oceanic dFe profiles worldwide, with dust considered to be the main oceanic dFe source. Since 2008, facilitated by the extensive field campaign and rigorous intercalibration of the international GEOTRACES program, there has been an “explosion” in the availability of oceanic dFe data, with hundreds of profiles now available. Concurrently, there has been a paradigm shift to a view of the marine Fe cycle where multiple sources contribute, and some forms of dFe can be transported great distances through the intermediate and deep ocean. Here, we showcase the GEOTRACES dFe datasets across the different ocean basins, synthesize our current multi-source view of the oceanic Fe cycle, spotlight sediments as an important dFe source, and look to future directions for constraining oceanic dFe boundary exchange. 

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5. Water Table Depth Estimates over the Contiguous United States Using a Random Forest Model

   https://doi.org/10.1111/gwat.13362  

   Ma, Yueling; Leonarduzzi, Elena; Defnet, Amy; Melchior, Peter; Condon, Laura E.; Maxwell, Reed M. (October 2023, Groundwater)

   Abstract Water table depth (WTD) has a substantial impact on the connection between groundwater dynamics and land surface processes. Due to the scarcity of WTD observations, physically‐based groundwater models are growing in their ability to map WTD at large scales; however, they are still challenged to represent simulated WTD compared to well observations. In this study, we develop a purely data‐driven approach to estimating WTD at continental scale. We apply a random forest (RF) model to estimate WTD over most of the contiguous United States (CONUS) based on available WTD observations. The estimated WTD are in good agreement with well observations, with a Pearson correlation coefficient (r) of 0.96 (0.81 during testing), a Nash‐Sutcliffe efficiency (NSE) of 0.93 (0.65 during testing), and a root mean square error (RMSE) of 6.87 m (15.31 m during testing). The location of each grid cell is rated as the most important feature in estimating WTD over most of the CONUS, which might be a surrogate for spatial information. In addition, the uncertainty of the RF model is quantified using quantile regression forests. High uncertainties are generally associated with locations having a shallow WTD. Our study demonstrates that the RF model can produce reasonable WTD estimates over most of the CONUS, providing an alternative to physics‐based modeling for modeling large‐scale freshwater resources. Since the CONUS covers many different hydrologic regimes, the RF model trained for the CONUS may be transferrable to other regions with a similar hydrologic regime and limited observations. 

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