Front. Mar. Sci. Frontiers in Marine Science Front. Mar. Sci. 2296-7745 Frontiers Media S.A. 10.3389/fmars.2026.1740354 Original Research Subsurface biogeochemical response to Hurricane Idalia within a cyclonic eddy and river plume–stratified environment McWhorterJennifer K. 1 2 * † Conceptualization Data curation Formal analysis Investigation Methodology Project administration Resources Software Validation Visualization Writing – original draft Writing – review & editing LooneyLev B. 1 2 3 † Conceptualization Data curation Formal analysis Investigation Methodology Resources Software Validation Visualization Writing – review & editing Le HénaffMatthieu 1 2 † Conceptualization Formal analysis Investigation Methodology Project administration Resources Supervision Visualization Writing – review & editing Roman-StorkHeather L. 4 † Conceptualization Data curation Formal analysis Investigation Methodology Resources Software Validation Visualization Writing – review & editing FoltzGregory R. 1 † Conceptualization Investigation Methodology Resources Software Supervision Validation Visualization Writing – review & editing GomezFabian 1 5 † Conceptualization Data curation Methodology Resources Validation Visualization Writing – review & editing CornecMarin 6 7 8 † Conceptualization Data curation Investigation Methodology Resources Software Writing – review & editing SodenMadison 1 2 † Data curation Investigation Methodology Resources Software Writing – review & editing ZhangJun A. 1 2 † Conceptualization Investigation Methodology Resources Validation Writing – review & editing OsborneEmily B. 1 † Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing – review & editing NOAA/OAR/Atlantic Oceanographic and Meteorological Laboratory, Miami, FL, United States Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, FL, United States Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, FL, United States Global Science & Technology, Inc., Greenbelt, MD, United States Northern Gulf Institute, Mississippi State University, Starkville, MS, United States NOAA/OAR/Pacific Marine and Environmental Laboratory, Seattle, WA, United States School of Oceanography, University of Washington, Seattle, WA, United States CNRS, Laboratoire d’Océanographie de Villefranche, Villefranche-sur-Mer, France Correspondence: Jennifer K. McWhorter, jennifer.mcwhorter@noaa.gov

ORCID: Jennifer K. McWhorter, orcid.org/0000-0003-1297-6599; Lev B. Looney, orcid.org/0009-0008-6378-0653; Matthieu Le Hénaff, orcid.org/0000-0001-5548-2543; Heather L. Roman-Stork, orcid.org/0000-0003-2357-1343; Gregory R. Foltz, orcid.org/0000-0003-0050-042X; Fabian Gomez, orcid.org/0000-0002-2335-478X; Marin Cornec, orcid.org/0000-0002-7796-4081; Madison Soden, orcid.org/0009-0001-9960-129X; Jun A. Zhang, orcid.org/0000-0003-3713-0223; Emily B. Osborne, orcid.org/0000-0001-9579-5851

 20 02 2026 2026 13 1740354 05 11 2025 23 01 2026 14 01 2026 Copyright © 2026 McWhorter, Looney, Le Hénaff, Roman-Stork, Foltz, Gomez, Cornec, Soden, Zhang and Osborne. 2026 McWhorter, Looney, Le Hénaff, Roman-Stork, Foltz, Gomez, Cornec, Soden, Zhang and Osborne https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Satellite observations can reveal chlorophyll blooms in the wake of hurricane disturbances but their subsurface biogeochemical anomalies remain poorly described due to limited in situ observations. Here, we quantify the biogeochemical response across the ocean water column to Hurricane Idalia (2023) in the Gulf of America (also known as the Gulf of Mexico). We compile observations across the eastern Gulf using satellite data and two autonomous platforms: a profiling Biogeochemical-Argo (BGC-Argo) float and saildrone. Prior to the formation of Hurricane Idalia, an anomalously large extension of the Mississippi River plume spanned much of the eastern Gulf, contributing low-salinity and high-chlorophyll conditions. Following Idalia’s passage, the saildrone observed surface chlorophyll increases in the river plume extension, while the BGC-Argo float observed subsurface nitrate depletion and oxygen enrichment. These changes occurred as the float measured background ocean conditions evolving from the edge of the Loop Current to a cyclonic eddy, influenced by the river plume extension. Increases in chlorophyll concentration, decreases in nitrate, and elevated dissolved oxygen levels suggested increased primary production. BGC-Argo float observations revealed enhanced upwelling below the surface layer (~22 m) that shoaled the nitracline, fueling the increase in subsurface primary production (20–50 m depth). Our study provides a glimpse on the surface and subsurface ocean-biogeochemical changes associated with the Hurricane Idalia passage, highlighting the importance of the background mesoscale seascape on shaping the phytoplankton response to hurricane-induced disturbances. The combination of observations underscores the value of continuous in situ monitoring to better understand hurricane-driven impacts on the full ocean water column and the impacts these dynamics have on the base of the marine food web.

 Argo BGC-Argo Gulf of Mexico hurricane saildrone tropical cyclone The author(s) declared that financial support was received for this work and/or its publication. Support for this analysis was provided by NOAA Atlantic Oceanographic and Meteorological Laboratory. ML & LL This research was carried out [in part] under the auspices of the Cooperative Institute for Marine and Atmospheric Studies, a Cooperative Institute of the University of Miami and NOAA, cooperative agreement # NA20OAR4320472. Research reported in this publication was partly supported by the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine under award number 2000013149. It was also partly supported under Disaster Relief Supplemental Appropriations project NA22OAR4050666D. Part of this work performed by HR was funded under ST133017CQ0050_1332KP22FNEED0042. JAZ acknowledges support from NOAA grants NA22OAR4590118 and NA22OAR4050669D, Office of Naval Research grants N00014-24-1-2554 and N00014-24-1-2761, and National Science Foundation under awards 2211308 and 2532217. The study was also supported by the Northern Gulf Institute (NOAA cooperative agreement NA21OAR4320190). Data collected from the saildrone mission was supported by NOAA’s Office of Marine and Aviation Operations (OMAO) Uncrewed Operations Center (UxSOC), NOAA Atlantic Oceanographic and Meteorological Laboratory and NOAA Pacific Marine Environmental Laboratory. section-at-acceptance Ocean Observation Key Points

Autonomous observations described surface and subsurface biogeochemical patterns in the wake of a hurricane

A combination of hurricane- and eddy-induced upwelling increased nutrient concentrations and triggered subsurface primary production

Surface and subsurface oxygen concentrations increased post-hurricane in tandem with rising primary production signals

 Introduction

Tropical cyclones, referred to as hurricanes in the Atlantic once their maximum sustained wind speeds exceed 33 m/s (~64 kt), are among the most extensively studied atmospheric phenomena worldwide. Sea surface temperatures (SSTs) above 26 °C are essential for hurricane formation and intensification (Byers, 1974) and parameters such as ocean heat content, temperature, and salinity stratification are known to aid forecasting of storm intensity (Balaguru et al., 2018, 2020; John et al., 2023; Mainelli et al., 2008).

Hurricane-force winds induce strong vertical mixing in the upper ocean (~0-100 m) (Emanuel, 1999; Liu et al., 2009; Price, 1981; Shi and Wang, 2007; Walker et al., 2005), primarily cooling SSTs over horizontal distances of 30-150 km (Price, 1981; Walker et al., 2005) while increasing sea surface salinity (SSS) (Dickey et al., 1998; Lin et al., 2003; Price, 1981; Reul et al., 2021; Shang et al., 2008; Subrahmanyam et al., 2002). This mixing often results in surface chlorophyll blooms observed at the sea surface by satellite-based sensors, triggered by surface resuspension of chlorophyll and the upward flux of nutrients from the subsurface driving primary production (Babin et al., 2004; Chacko, 2017; Gierach and Subrahmanyam, 2008; Liu et al., 2009; Shi and Wang, 2007; Walker et al., 2005). These surface expressions of blooms usually last 2–4 weeks (Babin et al., 2004; Chacko, 2017, 2019; Shi and Wang, 2007) and often occur in the cool wakes of hurricanes (Babin et al., 2004; Walker et al., 2005).

Recent studies using profiling Biogeochemical-Argo (BGC-Argo) floats have observed subsurface primary production dynamics following hurricanes in the Bay of Bengal (Chacko, 2017, 2019; Girishkumar et al., 2019), the Arabian Sea (Wang et al., 2022), and the western Pacific (Zheng and Zhang, 2023), though such observations have yet to be reported in the Atlantic or the Gulf of America (also known as Gulf of Mexico; hereafter ‘Gulf’). Dissolved oxygen concentrations generally increase due to surface cooling and enhanced air-sea gas exchange (Lin et al., 2014; Xu et al., 2019; Yang and Langdon, 2025), and from photosynthetic dissolved oxygen production by phytoplankton (Dickey et al., 1998; Eppley and Renger, 1988; Girishkumar et al., 2019; Lin et al., 2003, 2014; Marra et al., 1990; Robarts et al., 1998; Yin and Harrison, 2007). Following an increase in primary production, dissolved oxygen levels subsequently decrease as phytoplankton blooms decay and organic matter re-mineralizes (Chen et al., 2017; Rabalais et al., 2002; Wang et al., 2017; Yang et al., 2024), and may also be influenced by advection (Xu et al., 2019; Yang et al., 2024). However, a consistent ocean response to subsurface hurricane-induced biogeochemical changes has not been observed across studies, making generalizations challenging. The synthesis of existing results suggests that the biogeochemical response of the ocean is highly contingent on pre-storm ocean conditions, local biogeochemical dynamics, storm strength as well as the spatial, temporal, and vertical resolution of sampling.

In the Gulf, there are on average 5 ± 2 named storms, 2 ± 2 hurricanes, and 1 ± 1 major hurricane per year based on a 30-year climatology (1995-2024) (Gahtan et al., 2024; Knapp et al., 2010). Numerous studies have shown that features such as the Loop Current and associated anticyclonic eddies in the Gulf significantly enhance hurricane intensification due to their deep warm-core structures and elevated ocean heat content (Hong et al., 2000; Jacob and Shay, 2003; Jaimes et al., 2016; Rappaport et al., 2010; Shay et al., 1992, 2000). Entering through the Yucatan Strait between Mexico and Cuba, the Loop Current can extend north to ~28°N before moving southward, often shedding westward-propagating large anticyclonic eddies (radius ~200 km, lifespan up to a year) (Auladell et al., 2010; Meunier et al., 2019). The Loop Current also generates cyclonic eddies and associated upward doming of isopycnals (layers of equal density) toward the ocean surface that shoal the nitracline and foster chlorophyll blooms through the introduction of nutrients (Chacko, 2017; Girishkumar et al., 2019; Lee-Sánchez et al., 2022; Roy Chowdhury et al., 2022; Vukovich and Maul, 1985; Wang et al., 2022; Zheng and Zhang, 2023). Walker et al. (2005) used satellite observations to show that Hurricane Ivan in 2004 intensified the upwelling associated with a cyclonic eddy, amplifying the biological productivity. However, Walker et al. (2005) only had access to surface data and were unable to describe the subsurface biogeochemical response to the hurricane passage.

The Loop Current system also transports nutrients from the Mississippi River plume throughout the Gulf, occasionally extending into the Straits of Florida under anomalous conditions (Brokaw et al., 2019; Hu et al., 2005; Otis et al., 2019; Shi et al., 2025). Together, the Loop Current and the Mississippi River plume, which can extend as far offshore as the Straits of Florida, also play a dominant role in shaping the biogeochemistry in the Gulf. As a recurring summer seasonal pattern on the West Florida shelf, the Mississippi River plume introduces a low-salinity and high-chlorophyll water signature (Brokaw et al., 2019; Morey et al., 2003a, 2003b; Osborne et al., 2022; Otis et al., 2019). Most recently, Shi et al. (2025) found that the rapid intensification of Hurricane Idalia (August 2023) was aided by the Mississippi River plume, associated with a 10–20 m thick barrier layer below the plume that inhibited vertical mixing. Results from Shi et al. (2025) were limited in terms of biogeochemistry, relying solely on satellite chlorophyll data.

Using satellite observations and autonomous platforms (BGC-Argo and saildrone), this study examines the impact of Hurricane Idalia (August 2023) on the eastern Gulf’s biogeochemistry. We characterize a significant increase in primary production resulting from the interplay between a cyclonic eddy and the Mississippi River plume, with subsurface blooms evidenced by measurable nitrate drawdown and oxygen supersaturation.

 Data and methods Hurricane Idalia data

Observations of Hurricane Idalia’s track, intensity, and structure were obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) (Gahtan et al., 2024; Knapp et al., 2010), based on the National Hurricane Center’s best track data (Cangialosi and Alaka, 2024). In this archive, the best-known center coordinates (within 0.1˚ latitude/longitude), maximum sustained wind speed (2.6 m/s increments), minimum sea level pressure (within 1 hPa), and 17 m/s (34 kt), 26 m/s (50 kt), and 33 m/s (64 kt) wind radii (within 10 nautical miles) for each quadrant (NE, SE, SW, NW) were input for 6-hour intervals.

To determine the location of the autonomous platforms (i.e., BGC-Argo float and saildrone) within Hurricane Idalia’s wind field, we interpolated the IBTrACS original 6-hourly data to a 1-minute resolution using Modified Akima piecewise cubic Hermite interpolation. To determine the horizontal extent of the wind field, at the time the interpolated intensity exceeded a wind speed radius threshold (17 m/s, 26 m/s, and 33 m/s), the radius for each quadrant (NE, SE, SW and NW) was interpolated to 1-minute resolution. We then determined the region within each radius using a time-interpolated 1-degree radial resolution.

Using the new spatiotemporally interpolated extent of Hurricane Idalia’s wind field, we determine when each autonomous platform was before, during, and after the passage of the 17 m/s wind field. These labels correspond to the blue (before Idalia), green (within Idalia), and magenta (after Idalia) colors in Figure 1.

Saildrone trajectories during August 19 - September 20 (colored lines) and Biogeochemical-Argo profiles during August 19 - October 1 (triangles) used in this study. Colors indicate the timing of the observations: pre-storm (blue), in-storm (green), and post-storm (magenta). The gray line shows the interpolated path of Hurricane Idalia shaded by its maximum sustained wind speed with circles depicting the best-track location and intensity from the National Hurricane Center. Red lines in the grayscale bar indicate when the interpolated intensity transitions from tropical depression to tropical storm (TS), tropical storm to category 1 hurricane (HU1), category 1 to 2 hurricane (HU2), and 2 to 3 major hurricane (MH3). Yellow, light pink, and dark pink shading show the regions within the radii of 17, 26, and 33 m/s sustained wind speeds from Hurricane Idalia.

Weather map showing the track and intensity of a tropical cyclone moving from near Cancún across the Gulf of Mexico towards Florida. Color bands indicate storm intensity with annotations for pre-storm, in-storm, and post-storm phases. The path includes circular and triangular markers. Legend and scale bar are included. Satellite data and analytical methods Satellite data

Daily fields of the National Oceanic and Atmospheric Administration (NOAA) CoastWatch gap-filled ocean color chlorophyll data were obtained at 9 km daily resolution. This product uses a Data INterpolating Empirical Orthogonal Function (DINEOF) algorithm to combine Multi-Sensor Level 1 and Level 2 (MSL12) ocean color data from multiple Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (Suomi National Polar-Orbiting Partnership (SNPP) and NOAA-20), to create a gap-filled analysis, which allows for a cloud-free view of ocean color derived chlorophyll (Liu and Wang, 2019). Sea level anomalies (SLA) and geostrophic currents from the NOAA Radar Altimeter Database System (RADS) Level 4 merged near-real time (NRT) product were used and are available on a 0.25° daily grid from 2017 (2019 for currents) through the present from NOAA CoastWatch (Scharroo et al., 2013). Blended 6-hourly wind stress and surface wind measurements from NOAA National Centers for Environmental Information (NCEI) Blended Seawinds version 2 product (NBSv2) were obtained from NOAA CoastWatch on a 0.25° grid. NBSv2 combines satellite observations from multiple scatterometers (up to 7 since 2002) with L-band and the AMSR2 all-weather channel observations (Saha and Zhang, 2022). NOAA’s Geo-polar night SSTs were obtained on a daily 9 km grid from NOAA CoastWatch (Maturi et al., 2017). Additional satellite-derived SSS from the Soil Moisture Active Passive (SMAP) mission processed by NASA’s Jet Propulsion Lab as the Combined Active Passive (CAP) version 5.0 product were obtained from PO.DAAC on 0.25° daily grids as an 8-day interpolated product (Fore et al., 2016).

BGC-Argo float positions were collocated with mesoscale eddies identified using the MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER) product suite from NOAA CoastWatch (McWhorter et al., 2024; Roman-Stork et al., 2023). MUNSTER is a threshold-free, closed-contour eddy tracking method adapted from algorithms originally developed by Chaigneau et al. (2008, 2009) and Pegliasco et al. (2015). An asymmetric Gaussian high-pass spatial filter with a 5°/10° latitude/longitude half-width was applied to the daily NOAA RADS SLA field to remove planetary wave contamination. Eddies in MUNSTER are identified based on closed contours surrounding local maxima (anticyclonic eddies) and minima (cyclonic eddies) of filtered daily NOAA RADS SLA at a 0.1 cm contour interval. For the purposes of this study, eddy contours from MUNSTER were used to collocate the BGC-Argo float profiles and identify when the float surfaced within or outside of an eddy.

 Satellite data analysis

The total surface current was calculated as the sum of the geostrophic current and the Ekman current (Equation 1), where the geostrophic current is from satellite altimetry, and the Ekman current is considered to be at 15 m depth, as in Sudre and Morrow (2008):

(ue+ive) = Beiθ(τx+iτy)

where ue and ve are the zonal and meridional components of the Ekman current at 15 m depth, τx and τy are the zonal and meridional components of the wind stress from NBSv2. Following Sudre and Morrow (2008) and the tropical transition proposed in Lagerloef et al. (1999), a varying B (Equation 2) and θ (Equation 3) relative to latitude were calculated as follows:

B = 1ρ(r2+f2hmd2)−1/2 θ= arctan(fhmdr)

where ρ is the seawater density in kg/m3 calculated from satellite SSS and SST, f is the Coriolis parameter, and the values of r and hmd, the frictional parameter and mixing depth scale respectively, are taken to be constant from values calculated in Lagerloef et al. (1999), such that r = 2.15x10–4 m s-1 and hmd = 32.5 m.

Surface advection of SSS (SSS adv; Equation 4) and ocean color chlorophyll-a (Chla adv; Equation 5) were then calculated using the total current as follows:

SSS adv = −uT∂SSS∂x−vT∂SSS∂y Chla adv = −uT∂Chla∂x−vT∂Chla∂y

where uT and vT are the zonal and meridional components of the total current, and x and y are the changes in longitude and latitude on the 0.25° horizontal grid associated with gridded altimetry. Ocean color was regridded to 0.25° horizontal resolution prior to calculation.

Isotherm upwelling (Equation 6, Δη) within a cyclonic eddy due to the passage of Idalia was calculated as in Walker et al. (2005) using the reduced gravity approximation (Shay et al., 2000; Walker et al., 2005):

 Δη=−gg'Δh

such that g is the acceleration due to gravity, g’ is 0.03 m s-2 and is taken to be representative of Gulf-wide conditions as in Walker et al. (2005), and Δh is taken to be the change in sea level within the cyclonic eddy in question from altimetry in m. Here the magnitude of a cyclonic eddy located at 84.875°W, 25.125°N on August 19, 2023 was compared with the same eddy after the passage of Idalia at 85.125°W, 24.625°N on September 7, 2023, resulting in a Δh of 18 cm, and a Δη of 58 m. The same calculation was then performed for the location of maximum chlorophyll from saildrone observations (84.625°W, 26.5303°N), which resulted in a net downwelling of isotherms by 36 m. These values were used to contextualize the upwelling signals (cyclonic eddy and Hurricane Idalia) from the BGC-Argo float observations.

Ocean color and SST suffer from cloud contamination, while SSS, surface wind, and altimetric observations are not affected by clouds and are more likely to represent the surface conditions where cloud cover is present, such as within storms. That said, Geo-polar SST incorporates geostationary observations which can supplement for temporary cloud contamination and rapidly moving systems, which results in a product with a very low global standard deviation compared to in situ buoy data (Maturi et al., 2017). The DINEOF algorithm employed in the gap-filled ocean color chlorophyll-a combine’s observations from two VIIRS satellites that follow the same path 50 minutes apart, and uses information from previous observations to better extrapolate the missing information when clouds prevent direct observation (Liu and Wang, 2019). As such, while both SST and ocean color do experience these observational difficulties, the algorithms employed in the products used take measures to account for them. The satellite datasets used here are interpolated between 9 km grid points for temperature and ocean color, and 0.25° latitude and longitude for salinity, and thus miss finer scale features detected by the saildrone.

 Saildrone data

Saildrones are equipped with oceanographic and meteorological sensors to measure near-surface wind velocity, air temperature, relative humidity, barometric pressure, solar radiation, SST, SSS, dissolved oxygen concentration, chlorophyll concentration, wave height and period, and profiles of ocean currents over depths from ~ 6 to ~80 m with 2 m resolution (Supplementary Information Table 1; Zhang et al., 2023). These sensors are validated before the mission to ensure data quality control. Wind is measured at a height of ~3.45 m and air temperature and humidity are measured at ~2.3 m. It should be noted that the exact height per measurement may vary based on the vehicle’s pitch and roll. However, we found that this was minimal with the mean 1-minute height of the wind measurements in Idalia being 3.36 ± 0.03 m. All measurements reported here have not been adjusted to the standard 10 m height, which would increase wind speed by about 10 - 15%, assuming neutral atmospheric stability. SST, SSS, dissolved oxygen, and chlorophyll are measured at depths of ~1.7 m. Saildrones are operated remotely through satellite communications, powered by solar radiation, and propelled by the wind. Through a partnership between NOAA and Saildrone Inc. from 2021 to 2024, saildrone uncrewed surface vehicles have been used to observe hurricanes and transmit their 1-minute measurements in near-real time, with higher resolution data (1 to 20 Hz) recorded and downloaded upon retrieval at the end of the mission. During the 2023 mission, saildrone 1083 (hereafter ‘SD-1083’) was directed to, and intercepted, Hurricane Idalia in the eastern Gulf.

Prior to being directed for intercept on August 24, SD-1083 was positioned 25–50 km west of Hurricane Idalia’s eventual track. SD-1083 moved ~45 km to the SE from August 27–28 working best with the prevalent winds and currents to better position for intercept. On August 29, SD-1083 traveled to the NE for its final intercept positioning. SD-1083 started measuring tropical-storm-force 1-minute sustained winds on August 29 at 14:32 UTC, with sustained winds peaking at 36 m/s and maximum gusts of 43 m/s nearly 8 hours later. SD-1083 then entered the northeastern eye of Idalia around August 29 at 22:24 UTC and remained in the eye until 22:59 UTC, when it exited through the southeastern eyewall. The surface pressure reached a minimum of 964.4 hPa and the significant wave height peaked at 9.6 m. SD-1083 measured its final tropical-storm-force sustained wind from Idalia over 14 hours after tropical-storm-force winds began. The track of SD-1083 is shown in Figure 1.

Following the intercept of Hurricane Idalia, SD-1083 drifted ~60 km to the NE within one day and remained over the next four days. Then, SD-1083 traveled back and remained within 20 km of the intercept location for the following 8 days, reaching a minimum distance of less than 150 m on September 9. On September 11, 2023 12:00 UTC, more than 12.5 days after the intercept, SD-1083 began to travel offshore of its intercept location.

Pre-storm measurements from SD-1083 were defined as the average over a set period of time (i.e., 6 hours, 12 hours, 24 hours, 3 days, 5 days, and 10 days) ending 6-hours prior to SD-1083 entering into the region of 17 m/s winds. Multiple periods were used to define the uncertainty. A similar analysis was done to define post-storm SST, with averaging periods of 6 hours, 12 hours, and 24 hours starting 6 hours following SD-1083 exiting the region of 17 m/s winds.

Following Chiodi et al. (2024) and Brenner et al. (2023), in the absence of vertical measurements of temperature and salinity, which prevent the estimate of the mixed layer depth based on a density criterion, the mixed layer depth at the saildrone’s location (MLD_saildrone) was derived from the vertical shear of the horizontal currents measured by the saildrone’s downward-looking 300 kHz ADCP. ADCP observations where the total percentage of good pings per ensemble of less than 50% were removed, which on average, removed the lowest 10 m, where noise is high from the ADCP’s backscatter signal. Subsequent results are not sensitive to the threshold of good pings used. The magnitude of the vertical shear was calculated based on the vector differences of the horizontal components (i.e., u, v) of the current for each time step. The depths that met the following three criteria at each time step were identified: (1) the shear had a value within half a standard deviation of the maximum value of shear across depths, (2) the shear had a value greater than one standard deviation above the mean shear across depths, and (3) the shear was not the deepest nor shallowest depth. Less than 4% of the total time steps failed to have depths that met these criteria. Following this, if the standard deviation of the identified depths at a given time step was 10 m or greater (occurring less than 9% of the time), then that time step was deemed invalid. For all remaining valid time steps, the identified depths were then averaged to determine the initial MLD_saildrone. Since each time step is treated independently, it is possible that some unrealistic rapid changes in the initial MLD_saildrone exist. In order to remove these, all times when each depth was greater than one standard deviation away from the mean based on a 6-hour moving window were deemed invalid (17% of the time). The MLD_saildrone used was then calculated from a 6-hourly moving average of the remaining times (over 70% of the total).

 Biogeochemical-Argo data

BGC-Argo floats profile autonomously and drift with ocean currents, typically measuring from 0–2000 m depth every 10 days. Floats carry sensor packages that measure temperature, salinity, dissolved oxygen, nitrate, pH, chlorophyll, optical backscatter, and irradiance (Bittig et al., 2019; Claustre et al., 2020; Johnson et al., 2013; Maurer et al., 2021; Riser et al., 2018; Roemmich et al., 2019). A BGC-Argo float, identified as World Meteorological Organization (WMO) 4903624, operated by NOAA/AOML was located directly in the path of Hurricane Idalia. This particular BGC-Argo float was equipped with sensors that were collecting temperature, salinity, dissolved oxygen, nitrate, and pressure during the passage of Idalia (Supplementary Information Table 2). Chlorophyll and optical backscatter measurements were unavailable due to a sensor failure. Float operators sent instructions to this float to abandon its standard 10-day mission and employ a rapid cycle mission on August 29, 2023. For 12 days (August 29 - September 10), the float was instructed to profile every ~18 hours yielding a total of 15 profiles (cycle numbers 71-86) following Hurricane Idalia.

The eye of Hurricane Idalia passed over float 4903624 on August 29 16:00 UTC. Due to the presence of low-salinity waters from the Mississippi River plume at the sea surface (Figure 2g), the float, which is ballasted to operate over a predicted density range, did not have sufficient buoyancy to penetrate into the low-salinity waters and reach the sea surface from August 30 - September 10 (14 profiles), following the storm. Instead, the float stopped at an average depth of 22.1 m (± 2.02 m) over this 12-day period. Salinity in the Mississippi River plume is usually close to uniform from the ocean surface to the bottom of the plume (Le Hénaff et al., 2021; Shi et al., 2025), where in contrast the salinity gradient, and therefore the density gradient, are very pronounced. It is therefore reasonable to assume that the depth at which the float was blocked from reaching the surface corresponds to the bottom of the river plume, and that it is a good approximation of the mixed layer depth at the location of the Biogeochemical-Argo (MLD_float), in the absence of complementary observations. This depth reflects an instrumental limitation rather than a formal MLD definition. The 22 m average thickness of the Mississippi River plume is consistent with the ~20 m plume thickness reported in Shi et al. (2025). The nitracline was calculated as the depth at which there was a concentration difference of 1 µmol L-1 in reference to the surface value (Cornec et al., 2021; Lavigne et al., 2015). Due to the inability of the float to surface, a GPS fix could not be collected in association with these 14 cycles. The float GPS fixes changed minimally between cycles 71 and 86 (~8 nautical miles); therefore, a linear interpolation was applied to estimate profile locations.

Satellite observations of sea level anomalies (SLA; m; a, f, k), sea surface salinity (SSS; psu; b, g, l), sea surface temperature (SST; °C; c, h, m), ocean color chlorophyll-a (Chla; mg/m3; d, i, n), and 10 m winds (Wind; m/s; e, j, o) overlaid with total surface currents (m/s; a–d, f–i, k–n) and 10 m winds (m/s; e, j, o) are used to show the extension of the Mississippi River plume into the region of the saildrone (purple star) and the Biogeochemical-Argo float (purple square). Hurricane Idalia passed over the Biogeochemical-Argo float and saildrone on August 29, 2023. The first row (a–e) shows the ocean state before the storm, second row (f–j) during the storm, and third (k–o) row shows the mixing following the storm.

A series of maps displaying sea surface conditions in the Gulf of Mexico on three dates: August 19, August 29, and September 7. Each row corresponds to one date, showing variations in sea level anomaly (SLA), sea surface salinity (SSS), sea surface temperature (SST), chlorophyll-a concentration (Chla), and wind speed. The maps are color-coded with arrows indicating current directions. The visualizations highlight changes over time across different oceanographic parameters.

Data collected by BGC-Argo float 4903624 underwent Delayed Mode Quality Control (DMQC) procedures to determine and apply corrections to collected temperature, salinity, pressure, nitrate, and dissolved oxygen. DMQC for core Argo parameters (temperature, salinity, and pressure) confirmed that no adjustments to these data were needed (Wong et al., 2020). Offset corrections for dissolved oxygen and nitrate data were determined and applied using Maurer et al. (2021) SAGE-O2 and SAGE software. When possible, in-air oxygen measurements are used to determine corresponding profile gain and drift corrections that are applied to the full profile. This is due to in-air corrections (± 2 m partial pressure oxygen), relative to correcting based on deep reference values pulled from ocean climatologies such as World Ocean Atlas (± 4–6 m partial pressure oxygen), yield the lowest estimated sensor errors for corrected float oxygen data (Bittig et al., 2015; Bushinsky et al., 2016; Johnson et al., 2015; Thierry and Bittig, 2021). For cycles 72 through 85 when float 4903624 did not surface, in-air values were calculated using a linear regression. Measured average offset in-air values were 0.014 μmol/kg, representing a small change of 0.027 μmol/kg over the 12 days where interpolated correction values were applied. Nitrate gain and offset values were applied using a reference depth of 1480–1520 for cycles pre-Idalia (1-71) and post-Idalia (86-94). For the rapid cycles collected during or shortly after the storm (72-85) a wider reference depth of 1380–1520 m was applied to account for the shoaled profiles that were collected during rapid cycling, and lack of data below 1480 m. This compound reference depth in combination with an Empirical Seawater Property Estimation Routines (ESPER-NN) neural network is determined as a deep reference value for the location of the collected profiles (Maurer et al., 2021). DMQC data were submitted to the Argo Global Data Assembly centers and are available for public use (See Data Availability).

 Results Track of Hurricane Idalia

The National Hurricane Center began issuing statements for what would become Major Hurricane Idalia on August 26, 2023. Idalia entered the southeastern Gulf early on August 29, intensifying to a Category 1 hurricane moving northward at ~6 m/s with maximum winds increasing by 20 m/s within 24 hours (Figure 1). Hurricane Idalia reached a peak intensity of 59 m/s and a minimum pressure of 942 mb (major hurricane, Category 4 on the Saffir-Simpson Scale) in the northeastern Gulf. Idalia made landfall near Keaton Beach, Florida on August 30, 2023 at 11:45 UTC as a Category 3 major hurricane, with maximum sustained winds of 51 m/s. Hurricane Idalia then turned to the Northeast and moved off the coast of South Carolina the following day, where it later transitioned to an extratropical cyclone (Cangialosi and Alaka, 2024). Figure 1 shows the track of Idalia until August 30, 2023 at 16 UTC.

Both the BGC-Argo float WMO-4903624 and saildrone SD-1083 were operating along the west Florida shelf in the path of Hurricane Idalia (Figure 1), although in different mesoscale oceanic features (1. SD-1083: river plume, 2. BGC-Argo: river plume and cyclonic eddy). Hurricane Idalia was a rapidly intensifying, strong Category 1 hurricane (winds 40 m/s) when it passed over the BGC-Argo float (3.3 nautical miles from the interpolated center) on August 29 at 16:24 UTC. SD-1083 reached its closest position (5.8 nautical miles to the interpolated center) on August 29 at 22:42 UTC (just over 6 hours following the passage over the BGC-Argo float, 100 nautical miles north), when Idalia’s interpolated maximum wind speed was 45 m/s, reaching Category 2 intensity (Figure 1).

The conditions off the West Florida Shelf pre-Idalia were well-suited for the rapid intensification of Idalia with SSTs exceeding 31 °C and a large low-salinity extension (< 35 psu) from the Mississippi River (Figures 2b, c). Prior to Idalia’s passage, the region experienced comparatively slow currents near the Florida coast, light northerly winds, and a widespread low-salinity layer (Figures 2a–e), which was associated with the presence of a barrier layer (Shi et al., 2025). The center of Hurricane Idalia passed to the east of the Loop Current (Figures 2a, f), over the high SSTs (Figures 2c, h) and a low-salinity layer mainly associated with the Mississippi River plume and potentially from the Apalachicola River plume (Figures 2b, g; Morey et al., 2009). The region to the east of Idalia showed the largest change from Hurricane Idalia with an increase in salinity (Figures 2b, g, l), a decrease in temperature (Figures 2c, h, m) and an increase in chlorophyll (Figures 2d, i, n) with the highest surface chlorophyll signal off the West Florida Shelf, north of SD-1083, where Idalia rapidly intensified (Figures 1, 2d, i).

The Mississippi River plume prior to Idalia extended as far south as the Straits of Florida (Figure 2b; Shi et al., 2025). This southward river plume extension along the West Florida Shelf was likely driven by a combination of the eastward Ekman currents from persistent southerly winds (Liu and Weisberg, 2012; Morey et al., 2009, 2003a; Schiller et al., 2011; Zhang and Hu, 2021) and entrainment into the Loop Current (Figure 3; Hu et al., 2005; Le Hénaff and Kourafalou, 2016; Ortner et al., 1995; Otis et al., 2019). Surface advection of low-salinity was present throughout Idalia, largely due to the Mississippi River plume and surface entrainment by the Loop Current system (Figures 3a, c, e). Chlorophyll advection occurred as a response to Idalia at the edge of the Loop Current and extended further east, along the hurricane’s path (Figures 3b, d, f). In the southeastern Gulf, in the location of the BGC-Argo float, a cyclonic eddy adjacent to the Loop Current intensified during the passage of Idalia (Figures 2f, k; Gopalakrishnan et al., 2025). Wind-induced currents enhanced the cyclonic flow, intensifying upwelling and horizontal advection at the surface (Figure 3).

Satellite-derived calculations of SSS advection (SSS adv; a, c, e) and chlorophyll surface advection (Chla adv; b, d, f) overlaid with total surface currents (m/s) The locations of the saildrone (purple star) and Biogeochemical-Argo float (purple square) are indicated in purple and the best track of Hurricane Idalia is indicated by a black line. The first row (a, b) is to show the state before the storm, second row (c, d) during the storm, and third (e, f) row show the mixing following the storm.

Maps showing sea surface salinity (SSS) and chlorophyll-a (Chla) advection near the Gulf of Mexico on August 19, August 29, and September 7. Panels a, c, and e display SSS with gradients in blue to red indicating changes. Panels b, d, and f illustrate Chla advection with similar color gradations. Black vectors represent current directions. Each map includes geographical coordinates, with a star and square indicating specific locations. Surface bloom

Hurricane Idalia’s maximum sustained winds were 45 m/s when it passed over SD-1083 (Figure 1). SD-1083 was largely in the Mississippi River plume extension before, during, and after Idalia with SSS generally< 35 psu (Fournier et al., 2016; Figures 2b, g, i). SD-1083 was not in a Loop Current feature, in contrast to the BGC-Argo observations (Figures 2a, f, k). Directly following Hurricane Idalia, SD-1083 observed an increase in the ADCP-derived mixed layer depth, MLD_saildrone, from ~14 m to 53 m (Figure 4b), a decrease in SST of ~2 °C (1.8 - 2.1 ˚C; Figure 4c) and an increase in salinity of ~2 psu (1.6 - 2.1 psu; Figure 4c).

Saildrone observations of (a) running hourly extreme 1-minute sustained winds and pressure, (b) 5-minute horizontal ocean current speed with derived mixed layer depth (black line), (c) hourly averaged sea surface temperature and salinity, (d) hourly averaged chlorophyll-a and oxygen concentrations, and (e) distance and direction from Hurricane Idalia’s best track. The shaded area represents ‘in-storm’, or when the saildrone was within the extent of 17 m/s winds. The vertical dotted purple lines represent 10 days before and after the closest approach of Hurricane Idalia to the saildrone.

Five-panel chart showing data related to Hurricane Idalia from August 19 to September 16. Panel (a) displays hourly maximum wind speed and minimum pressure. Panel (b) shows horizontal velocity with mixed layer depth. Panel (c) depicts sea surface temperature and salinity. Panel (d) presents chlorophyll and oxygen levels. Panel (e) illustrates distance and direction from the hurricane's track. Each panel highlights a marked period in early September.

SD-1083 recorded a near twofold increase in chlorophyll. Chlorophyll values of ~1.28 mg/m3 in late August (10 days pre-Idalia passage), associated with the Mississippi River plume influence, were followed by a post-storm peak of 2.42 mg/m3 on September 9 (10 days after Idalia; Figure 4d). SD-1083 moved away from the original hurricane intercept location immediately following the intercept, showing values similar to pre-Idalia chlorophyll values (Figure 1). It then returned to the intercept location 4.5 days later, where the highest chlorophyll signal was measured (September 2, 2023 22:32 UTC; Figures 1, 4d, e). The bloom remained evident for the following seven days until September 10, 2023 00:00 UTC when the subsequent 6-day average chlorophyll concentration was below 0.08 mg/m3 (Figures 1, 4d).

Mirroring the increases in chlorophyll concentration, surface dissolved oxygen concentrations increased by 6% (+14 μmol/kg; from 220 μmol/kg 10 days pre-Idalia maximum to 234 μmol/kg 10 days post-Idalia maximum; Figure 4d). Oxygen concentrations fluctuated more than chlorophyll and declined after September 10 (Figure 4d).

 Subsurface bloom, inferred from nitrate drawdown and oxygen supersaturation

BGC-Argo float (WMO 4903624) was located within both the Mississippi River plume extension and a cyclonic eddy (upwelling), following the passage of Hurricane Idalia over the float’s location (Figure 2f). Pre-Idalia, the BGC-Argo float was profiling on the edge of the Loop Current, which had a downwelling signal characteristic of an anticyclonic eddy, and then moved into a cyclonic eddy (upwelling) during and after Idalia (Figures 2a, f, k, 5). The combination of upwelling driven by the cyclonic eddy and surface water displacement due to Idalia winds resulted in a shoaling of the thermocline by 58 m (See methods section, 2.2.2 Satellite Data Analysis). This combined upwelling also shoaled the nitracline from 151 m to 47 m based on the first BGC-Argo profile following the passage of Idalia (Figure 5c). While vertical mixing was limited by the Mississippi River low-salinity surface layer, the combination of hurricane winds (Figures 1, 2e, j, o) and cyclonic eddy circulation enhanced upwelling (Figures 2a, f, k), uplifting nutrient-rich layers.

Pre-Idalia, during-Idalia, and post-Idalia profiles of (a) temperature, (b) salinity, (c) nitrate, and (d) oxygen from Biogeochemical-Argo float (WMO-4903624) are shown. The Idalia mixed layer depth (MLD_float; dashed line) subsurface signal location was derived based on the depth of missing near the surface post-Idalia (~0–22 m, 15 profiles) data for 12 days due to ballasting issues representing a change in density.

Four graphs showing oceanic data related to pressure and chemical properties: (a) Temperature in degrees Celsius, indicating variation over time at different pressures. (b) Salinity in PSU, showing changes with respect to pressure. (c) Nitrate concentration in micromoles per kilogram, with three time points: before, during, and after specific dates in August and September. (d) Oxygen levels in micromoles per kilogram across time and pressure. Each graph includes a horizontal dotted line labeled “MLD float,” representing a reference depth.

In the absence of an operating bio-optical sensor to measure chlorophyll and particulate backscatter, nitrate and oxygen were used as tracers of biological activities/microbial dynamics. Nitrate values pre-Idalia (BGC-Argo profile on August 19) were at a maximum of 3.2 μmol kg-1 near the surface and 3.5 μmol kg-1 at the nitracline depth of 151 m while the float was located on the edge of the Loop Current (downwelling signal) and entrained in the Mississippi River plume (Figures 2a, b, 5c). Directly following Idalia, the cyclonic eddy and hurricane-induced upwelling raised the nitracline, resulting in an infusion of nutrients available to stimulate primary production, which was observed between ~20–50 m (Figure 5c). Subsurface nitrate concentrations declined to values < 1 μmol kg-1 following the passage of Idalia, suggesting that nutrients were rapidly consumed by phytoplankton growth (Figures 5c, 6b). Subsurface dissolved oxygen concentrations increased post-Idalia to values > 200 μmol kg-1, although these values were slightly delayed in response by 3 days (Figures 5d, 6b) likely reflecting increased photosynthetic activity. The nitrate decrease and dissolved oxygen increase relationship, shown in Figure 7a shows an enhanced case of upwelling intensification by Idalia and a cyclonic eddy when compared to average Gulf-wide cyclonic eddy conditions without hurricane-enhancement in Figure 7b. Importantly, the Gulf-wide anticyclonic eddy (downwelling eddy) conditions show a more homogenous relationship between oxygen and nitrate from 0–100 m (Figure 7d) while Figure 7c, Gulf-wide no eddy conditions, does not show either a downwelling eddy (Figure 7d) or upwelling eddy signal (Figures 7, b). The largest ratio of nitrate to dissolved oxygen was observed between 40–50 m (denoted by the dashed lines in Figures 6, 7a), deeper than the MLD_float (~22 m) and surrounding the nitracline. Following September 9, nitrate levels between 40–50 m recovered to higher values (>2 μmol kg-1) while oxygen remained within range of the Idalia disturbance (> 200 μmol kg-1), not yet showing a microbial regeneration of organic matter (Figure 6b).

The time series shows the Biogeochemical-Argo float data (WMO ID 4903624) (a) temperature (orange), salinity (purple), (b) oxygen (red), and nitrate (green), from the depth range 40–50 m where the largest biogeochemical change from Idalia occurred. Hurricane Idalia’s passage off the southwestern Florida shelf was on the evening of August 29, 2023 (vertical black line) and 10-days pre- and post-Idalia are denoted by dashed vertical lines. Closed blue circles represent cyclonic eddies and open black circles represent no eddy.

Line graphs illustrating oceanographic data over time. Panel (a) shows temperature in degrees Celsius (orange) and salinity in practical salinity units (purple). Panel (b) displays oxygen in micromoles per kilogram (brown) and nitrate in micromoles per kilogram (green). Data points are marked for no eddy (open circles) and cyclonic eddy (blue circles). Dates range from August 22 to October 1.

Nitrate and dissolved oxygen are shown from (a) the Biogeochemical-Argo float (WMO ID 4903624) in the hurricane-driven mixing event up to 10 days following Hurricane Idalia (August 29, 2023 to September 10, 2023) from 0–100 (m) (b) Cyclonic (n=60), (c) no eddy (n=82), and (d) anticyclonic eddy (n=141) plots were similarly created but using the Biogeochemical-Argo array in the Gulf, excluding the Idalia cycles (WMO IDs 4903624, 4903625, 4903622, 7901009; Sept 2021-2023). Dashed lines mark the enhanced minimum nitrate and maximum oxygen values post-Idalia on Sept 07, 2025 for comparison to other cyclonic eddies.

Graph showing the relationship between nitrate and oxygen concentrations across different depths, represented by different colors, in three scenarios: cyclonic eddy, no eddy, and anticyclonic eddy. Each scenario displays a distinct trend in nitrate and oxygen levels, with depth ranges categorized from less than 30 meters to 90-100 meters, indicated by a color gradient from red to dark blue. Discussion

The recorded biogeochemical changes under Hurricane Idalia resulted from the interplay between multiple physical factors, including the Mississippi River plume entrained in the Loop Current which drove the strong salinity-induced stratification, and the presence of a cyclonic eddy. These physical factors translated into a biogeochemical response of increased nutrient availability within the upper water column, an increase in chlorophyll, and a simultaneous increase in dissolved oxygen concentrations.

Shi et al. (2025) also reported that the Mississippi River plume transported nutrients and enhanced the chlorophyll bloom post-Idalia. Pre-Idalia, the plume contained anomalously high chlorophyll concentrations (~0.5 - 1.3 mg/m³) that exceeded the 20-year climatological mean (<0.5 mg/m³) (Shi et al., 2025). The maximum chlorophyll anomaly reported by Shi et al. (2025) of 1.3 mg/m³ was recorded near the saildrone location within a week after the passage of Idalia. The saildrone recorded chlorophyll values as high as 2.4 mg/m3, nearly double the values in Shi et al. (2025), emphasizing the value of collecting both in situ and satellite-based observations (Supplementary Figure 1). Satellite data proved to be critical for understanding regional spatial and temporal processes, such as the regional extent of the Mississippi River Plume (Figure 2), the locations of eddy fronts and contours (Figure 2), and horizontal advection of ocean properties (Figure 3); however, there were gaps in the magnitude of salinity (a difference of 2.08 psu), temperature (a difference of 1.27 °C) and chlorophyll measurements when compared to the saildrone (Supplementary Figure 1). The magnitude of the chlorophyll bloom measured by SD-1083 was over three times greater than the satellite data (0.51 mg/m² vs. 1.72 mg/m²; Supplementary Figure 1). Despite their differences and drawbacks in both methods, these observational platforms work together synergistically, and improving the frequency of observations, both remotely sensed and in situ, feeds back through calibration/validation efforts, algorithm development, and L4 blended products to improve our overall understanding of ocean processes and dynamics. As such, satellite data and in situ observations can complement one another in gathering a more complete picture of a phenomenon, with satellite data providing significantly better spatial coverage and in situ data providing more fine-scale information at a point.

Vertical mixing was relatively limited in the region prior to Idalia due to wide-spread low-salinity-driven stratification associated with the extension of the Mississippi River plume. The region was also experiencing a marine heatwave with SSTs in parts of the Gulf exceeding 29 °C in August and September (Figures 2c, h, m; Shi et al., 2025). The saildrone and BGC-Argo float were both in a low-salinity surface layer with values as low as 34-34.5 psu (Figure 4c) in comparison to typical regional values of 36–37 psu (Figures 2b, g, l). The low-salinity layer sustained elevated SSTs and suppressed vertical mixing which fueled the rapidly intensifying Hurricane Idalia from a Category 1 to Category 4 within 24 hours (Shi et al., 2025). The biogeochemical changes occurring below the surface, or beneath the MLD_float, were driven by upwelling (cyclonic eddy and hurricane-induced upwelling).

In a complementary Gulf study, yet limited to surface measurements, Walker et al. (2005) demonstrated enhanced primary production due to a cyclonic eddy and Hurricane Ivan-induced upwelling. The isothermal displacements of 50–65 m ventilated the thermocline and nitracline, triggering a phytoplankton bloom (chlorophyll increased as much as 0.75 mg/m-3) (Walker et al., 2005). Our results align with Walker et al. (2005) demonstrating an isothermal displacement of 58 m based on a change in sea surface height of 18 cm within the cyclonic eddy (Figures 2a, f, k) under Hurricane Idalia’s wind field (Figure 1, Figures 2e, j, o). BGC-Argo float profiles revealed that upwelling raised the nitracline inducing a subsurface phytoplankton bloom.

Nutrients in the upper 100 m can be supplied through riverine input (Zheng and Zhang, 2023), upwelling associated with cyclonic eddies, and eastern boundary currents (Girishkumar et al., 2019; Lee-Sánchez et al., 2022). Once mixing occurs, phytoplankton rapidly utilize these nutrients for growth in the euphotic zone (Falkowski and Stone, 1975). The BGC-Argo float measured elevated nitrate concentrations prior to Idalia and their depletion post-storm, suggesting nutrient utilization from primary production. Although past studies have documented post-hurricane nitrate surges (Girishkumar et al., 2019; Jayalakshmi et al., 2015; Naik et al., 2008), observations of nitrate depletion following the surge have been limited due to low temporal resolution. Enhanced biological activity appears to be related to elevated surface and subsurface dissolved oxygen following Idalia, consistent with oxygen production through phytoplankton photosynthesis. Air–sea gas exchange under hurricane-force winds likely plays a dominant role increasing dissolved oxygen alongside biological processes.

The fluctuations in oxygen observed, alongside high-chlorophyll, at the surface with the saildrone were likely due to varying light availability for primary production such as diurnal variability and due to variability in horizontal advection along the glider trajectory. When the chlorophyll bloom declined after September 10, oxygen values declined likely due to sampling in the Loop Current since the salinity also increased (Figures 4c, d). A decline in chlorophyll could also be in response to microbial remineralization of organic matter. Previous studies (Girishkumar et al., 2019; Li et al., 2021) similarly reported that hurricane-induced upwelling stimulates phytoplankton blooms, raising oxygen levels. These dissolved oxygen increases are likely due to the increase in photosynthetic activity and increases in air-sea gas exchange. The decreases in oxygen following the phytoplankton bloom can be attributed to remineralization and the vertical movement of the shallow oxygen minimum zone (Xu et al., 2019). While BGC-Argo floats typically operate on 10-day cycles, the rapid-cycling mode (~18 hours) employed here allowed for finer-resolution tracking of nitrate and oxygen dynamics under phytoplankton growth simulation by a hurricane. Our findings suggest that, post-hurricane, there are optimal conditions for phytoplankton growth at depth due to a shoaled nitracline relative to light availability.

 Conclusion

This study characterizes the three-dimensional biogeochemical response of the eastern Gulf to Hurricane Idalia (2023) by integrating satellite observations with high-resolution in situ data from saildrone and a profiling BGC-Argo float. Our results suggest that the hurricane’s passage over a cyclonic eddy and the Mississippi River plume stimulate primary production across the upper 50 m of the water column, with the background mesoscale ocean patterns playing a key role in modulating phytoplankton growth changes. Indeed, while the Mississippi River plume facilitated a surface bloom through horizontal advection of chlorophyll and limited vertical mixing, the interaction of the surface wind with the cyclonic eddy enhanced the eddy-induced upwelling, leading to a significant subsurface (20–50 m depth) nitrate increase recorded by the BGC-Argo float. Following Idalia, the BGC-Argo float recorded a drawdown in subsurface nitrate and a corresponding increase in dissolved oxygen concentrations, both signatures of phytoplankton production. These findings illustrate that surface chlorophyll observations alone may underestimate the total biological impact of hurricanes, as phytoplankton growth can be stimulated below the mixed layer. Our study provides an example of the complexities and challenges for observing extreme synoptic events using autonomous observational devices, and highlights the necessity of continuous, depth-resolved monitoring to fully capture the impact of hurricanes in modulating nutrient dynamics and ecosystem productivity in a rapidly changing ocean.

 Data availability statement

The datasets presented in this study can be found in online repositories. The name of the repository/repositories and accession number(s) can be found on zenodo (10.5281/zenodo.18486624). Publicly available datasets were analyzed in this study. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System. The complete dataset for the Biogeochemical-Argo floats can be found in either of the two Global Data Assembly Centers (GDACs), operated by Ifremer and NCEI, the main repositories for both real-time and delayed-mode data. Saildrone uncrewed surface vehicle hurricane data are available at https://www.pmel.noaa.gov/saildrone-hurricane/. The saildrone hurricane observations are supported by NOAA’s Office of Marine and Aviation Operations Uncrewed Systems Program Office. NOAA RADS SLA and geostrophic currents were obtained from NOAA CoastWatch and are available in near-real time from 2019 through present: https://coastwatch.noaa.gov/cwn/products/sea-level-anomaly-and-geostrophic-currents-multi-mission-global-optimal-interpolation.html. NOAA’s Geo-polar SSTs were obtained from NOAA CoastWatch and are available from 2002 through present: https://coastwatch.noaa.gov/cwn/products/noaa-geo-polar-blended-global-sea-surface-temperature-analysis-level-4.html. NOAA MSL12 DINEOF science quality Ocean Color data were obtained from NOAA CoastWatch and are available from 2018 through present: https://coastwatch.noaa.gov/cwn/products/noaa-msl12-ocean-color-science-quality-viirs-multi-sensor-snpp-noaa-20-chlorophyll-dineof.html. NBSv2 winds and wind stress are available through NOAA CoastWatch in daily and 6-hrly format from 1987 through present: https://coastwatch.noaa.gov/cwn/products/noaa-ncei-blended-seawinds-nbs-v2.html. MUNSTER eddy contours, properties, and trajectories are available from NOAA CoastWatch from 2020 through present: https://coastwatch.noaa.gov/cwn/products/experimental-eddy-products.html. Daily JPL SMAP v5 8-day averaged SSS are available from PO.DAAC and NASA Earthdata from 2015 through present: https://podaac.jpl.nasa.gov/dataset/SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5.

 Author contributions

JM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. LL: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – review & editing. ML: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – review & editing. HR: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – review & editing. GF: Conceptualization, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. FG: Conceptualization, Data curation, Methodology, Resources, Validation, Visualization, Writing – review & editing. MC: Conceptualization, Data curation, Investigation, Methodology, Resources, Software, Writing – review & editing. MS: Data curation, Investigation, Methodology, Resources, Software, Writing – review & editing. JZ: Conceptualization, Investigation, Methodology, Resources, Validation, Writing – review & editing. EO: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing.

 Acknowledgments

We thank our internal NOAA reviewer, Dr. Heidi Hirsh as well as our external reviewers. This research would not have been possible without the foundation of the Argo network and the many contributors to this invaluable dataset. We thank the Saildrone pilots and mission management team and NOAA’s saildrone Hurricane science team for the successful intercept of Hurricane Idalia and resultant data. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Gulf Research Program or the National Academies of Sciences, Engineering, and Medicine.

 Conflict of interest

HR was employed by company Global Science & Technology, Inc.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Author disclaimer

The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the authors and do not necessarily reflect those of NOAA or the Department of Commerce.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2026.1740354/full#supplementary-material

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Edited by: Manuel Bensi, National Institute of Oceanography and Applied Geophysics (Italy), Trieste, Italy

Reviewed by: Borja Aguiar-González, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

Leandro Calado, Instituto de Estudos do Mar Almirante Paulo Moreira, Arraial do Cabo, Brazil