Air-sea CO2 exchange in the Eastern Atlantic and the Mediterranean Sea based on autonomous surface measurements

Introduction: The ATL2MED mission, conducted between October 2019 and July 2020, investigated the variability of air–sea CO₂ exchange in the Eastern Atlantic and the Mediterranean Sea. The main objectives were to assess the spatial and temporal variability of the seawater partial pressure of CO₂ (pCO_2sw), identify its controlling physical and biogeochemical processes, estimate the CO₂ fluxes across the sea–air interface, and evaluate the performance of neural network-based predictions (CANYON-MED) in contrasting oceanographic regions.

Methods: High-resolution autonomous measurements were collected using Saildrone Unmanned Surface Vehicles (USVs), complemented by fixed ocean stations, gliders, and research vessels. Data quality was ensured through cross-validation among platforms, despite challenges such as sensor drift caused by biofouling.

Results: The results reveal pronounced regional differences in pCO_2sw and CO₂ fluxes, driven by thermal effects, biological activity, and physical mixing. In the Eastern Atlantic, upwelling systems off northwest Africa induced strong outgassing, while the western Mediterranean acted as a CO₂ sink during the spring bloom. Conversely, the Adriatic Sea exhibited episodic CO₂ outgassing, particularly in its southern and northern basins, associated with thermal stratification, river plumes, and coastal upwelling.

Discussion: Saildrone measurements successfully resolved sub-mesoscale processes typically missed by fixed platforms, demonstrating their potential to enhance ocean CO₂ observations in under-sampled or logistically constrained regions. The good agreement with neural network-based estimates further supports the robustness of the dataset. Overall, these findings emphasize the value of high-resolution, multi-platform approaches for accurately quantifying CO₂ fluxes and improving predictive capabilities in a changing ocean.

1 Introduction

The atmospheric carbon dioxide (CO₂) concentration has increased by more than 50% since pre-industrial time and has currently reached a concentration of 419.31 ppm (Friedelingstein et al., 2025). The increase is due to human activities (IPCC, 2021) and would have been even greater if the ocean and land would have not absorbed approximately 28% each of the emissions and thus dampening the effect of anthropogenic CO₂. The ocean interior stores about 60 times more CO₂ than the atmosphere, but this comes at a cost. Increasing levels of inorganic carbon in the ocean are affecting marine ecosystems by increasing hydrogen ion concentration and decreasing pH, which is referred to as ocean acidification (Gattuso and Hansson, 2011).

The variability of seawater CO₂ partial pressure (pCO_2sw) results from a complex interplay of physical–chemical, biological, and dynamic oceanic processes that regulate both the exchange of CO₂ across the air–sea interface and the long-term oceanic carbon sequestration (Zeebe, 2012; Middelburg, 2019; Tanhua et al., 2021; Bakker et al., 2023; Chakraborty et al., 2023). Among the physical drivers, sea surface temperature (SST) plays a dominant role: warming reduces CO₂ solubility, thereby increasing pCO_2sw, whereas cooling enhances CO₂ uptake (Weiss, 1974; Jersild and Ito, 2020; Gu et al., 2023; Li et al., 2025). Furthermore, increasing temperature also affects the thermodynamic constants of the carbonic acids equilibria shifting toward an increase of dissolved CO₂. Salinity also modulates solubility: although its large-scale impact is comparatively small, short-term and regional variability in freshwater inputs from rivers, precipitation, or evaporation can significantly alter pCO_2sw and delineate distinct biogeochemical regimes (Jersild and Ito, 2020; Curbelo-Hernández et al., 2024). Ocean circulation and mixing further redistribute pCO_2sw. Mesoscale and submesoscale eddies contribute to CO₂ variability in contrasting ways: cyclonic eddies are often associated with lower SST and enhanced net community production, thereby increasing the CO₂ uptake, while anticyclonic eddies may initially promote outgassing, this process can also enhance uptake through vertical pCO_2sw transport (Ford et al., 2023; Guo and Timmermans, 2024; Gregor et al., 2024; Liu et al., 2025; Li et al., 2025). Upwelling events bring CO₂ and nutrient-rich waters to the surface, typically increasing pCO_2sw but simultaneously stimulating primary production that draws it down, creating a dynamic interplay between physical and biological effects (Damien et al., 2022; Chakraborty et al., 2023; Roobaert et al., 2024). Vertical mixing and winter convection can also inject into surface CO₂-rich waters from depth, leading to transient outgassing (Rodgers et al., 2023; Peter et al., 2025). Finally, air–sea gas transfer is strongly influenced by wind speed and wave dynamics, with short-term fluctuations (days to weeks) leaving a pronounced imprint on CO₂ flux estimates (Wanninkhof, 2014; Landschützer et al., 2020; Song et al., 2025).

Biological processes exert an equally important control. Phytoplankton photosynthesis lowers surface pCO_2sw through CO₂ fixation, whereas respiration and remineralization return CO₂ to the water column (Chakraborty et al., 2023; Kerr et al., 2025). Nutrient availability, particularly nitrate, phosphate, silicate, and iron, sets the potential for biological uptake, and as described before in upwelling region, physical and biological controls often become competing drivers and generate strong spatial and temporal variability (Chakraborty et al., 2023; Rodgers et al., 2023). To disentangle these contributions, pCO_2sw variability is frequently decomposed into thermal and non-thermal components (see section 2.3). Finally, air–sea gas transfer is strongly influenced by wind speed and wave dynamics, with short-term fluctuations (days to weeks) leaving a pronounced imprint on CO₂ flux estimates (Wanninkhof, 2014; Landschützer et al., 2020; Song et al., 2025).

This complexity underlines the need for sustained and co-located observations of both physical and biogeochemical variables, as emphasized by recent studies reporting a worrying decline in standardized datasets, especially after 2017 (Tanhua et al., 2021; Hassoun et al., 2022; Bakker et al., 2023). This gap is particularly evident in the Mediterranean Sea, where many sub-basins, straits, channels, and deep- and intermediate-water formation areas are still poorly investigated (Hassoun et al., 2022).

In this context the Integrated Carbon Observation System (ICOS-ERIC) provides high-quality measurements of CO₂ and other GHGs across the atmosphere, the European terrestrial biosphere, the Atlantic Ocean, and the Mediterranean Sea. Its ocean network, consisting of research vessels, commercial ships, and fixed observatories, focuses on air–sea gas exchange, ocean acidification, and their impacts through long-term surface pCO_2sw observations and ancillary variables (Steinhoff et al., 2019). The frequency of these measurements is limited by power constraints, moreover, some of the data obtained from sea campaigns do not support comprehensive studies of CO₂ flux variability at the basin scale over several years. To overcome these issues, recent European initiatives such as EMSO ERIC (https://emso.eu), Euro-Argo One (https://www.euro-argo.eu/EU-Projects/Euro-Argo-ONE-2025-2027 focused on the BGC-Argo mission) and the GEORGE project (https://george-project.eu/) have made efforts towards the technological development of sensors aimed at autonomous in situ measurements of a variety of variables from fixed ocean stations and mobile platforms, including pCO_2sw and, for some observatories, also atmospheric pCO₂ (pCO_2atm). While waiting for progress on these initiatives, the ocean community frequently uses membrane sensors which usually perform in situ analyses of gas with zero CO₂ but lack daily calibrations with one or more span gases, relying only on factory calibrations. To address these challenges, we organised a mission including two Unmanned Surface Vehicles (USV) from Saildrone Inc. (USA), which operated in the space between and around fixed ocean stations, gliders, and a research vessel (Skjelvan et al., 2021; Martellucci et al., 2024). By bridging the gaps between fixed ocean stations, this approach enabled the study of air-sea gas exchange over a vast area. The demonstration mission was especially notable during the COVID pandemic when access to marine platforms and ship visits were restricted.

The present study investigates the variability of CO₂ in seawater and of CO₂ fluxes between air and water. The CO₂ exchanges were estimated based on direct measurements of the wind speed as well as pCO_2sw and pCO_2atm carried out in the East Atlantic Ocean and in the Mediterranean Sea, focusing on the factors driving regional differences in these ocean areas. Moreover the data acquired by USVs were compared with pCO₂ measurements from fixed ocean stations and estimates obtained through neural network-based methods, i.e., CANYON-MED (Fourrier et al., 2020) which have proven to be a promising approach to overcome data limitations and fill the gaps in CO₂ measurements (e.g., Landschützer et al., 2014; Ben Mustapha et al., 2014; Fourrier et al., 2020, 2022). The data used in this study are primarily derived from the ATL2MED mission that took place between 18 October 2019 and 17 July 2020 in the East Atlantic (encompassing both subtropical and tropical regions) and the Mediterranean Sea (Figure 1). Several European academic institutions took part in the ATL2MED mission experiment, which encompassed fixed ocean stations, gliders, research vessels, and USVs (see Supplementary Table S1 in the Supplementary material).

Figure 1

Figure 1. The map shows the sailing route of the two Saildrone vehicles (SD 1030: black lines and SD 1053: red lines) during the ATL2MED mission from 19 October 2019 to 17 July 2020. The East Atlantic Ocean encompasses subtropical and tropical regions, the Mediterranean Sea includes the Strait of Gibraltar, the Alboran Sea, and several regions in the northwestern and central Mediterranean sea such as the Ligurian Sea, the Tyrrhenian Sea, the Ionian Sea and the Adriatic Sea.

2 Data and methods

2.1 Study areas

The study area encompasses marine regions with diverse climatic and oceanographic characteristics, including the tropical Atlantic off the west African coast and various basins of the Mediterranean Sea.

The Tropical North Atlantic, is characterised by a clockwise circulation that drives water masses southwards along the northwestern coast of Africa as the Canary Current, which in turn feeds into the westward North Equatorial Current. The relatively cold Canary Current generates numerous eddies along the West African coast and is warming at a rate of 0.20 ± 0.05 °C decade^-1 over the period 1980-2020 (Kessler et al., 2022). The Eastern Boundary Upwelling System off northwest Africa is one of the four major Eastern Boundary Current systems within the trade wind belts of the subtropics. This system is driven by the interaction of winds and ocean currents, leading to the upwelling of nutrient and CO₂-rich water to the surface (Cropper et al., 2014; Fischer et al., 2016). In the southern area (from 10°N to 19°N) weak seasonal coastal upwelling occurs in winter and spring. In the northern region, from the Canary Islands to the Strait of Gibraltar, the year-round upwelling is weak in winter and spring and intense in summer and fall (Pelegrí and Benazzouz, 2015). The permanent upwelling region (20-26°N) is always a source of CO₂, except in spring when biological consumption prevails, causing a sink of CO₂ (Lefèvre et al., 2023). However, during autumn, González-Dávila et al. (2017) found that only the area between 20 and 23°N acted as a source of CO₂, while the rest was almost in equilibrium. Using merchant ships equipped with underway CO₂ systems from 2010 to 2022, Lefèvre et al. (2023) showed that off the northwest African coast, the pCO_2sw increased at a rate ranging from 1.82 μatm yr⁻¹ to 2.10 μatm yr⁻¹ close to the atmospheric increase and the pH decreased at a rate between 0.0016 and 0.0022 yr^{− 1}.

The Mediterranean Sea is characterised by a basin-wide counterclockwise circulation, with numerous cyclonic and anticyclonic eddies populating the entire basin as well as three areas of deep water formation (the northwestern Mediterranean, the Adriatic Sea, and the Aegean Sea). It is the only basin in the temperate latitudes where the deep open ocean convection reaches the seafloor (Testor et al., 2018). The Mediterranean Sea is a hotspot of climate change such as temperature increase (Reale et al., 2022; Marullo et al., 2023; Kubin et al., 2023; Pirro et al., 2024), decrease in precipitation, and occurrence and frequency of extreme events (e.g., Tintoré et al., 2019). This area hosts 30% of the world’s tourism and is crossed by 30% of the world’s maritime trade in volume. It has been identified as an important region for anthropogenic carbon storage, where the column inventory can be much higher than in the Atlantic or Pacific Oceans (e.g., Schneider et al., 2010). The Mediterranean Sea is affected by ocean acidification, which is detectable even in deep water masses and could have a considerable impact on the biodiversity of Mediterranean ecosystems (Hassoun et al., 2022). In the coastal waters of the northwestern Mediterranean Sea the surface pCO_2sw increases at a rate of 3.53 μatm yr^-1 and pH decreases by -0.0028 yr^-1 (Kapsenberg et al., 2017) whereas in the open sea the estimated increase of pCO_2sw is of 2.30 ± 0.23 μatm yr^-1 (Merlivat et al., 2018). The northern Adriatic Sea is an effective CO₂ sink during winter, spring and autumn and a source in summer (Cantoni and Luchetta, 2024 and references therein). In the northern Adriatic, Cantoni et al. (2024) found that pH decreases by 0.003 pH_T units yr^-1, similarly to the estimates for the Mediterranean open waters (Yao et al., 2016), for the surface coastal waters of the northwestern Mediterranean sea (Kapsenberg et al., 2017) and for the Strait of Gibraltar (Garcia-Ibañez et al., 2024).

2.2 Dataset

Only the facilities which performed pCO_2sw measurements during the ATL2MED mission were considered in this work: the fixed ocean stations DYFAMED, W1M3A, E2M3A, PALOMA, and MIRAMARE, the R/V Meteor, and the Saildrone vehicle (hereafter SD) 1030. In addition, the data of SD 1053 were used to supplement the data of SD 1030. Details of the data, infrastructures, and various sensors, including CO₂ sensors used, were described in detail in Skjelvan et al. (2021); Martellucci et al. (2024), and in Supplementary Table S2.

Moreover we use high resolution observations and output from numerical models provided by the Copernicus Marine Service:

1. Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis (https://doi.org/10.48670/moi-00165) (Good et al., 2020).

2. High-resolution and ultra-high-resolution satellite data of the sea surface temperature of the Mediterranean Sea (https://doi.org/10.48670/moi-00172) (Buongiorno Nardelli et al., 2013).

3. Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time) (https://doi.org/10.48670/moi-00279).

4. Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing) (https://doi.org/10.48670/moi-00300) (Volpe et al., 2018).

5. Mediterranean Sea Physics Reanalysis (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1) (Escudier et al., 2020, 2021; Nigam et al., 2021).

6. Copernicus Climate Change Services: ERA5 wind data at individual levels from 1940 to present (DOI: 10.24381/cds.adbb2d47) (Hersbach et al., 2023).

Ancillary satellite and model data were used to evaluate the SST, sea surface salinity (SSS) and chlorophyll a (Chl-a) distribution during the mission. The data were represented as isosurface maps.

During the ALT2MED mission, the SD’s speed was influenced by wind, ocean currents, and hull friction, which significantly reduced its velocity and complicated the mission planning and station visits. In addition, the long deployment (9 months) in biologically active regions led to sensor drift due to biofouling and technological limitations. Despite these challenges, extensive data cleaning and correction for SSS, dissolved oxygen (O₂), and pCO_2sw was carried out as documented in the recent publication by Martellucci et al. (2024).

The SSS data from the SD were compared with a few available in situ SSS observations, which revealed substantial differences and drift. Therefore the SD SSS data were corrected by aligning daily averages with CMEMS reanalysis products (Global Ocean 1/12° Physics Analysis and Forecast and Mediterranean Sea Physics Analysis and Forecast) using a linear regression method with a significance level of p < 0.05, while no correction was applied during periods when initial differences were smaller than 0.1. O₂ data, lacking discrete validation samples, were adjusted through an in-air calibration approach adapted from the Argo program (Bittig et al., 2018) and a daily gain factor obtained from the ratio between expected and measured values was applied to raw O₂ data after excluding outliers. The corrected O₂ concentrations were consistent with variability driven by temperature and phytoplankton biomass as indicated by Chl-a. The pCO_2sw dataset was obtained from SD 1030, equipped with a PMEL ASVCO₂ system measuring xCO₂ with an infrared detector after equilibration, and although initially calibrated before deployment, post-mission evaluation revealed that the span gas was set too low, necessitating recalibration at PMEL and the derivation of new coefficients. The corrected SD pCO_2sw showed consistency with station data, especially at DYFAMED and MIRAMARE, and reflected ecosystem variability when compared with temperature and satellite-derived Chl-a. These approaches allowed the production of a homogenized and reliable dataset despite the limited availability of in situ validation, supporting the use of SD data for the study of air–sea interactions.

Here, we present the corrected data with the aim of interpreting the variability of the biogeochemical measurements.

2.3 Thermal and non-thermal components of pCO_2sw

The temporal and spatial variability of surface ocean pCO_2sw can be attributed to several factors (e.g., Zeebe, 2012; Middelburg, 2019), leading to fluctuations across timescales from days to decades (Chakraborty et al., 2023; Rodgers et al., 2023). To distinguish these contributions, pCO_2sw variability is decomposed into thermal and non-thermal components, with the latter encompassing the effects of circulation, changes in SSS, dissolved inorganic carbon (DIC), and total alkalinity (TA), and biological activity (Fassbender et al., 2022; Rodgers et al., 2023).

The decomposition of pCO_2sw was following equations proposed by Takahashi et al. (2002):

$$pC{O}_{2sw\_N}=pC{O}_{2sw}\times ex{p}^{0.0423(SS{T}_{mean}- SS{T}_{obs})}$$

$$pC{O}_{2sw\_TD}=mean(pC{O}_{2sw})\times ex{p}^{0.0423(SS{T}_{obs}- SS{T}_{mean})}$$

where pCO_2sw (μatm) was measured during the study period, mean(pCO_2sw) is the mean pCO_2sw over the studied period in every study area (as shown in Figure 2), SST_mean (°C) is the average sea surface temperature (during the whole demonstration mission), and SST_obs (°C) is the in situ sea surface temperature. The pCO_{2sw_TD} refers to changes in pCO_2sw driven only by temperature variations, while the pCO_{2sw_N} refers to changes in pCO_2sw that are not directly caused by temperature variations but rather by other factors such as biological activity (photosynthesis and respiration), mixing or advective processes, and gas exchange with the atmosphere.

Figure 2

Figure 2. Time series of (A) sea surface temperature (SST; °C), (B) pCO_2sw (μatm) and pCO_2atm (μatm) drown in black and yellow respectively, (C) fluxes of CO₂ (mmol CO₂ m^-2 d^-1) and wind speed (m s^-1) represented by the magenta line and grey dots respectively acquired by the SD 1030. The periods without data correspond to the different maintenance events, see Martellucci et al. (2024).

The ratio (R) of between the thermal (pCO_{2sw_TD}) and non-thermal (pCO_{2sw_N}) components is used to identify the dominant driver of variability. An R value greater than one indicates that thermal effect prevail, whereas an R value less than one points to the dominance of non-thermal contribution.

$$R = pC{O}_{2sw\_TD} / pC{O}_{2sw\_N}$$

2.4 CO₂ flux calculations

The fluxes of CO₂ (FCO₂) were calculated according to:

$$FC{O}_2= K_0 \times k_s \times (pC{O}_{2sw} - pC{O}_{2atm})$$

where K₀ is the solubility of CO₂ (mol m^-3 atm^-1), k_s is the gas transfer velocity (cm h^-1) from Sweeney et al. (2007), and pCO_2sw and pCO_2atm were measured from the SD 1030 using an ASVCO₂ sensor (Sutton et al., 2014). Wind speed was measured on both SDs, but for the SD 1030, wind measurements were discontinued in March 2020. Additional information was therefore required to determine the wind speed over the entire duration of the mission. When the two SDs were closer than 12 nautical miles, the wind measurements of SD 1053 were used to compute the CO₂ fluxes. When the distance between the two SD was larger, wind data from the ERA5 reanalysis product (0.25° x 0.25° grid; Hersbach et al., 2023) were used to fill the gaps. Finally, to achieve the true wind speed, the wind speed was corrected for the movement of the SD.

Supplementary Figure S1 (Supplementary Material), compares the mole fraction of atmospheric CO₂ (xCO_2atm in ppm) measured by the SD 1030 with xCO_2atm measurements from two atmospheric stations in the East Atlantic and one in the Mediterranean Sea: Izaña Atmospheric Observatory (Tenerife, Spain), Cape Verde Atmospheric Observatory (CVAO, Cape Verde), and ENEA Station for Climate Observations (Lampedusa, Italy). The deviation between the xCO_2atm measurements of the atmospheric stations and the SD 1030 varied between 3.5 and 4.6 ppm during the first 4 months of the demonstration mission (October 2019 - February 2020) and was reduced to 2.5 ppm in June 2020 (Supplementary Table S2). This deviation might be related to the prevailing winds and the distance between the atmospheric stations and SD 1030, but further investigation is beyond the scope of this work.

2.5 Neural-network derived carbon data

Carbonate system variables (DIC, TA and pH) were predicted along the SD tracks using the CANYON-MED neural network (Fourrier et al., 2020). This neural network was employed in the Western Mediterranean Sea, and produced estimates for DIC, TA, pH, and nutrients. From this, the pCO_2sw long trends in surface, intermediate, and deep waters in the Western Mediterranean were predicted (Fourrier et al., 2022). In brief, neural network ensembles for each variable were developed and the network was trained with a reference dataset, facilitating a densification of the dataset. This neural network-based method allows the prediction of the carbonate system variables from geolocation, time of sampling, pressure, SST and O₂ concentration specifically for the Mediterranean Sea. In this study, the spring and summer 2020 data from the SD 1030 and SD 1053 were used to complement in situ pCO_2sw data retrieved from SD 1030 after employing the Python PyCO₂SYS toolbox for pCO_2sw estimation (Humphreys et al., 2022). The method was applied to the SSS and O₂ of both SD 1030 and SD 1053. The predicted carbonate variables were quality controlled and pCO_2sw was calculated using DIC and pH as input in the CO₂SYSv2 (Orr et al., 2018) and further, compared to pCO_2sw from the SD 1030 and from discrete sampling along the track (DYFAMED, PALOMA, MIRAMARE). The uncertainty of the predicted pCO_2sw was based on a combination of the error of predicted DIC (12 µmol kg^-1) and pH (0.014) of CANYON-MED for the Mediterranean Sea (Fourrier et al., 2022). The error package of the CO₂SYS toolbox (Orr et al., 2018) provided a pCO_2sw uncertainty estimated at 13.3 μatm using the constants K1 and K2 from Lueker et al. (2000).

3 Results and discussion

This study first examines the fluxes of CO₂ comparing the East Atlantic with the Mediterranean Sea with a particular focus on their temporal and spatial variability. This large-scale view is followed by an investigation of regional and local processes and features that were observed during the duration of the ATL2MED mission. Specifically, local processes influencing the pCO_2sw such as primary production and respiration, air-sea gas exchange, mixing, advection of water masses and temperature changes are discussed.

3.1 Large scale view

The ATL2MED mission contributed with pCO_2sw data of higher spatial density than ever before for the study area, allowing for a deeper investigation of the carbon dynamics in the eastern Atlantic and the Mediterranean Sea. During their path, the SDs crossed different geographical regions in different times of the year, resulting in large variability in SST and pCO_2sw, that ranged between 14 and 29°C (Figure 2A) and 320 and 520 µatm (Figure 2B), respectively. At the beginning of the mission (from the end of October to the end of December 2019), when the SDs crossed the eastern Atlantic, the area was a CO₂ source (Figure 2C) driven by the presence of a permanent upwelling system off the coast of Africa. When the SDs moved further south, the efflux of CO₂ to the atmosphere decreased due to local dynamics (see section 3.2.1).

In total, the Eastern Atlantic absorbed more than 0.6 mol CO₂ m^-2 over 4.5 months (18 October 2019 to 6 March 2020). This is higher than the CO₂ flux into the ocean (0.16 mol CO₂ m^-2) between December and May estimated by González-Dávila et al. (2017), who used data between 2005 and 2012 from the Mauritanian - Cape Verde upwelling area (10-27°N, 15-18°E). This difference can be due to the slightly different time of year in González-Dávila et al. (2017) and to the proximity to the shore of their area compared to our area and thus, their results might be more affected by upwelled water oversaturated with CO₂. Pérez et al. (2024) used interpolated in situ pCO_2sw data from the SOCAT database (Bakker et al., 2016) to estimate the CO₂ uptake in the North Atlantic Subtropical Gyre over the period 1985-2018. Their Figure 5 indicates an average winter flux of 1 mol CO₂ m^-2 yr^-1, which is larger than our CO₂ estimate. However, their values are based on data from a much larger area than that along the SD tracks in the eastern Atlantic.

As the SDs moved from the tropical region to the Strait of Gibraltar (December 2019 to March 2020), and the water column became well-mixed due to winter storms and cooling, photosynthetic activity was reduced, leading to less CO₂ uptake by phytoplankton, which kept pCO_2sw relatively high (Figure 2B).

In the Mediterranean Sea, the SD measurements highlighted a more complex interplay between physical and biological factors. Here, along the SDs tracks, strong seasonal shifts in CO₂ content and CO₂ fluxes were also driven by regional variations of SST and wind speed.

In March 2020 highly negative (-85 mmol CO₂ m^-2 d^-1) CO₂ fluxes (Figure 2C) were observed between the Alboran Sea and Gulf of Lion in response to water mixing and the active primary productivity usually observed in the area in this season.

In the Ligurian Sea the fluxes were generally close to zero with episodic events of strong CO₂ sink concurrently with wind increase and water cooling.

At the beginning of May 2020, the CO₂ fluxes showed positive values, as the pCO_2atm was lower than the pCO_2sw (Figure 2B). Until early June, the SDs did not acquire data, due to maintenance activities. Moving from the Ionian Sea to the Adriatic Sea (mid-June 2020), the pCO_2sw increased to well above the pCO_2atm and reached the highest values recorded during the mission (530 µatm) at the beginning of July 2020 in the southern Adriatic (Figure 2B). This resulted in a strong efflux of CO₂ to the atmosphere. This was even more evident in the northern Adriatic, where the highest positive CO₂ fluxes (up to 80 mmol m^-2 d^-1) recorded during the demonstration mission was observed (Figure 2C).

In total, the Mediterranean absorbed approximately 2.3 mol CO₂ m^-2 over 4.5 months (7 March to 17 July 2020). However, when splitting this sea into regions, the west and northwestern Mediterranean Sea clearly acted as a sink absorbing 205.5 mol CO₂ m^-2 over 2.5 months (7 March to 26 May 2020), while the area from the Ionian and the Adriatic Sea acted as a source of CO₂ emitting 314.2 mol CO₂ m^-2 over 3.5 weeks (6 June to 17 July 2020).

These findings are consistent with previous studies highlighting the marked spatial variability of CO₂ fluxes in the Mediterranean Sea, where the northwestern sub-basins and the Adriatic often act as carbon sinks on an annual scale (e.g., Copin-Montégut and Bégovic, 2002; Urbini et al., 2020; Cossarini et al., 2021; Cantoni et al., 2024), while the southeastern basins tend to act as sources (Cossarini et al., 2021). Our high-resolution measurements further emphasize the importance of sampling strategies capable of resolving seasonal and regional dynamics: for instance, data collected in the northwestern Mediterranean revealed a strong CO₂ uptake, likely enhanced by the timing of observations during a productive period when phytoplankton blooms can rapidly draw down CO₂ and generate transient sinks (Roobaert et al., 2024; Frazão et al., 2025). In contrast, summer measurements in the Adriatic indicated a source of CO₂, reflecting the dominant role of thermal forcing in shaping seasonal variability of surface pCO₂ (Takahashi et al., 2002; Jersild and Ito, 2020; Roobaert et al., 2024);. Mesoscale features encountered during the mission also revealed pronounced short-term variability in CO₂ fluxes, particularly in the Eastern Atlantic, consistent with evidence that mesoscale eddies and circulation patterns modulate surface carbon exchange in many oceanic regions (Harrison et al., 2018; Ford et al., 2023; Guo and Timmermans, 2024; Liu et al., 2025). The high-frequency resolution of our dataset proved crucial in capturing such variability, which is generally underrepresented in large-scale climatologies (Landschützer et al., 2020; Hassoun et al., 2022).

3.2 Regional events

3.2.1 The northwest African upwelling system in the East Atlantic

The CO₂ flux to the atmosphere in the East Atlantic during November 2019 was driven by upwelled water with low SST, low Chl-a concentration and high wind speed (Supplementary Figure S3, Supplementary Material). In this context, the physical upwelling of CO₂-rich deep waters to the surface can exceed the capacity of biological drawdown, leading to persistent CO₂ outgassing (Siddiqui et al., 2023; Roobaert et al., 2024).

The pCO_2sw thermal component (pCO_{2sw_TD}) followed the changes in SST and decreased by more than 100 µatm from the beginning of November 2019, when the region was characterised by elevated temperature (> 24 °C), to January 2020 over a period of which SST decreased more than 14 °C (Supplementary Figure S2, Supplementary Material). However, the non-thermal component, pCO_{2sw_N}, counteracted the changes in pCO_{2sw_TD} by increasing approximately 100 µatm over the same period (Supplementary Figure S2, Supplementary Material). Between 25 and 28 October 2019 the SD 1030 moved southward and crossed an area with higher Chl-a, which primarily affected the pCO_{2sw_N} and decreased the CO₂ flux. In the first weeks of November 2019, heading southwards, the CO₂ flux was positive (directed into the atmosphere), mainly driven by the SST increase (Figure 3B) and the relatively low Chl-a concentrations. During this period, some small negative CO₂ flux values were observed concurrently with a strong reduction in pCO_2sw (Figure 3D) suggesting that the SDs crossed an area previously characterised by elevated primary productivity (e.g., Supplementary Figure S3A, 28 October 2019. After 28 November 2019, the SDs crossed an upwelling area with lower SST and the pCO_2sw increased by about 30 µatm as the surface water was enriched with CO₂ from below (Figures 3A, D). Here, the CO₂ flux was mainly driven by the non-thermal component (pCO_{2sw_N}). After 6 December 2019, the SDs moved away from the upwelling area, and a sharp decrease in pCO_2sw was observed concurrently with an increase in SST. Until 16 December the flux was almost close to zero with the exception of a strong peak that occurred on 12 December, mainly triggered by wind driven coastal upwelling (i.e., increase in wind speed and strong decrease in SST; Supplementary Figure S3, Supplementary Material). From January to early March 2020, moving northwards from the Canary upwelling system, the SDs sampled an area characterised by low variability in pCO_2sw and pCO_2atm. In this period the thermal and non-thermal components of pCO_2sw were relatively similar, and at the end of this period, the non-thermal component started to increase inversely to the thermal component (Supplementary Figure S2, Supplementary Material).

Figure 3

Figure 3. (A) Map of SD trajectory in the East Atlantic from 18 October to 16 December 2019. Temporal evolution of (B) sea surface temperature (SST; °C) and sea surface salinity (SSS), (C) dissolved oxygen concentration (O₂; μmol kg^-1) and oxygen saturation (%), (D) pCO_2sw (µatm) and ratio between thermal and non-thermal components of pCO_2sw (R), (E) CO₂ flux (FCO₂; mmol CO₂ m^-2 day^-1) and wind speed (m s^-1) in the East Atlantic from 18 October to 16 December 2019.

Eddies are frequently observed in this area (McGillicuddy et al., 2003; Katzenmeier et al., 2024), which was also the case during the R/V Meteor cruise (M160) in fall 2019. Cyclonic eddies promote the upwelling of deeper water rich in CO₂ and nutrients from remineralized organic matter. Inside these eddies, the surface pCO_2sw can easily exceed the pCO_2atm, and thus facilitating the CO₂ efflux to the atmosphere. The CO₂ flux may also have been influenced by the island orography, which affected wind patterns and led to the formation of small frontal systems. North of Cape Verde and towards the Strait of Gibraltar, the sea surface was significantly undersaturated with respect to CO₂, which resulted in CO₂ influx.

A particularly interesting aspect is the high variability in SST, SSS, and O₂ (Figure 3C) concentration observed between 11 and 16 December, coinciding with measurements conducted by METEOR (Devresse et al., 2023). During this period, the SD crossed twice a small cyclonic eddy, and the CO₂ flux response was markedly different in the same area just two days apart (Supplementary Figure S4). The signal clearly indicates the presence of an upwelling, as evidenced by a sudden decrease in SST accompanied by a corresponding increase in SSS, which is also clearly observed in the METEOR data (Devresse et al., 2023), along with a strong increase in O₂ concentration and oxygen supersaturation coinciding with the SST minima.

This structure, which was identified as a cyclonic eddy (Brava eddy, Devresse et al., 2023) caused a general increase in CO₂, mainly due to outgassing associated with upwelled deep waters enriched in CO₂. In the core of the eddy the water mass was colder with higher concentration of dissolved inorganic nitrogen and Chl-a concentrations than the surrounding waters with a strong doming of the nitracline. This could explain the sudden changes of CO₂ fluxes observed around mid-December (Figure 3E). Furthermore, wind effects, which were stronger on the 11^th December than on the 16^th December, influenced phytoplankton biomass, leading to its increase following the reduction in wind intensity. The non-thermal component of pCO_2sw increased, resulting in a decrease in R. This can be explained by the initial upwelling-driven outgassing, enhanced by wind, followed by a reversal of the CO₂ flux toward the ocean due to the biological response associated with primary production, which removed CO₂ from the surface waters. This highlights how high resolution observations are essential to capture the strong variability of CO₂ fluxes associated with fine-scale oceanographic features such as mesoscale and submesoscale eddies.

3.2.2 Alboran Sea

After 7 March 2020 the SDs reached the Mediterranean and crossed the Alboran Sea in almost a week (Figure 4A). The area showed strong variations in SST and SSS (Figure 4B), which influenced the biogeochemical variables. In early March 2020 the variability of pCO_2sw increased, and pCO_2sw reached the lowest value recorded during the mission (350 µatm). The observed variability can be explained taking into account the characteristics of the region, where Atlantic and Mediterranean waters are coexisting and interact at different spatial and temporal scales (Capó et al., 2021; Poulain et al., 2021). Respiration processes, winter mixing, and spring blooms played a key role when the SDs moved from the Alboran Sea to the Ligurian Sea. Eddies are widespread in the area, as shown in Figure 4A which highlights the anticyclonic eddies in both the western and eastern Alboran Sea from satellite SST images. These eddies are caused by the surface inflow of Atlantic water that crosses the Strait of Gibraltar, which gradually mixes with the resident Mediterranean water (Tintoré et al., 1991; Mojica et al., 2018; Sánchez-Garrido and Nadal, 2022).

In the Alboran Sea, the SDs crossed two chlorophyll fronts (Chl-a >1.5 mg m^-3, Figure 4A), as evidenced by the O₂ supersaturation (Figure 4C). This influenced both the pCO2sw concentrations, which in the region was mostly driven by non thermal processes (Figure 4D), and CO2 flux (Figure 4E). Indeed as observed by Roobaert et al. (2024) and Frazão et al. (2025) phytoplankton blooms can rapidly draw down CO₂, creating transient CO₂ sinks during productive seasons especially in temperate ocean where spring blooms can induce sharp declines in pCO_2sw over a few days to weeks, often exceeding the influence of temperature during the bloom peak (Lévy et al., 2024).

Figure 4

Figure 4. (A) Map of sea surface temperature (SST), chlorophyll a (Chl-a) concentration and SDs trajectory (SD 1030 and SD 1053 in black and red respectively) in the Alboran Sea on 10 and 12 March 2020. Temporal evolution of (B) sea surface temperature (SST; °C) and sea surface salinity (SSS), (C) dissolved oxygen concentration (O₂; µmol kg^–1) and oxygen saturation (%), (D) pCO_2sw (µatm) and ratio between thermal and non-thermal components of pCO_2sw (R), (E) CO₂ flux (FCO₂; mmol CO₂ m^—2 day^–1) and wind speed (m s^–1) in the Alboran Sea from 8 to 15 March 2020.

The Alboran Sea is one of the most productive areas of the Mediterranean Sea according to estimates based on satellite data by Uitz et al. (2012). The Chl-a concentration in boreal spring along the northern flank of the Alboran Sea is influenced by the El Niño Southern Oscillation (ENSO) that is responsible for the development of anomalous distribution patterns of Chl-a. Furthermore, ENSO influences the western Mediterranean by altering the winds over the Alboran region and, consequently, the wind-driven upwelling that occurs along the southern Spanish coast in spring. This, in turn, modifies the surface Chl-a concentration: a high (low) Chl-a concentration in the northern Alboran Sea during spring is expected under La Niña (El Niño) conditions in the tropical Pacific during the preceding winter (Lopez-Parages et al., 2022). Also in the past elevated Chl-a concentrations have been reported for the western Alboran Sea (Minas et al., 1991; Rodríguez et al., 1998; Lopez-Parages et al., 2022), while particulate primary production can reach up to 632 mg C m^–2 d^–1 along a coast-to-coast gradient crossing the western Alboran Sea gyre (Moran and Estrada, 2001) and 700 mg C m^–2 d^–1 in the northern part of the eastern gyre (Videau et al., 1994).

3.2.3 Ligurian Sea

The SDs arrived in the Ligurian Sea at the beginning of April 2020 after crossing the Gulf of Lion (Figure 5A). The pCO_2sw fluctuation was high and the pCO_2sw concentration low in the northwestern Mediterranean at this time of year. The area was characterised by low SST and undersaturated O₂ concentrations highlighting the presence of intense vertical mixing (see Figure 5A in Martellucci et al., 2024). Convection can be intense in February and March in the Gulf of Lion due to cold and dry winds and preconditioning events (Schroeder et al., 2008; Houpert et al., 2016; Testor et al., 2018; Many et al., 2021), which refers to the set of oceanic conditions that favour deep convection, such as the presence of a weakly stratified water column and the accumulation of dense water from previous winters. These conditions induce an increase of surface water density sensitive to the winds blowing. The convection process is less pronounced in the Ligurian Sea where wind forcing and air-sea heat flux are usually less intense (Coppola et al., 2018). A recent study from Ulses et al. (2023) demonstrated that the deep convection zone in the northwestern Mediterranean Sea served as a moderate CO₂ sink for the atmosphere in the years 2012 to 2013, when convection was particularly strong. Between the beginning of March and the end of April 2020, the pCO_{2sw_N} dominated the pCO_2sw signal (Supplementary Figure S2, Supplementary Material). From the Alboran Sea to the Ligurian Sea, respiration possibly prevailed over photosynthesis, increasing the pCO_{2sw_N} while O₂ was consumed. In the western Mediterranean, the end of deep vertical mixing favours the onset of a spring phytoplankton bloom with a peak in primary production and phytoplankton concentration at the surface which generally occurs in April in this region (Mayot et al., 2017; Kessouri et al., 2020). The 2020 spring bloom produced a strong increase in Chl-a between March and April 2020 (Supplementary Figure S4, Supplementary Material) resulting in a pronounced uptake of CO₂ from the water (Figure 5E), decreasing pCO_{2sw_N}, while O₂ production increased during the bloom event (O_sat > 106, Figure 5C).

Figure 5

Figure 5. (A) Map of SD trajectory in the northwestern Mediterranean from 9 April to 12 May 2020, glider measurements (green dots), DYFAMED (red star) and W1M3A (blue star). Temporal evolution of (B) sea surface temperature (SST; °C) and sea surface salinity (SSS), (C) dissolved oxygen concentration (O₂; µmol/kg) and oxygen saturation (%), (D) pCO_2sw (µatm) and ratio between thermal and non-thermal components of pCO_2sw (R), blue and red diamonds represent the pCO_2sw recorded at fixed stations (see Figures 10a and b in Martellucci et al., 2024), and (E) CO₂ flux (FCO₂; mmol CO₂ m^-2 day^-1) and wind speed (m s^-1) in the East Atlantic from 18 October to 16 December 2019.

On 9 April 2020 the SDs left the area off Toulon moving towards the W1M3A site and sailing about 40 km from the coast. The area is characterized by the Northern Current (NC) that is a key boundary current in the western Mediterranean (Berta et al., 2018). This current originates in the Ligurian Sea from the convergence of the Western and Eastern Corsica currents. At surface, it transports the old Atlantic Water (AW) southwest ward, influencing the circulation of the northwest Mediterranean. The NC extends about 40 km off the coast and its variability is influenced by winds and freshwater inputs. This pattern was clearly detected by the SDs until 15th of April (Figure 5B). Until then the pCO_2sw values ranged from 360 to 400 µatm and the SST varied by 2 °C. This can be ascribed to the presence of strong mixing processes, driven by northwesterly winds, that influenced the pathways of the NC current forming a frontal zone near the coast, separating warm coastal waters from colder mixed waters of the Gulf of Lion (Prieur et al., 2020).

From mid-April 2020 SST increased as the SDs moved eastwards (Figure 5A), and pCO_2sw increased from 360 µatm to 400 µatm. This increase was consistent with the values reported by Coppola et al. (2020) for the seasonal cycle in the Ligurian Sea. Here, pCO_2sw values usually range from 300 to 550 μatm, as already reported for the DYFAMED site. The glider deployed during the mission helps to spatially characterize the observed area and to generate predicted carbonate system variables using the CANYON-MED neural network (see Section 2.5). The glider moving from Nice to Calvi measured SST similar to those measured by the two SDs at matching locations.

The O₂ concentration decreased from 260 µmol kg^-1 to almost 230 µmol kg^-1 as the SDs left the Ligurian Sea. pCO_2sw was comparable between the platforms.

In the Ligurian Sea, the main processes governing the pCO_2sw at the sea surface on an annual scale, after removing the temperature effect, are vertical mixing and biological production (Copin-Montégut et al., 2004; Merlivat et al., 2018; Coppola et al., 2023). However, during the studied period, in the Ligurian Sea the non-thermal component of pCO_2sw prevailed over the thermal one (R<1; Figure 5D) suggesting that biological activity might have influenced the pCO_2sw variability in this area during April 2020 (Supplementary Figure S4, Supplementary Material).

In early May 2020, the pCO_2sw started to increase (from 350 µatm to 450 µatm) crossing the Tyrrhenian Sea, concurrently with temperature increase (Figure 2A). The pCO_{2sw_TD} reflected the change in SST, being predominant in the overall pCO_2sw signal in the region.

The neural network (CANYON-MED) derived estimates of pCO_2sw closely matched the SD measurements (SD 1030, R² = 0.95, p<0.05; SD 1030, R² = 0.88, p<0.05), with only a small offset of 15–20 µatm observed as the SD moved along its path (Figure 6, Supplementary Figure S6). This offset fell within the expected error range of the neural network-derived pCO_2sw estimates (see section 2.5) and aligned with data collected by the W1M3A fixed ocean station. However, it is important to note that between 28 April and 1 May 2020, when the SDs circled the W1M3A station, sea conditions shifted from calm to very rough (significant wave height of 2.5 m), leading to water mixing that may have influenced the measurements. By 4 May 2020, sea conditions had returned to calm.

Figure 6

Figure 6. Time series of pCO_2sw in the Mediterranean Sea obtained from CANYON-MED (CMED) neural networks for SD 1030 (in orange) and SD 1053 (in green), alongside pCO_2sw data from SD 1030 (in black). The reference data are derived from fixed stations equipped with pCO_2sw sensors, corresponding to the dates when SD 1030 (and SD 1053 for DYFAMED) was in proximity to these sites: DYFAMED (represented by a yellow square and triangle), W1M3A (yellow triangle down), PALOMA (yellow circle), and MIRAMARE (yellow star).

Further analysis of discrepancies between pCO_2sw measurements from W1M3A and the SD revealed that differences primarily stemmed from the total gas stream pressure measurement provided which was used to calculate pCO_2sw. In contrast, better agreement was observed when comparing xCO_2sw measurements, with an absolute mean error of 3.9 ppm. Figure 6 illustrates that CANYON-MED predictions for pCO_2sw are consistent with direct measurements from SD 1030 and sensors deployed on fixed buoys (DYFAMED and W1M3A).

Occasionally, discrepancies in pCO_2sw between CANYON-MED predictions and in situ measurements are seen (Figure 6). These are mainly due to the neural network’s limited spatial resolution, which cannot capture sub-mesoscale variability. This is especially evident in the northern Adriatic Sea, where complex coastal influences (e.g., river inputs, groundwater discharges) fall outside the neural network original training scope (open sea in the Mediterranean Sea), leading to notable mismatches with data from SDs and fixed stations like PALOMA and MIRAMARE. Despite these limitations near the coast, CANYON-MED performs well in offshore regions such as the Ligurian, Ionian, and southern Adriatic Sea, where its predictions align closely with SD measurements. This suggests that CANYON-MED can effectively complement the in situ measurements and help fill observational gaps where direct pCO_2sw measurements are unavailable due to sensor failure or maintenance.

3.2.4 Southern Adriatic Sea

Starting from mid-June to the beginning of July 2020, the SDs crossed the southern Adriatic Sea (Figure 7A). This area exhibits a strong variability triggered by the inflow of cold and less salty water from the northern Adriatic and warm and salty water entering the basin through the Otranto strait in the south (Ingrosso et al., 2017; Cantoni et al., 2024; Martellucci et al., 2024). This, drives the formation of small scale eddies (e.g., baroclinic instability) mixing these different water masses. The SD pCO_2sw showed an increasing trend over the entire period (25 June - 1 July 2020) which was similar to the measurements at the E2M3A site (Figure 7D). This was likely due to the increasing SST (Figure 7B) as confirmed by the analysis of the thermal and non-thermal components of pCO_2sw suggesting a strong influence of temperature rather than biological processes (R>1; Figure 7D). This was also highlighted by very low fluctuation in the O₂ concentration Figure 7C over the period that could have been related to biological processes, as the southern Adriatic is an oligotrophic area. Pronounced day-night variations of SD pCO_2sw were also evident in correspondence with SST relative maxima. The CO₂ fluxes (Figure 7E) showed positive values over the entire period indicating CO₂ outgassing from the sea. While Ingrosso et al. (2017) reported that dense water formation was among the primary drivers of CO₂ uptake in the area in winter, the SD measurements highlighted an opposite pattern during summer. In addition, the CO₂ fluxes calculated from SD measurements and the fluxes calculated from the pCO_2sw data of E2M3A show again good agreement in their general patterns (R₂ = 0.72, p ≤ 0.05). SST and SSS measured by the two SDs between 25 June and 1 July 2020 showed similar values at distance less than 10 km apart and also a good agreement with the E2M3A data (R₂ = 0.72, p < 0.005; in contrast to the other fixed stations, the SD surrounded E2M3A for a sufficient amount of time to perform reliably statistics). However, during 26–28 June 2020, when the SDs sailed eastward along the Bari-Dubrovnik track, the SSS decreased from that measured at E2M3A, evidencing that the SDs entered the less salty coastal waters.

Figure 7

Figure 7. (A) Map of the SD trajectory in the southern Adriatic Sea. Glider measurements (green dots), the E2M3A site (magenta star) are also displayed. The measurements were performed during June-July 2020. Temporal evolution of (B) sea surface temperature (SST; °C) and sea surface salinity (SSS), (C) dissolved oxygen concentration (O₂; μmol kg^-1) and oxygen saturation (%), (D) pCO_2sw (µatm) and ratio between thermal and non-thermal components of pCO_2sw (R), where red diamonds represent the pCO_2sw, recorded at E2M3A (the temporal discrepancy between the two time series is due to the different frequency of data acquisition: every hour and every four hours for SD and E2M3A respectively), and (E) flux of CO₂ (FCO₂; mmol CO₂ m^-2 day^-1) and wind speed (m s^-1).

3.2.5 Northern Adriatic Sea

The carbonate balance and dynamics in the shallow (mean depth is approx. 30 m) northern Adriatic sea is strongly influenced by river discharges. The Po river is the major source of freshwater and nutrients in the area (e.g., Cozzi and Giani, 2011), but also smaller rivers influence the carbon chemistry of the region (Giani et al., 2023). Between 13 and 14 July 2020, the SDs crossed a plume of the Po river in the northern Adriatic (Figure 8A). The plume extended eastward from the Po delta on the western Italian coast as evidenced by the lower SSS and higher SST measured by the SD (Figure 8B). The eastward extension of the plume was caused by the northeasterly katabatic Bora wind blowing in the area from 12 to 15 July (Supplementary Figure S6, Supplementary Material). Chl-a in the plume was higher (Supplementary Figure S7, Supplementary Material) as a response to the river borne nutrients, and the corresponding phytoplankton bloom caused a marked decrease in pCO_2sw reaching its lowest values of about 370 µatm which resulted in a CO₂ influx (Figures 8D, E). The CO₂ drawdown by phytoplankton can be seen by the increase in the pCO₂ ratio (green line in Figure 8D, Supplementary Figure S2) due to the decrease of the non thermal pCO₂. This was the only occurrence of CO₂ influx in the Adriatic Sea during the demonstration mission (Figure 2B).

Figure 8

Figure 8. (A) Map of the SD tracks in the northern Adriatic Sea between 12 and 17 July 2020. The position of PALOMA (blue star) and MIRAMARE (red star) stations are indicated. Time series of (B) sea surface temperature (SST; °C) and sea surface salinity (SSS), (C) dissolved oxygen concentration (O₂; μmol kg^-1) and oxygen saturation (%), (D) pCO_2sw (µatm) and ratio between thermal and non-thermal components of pCO_2sw, and (E) flux of CO₂ (FCO₂; mmol CO₂ m^-2 day^-1) and wind speed (m s^-1). In d), pCO_2sw acquired from PALOMA and MIRAMARE are indicated as blue and red diamonds, respectively.

On 16 July 2020, before entering the Gulf of Trieste, the SDs crossed the Isonzo river plume (Figure 8A) as marked by a decrease in SSS (~ -1.6) and increase in SST (~ +1.1°C) (Figure 8B). However, differently from the crossing of the Po river plume, the SDs did not record marked pCO_2sw changes. The pCO_2sw increase due to the thermal effect was compensated by the non-thermal decrease presumably due to the CO₂ drawdown caused by primary productivity as also suggested by the increase of O₂ saturation (Figure 8C). Successively, from 17 to 18 of July 2020 a new outburst of the Bora wind was associated to an increase in pCO_2sw, despite the decrease in SST (~ -1.0 °C) and a switch from thermal (R = 1.14) to non thermal (R = 0.95) control. Strong positive CO₂ fluxes from the sea, up to 80 mmol m^-2d^-1, were measured (Figure 8E) turning the Gulf of Trieste from a weak (FCO₂ = 2.6 mmol m^-2d^-1 on average) to a strong (FCO₂ = 30.7 mmol m^-2d^-1 on average) CO₂ source. Contrary to what might be expected due to CO₂ outgassing, this increase in pCO_2sw can be explained by the mixing with bottom waters induced by northeasterly winds, which promote coastal upwelling. When the SDs entered the Gulf of Trieste, the water column was strongly stratified, with bottom waters depleted in O₂ (down to 132 µmol kg^-1; 56% O₂ sat) and enriched in CO₂ (up to 641 µatm in the middle of the gulf; Supplementary Figure S8, Supplementary Material). The increase of DIC and pCO_2sw and the decrease of pH and O₂ concentration below the pycnocline is typically observed in the Gulf of Trieste (Cantoni et al., 2012; Ingrosso et al., 2016a, b) during summer when the microbial respiration prevails over the primary production (Fonda Umani et al., 2012). The results reported here are consistent with previous findings by Turk et al. (2010); Cossarini et al. (2015) and Urbini et al. (2020) and further strengthens the role of this area as a CO₂ source during summer and highlights the interplay of multiple factors in controlling sea surface pCO_2sw variability. This phenomenon further strengthens the role of this area as a CO₂ source during summer and highlights the interplay of multiple factors in controlling sea surface pCO_2sw variability.

4 Conclusion

The nine months of the ATL2MED mission provided insights into the regional air-sea CO₂ exchange dynamics of the East Atlantic and the Mediterranean Sea, two areas that are generally under-sampled (Landschützer et al., 2020; Tanhua et al., 2021; Hassoun et al., 2022; Bakker et al., 2023; Chakraborty et al., 2023). At a broad and regional scale, our measurements revealed that the Eastern Atlantic and the Mediterranean Sea exhibit distinct CO₂ flux patterns driven by different processes. However, it must be kept in mind that the ATL2MED mission took place over three seasons, in which different processes contributed to the pCO_2sw variations, making direct comparisons between the regions challenging.

In the northwest African upwelling system, specifically in the upwelled waters characterized by low Chl-a concentration and strong winds, the pCO_2sw variability was mainly driven by the thermal component pCO_{2sw_TD} however in the cyclonic eddy also the primary productivity contributed to reverse the CO₂ fluxes from the sea to the atmosphere.

On the contrary, in the highly productive regions of the Alboran sea and the Gulf of Lion biological activity played a significant role in driving the variability of pCO_2sw, resulting in strongly negative CO₂ fluxes, occurring during the spring bloom season. In these areas, the non-thermal component of pCO_2sw (pCO_{2sw_N}) was dominant over pCO_{2sw_TD}. This is due to the low temperature and the presence of upwelling, which brings CO₂-rich deep waters to the surface, counterbalancing the CO₂ uptake by phytoplankton. In the Adriatic during the summer the pCO₂ increase was mainly driven by the seasonal warming, observed in the high values of pCO_{2sw_TD}, leading to the highest CO₂ effluxes recorded during the mission, and also strong wind and respiration processes further increased the CO₂ efflux. However in presence of riverine inputs (i.e. Po river in the northern Adriatic), the pCO₂ strongly decreases driven by the uptake of phytoplankton, causing an influx of pCO₂ from the atmosphere reflecting the high variability of the area. Indeed as observed in Borges et al., 2018 and Chen et al., 2020, river-borne nutrients can stimulate primary production, promoting a transient atmospheric CO₂ sink during phytoplankton blooms. These measurements captured fine-scale processes, including the influence of small eddies (i.e. eastern Atlantic), localized mixing events, and the impact of river plumes (i.e. northern Adriatic), which are often masked by broader-scale observations. Such localized phenomena can drive rapid and substantial changes in CO₂ exchange dynamics, highlighting the need for high-resolution measurements to fully understand the interplay between physical and biogeochemical processes at the air-sea interface. Moreover, the ATL2MED mission showed a close match between the CANYON-MED derived pCO_2sw estimates and the SD measurements, where the offset of 15–20 µatm fell within the expected error range of the neural network-derived pCO_2sw estimates. Due to limited spatial resolution of CANYON-MED, there are some differences when comparing to the SD pCO_2sw measurements, however CANYON-MED complements pCO_2sw measurements from other platforms like the SDs. It is also confirmed that while these models effectively capture broader patterns, they may overlook important sub-mesoscale features critical for precise CO₂ flux estimation as for the northern Adriatic case.

Despite the various challenges of the ATL2MED mission (e.g., the Covid pandemic), the SDs have proven to be a valuable tool for increasing the spatial pCO₂ data density, underscoring the complementary role that autonomous vehicles such as Saildrones can play in global carbon monitoring efforts. Overall, these findings emphasize that a multi-scale monitoring approach is essential to refining our understanding of marine carbon dynamics and improving predictive capabilities in a changing climate.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.5194/essd-16-5333-2024, 2024.

Author contributions

RM: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft. CD: Data curation, Formal analysis, Investigation, Writing – original draft. LC: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – original draft, Writing – review & editing, Software. IS: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. MG: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing, Visualization. SP: Data curation, Formal analysis, Funding acquisition, Investigation, Resources, Writing – original draft, Writing – review & editing, Methodology. CC: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing. VC: Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing. MF: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft. RB: Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing. MP: Data curation, Formal analysis, Resources, Validation, Writing – review & editing. EM: Data curation, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. The ATL2MED mission received support regarding coordination, operation, and data deliverance from Saildrone Inc. and funding from the US company PEAK 6 Invest, GEOMAR Helmholtz Centre for Ocean Research (GEOMAR), Integrated Carbon Observation System -Ocean Thematic Centre (ICOS-OTC), the French National Centre for Scientific Research (CNRS), Oceanography Laboratory of Villefranche (LOV), the Oceanic Platform of the Canary Islands (PLOCAN), Ocean Science Centre Mindelo (OSCM), the Hydrographic Institute of Portugal (IH), Balearic Islands Coastal Observing and Forecasting System (SOCIB), Italian National Institute of Oceanography and Applied Geophysics (OGS), Helmholtz Zentrum Geesthacht (HZG), Centre Scientifique de Monaco (CSM), National Research Council-Institute of Marine Sciences (CNR-ISMAR), and National Research Council -Institute for the study of Anthropic Impact and Sustainability in the Marine Environment (CNR-IAS). PEAK 6 Invest was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

Acknowledgments

We thank the OGS engineers Paolo Mansutti and Giuseppe Siena for the assistance during the final recovery of the SDs, and Piero Zuppelli, Riccardo Gerin, Antonio Bussani and Massimo Pacciaroni for piloting the OGS glider. Furthermore, we thank Benjamin Pfeil and Björn Fiedeler for initialising the mission and Fiedeler for executing the first phase of the mission. Finally, we are very grateful for the help from Adrienne Sutton and Stacy Manner regarding correction of the ASVCO₂ pCO_2sw and pCO_2atm data. The authors would like to thank the ICOS Ocean Thematic Centre for providing the data of the SAILDRONE mission and of the ICOS stations (W1M3A, E2M3A, Paloma and MIRAMARE).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2025.1633617/full#supplementary-material

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