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IntroductionThe 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 (pCO2sw), 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.MethodsHigh-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.ResultsThe results reveal pronounced regional differences in pCO2sw 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.DiscussionSaildrone 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.

There is currently a lack of representative, systematic and harmonised greenhouse gas (GHG) observations covering the variety of natural and human-altered biomes that occur in Africa. This impedes the long-term assessment of the drivers of climate change, in addition to their impacts and feedback loops at the continental scale, but also limits our understanding of the contribution of the African continent to the global carbon (C) cycle. Given the current and projected transformation of socio-economic conditions in Africa (i.e. the increasing trend of urbanisation and population growth) and the adverse impacts of climate change, the development of a GHG research infrastructure (RI) is needed to support the design of suitable mitigation and adaptation strategies required to assure food, fuel, nutrition and economic security for the African population. This paper presents the initial results of the EU-African SEACRIFOG project, which aims to design a GHG observation RI for Africa. The first stages of this project included the identification and engagement of key stakeholders, the definition of the conceptual monitoring framework and an assessment of existing infrastructural capacity. Feedback from stakeholder sectors was obtained through three Stakeholder Consultation Workshops held in Kenya, Ghana and Zambia. Main concerns identified were data quality and accessibility, the need for capacity building and networking among the scientific community, and adaptation to climate change, which was confirmed to be a priority for Africa. This feedback in addition to input from experts in the atmospheric, terrestrial and oceanic thematic areas, facilitated the selection of a set of 'essential variables' that need to be measured in the future environmental RI. An inventory of 47 existing and planned networks across the continent allowed for an assessment of the current RIs needs and gaps in Africa. Overall, the development of a harmonised and standardised pan-African RI will serve to address the continent's primary societal and scientific challenges through a potential cross-domain synergy among existing and planned networks at regional, continental and global scales.

Global population projections foresee the biggest increase to occur in Africa with most of the available uncultivated land to ensure food security remaining on the continent. Simultaneously, greenhouse gas emissions are expected to rise due to ongoing land use change, industrialisation, and transport amongst other reasons with Africa becoming a major emitter of greenhouse gases globally. However, distinct knowledge on greenhouse gas emissions sources and sinks as well as their variability remains largely unknown caused by its vast size and diversity and an according lack of observations across the continent. Thus, an environmental research infrastructure—as being setup in other regions—is more needed than ever. Here, we present the results of a design study that developed a blueprint for establishing such an environmental research infrastructure in Africa. The blueprint comprises an inventory of already existing observations, the spatial disaggregation of locations that will enable to reduce the uncertainty in climate forcing’s in Africa and globally as well as an overall estimated cost for such an endeavour of about 550 M€ over the next 30 years. We further highlight the importance of the development of an e-infrastructure, the necessity for capacity development and the inclusion of all stakeholders to ensure African ownership.

Over the ocean, eddy correlation measurements of the air‐sea CO2 flux obtained with open‐path sensors have typically been an order of magnitude larger than those estimated by other techniques or sensors. It is shown here that this discrepancy is due to cross sensitivity to water vapor fluctuations: a novel correction procedure is demonstrated, tested against an independent data set and proved to be robust. After correction, the observed gas transfer velocities are in reasonable agreement with published values obtained using closed‐path sensors or by tracer techniques. Data from open‐path sensors may now be used for air‐sea CO2 flux estimation, greatly increasing the information available on air‐sea gas transfer velocity.

We developed a simple method to refine existing open-ocean maps and extend them towards different coastal seas. Using a multi-linear regression we produced monthly maps of surface ocean fCO2 in the northern European coastal seas (the North Sea, the Baltic Sea, the Norwegian Coast and the Barents Sea) covering a time period from 1998 to 2016. A comparison with gridded Surface OceanCO2 Atlas (SOCAT) v5 data revealed mean biases and standard deviations of 0 ± 26 µatm in the North Sea, 0 ± 16 µatm along the Norwegian Coast, 0 ± 19 µatm in the Barents Sea and 2 ± 42 µatm in the Baltic Sea. We used these maps to investigate trends in fCO2, pH and air–seaCO2 flux. The surface ocean fCO2 trends are smaller than the atmospheric trend in most of the studied regions. The only exception to this is the western part of the North Sea, where sea surface fCO2 increases by 2 µatmyr-1, which is similar to the atmospheric trend. The Baltic Sea does not show a significant trend. Here, the variability was much larger than the expected trends. Consistently, the pH trends were smaller than expected for an increase in fCO2 in pace with the rise of atmosphericCO2 levels. The calculated air–sea CO2 fluxes revealed that most regions were net sinks for CO2. Only the southern North Sea and the Baltic Sea emitted CO2 to the atmosphere. Especially in the northern regions the sink strength increased during the studied period.

We developed a simple method to refine existing open-ocean maps and extend them towards different coastal seas. Using a multi-linear regression we produced monthly maps of surface ocean fCO.sub.2 in the northern European coastal seas (the North Sea, the Baltic Sea, the Norwegian Coast and the Barents Sea) covering a time period from 1998 to 2016. A comparison with gridded Surface Ocean CO.sub.2 Atlas (SOCAT) v5 data revealed mean biases and standard deviations of 0 ± 26 µatm in the North Sea, 0 ± 16 µatm along the Norwegian Coast, 0 ± 19 µatm in the Barents Sea and 2 ± 42 µatm in the Baltic Sea. We used these maps to investigate trends in fCO.sub.2, pH and air-sea CO.sub.2 flux. The surface ocean fCO.sub.2 trends are smaller than the atmospheric trend in most of the studied regions. The only exception to this is the western part of the North Sea, where sea surface fCO.sub.2 increases by 2 µatm yr.sup.-1, which is similar to the atmospheric trend. The Baltic Sea does not show a significant trend. Here, the variability was much larger than the expected trends. Consistently, the pH trends were smaller than expected for an increase in fCO.sub.2 in pace with the rise of atmospheric CO.sub.2 levels. The calculated air-sea CO.sub.2 fluxes revealed that most regions were net sinks for CO.sub.2 . Only the southern North Sea and the Baltic Sea emitted CO.sub.2 to the atmosphere. Especially in the northern regions the sink strength increased during the studied period.

We developed a simple method to refine existing open-ocean maps and extend them towards different coastal seas. Using a multi-linear regression we produced monthly maps of surface ocean fCO2 in the northern European coastal seas (the North Sea, the Baltic Sea, the Norwegian Coast and the Barents Sea) covering a time period from 1998 to 2016. A comparison with gridded Surface Ocean CO2 Atlas (SOCAT) v5 data revealed mean biases and standard deviations of 0 ± 26 µatm in the North Sea, 0 ± 16 µatm along the Norwegian Coast, 0 ± 19 µatm in the Barents Sea and 2 ± 42 µatm in the Baltic Sea. We used these maps to investigate trends in fCO2, pH and air–sea CO2 flux. The surface ocean fCO2 trends are smaller than the atmospheric trend in most of the studied regions. The only exception to this is the western part of the North Sea, where sea surface fCO2 increases by 2 µatm yr−1, which is similar to the atmospheric trend. The Baltic Sea does not show a significant trend. Here, the variability was much larger than the expected trends. Consistently, the pH trends were smaller than expected for an increase in fCO2 in pace with the rise of atmospheric CO2 levels. The calculated air–sea CO2 fluxes revealed that most regions were net sinks for CO2. Only the southern North Sea and the Baltic Sea emitted CO2 to the atmosphere. Especially in the northern regions the sink strength increased during the studied period.

Due to low calcium carbonate saturation states, and winter mixing that brings anthropogenic carbon to the deep ocean, the Nordic Seas and their cold-water corals are vulnerable to ocean acidification. Here, we present a detailed investigation of the changes in pH and aragonite saturation in the Nordic Seas from preindustrial times to 2100, by using in situ observations, gridded climatological data, and projections for three different future scenarios with the Norwegian Earth System Model (NorESM1-ME). During the period of regular ocean biogeochemistry observations from 1981–2019, the pH decreased with rates of 2–3 × 10−3 yr−1 in the upper 200 m of the Nordic Seas. In some regions, the pH decrease can be detected down to 2000 m depth. This resulted in a decrease in the aragonite saturation state, which is now close to undersaturation in the depth layer of 1000–2000 m. The model simulations suggest that the pH of the Nordic Seas will decrease at an overall faster rate than the global ocean from the preindustrial era to 2100, bringing the Nordic Seas' pH closer to the global average. In the esmRCP8.5 scenario, the whole water column is projected to be undersaturated with respect to aragonite at the end of the 21st century, thereby endangering all cold-water corals of the Nordic Seas. In the esmRCP4.5 scenario, the deepest cold-water coral reefs are projected to be exposed to undersaturation. Exposure of all cold-water corals to corrosive waters can only be avoided with marginal under the esmRCP2.6 scenario. Over all timescales, the main driver of the pH drop is the increase in dissolved inorganic carbon (CT) caused by the raising anthropogenic CO2, followed by the temperature increase. Thermodynamic salinity effects are of secondary importance. We find substantial changes in total alkalinity (AT) and CT as a result of the salinification, or decreased freshwater content, of the Atlantic water during all time periods, and as a result of an increased freshwater export in polar waters in past and future scenarios. However, the net impact of this decrease (increase) in freshwater content on pH is negligible, as the effects of a concentration (dilution) of CT and AT are canceling.

We report on a pilot study using a CO2 optode deployed on a Seaglider in the Norwegian Sea from March to October 2014. The optode measurements required drift and lag correction and in situ calibration using discrete water samples collected in the vicinity. We found that the optode signal correlated better with the concentration of CO2,c(CO2), than with its partial pressure, p(CO2). Using the calibratedc(CO2) and a regional parameterisation of total alkalinity (AT) as a function of temperature and salinity, we calculated total dissolved inorganic carbon content, c(DIC), which had a standard deviation of 11 µmol kg-1 compared with in situ measurements. The glider was also equipped with an oxygen (O2) optode. The O2 optode was drift corrected and calibrated using a c(O2) climatology for deep samples. The calibrated data enabled the calculation of DIC- and O2-based net community production, N(DIC) and N(O2). To derive N, DIC and O2 inventory changes over time were combined with estimates of air–sea gas exchange, diapycnal mixing and entrainment of deeper waters. Glider-based observations captured two periods of increased Chl a inventory in late spring (May) and a second one in summer (June). For the May period, we found N(DIC) = (21±5) mmol m-2 d-1,N(O2) = (94±16) mmol m-2 d-1 and an (uncalibrated) Chl a peak concentration of craw(Chl a) = 3 mg m-3. During the June period,craw(Chl a) increased to a summer maximum of 4 mg m-3, associated with N(DIC) = (85±5) mmol m-2 d-1 andN(O2) = (126±25) mmol m-2 d-1. The high-resolution dataset allowed for quantification of the changes in N before, during and after the periods of increased Chl a inventory. After the May period, the remineralisation of the material produced during the period of increased Chl a inventory decreased N(DIC) to (-3±5) mmol m-2 d-1 and N(O2) to(0±2) mmol m-2 d-1. The survey area was a source of O2 and a sink of CO2 for most of the summer. The deployment captured two different surface waters influenced by the Norwegian Atlantic Current (NwAC) and the Norwegian Coastal Current (NCC). The NCC was characterised by lowerc(O2) and c(DIC) than the NwAC, as well as lower N(O2) andcraw(Chl a) but higher N(DIC). Our results show the potential of glider data to simultaneously capture time- and depth-resolved variability in DIC and O2 concentrations.

The ATL2MED demonstration experiment involved two autonomous surface vehicles from Saildrone Inc. (SD) which travelled a route from the eastern tropical North Atlantic to the Adriatic Sea between October 2019 and July 2020 (see Table A6). This 9-month experiment in a transition zone between the temperate and tropical belts represents a major challenge for the SD's operations. The sensors on board were exposed to varying degrees of degradation and biofouling depending on the geographical area and season, which led to a deterioration in the measurements. As a result, some maintenance measures were required during the mission.We address the difficulty of correcting the data during a period of COVID-19 restrictions, which significantly reduced the number of discrete samples planned for the SD salinity and dissolved oxygen validation. This article details alternative correction methods for salinity and dissolved oxygen. Due to the lack of in situ data, model products have been used to correct the salinity data acquired by the SD instruments, and then the resulting corrected salinity was validated with data from fixed ocean stations, gliders, and Argo floats. In addition, dissolved oxygen data acquired from the SD instruments after correction using air oxygen measurements were tested and found to be coherent with the variation in oxygen concentrations expected from changes in temperature and phytoplankton abundance (from chlorophyll a). The correction methods are relevant and useful in situations where validation capabilities are lacking, which was the case during the ATL2MED demonstration experiment. For future experiments, a more frequent sample collection would improve the data qualification and validation.