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Ocean surface pCO 2 estimates are of great interest for the calculation of air–sea CO 2 fluxes, oceanic uptake of anthropogenic CO 2 , and eventually the Global Carbon Budget. They are accessible from direct observations, which are discrete in space and time and thus always sparse, or from biogeochemical models, which only approximate reality. Here, a combined method for the extrapolation of pCO 2 observations is presented that uses (1) model-based patterns of variability from an empirical orthogonal function (EOF) analysis of variability with (2) observational data to constrain EOF pattern amplitudes in (3) an ensemble approach, which locally adjusts the spatial scale of the mapping to the density of the observations. Thus, data-constrained, gap- and discontinuity-free mapped fields including local error estimates are obtained without the need for or dependence on ancillary data (e.g. satellite sea surface temperature maps). This extrapolation approach is generic in that it can be applied to any oceanic or coastal region covered by a suitable model and observations. It is used here to establish a regional pCO 2 climatology of the Baltic Sea (: 10.1594/PANGAEA.961119), largely based on ICOS-DE ship of opportunity (SOOP) Finnmaid surface pCO 2 observations between Lübeck-Travemünde (Germany) and Helsinki (Finland). The climatology can serve as improved input for atmosphere–ocean CO 2 flux estimation in this coastal environment. [ABSTRACT FROM AUTHOR]

Ocean surface pCO2 estimates are of great interest for the calculation of air-sea CO2 fluxes, oceanic uptake of anthropogenic CO2, and eventually the Global Carbon Budget. They are accessible from direct observations, which are discrete in space and time and thus always sparse, or from biogeochemical models, which only approximate reality. Here, a combined method for the extrapolation of pCO2 observations is presented that uses (1) model-based patterns of variability from an EOF analysis of variability with (2) observational data to constrain EOF pattern amplitudes in (3) an ensemble approach, 5 which locally adjusts the spatial scale of the mapping to the density of the observations. Thus, data-constrained, gap- and discontinuity-free mapped fields including local error estimates are obtained without the need for or dependence on ancillary data (like, e.g., satellite sea surface temperature maps). This extrapolation approach is generic in that it can be applied to any oceanic or coastal region covered by a suitable model and observations. It is used here to establish a regional pCO2 climatology of the Baltic Sea, largely based on ICOS-DE SOOP Finnmaid surface pCO2 observations between Lübeck-Travemünde 10 (Germany) and Helsinki (Finland). The climatology can serve as improved input for atmosphere-ocean CO2 flux estimation in this coastal environment. [ABSTRACT FROM AUTHOR]

Organic matter production by cyanobacteria blooms is a major environmental concern for the Baltic Sea, as it promotes the spread of anoxic zones. Partial pressure of carbon dioxide (p CO2) measurements carried out on Ships of Opportunity (SOOP) since 2003 have proven to be a powerful tool to resolve the carbon dynamics of the blooms in space and time. However, SOOP measurements lack the possibility to directly constrain depth-integrated net community production (NCP) in moles of carbon per surface area due to their restriction to the sea surface. This study tackles the knowledge gap through (1) providing an NCP best guess for an individual cyanobacteria bloom based on repeated profiling measurements of p CO2 and (2) establishing an algorithm to accurately reconstruct depth-integrated NCP from surface p CO2 observations in combination with modelled temperature profiles. Goal (1) was achieved by deploying state-of-the-art sensor technology from a small-scale sailing vessel. The low-cost and flexible platform enabled observations covering an entire bloom event that occurred in July–August 2018 in the Eastern Gotland Sea. For the biogeochemical interpretation, recorded p CO2 profiles were converted to CT* , which is the dissolved inorganic carbon concentration normalised to alkalinity. We found that the investigated bloom event was dominated by Nodularia and had many biogeochemical characteristics in common with blooms in previous years. In particular, it lasted for about 3 weeks, caused a CT* drawdown of 90 µmolkg-1 , and was accompanied by a sea surface temperature increase of 10 ∘C. The novel finding of this study is the vertical extension of the CT* drawdown up to the compensation depth located at around 12 m. Integration of the CT* drawdown across this depth and correction for vertical fluxes leads to an NCP best guess of ∼1.2 molm-2 over the productive period. Addressing goal (2), we combined modelled hydrographical profiles with surface p CO2 observations recorded by SOOP Finnmaid within the study area. Introducing the temperature penetration depth (TPD) as a new parameter to integrate SOOP observations across depth, we achieve an NCP reconstruction that agrees to the best guess within 10 % , which is considerably better than the reconstruction based on a classical mixed-layer depth constraint. Applying the TPD approach to almost 2 decades of surface p CO2 observations available for the Baltic Sea bears the potential to provide new insights into the control and long-term trends of cyanobacteria NCP. This understanding is key for an effective design and monitoring of conservation measures aiming at a Good Environmental Status of the Baltic Sea. [ABSTRACT FROM AUTHOR]

Autonomous measurements aboard ships of opportunity (SOOP) provide in situ data sets with high spatial and temporal coverage. In this study, we use 8 years of carbon dioxide (CO2) and methane (CH4) observations from SOOP Finnmaid to study the influence of upwelling on trace gas dynamics in the Baltic Sea. Between spring and autumn, coastal upwelling transports water masses enriched with CO2 and CH4 to the surface of the Baltic Sea. We study the seasonality, regional distribution, relaxation, and interannual variability in this process. We use reanalysed wind and modelled sea surface temperature (SST) data in a newly established statistical upwelling detection method to identify major upwelling areas and time periods. Large upwelling-induced SST decrease and trace gas concentration increase are most frequently detected around August after a long period of thermal stratification, i.e. limited exchange between surface and underlying waters. We found that these upwelling events with large SST excursions shape local trace gas dynamics and often lead to near-linear relationships between increasing trace gas levels and decreasing temperature. Upwelling relaxation is mainly driven by mixing, modulated by air–sea gas exchange, and possibly primary production. Subsequent warming through air–sea heat exchange has the potential to enhance trace gas saturation. In 2015, quasi-continuous upwelling over several months led to weak summer stratification, which directly impacted the observed trace gas and SST dynamics in several upwelling-prone areas. Trend analysis is still prevented by the observed high variability, uncertainties from data coverage, and long water residence times of 10–30 years. We introduce an extrapolation method based on trace gas–SST relationships that allows us to estimate upwelling-induced trace gas fluxes in upwelling-affected regions. In general, the surface water reverses from CO2 sink to source, and CH4 outgassing is intensified as a consequence of upwelling. We conclude that SOOP data, especially when combined with other data sets, enable flux quantification and process studies addressing the process of upwelling on large spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Organic matter production by cyanobacteria blooms is a major environmental concern for the Baltic Sea as it promotes thespread of anoxic zones. Partial pressure of carbon dioxide (pCO2) measurements carried out on Ships of Opportunity (SOOP) since 2003 have proven to be a powerful tool to resolve the carbon dynamics of the blooms in space and time. However, SOOP measurements lack the possibility to directly constrain the depth--integrated net community production (NCP) due to their restriction to the sea surface. This study tackles the resulting knowledge gap through (1) providing a best--guess NCP estimatefor an individual cyanobacteria bloom based on repeated profiling measurements of pCO2 and (2) establishing an algorithm to accurately reconstruct depth--integrated NCP from surface pCO2 observations in combination with modelled temperature profiles. Goal (1) was achieved by deploying state--of--the--art sensor technology from a small--scale sailing vessel. The low--cost and flexible platform enabled observations covering an entire bloom event that occurred in July and August 2018 in the Eastern Gotland Sea. For the biogeochemical interpretation, recorded pCO2 profiles were converted to CT*, which is the dissolved inorganic carbon concentration normalised to alkalinity. We found that the investigated Nodularia--dominated bloom event had many biogeochemical characteristics in common with blooms in previous years. In particular, it lasted for about three weeks, caused a CT* drawdown of 80 µmol kg-1, and was accompanied by a sea surface temperature increase of 10 °C. The novel finding of this study is the vertical extension of the CT* drawdown up to 12 m water depth. Integration of the CT* drawdown across this depth and correction for vertical fluxes permit a best--guess NCP estimate of ~1.2 mol--C m-2. Addressing goal (2), we combined modelled hydrographical profiles with surface pCO2 observations recorded by SOOP Finnmaid within the study area. Introducing the temperature penetration depth (TPD) as a new parameter to integrate SOOP observations across depth, we achieve a reconstructed NCP estimate that agrees to the best--guess within 10 %. Applying the TPD approach to almost two decades of surface pCO2 observations available for the Baltic Sea bears the potential to provide new insights into the control and long--term trends of cyanobacteria NCP. This understanding is key for an effective design and monitoring of conservation measures aiming at a Good Environmental Status of the Baltic Sea. [ABSTRACT FROM AUTHOR]

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 f CO2 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 f CO2 , pH and air–sea CO2 flux. The surface ocean f CO2 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 f CO2 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 f CO2 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. [ABSTRACT FROM AUTHOR]

Autonomous measurements aboard ships of opportunity (SOOP) provide in situ data sets with high spatial and temporal coverage. In this study, we use 8 years of carbon dioxide (CO2) and methane (CH4) observations from SOOP Finnmaid to study the influence of upwelling on trace gas dynamics in the Baltic Sea. Between spring and autumn, coastal upwelling transports water masses enriched with CO2 and CH4 to the surface of the Baltic Sea. We study the seasonality, regional distribution, relaxation, and interannual variability of this process. We use reanalysed wind and modelled sea surface temperature (SST) data in a newly established statistical upwelling detection method to identify major upwelling areas and time periods. Strong upwelling events are most frequently detected around August after a long period of thermal stratification, i.e. limited exchange between surface and underlying waters. We found that these strong upwelling events with large SST excursions shape local trace gas dynamics and often lead to near-linear relationships between increasing trace gas levels and decreasing temperature. Upwelling relaxation is mainly driven by mixing and modulated by air-sea gas exchange and possibly primary production. Subsequent warming through air-sea heat exchange has the potential to enhance trace gas saturation. In 2015, quasi-continuous upwelling over several months led to weak summer stratification, which directly impacted the observed trace gas and SST dynamics in several upwelling-prone areas. We introduce an extrapolation method based on trace gas - SST relationships that allows us to estimate upwelling-induced trace gas fluxes in upwelling-affected regions. In general, the surface water reverses from CO2 sink to source and CH4 outgassing is intensified as a consequence of upwelling. We conclude that upwelling is an important and relevant process controlling trace gas dynamics in near-coastal environments in the Baltic Sea, and that SOOP data, especially when combined with other data sets, enable flux quantification and process studies on larger spatial and temporal scales. [ABSTRACT FROM AUTHOR]