Cyanobacteria net community production in the Baltic Sea as inferred from proﬁling p CO 2 measurements

. 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 CO 2 ) 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 5 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 CO 2 and (2) establishing an algorithm to accurately reconstruct depth–integrated NCP from surface p CO 2 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 10 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,

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 two decades of surface pCO 2 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. 25 1 Introduction

Net community production (NCP) in marine ecosystems
Net community production (NCP) of organic matter triggers many biogeochemical processes that control the functioning and state of marine ecosystems. Globally relevant examples are the biological carbon pump (Henson et al., 2011;Sanders et al., 2014) and the establishment of oxygen minimum zones (Gilly et al., 2013;Oschlies et al., 2018). In this biogeochemical 30 context, we define NCP as the net amount of carbon fixed in organic matter (gross production minus respiration) that is produced in a defined water volume over a defined period. This definition implies that the choice of an integration depth is a critical component of any NCP estimate. Traditionally, NCP is constrained either to the depth of the euphotic zone, the compensation depth at which gross production equals respiration, or the mixed layer depth (Sarmiento and Gruber, 2006). Of those approaches, only the integration to the compensation depth is directly linked to the vertical distribution of carbon fixation 35 and remineralisation and therefore quantifies the amount of formed organic matter that can potentially be exported. The reliable quantification of this potential export is a prerequisite to understand subsequent biogeochemical transformation of the organic matter and its imprint on environmental conditions in any aquatic system.

Baltic Sea
On a regional scale, NCP quantification is of particular importance to study the formation of anoxic conditions in :::::::::::: deoxygenation 40 :: of stratified water bodies caused by the remineralisation of organic matter that was exported across a permanent pycnocline.
This :::::::::::: hydrographical : situation is typically encountered in semi-enclosed, silled estuaries such as the Baltic Sea. The deep basins of the Baltic Sea receive substantial amounts of oxygenated, salty water from the North Sea only during occasional major inflow events. Between inflow events, those water masses can stagnate for more than a decade below the permanent halocline (Mohrholz et al., 2015), which is located at around 60 m water depth in the Central Baltic Sea. The export of organic 45 matter into the deep waters is considered the ultimate cause for the expansion of anoxic areas in the Baltic Sea, which are nowadays among the largest anthropogenically induced anoxic areas in the world (Carstensen et al., 2014). Although the actual oxygenation state of the deep basins of the Baltic Sea is modulated by the frequency and strength of inflow events (Mohrholz et al., 2015;Neumann et al., 2017) and the biogeochemical properties of the inflowing waters , the long-term expansion of the anoxic water body was primarily attributed to increased nutrient inputs from land (Jokinen 50 et al., 2018;Meier et al., 2019;Carstensen et al., 2014;Mohrholz, 2018) that fueled the organic matter production in surface waters. Therefore, a quantitative and mechanistic understanding of organic matter production is key to understand, predict, and eventually counteract the expansion of the anoxic areas. Such measures to reduce eutrophication and deep water anoxia actually represent a core component of the EU Marine Strategy Framework Directive (MSFD), which is implemented as the HELCOM Baltic Sea Action Plan (BSAP) and aims at a Good Environmental Status (GES) of the Baltic Sea. 55

Cyanobacteria blooms
The annual cycle of organic matter production in the Central Baltic Sea can be broadly divided into two phases (Schneider and Müller, 2018). The first production phase is the spring bloom, which is controlled by the availability of nitrate and shifted from being dominated by diatoms to dinoflagellates in the late 1980s Spilling et al., 2018). After a so-called blue water period with close-to-zero NCP rates, the second production phase consists of mid-summer blooms dominated 60 by nitrogen-fixing cyanobacteria that develop in most years depending on meteorological conditions. Although cyanobacteria NCP is yet poorly constrained, its relative contribution to the annual NCP in the Eastern Gotland Sea in 2009 was estimated in the order of 40% (Schneider and Müller, 2018;Schneider et al., 2014), though the uncertainty :: of ::: this ::::::: estimate : is high. This preliminary estimate further needs to be interpreted with care as cyanobacteria NCP varies significantly between years and regions. The blooming of cyanobacteria is limited to the months of June to August (Kownacka et al., 2020) and represents 65 a common feature of the Baltic Sea ecosystem at least since the 1960s (Finni et al., 2001). The blooms are a major public concern, because they produce toxins and form thick surface scums lowering the recreational value of the Baltic Sea. From a biogeochemical perspective, the ability to fix nitrogen makes cyanobacteria independent from nitrate and aggravates the eutrophication state of the Baltic Sea. Whether their growth is limited by the availability of phosphate remains an ongoing debate (Nausch et al., 2012), although the highly variable C:P ratio of their biomass (Nausch et al., 2009) indicates phenotypic 70 plasticity. Other ongoing debates in the field of cyanobacteria research address the fate of the produced organic matter and its transfer into the food web (Karlson et al., 2015), the intensification of cyanobacteria blooms through positive feedback loops between organic matter production, deep water anoxia and the release of phosphate from anoxic sediments (Vahtera et al., 2007), as well as their response to ongoing changes in salinity, temperature and the partial pressure of carbon dioxide, pCO 2 (Olofsson et al., 2019(Olofsson et al., , 2020. The limited understanding of the factors that control the blooms hinders the reliable prediction 75 of the future state of the Baltic Sea and therefore the prioritisation of conservation measures (Elmgren, 2001). In particular, it remains challenging to disentangle how expected trends -including warming, reduced nutrient loads, and increasing pCO 2might impact cyanobacteria growth Saraiva et al., 2019). A long-term hindcast of cyanobacteria NCP and the attribution of its strength to prevailing environmental conditions in particular years could improve our understanding of controlling factors and facilitate more reliable predictions of the blooms. However, such a hindcast of cyanobacteria NCP was 80 so far impossible due to missing vertically-resolved observations that would allow to constrain their organic matter production.

Quantification of NCP
Striving for a better understanding of the ecosystem impact of cyanobacteria blooms, the accurate quantification of produced organic matter is key. In this regard, NCP could in principle be quantified directly as an increase in particulate organic carbon (Wasmund et al., 2005) and also fail to achieve the required spatio-temporal resolution due to a low degree of automation. As an alternative, it is possible to quantify NCP through the drawdown of dissolved inorganic carbon (C T ) from the water column (Schneider et al., 2003). From a biogeochemical perspective, the determination of NCP in terms of carbon is ideal, because carbon is the major component of organic matter and directly related to the amount of oxygen (O 2 ) that is consumed during remineralisation. In principle, NCP could as well be estimated from O 2 time series. However, the equilibrium reactions of 90 carbon dioxide (CO 2 ) in seawater result in slower re-equilibration of CO 2 with the atmosphere compared to O 2 (Wanninkhof, 2014). This results in substantially longer preservation of the C T signal and :::: thus a lower uncertainty contribution of required air-sea CO 2 flux corrections, and makes :::::: making : C T the preferred tracer for NCP. During the Baltic Sea spring bloom, the tracing of nutrient drawdown is a meaningful alternative to quantify NCP and convincingly leads to comparable results to the C T approach (Wasmund et al., 2005). However, time series of nutrient drawdown do not allow for determining NCP of algae 95 blooms dominated by nitrogen-fixing organisms and those with highly variable C:P ratios. As both characteristics are typical for Baltic Sea cyanobacteria blooms (Nausch et al., 2009), the well established C T approach remains the favorable method to determine mid-summer NCP in this region. However, it should be noted that NCP estimates derived from this approach include the formation of POC and dissolved organic carbon (DOC). The produced DOC contributes~20% to NCP (Hansell and Carlson, 1998;Schneider and Kuss, 2004) and is not likely to be vertically exported.

Previous studies
Among previous attempts to trace and quantify the organic matter production of cyanobacteria blooms, automated pCO 2 measurements on the Ship of Opportunity (SOOP) Finnmaid played a pivotal role. Those measurements were started in 2003 and it was demonstrated that highly accurate time series of changes (not absolute values) in C T can be derived from pCO 2 observations (Schneider et al., 2006). The conversion from pCO 2 to C T relies on a fixed alkalinity (A T ) estimate and is applicable under the condition that internal :::: sinks ::: and : sources of A T can be excluded, which is the case in the Baltic Sea due to the absence of calcifying plankton (Tyrrell et al., 2008). The derived parameter is comparable to directly measured C T normalised to A T , and in the following referred to as C T *. For several years of SOOP observations, it was shown that the C T * drawdown during mid-summer cyanobacteria blooms occurs in pulses of days to weeks, primarily during calm, sunny days. Further, it was found that the C T * drawdown correlates well with the co-occurring increase in sea surface temperature (SST), rather than 110 with absolute SST. This relationship was attributed to a common driver, which is the light dose received by the water mass under consideration (Schneider and Müller, 2018).
Despite the successful investigation of cyanobacteria blooms through SOOP pCO 2 observations, providing a depth-integrated estimate of NCP in units of moles carbon fixed per surface area remains challenging due to the restriction of SOOP observations to surface waters. Previous studies aiming at a depth-integrated NCP estimate either simply assumed that the C T * drawdown 115 reached as far down as the water inlet of the measurement system (Schneider and Müller, 2018) or relied on a modelled mixed layer depth for the vertical integration of surface observations (Schneider et al., 2014). However, in the absence of any vertically resolved measurements, neither approach could be validated. Likewise, remote sensing approaches were capable to resolve the station network to average bloom patchiness.

This study
This study builds upon the previous success to determine NCP based on pCO 2 time series, but extends the approach to vertically resolved observations for the first time. The primary goals of this study are to (1) provide a best-guess for the depth-integrated NCP of an individual cyanobacteria bloom based on the full suite of 130 depth-resolved in situ measurements and (2) establish an algorithm to reconstruct depth-integrated NCP based on surface pCO 2 observations and modelled hydrographical profiles Achieving goal (2) and applying the algorithm to almost two decades of SOOP pCO 2 observations in the Baltic Sea would not only allow to determine long-term trends of cyanobacteria NCP, but also enable disentangling its drivers through a comparison 135 of NCP estimates from different years characterized by particular environmental conditions such as SST, pCO 2 and nutrient availability.

Overview
Profiling in situ sensor measurements and water sampling were performed on board the 27ft sailing vessel SV Tina V in the 140 framework of the field sampling campaign "BloomSail". The study area was located in the Central Baltic Sea and extended about 25 nautical miles from the coast of Gotland into the Eastern Gotland Basin (Fig. 1). Measurements were performed during eight cruises covering the period July 6 to August 16, 2018 (Fig. 2).
A custom-made sensor package configured at IOW's Innovative Instrumentation department was deployed to perform pCO 2 and conductivity, temperature and depth (CTD) measurements. The sensor package was either towed near the sea surface while 145 cruising or lowered to at least 25 m water depth at designated profiling stations. This study focuses exclusively on the vertical profiles recorded at stations 02 -12 (Fig. 1b), whereas profiles at stations with water depths below ::: less ::: than : 60 m were not taken into account to avoid the impact of coastal processes. In addition to the sensor measurements, discrete samples for dissolved inorganic carbon (C T ), total alkalinity (A T ) and phytoplankton counts were collected. Track coordinates were continuously recorded with a tablet computer (Galaxy Tab Active, Samsung Electronics, Suwon, South Korea). In addition to the field sampling campaign, atmospheric measurements of wind speed and pCO 2 :::: 2,atm were provided by an ICOS (Integrated Carbon Observation System) station permanently operated on the island Östergarnsholm (Fig. 1b). Furthermore, sea surface pCO 2 and temperature (SST) were also determined on the SOOP Finnmaid, regularly crossing the field study area ( Fig. 1b) Germany; now -4H-JENA engineering, Jena, Germany), uses membrane equilibration of a headspace and subsequent optical Non-Dispersive Infra-Red (NDIR) absorption to determine the pCO 2 in water (Fietzek et al., 2014).
A pre-and post-deployment calibration of the sensor was performed by the manufacturer. pCO 2 data were post-processed 170 taking into account the pre-and post-deployment calibration polynomials, as well as zeroing signals regularly recorded during each deployment. Given the statistics of the pre-and post-deployment calibration, the small drift encountered throughout the deployment and the otherwise smooth performance and regular cleaning of the sensor during the deployment, the accuracy of the drift corrected pCO 2 data is considered to be within 1% of reading as also found by Fietzek et al. (2014). For details concerning sensor calibration, configuration, and signal post-processing, see Appendices A1 -A3.

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Although the pCO 2 sensor achieves low and reproducible response times through active pumping of water onto the membrane, a correction of the response time (τ ) was applied following previously developed procedures (Miloshevich et al., 2004;Fiedler et al., 2013;Atamanchuk et al., 2015). After the response time correction, the mean absolute pCO 2 difference between the up-and downcast profile was <2.5 µatm in the upper 5 m of the water column and <7.5 µatm across the upper 20 m (Fig.   A2). For details concerning the response time correction, see Appendix A4.

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The biogeochemical interpretation of the pCO 2 data was based on downcast profiles only. Since downcasts were started after complete equilibration of the pCO 2 sensor in near-surface waters, the applied response time correction has only a minor impact on the derived NCP estimate.

Atmospheric measurements
Meteorological observations were provided by the ICOS flux tower (Fig. 1b)  Biosciences, Lincoln, USA). Wind speed was measured with a wind monitor (Young, Michigan, USA) at 12 m above mean sea level. Wind speed and pCO 2 data were averaged over 30 min intervals for further analysis. Measured wind speed was converted to U 10 , the wind speed at 10 m above sea level (Winslow et al., 2016), to be consistent with the gas exchange parameterisation (see Sect. 2.5.2).

C T * calculation 205
The dissolved inorganic carbon concentration (C T *) was calculated from the measured profiles of temperature and response time corrected pCO 2 (Schneider et al., 2014), as well as the mean A T (1720 µmol kg −1 ) and mean salinity (6.9) determined from discrete samples collected across the upper 20 m of the water column and over the entire observation period (Fig. B1).
Calculations were performed with the R package seacarb (Gattuso et al., 2020), using the CO 2 dissociation constants for estuarine waters from Millero (2010).

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The calculated C T * represents an alkalinity-and salinity-normalised estimate of the dissolved inorganic carbon concentration. C T * is suitable to accurately determine changes rather than absolute values of the dissolved inorganic carbon concentration and therefore the preferred variable to quantify NCP. The uncertainty in the determination of changes of C T * is below 2 µmol kg −1 when the mean A T is constrained within the observed standard deviation of ±27 µmol kg −1 (see Appendix C1 for a detailed assessment).

NCP best-guess
The determination of NCP in this study ::: our :::: NCP :::::::::: best-guess relies on the interpretation of observed temporal changes in C T * (∆C T *) across the water column. Conceptually, our calculations follow the idea of a one-dimensional box model approach, which does not resolve regional variability within the research area, i.e. it neglects lateral water mass transport. The calculation of the underlying ∆C T * profiles requires a vertical gridding of measured profiles into discrete depth intervals δz and their 220 regional averaging across all stations (for details see Sect. 2.5.1). According to equation 1, we derive the column inventory of incremental changes of ∆C T * (i∆C T *) between two cruise events through vertical integration of ∆C T * from the sea surface to the compensation depth (cd ::: CD), i.e. the depth (z) at which no net drawdown of CO 2 was observed: NCP best-guess ::::: Incremental NCP estimates between cruise events are further added up to derive cumulative NCP over the study period. We refer to the derived NCP estimate as our best-guess, as it is well-constrained by high-quality measurements and therefore as close to the truth as currently possible.

Air-sea CO 2 flux
The air-sea gas exchange of CO 2 (F air-sea ) was calculated from sea surface pCO 2 , salinity and temperature, in combination with atmospheric pCO 2 and wind speed ( :::: 2,atm ::: and : U 10 ) according to Wanninkhof (2014). For the calculation, sea surface 240 observations were linearly interpolated to match the temporal resolution of atmospheric measurements. A negative sign of F air-sea indicates uptake of CO 2 from the atmosphere.

Vertical entrainment flux of CO 2 through mixing
Between June 6 and August 7, vertical mixing of C T * into the surface layer (F mix ) was neglected, because a stable thermocline coincided with the integration depth for the NCP calculation (i.e. the compensation depth). However, clear signals for 245 significant vertical entrainment of C T * across this layer were observed between August 7 and 16 (Fig. 3). This entrainment was quantified assuming an instantaneous complete vertical mixing to 17 m water depth after August 7. For this simplified scenario, F mix was estimated based on a mass-balance of C T *, which behaves conservatively with respect to mixing (see Appendix C2 for details). A negative sign of F mix indicates entrainment of CO 2 into the surface layer.

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SOOP Finnmaid regularly commutes between Helsinki in Finnland and Travemünde in Germany thereby crossing the entire Central Baltic Sea and our study area on the east coast of Gotland every 1 -2 days. On board SOOP Finnmaid, pCO 2 is measured with a bubble-type equilibrator system supplied with water from an inlet at around 3 m water depth. Details of the measurement set-up are described in Schneider et al. (2014) and data are submitted on a regular basis to the Surface Ocean CO 2 Atlas SOCAT (Bakker et al., 2016). The primary measurement system used to determine pCO 2 in this study is a NDIR sensor 275 (LI-6262, LI-COR Biosciences, Lincoln, USA). The ferrybox unit is also equipped with an additional methane/carbon dioxide analyzer (Greenhouse Gas Analyzer DLT 100, type 908-0011, Los Gatos Research, San Jose, USA), providing independent pCO 2 observations (Gülzow et al., 2011). Intercomparison of both systems is routinely used to ensure the correct functioning of the instrumentation. In this study, a data gap caused by malfunctioning of the primary LI-COR system was filled by including data recorded with the Los Gatos system on six cruises between July 8 and 16 (see Appendix D for details). The mean regional 280 pCO 2 , sea surface temperature (SST) and salinity (SSS) were calculated for each crossing of the study area (Fig. 1b). Based on the mean pCO 2 and SST values, C T * was calculated following the procedure outlined in Sect. 2.4. A remaining gap in the SOOP time series was filled with two in situ C T * observations from the BloomSail campaign (July 19 and 24).
numerical ocean model of the Baltic Sea. The deployed General Estuarine Turbulence Model (GETM) has a horizontal resolution of 1 nautical mile and 50 vertical terrain-following levels. The uppermost level has a thickness of maximum 50 cm to properly represent SST and ocean-atmosphere fluxes. The computation of the atmospheric fluxes is based on the parameterisation of Kara et al. (2005). The model covers the entire Baltic Sea and the period 1961 -2019. A detailed analysis of the model performance is given in Placke et al. (2018) and Gräwe et al. (2019). For the present study, we used a model run restarted in 290 2003 with the atmospheric forcing from the operational reanalysis data set of the German weather service (Zängl et al., 2015).
Additionally, we implemented the Langmuir-circulation parameterisation of Axell (2002), to account for wind-wave induced variation in the mixed layer depth. Model results were averaged over 24 h and interpolated to a standardised section with 2 km horizontal and 1 m vertical resolution, which follows the mean Finnmaid cruise track. Based on this standard section, daily mean profiles within the study area (characterized by little regional variability) were computed and linearly interpolated to 295 match the exact times of Finnmaid crossings.
2.6.3 Parameterisation of the integration depth ::: In this study, two parameters were used to integrate surface observations across depth, namely the classical mixed layer depth (MLD) and the newly introduced temperature penetration depth (TPD).
MLD was defined as the shallowest depth at which seawater density exceeds the density at the surface by more than 300 0.1 kg m −3 (Roquet et al., 2015). According to this definition, MLD characterises the thermohaline structure of the water column and often (but not necessarily) approximates the depth to which surfaces water masses are actively mixed. The definition through a fixed density threshold further implies that gradual changes of temperature with depth are not reflected by this parameter.
TPD characterises the mean penetration depth of surface warming that occurred between two sampling events. TPD was 305 defined as the SST increase divided by the integrated warming signal across the water column, i.e. the sum of all positive temperature changes within 1m depth intervals, ::::::: divided :: by ::: the :::: SST ::::::: increase : (for a graphical illustration see Fig. C4a). According to this definition and in contrast to MLD, TPD takes gradual changes of temperature across depth into account and does not require a fixed threshold value. TPD is only applicable when SST increases and has units of metres. To illustrate the TPD concept, it should be noted that a homogeneous warming signal that ceases abruptly at 10 m water depth would result in the 310 same TPD as a warming signal that decreases linearly from the surface to 20 m water depth (TPD is 10 m in both cases). The TPD approach is motivated by the assumption that primary production and temperature increase are both primarily controlled by the light dose that a water parcel received (Schneider et al., 2014) and therefore show similar :::::: vertical : patterns.
Based on MLD or TPD, vertically integrated changes of C T * were reconstructed as the product of incremental changes of surface C T * between cruise days and one of the two integration depth estimates. The reconstructed integrated changes of C T * 315 were further corrected for air-sea fluxes of CO 2 according to section 2.5.2. Please note that neither the MLD nor the TPD approach allows to resolve vertical entrainment fluxes, because profiles of C T * are not reconstructed (compare section 2.5.3).

Results
3.1 Dynamics of temperature, pCO 2 , C T * and phytoplankton biomass Between July 6 and August 16, a total number of 78 complete vertical CTD and pCO 2 downcast profiles were recorded ( Fig.   2 and 3). C T * was calculated and profiles were regionally averaged for each of the eight cruise events (Fig. 4). Since the first cruise of the BloomSail expedition on July 6, sea surface temperature (SST) increased steadily from~15°C to peak values of 325~2 5°C ( Fig. 4 and 5) observed on August 3. Sea surface pCO 2 was already as low as~100 µatm at the beginning of July (Fig.   5a) and decreased further to the lowest values of~70 µatm on July 24. The drop in pCO 2 and the simultaneous increase in SST correspond to a decrease of C T * of almost 90 µmol kg −1 (Fig. 4). During this period of intense primary production, the regional variability of SST, pCO 2 , and C T * across stations was low compared to their temporal change ( Fig. 5a-b; Fig. C3).
The regional variability is slightly higher when including the coastal stations 01, 13, and 14 (results not shown), but is generally 330 lower than one could expect from the bloom patchiness typically observed through remote sensing (Fig. 1a). With respect to pCO 2 dynamics, it should be noted that (i) the observed temperature increase and C T * drawdown have opposing effects on pCO 2 and (ii) the change of pCO 2 per change in C T * is generally low at low absolute pCO 2 . The observed C T * dynamics in surface waters are clearly attributable to the primary production activity of phytoplankton and go along with an observed increase of the biomass of Nodularia sp. (Fig. B2), which also peaked on July 24. ::::::::::: Furthermore, ::: we ::::: found ::: that :::: C T * ::::::::: calculated 335 :::: from ::::: pCO 2 :::::: agreed :::: with :::: C T * :::::: derived ::::: from :::::: discrete ::::::: samples :::::: within ::: the ::::::::: uncertainty ::::: range ::::::::: attributed :: to ::::::: regional :::::::: variability ::::: (Fig.   ::: 5c). : Between the extremes of pCO 2 and C T * (minimum on July 24) and SST (maximum on August 3), a noticeable increase of surface C T * was observed on July 31, which was accompanied by a higher regional variability across the station network ( Fig. 5a,c). The temporary C T * increase was limited to the north-eastern stations 07 -10 ( Fig. C3) and paralleled by a drop in 340 salinity and elevated A T at the same stations (Fig. B1). It is therefore attributable to the lateral exchange of water masses. All signals of this lateral intrusion vanished within a week. At the other stations (02 -06 and 11 -12), no noticeable signs of water mass exchange or C T * changes were observed between July 24 and August 3, indicating that the production and respiration of organic matter were balanced during this period. During the first two weeks of August the study area was affected by increased wind speeds, causing a decrease of SST back to~18°C. The simultaneous return of surface pCO 2 to~150 µatm corresponded 345 to a C T * increase of~100 µmol kg −1 .
The observed surface warming and C T * drawdown extended vertically to a water depth of~10 m (Fig. 4). On the first cruise day (July 6), the vertical distribution of C T * and temperature was still relatively homogenous ::::::::::: homogeneous. C T * at 25 m water depth was~70 µmol kg −1 higher than at the surface. Likewise, the temperature gradient covered only~3°C from 16°C at the a steep increase of C T * in the surface water and a loss of C T * between 11 -17 m ( Fig. 3 and 4).

NCP best-guess based on profiling measurements
Net community production (NCP) was determined through vertical integration of the observed drawdown of C T * from the surface to the compensation depth located at 12 m. The determined compensation depth reflects the maximum penetration depth of the incremental (i.e. between cruise days), as well as the cumulative (i.e. from July 6 -24) : , C T * drawdown (Fig. 4).

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Likewise, about 95% of the cumulative warming signal, which refers to positive temperature changes integrated over depth, occurred above 12 m.

NCP reconstruction based on surface pCO 2 and hydrographical profiles
The reconstruction of depth-integrated NCP was tested for two data sets containing the same type of information, namely the observed changes in surface pCO 2 and vertical profiles of seawater salinity and temperature. The first data set "SV Tina V (surface only)" contains the surface pCO 2 data recorded during the BloomSail expedition, as well as the complete CTD profiles. The second data set ("SOOP Finnmaid + GETM model") combines surface pCO 2 observations from SOOP Finnmaid 385 with seawater salinity and temperature as estimated with the GETM model.
An almost identical decrease of surface C T * of~50 µmol kg −1 was determined between July 6 and 16 (Fig. 6a), based on the completely independent pCO 2 data recorded on SOOP Finnmaid and SV Tina V. Likewise, a very similar increase in C T * between August 6 and 15 was determined from both independent observational data sets. The good agreement between the independent observations justifies that a data gap due to failure of instrumentation on the SOOP was filled with two observations 390 from SV Tina V on July 19 and 24 (open circles in (Fig. 6a).
Good agreement was also found for the spatio-temporal dynamics of observed and modelled seawater temperature (Fig. 6b).
Observed and modelled SST agreed within 1°C over the entire observation period, despite an absolute change spanning almost 10°C. Slightly higher deviations between observed and modelled temperature were found around the thermocline, where the observational record revealed a stronger temperature gradient. This difference is likely due to an imperfect representation of 395 Langmuir circulation in the model (Axell, 2002), whereas the absence of increased light attenuation caused by phytoplankton particles was previously found to have only minor impacts on modeled SST dynamics (Löptien and Meier, 2011). Most importantly, the mean temperature penetration depths (TPD) derived from the observational and model data differ less than 1 m, indicating that surface warming and the integrated heat uptake are accurately represented by the model. The TPD (mean ± SD) over the observed productive period between July 6 and 24 was determined as 12.3 ± 2.5 m and 11.4 ± 2.3 m for the 400 observational and model data, respectively (Fig. 6b). The TPD estimates are considerably higher than the respective mixed layer depth (MLD) estimates (6.0 ± 1.9 m and 5.5 ± 1.2 m) and agree better with the observed penetration depth of C T * drawdown, indicating that TPD is the favourable approximation ::::::::::::: parameterisation of the integration depth.
Based on SOOP observations before July 6, first signs of the onset of the investigated bloom event were detected :::::: already on July 3. Between July 3 and 6, an SST increase of~1°C was accompanied by a C T * drawdown of~10 µmol kg −1 (data not shown). Still, in the absence of any vertically resolved observation for this time period, the following comparison of the reconstructions to the best-guess needs to be restricted to the period July 6 -24, during which the bulk of NCP occurred.
The NCP reconstruction based on TPD is generally higher than the MLD-based estimate (Fig. 6c). Comparing peak cumulative NCP estimates for July 24, the TPD-approach results in a~10% overestimation compared to the best-guess, i.e. the value derived from vertically resolved measurements. In contrast, the MLD-based NCP estimate is~30% lower than the best-guess.

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The reconstructed NCP estimates are very similar for both test data sets, as the good agreement between the underlying C T *, MLD and TPD time series suggests.
Comparing the deviation between the best-guess and reconstructed NCP estimates in the light of the lateral variability observed within the study area, it must be emphasised that between July 6 and 24, the mean standard deviation of pCO 2 and C T * across stations amounted to ± 6 µatm and ± 11 µmol kg −1 , respectively. This is higher than the likely uncertainty 415 associated with the pCO 2 measurements (see Methods), as well as its response time correction (see Methods and Appendix A4) or conversion to C T * (see Appendix C1). Therefore, the lateral variability of seawater chemistry and the production signal are generally considered the highest source of uncertainty to our NCP estimates. Still, this lateral variability is small compared to the signal to be resolved (i.e. the C T * drawdown of~90 µmol kg −1 ). However, on a relative scale the lateral C T * variability is about as large as the difference between the best-guess and the TPD-based NCP reconstruction (~10%), suggesting that the 420 bias of the reconstruction falls within the uncertainty range of the best-guess. In contrast, the lateral variability is smaller than the deviation between the best-guess and the MLD-based NCP reconstruction.
All reconstructed NCP estimates include the correction of air-sea fluxes of CO 2 , but it is impossible to quantify and correct vertical entrainment fluxes due to mixing, because the vertical distribution of C T * across the water column can not be resolved.
The strong deviation between the best-guess NCP and the MLD-based reconstruction on August 16 is due to this missing 425 correction of vertical mixing. This deviation highlights that the reconstruction approach is only applicable to production periods with a stable or shoaling thermocline. The TPD-based approach does not allow for any estimate during the last two weeks of the observations period, as the TPD is per definition only applicable to periods of warming surface waters. 2009 and 2011, the authors found average daily rates of C T * drawdown ranging from 3 to 8 µmol kg −1 d −1 , which comprises the mean rate of 5 µmol kg −1 d −1 determined in this study (i.e. the average C T * drawdown of~90 µmol kg −1 over 18 days, Fig. 4). The individual production events identified by Schneider et al. (2014) lasted 1 to 5 weeks, similar to the bloom duration described in this study. Finally, Schneider et al. (2014) also provided a depth-integrated NCP estimate based on daily modelled mixing depths, which ranged from 3 -20 m and were derived from the vertical distribution of a tracer one day after its injection 440 into the surface. Although this approach is primarily useful to estimate the vertical distribution of air-sea CO 2 fluxes and does not necessarily reflect the vertical extent of organic matter production, their determined mid-summer NCP estimates (1 -2.1 mol m −2 ) are in the same order of magnitude as the best-guess derived in this study. It should be noted that the NCP estimates by Schneider et al. (2014) refer to the cumulative NCP of one to three production pulses per years, whereas our estimate of 1.2 mol m −2 refers to a single bloom event.
445 Wasmund et al. (2001) conducted 14 C incubation experiments at different water depths to determine instantaneous rates of daytime primary production during a cyanobacteria bloom. Their reported carbon fixation rates in surface waters (0.4 -0.8 mmol C m −3 h −1 ) are in the same order of magnitude as the mean rate found in this study (5 µmol kg −1 d −1 , equivalent to 0.2 mmol C m −3 h −1 ), despite representing daytime production rates and diurnal averages, respectively. More important than the agreement between the fixation rates at the sea surface, is the fact that Wasmund et al. (2001) also found significantly 450 lower fixation rates below 10 m water depth (< 0.2 mmol-C m −3 h −1 ), which agrees well with the depth distribution of NCP observed in this study.
Furthermore, the succession of different cyanobacteria genera observed in 2018, with the Nodularia dominated bloom following an earlier presence of Aphanizomenon (Fig. B2), was previously described as a typical pattern (Wasmund, 2017), as well as the fact that increased wind speed and turbulence can inhibit N-fixation of cyanobacteria and cause the termination of 455 the bloom (Wasmund, 1997).
In conclusion, the bloom event duration, C T * drawdown, and NCP, as well as the vertical extend of carbon fixation and the succession of the bloom observed in this study agree well with observations in previous years, and distinct differences cannot be found. We therefore conclude that the findings of this study are representative for Baltic Sea cyanobacteria blooms in general, although the SST and pCO 2 levels in 2018 were at the upper and lower end, respectively, of the conditions observed 460 in previous years (Schneider and Müller, 2018).

Biogeochemical relevance and interpretation
Our best-guess of cumulative NCP on July 24 (~1.2 mol m −2 ) represents the net amount of organic matter that was produced throughout the bloom event in the surface waters above the compensation depth at 12 m. After subtracting~20 % dissolved organic carbon (DOC) production :::::::::::::::::::::::::::::::::::::::::::: (Hansell and Carlson, 1998;Schneider and Kuss, 2004), our NCP estimate equals the pro-465 duced particulate organic carbon (POC) that is potentially available for export. In contrast, NCP estimates derived from other traditional methods for the integration across depth (such as the lower bound of the euphotic zone or the mixed layer depth) would not directly relate to the POC export potential.
However, the potential POC export constraint by our NCP estimate is not equivalent to the supply of organic matter to the deep waters of the Gotland Basin, because POC might be (partly) remineralised before sinking beneath the permanent 470 halocline. Remineralisation of POC that occurs during the bloom event above the compensation depth is -according to our definition of NCP -already included in our estimate. In contrast, any additional remineralisation of POC that occurs between the compensation depth and the halocline, or above the compensation depth after the end of the bloom event, reduces the organic matter supply to the deep waters and thereby mitigates deoxygenation. Indeed, our profiling measurements indicate a steady accumulation of C T * beneath the compensation depth (Fig. 4), likely fueled by the remineralisation of organic matter.

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However, our measurements do neither allow to constrain the budget of this C T * accumulation, nor could we attribute the source of organic matter.
In contrast to shallow remineralisation processes, the deepening of the mixed layer that marked the end of the studied bloom event may facilitate the efficient transport of POC from the surface layer to depth. Focusing on the accumulation of remineralisation products beneath 150 m in the Gotland basin, a previous study revealed that -in accordance with the 480 main input of POC during the productive period -remineralisation rates exhibit a pronounced seasonality (Schneider et al., 2010). This seasonality was found to be most pronounced in the water layers closest to the sediment surface, suggesting that beneath 150 m the remineralisation takes place mainly at the sediment surface and is of minor importance during particle sinking through the deep water column. The pronounced seasonality further confirms that surface organic matter production and deep water oxygen consumption are indeed tightly coupled, despite a potential degradation of POC before export across 485 the permanent halocline.
We conclude that NCP estimates determined with the methods developed in this study are of direct relevance to quantify the drivers for deep water deoxygenation. However, a better understanding of the organic matter remineralisation processes would be required to close the budget of biogeochemical transformations. New observational platforms, such as recently deployed biogeochemical ARGO floats (Haavisto et al., 2018), will complement the existing SOOP infrastructure and help to provide 490 the required observational constraints throughout the water column.

Recommendations and caveats for NCP reconstruction from SOOP and model data
The good agreement between our best-guess and the TPD-based NCP reconstruction on July 24 (Fig. 6c) indicates that it is possible to determine NCP from surface pCO 2 observations and vertically resolved seawater temperature with little uncertainty.
For the NCP calculation based on surface pCO 2 observations from SOOP and modelled temperature profiles, we recommend 495 to: 1. Convert surface pCO 2 to C T * based on a mean A T estimate for the region under consideration.

Identify production pulses dominated by cyanobacteria as periods characterised by a decrease in C T * that occurs between
June and August.
3. Integrate observed surface C T * changes to the temperature penetration depth (TPD) estimated from modelled temperature profiles, rather than using a mixed layer depth (MLD) estimate.
4. Perform the integration individually for each production pulse and limit NCP reconstruction to periods characterised by a stable or shoaling thermocline.
The NCP reconstruction approach presented in this study was derived from observations covering a single bloom event within the Central Baltic Sea. In the lack of comparable comprehensive observational data that underlie our best-guess, the 520 applicability of this approach could not be tested for other regions or bloom events. However, the dynamics and intensity of the bloom event described here are comparable to previous, independent descriptions of cyanobacteria blooms. Therefore, it is assumed that underlying biogeochemical mechanisms are representative and that the NCP reconstruction approach can be applied to other cyanobacteria bloom events. Specifically, we assume that the findings represented here can be applied to evaluate past and future pCO 2 observations made on Finnmaid and other SOOP in the Central Baltic Sea without compromise.

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However, larger uncertainties should be expected when applying our NCP reconstruction approach to other regions outside the Central Baltic Sea.

Conclusions
In this study, the depth-integrated quantification of NCP that occurred during a cyanobacteria bloom in the Baltic Sea in 2018 is achieved through the interpretation of profiling measurements of pCO 2 that covered the entire bloom event. Furthermore, 530 it is demonstrated that this best-guess can be reconstructed with small bias from SOOP pCO 2 observations and modelled temperature profiles. Recommendations to apply our reconstruction approach to the comprehensive long-term record of surface pCO 2 data available for the Baltic Sea are given. The application of this approach will allow for the detection and attribution of trends in cyanobacteria NCP over decades. In particular the comparison of NCP estimates of bloom events that occurred under different environmental conditions will provide a better understanding of the controlling factors. Factors to be tested include 535 the environmental parameters used to constrain NCP (pCO 2 , SST, and TPD), but also additional observations of nutrients and phytoplankton composition routinely determined on SOOP Finnmaid and in the framework of the Baltic Sea monitoring program. The recently started initiative to deploy biogeochemical ARGO floats in the Baltic Sea will further aid to link surface NCP estimates and deep water deoxygenation, and thereby constrain biogeochemical budgets in the Baltic Sea. Ultimately, this knowledge will inform the design and monitoring of conservation measures aiming at a Good Environmental Status of the 540 Baltic Sea and potentially other regions.
Code and data availability.
Website: Following the concept of literature programming and relying on the R package workflowr (Blischak et al., 2019), the code, plain text comments, and graphical output of this study are compiled as a website available at: https://jens-daniel-mueller.github.io/BloomSail/.
Code and raw data: A release of the Github repository underlying the website and containing all code was tagged as "bg-2021-545 40_resubmission" and archived on https://zenodo.org/. All raw data required to run the analysis were uploaded manually to this archive.
Processed environmental data: Processed in situ observation of this study will be made available through https://www.pangaea.de/ upon acceptance of the manuscript.

A1 Sensor calibration
The CONTROS HydroC® CO 2 sensor used in this study (serial number CO 2 -0618-001) was calibrated in water by the manufacturer at 15°C before (June 2018) and after (October 2018) the deployment for a measuring range of 100 to 500 µatm. The pre-and post-deployment calibration polynomials met the 6 steps per calibration with an R 2 of 0.999999 (pre) and 0.999993 (post) at an RMSE of 0.13 µatm (pre) and 0.43 µatm (post). The time between the calibrations was about 107 days and the 555 sensor runtime during this interval was about 506 hours or little more than 21 days. The zero drift observed between the two calibrations was only 0.89 µatm.

A2 Sensor configuration and operation
The instrument periodically records zeroing values, during which the CO 2 within the gas stream is scrubbed by a soda lime cartridge. Zeroings of two minutes duration were recorded every five hours during the field deployment. A period of 600 560 seconds after the zeroing was flagged as a flush period, during which the sensor signal recovers to environmental conditions.
Recordings during the flush and zeroing period were removed before further biogeochemical interpretation.
For the majority of the measurements, the sensor was operated with a 8W-pump (SBE-5T; Sea-Bird Electronics, Bellevue, USA) and the logging interval was set to 1 second. Only for the first two cruise days on July 6 and 10, a 1W-pump (SBE-5M, Sea-Bird Electronics) was used and the logging interval set to 10 seconds.

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The downcast profiles were always recorded continuously and with a steady profiling speed of~2 m min −1 . The upcast profiles were either performed continuously as well, or with a stop to record an equilibrated reference pCO 2 value at a desired depth. Only continuous downcast profiles were used for biogeochemical interpretation.
Zeroing signals were recorded by the CTD unit from the analogue sensor output, as well as in the internal sensor memory.
Both records were used to ensure exact temporal match of the CTD and pCO 2 time series. Only pCO 2 data stored with higher 570 temporal resolution in the internal memory were used during further analysis.

A3 Data post-processing
A drift correction as discussed in Fietzek et al. (2014) was applied to the field data to improve the data quality. This postprocessing considers information from the pre-and post-deployment calibrations (i.e. concentration dependent or span drift) and the regular in situ zeroings (i.e. zero drift).

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The first 60 seconds within every zeroing interval were discarded to only consider smooth zero-gas measurements that are not affected by the signal drop from ambient pCO 2 to the zero value. Zero signals for every point of the deployment were obtained by linear interpolation of the zero measurements. In case of data gaps larger than 2 hours within the deployment data, the course of the 2 zero signals before or after the gap was linearly extrapolated forward or backward, respectively, instead of an interpolation over the time of the measuring gap. A concentration-dependent drift of the sensor was considered 580 by transforming the pre-into the post-deployment calibration polynomial according to the actual sensor runtime (and not according to the course of the zero measurements as applied within Fietzek et al. (2014)).
Approx. 100 unrealistic outliers were found within the sensor temperature record (T sensor parameter) of the HydroC®. These were identified to be electronic artefacts and the values replaced by the constant temperatures recorded before and after these events that only lasted a few seconds at most.

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A4 pCO 2 response time correction The pCO 2 response time correction applies a common "growth-law equation" and follows a two-step procedure (Miloshevich et al., 2004) that was previously successfully applied to pCO 2 data recorded with the same type of instrument as used in this study (Fiedler et al., 2013;Fietzek et al., 2014;Atamanchuk et al., 2015). In a first step, the actual in situ response times (τ ) of the sensor were determined by fitting an exponential function to the signal recovery following a zeroing (Sect. A4.1). In 590 a second step, the determined τ values were used to correct the signal delay (Sect. A4.2). A quality assessment of the pCO 2 response time correction is given in Sect. A4.3.

A4.1 Response time determination
In situ response times (τ ) were determined from pCO 2 data recorded during the flush period after each zeroing. Data recorded during the initial 20 seconds of each flush period were removed as those are affected by the mixing of residual gas volumes 595 inside the sensor. Individual τ values were determined by fitting the non-linear model pCO 2 (t) = pCO 2 (t end ) + (pCO 2 (t 0 ) − pCO 2 (t end )) · e (−dt/τ ) where pCO 2 (t) is the recorded pCO 2 at time t, pCO 2 (t 0 ) and pCO 2 (t end ) are the fitted pCO 2 values at the beginning and the end of the equilibration process, and dt is the time since the beginning of the equilibration process. In situ τ was determined for a fit interval length of 300 seconds. Flush periods were discarded when the mean of absolute residuals from the fit exceeded 600 1% of the final pCO 2 , a condition which indicated unstable environmental pCO 2 (e.g. due to unintended heaving of the sensor package).
Similar to previous studies, a decrease of τ with increasing in situ temperature was found. The dependence of τ on temperature was fitted with linear regression models, separately for the deployments with the 1W-and 8W-pump. The sensor was carefully cleaned after each cruise and no signs of a changing sensor response time over time as an indicative of fouling on the 605 sensor's membrane were detected.

A4.2 Correction procedure
For each recorded pCO 2 value, the corresponding τ was calculated from measured in situ temperature. The response time correction was then applied based on a rearranged version of equation A1: pCO 2,insitu (t i+1 ) = pCO 2,obs (t i+1 ) − pCO 2,obs (t i ) · e (−dt/τ ) 1 − e (−dt/τ ) (A2) Figure A1. Exemplary determination of the response time τ through fitting an exponential function (red curve) to the pCO2 signal recovery following a zeroing measurement. The determined response time τ and pCO2(t=τ ) are indicated by a vertical and horizontal line, respectively.
where pCO 2,insitu is the true in situ pCO 2 time series, pCO 2,obs the pCO 2 time series as recorded by the sensor, and τ the response time for the interval between t i and t i+1 . A rolling mean with a window width of 30 sec was applied to the response time corrected pCO 2,insitu time series to remove short term noise. Please note that throughout the rest of the manuscript pCO 2,insitu is referred to as pCO 2 .

A4.3 Quality assessment 615
The improvements by the response time correction were investigated based on the difference between up-and downcast pCO 2 profiles vertically gridded into 1m depth intervals. To focus this quality assessment on the conditions in near surface waters which are subject of this study, profiles were discarded which exceeded a maximum depth of 30 m and/or a maximum pCO 2 of 300 µatm. Those profiles were excluded only for the quality assessment (not for the biogeochemical interpretation) to avoid a bias through exposure to very high pCO 2 at greater depth. Furthermore, profiles were removed with a maximum number 620 of missing observations from two or more depth intervals, which occasionally occurred when a sensor zeroing started while profiling. Based on this subset of response time corrected pCO 2 profiles it was found that the mean absolute pCO 2 difference between the up-and downcast profile was <2.5 µatm averaged across the upper 5 m of the water column and <7.5 µatm across the upper 20 m. The highest offset was found at around 10 m water depth and results from the steep environmental pCO 2 gradient around the thermocline.  Lincoln, USA), where the peak area is determined. Comparison to measurements performed on certified reference materials (CRM Batch 173; Dickson et al., 2003) allows for the calculation of C T . Triplicated measurements were performed on each sample and a precision of 2 µmol kg −1 was achieved.
Total alkalinity (A T ) was analysed by open cell titration of 125 -140 g of sample. The method involves a two-stage titration.
After a first, single addition of hydrochloric acid to achieve a pH 4 -3.5, A T is determined during a continued, stepwise titration 635 to pH 3, during which pH is recorded potentiometrically (Dickson et al., 2007). Measurements were referenced to CRM batch 173 (Dickson et al., 2003).
C T * calculated for discrete samples refers to a classical alkalinity-normalised C T , and was defined as C T * = C T · A T,mean / A T . C T * derived from discrete samples or pCO 2 sensor data are directly comparable (Fig. 5c) because they are referenced to the same mean A T of the discrete samples (1720 µmol kg −1 ).

B2 Phytoplankton
Phytoplankton samples were fixed with Lugol solution within no more than 24 hours after sampling. Samples were stored dark, before being transported to IOW and analysed in the laboratory within no more than 3 months after sampling. Phytoplankton community composition and biomass were determined by the Utermöhl method (HELCOM, 2017), which relies on microscope counts and the conversion of cell shape and size to biomass units.
Appendix C: Net community production estimation C1 Conversion from pCO 2 to C T * The approach to estimate temporal changes (rather than absolute values) in the dissolved inorganic carbon concentration (C T ) from a pCO 2 time series was previously established and theoretically examined (Schneider et al., 2014, and references therein).
It relies on a fixed estimate of alkalinity (A T ) and is only applicable when noticeable internal changes in A T can be excluded, as 650 is the case in the Baltic Sea due to the absence of calcifying plankton (Tyrrell et al., 2008). To avoid confusion with measured or absolute C T values and for consistency with previous studies, the calculated variable is referred to as C T *.
To evaluate the applicability of this approach under the specific pCO 2 and temperature conditions observed in summer 2018, we calculated C T * changes between Jul 6 and 24 for a range of A T values covering three times the standard deviation of A T observations (Fig. B1). For assumed A T values of 1747 µmol kg −1 and 1693 µmol kg −1 , which is 1 standard deviation of the 655 observations (27 µmol kg −1 ) higher and lower than the mean A T (1720 µmol kg −1 ), the bias of the derived change in C T * amounts to ± 1.6 µmol kg −1 . This bias is <2% compared to the signal of interest, i.e. the absolute drawdown of C T * (89 µmol kg −1 ). Figure C1. Bias of changes in CT* as a function of the bias in mean AT used for calculation (see Fig. B1). Results correspond to the pCO2 and temperature conditions observed in this study and are expressed in absolute and relative units. Grey areas highlight ±1 standard deviation around the mean AT.
It should be noted that the bias assessment presented here reflects two types of errors, namely (i) the assignment of an erroneous mean A T value for the calculation and (ii) the lateral exchange of water masses with different A T but identical 660 initial pCO 2 during the observation period. The robustness of this approach to the latter aspect is the reason why pCO 2 observations are more suitable to determine NCP than direct C T measurements, when those are not normalised to corresponding A T measurements.
C2 Calculation of the vertical entrainment flux of C T * The vertical entrainment flux of C T * that occured across the 12 m integration depth layer between Aug 7 and 16 was estimated 665 assuming an instantaneous complete vertical mixing to 17 m water depth after Aug 7. For this scenario, the hypothetical homogeneous C T * concentration after the mixing event (C T *mix) equals the mean volume-weighted C T * concentration between 0 -17 m (Fig. C2). Furthermore, the entrainment flux (C T *flux) into the surface water column (0 -12 m) is equal to the concentration difference between observed C T * on Aug 7 and C T *mix, integrated from 12 to 17 m. Figure C2. Illustration of the approximation of the entrainment flux of CT* due to vertical mixing. (a) The estimated deepening of the mixed layer from 12 to 17 m water depth between Aug 7 and 16 is based on the observed changes in the temperature profiles. (b) Assuming a complete, instantaneous mixing of the water column after Aug 7, the hypothetical homogeneous concentration of CT* (CT*mix) can be used to approximate the entrainment flux of CT* (grey area). Figure C3. Individual profiles of CT* (left panels) and temperature (right) displayed separately for each cruise day (rows) and station (color).
Grey ribbons indicate the minimum and maximum values observed over the entire study period.
C4 Temperature penetration depth (TPD) concept Figure C4. Illustration of the temperature and CT* penetration depth concept, short TPD and CPD. Shown are exemplary profiles of incremental changes of (a) temperature and (b) CT* observed between the cruises on July 6 and 10. TPD and CPD (red horizontal lines) are defined as the depth-integrated positive (for temperature) and negative (for CT*) changes (grey areas) divided by the change at the surface.
TPD and CPD are expressed in units of metres.
Appendix D: SOOP Finnmaid pCO 2 For SOOP Finnmaid transects recorded between July 7 and July 16, pCO 2 data were not available from the LI-COR system because of technical failure. Therefore, data generated by the Los Gatos (LGR) system were used to fill the gap. Unfortunately, the comparison of LI-COR and LGR measurements before July 7 indicated a small leakage in the LGR system, which was later 675 also physically detected and fixed. The resulting difference between the two systems was clearly correlated with absolute pCO 2 , as expected from contamination with ambient air. For data from the transect on July 5, the linear regression model pCO 2,true = pCO 2,LGR + 0.038 * pCO 2,LGR -24.2 was fitted, assuming that the LI-COR system had delivered the "true" pCO 2,true before its failure. Assuming further that the effect of the contamination remained constant, this relationship was then applied to reconstruct pCO 2,true from pCO 2,LGR for the period without LI-COR data. To validate this adjustment, pCO 2,true was 680 also reconstructed from pCO 2,LGR on July 4 and compared to pCO 2 directly measured with the LI-COR system. The mean difference was below 2 µatm for the entire transect as well as for a data subset within the study region, giving confidence to the high accuracy of the adjusted pCO 2,true . It should be noted that the adjusted SOOP pCO 2 data recorded between July 7 and July 16 agree well with the in situ pCO 2 recorded by the sailing campaign, i.e. the standard deviations of all surface measurements in the study region overlap.
Author contributions. JDM was the lead author of this study and involved in all parts of it, in particular the conceptualization, funding acquisition, project administration, field sampling, data curation, visualisation and analysis, and manuscript writing. BS was involved in the conceptualization, formal analysis, and original draft preparation. UG generated and processed the GETM model data. PF supported the HydroC® sensor configuration and performed the data post processing. MBW and AR provided the atmospheric data and supported the field campaign logistically. NW was responsibly for the collection, analysis and interpretation of phytoplankton samples. SK designed and