Coccolithophore blooms boost particle fluxes in the Nordic Seas

Coccolithophore blooms boost particle fluxes in the Nordic Seas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Coccolithophore blooms boost particle fluxes in the Nordic Seas Rafael Rasse, Griet Neukermans This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6422717/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The biological gravitational pump is the primary mechanism exporting particulate organic carbon from the sunlit surface to the deep ocean, particularly inhigh-latitude regions. However, mesopelagic carbon budget deficits indicate additional, unaccounted-for mechanisms that enhance particle export. One hypothesis suggests that biominerals produced by coccolithophores—calcifying phytoplankton—increase the density and sinking speed of marine snow aggregates, thereby boosting carbon export. Yet, the extent of this effect remains unclear, due to limited in situ observations of the associated particle fluxes. Here, we use autonomous BioGeoChemical-Argo float and satellite observations to assess, at a basin scale, how coccolithophore blooms influence carbon export in the Nordic Seas. Our findings reveal that coccolithophore bloom intensity correlates with increased particle export and deep fluxes, with important implications for oceanic carbon sequestration. Earth and environmental sciences/Ocean sciences/Marine biology Earth and environmental sciences/Biogeochemistry/Carbon cycle Earth and environmental sciences/Ocean sciences/Marine chemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The biological gravitational pump (BGP) sequesters atmospheric carbon dioxide (CO₂) in the ocean interior through the sinking of particulate organic carbon (POC) 1,2 . Its strength is governed by three key factors: the amount of surface-produced POC available for export to the mesopelagic zone, globally estimated at 6-10 Pg C per year 3–5 ; the size and composition of sinking particles, which influence their sinking speed, depth penetration, and carbon storage time 6–8 ; and physical ocean features such as eddies and fronts which enhance vertical transport of carbon 2,9,10 . Understanding the relative importance of these drivers is critical to understanding the BGP’s role in the global carbon cycle . High-latitude oceans contribute approximately 40% of global POC export, underscoring their importance in oceanic carbon sequestration 4,5 . In these regions, the BGP operates seasonally, driven by phytoplankton blooms in the sunlit surface ocean in spring and summer 2 . In many high-latitude areas, the spring bloom is succeeded by a summer bloom of coccolithophores 11 —calcifying phytoplankton—that can produce substantial amounts of particulate inorganic carbon (PIC) 12–14 in the form of calcite platelets, called coccoliths. It has been hypothesized that PIC enhances the BGP by increasing aggregate density and sinking speed, facilitating deeper carbon transfer and storage 7,15,16 . Here, we examine this so-called ballast hypothesis in the Nordic Seas using BioGeoChemical-Argo (BGC-Argo) floats (Fig. 1) and ocean colour satellite observations. Testing the ballast hypothesis is indeed crucial, as coccolithophore blooms may significantly enhance POC export 17 . However, the timing and magnitude of mesopelagic particle fluxes associated with these blooms remain poorly constrained due to limitations in conventional carbon flux measurements 18 . BGC-Argo floats and satellite observations offer a powerful means to overcome these limitations, providing high-resolution insights into how coccolithophore blooms influence carbon fluxes. Using these tools, we tracked coccolithophore bloom timing and their associated mesopelagic carbon fluxes across the Nordic Seas. Our findings offer unprecedented insights into the interplay between coccolithophore blooms, BGP efficiency, and the influence of physical oceanographic features. Addressing these knowledge gaps is essential for refining oceanic carbon budgets and improving the accuracy of IPCC carbon flux projections, which currently vary by up to a factor of four for the year 2100 19 . 2. Sampling the Nordic Seas The Nordic Seas comprise the Greenland, Norwegian and Lofoten basins. These basins are characterized by permanent anticyclonic currents, receive warm and salty waters from the North Atlantic via the Norwegian current system, and are deeper than 3000 m (Fig. 1 ) 20 . Here, we used BGC-Argo float data recorded in the upper 1000 m of the water column from 14 floats operating in the basins of the Nordic Seas between June 2011 and September 2023 (Fig. 1 ; Table 1 S). This array of floats profiled at a temporal resolution of 5–10 days and vertical resolution of 3–10 m and collected 936 upward profiles from 1000 m depth to the surface over the periods of interest. Bio-optical sensor data processing and quality control is described in Section 4 . In addition, Sections 4.3 – 4.5 describe the methods used to track coccolithophore bloom timing, demarcate BGP operation events, identify particle assemblages with low and high PIC contributions, assess the relative abundance of aggregates and compute net carbon fluxes across the mesopelagic layer. Finally, the Norwegian and Lofoten basins show strong hydrography gradients due to the combined effect of anticyclonic currents and nearby intrusions of North Atlantic water masses (Fig. 1 ) 20 , 21 . The sub-surface water layers (1-400 m) of the latter two basins become warmer and less dense from west (north) to east (south) due to the downward intrusions of the Atlantic water masses (Fig. 1 S) 20 , which can ultimately increase the vertical transport of particles. We therefore combine an approach based on isopycnals and spiciness to detect this intrusion (Section 4.3 ), and assess its effect on the relative abundance of aggregates and net carbon fluxes. 3. Results and discussion 3.1. Biological gravitational pump events and coccolithophore blooms In the Nordic Seas, the biological gravitational pump (BGP) operated between May and September since the mixed layer depth (MLD) was equal to or shallower than the base of the productive layer ( z p ), and surface waters were strongly stratified during this period (e.g. Figure 2 c, Section 4.3 ). BGP events were characterized by the accumulation of surface POC, as surface chlorophyll-a levels ( chl S ) began rising in April and peaked between May and September, reaching median values between 0.42 mg m⁻³ and 1.14 mg m⁻³ (e.g. Figure 2 b). After the BGP events ended, surface chl S levels tended to drop again, reaching minimum values between November and March due to reduced organic matter production and dilution from deeper vertical mixing 22 (e.g. Figure 2 b-c). BGP events also coincided with the timing of coccolithophore blooms (April–September). For instance, coccolithophore blooms typically initiated between April and May and peaked in July (e.g. Figure 2 a), as evidenced by the time series of satellite-derived PIC and float-derived b bpS : chl S ratios, an optical proxy for coccolithophore blooms 23 (Section 4.4 ). By September, PIC levels had returned to their background values, indicating bloom termination (e.g. Figure 2 a, and Section 4 S for the remaining floats). We recorded five BGP events associated with intense coccolithophore blooms (four of which occurred in the Lofoten basin), characterized by peak PIC concentrations exceeding 1.0 mmol m − 3 , with b bpS ≥ 0.0033 m⁻¹ and b bpS : chl S ≥ 0.0063 m² mg⁻¹ (e.g. Figure 2 a-b). We also identified 25 other BGP events associated with PIC concentrations below 1.0 mmol m − 3 . Ten of these were influenced by downward intrusions of surface Atlantic waters into the mesopelagic layers (Fig. 3 ), primarily along the southern borders of the Norwegian and Lofoten basins (Fig. 1 a). We thus partitioned the dataset of 30 BGP events into three categories associated with: (1) intense coccolithophore blooms (5 events), (2) weak coccolithophore blooms without effects of physical intrusions, (reference category, 15 events) and (3) weak blooms with impacts of physical intrusions (10 events, see Table 3 S). We then compared observations among these categories to assess the impact of coccolithophore bloom intensity and physical intrusion on mesopelagic particle fluxes in the Nordic Seas (Sections 3.2 – 3.4 ). 3.2. Comparison of mesopelagic particle pulses among BGP event categories Here, we characterize the dynamics of mesopelagic particle pulses derived from the fragmentation of sinking aggregates 24 for each BGP event category. To this end, b bpS and chl S stocks at the surface were used as metrics of organic matter abundance contributing to export, while vertical profiles of b bpS were applied to track the intensity and mesopelagic fate of exported particles. We found that b bpS was elevated in the surface layers during all BGP events (e.g. Figure 2 b) due to high production of small particles 25 and their retention in the strongly stratified surface layers (e.g. Figure 2 c). Similar chlorophyll-a stocks were observed across all BGP event categories, with median values ranging from 28 to 31 mg m⁻² (Section 5S), indicating comparable organic matter production within the productive layer for the three event categories. Pulses of small particles were consistently recorded beneath z p over the BGP events (e.g. Figure 2 c). We however observed stark differences in the intensity and mesopelagic fate of these small-particle pulses among the BGP event categories. BGP events associated with intense coccolithophore blooms produced continuous, strong pulses of small particles that penetrated deep into the mesopelagic layer. Specifically, we observed that small particles progressively sank to 700-900m from the onset of the BGP event to August-September (e.g. Figure 4 b), as surface PIC levels dropped to minimum values (Fig. 4 a). As coccoliths of the bloom forming species E. huxleyi are too small to sink as individual particles 26 , this suggests that surface PIC was transferred to the mesopelagic either in sinking aggregates or in sinking faecal pellets. This hypothesis is supported by the highest absolute PIC fluxes associated with aggregates, as recorded by sediment traps at mesopelagic depths between July and September (Fig. 5 h). In contrast, BGP events associated with weak coccolithophore blooms without effects of physical intrusions generated episodic and less intense small-particle pulses that typically settled in the shallowest mesopelagic layers (< 500 m; Fig. 4 b). However, these pulses became more intense and reached greater depths as chlorophyll-a stocks increased (Fig. 4 a-b). These results suggest that the magnitude of pulses from small particles was highly sensitive to the amount of organic matter produced within the productive layer, z p . Lastly, weak coccolithophore blooms influenced by physical intrusions also caused episodic and fairly intense pulses of small particles that reached depths of 500–700 m (e.g. Figure 4 b). Here, particle penetration depths were determined by the maximum intrusion depth of North Atlantic water masses into the mesopelagic layers (e.g. Figure 3 b-c). These results show that the downward injection of North Atlantic water is a key mechanism that must be considered, along with the structural composition of the particles, as it can enhance particle pulses during weak bloom events. 3.3. Comparison of the relative abundance of mesopelagic large particles among BGP event categories Here, we explore differences among BGP categories on the abundance of sinking aggregates, which can be detected as spikes in the bulk signal of particle backscattering ( b bp ) profiles in the mesopelagic layer. Their relative abundance can be derived from the percentage of b bp -spikes along each b bp profile (see methods) 27 . Figure 5 c illustrates the percentage of mesopelagic b bp -spikes ( %spikes ) calculated for the three BGP categories, highlighting two key features. First, the highest percentage of mesopelagic b bp − spikes (10.6%, Fig. 5 a-c) was found during BGP events associated with intense blooms, while those with weak blooms—both with and without the influence of physical intrusions—had the second-highest (8.5%) and lowest (5.6%) abundances of mesopelagic aggregates, respectively (Fig. 5 a-c). These results suggest that intense coccolithophore blooms (PIC ≥ 1 mmol m⁻³) were the predominant factor that contributed to fostering the formation and export of PIC-enriched aggregates 28 , 29 . Second, the temporal pattern of %spikes aligned with those observed for small-particle pulses (Section 3.2 ). For BGP events with intense blooms, %spikes clearly increased over time, mirroring the behavior of small particle pulses (Fig. 4 b-c), and peaking 2–4 weeks after surface values of PIC peaked. These findings demonstrate that mesopelagic pulses of small particles originate from the fragmentation of surface-derived sinking aggregates 22 , 24 . In contrast, temporal changes in %spikes were rather featureless in the other BGP categories (Fig. 4 b-c). 3.4. Comparison of particle carbon fluxes among BGP event categories Our results show that the rate of increase in mesopelagic stocks of small particles and mesopelagic net POC fluxes 22 were significantly higher for BGP events associated with intense blooms than for weak blooms and physical intrusion categories (Fig. 4 d). BGP events associated with intense blooms generated POC fluxes that were 80–120% and 40–80% higher in the upper and lower mesopelagic layers, respectively, compared to the other two BGP categories (Fig. 5 d-f). As shown earlier, BGP events with intense blooms are also characterized by the highest abundance of sinking aggregates and the deepest pulses of particles (Sections 3.2 and 3.3 ), while sediment traps show high PIC:POC rain ratios (0.21–0.48) and strong linear correlations between POC and PIC fluxes during coccolithophore bloom conditions (Fig. 5 g-h). Alltogether, these observations suggest that intense coccolithophore blooms strengthen the BGP by enhancing the export of large and small particles to the ocean interior, thereby providing support for the so-called ballast hypothesis, which states that PIC particles are a major driver of POC flux by increasing the density of aggregates and thus their sinking speed 15 , or by physically protecting the more labile POC in aggregates from degradation while sinking to the deep ocean 16 . 3.5. Implications of coccolithophore blooms on the strength of the BGP Our results demonstrate that intense coccolithophore blooms (PIC ≥ 1 mmol m⁻³) in the Nordic Seas can double the export of POC into mesopelagic layers where this carbon can be stored for decades to 100 years, compared to areas with weaker blooms without physical intrusions (Fig. 5 ). Sediment traps further reveal high PIC:POC rain ratios and strong positive linear correlations between POC and PIC fluxes in the mesopelagic layers of the Nordic Seas during BGP events, demonstrating a strong contribution of PIC to the overall particle flux (Fig. 5 ). Another study in the Southern Ocean has shown that intense coccolithophore blooms can triple mesopelagic carbon export compared to other phytoplankton types 17 . Notably, carbon flux and its transfer efficiency to the deep ocean were also found to be linked to PIC fluxes rather than to the net production of organic particles in the surface waters 30 , the latter being the primary metric currently used by climate models to estimate carbon fluxes 31 . These findings underscore the critical role of coccolithophore blooms in driving carbon fluxes and highlight the importance of including mineral ballasting mechanisms in climate models, especially given that only 25% currently include this effect 19 . Incorporating mineral ballasting into climate models is essential, as regions influenced by coccolithophore blooms— primarily located in the high-latitude areas of the North Atlantic and Southern Ocean—cover more than 50% of the ocean surface 11 . These two regions alone may contribute up to 50% of global POC export 32 , 33 , even without accounting for the effects of coccolithophore blooms or physical intrusions. Furthermore, mesopelagic carbon demand is estimated to be two to three times higher than the supply from sinking POC 34 , mirroring the two- to threefold increases in carbon flux observed during intense coccolithophore bloom events observed in this study as well as in the Southern Ocean. We suggest that explicitly representing ballast effects associated with coccolithophore blooms in climate models may help to reconcile current discrepancies between POC sources and demand. We recommend that this incorporation should account for both the intensity of blooms (intense vs. weak) and the role of physical intrusions, as both factors significantly influence mesopelagic carbon fluxes. Incorporating these mechanisms could help resolve the large spread in carbon flux projections among IPCC climate models that include the ballast effect—some of which still differ by up to a factor of four in their year-2100 estimates 19 . This highlights the need for more mechanistic, consensus-driven approaches to modelling the ballast effect and improving climate predictions. 4. Methodology 4.1. Bio-optical and physicochemical data recorded by the BGC-Argo floats The floats were mainly equipped with two sensors: (1) a SBE-41 CP conductivity–T–depth sensor (Sea-Bird Scientific), and (2) a WETLabs ECO Triplet Puck. These sensors measured upward profiles of (1) temperature (T), conductivity, and depth, and (2) chlorophyll-a fluorescence (F chl ) and optical backscattering at 700 nm. Backscattering and fluorescence measurements were converted into bulk signals of particle backscattering ( b bp ) and of ( chl) concentration following standard procedures 35 , 36 . This standard protocol included the non-photochemical quenching correction for chl 37 . We used quality-controlled Argo Sprof files to compute carbon fluxes and Bprof files to compute spikes from b bp data. These files include data corrected following state-of-the-art protocols (SBE-4, chl , and b bp ) 35 , 36 . 4.2. Defining the water layers The mixed-layer depth (MLD) was calculated as the depth at which density differed from 0.10 kg m − 3 relative to the density recorded at 1 m depth 38 . We used a chlorophyll threshold of 0.025 mg m − 3 to define the base of the productive layer ( z p ) 39 . We applied chlorophyll to define the productive layer where living phytoplankton are present because our technique to compute export requires that no production occurs below a given layer of the water column 22 . Indeed, it has been demonstrated that chlorophyll is an accurate metric for defining the base of the productive layer to compute carbon fluxes 40 . We define the mesopelagic layer and its progressively deeper water layers as those respectively found between (1) z p -1000 m and (2) z p + z i and 1000 m depth, where z i refers to a progressive increment of 50 m beneath the productive layer (i.e. zi = 50, 100, 150…600 m). We defined mesopelagic water layers to calculate stocks and fluxes of small particles 22 , and abundance of large particles 41 (Section 4.5 ). 4.3. Biological gravitational pump events and physical intrusions The biological gravitational pump (BGP) events were defined as those when the mixed layer depth (MLD) was equal to or shallower than the base of the productive layer ( z p , e.g. Figure 2 c). We applied this metric to define the BGP events because it excludes times when the mixed layer pump was operational (i.e. PL ≥ MLD) 42 . Since the BGP coincides with periods of strong surface stratification, we also computed the difference in potential density (σ ɵ ) between 300 m and 10 m depths to track and link the temporal dynamics of surface stratification with the BGP events 43 . Spiciness (π) along the isopycnal 27.6 kg m⁻³ was ultimately used to identify the BGP events influenced by vertical intrusions of surface Atlantic water masses into the mesopelagic. Spiciness refers to variations in seawater’s density-compensated temperature and salinity gradients, commonly used to contrast warm, salty water masses (i.e. spicy, mainly formed in the tropics) with cold, fresh ones in high altitudes 20 . Thus, spiciness along the isopycnal 27.6 kg m⁻³ allowed us to identify physical intrusions of surface waters by defining the vertical extent of warmer, saltier Atlantic water masses (e.g. those above the isopycnal 27.6 kg m⁻³ and π ≥ 0.8 kg m⁻³), which overlay the cold, fresh deep-water masses of the Nordic Seas (e.g. those beneath the isopycnal 27.6 kg m⁻³ and π < 0.8 kg m⁻³) 20 . 4.4. Timing and bio-optics of coccolithophore blooms We used the GlobColour ( https://hermes.acri.fr ) daily PIC satellite product with a spatial resolution of 4 km. We extracted time series of satellite PIC that matched with the sampling time and location of the floats following Terrats et al. 2020. Briefly, matchups of satellite PIC were averaged for temporal and spatial windows of 9 days and 20 x 20 km 23 . Coccolithophore bloom phenology metrics (bloom start, peak, and end) were calculated as described in Hopkins et al., 2015 for defining the entire blooming periods. As a complementary approach for assessing the bulk composition of particles under coccolithophore blooming conditions (i.e., high and low PIC levels relative to organic matter), we determined the bio-optical characteristics of the particles using metrics of particle backscattering and chlorophyll-a due to small particles ( b bpS and chl S , respectively). To this end, we first applied a smoothing function to each profile of b bp and chl to extract b bpS and chl S , respectively. Details regarding the smoothing method are given in section 4.5 for b bp and section 2 S for chl. The b bpS and b bpS : chl S values, with thresholds of ≥ 0.0033 m − 1 and ≥ 0.0063 m 2 mg − 1 at the surface, were used to identify particle assemblages with a very high content of PIC per unit organic matter 23 . 4.5. Quantifying large and small particles The un-spiked and spiked components of the b bp signal are quantitative metrics of the concentration of small (0.2–20 µm) and large particles (~ 100 µm) in the mesopelagic layer 44 , 45 . We partitioned the b bp signal due to b bp s and large particles ( b bpL ) by applying a smoothing function with window sizes of 3 and 7 points to each profile with 10, and 3-m vertical resolution, respectively 45 . The resulting baseline refers to b bp s while the residual includes b bpL and a spike blank (Fig. 4 S, residual = b bp - b bp s ). Float-specific spike blanks were calculated following Briggs et al., 2020 and their values ranged between [1.3-2.0]x10 − 4 m − 1 (1.6 ± 0.2 m − 1 x10 − 4 ). b bpL was computed as the residual values above the spike blank and beneath 0.0028 m − 1 . We excluded residual values above 0.0028 m − 1 likely caused by zooplankton 46 . We finally used b bpS and b bpL to correspondingly calculate the stocks and fluxes of small particles and the abundance of large particles as described below. 4.5.1. Mesopelagic abundance of large particles The vertical resolution of float profiles was unfortunately insufficient to examine particle fragmentation and sinking speeds directly 24 . Instead, we estimated the relative abundance of large particles in the mesopelagic ( %Spikes ) quantified as the number of b bp spikes divided by the number of b b p measurements along each profile 41 . This metric provides us with insights regarding the influence that coccolithophore blooms have on the abundance of large particles. 4.5.2. Mesopelagic stocks and fluxes of small particles We translated b bpS into POC units by combining published linear empirical correlations 47 , 48 and exponential models 49 , 50 (Section 3 S). Depth-integrated stocks of POC were calculated by vertically integrating mean POC values over sections of the water column that included: (1) the mesopelagic layer ( \(\:{iPOC}_{zp}^{1000}\) ) and (2) progressively deeper sections of the mesopelagic layer as defined in section 4.2 . ( \(\:{iPOC}_{zp+zi}^{1000}\) ) 22 . Net fluxes of small POC (E zi ) were calculated in the defined water layers by regressing the stocks of POC 22 from the start of the BGP event until the day with the maximum stock value (e.g. Figure 4 d). Fluxes reported here refer to the accumulation rates of small particles derived from the fragmentation of the larger ones under non-stationary state conditions. Fluxes due to large particles were not computed because vertical and temporal resolution of bio-optical profiles was too low to work out the sinking speed of large particles. 4.6. Absolute fluxes of POC and PIC from sediment traps We used POC and PIC flux data recorded by sediment traps deployed at 1000 m in two Nordic Seas basins from 1990 to 1996 51 (Fig. 1 a). This data served as complementary information to support our hypothesis. We selected fluxes recorded at 1000 m to minimize errors in POC flux estimates associated with sediment trap sampling methods at shallower depths 3 , 52 . Declarations Acknowledgements This work is a contribution to the following projects: CarbOcean (European Research Council under the European Union’s Horizon 2020 research and innovation programme Grant agreement No. 853516) and DTO-BioFlow (European Union’s under the Horizon Programme, Grant Agreement No. 101112823). Data availability The Argo Program is part of the Global Ocean Observing System. All BGC-Argo data are available at ftp://ftp.ifremer.fr/ifremer/argo/dac/. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). Daily-merged ocean-colour satellite data were downloaded from the GlobColour project (ftp://ftp.hermes.acri.fr). POC and PIC flux data recorded by sediment traps were downloaded from the PANGAEA repository (https://doi.pangaea.de/10.1594/PANGAEA.807946). 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Earth’s Future vol. 12 Preprint at https://doi.org/10.1029/2023EF004375 (2024). Boyd, P. W. et al. The role of biota in the Southern Ocean carbon cycle. Nature Reviews Earth and Environment vol. 5 390–408 Preprint at https://doi.org/10.1038/s43017-024-00531-3 (2024). Sanders, R. et al. The Biological Carbon Pump in the North Atlantic. Prog Oceanogr 129 , (2014). Burd, A. B. et al. Assessing the apparent imbalance between geochemical and biochemical indicators of meso- and bathypelagic biological activity: What the @$#! is wrong with present calculations of carbon budgets? Deep Sea Res 2 Top Stud Oceanogr 57 , (2010). Schmechtig Catherine, P. A. C. H. D. F. D. G. B. E. Processing Bio-Argo particle backscattering at the DAC level. Argo data management. . https://doi.org/10.13155/39459 (2018). Schmechtig, C., Claustre, H., Poteau, A. & D’Ortenzio, F. Bio-Argo quality control manual for the Chlorophyll-A concentration. Argo Data Management (2014). Xing, X. et al. Quenching correction for in vivo chlorophyll fluorescence acquired by autonomous platforms: A case study with instrumented elephant seals in the Kerguelen region (Southern Ocean). Limnol Oceanogr Methods 10 , (2012). de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A. & Iudicone, D. Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology. Journal of Geophysical Research C: Oceans 109 , (2004). Rasse, R. & Dall’Olmo, G. Do Oceanic Hypoxic Regions Act as Barriers for Sinking Particles? A Case Study in the Eastern Tropical North Atlantic. Global Biogeochem Cycles (2019) doi:10.1029/2019GB006305. Buesseler, K. O., Boyd, P. W., Black, E. E. & Siegel, D. A. Metrics that matter for assessing the ocean biological carbon pump. Proceedings of the National Academy of Sciences of the United States of America vol. 117 Preprint at https://doi.org/10.1073/pnas.1918114117 (2020). Terrats, L. et al. BioGeoChemical-Argo Floats Reveal Stark Latitudinal Gradient in the Southern Ocean Deep Carbon Flux Driven by Phytoplankton Community Composition. Global Biogeochem Cycles 37 , (2023). Dall’Olmo, G., Dingle, J., Polimene, L., Brewin, R. J. W. & Claustre, H. Substantial energy input to the mesopelagic ecosystem from the seasonal mixed-layer pump. Nat Geosci 9 , (2016). Lozier, M. S., Dave, A. C., Palter, J. B., Gerber, L. M. & Barber, R. T. On the relationship between stratification and primary productivity in the North Atlantic. Geophys Res Lett 38 , (2011). Organelli, E. et al. The open-ocean missing backscattering is in the structural complexity of particles. Nat Commun 9 , (2018). Briggs, N. et al. High-resolution observations of aggregate flux during a sub-polar North Atlantic spring bloom. Deep Sea Res 1 Oceanogr Res Pap (2011) doi:10.1016/j.dsr.2011.07.007. Haëntjens, N. et al. Detecting Mesopelagic Organisms Using Biogeochemical-Argo Floats. Geophys Res Lett 47 , (2020). Rasse, R. et al. Evaluating optical proxies of particulate organic carbon across the surface Atlantic ocean. Front Mar Sci 4 , (2017). Stramski, D. et al. Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans. Biogeosciences 5 , (2008). Galí, M., Falls, M., Claustre, H., Aumont, O. & Bernardello, R. Bridging the gaps between particulate backscattering measurements and modeled particulate organic carbon in the ocean. Biogeosciences 19 , (2022). Bol, R., Henson, S. A., Rumyantseva, A. & Briggs, N. High-Frequency Variability of Small-Particle Carbon Export Flux in the Northeast Atlantic. Global Biogeochem Cycles 32 , (2018). Torres Valdés, S., Painter, S. C., Martin, A. P., Sanders, R. & Felden, J. Data compilation of fluxes of sedimenting material from sediment traps in the Atlantic ocean. Earth Syst Sci Data 6 , (2014). Francois, R., Honjo, S., Krishfield, R. & Manganini, S. Factors controlling the flux of organic carbon to the bathypelagic zone of the ocean. Global Biogeochem Cycles (2002) doi:10.1029/2001gb001722. Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6422717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":442887231,"identity":"a7afc9e9-be93-4e86-b5dd-f613497fa891","order_by":0,"name":"Rafael Rasse","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYFCCA2BSDsx8QIoWYzAzgRS7EhtAJFFazBvPPvz4449N+vywww+BttjJ6TYQ0CJz4LixNG9bWu7G22kGQC3JxmYHCGiRYDjGIM3YcDh34+wEkJYDiduI0ML888ef/+mGs9M/EK2FTYKH7UCCvHQO8bawWfO2JRtukM4pOJBgQIxfJI4x3/zxx05efnb65g8fKuzkCGphkICqMADTBoSUgwB/A4SWbyBG9SgYBaNgFIxIAAByx0Y+K9MTIwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9082-3315","institution":"Ghent University","correspondingAuthor":true,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Rasse","suffix":""},{"id":442887232,"identity":"ac81c9eb-25e0-4969-b913-a66aa1958ee0","order_by":1,"name":"Griet Neukermans","email":"","orcid":"https://orcid.org/0000-0002-8258-3590","institution":"Universiteit Gent","correspondingAuthor":false,"prefix":"","firstName":"Griet","middleName":"","lastName":"Neukermans","suffix":""}],"badges":[],"createdAt":"2025-04-10 19:05:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6422717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6422717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80701451,"identity":"93c2e206-66dd-4257-b5d6-5e7454cec84a","added_by":"auto","created_at":"2025-04-16 07:43:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":646220,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Main basins of the Nordic Seas. NB: Norwegian basin. LB: Lofoten basin; GB: Greenland basin. Anticyclonic permanent currents are indicated as black arrows for each basin. Red arrows indicate the main pathways of North Atlantic water masses. NWAFC: Norwegian Atlantic Frontal Current. NwASC: Norwegian Atlantic Slope Current. The two grey triangles in (a) are the locations of the neutrally buoyant sediment traps. \u0026nbsp;(b) Sampling locations of the profiling floats collected in the Nordic Seas over biological gravitational pump sampled between 2011 and 2023. The background map in (b) shows \u0026nbsp;summer climatology satellite-derived PIC concentrations (2002–2025, data source: https://oceandata.sci.gsfc.nasa.gov/l3/).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6422717/v1/b973926298b7cc12fa45b426.png"},{"id":80701759,"identity":"b1a398e0-3f08-4540-be77-bbe500d68d92","added_by":"auto","created_at":"2025-04-16 07:51:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":287558,"visible":true,"origin":"","legend":"\u003cp\u003eMatchups of satellite data and bio-optical parameters recorded by float 6900799. (a) Matchups of satellite PIC (purple bars). (b) \u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e, particle backscattering (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e\u003csub\u003e, \u003c/sub\u003eorange bars) and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e:chl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e ratios (grey line with circles) recorded by the float at the surface. (c) Time series of \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e\u003csub\u003e \u003c/sub\u003ein the entire water column. Gray line in (c) describes the temporal evolution of the stratification index (Δσ\u003csub\u003eθ\u003c/sub\u003e = σ\u003csub\u003eθ300 - \u003c/sub\u003eσ\u003csub\u003eθ10\u003c/sub\u003e). Shaded areas demarcate the start and end of coccolithophore blooms in (a-b) and BGP periods in (c). (c) blue line is the MLD, green line is the productive layer (\u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6422717/v1/48d9ce331065981151c958b2.png"},{"id":80701455,"identity":"87b7a4fd-67a1-4e72-829f-6cda5534c941","added_by":"auto","created_at":"2025-04-16 07:43:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":419457,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of (a) chlorophyll-a of small particles (\u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e), (b) spiciness (π), and (c) small particle backscattering (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e). The first (left) panel represents the reference category (weak blooms without physical intrusions), while the remaining three panels show examples of BGP events (indicated by transparent shadows) influenced by weak coccolithophore blooms and the vertical intrusion of North Atlantic waters into the mesopelagic.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6422717/v1/e59baa5b679eb18503261327.png"},{"id":80701454,"identity":"9db5ffe8-3b63-46af-ad61-52fb069dddd0","added_by":"auto","created_at":"2025-04-16 07:43:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":287713,"visible":true,"origin":"","legend":"\u003cp\u003eParticle stocks and fluxes in each BGP event category. (a) Matchups of satellite PIC (purple bars) and stocks of chlorophyll-a due to small particles from float data (s\u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, \u003c/em\u003egreen line\u003cem\u003e)\u003c/em\u003e within the productive layer. (b) \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e data recorded by floats in the entire water column. (c) Percentage spikes in the mesopelagic layer, and (d) \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e\u003csub\u003e \u0026nbsp;\u003c/sub\u003eand small-particle POC\u003csub\u003e \u003c/sub\u003estocks in different depth layers of the mesopelagic.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6422717/v1/141486a6eddc478f2a06de22.png"},{"id":80701453,"identity":"1cbe8890-8643-4f5d-a977-b7fb9bf50fa7","added_by":"auto","created_at":"2025-04-16 07:43:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135411,"visible":true,"origin":"","legend":"\u003cp\u003e(a-c)\u003cstrong\u003e \u003c/strong\u003eFrequency distribution of the \u003cem\u003e%spikes\u003c/em\u003eand (d-f) net seasonal fluxes of POC computed by all floats for the three categories of BGP events. Blue, red, and purple colors in (a-f) respectively refers to the BGP events influenced by weak blooms, physical-intrusion, and intense blooms (g) PIC:POC rain ratios, and (h) linear correlations between POC and PIC fluxes recorded by historical (1990-1996) sediment traps deployed at 1000m during BGP events. Solid and dotted lines in (h) represent the best linear fits to the data and the 1:1 relationships, respectively.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6422717/v1/193a6647937675fd5b58641b.png"},{"id":85886930,"identity":"86b5ef9a-cef4-4bfb-af50-890cfe26ff29","added_by":"auto","created_at":"2025-07-02 18:03:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2796983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6422717/v1/cf9b06f5-8163-4618-9c91-3ed8c58fd776.pdf"},{"id":80701461,"identity":"460cf2d3-1b7e-405b-95ad-1b0731a6a1d8","added_by":"auto","created_at":"2025-04-16 07:43:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7372454,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6422717/v1/fd19737611c285f8d741aeb0.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Coccolithophore blooms boost particle fluxes in the Nordic Seas","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe biological gravitational pump (BGP) sequesters atmospheric carbon dioxide (CO₂) in the ocean interior through the sinking of particulate organic carbon (POC)\u003csup\u003e1,2\u003c/sup\u003e. Its strength is governed by three key factors: the amount of\u0026nbsp;surface-produced\u0026nbsp;POC\u0026nbsp;available for export\u0026nbsp;to the mesopelagic\u0026nbsp;zone, globally estimated at 6-10 Pg C per year\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e; the \u003cstrong\u003esize and composition\u003c/strong\u003e of sinking particles, which influence their sinking speed, depth penetration, and carbon storage time\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e; and\u0026nbsp;physical\u0026nbsp;ocean\u0026nbsp;features\u0026nbsp;such as eddies and fronts which enhance vertical\u0026nbsp;transport of\u0026nbsp;carbon\u003csup\u003e2,9,10\u003c/sup\u003e. Understanding the relative importance of these drivers is critical to understanding the BGP\u0026rsquo;s role in the \u003cstrong\u003eglobal carbon cycle\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHigh-latitude oceans contribute approximately 40% of global POC export, underscoring their importance in oceanic carbon sequestration\u003csup\u003e4,5\u003c/sup\u003e. In these regions, the BGP operates seasonally, driven by phytoplankton blooms in the sunlit surface ocean in spring and summer \u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;In many high-latitude areas, the spring bloom is succeeded by a\u0026nbsp;summer\u0026nbsp;bloom of\u0026nbsp;coccolithophores\u003csup\u003e11\u003c/sup\u003e\u0026mdash;calcifying phytoplankton\u0026mdash;that can produce\u0026nbsp;substantial\u0026nbsp;amounts\u0026nbsp;of particulate inorganic carbon (PIC)\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003ein the form of calcite platelets, called coccoliths. It has been hypothesized\u0026nbsp;that PIC enhances the\u0026nbsp;BGP\u0026nbsp;by\u0026nbsp;increasing\u0026nbsp;aggregate\u0026nbsp;density and sinking speed,\u0026nbsp;facilitating deeper carbon transfer and storage\u003csup\u003e7,15,16\u003c/sup\u003e. Here, we examine this so-called ballast hypothesis in the Nordic Seas using BioGeoChemical-Argo (BGC-Argo) floats (Fig. 1) and ocean colour satellite observations. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTesting the ballast hypothesis is indeed crucial, as coccolithophore blooms may significantly enhance POC export\u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;However, the timing and magnitude of mesopelagic particle fluxes associated with these blooms remain poorly constrained due to limitations in conventional carbon flux measurements\u003csup\u003e18\u003c/sup\u003e. BGC-Argo floats and satellite observations offer a powerful means to overcome these limitations, providing high-resolution insights into how coccolithophore blooms influence carbon fluxes.\u003c/p\u003e\n\u003cp\u003eUsing these tools, we tracked coccolithophore bloom timing and their associated mesopelagic carbon fluxes across the Nordic Seas. Our findings offer unprecedented insights into the interplay between coccolithophore blooms, BGP efficiency, and the influence of physical oceanographic features. Addressing these knowledge gaps is essential for refining oceanic carbon budgets and improving the accuracy of IPCC carbon flux projections, which currently vary by up to a factor of four for the year 2100\u003csup\u003e\u0026nbsp;19\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Sampling the Nordic Seas","content":"\u003cp\u003eThe Nordic Seas comprise the Greenland, Norwegian and Lofoten basins. These basins are characterized by permanent anticyclonic currents, receive warm and salty waters from the North Atlantic via the Norwegian current system, and are deeper than 3000 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Here, we used BGC-Argo float data recorded in the upper 1000 m of the water column from 14 floats operating in the basins of the Nordic Seas between June 2011 and September 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS). This array of floats profiled at a temporal resolution of 5\u0026ndash;10 days and vertical resolution of 3\u0026ndash;10 m and collected 936 upward profiles from 1000 m depth to the surface over the periods of interest. Bio-optical sensor data processing and quality control is described in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In addition, Sections \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4.5\u003c/span\u003e describe the methods used to track coccolithophore bloom timing, demarcate BGP operation events, identify particle assemblages with low and high PIC contributions, assess the relative abundance of aggregates and compute net carbon fluxes across the mesopelagic layer.\u003c/p\u003e \u003cp\u003eFinally, the Norwegian and Lofoten basins show strong hydrography gradients due to the combined effect of anticyclonic currents and nearby intrusions of North Atlantic water masses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The sub-surface water layers (1-400 m) of the latter two basins become warmer and less dense from west (north) to east (south) due to the downward intrusions of the Atlantic water masses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003eS)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, which can ultimately increase the vertical transport of particles. We therefore combine an approach based on isopycnals and spiciness to detect this intrusion (Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e), and assess its effect on the relative abundance of aggregates and net carbon fluxes.\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Biological gravitational pump events and coccolithophore blooms\u003c/h2\u003e \u003cp\u003eIn the Nordic Seas, the biological gravitational pump (BGP) operated between May and September since the mixed layer depth (MLD) was equal to or shallower than the base of the productive layer (\u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e), and surface waters were strongly stratified during this period (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e). BGP events were characterized by the accumulation of surface POC, as surface chlorophyll-a levels (\u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e) began rising in April and peaked between May and September, reaching median values between 0.42 mg m⁻\u0026sup3; and 1.14 mg m⁻\u0026sup3; (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). After the BGP events ended, surface \u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e levels tended to drop again, reaching minimum values between November and March due to reduced organic matter production and dilution from deeper vertical mixing\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-c).\u003c/p\u003e \u003cp\u003eBGP events also coincided with the timing of coccolithophore blooms (April\u0026ndash;September). For instance, coccolithophore blooms typically initiated between April and May and peaked in July (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), as evidenced by the time series of satellite-derived PIC and float-derived \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e:\u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e ratios, an optical proxy for coccolithophore blooms\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e (Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e). By September, PIC levels had returned to their background values, indicating bloom termination (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, and Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003eS for the remaining floats).\u003c/p\u003e \u003cp\u003eWe recorded five BGP events associated with intense coccolithophore blooms (four of which occurred in the Lofoten basin), characterized by peak PIC concentrations exceeding 1.0 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, with \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e \u0026ge; 0.0033 m⁻\u0026sup1; and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e:\u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e \u0026ge; 0.0063 m\u0026sup2; mg⁻\u0026sup1; (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). We also identified 25 other BGP events associated with PIC concentrations below 1.0 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. Ten of these were influenced by downward intrusions of surface Atlantic waters into the mesopelagic layers (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e), primarily along the southern borders of the Norwegian and Lofoten basins (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). We thus partitioned the dataset of 30 BGP events into three categories associated with: (1) intense coccolithophore blooms (5 events), (2) weak coccolithophore blooms without effects of physical intrusions, (reference category, 15 events) and (3) weak blooms with impacts of physical intrusions (10 events, see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eS). We then compared observations among these categories to assess the impact of coccolithophore bloom intensity and physical intrusion on mesopelagic particle fluxes in the Nordic Seas (Sections \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Comparison of mesopelagic particle pulses among BGP event categories\u003c/h2\u003e \u003cp\u003eHere, we characterize the dynamics of mesopelagic particle pulses derived from the fragmentation of sinking aggregates\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e for each BGP event category. To this end, \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e stocks at the surface were used as metrics of organic matter abundance contributing to export, while vertical profiles of \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e were applied to track the intensity and mesopelagic fate of exported particles. We found that \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e was elevated in the surface layers during all BGP events (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) due to high production of small particles\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and their retention in the strongly stratified surface layers (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Similar chlorophyll-a stocks were observed across all BGP event categories, with median values ranging from 28 to 31 mg m⁻\u0026sup2; (Section 5S), indicating comparable organic matter production within the productive layer for the three event categories.\u003c/p\u003e \u003cp\u003ePulses of small particles were consistently recorded beneath \u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e over the BGP events (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). We however observed stark differences in the intensity and mesopelagic fate of these small-particle pulses among the BGP event categories. BGP events associated with intense coccolithophore blooms produced continuous, strong pulses of small particles that penetrated deep into the mesopelagic layer. Specifically, we observed that small particles progressively sank to 700-900m from the onset of the BGP event to August-September (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), as surface PIC levels dropped to minimum values (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). As coccoliths of the bloom forming species \u003cem\u003eE. huxleyi\u003c/em\u003e are too small to sink as individual particles\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, this suggests that surface PIC was transferred to the mesopelagic either in sinking aggregates or in sinking faecal pellets. This hypothesis is supported by the highest absolute PIC fluxes associated with aggregates, as recorded by sediment traps at mesopelagic depths between July and September (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003eIn contrast, BGP events associated with weak coccolithophore blooms without effects of physical intrusions generated episodic and less intense small-particle pulses that typically settled in the shallowest mesopelagic layers (\u0026lt;\u0026thinsp;500 m; Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). However, these pulses became more intense and reached greater depths as chlorophyll-a stocks increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-b). These results suggest that the magnitude of pulses from small particles was highly sensitive to the amount of organic matter produced within the productive layer, \u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eLastly, weak coccolithophore blooms influenced by physical intrusions also caused episodic and fairly intense pulses of small particles that reached depths of 500\u0026ndash;700 m (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Here, particle penetration depths were determined by the maximum intrusion depth of North Atlantic water masses into the mesopelagic layers (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c). These results show that the downward injection of North Atlantic water is a key mechanism that must be considered, along with the structural composition of the particles, as it can enhance particle pulses during weak bloom events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Comparison of the relative abundance of mesopelagic large particles among BGP event categories\u003c/h2\u003e \u003cp\u003eHere, we explore differences among BGP categories on the abundance of sinking aggregates, which can be detected as spikes in the bulk signal of particle backscattering (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e) profiles in the mesopelagic layer. Their relative abundance can be derived from the percentage of \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-spikes\u003c/em\u003e along each \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e profile (see methods)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ec illustrates the percentage of mesopelagic \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-spikes\u003c/em\u003e (\u003cem\u003e%spikes\u003c/em\u003e) calculated for the three BGP categories, highlighting two key features. First, the highest percentage of mesopelagic \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u0026minus;\u003c/sub\u003e\u003cem\u003espikes\u003c/em\u003e (10.6%, Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c) was found during BGP events associated with intense blooms, while those with weak blooms\u0026mdash;both with and without the influence of physical intrusions\u0026mdash;had the second-highest (8.5%) and lowest (5.6%) abundances of mesopelagic aggregates, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). These results suggest that intense coccolithophore blooms (PIC\u0026thinsp;\u0026ge;\u0026thinsp;1 mmol m⁻\u0026sup3;) were the predominant factor that contributed to fostering the formation and export of PIC-enriched aggregates\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSecond, the temporal pattern of \u003cem\u003e%spikes\u003c/em\u003e aligned with those observed for small-particle pulses (Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e). For BGP events with intense blooms, \u003cem\u003e%spikes\u003c/em\u003e clearly increased over time, mirroring the behavior of small particle pulses (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c), and peaking 2\u0026ndash;4 weeks after surface values of PIC peaked. These findings demonstrate that mesopelagic pulses of small particles originate from the fragmentation of surface-derived sinking aggregates\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In contrast, temporal changes in \u003cem\u003e%spikes\u003c/em\u003e were rather featureless in the other BGP categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Comparison of particle carbon fluxes among BGP event categories\u003c/h2\u003e \u003cp\u003eOur results show that the rate of increase in mesopelagic stocks of small particles and mesopelagic net POC fluxes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e were significantly higher for BGP events associated with intense blooms than for weak blooms and physical intrusion categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). BGP events associated with intense blooms generated POC fluxes that were 80\u0026ndash;120% and 40\u0026ndash;80% higher in the upper and lower mesopelagic layers, respectively, compared to the other two BGP categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ed-f). As shown earlier, BGP events with intense blooms are also characterized by the highest abundance of sinking aggregates and the deepest pulses of particles (Sections \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e and \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e), while sediment traps show high PIC:POC rain ratios (0.21\u0026ndash;0.48) and strong linear correlations between POC and PIC fluxes during coccolithophore bloom conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eg-h).\u003c/p\u003e \u003cp\u003eAlltogether, these observations suggest that intense coccolithophore blooms strengthen the BGP by enhancing the export of large and small particles to the ocean interior, thereby providing support for the so-called ballast hypothesis, which states that PIC particles are a major driver of POC flux by increasing the density of aggregates and thus their sinking speed\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, or by physically protecting the more labile POC in aggregates from degradation while sinking to the deep ocean\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Implications of coccolithophore blooms on the strength of the BGP\u003c/h2\u003e \u003cp\u003eOur results demonstrate that intense coccolithophore blooms (PIC\u0026thinsp;\u0026ge;\u0026thinsp;1 mmol m⁻\u0026sup3;) in the Nordic Seas can double the export of POC into mesopelagic layers where this carbon can be stored for decades to 100 years, compared to areas with weaker blooms without physical intrusions (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Sediment traps further reveal high PIC:POC rain ratios and strong positive linear correlations between POC and PIC fluxes in the mesopelagic layers of the Nordic Seas during BGP events, demonstrating a strong contribution of PIC to the overall particle flux (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother study in the Southern Ocean has shown that intense coccolithophore blooms can triple mesopelagic carbon export compared to other phytoplankton types\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Notably, carbon flux and its transfer efficiency to the deep ocean were also found to be linked to PIC fluxes rather than to the net production of organic particles in the surface waters\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, the latter being the primary metric currently used by climate models to estimate carbon fluxes\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These findings underscore the critical role of coccolithophore blooms in driving carbon fluxes and highlight the importance of including mineral ballasting mechanisms in climate models, especially given that only 25% currently include this effect\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIncorporating mineral ballasting into climate models is essential, as regions influenced by coccolithophore blooms\u0026mdash; primarily located in the high-latitude areas of the North Atlantic and Southern Ocean\u0026mdash;cover more than 50% of the ocean surface\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These two regions alone may contribute up to 50% of global POC export\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, even without accounting for the effects of coccolithophore blooms or physical intrusions. Furthermore, mesopelagic carbon demand is estimated to be two to three times higher than the supply from sinking POC\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, mirroring the two- to threefold increases in carbon flux observed during intense coccolithophore bloom events observed in this study as well as in the Southern Ocean.\u003c/p\u003e \u003cp\u003eWe suggest that explicitly representing ballast effects associated with coccolithophore blooms in climate models may help to reconcile current discrepancies between POC sources and demand. We recommend that this incorporation should account for both the intensity of blooms (intense vs. weak) and the role of physical intrusions, as both factors significantly influence mesopelagic carbon fluxes. Incorporating these mechanisms could help resolve the large spread in carbon flux projections among IPCC climate models that include the ballast effect\u0026mdash;some of which still differ by up to a factor of four in their year-2100 estimates\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This highlights the need for more mechanistic, consensus-driven approaches to modelling the ballast effect and improving climate predictions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Bio-optical and physicochemical data recorded by the BGC-Argo floats\u003c/h2\u003e \u003cp\u003eThe floats were mainly equipped with two sensors: (1) a SBE-41 CP conductivity\u0026ndash;T\u0026ndash;depth sensor (Sea-Bird Scientific), and (2) a WETLabs ECO Triplet Puck. These sensors measured upward profiles of (1) temperature (T), conductivity, and depth, and (2) chlorophyll-a fluorescence (F\u003cem\u003echl\u003c/em\u003e) and optical backscattering at 700 nm. Backscattering and fluorescence measurements were converted into bulk signals of particle backscattering (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e) and of (\u003cem\u003echl)\u003c/em\u003e concentration following standard procedures\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This standard protocol included the non-photochemical quenching correction for \u003cem\u003echl\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. We used quality-controlled Argo Sprof files to compute carbon fluxes and Bprof files to compute spikes from \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e data. These files include data corrected following state-of-the-art protocols (SBE-4, \u003cem\u003echl\u003c/em\u003e, and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Defining the water layers\u003c/h2\u003e \u003cp\u003eThe mixed-layer depth (MLD) was calculated as the depth at which density differed from 0.10 kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e relative to the density recorded at 1 m depth\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. We used a chlorophyll threshold of 0.025 mg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to define the base of the productive layer (\u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. We applied chlorophyll to define the productive layer where living phytoplankton are present because our technique to compute export requires that no production occurs below a given layer of the water column\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Indeed, it has been demonstrated that chlorophyll is an accurate metric for defining the base of the productive layer to compute carbon fluxes\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe define the mesopelagic layer and its progressively deeper water layers as those respectively found between (1) \u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e-1000 m and (2) \u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e + \u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and 1000 m depth, where \u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e refers to a progressive increment of 50 m beneath the productive layer (i.e. \u003cem\u003ezi\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50, 100, 150\u0026hellip;600 m). We defined mesopelagic water layers to calculate stocks and fluxes of small particles\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and abundance of large particles\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e(Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4.5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Biological gravitational pump events and physical intrusions\u003c/h2\u003e \u003cp\u003eThe biological gravitational pump (BGP) events were defined as those when the mixed layer depth (MLD) was equal to or shallower than the base of the productive layer (\u003cem\u003ez\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). We applied this metric to define the BGP events because it excludes times when the mixed layer pump was operational (i.e. PL\u0026thinsp;\u0026ge;\u0026thinsp;MLD)\u003csup\u003e42\u003c/sup\u003e. Since the BGP coincides with periods of strong surface stratification, we also computed the difference in potential density (σ\u003csub\u003eɵ\u003c/sub\u003e) between 300 m and 10 m depths to track and link the temporal dynamics of surface stratification with the BGP events\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSpiciness (π) along the isopycnal 27.6 kg m⁻\u0026sup3; was ultimately used to identify the BGP events influenced by vertical intrusions of surface Atlantic water masses into the mesopelagic. Spiciness refers to variations in seawater\u0026rsquo;s density-compensated temperature and salinity gradients, commonly used to contrast warm, salty water masses (i.e. spicy, mainly formed in the tropics) with cold, fresh ones in high altitudes\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Thus, spiciness along the isopycnal 27.6 kg m⁻\u0026sup3; allowed us to identify physical intrusions of surface waters by defining the vertical extent of warmer, saltier Atlantic water masses (e.g. those above the isopycnal 27.6 kg m⁻\u0026sup3; and π\u0026thinsp;\u0026ge;\u0026thinsp;0.8 kg m⁻\u0026sup3;), which overlay the cold, fresh deep-water masses of the Nordic Seas (e.g. those beneath the isopycnal 27.6 kg m⁻\u0026sup3; and π\u0026thinsp;\u0026lt;\u0026thinsp;0.8 kg m⁻\u0026sup3;)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Timing and bio-optics of coccolithophore blooms\u003c/h2\u003e \u003cp\u003eWe used the GlobColour (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hermes.acri.fr\u003c/span\u003e\u003cspan address=\"https://hermes.acri.fr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) daily PIC satellite product with a spatial resolution of 4 km. We extracted time series of satellite PIC that matched with the sampling time and location of the floats following Terrats et al. 2020. Briefly, matchups of satellite PIC were averaged for temporal and spatial windows of 9 days and 20 x 20 km\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Coccolithophore bloom phenology metrics (bloom start, peak, and end) were calculated as described in Hopkins et al., 2015 for defining the entire blooming periods.\u003c/p\u003e \u003cp\u003eAs a complementary approach for assessing the bulk composition of particles under coccolithophore blooming conditions (i.e., high and low PIC levels relative to organic matter), we determined the bio-optical characteristics of the particles using metrics of particle backscattering and chlorophyll-a due to small particles (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e, respectively). To this end, we first applied a smoothing function to each profile of \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003echl\u003c/em\u003e to extract \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e, respectively. Details regarding the smoothing method are given in section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4.5\u003c/span\u003e for \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e and section \u003cspan refid=\"Sec1\" class=\"InternalRef\"\u003e2\u003c/span\u003eS for \u003cem\u003echl.\u003c/em\u003e The \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e:\u003cem\u003echl\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e values, with thresholds of \u0026ge;\u0026thinsp;0.0033 m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and \u0026ge;\u0026thinsp;0.0063 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at the surface, were used to identify particle assemblages with a very high content of PIC per unit organic matter \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Quantifying large and small particles\u003c/h2\u003e \u003cp\u003eThe un-spiked and spiked components of the \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e signal are quantitative metrics of the concentration of small (0.2\u0026ndash;20 \u0026micro;m) and large particles (~\u0026thinsp;100 \u0026micro;m) in the mesopelagic layer\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. We partitioned the \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e signal due to \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e\u003cem\u003es\u003c/em\u003e and large particles (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpL\u003c/em\u003e\u003c/sub\u003e) by applying a smoothing function with window sizes of 3 and 7 points to each profile with 10, and 3-m vertical resolution, respectively\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The resulting baseline refers to \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e\u003cem\u003es\u003c/em\u003e while the residual includes \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpL\u003c/em\u003e\u003c/sub\u003e and a spike blank (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eS, residual\u0026thinsp;=\u0026thinsp;\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e - \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e\u003cem\u003es\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eFloat-specific spike blanks were calculated following Briggs et al., 2020 and their values ranged between [1.3-2.0]x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ex10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpL\u003c/em\u003e\u003c/sub\u003e was computed as the residual values above the spike blank and beneath 0.0028 m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. We excluded residual values above 0.0028 m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e likely caused by zooplankton\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. We finally used \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpL\u003c/em\u003e\u003c/sub\u003e to correspondingly calculate the stocks and fluxes of small particles and the abundance of large particles as described below.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1. Mesopelagic abundance of large particles\u003c/h2\u003e \u003cp\u003eThe vertical resolution of float profiles was unfortunately insufficient to examine particle fragmentation and sinking speeds directly\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Instead, we estimated the relative abundance of large particles in the mesopelagic (\u003cem\u003e%Spikes\u003c/em\u003e) quantified as the number of \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e spikes divided by the number of \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003ep\u003c/sub\u003e measurements along each profile\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This metric provides us with insights regarding the influence that coccolithophore blooms have on the abundance of large particles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2. Mesopelagic stocks and fluxes of small particles\u003c/h2\u003e \u003cp\u003eWe translated \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ebpS\u003c/em\u003e\u003c/sub\u003e into POC units by combining published linear empirical correlations\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and exponential models\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e (Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e3\u003c/span\u003eS). Depth-integrated stocks of POC were calculated by vertically integrating mean POC values over sections of the water column that included: (1) the mesopelagic layer (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{iPOC}_{zp}^{1000}\\)\u003c/span\u003e\u003c/span\u003e) and (2) progressively deeper sections of the mesopelagic layer as defined in section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e. (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{iPOC}_{zp+zi}^{1000}\\)\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNet fluxes of small POC (E\u003csub\u003e\u003cem\u003ezi\u003c/em\u003e\u003c/sub\u003e) were calculated in the defined water layers by regressing the stocks of POC\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e from the start of the BGP event until the day with the maximum stock value (e.g. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Fluxes reported here refer to the accumulation rates of small particles derived from the fragmentation of the larger ones under non-stationary state conditions. Fluxes due to large particles were not computed because vertical and temporal resolution of bio-optical profiles was too low to work out the sinking speed of large particles.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Absolute fluxes of POC and PIC from sediment traps\u003c/h2\u003e \u003cp\u003eWe used POC and PIC flux data recorded by sediment traps deployed at 1000 m in two Nordic Seas basins from 1990 to 1996\u003csup\u003e51\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). This data served as complementary information to support our hypothesis. We selected fluxes recorded at 1000 m to minimize errors in POC flux estimates associated with sediment trap sampling methods at shallower depths\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is a contribution to the following projects: CarbOcean (European Research Council under the European Union\u0026rsquo;s Horizon 2020 research and innovation programme Grant agreement No. 853516) and DTO-BioFlow (European Union\u0026rsquo;s under the Horizon Programme, Grant Agreement No. 101112823). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Argo Program is part of the Global Ocean Observing System. All BGC-Argo data are available at ftp://ftp.ifremer.fr/ifremer/argo/dac/. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). Daily-merged ocean-colour satellite data were downloaded from the GlobColour project (ftp://ftp.hermes.acri.fr). POC and PIC flux data recorded by sediment traps were downloaded from the PANGAEA repository (https://doi.pangaea.de/10.1594/PANGAEA.807946).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.R. developed the methods, conducted the analyses, created the figures and tables, and wrote the initial draft. \u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003eN.\u003c/strong\u003e conceptualized the study, provided guidance, and acquired the funding. \u0026nbsp;Both authors contributed to manuscript editing and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVolk, T. \u0026amp; Hoffert, M. I. Ocean carbon pumps: analysis of relative strengths and efficiencies in ocean-driven atmospheric CO2 changes. \u003cem\u003eThe carbon cycle and atmospheric CO\u003c/em\u003e (1985).\u003c/li\u003e\n\u003cli\u003eBoyd, P. 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Factors controlling the flux of organic carbon to the bathypelagic zone of the ocean. \u003cem\u003eGlobal Biogeochem Cycles\u003c/em\u003e (2002) doi:10.1029/2001gb001722.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6422717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6422717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe biological gravitational pump is the primary mechanism exporting particulate organic carbon from the sunlit surface to the deep ocean, particularly inhigh-latitude regions. However, mesopelagic carbon budget deficits indicate additional, unaccounted-for mechanisms that enhance particle export. One hypothesis suggests that biominerals produced by coccolithophores—calcifying phytoplankton—increase the density and sinking speed of marine snow aggregates, thereby boosting carbon export. Yet, the extent of this effect remains unclear, due to limited in situ observations of the associated particle fluxes. Here, we use autonomous BioGeoChemical-Argo float and satellite observations to assess, at a basin scale, how coccolithophore blooms influence carbon export in the Nordic Seas. Our findings reveal that coccolithophore bloom intensity correlates with increased particle export and deep fluxes, with important implications for oceanic carbon sequestration.\u003c/p\u003e","manuscriptTitle":"Coccolithophore blooms boost particle fluxes in the Nordic Seas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 07:42:59","doi":"10.21203/rs.3.rs-6422717/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"362286ca-4565-45ee-a37e-25b28553941f","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47151962,"name":"Earth and environmental sciences/Ocean sciences/Marine biology"},{"id":47151963,"name":"Earth and environmental sciences/Biogeochemistry/Carbon cycle"},{"id":47151964,"name":"Earth and environmental sciences/Ocean sciences/Marine chemistry"}],"tags":[],"updatedAt":"2025-07-02T17:55:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 07:42:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6422717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6422717","identity":"rs-6422717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}