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© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cael, B. B., Bisson, K., Conte, M., Duret, M. T., Follett, C. L., Henson, S. A., Honda, M. C., Iversen, M. H., Karl, D. M., Lampitt, R. S., Mouw, C. B., Muller-Karger, F., Pebody, C. A., Smith, K. L., & Talmy, D. Open ocean particle flux variability from surface to seafloor. Geophysical Research Letters, 48(9), (2021): e2021GL092895, https://doi.org/10.1029/2021GL092895., The sinking of carbon fixed via net primary production (NPP) into the ocean interior is an important part of marine biogeochemical cycles. NPP measurements follow a log-normal probability distribution, meaning NPP variations can be simply described by two parameters despite NPP's complexity. By analyzing a global database of open ocean particle fluxes, we show that this log-normal probability distribution propagates into the variations of near-seafloor fluxes of particulate organic carbon (POC), calcium carbonate, and opal. Deep-sea particle fluxes at subtropical and temperate time-series sites follow the same log-normal probability distribution, strongly suggesting the log-normal description is robust and applies on multiple scales. This log-normality implies that 29% of the highest measurements are responsible for 71% of the total near-seafloor POC flux. We discuss possible causes for the dampening of variability from NPP to deep-sea POC flux, and present an updated relationship predicting POC flux from mineral flux and depth., B. B. Cael and S. A. Henson acknowledge support from the National Environmental Research Council (NE/R015953/1) and the Horizon 2020 Framework Programme (820989, project COMFORT). The work reflects only the authors' views; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains. S. A. Henson also acknowledges support from a European Research Council Consolidator grant (GOCART, agreement number 724416). C. L. Follett acknowledges support from the Simons Foundation (grants #827829 and #553242). M. H. Iversen acknowledges support from the DFG-Research Center/Cluster of Excellence “The Ocean Floor – Earth's Uncharted Interface”: EXC-2077-390741603 and the HGF Young Investigator Group SeaPump “Seasonal and regional food web interactions with the biological pump”: VH-NG-1000. M. C. Honda acknowledges financial support from the Ministry of Education, Culture, Sports, Science, and Technology – Japan (grants #: KAKENHI JP18H04144 and JP19H05667). M. Conte acknowledges support from the US National Science Foundation, Division of Ocean Sciences for support for the Oceanic Flux Program time-series since inception, most recently by NSF OCE grant 1829885. D. M. Karl acknowledges support from the Gordon and Betty Moore Foundation (#3794) and the Simons Foundation (SCOPE #329108).

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cael, B. B., Bisson, K., Conte, M., Duret, M. T., Follett, C. L., Henson, S. A., Honda, M. C., Iversen, M. H., Karl, D. M., Lampitt, R. S., Mouw, C. B., Muller-Karger, F., Pebody, C. A., Smith, K. L., & Talmy, D. Open ocean particle flux variability from surface to seafloor. Geophysical Research Letters, 48(9), (2021): e2021GL092895, https://doi.org/10.1029/2021GL092895., The sinking of carbon fixed via net primary production (NPP) into the ocean interior is an important part of marine biogeochemical cycles. NPP measurements follow a log-normal probability distribution, meaning NPP variations can be simply described by two parameters despite NPP's complexity. By analyzing a global database of open ocean particle fluxes, we show that this log-normal probability distribution propagates into the variations of near-seafloor fluxes of particulate organic carbon (POC), calcium carbonate, and opal. Deep-sea particle fluxes at subtropical and temperate time-series sites follow the same log-normal probability distribution, strongly suggesting the log-normal description is robust and applies on multiple scales. This log-normality implies that 29% of the highest measurements are responsible for 71% of the total near-seafloor POC flux. We discuss possible causes for the dampening of variability from NPP to deep-sea POC flux, and present an updated relationship predicting POC flux from mineral flux and depth., B. B. Cael and S. A. Henson acknowledge support from the National Environmental Research Council (NE/R015953/1) and the Horizon 2020 Framework Programme (820989, project COMFORT). The work reflects only the authors' views; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains. S. A. Henson also acknowledges support from a European Research Council Consolidator grant (GOCART, agreement number 724416). C. L. Follett acknowledges support from the Simons Foundation (grants #827829 and #553242). M. H. Iversen acknowledges support from the DFG-Research Center/Cluster of Excellence “The Ocean Floor – Earth's Uncharted Interface”: EXC-2077-390741603 and the HGF Young Investigator Group SeaPump “Seasonal and regional food web interactions with the biological pump”: VH-NG-1000. M. C. Honda acknowledges financial support from the Ministry of Education, Culture, Sports, Science, and Technology – Japan (grants #: KAKENHI JP18H04144 and JP19H05667). M. Conte acknowledges support from the US National Science Foundation, Division of Ocean Sciences for support for the Oceanic Flux Program time-series since inception, most recently by NSF OCE grant 1829885. D. M. Karl acknowledges support from the Gordon and Betty Moore Foundation (#3794) and the Simons Foundation (SCOPE #329108).

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cael, B. B., & Bisson, K. (2018). Particle flux parameterizations: Quantitative and mechanistic similarities and differences. Frontiers in Marine Science, 5, (2018): 395. doi:10.3389/fmars.2018.00395., The depth-attenuation of sinking particulate organic carbon (POC) is of particular importance for the ocean's role in the global carbon cycle. Numerous idealized flux-vs.-depth relationships are available to parameterize this process in Earth System Models. Here we show that these relationships are statistically indistinguishable from available POC flux profile data. Despite their quantitative similarity, we also show these relationships have very different implications for the flux leaving the upper ocean, as well as for the mechanisms governing POC flux. We discuss how this tension might be addressed both observationally and in modeling studies., The National Aeronautics and Space Administration supported this work (awards NNX16AR47G and NNX16AR49G).

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cael, B. B., & Bisson, K. (2018). Particle flux parameterizations: Quantitative and mechanistic similarities and differences. Frontiers in Marine Science, 5, (2018): 395. doi:10.3389/fmars.2018.00395., The depth-attenuation of sinking particulate organic carbon (POC) is of particular importance for the ocean's role in the global carbon cycle. Numerous idealized flux-vs.-depth relationships are available to parameterize this process in Earth System Models. Here we show that these relationships are statistically indistinguishable from available POC flux profile data. Despite their quantitative similarity, we also show these relationships have very different implications for the flux leaving the upper ocean, as well as for the mechanisms governing POC flux. We discuss how this tension might be addressed both observationally and in modeling studies., The National Aeronautics and Space Administration supported this work (awards NNX16AR47G and NNX16AR49G).

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 32 (2018): 1312-1328, doi:10.1029/2018GB005934., Ocean biological processes mediate the transport of roughly 10 petagrams of carbon from the surface to the deep ocean each year and thus play an important role in the global carbon cycle. Even so, the globally integrated rate of carbon export out of the surface ocean remains highly uncertain. Quantifying the processes underlying this biological carbon export requires a synthesis between model predictions and available observations of particulate organic carbon (POC) flux; yet the scale dissimilarities between models and observations make this synthesis difficult. Here we compare carbon export predictions from a mechanistic model with observations of POC fluxes from several data sets compiled from the literature spanning different space, time, and depth scales as well as using different observational methodologies. We optimize model parameters to provide the best match between model‐predicted and observed POC fluxes, explicitly accounting for sources of error associated with each data set. Model‐predicted globally integrated values of POC flux at the base of the euphotic layer range from 3.8 to 5.5 Pg C/year, depending on the data set used to optimize the model. Modeled carbon export pathways also vary depending on the data set used to optimize the model, as well as the satellite net primary production data product used to drive the model. These findings highlight the importance of collecting field data that average over the substantial natural temporal and spatial variability in carbon export fluxes, and advancing satellite algorithms for ocean net primary production, in order to improve predictions of biological carbon export., NASA Ocean Biology and Biogeochemistry Program Grant Numbers: NNX16AR49G, NNXA122G, NNX16AR47G, OBB16_2‐0031; National Science Foundation, 2019-03-13

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Microbiology 9 (2018): 358, doi:10.3389/fmicb.2018.00358., Viruses are key players in ocean ecology and biogeochemistry, not only because of their functional roles but also partially due to their sheer abundance (Fuhrman, 1999; Wilhelm and Suttle, 1999). Because viruses cannot replicate without their hosts' machinery, their abundance is inextricably related to that of their (mostly microbial) hosts. The relationship between viral and microbial abundances is thus of great interest.

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 32 (2018): 1312-1328, doi:10.1029/2018GB005934., Ocean biological processes mediate the transport of roughly 10 petagrams of carbon from the surface to the deep ocean each year and thus play an important role in the global carbon cycle. Even so, the globally integrated rate of carbon export out of the surface ocean remains highly uncertain. Quantifying the processes underlying this biological carbon export requires a synthesis between model predictions and available observations of particulate organic carbon (POC) flux; yet the scale dissimilarities between models and observations make this synthesis difficult. Here we compare carbon export predictions from a mechanistic model with observations of POC fluxes from several data sets compiled from the literature spanning different space, time, and depth scales as well as using different observational methodologies. We optimize model parameters to provide the best match between model‐predicted and observed POC fluxes, explicitly accounting for sources of error associated with each data set. Model‐predicted globally integrated values of POC flux at the base of the euphotic layer range from 3.8 to 5.5 Pg C/year, depending on the data set used to optimize the model. Modeled carbon export pathways also vary depending on the data set used to optimize the model, as well as the satellite net primary production data product used to drive the model. These findings highlight the importance of collecting field data that average over the substantial natural temporal and spatial variability in carbon export fluxes, and advancing satellite algorithms for ocean net primary production, in order to improve predictions of biological carbon export., NASA Ocean Biology and Biogeochemistry Program Grant Numbers: NNX16AR49G, NNXA122G, NNX16AR47G, OBB16_2‐0031; National Science Foundation, 2019-03-13

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 32 (2018): 1312-1328, doi:10.1029/2018GB005934., Ocean biological processes mediate the transport of roughly 10 petagrams of carbon from the surface to the deep ocean each year and thus play an important role in the global carbon cycle. Even so, the globally integrated rate of carbon export out of the surface ocean remains highly uncertain. Quantifying the processes underlying this biological carbon export requires a synthesis between model predictions and available observations of particulate organic carbon (POC) flux; yet the scale dissimilarities between models and observations make this synthesis difficult. Here we compare carbon export predictions from a mechanistic model with observations of POC fluxes from several data sets compiled from the literature spanning different space, time, and depth scales as well as using different observational methodologies. We optimize model parameters to provide the best match between model‐predicted and observed POC fluxes, explicitly accounting for sources of error associated with each data set. Model‐predicted globally integrated values of POC flux at the base of the euphotic layer range from 3.8 to 5.5 Pg C/year, depending on the data set used to optimize the model. Modeled carbon export pathways also vary depending on the data set used to optimize the model, as well as the satellite net primary production data product used to drive the model. These findings highlight the importance of collecting field data that average over the substantial natural temporal and spatial variability in carbon export fluxes, and advancing satellite algorithms for ocean net primary production, in order to improve predictions of biological carbon export., NASA Ocean Biology and Biogeochemistry Program Grant Numbers: NNX16AR49G, NNXA122G, NNX16AR47G, OBB16_2‐0031; National Science Foundation, 2019-03-13

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Microbiology 9 (2018): 358, doi:10.3389/fmicb.2018.00358., Viruses are key players in ocean ecology and biogeochemistry, not only because of their functional roles but also partially due to their sheer abundance (Fuhrman, 1999; Wilhelm and Suttle, 1999). Because viruses cannot replicate without their hosts' machinery, their abundance is inextricably related to that of their (mostly microbial) hosts. The relationship between viral and microbial abundances is thus of great interest.

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Geophysical Research Letters 44 (2017): 1886–1891, doi:10.1002/2016GL072223., Here we explore the relationship between the mean salinity S of the ocean and the strength of its Atlantic and Pacific Meridional Overturning Circulations (AMOC and PMOC). We compare simulations performed with a realistically configured coarse-grained ocean model, spanning a range of mean salinities. We find that the AMOC strength increases approximately linearly with S. In contrast, the PMOC strength declines approximately linearly with inline image until it reaches a small background value similar to the present-day ocean. Well-established scaling laws for the overturning circulation explain both of these dependencies on S, National Science Foundation Graduate Research Fellowship Program Grant Number: 2388357; National Science Foundation Grant Numbers: OCE-1315201, OCE-1536515

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Geophysical Research Letters 44 (2017): 209–218, doi:10.1002/2016GL071378., Global lake volume estimates are scarce, highly variable, and poorly documented. We developed a rigorous method for estimating global lake depth and volume based on the Hurst coefficient of Earth's surface, which provides a mechanistic connection between lake area and volume. Volume-area scaling based on the Hurst coefficient is accurate and consistent when applied to lake data sets spanning diverse regions. We applied these relationships to a global lake area census to estimate global lake volume and depth. The volume of Earth's lakes is 199,000 km3 (95% confidence interval 196,000–202,000 km3). This volume is in the range of historical estimates (166,000–280,000 km3), but the overall mean depth of 41.8 m (95% CI 41.2–42.4 m) is significantly lower than previous estimates (62–151 m). These results highlight and constrain the relative scarcity of lake waters in the hydrosphere and have implications for the role of lakes in global biogeochemical cycles., Knut and Alice Wallenberg Foundation; National Science Foundation Graduate Research Fellowship Program Grant Number: 2388357; National Science Foundation Grant Number: OCE-1315201

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Geophysical Research Letters 44 (2017): 1886–1891, doi:10.1002/2016GL072223., Here we explore the relationship between the mean salinity S of the ocean and the strength of its Atlantic and Pacific Meridional Overturning Circulations (AMOC and PMOC). We compare simulations performed with a realistically configured coarse-grained ocean model, spanning a range of mean salinities. We find that the AMOC strength increases approximately linearly with S. In contrast, the PMOC strength declines approximately linearly with inline image until it reaches a small background value similar to the present-day ocean. Well-established scaling laws for the overturning circulation explain both of these dependencies on S, National Science Foundation Graduate Research Fellowship Program Grant Number: 2388357; National Science Foundation Grant Numbers: OCE-1315201, OCE-1536515

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Geophysical Research Letters 44 (2017): 209–218, doi:10.1002/2016GL071378., Global lake volume estimates are scarce, highly variable, and poorly documented. We developed a rigorous method for estimating global lake depth and volume based on the Hurst coefficient of Earth's surface, which provides a mechanistic connection between lake area and volume. Volume-area scaling based on the Hurst coefficient is accurate and consistent when applied to lake data sets spanning diverse regions. We applied these relationships to a global lake area census to estimate global lake volume and depth. The volume of Earth's lakes is 199,000 km3 (95% confidence interval 196,000–202,000 km3). This volume is in the range of historical estimates (166,000–280,000 km3), but the overall mean depth of 41.8 m (95% CI 41.2–42.4 m) is significantly lower than previous estimates (62–151 m). These results highlight and constrain the relative scarcity of lake waters in the hydrosphere and have implications for the role of lakes in global biogeochemical cycles., Knut and Alice Wallenberg Foundation; National Science Foundation Graduate Research Fellowship Program Grant Number: 2388357; National Science Foundation Grant Number: OCE-1315201

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 43 (2016): 5170–5175, doi:10.1002/2016GL068877., Quantifying the fraction of primary production exported from the euphotic layer (termed the export efficiency ef) is a complicated matter. Studies have suggested empirical relationships with temperature which offer attractive potential for parameterization. Here we develop what is arguably the simplest mechanistic model relating the two, using established thermodynamic dependencies for primary production and respiration. It results in a single-parameter curve that constrains the envelope of possible efficiencies, capturing the upper bounds of several ef-T data sets. The approach provides a useful theoretical constraint on this relationship and extracts the variability in ef due to temperature but does not idealize out the remaining variability which evinces the substantial complexity of the system in question., National Science Foundation; Gordon and Betty Moore Foundation's Marine Microbiology Initiative; Simons Foundation, 2016-11-18

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS One 10 (2015): e0143299, doi:10.1371/journal.pone.0143299., For phytoplankton and other microbes, nutrient receptors are often the passages through which viruses invade. This presents a bottom-up vs. top-down, co-limitation scenario; how do these would-be-hosts balance minimizing viral susceptibility with maximizing uptake of limiting nutrient(s)? This question has been addressed in the biological literature on evolutionary timescales for populations, but a shorter timescale, mechanistic perspective is lacking, and marine viral literature suggests the strong influence of additional factors, e.g. host size; while the literature on both nutrient uptake and host-virus interactions is expansive, their intersection, of ubiquitous relevance to marine environments, is understudied. I present a simple, mechanistic model from first principles to analyze the effect of this co-limitation scenario on individual growth, which suggests that in environments with high risk of viral invasion or spatial/temporal heterogeneity, an individual host’s growth rate may be optimized with respect to receptor coverage, producing top-down selective pressure on short timescales. The model has general applicability, is suggestive of hypotheses for empirical exploration, and can be extended to theoretical studies of more complex behaviors and systems., This work was supported by the Massachusetts Institute of Technology Charles Vest Presidential Fellowship.

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 43 (2016): 5170–5175, doi:10.1002/2016GL068877., Quantifying the fraction of primary production exported from the euphotic layer (termed the export efficiency ef) is a complicated matter. Studies have suggested empirical relationships with temperature which offer attractive potential for parameterization. Here we develop what is arguably the simplest mechanistic model relating the two, using established thermodynamic dependencies for primary production and respiration. It results in a single-parameter curve that constrains the envelope of possible efficiencies, capturing the upper bounds of several ef-T data sets. The approach provides a useful theoretical constraint on this relationship and extracts the variability in ef due to temperature but does not idealize out the remaining variability which evinces the substantial complexity of the system in question., National Science Foundation; Gordon and Betty Moore Foundation's Marine Microbiology Initiative; Simons Foundation, 2016-11-18

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS One 10 (2015): e0143299, doi:10.1371/journal.pone.0143299., For phytoplankton and other microbes, nutrient receptors are often the passages through which viruses invade. This presents a bottom-up vs. top-down, co-limitation scenario; how do these would-be-hosts balance minimizing viral susceptibility with maximizing uptake of limiting nutrient(s)? This question has been addressed in the biological literature on evolutionary timescales for populations, but a shorter timescale, mechanistic perspective is lacking, and marine viral literature suggests the strong influence of additional factors, e.g. host size; while the literature on both nutrient uptake and host-virus interactions is expansive, their intersection, of ubiquitous relevance to marine environments, is understudied. I present a simple, mechanistic model from first principles to analyze the effect of this co-limitation scenario on individual growth, which suggests that in environments with high risk of viral invasion or spatial/temporal heterogeneity, an individual host’s growth rate may be optimized with respect to receptor coverage, producing top-down selective pressure on short timescales. The model has general applicability, is suggestive of hypotheses for empirical exploration, and can be extended to theoretical studies of more complex behaviors and systems., This work was supported by the Massachusetts Institute of Technology Charles Vest Presidential Fellowship.

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Physical Review E Statistical, Nonlinear, and Soft Matter Physics 92 (2015): 052128, doi: 10.1103/PhysRevE.92.052128., Studies over the past decade have reported power-law distributions for the areas of terrestrial lakes and Arctic melt ponds, as well as fractal relationships between their areas and coastlines. Here we report similar fractal structure of ponds in a tidal flat, thereby extending the spatial and temporal scales on which such phenomena have been observed in geophysical systems. Images taken during low tide of a tidal flat in Damariscotta, Maine, reveal a well-resolved power-law distribution of pond sizes over three orders of magnitude with a consistent fractal area-perimeter relationship. The data are consistent with the predictions of percolation theory for unscreened perimeters and scale-free cluster size distributions and are robust to alterations of the image processing procedure. The small spatial and temporal scales of these data suggest this easily observable system may serve as a useful model for investigating the evolution of pond geometries, while emphasizing the generality of fractal behavior in geophysical surfaces., This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 2388357, the Gordon and Betty Moore Foundation, and the National Science Foundation, Award No. OCE-1315201.

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Physical Review E Statistical, Nonlinear, and Soft Matter Physics 92 (2015): 052128, doi: 10.1103/PhysRevE.92.052128., Studies over the past decade have reported power-law distributions for the areas of terrestrial lakes and Arctic melt ponds, as well as fractal relationships between their areas and coastlines. Here we report similar fractal structure of ponds in a tidal flat, thereby extending the spatial and temporal scales on which such phenomena have been observed in geophysical systems. Images taken during low tide of a tidal flat in Damariscotta, Maine, reveal a well-resolved power-law distribution of pond sizes over three orders of magnitude with a consistent fractal area-perimeter relationship. The data are consistent with the predictions of percolation theory for unscreened perimeters and scale-free cluster size distributions and are robust to alterations of the image processing procedure. The small spatial and temporal scales of these data suggest this easily observable system may serve as a useful model for investigating the evolution of pond geometries, while emphasizing the generality of fractal behavior in geophysical surfaces., This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 2388357, the Gordon and Betty Moore Foundation, and the National Science Foundation, Award No. OCE-1315201.