An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐<i>a</i> based models

Lee, Younjoo; Matrai, Patricia A.; Friedrichs, Marjorie A. M.; Saba, Vincent S.; Antoine, David; Ardyna, Mathieu; Asanuma, Ichio; Babin, Marcel; Bélanger, Simon; Benoît‐Gagné, Maxime; Devred, Émmanuel; Fernández‐Méndez, Mar; Gentili, Bernard; Hirawake, Toru; Kang, Sung‐Ho; Kameda, Takahiko; Katlein, Christian; Lee, Sang H.; Lee, Zhongping; Mélin, Frédéric; Scardi, Michele; Smyth, Tim; Tang, Shilin; Turpie, Kevin R.; Waters, Kirk; Westberry, T. K.

doi:10.1002/2015jc011018

Cited by 112 publications

(94 citation statements)

References 136 publications

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“…Accurately assessing sea ice associated NPP in models is of particular importance since it can represent a dominant portion of total (water plus sea ice) NPP in regions covered by sea ice for most of the year (e.g., Gosselin et al, 1997;Fernández-Méndez et al, 2015). In general, pan-Arctic models of NPP, which include sea ice contributions to NPP are very limited (e.g., Lee et al, 2015) highlighting the need for improved sea ice algae model parameterizations.…”

Section: Sea Ice Classesmentioning

confidence: 99%

“…Large-scale estimates of sea ice algal chl a biomass and PP are limited to modeling studies as satellites are unable to observe the underside of sea ice. Lee et al (2015) demonstrated that pelagic phytoplankton PP models for the Arctic Ocean were highly sensitive to uncertainties in chlorophyll a (chl a) and performed best with in situ chl a data. In situ ice algal chl a estimates used in models, however, are typically based on a small number of ice core observations (e.g., Fernández-Méndez et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Spatial Variability of Ice Algal Chlorophyll a and Net Primary Production between Sea Ice Habitats Using Horizontal Profiling Platforms

et al. 2017

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Assessing the role of sea ice algal biomass and primary production for polar ecosystems remains challenging due to the strong spatio-temporal variability of sea ice algae. Therefore, the spatial representativeness of sea ice algal biomass and primary production sampling remains a key issue in large-scale models and climate change predictions of polar ecosystems. To address this issue, we presented two novel approaches to up-scale ice algal chl a biomass and net primary production (NPP) estimates based on profiles covering distances of 100 to 1,000 s of meters. This was accomplished by combining ice core-based methods with horizontal under-ice spectral radiation profiling conducted in the central Arctic Ocean during summer 2012. We conducted a multi-scale comparison of ice-core based ice algal chl a biomass with two profiling platforms: a remotely operated vehicle and surface and under ice trawl (SUIT). NPP estimates were compared between ice cores and remotely operated vehicle surveys. Our results showed that ice core-based estimates of ice algal chl a biomass and NPP do not representatively capture the spatial variability compared to the remotely operated vehicle-based estimates, implying considerable uncertainties for pan-Arctic estimates based on ice core observations alone. Grouping sea ice cores based on region or ice type improved the representativeness. With only a small sample size, however, a high risk of obtaining non-representative estimates remains. Sea ice algal chl a biomass estimates based on the dominant ice class alone showed a better agreement between ice core and remotely operated vehicle estimates. Grouping ice core measurements yielded no improvement in NPP estimates, highlighting the importance of accounting for the spatial variability of both the chl a biomass and bottom-ice light in order to get representative estimates. Profile-based measurements of ice algae chl a biomass identified sea ice ridges as an underappreciated component of the Arctic ecosystem Lange et al.Ice-Algal Biomass and NPP because chl a biomass was significantly greater in this unique habitat. Sea ice ridges are not easily captured with ice coring methods and thus require more attention in future studies. Based on our results, we provide recommendations for designing an efficient and effective sea ice algal sampling program for the summer season.

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Section: Sea Ice Classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterizing Spatial Variability of Ice Algal Chlorophyll a and Net Primary Production between Sea Ice Habitats Using Horizontal Profiling Platforms

et al. 2017

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“…Although global estimates of marine primary production tend to converge on a number around 40-50 GT yr −1 , the accuracy and precision on regional scales of the estimation protocols remain relatively poor, partly as a result of an incomplete understanding of how the photosynthetic performance of marine phytoplankton varies in the global ocean (Carr et al, 2006;Lee et al, 2015). Photosynthesis-irradiance (P-E) parameters derived from carbon uptake experiments conducted over a controlled range of available-light levels provide a means of comparing the photosynthetic characteristics of marine phytoplankton across different natural populations and cultured isolates Prézelin et al, 1989;MacIntyre et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Photosynthesis–irradiance parameters of marine phytoplankton: synthesis of a global data set

Bouman

Platt

Doblin

et al. 2018

Earth Syst. Sci. Data

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Abstract. The photosynthetic performance of marine phytoplankton varies in response to a variety of factors, environmental and taxonomic. One of the aims of the MArine primary Production: model Parameters from Space (MAPPS) project of the European Space Agency is to assemble a global database of photosynthesisirradiance (P-E) parameters from a range of oceanographic regimes as an aid to examining the basin-scale variability in the photophysiological response of marine phytoplankton and to use this information to improve the assignment of P-E parameters in the estimation of global marine primary production using satellite data. The MAPPS P-E database, which consists of over 5000 P-E experiments, provides information on the spatiotemporal variability in the two P-E parameters (the assimilation number, P B m , and the initial slope, α B , where the superscripts B indicate normalisation to concentration of chlorophyll) that are fundamental inputs for models (satellite-based and otherwise) of marine primary production that use chlorophyll as the state variable. Qualitycontrol measures consisted of removing samples with abnormally high parameter values and flags were added to denote whether the spectral quality of the incubator lamp was used to calculate a broad-band value of α B . The MAPPS database provides a photophysiological data set that is unprecedented in number of observations and in spatial coverage. The database will be useful to a variety of research communities, including marine ecologists, Published by Copernicus Publications.

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“…There are two main types of numerical models that have been applied to estimate NPP in the Arctic Ocean: satellite-based diagnostic models (reviewed in Babin et al [2015] and International Ocean Colour Coordinating Group (IOCCG) [2015], and assessed in Lee et al [2015]) and process-based coupled dynamic physicalbiological models run in either retrospective or predictive modes. This latter class of models has been applied to improve our understanding of the Arctic Ocean carbon sinks and sources using regional [e.g., Deal et al, 2011;Manizza et al, 2013;Slagstad et al, 2015;Zhang et al, 2010cZhang et al, ,2015 to global [e.g., Popova et al, 2010;Vancoppenolle et al, 2013] ice-ocean-atmosphere carbon cycle simulations.…”

Section: Introductionmentioning

confidence: 99%

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

Lee

Matrai

Friedrichs

et al. 2016

J. Geophys. Res. Oceans

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The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality‐controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan‐Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice‐free versus ice‐influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

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An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐a based models

Cited by 112 publications

References 136 publications

Characterizing Spatial Variability of Ice Algal Chlorophyll a and Net Primary Production between Sea Ice Habitats Using Horizontal Profiling Platforms

Characterizing Spatial Variability of Ice Algal Chlorophyll a and Net Primary Production between Sea Ice Habitats Using Horizontal Profiling Platforms

Photosynthesis–irradiance parameters of marine phytoplankton: synthesis of a global data set

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

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