Massive Phytoplankton Blooms Under Arctic Sea Ice

Arrigo, Kevin R.; Perovich, Donald K.; Pickart, Robert S.; Brown, Zachary W.; Dijken, Gert L. van; Lowry, Kate E.; Mills, Matthew M.; Palmer, Molly A.; Balch, William M.; Bahr, Frank; Bates, Nicholas R.; Benitez-Nelson, Claudia R.; Bowler, Bruce C.; Brownlee, Emily F.; Ehn, Jens K.; Frey, Karen E.; Garley, Rebecca; Laney, Samuel R.; Lubelczyk, Laura C.; Mathis, Jeremy T.; Matsuoka, Atsushi; Mitchell, B. Greg; Moore, G. W. K.; Ortega−Retuerta, Eva; Pal, Sanjay Kumar; Polashenski, Chris; Reynolds, Rick A.; Schieber, Brian; Sosik, Heidi M.; Stephens, Michael; Swift, James H.

doi:10.1126/science.1215065

Cited by 670 publications

(624 citation statements)

References 14 publications

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“…The length scale of melt pond variability on the floe was 9.3 m as determined by variogram analysis of the classified aerial image (range value). The thick ice, the considerable surface layer, the geographic position close to Greenland and the timing early in the melting season are the reason for the low light transmittances when compared to other studies from other regions or later in the summer [ Arrigo et al ., 2012; Nicolaus et al ., 2012]. …”

Section: Resultsmentioning

confidence: 99%

Influence of ice thickness and surface properties on light transmission through Arctic sea ice

et al. 2015

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The observed changes in physical properties of sea ice such as decreased thickness and increased melt pond cover severely impact the energy budget of Arctic sea ice. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role for amount and timing of sea‐ice‐melt and under‐ice primary production. Recent developments in underwater technology provide new opportunities to study light transmission below the largely inaccessible underside of sea ice. We measured spectral under‐ice radiance and irradiance using the new Nereid Under‐Ice (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H‐ROV) designed for both remotely piloted and autonomous surveys underneath land‐fast and moving sea ice. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under‐ice optical measurements with three dimensional under‐ice topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying ice‐thickness and surface properties on the spatial variability of light transmittance during summer. Our results show that surface properties such as melt ponds dominate the spatial distribution of the under‐ice light field on small scales (<1000 m 2 ), while sea ice‐thickness is the most important predictor for light transmission on larger scales. In addition, we propose the use of an algorithm to obtain histograms of light transmission from distributions of sea ice thickness and surface albedo.

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Section: Resultsmentioning

confidence: 99%

Influence of ice thickness and surface properties on light transmission through Arctic sea ice

et al. 2015

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“…Nutrient depletion near the surface is observed during each summer period, when the light limitation decreases and the sea ice breaks up, allowing phytoplankton blooms to spread along the base of the sea ice (Arrigo et al, 2012). Remarkably, surface N/P ratios were low in both basins and never reached the expected Redfield ratio (16/1) (Fig.…”

Section: Pml and Haloclinementioning

confidence: 99%

Climate relevant trace gases (N2O and CH4) in the Eurasian Basin (Arctic Ocean)

Verdugo

Damm

Snoeijs

et al. 2016

Deep Sea Research Part I: Oceanographic Research Papers

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A B S T R A C TThe concentration of greenhouse gases, including nitrous oxide (N 2 O), methane (CH 4 ), and compounds such as total dimethylsulfoniopropionate (DMSP t ), along with other oceanographic variables were measured in the icecovered Arctic Ocean within the Eurasian Basin (EAB). The EAB is affected by the perennial ice-pack and has seasonal microalgal blooms, which in turn may stimulate microbes involved in trace gas cycling. Data collection was carried out on board the LOMROG III cruise during the boreal summer of 2012. Water samples were collected from the surface to the bottom layer (reaching 4300 m depth) along a South-North transect (SNT), from 82.19°N, 8.75°E to 89.26°N, 58.84°W, crossing the EAB through the Nansen and Amundsen Basins. The Polar Mixed Layer and halocline waters along the SNT showed a heterogeneous distribution of N 2 O, CH 4 and DMSP t , fluctuating between 42-111 and 27-649% saturation for N 2 O and CH 4, respectively; and from 3.5 to 58.9 nmol L −1 for DMSP t . Spatial patterns revealed that while CH 4 and DMSP t peaked in the Nansen Basin, N 2 O was higher in the Amundsen Basin. In the Atlantic Intermediate Water and Arctic Deep Water N 2 O and CH 4 distributions were also heterogeneous with saturations between 52% and 106% and 28% and 340%, respectively. Remarkably, the Amundsen Basin contained less CH 4 than the Nansen Basin and while both basins were mostly under-saturated in N 2 O. We propose that part of the CH 4 and N 2 O may be microbiologically consumed via methanotrophy, denitrification, or even diazotrophy, as intermediate and deep waters move throughout EAB associated with the overturning water mass circulation. This study contributes to baseline information on gas distribution in a region that is increasingly subject to rapid environmental changes, and that has an important role on global ocean circulation and climate regulation.

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“…It should be noted that the similarity could be due to biased seasonal coverage by field studies in ice-free waters, introducing more measurements collected in late summer and fall (i.e., September), thus lowering the ice-free iNPP average (not shown). Alternatively, the similarity in simulated iNPP values in ice-free and ice-influenced areas could result from the averaging of regional differences previously observed in field and satellite-derived NPP data [e.g., Arrigo et al, 2012;Hill et al, 2013;Ardyna et al, 2013] as well as in model simulations [e.g., Zhang et al, 2015]. Recently, Jin et al [2016] showed that simulated under-ice production is also regionally different (i.e., higher in the Arctic shelves, possibly due to enhanced nutrient supply) [Zhang et al, 2015], but not necessarily related to the recent decrease in sea ice concentration, especially in marginal ice zones.…”

Section: 1002/2016jc011993mentioning

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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Massive Phytoplankton Blooms Under Arctic Sea Ice

Cited by 670 publications

References 14 publications

Influence of ice thickness and surface properties on light transmission through Arctic sea ice

Influence of ice thickness and surface properties on light transmission through Arctic sea ice

Climate relevant trace gases (N2O and CH4) in the Eurasian Basin (Arctic Ocean)

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

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