Subsurface plankton layers in the Arctic Ocean

Churnside, James H.; Marchbanks, Richard D.

doi:10.1002/2015gl064503

Cited by 57 publications

(36 citation statements)

References 33 publications

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“…Our observations using the WARM buoy system provide evidence that light availability was sufficient to support high phytoplankton growth rates beneath seasonal sea ice cover in the Arctic Ocean. The mechanistic confirmation provided by our bio‐optical model provides further evidence that dense phytoplankton populations recently observed under the ice (Arrigo et al, ; Assmy et al, ; Churnside & Marchbanks, ) are likely to have grown in place and were not laterally advected from ice‐free areas. As seasonal ice cover becomes more dominant in the Arctic, its thinner snow cover which is more quickly melted will be the primary agent in determining the magnitude of the under‐ice light field, followed by ice thickness and earlier surface melt pond formation.…”

Section: Discussionsupporting

confidence: 81%

“…The Arctic‐wide replacement of thick multiyear sea ice with thinner, more transparent seasonal ice could result in an earlier initiation and prolonged window for seasonal phytoplankton growth. High concentrations of phytoplankton recently observed in the water column beneath ice as far as 100 km from the ice edge may indicate that phytoplankton growth can now be initiated and sustained beneath a seasonal ice cover (Arrigo et al, ; Assmy et al, ; Churnside & Marchbanks, ). Horvat et al () conclude that ice thinning has increased the prevalence of conditions conducive for under‐ice blooms, such that as much as 30% of the ice‐covered Arctic in July could now support phytoplankton growth.…”

Section: Introductionmentioning

confidence: 99%

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Light Availability and Phytoplankton Growth Beneath Arctic Sea Ice: Integrating Observations and Modeling

et al. 2018

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Observations of the seasonal light field in the upper Arctic Ocean are critical to understanding the impacts of changing Arctic ice conditions on phytoplankton growth in the water column. Here we discuss data from a new sensor system, deployed in seasonal ice cover north‐east of Utqiaġvik, Alaska in March 2014. The system was designed to provide observations of light and phytoplankton biomass in the water column during the formation of surface melt ponds and the transition from ice to open water. Hourly observations of downwelling irradiance beneath the ice (at 2.9, 6.9, and 17.9 m depths) and phytoplankton biomass (at 2.9 m depth) were transmitted via Iridium satellite from 9 March to 10 November 2014. Evidence of an under‐ice phytoplankton bloom (Chl a ∼8 mg m−3) was seen in June and July. Increases in light intensity observed by the buoy likely resulted from the loss of snow cover and development of surface melt ponds. A bio‐optical model of phytoplankton production supported this probable trigger for the rapid onset of under‐ice phytoplankton growth. Once under‐ice light was no longer a limiting factor for photosynthesis, open water exposure almost marginally increased daily phytoplankton production compared to populations that remained under the adjacent ice. As strong effects of climate change continue to be documented in the Arctic, the insight derived from autonomous buoys will play an increasing role in understanding the dynamics of primary productivity where ice and cloud cover limit the utility of ocean color satellite observations.

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Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Light Availability and Phytoplankton Growth Beneath Arctic Sea Ice: Integrating Observations and Modeling

et al. 2018

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“…In addition, the northern line was flown on 15 July and on the early morning of 17 July. With slight modifications, the lidar is the same as has been described in previous publications [29,[35][36][37]. For this application, the transmitted pulse energy was 100 mJ, pulse length was 12 ns, pulse repetition rate was 30 s −1 , wavelength was 532 nm, polarization was linear, and the beam divergence was 17 mrad.…”

Section: Methodsmentioning

confidence: 99%

“…This technique has been particularly effective in measuring enhanced scattering layers in the ocean [29,30] and dynamical processes such as internal waves [31,32]. A direct comparison of airborne lidar estimates of b bp with ship based measurements produced a root-mean square difference of 9.4 × 10 −4 m −1 over a range of values from 5 × 10 −4 m −1 to 9 × 10 −3 m −1 [33].…”

Section: Introductionmentioning

confidence: 99%

Optical Backscattering Measured by Airborne Lidar and Underwater Glider

et al. 2017

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Abstract:The optical backscattering from particles in the ocean is an important quantity that has been measured by remote sensing techniques and in situ instruments. In this paper, we compare estimates of this quantity from airborne lidar with those from an in situ instrument on an underwater glider. Both of these technologies allow much denser sampling of backscatter profiles than traditional ship surveys. We found a moderate correlation (R = 0.28, p < 10 −5 ), with differences that are partially explained by spatial and temporal sampling mismatches, variability in particle composition, and lidar retrieval errors. The data suggest that there are two different regimes with different scattering properties. For backscattering coefficients below about 0.001 m −1 , the lidar values were generally greater than the glider values. For larger values, the lidar was generally lower than the glider. Overall, the results are promising and suggest that airborne lidar and gliders provide comparable and complementary information on optical particulate backscattering.

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“…The factor-of-ten enhancement is consistent with reported values that include a range of 4-55 in Monterey Bay, California 56 and a median value of 12 in open water in the Arctic Ocean. 57 A minimum value of three was used as a criterion for the existence of a layer in East Sound, Washington, 40 suggesting typical values were much higher. However, other measurements in Monterey Bay have produced average values of about three, 42,58 suggesting that there is a high degree of variability.…”

Section: Stratified Water Examplementioning

confidence: 99%

Bio-optical model to describe remote sensing signals from a stratified ocean

Churnside

2015

J. Appl. Remote Sens

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Abstract. We use a bio-optical model of the optical properties of natural seawater to investigate the effects of subsurface chlorophyll layers on passive and active remote sensors. A thin layer of enhanced chlorophyll concentration reduces the remote sensing reflectance in the blue, while having little effect in the green. As a result, the chlorophyll concentration inferred from ocean color instruments will fall between the background concentration and the concentration in the layer, depending on the concentrations and the depth of the layer. For lidar, an iterative inversion algorithm is described that can reproduce the chlorophyll profile within the limits of the model. The model is extended to estimate column-integrated primary productivity, demonstrating that layers can contribute significantly to overall productivity. This contribution also depends on the chlorophyll concentrations and the depth of the layer. Using passive remote sensing alone to estimate primary productivity can lead to significant underestimation in the presence of subsurface plankton layers. Active remote sensing is not affected by this bias. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Subsurface plankton layers in the Arctic Ocean

Cited by 57 publications

References 33 publications

Light Availability and Phytoplankton Growth Beneath Arctic Sea Ice: Integrating Observations and Modeling

Light Availability and Phytoplankton Growth Beneath Arctic Sea Ice: Integrating Observations and Modeling

Optical Backscattering Measured by Airborne Lidar and Underwater Glider

Bio-optical model to describe remote sensing signals from a stratified ocean

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