A New Remotely Operated Sensor Platform for Interdisciplinary Observations under Sea Ice

Katlein, Christian; Schiller, Martin; Belter, Hans Jakob; Coppolaro, Veronica; Wenslandt, David; Nicolaus, Marcel

doi:10.3389/fmars.2017.00281

Cited by 668 publications

(43 citation statements)

References 26 publications

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“…Journal of Geophysical Research: Oceans respectively. In 2011, 2012, and 2015, optical sensors were carried on board a V8Sii observation class ROV (Ocean Modules, Atvidaberg, Sweden; Nicolaus & Katlein, 2013), while in 2016 and 2017 we used a purpose-built improved M500 ROV (Ocean Modules, Atvidaberg, Sweden) as measurement platform (Katlein et al, 2017). Both used ROV systems were operated directly from the examined ice floe through an individual hole in the ice or in rare cases over the ice edge.…”

Section: 1029/2018jc014833mentioning

confidence: 99%

Seasonal Evolution of Light Transmission Distributions Through Arctic Sea Ice

et al. 2019

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Light transmission through sea ice is a critical process for energy partitioning at the polar atmosphere‐ice‐ocean boundary. Transmission of sunlight strongly impacts sea ice melting by absorption, as well as heat deposition, and primary productivity in the upper ocean. While earlier observations relied on a limited number of point observations, the recent years have seen an increase in spatially distributed light measurements underneath sea ice using remotely operated vehicles covering a wide range of ice conditions. These measurements allow us to reconstruct the seasonal evolution of the spatial variability in light transmission. Here we present measurements of sea ice light transmittance distributions from 6 years of Arctic under‐ice remotely operated vehicle operations. The data set covers the entire melt period of Central Arctic sea ice. Data are combined into a pseudo time series describing the seasonal evolution of the spatial variability of sea ice optical properties from spring to autumn freezeup. In spring, snowmelt increases light transmission continuously, until a secondary mode originating from translucent melt ponds appears in the histograms of light transmittance. This secondary mode persists long into autumn, before snowfall reduces overall light levels again. Comparison to several autonomous time series measurements from single locations confirms the detected general patterns of the seasonal evolution of light transmittance variability. This also includes characteristic spectral features caused by biological processes at the ice underside. The results allow for the evaluation of three different light transmittance parameterizations, implying that light transmission in current ice‐ocean models may not be accurately represented on large scales throughout all seasons while ice thickness alone is a poor predictor of light transmittance.

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Section: 1029/2018jc014833mentioning

confidence: 99%

Seasonal Evolution of Light Transmission Distributions Through Arctic Sea Ice

et al. 2019

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“…For the last decade, radiometers have been attached to remotely operated vehicles (ROV). Small-sized ROVs can be deployed through relatively small holes (<2 m) to cover areas in the order of a few hundred meters (Ambrose et al, 2005;Katlein et al, 2015Katlein et al, , 2017Lund-Hansen et al, 2018;Nicolaus et al, 2010). Navigating directly under sea ice, ROVs allow covering various types of sea ice, such as newly formed, ponded and snow-covered sea ice, as well as pressure ridges (Katlein et al, 2017).…”

Section: 1029/2019jc015007mentioning

confidence: 99%

“…ROV observations were taken using similar procedures as presented in Nicolaus and Katlein (2013) and Katlein et al (2017) using a V8 Sii ROV (Ocean Modules, Atvidaberg, Sweden) and RAMSES-ACC-VIS (TriOs…”

Section: Underwater Light Measurements 231 Rov Measurementsmentioning

confidence: 99%

Sensitivity of Phytoplankton Primary Production Estimates to Available Irradiance Under Heterogeneous Sea Ice Conditions

et al. 2019

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The Arctic ice scape is composed by a mosaic of ridges, hummocks, melt ponds, leads, and snow. Under such heterogeneous surfaces, drifting phytoplankton communities are experiencing a wide range of irradiance conditions and intensities that cannot be sampled representatively using single‐location measurements. Combining experimentally derived photosynthetic parameters with transmittance measurements acquired at spatial scales ranging from hundreds of meters (using a remotely operated vehicle, ROV) to thousands of meters (using a surface and underice trawl, SUIT), we assessed the sensitivity of water column primary production estimates to multiscale underice light measurements. Daily primary production calculated from transmittance from both the ROV and the SUIT ranged between 0.004 and 939 mgC·m−2·day−1. Upscaling these estimates at larger spatial scales using satellite‐derived sea ice concentration reduced the variability by 22% (0.004–731 mgC·m−2·day−1). The relative error in primary production estimates was two times lower when combining remote sensing and in situ data compared to ROV‐based estimates alone. These results suggest that spatially extensive in situ measurements must be combined with large‐footprint sea ice coverage sampling (e.g., remote sensing, aerial imagery) to accurately estimate primary production in ice‐covered waters. Also, the results indicated a decreasing error of primary production estimates with increasing sample size and the spatial scale at which in situ measurements are performed. Conversely, existing estimates of spatially integrated phytoplankton primary production in ice‐covered waters derived from single‐location light measurements may be associated with large statistical errors. Considering these implications is important for modeling scenarios and interpretation of existing measurements in a changing Arctic ecosystem.

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“…Direct measurements of convective turbulence can be made with specialized in‐lake instruments such as microstructure profilers, acoustic Doppler current profilers, and acoustic Doppler velocity meters (Kirillin et al and papers therein). Moreover, remotely operated and autonomous underwater vehicles are increasingly used as platforms to characterize physical dynamics in ice‐covered lakes (Katlein et al ). Aquatic eddy covariance systems, which make concurrent high‐frequency measurements of current velocities, temperature, conductivity, and dissolved oxygen, have been successfully used in lakes and under sea ice to measure turbulent exchanges and heat or solute fluxes at the ice–water interface and the sediment–water interface (McPhee ; McGinnis et al ; Else et al ).…”

Section: Measurements Of Physical Conditionsmentioning

confidence: 99%

The unique methodological challenges of winter limnology

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et al. 2018

Limnology & Ocean Methods

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Winter is an important season for many limnological processes, which can range from biogeochemical transformations to ecological interactions. Interest in the structure and function of lake ecosystems under ice is on the rise. Although limnologists working at polar latitudes have a long history of winter work, the required knowledge to successfully sample under winter conditions is not widely available and relatively few limnologists receive formal training. In particular, the deployment and operation of equipment in below 0 C temperatures pose considerable logistical and methodological challenges, as do the safety risks of sampling during the icecovered period. Here, we consolidate information on winter lake sampling and describe effective methods to measure physical, chemical, and biological variables in and under ice. We describe variation in snow and ice conditions and discuss implications for sampling logistics and safety. We outline commonly encountered methodological challenges and make recommendations for best practices to maximize safety and efficiency when sampling through ice or deploying instruments in ice-covered lakes. Application of such practices over a broad range of ice-covered lakes will contribute to a better understanding of the factors that regulate lakes during winter and how winter conditions affect the subsequent ice-free period.

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A New Remotely Operated Sensor Platform for Interdisciplinary Observations under Sea Ice

Cited by 668 publications

References 26 publications

Seasonal Evolution of Light Transmission Distributions Through Arctic Sea Ice

Seasonal Evolution of Light Transmission Distributions Through Arctic Sea Ice

Sensitivity of Phytoplankton Primary Production Estimates to Available Irradiance Under Heterogeneous Sea Ice Conditions

The unique methodological challenges of winter limnology

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