Average cosine coefficient and spectral distribution of the light field under sea ice: Implications for primary production

The Arctic spring phytoplankton bloom has been reported to commence under a melting sea ice cover as transmission of photosynthetically active radiation (PAR; 400–700 nm) suddenly increases with the formation of surface melt ponds. Spatial variability in ice surface characteristics, i.e., snow thickness or melt pond distributions, and subsequent impact on transmitted PAR makes estimating light-limited primary production difficult during this time of year. Added to this difficulty is the interpretation of data from various sensor types, including hyperspectral, multispectral, and PAR-band irradiance sensors, with either cosine-corrected (planar) or spherical (scalar) sensor heads. To quantify the impact of the heterogeneous radiation field under sea ice, spectral irradiance profiles were collected beneath landfast sea ice during the Green Edge ice-camp campaigns in May–June 2015 and June–July 2016. Differences between PAR measurements are described using the downwelling average cosine, μd, a measure of the degree of anisotropy of the downwelling underwater radiation field which, in practice, can be used to convert between downwelling scalar, E0d, and planar, Ed, irradiance. A significantly smallerμd(PAR) was measured prior to snow melt compared to after (0.6 vs. 0.7) when melt ponds covered the ice surface. The impact of the average cosine on primary production estimates, shown in the calculation of depth-integrated daily production, was 16% larger under light-limiting conditions when E0d was used instead of Ed. Under light-saturating conditions, daily production was only 3% larger. Conversion of underwater irradiance data also plays a role in the ratio of total quanta to total energy (EQ/EW, found to be 4.25), which reflects the spectral shape of the under-ice light field. We use these observations to provide factors for converting irradiance measurements between irradiance detector types and units as a function of surface type and depth under sea ice, towards improving primary production estimates.

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The impact of melt ponds on summertime microwave brightness temperatures and sea ice concentrations

10.5194/tc-2015-202 ◽

2016 ◽

Cited By ~ 2

Author(s):

S. Kern ◽

A. Rösel ◽

L. T. Pedersen ◽

N. Ivanova ◽

R. Saldo ◽

...

Keyword(s):

Sea Ice ◽

Open Water ◽

The Arctic ◽

Sea Ice Concentration ◽

Water Fraction ◽

Microwave Brightness Temperature ◽

Ice Melt ◽

Melt Ponds ◽

Retrieval Algorithms ◽

The Impact

Abstract. The sea ice concentration (SIC) derived from satellite microwave brightness temperature (TB) data are known to be less accurate during summer melt conditions – in the Arctic Ocean primarily because of the impact of melt ponds on sea ice. Using data from June to August 2009, we investigate how TBs and SICs vary as a function of the ice surface fraction (ISF) computed from open water fraction and melt pond fraction both derived from satellite optical reflectance data. SIC is computed from TBs using a set of eight different retrieval algorithms and applying a consistent set of tie points. We find that TB values change during sea ice melt non-linearly and not monotonically as a function of ISF for ISF of 50 to 100 %. For derived parameters such as the polarization ratio at 19 GHz the change is monotonic but substantially smaller than theoretically expected. Changes in ice/snow radiometric properties during melt also contribute to the TB changes observed; these contributions are functions of frequency and polarization and have the potential to partly counter-balance the impact of changing ISF on the observed TBs. All investigated SIC retrieval algorithms overestimate ISF when using winter tie points. The overestimation varies among the algorithms as a function of ISF such that the SIC retrieval algorithms could be categorized into two different classes. These reveal a different degree of ISF overestimation at high ISF and an opposite development of ISF over-estimation as ISF decreases. For one class, correlations between SIC and ISF are ≥ 0.85 and the associated linear regression lines suggest an exploitable relationship between SIC and ISF if reliable summer sea ice tie points can be established. This study shows that melt ponds are interpreted as open water by the SIC algorithms, while the concentration of ice between the melt ponds is in general being overestimated. These two effects may cancel each other out and thus produce seemingly correct SIC for the wrong reasons. This cancelling effect will in general only be "correct" at one specific value of MPF. Based on our findings we recommend to not correct SIC algorithms for the impact of melt ponds as this seems to violate physical principles. Users should be aware that the SIC algorithms available at the moment retrieve a combined parameter presented by SIC in winter and ISF in summer.

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Modelling the impact of riverine DON removal by marine bacterioplankton on primary production in the Arctic Ocean

Biogeosciences ◽

10.5194/bg-12-3385-2015 ◽

2015 ◽

Vol 12 (11) ◽

pp. 3385-3402 ◽

Cited By ~ 9

Author(s):

V. Le Fouest ◽

M. Manizza ◽

B. Tremblay ◽

M. Babin

Keyword(s):

Sea Ice ◽

Primary Production ◽

Arctic Ocean ◽

Dissolved Organic Nitrogen ◽

Organic Nitrogen ◽

The Arctic ◽

Biogeochemical Model ◽

The Impact ◽

Ice Model ◽

Annual Flux

Abstract. The planktonic and biogeochemical dynamics of the Arctic shelves exhibit a strong variability in response to Arctic warming. In this study, we employ a biogeochemical model coupled to a pan-Arctic ocean–sea ice model (MITgcm) to elucidate the processes regulating the primary production (PP) of phytoplankton, bacterioplankton (BP), and their interactions. The model explicitly simulates and quantifies the contribution of usable dissolved organic nitrogen (DON) drained by the major circum-Arctic rivers to PP and BP in a scenario of melting sea ice (1998–2011). Model simulations suggest that, on average between 1998 and 2011, the removal of usable riverine dissolved organic nitrogen (RDON) by bacterioplankton is responsible for a ~ 26% increase in the annual BP for the whole Arctic Ocean. With respect to total PP, the model simulates an increase of ~ 8% on an annual basis and of ~ 18% in summer. Recycled ammonium is responsible for the PP increase. The recycling of RDON by bacterioplankton promotes higher BP and PP, but there is no significant temporal trend in the BP : PP ratio within the ice-free shelves over the 1998–2011 period. This suggests no significant evolution in the balance between autotrophy and heterotrophy in the last decade, with a constant annual flux of RDON into the coastal ocean, although changes in RDON supply and further reduction in sea-ice cover could potentially alter this delicate balance.

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Drone-based sea ice albedo measurements and photogrammetry during the Arctic freeze-up in the MOSAiC expedition

10.5194/egusphere-egu21-11377 ◽

2021 ◽

Author(s):

Roberta Pirazzini ◽

Henna-Reetta Hannula ◽

David Brus ◽

Ruzica Dadic ◽

Martin Scnheebeli

Keyword(s):

Surface Roughness ◽

Sea Ice ◽

Surface Topography ◽

Near Infrared ◽

Snow Accumulation ◽

The Arctic ◽

Ocean Optics ◽

Surface Features ◽

Ice Surface ◽

Melt Ponds

Aerial albedo measurements and detailed surface topography of sea ice are needed to characterize the distribution of the various surface types (melt ponds of different depth and size, ice of different thicknesses, leads, ridges) and to determine how they contribute to the areal-averaged albedo on different horizontal scales. These measurements represent the bridge between the albedo measured from surface-based platforms, which typically have metre-to-tens-of-meters footprint, and satellite observations or large-grid model outputs.Two drones were operated in synergy to measure the albedo and map the surface topography of the sea ice during the leg 5 of the MOSAiC expedition (August-September 2020), when concurrent albedo and surface roughness measurements were collected using surface-based instruments. The drone SPECTRA was equipped with paired Kipp and Zonen CM4 pyranometers measuring broadband albedo and paired Ocean Optics STS VIS (350 &#8211; 800 nm) and NIR (650-1100 nm) micro-radiometers measuring visible and near-infrared spectral albedo, and the drone Mavic 2 Pro was equipped with camera to perform photography mapping of the area measured by the SPECTRA drone.Here we illustrate the collected data, which show a drastic change in sea ice albedo during the observing period, from the initial melting state to the freezing and snow accumulation state, and demonstrate how this change is related to the evolution of the different surface features, melt ponds and leads above all. From the data analysis we can conclude that the 30m albedo is not significantly affected by the individual surface features and, therefore, it is potentially representative of the sea ice albedo in satellite footprint and model grid areas.The Digital Elevation Models (DEMs) of the sea ice surface obtained from UAV photogrammetry are combined with the DEMs based on Structure From Motion technique that apply photos manually taken close to the surface. This will enable us to derive the surface roughness from sub-millimeter to meter scales, which is critical to interpret the observed albedo and to develop correction methods to eliminate the artefacts caused by shadows.The UAV-based albedo and surface roughness are highly complementary also to analogous helicopter-based observations, and will be relevant for the interpretation of all the physical and biochemical processes observed at and near the sea ice surface during the transition from melting to freezing and growing.

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Modelling melt ponds in Global Circulation Models

10.5194/egusphere-egu2020-21530 ◽

2020 ◽

Author(s):

Jean Sterlin ◽

Thierry Fichefet ◽

François Massonnet ◽

Olivier Lecomte ◽

Martin Vancoppenolle

Keyword(s):

Aspect Ratio ◽

Sea Ice ◽

Climate Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Global Circulation ◽

Melt Pond ◽

Global Circulation Models ◽

Melt Ponds ◽

The Impact

Melt ponds appear during the Arctic summer on the sea ice cover when meltwater and liquid precipitation collect in the depressions of the ice surface. The albedo of the melt ponds is lower than that of surrounding ice and snow areas. Consequently, the melt ponds are an important factor for the ice-albedo feedback, a mechanism whereby a decrease in albedo results in greater absorption of solar radiation, further ice melt, and lower albedos&#160;To account for the effect of melt ponds on the climate, several numerical schemes have been introduced for Global Circulation Models. They can be classified into two groups. The first group makes use of an explicit relation to define the aspect ratio of the melt ponds. The scheme of Holland et al. (2012) uses a constant ratio of the melt pond depth to the fraction of sea ice covered by melt ponds. The second group relies on theoretical considerations to deduce the area and volume of the melt ponds. The scheme of Flocco et al. (2012) uses the ice thickness distribution to share the meltwater between the ice categories and determine the melt ponds characteristics.Despite their complexity, current melt pond schemes fail to agree on the trends in melt pond fraction of sea ice area during the last decades. The disagreement casts doubts on the projected melt pond changes. It also raises questions on the definition of the physical processes governing the melt ponds in the schemes and their sensitivity to atmospheric surface conditions.In this study, we aim at identifying 1) the conceptual difference of the aspect ratio definition in melt pond schemes; 2) the role of refreezing for melt ponds; 3) the impact of the uncertainties in the atmospheric reanalyses. To address these points, we have run the Louvain-la-Neuve Ice Model (LIM), part of the Nucleus for European Modelling of the Ocean (NEMO) version 3.6 along with two different atmospheric reanalyses as surface forcing sets. We used the reanalyses in association with Holland et al. (2012) and Flocco et al. (2012) melt pond schemes. We selected Holland et al. (2012) pond refreezing formulation for both schemes and tested two different threshold temperatures for refreezing.&#160;From the experiments, we describe the impact on Arctic sea ice and state the importance of including melt ponds in climate models. We attempt at disentangling the separate effects of the type of melt pond scheme, the refreezing mechanism, and the atmospheric surface forcing method, on the climate. We finally formulate a recommendation on the use of melt ponds in climate models.&#160;

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Importance of variable neutral drag coefficients for ocean-ice and air-ice fluxes in polar regions

10.5194/egusphere-egu21-12274 ◽

2021 ◽

Author(s):

Jean Sterlin ◽

Thierry Fichefet ◽

Francois Massonnet ◽

Michel Tsamados

Keyword(s):

Surface Roughness ◽

Sea Ice ◽

Surface Fluxes ◽

The Arctic ◽

Climate Modelling ◽

Form Drag ◽

Drag Coefficients ◽

Ice Surface ◽

Melt Ponds ◽

Ice Surface Roughness

Sea ice features a variety of obstacles to the flow of air and seawater at its top and bottom surfaces. Sea ice ridges, floe edges, ice surface roughness and melt ponds, lead to a form drag that interacts dynamically with the air-ice and ocean-ice fluxes of heat and momentum. In most climate models, surface fluxes of heat and momentum are calculated by bulk formulas using constant drag coefficients over sea ice, to reflect the mean surface roughness of the interfaces with the atmosphere and ocean. However, such constant drag coefficients do not account for the subgrid-scale variability of the sea ice surface roughness. To study the effect of form drag over sea ice on air-ice-ocean fluxes, we have implemented a formulation that estimates drag coefficients in ice-covered areas comprising the effect of sea ice ridges, floe edges and melt ponds, and ice surface skin (Tsamados et al., 2013) into the NEMO3.6-LIM3 global coupled ice-ocean model. In this work, we thoroughly analyse the impacts of this improvement on the model performance in both the Arctic and Antarctic. A particular attention is paid to the influence of this modification on the air-ice-ocean fluxes of heat and momentum, and the characteristics of the oceanic surface layers. We also formulate an assessment of the importance of variable drag coefficients over sea ice for the climate modelling community.

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Evolution of Backscattering Coefficients of Drifting Multi-Year Sea Ice during End of Melting and Onset of Freeze-up in the Western Beaufort Sea

Remote Sensing ◽

10.3390/rs12091378 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1378

Author(s):

Seung Hee Kim ◽

Hyun-Cheol Kim ◽

Chang-Uk Hyun ◽

Sungjae Lee ◽

Jung-Seok Ha ◽

...

Keyword(s):

Sea Ice ◽

Transition Period ◽

Incidence Angle ◽

Beaufort Sea ◽

Reanalysis Data ◽

The Arctic ◽

Angle Dependence ◽

Ice Surface ◽

Melt Ponds ◽

Increasing Trend

Backscattering coefficients of Sentinel-1 synthetic aperture radar (SAR) data of drifting multi-year sea ice in the western Beaufort Sea during the transition period between the end of melting and onset of freeze-up are analyzed, in terms of the incidence angle dependence and temporal variation. The mobile sea ice surface is tracked down in a 1 km by 1 km region centered at a GPS tracker, which was installed during a field campaign in August 2019. A total of 24 Sentinel-1 images spanning 17 days are used and the incidence angle dependence in HH- and HV-polarization are −0.24 dB/deg and −0.10 dB/deg, respectively. Hummocks and recently frozen melt ponds seem to cause the mixture behavior of surface and volume scattering. The normalized backscattering coefficients in HH polarization gradually increased in time at a rate of 0.15 dB/day, whereas the HV-polarization was relatively flat. The air temperature from the ERA5 hourly reanalysis data has a strong negative relation with the increasing trend of the normalized backscattering coefficients in HH-polarization. The result of this study is expected to complement other previous studies which focused on winter or summer seasons in other regions of the Arctic Ocean.

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Improved Sea Ice Shortwave Radiation Physics in CCSM4: The Impact of Melt Ponds and Aerosols on Arctic Sea Ice

Journal of Climate ◽

10.1175/jcli-d-11-00078.1 ◽

2012 ◽

Vol 25 (5) ◽

pp. 1413-1430 ◽

Cited By ~ 225

Author(s):

Marika M. Holland ◽

David A. Bailey ◽

Bruce P. Briegleb ◽

Bonnie Light ◽

Elizabeth Hunke

Keyword(s):

Twentieth Century ◽

Radiative Transfer ◽

Sea Ice ◽

Aerosol Deposition ◽

Arctic Sea Ice ◽

The Arctic ◽

Climate System Model ◽

Melt Ponds ◽

Arctic Sea ◽

The Impact

The Community Climate System Model, version 4 has revisions across all components. For sea ice, the most notable improvements are the incorporation of a new shortwave radiative transfer scheme and the capabilities that this enables. This scheme uses inherent optical properties to define scattering and absorption characteristics of snow, ice, and included shortwave absorbers and explicitly allows for melt ponds and aerosols. The deposition and cycling of aerosols in sea ice is now included, and a new parameterization derives ponded water from the surface meltwater flux. Taken together, this provides a more sophisticated, accurate, and complete treatment of sea ice radiative transfer. In preindustrial CO2 simulations, the radiative impact of ponds and aerosols on Arctic sea ice is 1.1 W m−2 annually, with aerosols accounting for up to 8 W m−2 of enhanced June shortwave absorption in the Barents and Kara Seas and with ponds accounting for over 10 W m−2 in shelf regions in July. In double CO2 (2XCO2) simulations with the same aerosol deposition, ponds have a larger effect, whereas aerosol effects are reduced, thereby modifying the surface albedo feedback. Although the direct forcing is modest, because aerosols and ponds influence the albedo, the response is amplified. In simulations with no ponds or aerosols in sea ice, the Arctic ice is over 1 m thicker and retains more summer ice cover. Diagnosis of a twentieth-century simulation indicates an increased radiative forcing from aerosols and melt ponds, which could play a role in twentieth-century Arctic sea ice reductions. In contrast, ponds and aerosol deposition have little effect on Antarctic sea ice for all climates considered.

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Impact of refreezing melt ponds on Arctic sea ice basal growth

10.5194/tc-2016-118 ◽

2016 ◽

Author(s):

Daniela Flocco ◽

Daniel L. Feltham ◽

David Schroeder ◽

Michel Tsamados

Keyword(s):

Sea Ice ◽

Climate Model ◽

Pond Water ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Growth ◽

Melt Pond ◽

Melt Ponds ◽

Ocean Properties ◽

The Impact

Abstract. Melt ponds forming over the sea ice cover in the Arctic profoundly impact the surface albedo inducing a positive feedback leading to further melting. Here we examine the processes involved in melt pond refreezing and their impact on basal sea ice growth. When ponds freeze, the ice that forms on them insulates the pond trapping it between the sea ice and the ice lid. Trapped melt ponds delay basal sea ice growth in Autumn: ice thickens only after (1) the pond water has been fully frozen and (2) a temperature gradient is established that will conduct heat away from the ocean. Sea ice thickening in the areas where ponds are present is mainly due to the pond's water refreezing. Pan-Arctic simulations with a stand-alone sea ice model and studies with a high-resolution one-dimensional, three-layer refreezing model are used to study the impact on sea ice growth of trapped melt ponds. Basal sea ice growth may be inhibited by up to two months. We estimate an inhibited basal growth of up to 228 km3, which represents 25 % of the basal sea ice growth estimated by PIOMAS during the months of September and October. The brine not released due to the inhibited basal growth during this period could have implications for the ocean properties and circulation. The impact of trapped melt ponds has not been accounted for so far in any climate model.

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Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

Remote Sensing ◽

10.3390/rs13193882 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3882

Author(s):

Jiechen Zhao ◽

Yining Yu ◽

Jingjing Cheng ◽

Honglin Guo ◽

Chunhua Li ◽

...

Keyword(s):

Sea Ice ◽

The Arctic ◽

High Concentration ◽

Sea Ice Concentration ◽

Melt Pond ◽

Ice Surface ◽

Melt Ponds ◽

Ice Concentration ◽

Microwave Imager

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

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Inter-comparison of melt pond products with melt/freeze-up dates and sea ice concentration data

10.5194/egusphere-egu2020-20874 ◽

2020 ◽

Author(s):

Sanggyun Lee ◽

Julienne Stroeve ◽

Michel Tsamados

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Solar Heat ◽

Melt Pond ◽

Ice Surface ◽

Melt Ponds ◽

Ice Concentration

&#160;Melt ponds are a dominant feature on the Arctic sea ice surface in summer, occupying up to about 50 &#8211; 60% of the sea ice surface during advanced melt. Melt ponds normally begin to form around mid-May in the marginal ice zone and expand northwards as the summer melt season progresses. Once melt ponds emerge, the scattering characteristics of the ice surface changes, dramatically lowering the sea ice albedo. Since 96% of the total annual solar heat into the ocean through sea ice occurs between May and August, the presence of melt ponds plays a significant role in this transfer of solar heat, influencing not only the sea ice energy balance, but also the amount of light available under the sea ice and ocean primary productivity. Given the importance melt ponds play in the coupled Arctic climate-ecosystem, mapping and quantification of melt pond variability on a Pan-Arctic basin scale are needed. Satellite-based observations are the only way to map melt ponds and albedo changes on a pan-Arctic scale. R&#246;sel et al. (2012) utilized a MODIS 8-day average product to map melt ponds on a pan-Arctic scale and over several years. In another approach, melt pond fraction and surface albedo were retrieved based on the physical and optical characteristics of sea ice and melt ponds without a priori information using MERIS.Here, we propose a novel machine learning-based methodology to map Arctic melt ponds from MODIS 500m resolution data. We provide a merging procedure to create the first pan-Arctic melt pond product spanning a 20-year period at a weekly temporal resolution. Specifically, we use MODIS data together with machine learning, including multi-layer neural network and logistic regression to test our ability to map melt ponds from the start to the end of the melt season. Since sea ice reflectance is strongly dependent on the viewing and solar geometry (i.e. sensor and solar zenith and azimuth angles), we attempt to minimize this dependence by using normalized band ratios in the machine learning algorithms. Each melt pond retrieval algorithm is different and validation ways are different as well producing somewhat dissimilar melt pond results. In this study, we inter-compare melt ponds products from different institutes, including university of Hamburg, university of Bremen, and university college London. The melt pond maps are compared with melt onset and freeze-up dates data and sea ice concentration. The melt pond maps are evaluated by melt pond fraction statistics from high resolution satellite (MEDEA) images that have not been used for the evaluation in melt pond products.&#160;

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