A simple model for the evolution of melt pond coverage on permeable Arctic sea ice

Abstract. As the melt season progresses, sea ice in the Arctic often becomes permeable enough to allow for nearly complete drainage of meltwater that has collected on the ice surface. Melt ponds that remain after drainage are hydraulically connected to the ocean and correspond to regions of sea ice whose surface is below sea level. We present a simple model for the evolution of melt pond coverage on such permeable sea ice floes in which we allow for spatially varying ice melt rates and assume the whole floe is in hydrostatic balance. The model is represented by two simple ordinary differential equations, where the rate of change of pond coverage depends on the pond coverage. All the physical parameters of the system are summarized by four strengths that control the relative importance of the terms in the equations. The model both fits observations and allows us to understand the behavior of melt ponds in a way that is often not possible with more complex models. Examples of insights we can gain from the model are that (1) the pond growth rate is more sensitive to changes in bare sea ice albedo than changes in pond albedo, (2) ponds grow slower on smoother ice, and (3) ponds respond strongest to freeboard sinking on first-year ice and sidewall melting on multiyear ice. We also show that under a global warming scenario, pond coverage would increase, decreasing the overall ice albedo and leading to ice thinning that is likely comparable to thinning due to direct forcing. Since melt pond coverage is one of the key parameters controlling the albedo of sea ice, understanding the mechanisms that control the distribution of pond coverage will help improve large-scale model parameterizations and sea ice forecasts in a warming climate.

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A mechanism that produces dichotomy in melt pond coverage in sea ice floes

10.5194/tc-2016-7 ◽

2016 ◽

Author(s):

Predrag Popović ◽

Dorian S. Abbot

Keyword(s):

Fixed Point ◽

Sea Ice ◽

Large Scale ◽

Bimodal Distribution ◽

Melting Behavior ◽

The Arctic ◽

Scale Model ◽

Melt Pond ◽

Melt Ponds ◽

Ice Floes

Abstract. Late in the melt season, sea ice floes in the Arctic have been observed to exhibit a large range in melt pond coverage, from heavily ponded to almost pond free. Some of these observations are consistent with a bimodal distribution in pond coverage with few intermediately ponded ice floes. We present a model for the evolution of melt ponds on sea ice floes in which conservation of hydrostatic balance in response to melt creates an unstable fixed point in pond coverage: if the initial pond coverage is below a threshold value the floe becomes unponded, and if it is above the threshold the floe becomes heavily ponded. Whether the fixed point is physically realistic depends on the differential melting rates of different points on the ice: ice at the perimeter of ponds needs to melt sufficiently slower than bare ice on average. Interestingly, this shows that the melting behavior of the narrow boundary between bare ice and melt ponds can govern the melt pond evolution of the entire ice floe. Since melt pond coverage is one of the key parameters controlling the albedo of sea ice, understanding the mechanisms that control the distribution of pond coverage will help us improve large-scale model parameterizations and sea ice forecasts in a warming climate.

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Regional melt-pond fraction and albedo of thin Arctic first-year drift ice in late summer

The Cryosphere ◽

10.5194/tc-9-255-2015 ◽

2015 ◽

Vol 9 (1) ◽

pp. 255-268 ◽

Cited By ~ 13

Author(s):

D. V. Divine ◽

M. A. Granskog ◽

S. R. Hudson ◽

C. A. Pedersen ◽

T. I. Karlsen ◽

...

Keyword(s):

Sea Ice ◽

Open Water ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Surface Type ◽

Melt Pond ◽

Melt Ponds

Abstract. The paper presents a case study of the regional (≈150 km) morphological and optical properties of a relatively thin, 70–90 cm modal thickness, first-year Arctic sea ice pack in an advanced stage of melt. The study combines in situ broadband albedo measurements representative of the four main surface types (bare ice, dark melt ponds, bright melt ponds and open water) and images acquired by a helicopter-borne camera system during ice-survey flights. The data were collected during the 8-day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic, north of Svalbard at 82.3° N, from 26 July to 3 August 2012. A set of > 10 000 classified images covering about 28 km2 revealed a homogeneous melt across the study area with melt-pond coverage of ≈ 0.29 and open-water fraction of ≈ 0.11. A decrease in pond fractions observed in the 30 km marginal ice zone (MIZ) occurred in parallel with an increase in open-water coverage. The moving block bootstrap technique applied to sequences of classified sea-ice images and albedo of the four surface types yielded a regional albedo estimate of 0.37 (0.35; 0.40) and regional sea-ice albedo of 0.44 (0.42; 0.46). Random sampling from the set of classified images allowed assessment of the aggregate scale of at least 0.7 km2 for the study area. For the current setup configuration it implies a minimum set of 300 images to process in order to gain adequate statistics on the state of the ice cover. Variance analysis also emphasized the importance of longer series of in situ albedo measurements conducted for each surface type when performing regional upscaling. The uncertainty in the mean estimates of surface type albedo from in situ measurements contributed up to 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea-ice cover.

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Snow topography on undeformed Arctic sea ice captured by an idealized model

10.5194/egusphere-egu2020-12836 ◽

2020 ◽

Cited By ~ 1

Author(s):

Predrag Popovic ◽

Justin Finkel ◽

Mary Silber ◽

Dorian Abbot

Keyword(s):

Sea Ice ◽

Large Scale ◽

Early Stage ◽

Arctic Sea Ice ◽

Conductive Heat ◽

Melt Pond ◽

Melt Ponds ◽

Detailed Surface ◽

Arctic Sea ◽

Analytic Expressions

Our ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity, and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. Here, we develop a simple model of pre-melt ice surface topography that accurately describes snow cover on flat, undeformed ice. The model considers a surface that is a sum of randomly sized and placed ``snow dunes'' represented as Gaussian mounds. This model generalizes the "void model" of Popovic et al. (2018) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the pre-melt snow topography. We show that the model snow-depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage evolution during an early stage of pond formation. We also formulate a criterion for ice to remain pond-free throughout the summer. Results from our model could be directly included in large-scale models, thereby improving our understanding of energy balance on sea ice and allowing for more reliable predictions of Arctic sea ice in a future climate.&#160;

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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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Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach

Satellite Oceanography and Meteorology ◽

10.18063/som.v3i3.692 ◽

2018 ◽

Vol 3 (3) ◽

Cited By ~ 1

Author(s):

Qi Liu 1 ◽

Yawen Zhang 1

Keyword(s):

Neural Network ◽

Sea Ice ◽

Satellite Data ◽

Arctic Sea Ice ◽

The Arctic ◽

Neural Network Approach ◽

Melt Pond ◽

Melt Ponds ◽

Arctic Sea ◽

Visible Imagery

During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multi-period visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.

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Inorganic carbon dynamics of melt pond-covered first year sea ice in the Canadian Arctic

Biogeosciences Discussions ◽

10.5194/bgd-11-7485-2014 ◽

2014 ◽

Vol 11 (5) ◽

pp. 7485-7519 ◽

Cited By ~ 3

Author(s):

N.-X. Geilfus ◽

R. J. Galley ◽

O. Crabeck ◽

T. Papakyriakou ◽

J. Landy ◽

...

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Ice Melt ◽

Melt Pond ◽

Melt Ponds ◽

Arctic Sea ◽

Melt Water

Abstract. Melt pond formation is a common feature of the spring and summer Arctic sea ice. However, the role of the melt ponds formation and the impact of the sea ice melt on both the direction and size of CO2 flux between air and sea is still unknown. Here we describe the CO2-carbonate chemistry of melting sea ice, melt ponds and the underlying seawater associated with measurement of CO2 fluxes across first year landfast sea ice in the Resolute Passage, Nunavut, in June 2012. Early in the melt season, the increase of the ice temperature and the subsequent decrease of the bulk ice salinity promote a strong decrease of the total alkalinity (TA), total dissolved inorganic carbon (TCO2) and partial pressure of CO2 (pCO2) within the bulk sea ice and the brine. Later on, melt pond formation affects both the bulk sea ice and the brine system. As melt ponds are formed from melted snow the in situ melt pond pCO2 is low (36 μatm). The percolation of this low pCO2 melt water into the sea ice matrix dilutes the brine resulting in a strong decrease of the in situ brine pCO2 (to 20 μatm). As melt ponds reach equilibrium with the atmosphere, their in situ pCO2 increase (up to 380 μatm) and the percolation of this high concentration pCO2 melt water increase the in situ brine pCO2 within the sea ice matrix. The low in situ pCO2 observed in brine and melt ponds results in CO2 fluxes of −0.04 to −5.4 mmol m–2 d–1. As melt ponds reach equilibrium with the atmosphere, the uptake becomes less significant. However, since melt ponds are continuously supplied by melt water their in situ pCO2 still remains low, promoting a continuous but moderate uptake of CO2 (~ −1mmol m–2 d–1). The potential uptake of atmospheric CO2 by melting sea ice during the Arctic summer has been estimated from 7 to 16 Tg of C ignoring the role of melt ponds. This additional uptake of CO2 associated to Arctic sea ice needs to be further explored and considered in the estimation of the Arctic Ocean's overall CO2 budget.

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Inorganic carbon dynamics of melt-pond-covered first-year sea ice in the Canadian Arctic

Biogeosciences ◽

10.5194/bg-12-2047-2015 ◽

2015 ◽

Vol 12 (6) ◽

pp. 2047-2061 ◽

Cited By ~ 18

Author(s):

N.-X. Geilfus ◽

R. J. Galley ◽

O. Crabeck ◽

T. Papakyriakou ◽

J. Landy ◽

...

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Co2 Fluxes ◽

Ice Melt ◽

Melt Pond ◽

Melt Ponds ◽

Arctic Sea

Abstract. Melt pond formation is a common feature of spring and summer Arctic sea ice, but the role and impact of sea ice melt and pond formation on both the direction and size of CO2 fluxes between air and sea is still unknown. Here we report on the CO2–carbonate chemistry of melting sea ice, melt ponds and the underlying seawater as well as CO2 fluxes at the surface of first-year landfast sea ice in the Resolute Passage, Nunavut, in June 2012. Early in the melt season, the increase in ice temperature and the subsequent decrease in bulk ice salinity promote a strong decrease of the total alkalinity (TA), total dissolved inorganic carbon (TCO2) and partial pressure of CO2 (pCO2) within the bulk sea ice and the brine. As sea ice melt progresses, melt ponds form, mainly from melted snow, leading to a low in situ melt pond pCO2 (36 μatm). The percolation of this low salinity and low pCO2 meltwater into the sea ice matrix decreased the brine salinity, TA and TCO2, and lowered the in situ brine pCO2 (to 20 μatm). This initial low in situ pCO2 observed in brine and melt ponds results in air–ice CO2 fluxes ranging between −0.04 and −5.4 mmol m−2 day−1 (negative sign for fluxes from the atmosphere into the ocean). As melt ponds strive to reach pCO2 equilibrium with the atmosphere, their in situ pCO2 increases (up to 380 μatm) with time and the percolation of this relatively high concentration pCO2 meltwater increases the in situ brine pCO2 within the sea ice matrix as the melt season progresses. As the melt pond pCO2 increases, the uptake of atmospheric CO2 becomes less significant. However, since melt ponds are continuously supplied by meltwater, their in situ pCO2 remains undersaturated with respect to the atmosphere, promoting a continuous but moderate uptake of CO2 (~ −1 mmol m−2 day−1) into the ocean. Considering the Arctic seasonal sea ice extent during the melt period (90 days), we estimate an uptake of atmospheric CO2 of −10.4 Tg of C yr−1. This represents an additional uptake of CO2 associated with Arctic sea ice that needs to be further explored and considered in the estimation of the Arctic Ocean's overall CO2 budget.

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Transition in the fractal geometry of Arctic melt ponds

The Cryosphere ◽

10.5194/tc-6-1157-2012 ◽

2012 ◽

Vol 6 (5) ◽

pp. 1157-1162 ◽

Cited By ~ 23

Author(s):

C. Hohenegger ◽

B. Alali ◽

K. R. Steffen ◽

D. K. Perovich ◽

K. M. Golden

Keyword(s):

Sea Ice ◽

Statistical Physics ◽

Climate Modeling ◽

Critical Length ◽

Arctic Sea Ice ◽

The Arctic ◽

Climate Projections ◽

Transmitted Radiation ◽

Melt Pond ◽

Melt Ponds

Abstract. During the Arctic melt season, the sea ice surface undergoes a remarkable transformation from vast expanses of snow covered ice to complex mosaics of ice and melt ponds. Sea ice albedo, a key parameter in climate modeling, is determined by the complex evolution of melt pond configurations. In fact, ice–albedo feedback has played a major role in the recent declines of the summer Arctic sea ice pack. However, understanding melt pond evolution remains a significant challenge to improving climate projections. By analyzing area–perimeter data from hundreds of thousands of melt ponds, we find here an unexpected separation of scales, where pond fractal dimension D transitions from 1 to 2 around a critical length scale of 100 m2 in area. Pond complexity increases rapidly through the transition as smaller ponds coalesce to form large connected regions, and reaches a maximum for ponds larger than 1000 m2, whose boundaries resemble space-filling curves, with D ≈ 2. These universal features of Arctic melt pond evolution are similar to phase transitions in statistical physics. The results impact sea ice albedo, the transmitted radiation fields under melting sea ice, the heat balance of sea ice and the upper ocean, and biological productivity such as under ice phytoplankton blooms.

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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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Melt pond retrieval based on LinearPolar algorithm using Landsat data

10.5194/egusphere-egu21-14243 ◽

2021 ◽

Author(s):

Yuqing Qin ◽

Jie Su ◽

Mingfeng Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Relative Error ◽

Arctic Sea Ice ◽

The Arctic ◽

Landsat 8 ◽

Landsat Data ◽

Melt Pond ◽

Melt Ponds ◽

Sentinel 2

The formation and distribution of melt ponds also have an important influence on the Arctic climate. It is necessary to obtain more accurate information of melt ponds on Arctic sea ice by remote sensing. Present large-scale melt pond products, especially melt pond fraction (MPF), still need a lot of verification, and it is a good way to use the very high resolution optical satellite remote sensing data to verify the retrieval MPF of low-resolution melt pond results.Most MPF algorithm such as Markus (Markus, et al., 2003) and PCA (Rosel et al., 2011) relying on fixed melt pond albedo, LinearPolar algorithm (Wang et. al., 2020) considers that the albedo of melt ponds albedo is variable, it has been proved the retrieval results of this algorithm has a high accuracy of the MPF than that of the previous algorithm based on Sentinel-2 data in Wang et al.&#8217;s work. In this paper, we applied this algorithm to Landsat 8 data. Meanwhile, Sentinel-2 data as well as SVM and ISODATA method are used as the comparison and verification data. The results show that the MPF obtained from Landsat 8 using LinearPolar algorithm is the much more closer to Sentinel-2 than Markus and PCA algorithms, and the correlation coefficients of the two MPF is as high as 0.95. The overall relative error of LinearPolar algorithm is 53.5% and 46.4% lower than Markus and PCA algorithms, respectively. And in the cases without obvious melt ponds, the relative error is reduced more than that with obvious melt ponds. This is because LinearPolar algorithm can identify 100% dark melt ponds and relatively small-scale melt ponds, and the latter contributes more to MPF retrieval.The application of LinearPolar algorithm on Landsat can cover a wider range than Sentinel and enhance the verification efficiency. Moreover, because of the longer time series of Landsat data than Sentinel data, the long-term variation trend of sea ice in fixed areas can be monitored.

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