Impact of refreezing melt ponds on Arctic sea ice basal growth

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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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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Investigating future changes in the volume budget of the Arctic sea ice in a coupled climate model

The Cryosphere ◽

10.5194/tc-12-2855-2018 ◽

2018 ◽

Vol 12 (9) ◽

pp. 2855-2868 ◽

Cited By ~ 4

Author(s):

Ann Keen ◽

Ed Blockley

Keyword(s):

Sea Ice ◽

21St Century ◽

Climate Model ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Growth ◽

Basal Ice ◽

Arctic Sea ◽

The Impact

Abstract. We present a method for analysing changes in the modelled volume budget of the Arctic sea ice as the ice declines during the 21st century. We apply the method to the CMIP5 global coupled model HadGEM2-ES to evaluate how the budget components evolve under a range of different forcing scenarios. As the climate warms and the ice cover declines, the sea ice processes that change the most in HadGEM2-ES are summer melting at the top surface of the ice due to increased net downward radiation and basal melting due to extra heat from the warming ocean. There is also extra basal ice formation due to the thinning ice. However, the impact of these changes on the volume budget is affected by the declining ice cover. For example, as the autumn ice cover declines the volume of ice formed by basal growth declines as there is a reduced area over which this ice growth can occur. As a result, the biggest contribution to Arctic ice decline in HadGEM2-ES is the reduction in the total amount of basal ice growth during the autumn and early winter. Changes in the volume budget during the 21st century have a distinctive seasonal cycle, with processes contributing to ice decline occurring in May–June and September to November. During July and August the total amount of sea ice melt decreases, again due to the reducing ice cover. The choice of forcing scenario affects the rate of ice decline and the timing and magnitude of changes in the volume budget components. For the HadGEM2-ES model and for the range of scenarios considered for CMIP5, the mean changes in the volume budget depend strongly on the evolving ice area and are independent of the speed at which the ice cover declines.

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New insight from CryoSat-2 sea ice thickness for sea ice modelling

10.5194/tc-2018-159 ◽

2018 ◽

Author(s):

David Schröder ◽

Danny L. Feltham ◽

Michel Tsamados ◽

Andy Ridout ◽

Rachel Tilling

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Atmospheric Conditions ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Ice Growth ◽

Melt Pond ◽

The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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Interannual variability in Transpolar Drift ice thickness and potential impact of Atlantification

10.5194/tc-2020-305 ◽

2020 ◽

Author(s):

H. Jakob Belter ◽

Thomas Krumpen ◽

Luisa von Albedyll ◽

Tatiana A. Alekseeva ◽

Sergei V. Frolov ◽

...

Keyword(s):

Time Series ◽

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Fram Strait ◽

Ice Growth ◽

Arctic Sea ◽

Transpolar Drift ◽

The Impact

Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exits the Arctic Ocean through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age and number of freezing degree days. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea, but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate the impact of Atlantification on sea ice growth in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea ice covered Arctic.

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Evidence for an increasing role of ocean heat in Arctic winter sea ice growth

Journal of Climate ◽

10.1175/jcli-d-20-0848.1 ◽

2021 ◽

pp. 1-42

Author(s):

Robert Ricker ◽

Frank Kauker ◽

Axel Schweiger ◽

Stefan Hendricks ◽

Jinlun Zhang ◽

...

Keyword(s):

Sea Ice ◽

Reanalysis Data ◽

Arctic Sea Ice ◽

Satellite Observations ◽

The Arctic ◽

Ice Growth ◽

Marginal Seas ◽

Air Temperatures ◽

Barents And Kara Seas ◽

The Impact

AbstractWe investigate how sea ice decline in summer and warmer ocean and surface temperatures in winter affect sea ice growth in the Arctic. Sea ice volume changes are estimated from satellite observations during winter from 2002 to 2019 and partitioned into thermodynamic growth and dynamic volume change. Both components are compared to validated sea ice-ocean models forced by reanalysis data to extend observations back to 1980 and to understand the mechanisms that cause the observed trends and variability. We find that a negative feedback driven by the increasing sea ice retreat in summer yields increasing thermodynamic ice growth during winter in the Arctic marginal seas eastward from the Laptev Sea to the Beaufort Sea. However, in the Barents and Kara Seas, this feedback seems to be overpowered by the impact of increasing oceanic heat flux and air temperatures, resulting in negative trends in thermodynamic ice growth of -2 km3month-1yr-1 on average over 2002-2019 derived from satellite observations.

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An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models

10.5194/tc-2019-314 ◽

2020 ◽

Author(s):

Ann Keen ◽

Ed Blockley ◽

David Bailey ◽

Jens Boldingh Debernard ◽

Mitchell Bushuk ◽

...

Keyword(s):

Sea Ice ◽

Climate Model ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Formation ◽

Mass Budget ◽

Ice Growth ◽

Arctic Sea ◽

Basal Melting

Abstract. We compare the mass budget of the Arctic sea ice for 14 models submitted to the latest Climate Model Inter-comparison Project (CMIP6), using new diagnostics that have not been available for previous model inter-comparisons. Using these diagnostics allows us to look beyond the standard metrics of ice cover and thickness, to compare the processes of sea ice growth and loss in climate models in a more detailed way than has previously been possible. For the 1960–89 multi-model mean, the dominant processes causing annual ice growth are basal growth and frazil ice formation, which both occur during the winter. The main processes by which ice is lost are basal melting, top melting and advection of ice out of the Arctic. The first two processes occur in summer, while the latter process is present all year. The sea-ice budgets for individual models are strikingly similar overall in terms of the major processes causing ice growth and loss, and in terms of the time of year during which each process is important. However, there are also some key differences between the models. The relative amounts of frazil and basal ice formation varies between the models. This is, to some extent at least, attributable to exactly how the frazil growth is formulated within each model. There are also differences in the relative amounts of top and basal melting. As the ice cover and mass decline during the 21st century, we see a shift in the timing of the top and basal melting in the multi-model mean, with more melt occurring earlier in the year, and less melt later in the summer. The amount of basal growth in the autumn reduces, but the amount of basal growth later in the winter increases due to the ice being thinner. Overall, extra ice loss in May–June and reduced ice growth in October-November is partially offset by reduced ice melt in August and increased ice growth in January–February. For the individual models, changes in the budget components vary considerably in terms of magnitude and timing of change. However, when the evolving budget terms are considered as a function of the changing ice state itself, behaviours common to all the models emerge, suggesting that the sea ice components of the models are fundamentally responding in a broadly consistent way to the warming climate. Additional results from a forced ocean-ice model show that although atmospheric forcing is crucial for the sea ice mass budget, the sea ice physics also plays an important role.

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Understanding model spread in sea ice volume by attribution of model differences in seasonal ice growth and melt

10.5194/tc-2021-351 ◽

2021 ◽

Author(s):

Alex West ◽

Ed Blockley ◽

Mat Collins

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

Surface Flux ◽

Early Summer ◽

Arctic Sea Ice ◽

Ice Growth ◽

Melt Pond ◽

Ice Volume ◽

Melt Ponds ◽

Future Loss

Abstract. Arctic sea ice is declining rapidly, but predictions of its future loss are made difficult by the large spread both in present-day and in future sea ice area and volume; hence, there is a need to better understand the drivers of model spread in sea ice state. Here we present a framework for understanding differences between modelled sea ice simulations based on attributing seasonal ice growth and melt differences. In the method presented, the net downward surface flux is treated as the principal driver of seasonal sea ice growth and melt. A system of simple models is used to estimate the pointwise effect of model differences in key Arctic climate variables on this surface flux, and hence on seasonal sea ice growth and melt. We compare three models with very different historical sea ice simulations: HadGEM2-ES, HadGEM3-GC3.1 and UKESM1.0. The largest driver of differences in ice growth / melt between these models is shown to be the ice area in summer (representing the surface albedo feedback) and the ice thickness distribution in winter (the thickness-growth feedback). Differences in snow and melt-pond cover during the early summer exert a smaller effect on the seasonal growth and melt, hence representing the drivers of model differences in both this and in the sea ice volume. In particular, the direct impacts on sea ice growth / melt of differing model parameterisations of snow area and of melt-ponds are shown to be small but non-negligible.

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Suppression of Arctic sea ice growth by winter clouds and snowfall

10.21203/rs.3.rs-84779/v1 ◽

2021 ◽

Author(s):

Won-il Lim ◽

Hyo-Seok Park ◽

Andrew Stewart ◽

Kyong-Hwan Seo

Keyword(s):

Sea Ice ◽

Longwave Radiation ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Growth ◽

Downward Longwave Radiation ◽

Arctic Sea ◽

The Impact

Abstract The ongoing Arctic warming has been pronounced in winter and has been associated with an increase in downward longwave radiation. While previous studies have demonstrated that poleward moisture flux into the Arctic strengthens downward longwave radiation, less attention has been given to the impact of the accompanying increase in snowfall. Here, utilizing state-of-the art sea ice models, we show that typical winter snowfall anomalies of 1.0 cm, accompanied by positive downward longwave radiation anomalies of ~5 W m-2 can decrease sea ice thickness by around 5 cm in the following spring over the Eurasian Seas. This basin-wide ice thinning is followed by a shrinking of summer ice extent in extreme cases. In the winter of 2016–17, anomalously strong warm/moist air transport combined with ~2.5 cm increase in snowfall decreased spring ice thickness by ~10 cm and decreased the following summer sea ice extent by 5–30%. Projected future reductions in the thickness of Arctic sea ice and snow will amplify the impact of anomalous winter snowfall events on winter sea ice growth and seasonal sea ice thickness.

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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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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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