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

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

The Cryosphere ◽

10.5194/tc-13-125-2019 ◽

2019 ◽

Vol 13 (1) ◽

pp. 125-139 ◽

Cited By ~ 8

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 ◽

Component Community ◽

Sea Ice Thickness ◽

Ice Growth ◽

Melt Pond ◽

The Impact

Abstract. Estimates of Arctic sea ice thickness have been 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 five ice thickness categories used in a sea ice component (Community Ice CodE, 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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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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Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model

Annals of Glaciology ◽

10.1017/aog.2020.13 ◽

2020 ◽

Vol 61 (82) ◽

pp. 97-105

Author(s):

Jun Ono ◽

Yoshiki Komuro ◽

Hiroaki Tatebe

Keyword(s):

Sea Ice ◽

Climate Model ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Predictive Skill ◽

The Pacific ◽

The Impact

AbstractThe impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September.

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Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness

The Cryosphere ◽

10.5194/tc-12-3419-2018 ◽

2018 ◽

Vol 12 (11) ◽

pp. 3419-3438 ◽

Cited By ~ 34

Author(s):

Edward W. Blockley ◽

K. Andrew Peterson

Keyword(s):

Sea Ice ◽

Ice Cover ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Forecast Errors ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Near Surface ◽

Arctic Sea ◽

The Impact

Abstract. Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere–ocean–sea-ice models. Although assimilation of sea-ice concentration into these systems is commonplace, sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. Here, for the first time, we directly assess the impact of winter sea-ice thickness initialization on the skill of summer seasonal predictions by assimilating CryoSat-2 thickness data into the Met Office's coupled seasonal prediction system (GloSea). We show a significant improvement in predictive skill of Arctic sea-ice extent and ice-edge location for forecasts of September Arctic sea ice made from the beginning of the melt season. The improvements in sea-ice cover lead to further improvement of near-surface air temperature and pressure fields across the region. A clear relationship between modelled winter thickness biases and summer extent errors is identified which supports the theory that Arctic winter thickness provides some predictive capability for summer ice extent, and further highlights the importance that modelled winter thickness biases can have on the evolution of forecast errors through the melt season.

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Using CryoSat-2 estimates to analyse sub-grid scale sea ice thickness distribution in HadGEM3 simulations for CMIP6

10.5194/egusphere-egu2020-18134 ◽

2020 ◽

Author(s):

David Schroeder ◽

Danny Feltham ◽

Michel Tsamados

Keyword(s):

Sea Ice ◽

Annual Cycle ◽

Thickness Distribution ◽

Arctic Sea Ice ◽

Area Fraction ◽

Strength Parameter ◽

Striking Difference ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Growth

<p>A sub-grid scale sea ice thickness distribution (ITD) is a key parameterization to enable a large-scale sea ice model to simulate winter ice growth and sea ice ridging processes realistically. Recent sophisticated developments, e.g. a melt pond model, a form drag parameterization, a floe-size distribution model, fundamentally depend on the ITD. In spite of its importance, knowledge is poor about the accuracy of the simulated ITD. Here, we derive the ITD from individual Arctic sea ice thickness estimates available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We bin the CS2 data into 5 ice thickness categories used by the sea ice component CICE of HadGEM3 climate simulations: (1) ice thickness h < 60 cm, (2) 60 cm < h < 1.4 m, (3) 1.4 m < h < 2.4 m, (4) 2.4 m < h < 3.6 m, (5) h > 3.6 m. Our analysis includes historical simulations and future projections with the HadGEM3-GC31 model as well as forced ocean-ice and standalone ice simulations with the same model components NEMO v3.6 and CICE v5.1.2. The most striking difference occurs regarding the annual cycle of area fraction of ice in the thickest category (> 3.6 m). According to CS2, in the Central Arctic the fraction is below 2% in October and increases to 15-40% in April. In contrast the annual cycle is weak in all simulations. The magnitude of the area fraction differs between the simulations. For simulations which agree best with CS2 for grid cell mean ice thickness, the area fraction of thick ice is around 5% constantly throughout the whole year. Potential reasons for the discrepancy are discussed and sensitivity experiments presented to study the impact of sea ice settings on the simulated ITD, e.g. ice strength parameter, parameter for participating in ridging, heat transfer coefficients.</p>

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Sea-ice freeboard or thickness? Design choices in the context of data assimilation in the coupled numerical prediction system EC-Earth3 for seasonal Arctic sea ice prediction

10.5194/egusphere-egu21-8317 ◽

2021 ◽

Author(s):

Francois Massonnet ◽

Sara Fleury ◽

Florent Garnier ◽

Ed Blockley ◽

Pablo Ortega Montilla ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

General Circulation ◽

Seasonal Prediction ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration

<p>It is well established that winter and spring Arctic sea-ice thickness anomalies are a key source of predictability for late summer sea-ice concentration. While numerical general circulation models (GCMs) are increasingly used to perform seasonal predictions, they are not systematically taking advantage of the wealth of polar observations available. Data assimilation, the study of how to constrain GCMs to produce a physically consistent state given observations and their uncertainties, remains, therefore, an active area of research in the field of seasonal prediction. With the recent advent of satellite laser and radar altimetry, large-scale estimates of sea-ice thickness have become available for data assimilation in GCMs. However, the sea-ice thickness is never directly observed by altimeters, but rather deduced from the measured sea-ice freeboard (the height of the emerged part of the sea ice floe) based on several assumptions like the depth of snow on sea ice and its density, which are both often poorly estimated. Thus, observed sea-ice thickness estimates are potentially less reliable than sea-ice freeboard estimates. Here, using the EC-Earth3 coupled forecasting system and an ensemble Kalman filter, we perform a set of sensitivity tests to answer the following questions: (1) Does the assimilation of late spring observed sea-ice freeboard or thickness information yield more skilful predictions than no assimilation at all? (2) Should the sea-ice freeboard assimilation be preferred over sea-ice thickness assimilation? (3) Does the assimilation of observed sea-ice concentration provide further constraints on the prediction? We address these questions in the context of a realistic test case, the prediction of 2012 summer conditions, which led to the all-time record low in Arctic sea-ice extent. We finally formulate a set of recommendations for practitioners and future users of sea ice observations in the context of seasonal prediction.</p>

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On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses

The Cryosphere ◽

10.5194/tc-13-521-2019 ◽

2019 ◽

Vol 13 (2) ◽

pp. 521-543 ◽

Cited By ~ 3

Author(s):

Leandro Ponsoni ◽

François Massonnet ◽

Thierry Fichefet ◽

Matthieu Chevallier ◽

David Docquier

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Length Scales ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration ◽

Arctic Sea Ice Thickness

Abstract. The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

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Better constraints on the sea-ice state using global sea-ice data assimilation

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-1627-2012 ◽

2012 ◽

Vol 5 (2) ◽

pp. 1627-1667 ◽

Cited By ~ 1

Author(s):

P. Mathiot ◽

C. König Beatty ◽

T. Fichefet ◽

H. Goosse ◽

F. Massonnet ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Ice Thickness ◽

Initial State ◽

Concentration Data ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Ice Concentration ◽

The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

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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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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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