Comparing sea ice, hydrography and circulation between NEMO3.6 LIM3 and LIM2

Abstract. A set of hindcast simulations with the new version 3.6 of the Nucleus for European Modelling of the Ocean (NEMO) ocean–ice model in the ORCA1 configuration and forced by the DRAKKAR Forcing Set version 5.2 (DFS5.2) atmospheric data was performed from 1958 to 2012. Simulations differed in their sea-ice component: the old standard version Louvain-la-Neuve Sea Ice Model (LIM2) and its successor LIM3. Main differences between these sea-ice models are the parameterisations of sub-grid-scale sea-ice thickness distribution, ice deformation, thermodynamic processes, and sea-ice salinity. Our main objective was to analyse the response of the ocean–ice system sensitivity to the change in sea-ice physics. Additional sensitivity simulations were carried out for the attribution of observed differences between the two main simulations.In the Arctic, NEMO-LIM3 compares better with observations by realistically reproducing the sea-ice extent decline during the last few decades due to its multi-category sea-ice thickness. In the Antarctic, NEMO-LIM3 more realistically simulates the seasonal evolution of sea-ice extent than NEMO-LIM2. In terms of oceanic properties, improvements are not as evident, although NEMO-LIM3 reproduces a more realistic hydrography in the Labrador Sea and in the Arctic Ocean, including a reduced cold temperature bias of the Arctic Intermediate Water at 250 m. In the extra-polar regions, the oceanographic conditions of the two NEMO-LIM versions remain relatively similar, although they slowly drift apart over decades. This drift is probably due to a stronger deep water formation around Antarctica in LIM3.

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On the influence of sea-ice physics in multi-decadal ocean-ice hindcasts

10.5194/gmd-2016-187 ◽

2016 ◽

Author(s):

Petteri Uotila ◽

Dorotea Iovino ◽

Martin Vancoppenolle ◽

Mikko Lensu ◽

Clement Rousset

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

The Arctic ◽

Ice Thickness ◽

Polar Regions ◽

Sea Ice Thickness ◽

Atmospheric Data ◽

Single Category ◽

Ice Model ◽

Thermodynamic Processes

Abstract. A set of hindcast simulations with the new NEMO3.6 ocean-ice model in the ORCA1 grid and forced by the DFS5.2 atmospheric data was performed from 1958–2012. We focussed on simulations that differ only in their sea-ice component: the old standard version LIM2 and its successor LIM3. Main differences between these sea-ice models are the parameterisations of sub-grid-scale sea-ice thickness distribution, ice deformation, thermodynamic processes, and sea-ice salinity. Our main objective was to diagnose the ocean-ice sensitivity to the updated NEMO-LIM sea-ice physics. Results of such analysis have not been published for this new NEMO version. In the polar regions, NEMO-LIM3 compares better with observations, while NEMO-LIM2 deviates more, producing too much ice in the Arctic, for example. Differences between NEMO-LIM2 and NEMO-LIM3 do not change in simulations even when the freshwater adjustments are turned off. In the extra-polar regions, the oceanographic conditions of the two NEMO-LIM versions remain relatively similar, although they slowly drift apart over decades. A simplified NEMO-LIM3 configuration, having a virtual, single-category sea-ice thickness distribution, produced sea ice with a skill sufficient for ocean-ice hindcasts that target oceanographic studies. We conclude that NEMO3.6 is ready to be used as a stand-alone ocean-ice model and as a component of coupled atmosphere-ocean models.

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Interannual sea ice thickness variability in the Bay of Bothnia

The Cryosphere ◽

10.5194/tc-12-3459-2018 ◽

2018 ◽

Vol 12 (11) ◽

pp. 3459-3476 ◽

Cited By ~ 5

Author(s):

Iina Ronkainen ◽

Jonni Lehtiranta ◽

Mikko Lensu ◽

Eero Rinne ◽

Jari Haapala ◽

...

Keyword(s):

Sea Ice ◽

Interannual Variability ◽

Thickness Distribution ◽

Ice Thickness ◽

Data Sets ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Thickness Variability ◽

Fast Ice ◽

Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

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On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model

Geoscientific Model Development ◽

10.5194/gmd-12-3745-2019 ◽

2019 ◽

Vol 12 (8) ◽

pp. 3745-3758 ◽

Cited By ~ 2

Author(s):

François Massonnet ◽

Antoine Barthélemy ◽

Koffi Worou ◽

Thierry Fichefet ◽

Martin Vancoppenolle ◽

...

Keyword(s):

Sea Ice ◽

General Circulation ◽

Large Scale ◽

Thickness Distribution ◽

Circulation Model ◽

Grid Cell ◽

The Arctic ◽

Global Ocean ◽

Ice Thickness ◽

Ice Model

Abstract. The ice thickness distribution (ITD) is one of the core constituents of modern sea ice models. The ITD accounts for the unresolved spatial variability of sea ice thickness within each model grid cell. While there is a general consensus on the added physical realism brought by the ITD, how to discretize it remains an open question. Here, we use the ocean–sea ice general circulation model, Nucleus for European Modelling of the Ocean (NEMO) version 3.6 and Louvain-la-Neuve sea Ice Model (LIM) version 3 (NEMO3.6-LIM3), forced by atmospheric reanalyses to test how the ITD discretization (number of ice thickness categories, positions of the category boundaries) impacts the simulated mean Arctic and Antarctic sea ice states. We find that winter ice volumes in both hemispheres increase with the number of categories and attribute that increase to a net enhancement of basal ice growth rates. The range of simulated mean winter volumes in the various experiments amounts to ∼30 % and ∼10 % of the reference values (run with five categories) in the Arctic and Antarctic, respectively. This suggests that the way the ITD is discretized has a significant influence on the model mean state, all other things being equal. We also find that the existence of a thick category with lower bounds at ∼4 and ∼2 m for the Arctic and Antarctic, respectively, is a prerequisite for allowing the storage of deformed ice and therefore for fostering thermodynamic growth in thinner categories. Our analysis finally suggests that increasing the resolution of the ITD without changing the lower limit of the upper category results in small but not negligible variations of ice volume and extent. Our study proposes for the first time a bi-polar process-based explanation of the origin of mean sea ice state changes when the ITD discretization is modified. The sensitivity experiments conducted in this study, based on one model, emphasize that the choice of category positions, especially of thickest categories, has a primary influence on the simulated mean sea ice states while the number of categories and resolution have only a secondary influence. It is also found that the current default discretization of the NEMO3.6-LIM3 model is sufficient for large-scale present-day climate applications. In all cases, the role of the ITD discretization on the simulated mean sea ice state has to be appreciated relative to other influences (parameter uncertainty, forcing uncertainty, internal climate variability).

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Sea ice thickness distribution in the Arctic Ocean

Cold Regions Science and Technology ◽

10.1016/0165-232x(87)90007-3 ◽

1987 ◽

Vol 13 (3) ◽

pp. 259-280 ◽

Cited By ~ 240

Author(s):

Robert H. Bourke ◽

Robert P. Garrett

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Thickness Distribution ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Arctic Ocean

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Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

The Cryosphere ◽

10.5194/tc-15-3207-2021 ◽

2021 ◽

Vol 15 (7) ◽

pp. 3207-3227

Author(s):

Timothy Williams ◽

Anton Korosov ◽

Pierre Rampal ◽

Einar Ólason

Keyword(s):

Sea Ice ◽

Initial Conditions ◽

Thickness Distribution ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Concentration ◽

Forecasting System ◽

Ice Model

Abstract. The neXtSIM-F (neXtSIM forecast) forecasting system consists of a stand-alone sea ice model, neXtSIM (neXt-generation Sea Ice Model), forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation of sea ice concentration. It uses the novel brittle Bingham–Maxwell (BBM) sea ice rheology, making it the first forecast based on a continuum model not to use the viscous–plastic (VP) rheology. It was tested in the Arctic for the time period November 2018–June 2020 and was found to perform well, although there are some shortcomings. Despite drift not being assimilated in our system, the sea ice drift is good throughout the year, being relatively unbiased, even for longer lead times like 5 d. The RMSE in speed and the total RMSE are also good for the first 3 or so days, although they both increase steadily with lead time. The thickness distribution is relatively good, although there are some regions that experience excessive thickening with negative implications for the summertime sea ice extent, particularly in the Greenland Sea. The neXtSIM-F forecasting system assimilates OSI SAF sea ice concentration products (both SSMIS and AMSR2) by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. The assimilation greatly improves the sea ice extent for the forecast duration.

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A Multithickness Sea Ice Model Accounting for Sliding Friction

Journal of Physical Oceanography ◽

10.1175/jpo2937.1 ◽

2006 ◽

Vol 36 (9) ◽

pp. 1719-1738 ◽

Cited By ~ 11

Author(s):

Alexander V. Wilchinsky ◽

Daniel L. Feltham ◽

Paul A. Miller

Keyword(s):

Sea Ice ◽

Sliding Friction ◽

Kinematic Model ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Model Code ◽

Ice Strength ◽

Discrete Element Simulations ◽

Ice Model

Abstract A multithickness sea ice model explicitly accounting for the ridging and sliding friction contributions to sea ice stress is developed. Both ridging and sliding contributions depend on the deformation type through functions adopted from the Ukita and Moritz kinematic model of floe interaction. In contrast to most previous work, the ice strength of a uniform ice sheet of constant ice thickness is taken to be proportional to the ice thickness raised to the 3/2 power, as is revealed in discrete element simulations by Hopkins. The new multithickness sea ice model for sea ice stress has been implemented into the Los Alamos “CICE” sea ice model code and is shown to improve agreement between model predictions and observed spatial distribution of sea ice thickness in the Arctic.

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Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice

10.5194/egusphere-egu2020-11436 ◽

2020 ◽

Author(s):

Alex Cabaj ◽

Paul Kushner ◽

Alek Petty ◽

Stephen Howell ◽

Christopher Fletcher

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Arctic Ocean ◽

Ice Model

Snow on Arctic sea ice plays multiple&#8212;and sometimes contrasting&#8212;roles in several feedbacks between sea ice and the global climate system. For example, the presence of snow on sea ice may mitigate sea ice melt by increasing the sea ice albedo and enhancing the ice-albedo feedback. Conversely, snow can inhibit sea ice growth by insulating the ice from the atmosphere during the sea ice growth season. In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. In particular, snow contributes to uncertainties in retrievals of sea ice thickness from satellite altimetry measurements, such as those from ICESat-2. Snow-on-sea-ice models can produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are absent over most of the Arctic Ocean, so it can be difficult to determine which reanalysis snowfall product is best suited to be used as input for a snow-on-sea-ice model.In the absence of in-situ snowfall rate measurements, measurements from satellite instruments can be used to quantify snowfall over the Arctic Ocean. The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rates can be retrieved. This instrument provides the most extensive high-latitude snowfall rate observation dataset currently available. CloudSat&#8217;s near-polar orbit enables it to make measurements at latitudes up to 82&#176;N, with a 16-day repeat cycle, over the time period from 2006-2016.We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM when different reanalysis inputs are used. In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.

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Sea ice representation in CMIP6 simulations with EC-Earth3-Veg

10.5194/egusphere-egu2020-20372 ◽

2020 ◽

Author(s):

Torben Koenigk ◽

Evelien Dekker

Keyword(s):

Sea Ice ◽

Emission Scenario ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

First Year ◽

Data Sets ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Arctic Sea

In this study, we compare the sea ice in ensembles of historical and future simulations with EC-Earth3-Veg to the sea ice of the NSIDC and OSA-SAF satellite data sets. The EC-Earth3-Veg Arctic sea ice extent generally matches well to the observational data sets, and the trend over 1980-2014 is captured correctly. Interestingly, the summer Arctic sea ice area minimum occurs already in August in the model. Mainly east of Greenland, sea ice area is overestimated. In summer, Arctic sea ice is too thick compared to PIOMAS. In March, sea ice thickness is slightly overestimated in the Central Arctic but in the Bering and Kara Seas, the ice thickness is lower than in PIOMAS.While the general picture of Arctic sea ice looks good, EC-Earth suffers from a warm bias in the Southern Ocean. This is also reflected by a substantial underestimation of sea ice area in the Antarctic.Different ensemble members of the future scenario projections of sea ice show a large range of the date of first year with a minimum ice area below 1 million square kilometers in the Arctic. The year varies between 2024 and 2056. Interestingly, this range does not differ very much with the emission scenario and even under the low emission scenario SSP1-1.9 summer Arctic sea ice almost totally disappears.

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A combined approach of remote sensing and airborne electromagnetics to determine the volume of polynya sea ice in the Laptev Sea

The Cryosphere Discussions ◽

10.5194/tcd-7-441-2013 ◽

2013 ◽

Vol 7 (1) ◽

pp. 441-473 ◽

Cited By ~ 2

Author(s):

L. Rabenstein ◽

T. Krumpen ◽

S. Hendricks ◽

C. Koeberle ◽

C. Haas ◽

...

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

Growth Conditions ◽

Ice Age ◽

The Arctic ◽

Ice Thickness ◽

Laptev Sea ◽

Volume Estimate ◽

Sea Ice Thickness ◽

Level Ice

Abstract. A combined interpretation of synthetic aperture radar (SAR) satellite images and helicopter electromagnetic (HEM) sea-ice thickness data has provided an estimate of sea-ice volume formed in Laptev Sea polynyas during the winter of 2007/08. The evolution of the surveyed sea-ice areas, which were formed between late December 2007 and middle April 2008, was tracked using a series of SAR images with a sampling interval of 2–3 days. Approximately 160 km of HEM data recorded in April 2008 provided sea-ice thicknesses along profiles that transected sea-ice varying in age from 1–116 days. For the volume estimates, thickness information along the HEM profiles was extrapolated to zones of the same age. The error of areal mean thickness information was estimated to be between 0.2 m for younger ice and up to 1.55 m for older ice, with the primary error source being the spatially limited HEM coverage. Our results have demonstrated that the modal thicknesses and mean thicknesses of level ice correlated with the sea-ice age, but that varying dynamic and thermodynamic sea-ice growth conditions resulted in a rather heterogeneous sea-ice thickness distribution on scales of tens of kilometers. Taking all uncertainties into account, total sea-ice area and volume produced within the entire surveyed area were 52 650 km2 and 93.6 ± 26.6 km3. The surveyed polynya contributed 2.0 ± 0.5% of the sea-ice produced throughout the Arctic during the 2007/08 winter. The SAR-HEM volume estimate compares well with the 112 km3 ice production calculated with a high resolution ocean sea-ice model. Measured modal and mean-level ice thicknesses correlate with calculated freezing-degree-day thicknesses with a factor of 0.87–0.89, which was too low to justify the assumption of homogeneous thermodynamic growth conditions in the area, or indicates a strong dynamic thickening of level ice by rafting of even thicker ice.

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Sea ice inertial oscillations in the Arctic Basin

The Cryosphere ◽

10.5194/tc-6-1187-2012 ◽

2012 ◽

Vol 6 (5) ◽

pp. 1187-1201 ◽

Cited By ~ 22

Author(s):

F. Gimbert ◽

D. Marsan ◽

J. Weiss ◽

N. C. Jourdain ◽

B. Barnier

Keyword(s):

Sea Ice ◽

Arctic Basin ◽

Original Method ◽

The Arctic ◽

Ice Thickness ◽

Inertial Motion ◽

Inertial Oscillations ◽

Dimensional Parameter ◽

Sea Ice Extent ◽

Sea Ice Thickness

Abstract. An original method to quantify the amplitude of inertial motion of oceanic and ice drifters, through the introduction of a non-dimensional parameter M defined from a spectral analysis, is presented. A strong seasonal dependence of the magnitude of sea ice inertial oscillations is revealed, in agreement with the corresponding annual cycles of sea ice extent, concentration, thickness, advection velocity, and deformation rates. The spatial pattern of the magnitude of the sea ice inertial oscillations over the Arctic Basin is also in agreement with the sea ice thickness and concentration patterns. This argues for a strong interaction between the magnitude of inertial motion on one hand, the dissipation of energy through mechanical processes, and the cohesiveness of the cover on the other hand. Finally, a significant multi-annual evolution towards greater magnitudes of inertial oscillations in recent years, in both summer and winter, is reported, thus concomitant with reduced sea ice thickness, concentration and spatial extent.

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