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

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

Geoscientific Model Development ◽

10.5194/gmd-10-1009-2017 ◽

2017 ◽

Vol 10 (2) ◽

pp. 1009-1031 ◽

Cited By ~ 12

Author(s):

Petteri Uotila ◽

Doroteaciro Iovino ◽

Martin Vancoppenolle ◽

Mikko Lensu ◽

Clement Rousset

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

The Arctic ◽

Ice Thickness ◽

Polar Regions ◽

Intermediate Water ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

System Sensitivity ◽

Ice Model

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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Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model

10.5194/gmd-2019-325 ◽

2019 ◽

Author(s):

Eduardo Moreno-Chamarro ◽

Pablo Ortega ◽

François Massonnet

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

The Arctic ◽

Ice Thickness ◽

Model Data ◽

Atmospheric Variability ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Ice Concentration ◽

Single Category

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability. Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data. Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume. In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.

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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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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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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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Variability and changes of Arctic sea ice thickness distribution under different AO/DA states

The Cryosphere Discussions ◽

10.5194/tcd-5-131-2011 ◽

2011 ◽

Vol 5 (1) ◽

pp. 131-167

Author(s):

A. Oikkonen ◽

J. Haapala

Keyword(s):

Sea Ice ◽

Seasonal Variability ◽

Large Scale ◽

Thickness Distribution ◽

Beaufort Sea ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Pack

Abstract. Changes of the mean sea ice thickness and concentration in the Arctic are well known. However, comparable little is known about the ice thickness distribution and the composition of ice pack in quantity. In this paper we determine the ice thickness distributions, mean and modal thicknesses, and their regional and seasonal variability in the Arctic under different large scale atmospheric circulation modes. We compare characteristics of the Arctic ice pack during the periods 1975–1987 and 1988–2000, which have a different distribution in the AO/DA space. The study is based on submarine measurements of sea ice draft. The prevalent feature is that the peak of sea ice thickness distributions has generally taken a narrower form and shifted toward thinner ice. Also, both mean and modal ice thickness have generally decreased. These noticeable changes result from a loss of thick, mostly deformed, ice. In the spring the loss of the volume of ice thicker than 5 m exceeds 35% in all regions except the Nansen Basin, and the reduction is as much as over 45% at the North Pole and in the Eastern Arctic. In the autumn the volume of thick, mostly deformed ice has decreased by more than 40% in the Canada Basin only, but the reduction is more than 30% also in the Beaufort Sea and in the Chukchi Sea. In the Beaufort Sea region the decrease of the modal draft has been so strong that the peak has shifted from multiyear ice to first-year type ice. Also, the regional and seasonal variability of the sea ice thickness has decreased, since the thinning has been the most pronounced in the regions with the thickest pack ice (the Western Arctic), and during the spring (0.6–0.8 m per decade).

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The effects of assimilating a sub-grid scale sea ice thickness distribution in a new Arctic sea ice data assimilation system

10.5194/egusphere-egu21-2657 ◽

2021 ◽

Author(s):

Nicholas Williams ◽

Nicholas Byrne ◽

Daniel Feltham ◽

Peter Jan Van Leeuwen ◽

Ross Bannister ◽

...

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Thickness Distribution ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Data Assimilation System ◽

Assimilation System

<div>A modified, standalone version of the Los Alamos Sea Ice Model (CICE) has been coupled to the Parallelized Data Assimilation Framework (PDAF) to produce a new Arctic sea ice data assimilation system CICE-PDAF, with routines for assimilating many types of recently developed sea ice observations. In this study we explore the effects of assimilating a sub-grid scale sea ice thickness distribution derived from Cryosat-2 Arctic sea ice estimates into CICE-PDAF. The true state of the sub-grid scale ice thickness distribution is not well established, and yet it plays a key role in large scale sea ice models and is vital to the dynamical and thermodynamical processes necessary to produce a good representation of the Arctic sea ice state. We examine how assimilating sub-grid scale sea ice thickness distributions can affect the evolution of the sea ice state in CICE-PDAF and better our understanding of the Arctic sea ice system.</div>

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