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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 albedo 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. 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.&nbsp

Melt ponds appear during the melt season in the Arctic, when the surface melt water collects in the depressions of the ice field. The albedo of the ponds is lower than the surrounding ice and snow areas, and for this reason the ponds are hot-spots for the ice-albedo feedback. There is two main approaches to represent the melt ponds in Global Climate Models. The first approach is empiric and relies on observations to determine the available water capacity of the ponds from the sea ice state. Then, a fraction of the surface melt water accumulates is the ponds. The second makes use of the Ice Thickness Distribution to infer the surface topography of the sea ice and distribute the melt water among the ice categories. Although the role of melt ponds has been extensively studied, less is known on the response of the ponds to climate change. Insights can be gained from using different reanalyses of the atmospheric surface state to force the ocean and ice components. Because of a lack of observations in remote areas, reanalyses still suffer from biases notably in the polar regions. The choice of a reanalysis has a strong influence on the representation of the sea ice state of the Antarctic. We expect similar deviations in the Northern Hemisphere. To evaluate the effect of the melt pond schemes on the sea ice when subject to uncertainties in the atmospheric state, we have run the empiric and topographic schemes forced with JRA-55, DFS 5.2, and NCEP/NCAR atmospheric reanalyses. From the simulations, We expect to see the degree of difference between the pond schemes and the influence of the forcing onto their climatic response. We will be able to assess the importance of the melt ponds for the climate and check the consistency of the parameterizations. This will allow us to formulate a recommendation on the use of melt ponds in climate models.

Melt ponds develop during summer in the Arctic when surface freshwater collect into the depressions of the ice field. Because of the liquid state of the water, the albedo in the ponds is lower than the surrounding sea ice cover. Consequently, melt ponds are hot-spots for greater solar absorption and further ice melt. It is customary in GCM to take into account melt pond effects indirectly by tuning the albedo of the ice to lower values. However, this method does not reflect the complexity the ponds and their contribution to the mean ice albedo. To include melt ponds in sea ice model, a first approach consists in estimating the water capacity of the ponds empirically from the sea ice state. Then, a fraction of the surface melt water accumulates is the ponds. A second approach makes use of the Ice Thickness Distribution to infer the surface topography of the sea ice and distribute the melt water among the ice categories. Although the role of melt ponds has been extensively studied, less is known on the response of the ponds to atmospheric uncertainties. Insights can be gained from using different reanalyses of the atmospheric surface state to force the ocean and ice components. Because of a lack of observations in remote areas, reanalyses still suffer from biases notably in the polar regions. The choice of a reanalysis has a strong influence on the representation of the sea ice state of the Antarctic. We expect similar deviations in the Northern Hemisphere. To evaluate the effect of the melt pond schemes on the sea ice when subject to uncertainties in the atmospheric state, we have run the empiric and topographic schemes forced with JRA-55, DFS 5.2, and NCEP/NCAR atmospheric reanalyses. From the simulations, We expect to see the degree of difference between the pond schemes and the influence of the forcing onto their climatic response. We will be able to assess the importance of the melt ponds for the climate and check the consistency of the parameterizations. This will allow us to formulate a recommendation on the use of melt ponds in climate models.

Sea ice comes in a variety of sizes and shapes depending on the mechanical and thermodynamical events it has undergone. New ice offers little resistance to the winds and currents, while deformed ice contains hummocks and ridges that influence how heat and momentum are transferred at the atmosphere-ice-ocean interfaces. In most climate models, the surface fluxes are determined from "bulk formulas" with constant drag coefficients based on roughness length estimates. Therefore, these formulations do not account for the space-time variability of transfer coefficients due to variations in ice roughness. However, the ice roughness can be estimated from the models by quantifying the amount of deformed ice (Tsamados et al, 2013). To study the effect of ice deformation on the surface fluxes and the associated impact on the sea ice, we implement a tracer of deformed ice into the ocean-ice model NEMO-LIM3 v3.6 and modify the drag coefficients accordingly. From a run of NEMO-LIM3 between 1990 and 2010 at 1 degree resolution, we examine the spatial and temporal evolution of the drag coefficients in the Arctic and Antarctic regions. We investigate possible effects on the surface fluxes and impacts on the sea ice state. This study allows us to formulate an initial assessment on the importance of deformed ice variability for the current climate models.

Sea ice comes in a variety of sizes and shapes depending on the mechanical and thermodynamical events it has undergone. New ice offers little resistance to the winds and currents, while deformed ice contains hummocks and ridges that influence how heat and momentum are transferred at the atmosphere-ice-ocean interfaces. In most climate models, the surface fluxes are determined from "bulk formulas" with constant drag coefficients based on roughness length estimates. Therefore, these formulations do not account for the space-time variability of transfer coefficients due to variations in ice roughness. However, the ice roughness can be estimated from the models by quantifying the amount of deformed ice (Tsamados et al, 2013). To study the effect of ice deformation on the surface fluxes and the associated impact on the sea ice, we implement a tracer of deformed ice into the ocean-ice model NEMO-LIM3 v3.6 and modify the drag coefficients accordingly. From a run of NEMO-LIM3 between 1990 and 2010 at 1 degree resolution, we examine the spatial and temporal evolution of the drag coefficients in the Arctic and Antarctic regions. We investigate possible effects on the surface fluxes and impacts on the sea ice state. This study allows us to formulate an initial assessment on the importance of deformed ice variability for the current climate models.

Sea ice features a variety of obstacles to the flow of air and ocean near its surface. The distribution of ridges on the ice bottom and top surfaces, as well as the edges of the floes, the ice surface roughness and the melt ponds edges interact with the exchanges of heat and momentum between the ice and ocean and atmosphere systems. In most climate models, the surface fluxes of heat and momentum are estimated with bulk formulas, using a drag coefficient that reflects the surface roughness of the interface. Drag coefficients over sea ice are usually set to constant, which do not account for the variability of the ice surface roughness. However, drag coefficients can be estimated as a function the sea ice state (Tsamados et al, 2013). To study the effect of variable drag coefficients on the sea ice and its subsequent impact on the ocean surface, we have implemented the formalism of Tsamados et al (2013). We performed a first simulation with constant drag coefficients and a second with variable drag coefficients, using NEMO-LIM3 and a prescribed atmospheric state of the past decades. The differences between the two simulations allow us to formulate an initial assessment of the importance of variable drag coefficients over sea ice for the ocean surface in the Arctic.

Landfast ice is the part of sea ice fastened to the coast. As the landfast ice is immobile, its has an influence on the interactions between the ocean and the atmosphere, the fresh water budgets, deep water formations and the stability of the ice cover. It plays equally a role for coastal ecosystems in the Arctic and Antarctic. Two main mechanisms for landfast ice formation are known in the Arctic. The first occurs when the ice is thick enough to ground on the sea floor. The weight of ice unbalanced by buoyancy forces leads to basal stress limiting its displacements. Over deeper waters, landfast ice can also be sustained by tensile strength. The fast ice develops as arches anchored to islands, grounded icebergs, or other points such as the shoreline. To model the landfast ice, grounding schemes have been introduced (Rousset et al., 2013; Lemieux et al., 2015) while the yield curve of the ice have been modified to account for tensile strength (Dumont et al., 2009; Lemieux et al., 2016; Olason, 2016), showing promising results for regional modelling. However, for global models, little is known on the behaviour of the grounding schemes with ice thickness distribution and coarser bathymetries, neither the effects of tensile strength on the ice dynamics at the poles, nor the impact of landfast ice on the global climate on the decadal time scale. In this study, we use NEMO-LIM3 to test Lemieux et al. (2015) grounding scheme. We introduce isotropic tensile strength in the ice rheology. We validate the representation of fast ice and the dynamic of drift ice. We then run a simulation on global ORCA grid at 1 degree resolution, from the years 1958 to 2015. We formulate an initial assessment of the importance of landfast ice for current climate models and we suggest of set of paramaters that can be used.

Sea ice comes in a variety of sizes and shapes depending on the mechanical and thermodynamical events it has undergone. New ice offers little resistance to the winds and currents, while deformed ice contains hummocks and ridges that influence how heat and momentum are transferred at the atmosphere-ice-ocean interfaces. In most climate models, the surface fluxes are determined from "bulk formulas" with constant drag coefficients based on roughness length estimates. Therefore, these formulations do not account for the space-time variability of transfer coefficients due to variations in ice roughness. However, the ice roughness can be estimated from the models by quantifying the amount of deformed ice (Tsamados et al, 2013). To study the effect of ice deformation on the surface fluxes and the associated impact on the sea ice, we implement a tracer of deformed ice into the ocean-ice model NEMO-LIM3 v3.6 and modify the drag coefficients accordingly. From a run of NEMO-LIM3 between 1990 and 2010 at 1 degree resolution, we examine the spatial and temporal evolution of the drag coefficients in the Arctic and Antarctic regions. We investigate possible effects on the surface fluxes and impacts on the sea ice state. This study allows us to formulate an initial assessment on the importance of deformed ice variability for the current climate models.

During the Arctic summer, a fraction of the surface melt water and liquid precipitation collect on the ice surface in pools known as melt ponds. The albedo of the ponds is lower than the surrounding snow and ice surfaces. Consequently, the melt ponds are an important factor for the ice-albedo feedback. The feedback is a mechanism whereby a decrease in albedo results in greater absorption of solar radiation, further ice melt, and lower albedo. Several numerical schemes have been proposed for Global Circulation Models. They can be classified into two groups. The first makes use of an explicit relation to define the geometrical aspect of the ponds. The scheme of Holland et al. (2012) falls in this category. The second relies on theoretical considerations to infer the ponds characteristics. The scheme of Flocco et al. (2012) uses the ice thickness distribution to distribute the melt water among the ice categories. Despite their current complexity, the melt pond schemes fail to agree on the future evolution of the ponds in the next decades. The difference of trends casts doubts on the definition of the physical processes governing the melt pond evolution. It also raises questions about the sensitivity of the schemes to the atmospheric surface forcing method. In this study, we aim at identifying 1) the conceptual difference of the aspect ratio definition in melt pond schemes; 2) the role of the refreezing of the ponds; 3) the impact of the uncertainties in the atmospheric reanalyses on the simulations. 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 forcing sets: JRA-55 and DFS5.2. We implemented Holland et al. (2012) and Flocco et al. (2012) melt pond schemes in the model. We selected Holland et al. (2012) pond refreezing formulation for both schemes and tried two different threshold temperatures for refreezing. From the experiments, we state the importance of melt ponds for climate models. We describe the impact on the sea ice in the Arctic. We attempt at disentangling the separate effects of the atmospheric surface forcing method, the type of melt pond schemes, and the refreezing mechanism. From our results, we formulate a recommendation on the use of melt ponds in climate models.

Melt ponds appear during the melt season in the Arctic, when the surface melt water collects in the depressions of the ice field. The albedo of the ponds is lower than the surrounding ice and snow areas, and for this reason the ponds are hot-spots for the ice-albedo feedback. There is two main approaches to represent the melt ponds in Global Climate Models. The first approach is empiric and relies on observations to determine the available water capacity of the ponds from the sea ice state. Then, a fraction of the surface melt water accumulates is the ponds. The second makes use of the Ice Thickness Distribution to infer the surface topography of the sea ice and distribute the melt water among the ice categories. Although the role of melt ponds has been extensively studied, less is known on the response of the ponds to climate change. Insights can be gained from using different reanalyses of the atmospheric surface state to force the ocean and ice components. Because of a lack of observations in remote areas, reanalyses still suffer from biases notably in the polar regions. The choice of a reanalysis has a strong influence on the representation of the sea ice state of the Antarctic. We expect similar deviations in the Northern Hemisphere. To evaluate the effect of the melt pond schemes on the sea ice when subject to uncertainties in the atmospheric state, we have run the empiric and topographic schemes forced with JRA-55, DFS 5.2, and NCEP/NCAR atmospheric reanalyses. From the simulations, We expect to see the degree of difference between the pond schemes and the influence of the forcing onto their climatic response. We will be able to assess the importance of the melt ponds for the climate and check the consistency of the parameterizations. This will allow us to formulate a recommendation on the use of melt ponds in climate models.

During the Arctic summer, a fraction of the surface melt water and liquid precipitation collect on the ice surface in pools known as melt ponds. The albedo of the ponds is lower than the surrounding snow and ice surfaces. Consequently, the melt ponds are an important factor for the ice-albedo feedback. The feedback is a mechanism whereby a decrease in albedo results in greater absorption of solar radiation, further ice melt, and lower albedo. Several numerical schemes have been proposed for Global Circulation Models. They can be classified into two groups. The first makes use of an explicit relation to define the geometrical aspect of the ponds. The scheme of Holland et al. (2012) falls in this category. The second relies on theoretical considerations to infer the ponds characteristics. The scheme of Flocco et al. (2012) uses the ice thickness distribution to distribute the melt water among the ice categories. Despite their current complexity, the melt pond schemes fail to agree on the future evolution of the ponds in the next decades. The difference of trends casts doubts on the definition of the physical processes governing the melt pond evolution. It also raises questions about the sensitivity of the schemes to the atmospheric surface forcing method. In this study, we aim at identifying 1) the conceptual difference of the aspect ratio definition in melt pond schemes; 2) the role of the refreezing of the ponds; 3) the impact of the uncertainties in the atmospheric reanalyses on the simulations. 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 forcing sets: JRA-55 and DFS5.2. We implemented Holland et al. (2012) and Flocco et al. (2012) melt pond schemes in the model. We selected Holland et al. (2012) pond refreezing formulation for both schemes and tried two different threshold temperatures for refreezing. From the experiments, we state the importance of melt ponds for climate models. We describe the impact on the sea ice in the Arctic. We attempt at disentangling the separate effects of the atmospheric surface forcing method, the type of melt pond schemes, and the refreezing mechanism. From our results, we formulate a recommendation on the use of melt ponds in climate models. &nbsp

In such a remote and harsh environment as the Arctic, the monitoring of essential climate variables is expensive and, therefore, sporadic. In turn, satellites provide observations constrained to the surface. Also, because of technical restrictions, satellites cannot sample, at least not year-round, a set of essential variables, such as the sea ice thickness. To overcome this difficulty, numerical models are key tools for studying and predicting the Arctic weather and climate. Numerical models aim at reproducing the interactions between different climate components such as the land, atmosphere, ocean, and sea ice. Such interactions are complex and described with non-linear functions. This is one reason numerical models are in constant improvement. The primary goals of Work Package 2 are to promote improvements in numerical models and establish the impact of these improvements on model results, both for the study of the Arctic climate and numerical weather prediction. This deliverable presents a set of model enhancements that are implemented and tested in fully coupled models (AWI-CM1, HadGEM3-GC3.1, EC-Earth3 PRIMAVERA, and ECMWF IFS CY45R1) and forced-mode (NEMO3.6-LIM3 and GELATO). All model developments aim to improve the representation of physical processes that take place in the sea ice or snow. These consist of a better representation of the turbulent exchanges of heat and momentum between the atmosphere and sea ice (form drag), a description of the melt ponds that appear on the sea ice surface during the melt season (melt ponds), a parameterization which takes into account the effect of the sea ice attached to the shore and ocean floor (landfast ice), a scheme which accounts for the size of the floes that form the sea ice cover (floe size distribution), and an improved representation of the snow both over land and sea ice (multilayer snow scheme). We assess the benefit of the model developments based on the following five aspects: (i) changes in various components of the Arctic surface energy budget, (ii) changes in the transfer of momentum from the atmosphere to the ocean, (iii) the overall realism of the simulated climate system, (iv) effects on the Arctic Ocean circulation, and (v) changes in the Arctic climate sensitivity. Common results emerged from the form drag experiments: increased sea ice drift speed in the marginal ice zone, a general decrease in ice thickness, and a marginal decrease of ice concentration at the ice edge in summer. The form drag parameterization improved the large-scale atmospheric and ocean-driven ocean circulation. The melt pond parameterization shows a clear impact on the albedo and sea ice variability and reinforces that a reduced sea ice regime in the Arctic impacts the large-scale, density-driven ocean circulation. The multilayer snow scheme makes the models more sensitive to the surface thermodynamic forcing than the control run with a single layer of snow and shows a more realistic albedo. In numerical weather prediction, the multilayer scheme leads to an improved prediction of the 2-m temperature diurnal cycle over land. Over sea ice, the new scheme reduces large positive biases of outgoing longwave radiation. Fast ice developments lead to more realistic sea ice conditions supported by evidence from observational data. Parameterization of floe size distribution reveals sea ice growth caused by large ice floes in the marginal ice zone during summer.

Melt ponds develop during summer in the Arctic when surface freshwater collect into the depressions of the ice field. Because of the liquid state of the water, the albedo in the ponds is lower than the surrounding sea ice cover. Consequently, melt ponds are hot-spots for greater solar absorption and further ice melt. It is customary in GCM to take into account melt pond effects indirectly by tuning the albedo of the ice to lower values. However, this method does not reflect the complexity the ponds and their contribution to the mean ice albedo. To include melt ponds in sea ice model, a first approach consists in estimating the water capacity of the ponds empirically from the sea ice state. Then, a fraction of the surface melt water accumulates is the ponds. A second approach makes use of the Ice Thickness Distribution to infer the surface topography of the sea ice and distribute the melt water among the ice categories. Although the role of melt ponds has been extensively studied, less is known on the response of the ponds to atmospheric uncertainties. Insights can be gained from using different reanalyses of the atmospheric surface state to force the ocean and ice components. Because of a lack of observations in remote areas, reanalyses still suffer from biases notably in the polar regions. The choice of a reanalysis has a strong influence on the representation of the sea ice state of the Antarctic. We expect similar deviations in the Northern Hemisphere. To evaluate the effect of the melt pond schemes on the sea ice when subject to uncertainties in the atmospheric state, we have run the empiric and topographic schemes forced with JRA-55, DFS 5.2, and NCEP/NCAR atmospheric reanalyses. From the simulations, We expect to see the degree of difference between the pond schemes and the influence of the forcing onto their climatic response. We will be able to assess the importance of the melt ponds for the climate and check the consistency of the parameterizations. This will allow us to formulate a recommendation on the use of melt ponds in climate models. &nbsp

In such a remote and harsh environment as the Arctic, the monitoring of essential climate variables is expensive and, therefore, sporadic. In turn, satellites provide observations constrained to the surface. Also, because of technical restrictions, satellites cannot sample, at least not year-round, a set of essential variables, such as the sea ice thickness. To overcome this difficulty, numerical models are key tools for studying and predicting the Arctic weather and climate. Numerical models aim at reproducing the interactions between different climate components such as the land, atmosphere, ocean, and sea ice. Such interactions are complex and described with non-linear functions. This is one reason numerical models are in constant improvement. The primary goals of Work Package 2 are to promote improvements in numerical models and establish the impact of these improvements on model results, both for the study of the Arctic climate and numerical weather prediction. This deliverable presents a set of model enhancements that are implemented and tested in fully coupled models (AWI-CM1, HadGEM3-GC3.1, EC-Earth3 PRIMAVERA, and ECMWF IFS CY45R1) and forced-mode (NEMO3.6-LIM3 and GELATO). All model developments aim to improve the representation of physical processes that take place in the sea ice or snow. These consist of a better representation of the turbulent exchanges of heat and momentum between the atmosphere and sea ice (form drag), a description of the melt ponds that appear on the sea ice surface during the melt season (melt ponds), a parameterization which takes into account the effect of the sea ice attached to the shore and ocean floor (landfast ice), a scheme which accounts for the size of the floes that form the sea ice cover (floe size distribution), and an improved representation of the snow both over land and sea ice (multilayer snow scheme). We assess the benefit of the model developments based on the following five aspects: (i) changes in various components of the Arctic surface energy budget, (ii) changes in the transfer of momentum from the atmosphere to the ocean, (iii) the overall realism of the simulated climate system, (iv) effects on the Arctic Ocean circulation, and (v) changes in the Arctic climate sensitivity. Common results emerged from the form drag experiments: increased sea ice drift speed in the marginal ice zone, a general decrease in ice thickness, and a marginal decrease of ice concentration at the ice edge in summer. The form drag parameterization improved the large-scale atmospheric and ocean-driven ocean circulation. The melt pond parameterization shows a clear impact on the albedo and sea ice variability and reinforces that a reduced sea ice regime in the Arctic impacts the large-scale, density-driven ocean circulation. The multilayer snow scheme makes the models more sensitive to the surface thermodynamic forcing than the control run with a single layer of snow and shows a more realistic albedo. In numerical weather prediction, the multilayer scheme leads to an improved prediction of the 2-m temperature diurnal cycle over land. Over sea ice, the new scheme reduces large positive biases of outgoing longwave radiation. Fast ice developments lead to more realistic sea ice conditions supported by evidence from observational data. Parameterization of floe size distribution reveals sea ice growth caused by large ice floes in the marginal ice zone during summer.

Melt ponds develop during summer in the Arctic when surface freshwater collect into the depressions of the ice field. Because of the liquid state of the water, the albedo in the ponds is lower than the surrounding sea ice cover. Consequently, melt ponds are hot-spots for greater solar absorption and further ice melt. It is customary in GCM to take into account melt pond effects indirectly by tuning the albedo of the ice to lower values. However, this method does not reflect the complexity the ponds and their contribution to the mean ice albedo. To include melt ponds in sea ice model, a first approach consists in estimating the water capacity of the ponds empirically from the sea ice state. Then, a fraction of the surface melt water accumulates is the ponds. A second approach makes use of the Ice Thickness Distribution to infer the surface topography of the sea ice and distribute the melt water among the ice categories. Although the role of melt ponds has been extensively studied, less is known on the response of the ponds to atmospheric uncertainties. Insights can be gained from using different reanalyses of the atmospheric surface state to force the ocean and ice components. Because of a lack of observations in remote areas, reanalyses still suffer from biases notably in the polar regions. The choice of a reanalysis has a strong influence on the representation of the sea ice state of the Antarctic. We expect similar deviations in the Northern Hemisphere. To evaluate the effect of the melt pond schemes on the sea ice when subject to uncertainties in the atmospheric state, we have run the empiric and topographic schemes forced with JRA-55, DFS 5.2, and NCEP/NCAR atmospheric reanalyses. From the simulations, We expect to see the degree of difference between the pond schemes and the influence of the forcing onto their climatic response. We will be able to assess the importance of the melt ponds for the climate and check the consistency of the parameterizations. This will allow us to formulate a recommendation on the use of melt ponds in climate models.

Sea ice features a variety of obstacles to the flow of air and ocean near its surface. The distribution of ridges on the ice bottom and top surfaces, as well as the edges of the floes, the ice surface roughness and the melt ponds edges interact with the exchanges of heat and momentum between the ice and ocean and atmosphere systems. In most climate models, the surface fluxes of heat and momentum are estimated with bulk formulas, using a drag coefficient that reflects the surface roughness of the interface. Drag coefficients over sea ice are usually set to constant, which do not account for the variability of the ice surface roughness. However, drag coefficients can be estimated as a function the sea ice state (Tsamados et al, 2013). To study the effect of variable drag coefficients on the sea ice and its subsequent impact on the ocean surface, we have implemented the formalism of Tsamados et al (2013). We performed a first simulation with constant drag coefficients and a second with variable drag coefficients, using NEMO-LIM3 and a prescribed atmospheric state of the past decades. The differences between the two simulations allow us to formulate an initial assessment of the importance of variable drag coefficients over sea ice for the ocean surface in the Arctic.

Landfast ice is the part of sea ice fastened to the coast. As the landfast ice is immobile, its has an influence on the interactions between the ocean and the atmosphere, the fresh water budgets, deep water formations and the stability of the ice cover. It plays equally a role for coastal ecosystems in the Arctic and Antarctic. Two main mechanisms for landfast ice formation are known in the Arctic. The first occurs when the ice is thick enough to ground on the sea floor. The weight of ice unbalanced by buoyancy forces leads to basal stress limiting its displacements. Over deeper waters, landfast ice can also be sustained by tensile strength. The fast ice develops as arches anchored to islands, grounded icebergs, or other points such as the shoreline. To model the landfast ice, grounding schemes have been introduced (Rousset et al., 2013; Lemieux et al., 2015) while the yield curve of the ice have been modified to account for tensile strength (Dumont et al., 2009; Lemieux et al., 2016; Olason, 2016), showing promising results for regional modelling. However, for global models, little is known on the behaviour of the grounding schemes with ice thickness distribution and coarser bathymetries, neither the effects of tensile strength on the ice dynamics at the poles, nor the impact of landfast ice on the global climate on the decadal time scale. In this study, we use NEMO-LIM3 to test Lemieux et al. (2015) grounding scheme. We introduce isotropic tensile strength in the ice rheology. We validate the representation of fast ice and the dynamic of drift ice. We then run a simulation on global ORCA grid at 1 degree resolution, from the years 1958 to 2015. We formulate an initial assessment of the importance of landfast ice for current climate models and we suggest of set of paramaters that can be used.

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development. The results discussed in this article were supported by the project APPLICATE (727862), funded by the European Union's Horizon 2020 research and innovation programme. PO was additionally supported by the Spanish fellowship RYC-2017-22772. Peer Reviewed Article signat per 29 autors/es: Pablo Ortega (1), Edward W. Blockley (2), Morten Køltzow (3), François Massonnet (4), Irina Sandu (5), Gunilla Svensson (6), Juan C. Acosta Navarro (1), Gabriele Arduini (5), Lauriane Batté (7), Eric Bazile (7), Matthieu Chevallier (8), Rubén Cruz-García (1), Jonathan J. Day (5), Thierry Fichefet (4), Daniela Flocco (9), Mukesh Gupta (4), Kerstin Hartung (6,10), Ed Hawkins (9), Claudia Hinrichs (11), Linus Magnusson (5), Eduardo Moreno-Chamarro (1), Sergio Pérez-Montero (1), Leandro Ponsoni (4), Tido Semmler (11), Doug Smith (2), Jean Sterlin (4), Michael Tjernström (6), Ilona Välisuo (7,12), and Thomas Jung (11,13) // (1) Barcelona Supercomputing Center, Barcelona, Spain | (2) Met Office, Exeter, UK | (3) Norwegian Meteorological Institute, Oslo, Norway | (4) Université catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Louvain-la-Neuve, Belgium | (5) European Centre for Medium-Range Weather Forecasts, Reading, UK | (6) Department of Meteorology, Stockholm University, Stockholm, Sweden | (7) CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France | (8) Météo-France, Toulouse, France | (9) National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK. | (10) Now at: Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany | (11) Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany | (12) Now at: Meteorology Unit, Finnish Meteorological Institute, Helsinki, Finland | (13) Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany