Coupled Sea Ice-ocean Model Research Articles

Bathymetry-constrained ocean geostrophic currents play a key role in shaping the sea ice circulation in the Canada Basin, Arctic Ocean

Abstract Satellite-based observations and a pan-Arctic coupled sea ice-ocean model are utilized to study the effect of ocean geostrophic currents on large-scale sea ice circulation in the Canada Basin, Arctic Ocean. We find that surface winds primarily drive sea ice drifts in the west-east direction, while the geostrophic currents in the Beaufort Gyre promote north-south ice drifts. Wind fluctuations can create variable ice drifts, yet geostrophic currents respond more slowly due to their larger vertical scale, serving as a slowly-evolving conveyor belt for maintaining the anticyclonic ice circulation. It is further demonstrated that the bathymetry can regulate the movement of sea ice via constraining the expansion of ocean circulation. This mechanism is indirect in the sense that the ice is far from the seafloor. Our research underscores the necessity of considering the bathymetry-constrained geostrophic currents in understanding Arctic sea ice dynamics. With the rapid retreat of Arctic sea ice, the multi-scale interactions between ice drifts and ocean currents may have significant implications for the Arctic ecosystem, climate, and shipping corridors.

Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model

Abstract. Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the individual satellite swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (especially critical in periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic are not used, providing less temporal resolution in the data assimilation (DA) analysis and, therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenges (i) and (ii) can be avoided by assimilating individual satellite swaths (level 3 uncollated) instead of daily means (level 3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE) together with the ensemble Kalman filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the mean absolute difference (MAD) shows a 10 % improvement at the end of the assimilation period and a 7 % improvement at the end of the 7 d forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation.

Mechanisms of Offshore Solid and Liquid Freshwater Flux from the East Greenland Current

Abstract The mechanisms that control the export of freshwater from the East Greenland Current, in both liquid and solid form, are explored using an idealized numerical model and scaling theory. A regional, coupled ocean–sea ice model is applied to a series of calculations in which key parameters are varied and the scaling theory is used to interpret the model results. The offshore ice flux, occurring in late winter, is driven primarily by internal stresses and is most sensitive to the thickness of sea ice on the shelf coming out of Fram Strait and the strength of alongshore winds over the shelf. The offshore liquid freshwater flux is achieved by eddy fluxes in late summer while there is an onshore liquid freshwater flux in winter due to the ice–ocean stress, resulting in only weak annual mean flux. The scaling theory identifies the key nondimensional parameters that control the behavior and reproduces the general parameter dependence found in the numerical model. Climate models predict that winds will increase and ice export from the Arctic will decrease in the future, both of which will lead to a decrease in the offshore flux of sea ice, while the influence on liquid freshwater may increase or decrease, depending on the relative changes in the onshore Ekman transport and offshore eddy fluxes. Additional processes that have not been considered here, such as more complex topography and synoptic wind events, may also contribute to cross-shelf exchange. Significance Statement The purpose of this study is to provide a basic understanding of what controls the flux of sea ice and low-salinity water from the East Greenland shelf into the interior of the Greenland and Iceland Seas. This is a potentially important process since it has been shown that sufficient freshening of the surface waters in the interior of the Nordic seas can inhibit deep convection and the associated air–sea heat flux and water mass transformation. A combination of idealized computer models and basic theory indicates that the fluxes of liquid and solid freshwater are controlled by different mechanisms and occur at different times of the year. Accurate representation in climate models will require representation of small-scale processes such as mesoscale eddies and gradients of ice thickness across the shelf.

Response of the Arctic sea ice–ocean system to meltwater perturbations based on a one-dimensional model study

Abstract. A one-dimensional coupled sea ice–ocean model is used to investigate how the Arctic Ocean stratification and sea ice respond to changes in meltwater. In the control experiments, the model is capable of accurately simulating seasonal changes in the upper-ocean stratification structure compared with observations, and the results suggest that ocean stratification is important for ice thickness development during the freezing season. The sensitivity experiments reveal the following: (1) a decrease in meltwater release weakens ocean stratification and creates a deeper, higher-salinity mixed layer. (2) Meltwater reduced ice melting by 17 % by strengthening ocean stratification. (3) The impact of meltwater released during the previous melting season on ice growth in winter depends on the strength of stratification. After removing all the meltwater during the summer, ice formation in areas with strong stratification increased by 12 % during the winter, while it decreased by 43 % in areas with weak stratification. (4) In some areas of the Nansen Basin where stratification is nearly absent, the warm Atlantic Water can reach the ice directly in early spring, leading to early melting of the sea ice in winter if all meltwater is removed from the model. These findings contribute to our understanding of the complex interactions between ocean stratification, meltwater and sea ice growth and have important implications for climate models and future change prediction in the Arctic.

Analysis of ecologically relevant sea ice and ocean variables for the Southern Ocean using a high-resolution model to inform ecosystem studies

Southern Ocean organisms are uniquely adapted to the extreme environmental conditions that characterise this region, making them especially vulnerable to climate change. Alterations to the physical environment have already been linked to alterations in the structure and functioning of entire ecosystems, and ecological disruptions are expected to continue to occur. Although our understanding of the physical processes driving ecological change in the Southern Ocean has improved in recent years, significant knowledge gaps remain largely as a result of insufficient observational data being available. High resolution ocean models are an important tool that can help us overcome data scarcity. However, models generally contain biases that may affect their ability to accurately represent environmental conditions in the region of interest. Thus, their outputs must be evaluated to understand if and how model outputs can be used to answer questions about ecological impacts. Here, we examined the suitability of ACCESS-OM2-01, a high-resolution coupled ocean–sea ice model, for ecological applications. Our results highlight the heterogeneous nature of the mean state of the environmental variables examined and their trends across the Southern Ocean. Our assessment shows that the ACCESS-OM2-01 model simulated well the observed seasonal cycle and broad baseline climatological conditions of the mixed layer depth and sea ice variables for the Southern Ocean over the past 50 years. However, the model performance varies across regions and at seasonal time scales: the model simulated much deeper winter mixed layer depth in the Weddell Sea and within the Antarctic Circumpolar Current, and the climatological sea-ice concentration for summer was lower in the model compared to available observations. Finally, we provide detailed Python-based scripts with all the code and steps for this analysis, which can be used as reference material to reapply elsewhere and evaluate the suitability of other model outputs for ecological applications.

Relative Contribution of Atmospheric Forcing, Oceanic Preconditioning and Sea Ice to Deep Convection in the Labrador Sea

The relative contribution of atmospheric forcing, oceanic preconditioning, and sea ice to Labrador Sea Deep Convection (LSDC) is investigated by conducting three ensemble experiments using a global coupled sea ice–ocean model for the first time. Simulated results show that the atmospheric activities dominate the interannual and decadal variability, accounting for 70% of LSDC. Oceanic preconditioning is more significant in the shallow LSDC years that the water column is stable, accounting for 21%, especially in the central Labrador Sea and Irminger Sea. Moreover, the sea ice contribution is negligible over the whole Labrador Sea, while its contribution is significant in the sea ice-covered slope regions, accounting for 20%. The increasingly importance of sea ice on LSDC and the water mass transformation will be found in the North Atlantic Ocean, if the Arctic sea ice declines continuously. Additionally, there is a 10 Sv increase (85%) in atmospheric forcing to the subpolar gyre in the North Atlantic Ocean, while oceanic preconditioning contributes a 7 Sv decrease (12%). These findings highlight the importance of summer oceanic preconditioning to LSDC and the subpolar gyre, and therefore it should be appropriately accounted for in future studies.

The Effects of Atmospheric and Lateral Buoyancy Fluxes on Labrador Sea Mixed Layer Depth

The depth of the Labrador Sea mixed layer during winter convection is a balance between atmospheric buoyancy loss and lateral buoyancy exchange, and is notoriously difficult to represent accurately in ocean and climate models. This study shows that lateral exchanges of heat and salt between the shelf and the interior are smaller in a regional coupled ocean–sea ice model at higher vertical resolution (75 levels compared with 50 levels), due in part to altered bathymetry along the Greenland shelf. Reduced lateral exchange results in a stronger stratification in the interior of the Labrador Sea, with stronger convection resistance which results in unrealistically shallow mixed layers. The westward fluxes of heat and salt associated with Irminger Water at Cape Farewell are 50 % and 33 % lower, respectively, with higher vertical resolution. Exchanges south of the Labrador Sea from the North Atlantic Current are also smaller, contributing to a reduction in salt and heat import into the Labrador Sea interior. When the high resolution model is forced with a stronger wintertime buoyancy loss at the ocean surface, this weakens the Labrador Sea stratification, allowing the forcing to break through the freshwater cap and increasing convection, bringing mixed layer depths back to observed values. A strong atmospheric forcing can therefore compensate for a reduction in lateral advection. The mixed layer depths, which are representative of convection and Labrador Sea water formation, will be the focus in this study. Therefore, this study suggests that convection and Labrador Sea Water formation is a complex interplay of surface and lateral fluxes, linked to stratification thresholds.

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

Abstract. The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Ice Algae Model Intercomparison Project phase 2 (IAMIP2)

Abstract. Ice algae play a fundamental role in shaping sea-ice-associated ecosystems and biogeochemistry. This role can be investigated by field observations; however the influence of ice algae at the regional and global scales remains unclear due to limited spatial and temporal coverage of observations and because ice algae are typically not included in current Earth system models. To address this knowledge gap, we introduce a new model intercomparison project (MIP), referred to here as the Ice Algae Model Intercomparison Project phase 2 (IAMIP2). IAMIP2 is built upon the experience from its previous phase and expands its scope to global coverage (both Arctic and Antarctic) and centennial timescales (spanning the mid-20th century to the end of the 21st century). Participating models are three-dimensional regional and global coupled sea-ice–ocean models that incorporate sea-ice ecosystem components. These models are driven by the same initial conditions and atmospheric forcing datasets by incorporating and expanding the protocols of the Ocean Model Intercomparison Project, an endorsed MIP of the Coupled Model Intercomparison Project phase 6 (CMIP6). Doing so provides more robust estimates of model bias and uncertainty and consequently advances the science of polar marine ecosystems and biogeochemistry. A diagnostic protocol is designed to enhance the reusability of the model data products of IAMIP2. Lastly, the limitations and strengths of IAMIP2 are discussed in the context of prospective research outcomes.

An analysis of Atlantic water in the Arctic Ocean using the Arctic subpolar gyre state estimate and observations

The Atlantic Water (AW) Layer in the Arctic Subpolar gyre sTate Estimate Release 1 (ASTE R1), a data-constrained, regional, medium-resolution coupled ocean-sea ice model, is analyzed for the period 2004–2017 in combination with available hydrographic data. The study, focusing on AW defined as the waters between two bounding isopycnals, examines the time-average, mean seasonal cycle and interannual variability of AW Layer properties and circulation. A surge of AW, marked by rapid increases in mean AW Layer potential temperature and AW Layer thickness, begins two years into the state estimate and traverses the Arctic Ocean along boundary current pathways at a speed of 1–2 cm/s. The surge also alters AW circulation, including a reversal in flow direction along the Lomonosov Ridge, resulting in a new quasi-steady AW circulation from 2010 through the end of the state estimate period. The time-mean AW circulation during this latter time period indicates that a significant amount of AW spreads over the Lomonosov Ridge rather than directly returning along the ridge to Fram Strait. A three-layer depiction of the time-averaged ASTE R1 overturning circulation within the Arctic Ocean reveals that more AW is converted to colder, fresher Surface Layer water than is transformed to Deep and Bottom Water (1.2 Sv vs. 0.4 Sv). ASTE R1 also exhibits an increase in the volume of AW over the study period at a rate of 1.4 Sv, with near compensating decrease in Deep and Bottom Water volume. Observed AW properties compared to ASTE R1 output reveal increasing misfit during the simulated period with the ASTE R1 AW Layer generally being warmer and thicker than in observations.

Internal Ocean Dynamics Control the Long-Term Evolution of Weddell Sea Polynya Activity

Open-ocean polynyas effectively couple the ocean and atmosphere through large ice-free areas within the sea-ice cover, release vast quantities of oceanic heat, and impact deep ocean ventilation. Changes in polynya activity, particularly in the Weddell Sea, may be key to longer time-scale climate fluctuations, feedbacks and abrupt change. While changes in the occurrence of Weddell Sea polynyas are generally attributed to changes in the atmospheric surface forcing, the role of internal ocean dynamics for polynya variability is not well-resolved. In this study we employ a global coupled ocean-sea ice model with a repeating annual atmospheric cycle to explore changes in Weddell Sea water mass properties, stratification and ocean circulation driven by open-ocean polynyas. During the 1300-year long simulation, two large polynyas occur in the central Weddell Sea. Our results suggest that Weddell polynyas may be triggered without inter-annual changes in the atmospheric forcing. This highlights the role of ocean processes in preconditioning and triggering open-ocean polynyas on multi-centennial time-scales. The simulated polynyas form due to internal ocean-sea ice dynamics associated with a slow build-up and subsequent release of subsurface heat. A strong stratification and weak vertical mixing is necessary for building the subsurface heat reservoir. Once the water column turns unstable, enhanced vertical mixing of warm and saline waters into the surface layer causes efficient sea ice melt and the polynya appears. Subsequent, vigorous deep convection is maintained through upwelling of warm deep water leading to enhanced bottom water formation. We find a cessation of simulated deep convection and polynya activity due to long-term cooling and freshening of the subsurface heat reservoir. As subsurface waters in the Southern Ocean are now becoming warmer and saltier, we speculate that larger and more persistent Weddell polynyas could become more frequent in the future.

Intercomparison of Arctic sea ice simulation in ROMS-CICE and ROMS-Budgell

Accurate representation of the complex ocean-sea ice interaction is still an ongoing effort. In this study, we have coupled the Community Ice Code (CICE) model and Regional Ocean Modeling System (ROMS) to develop a high-resolution regional coupled ocean-sea ice model for polar regions. This setup allows us to investigate the interaction between ocean and sea ice in detail. The Coupled-Ocean-Atmosphere-Wave-Sediment-Transport (COAWST) modeling system is the core of this coupled model. Currently, the ROMS model in COAWST uses the Budgell sea ice model, embedded as a sub-module in it but introducing a more comprehensive sea ice model (CICE) may provide a better treatment of sea ice. Here, we present our preliminary results based on the coupled ROMS-CICE and ROMS-Budgell simulation over the Arctic Ocean. Our results show that both CICE and Budgell models perform better in simulating sea ice concentration during winter than during summer. Compared to the satellite observations, sea ice concentrations from the CICE model in most subregions have higher correlations and smaller centered root mean square errors, showing higher simulation skills. The sea ice thickness biases are larger in the Budgell model in the early months of the year, whereas in the CICE model they are larger after October. Both CICE and Budgell models overestimate the sea ice extent and sea ice volume in summer, and their performances differ in the subregions.

Impacts of Changed Ice-Ocean Stress on the North Atlantic Ocean: Role of Ocean Surface Currents

The importance of considering ocean surface currents in ice-ocean stress calculation in the North Atlantic Ocean and Arctic sea ice is investigated for the first time using a global coupled ocean-sea ice model. Considering ocean surface currents in ice-ocean stress calculation weakens the ocean surface stress and Ekman pumping by about 7.7 and 15% over the North Atlantic Ocean, respectively. It also significantly reduces the mechanical energy input to ageostrophic and geostrophic currents, and weakens the mean and eddy kinetic energy by reducing the energy conversion rates of baroclinic and barotropic pathways. Furthermore, the strength of the Atlantic Meridional Overturning Circulation (AMOC), the Nordic Seas MOC, and the North Atlantic subpolar gyre are found to be reduced considerably (by 14.3, 31.0, and 18.1%, respectively). The weakened AMOC leads to a 0.12 PW reduction in maximum northward ocean heat transport, resulting in a reduced surface heat loss and lower sea surface temperature over the North Atlantic Ocean. This reduction also leads to a shrink in sea ice extent and an attenuation of sea ice thickness. These findings highlight the importance of properly considering both the geostrophic and ageostrophic components of ocean surface currents in ice-ocean stress calculation on ocean circulation and climate studies.

Atmospheric Warming Drives Growth in Arctic Sea Ice: A Key Role for Snow.

A number of feedbacks regulate the response of Arctic sea ice to local atmospheric warming. Using a realistic coupled ocean‐sea ice model and its adjoint, we isolate a mechanism by which significant ice growth at the end of the melt season may occur as a lagged response to Arctic atmospheric warming. A series of perturbation simulations informed by adjoint model‐derived sensitivity patterns reveal the enhanced ice growth to be accompanied by a reduction of snow thickness on the ice pack. Detailed analysis of ocean‐ice‐snow heat budgets confirms the essential role of the reduced snow thickness for persistence and delayed overshoot of ice growth. The underlying mechanism is a snow‐melt‐conductivity feedback, wherein atmosphere‐driven snow melt leads to a larger conductive ocean heat loss through the overlying ice layer. Our results highlight the need for accurate observations of snow thickness to constrain climate models and to initialize sea ice forecasts.

Introducing LAB60: A 1∕60° NEMO 3.6 numerical simulation of the Labrador Sea

Abstract. A high-resolution coupled ocean–sea ice model is set up within the Labrador Sea. With a horizontal resolution of 1∕60∘, this simulation is capable of resolving the multitude of eddies that transport heat and freshwater into the interior of the Labrador Sea. These fluxes strongly govern the overall stratification, deep convection, restratification, and production of Labrador Sea Water. Our regional configuration spans the full North Atlantic and Arctic; however, high resolution is only applied in smaller nested domains within the North Atlantic and Labrador Sea. Using nesting reduces computational costs and allows for a long simulation from 2002 to the near present. Three passive tracers are also included: Greenland runoff, Labrador Sea Water produced during convection, and Irminger Water that enters the Labrador Sea along Greenland. We describe the configuration setup and compare it against similarly forced lower-resolution simulations to better describe how horizontal resolution impacts the representation of the Labrador Sea in the model.

Respective influences of perturbed atmospheric and ocean–sea ice initial conditions on the skill of seasonal Antarctic sea ice predictions: A study with NEMO3.6–LIM3

Dynamical climate models have been extensively used over the last decade to perform seasonal sea ice predictions in the context of ensemble forecasting. To date, the sensitivity to the initial conditions has received the most attention through the evaluation of the theoretical limit of Antarctic sea ice predictability imposed by the chaotic evolution of the climate system. The respective contributions of perturbed ocean–sea ice initial conditions and perturbed atmospheric boundary conditions to this predictability remains unevaluated, though. Using the coupled ocean–sea ice model NEMO3.6–LIM3, we developed a suitable framework for evaluating both influences on seasonal Antarctic sea ice predictions. This study reveals that the uncertainty associated with the evolution of the atmospheric conditions is a major limitation to the realization of skilful sea ice extent (SIE) predictions. However, it has a limited impact on sea ice volume (SIV) predictions. The discrepancies between the SIE and SIV predictabilities have been attributed to the presence of very thin ice, which accounts for much of the SIE variability in winter. We also demonstrated that an incorrect estimate of the ocean–sea ice initial conditions has a weaker, but not negligible influence. The time evolution of different plausible ocean–sea ice initial conditions under perfect knowledge of the atmospheric conditions suggest that the initial SIE errors cannot be totally reduced by the atmospheric forcing. They even increase during the melt season. The high persistence of the SIV anomalies is found to be responsible for this behaviour. These findings imply that a correct initialization of the sea ice thickness (SIT) might be more important than previously thought for seasonal Antarctic sea ice predictions, especially if one wants to predict the SIE during the melt season.

Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm

AbstractImprovement and optimization of numerical sea ice models are of great relevance for understanding the role of sea ice in the climate system. They are also a prerequisite for meaningful prediction. To improve the simulated sea ice properties, we develop an objective parameter optimization system for a coupled sea ice–ocean model based on a genetic algorithm. To take the interrelation of dynamic and thermodynamic model parameters into account, the system is set up to optimize 15 model parameters simultaneously. The optimization is minimizing a cost function composed of the model–observation misfit of three sea ice quantities (concentration, drift, and thickness). The system is applied for a domain covering the entire Arctic and northern North Atlantic Ocean with an optimization window of about two decades (1990–2012). It successfully improves the simulated sea ice properties not only during the period of optimization but also in a validation period (2013–16). The similarity of the final values of the cost function and the resulting sea ice fields from a set of 11 independent optimizations suggest that the obtained sea ice fields are close to the best possible achievable by the current model setup, which allows us to identify limitations of the model formulation. The optimized parameters are applied for a simulation with a higher-resolution model to examine a portability of the parameters. The result shows good portability, while at the same time, it shows the importance of the oceanic conditions for the portability.

Simulated ballast water accumulation along Arctic shipping routes

The recent decline of the Arctic sea ice cover leads to an increasing number of vessels navigating through the Arctic shipping routes. Ballast water, essential for the vessel's safety during voyages and cargo transfers, however, is also considered one of the main vectors for transport and introduction of non-indigenous species. The aim of this paper is to investigate potential effects of the ballast water discharged in the main Arctic shipping routes on the local environment. For that a passive tracer was implemented in a fine resolution coupled ocean-sea ice model covering the entire Arctic and northern North Atlantic, to simulate the spread of the ballast water discharged based on release points along real ship positions from 2013. The model results showed that spring and summer were the seasons with the highest tendency for accumulation of ballast water tracer on the surface layers south of Novaya Zemlya and south of Spitsbergen, not only due to a higher number of vessels navigating in the area but also due to strong stratification. During winter and autumn, the tracer was mixed with and into deeper layers due to vertical convection. The simulated ballast water accumulation during spring and summer indicated that organisms, that survived the voyage in the ballast tanks, could establish a stable or growing population and eventually become invasive.

Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, which include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean–sea-ice model is used to assess the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application Facility, thin sea ice from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term estimation of sea-ice extent compared to the other assimilation systems. The other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, but possibly even longer.

Spatiotemporal dependence of Antarctic sea ice variability to dynamic and thermodynamic forcing: a coupled ocean–sea ice model study

Satellite-derived Antarctic sea ice extent has displayed a slight upward since 1979, but with strong temporal and regional variability—the drivers of which are poorly understood. Here, we conduct numerical experiments with a circum-Antarctic ocean–sea ice–ice shelf model driven by realistic atmospheric surface boundary conditions to examine the factors responsible for the temporal and spatial patterns in observed Antarctic sea ice variability. The model successfully reproduces observed seasonal and interannual variability in total sea ice extent and the temporal/spatial patterns of sea ice concentration and seasonality (days of advance and retreat and actual ice days) for 1979–2014. Sensitivity experiments are performed, in which the interannual variability in wind stress or thermodynamic surface forcing is ignored, to delineate their contributions to Antarctic sea ice fields. The results demonstrate that: (1) thermodynamic forcing plays a key role in driving interannual variability in sea ice extent and seasonality in most Antarctic sectors; (2) only in the Ross Sea the wind stress does become the main driver of sea ice extent variability; (3) thermodynamic forcing largely regulates interannual variability in the timing of sea ice advance, while wind stress largely controls the timing of the sea ice retreat; and (4) although both wind stress and thermodynamic forcing contribute to variability in total sea ice volume, the wind stress plays a dominant role in regulating sea ice volume variability in the near-coastal zone.