Sea Ice Prediction Research Articles

The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018

Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships, while limited by the capability of climate models. A bias correction methodology in this study was proposed and performed on raw products from two climate models, the First Institute Oceanography Earth System Model (FIOESM) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS), to improve 60 days predictions for Arctic sea ice. Both models were initialized on July 1, August 1, and September 1 in 2018. A 60-day forecast was conducted as a part of the official sea ice service, especially for the ninth Chinese National Arctic Research Expedition (CHINARE) and the China Ocean Shipping (Group) Company (COSCO) Northeast Passage voyages during the summer of 2018. The results indicated that raw products from FIOESM underestimated sea ice concentration (SIC) overall, with a mean bias of SIC up to 30%. Bias correction resulted in a 27% improvement in the Root Mean Square Error (RMSE) of SIC and a 10% improvement in the Integrated Ice Edge Error (IIEE) of sea ice edge (SIE). For the CFS, the SIE overestimation in the marginal ice zone was the dominant features of raw products. Bias correction provided a 7% reduction in the RMSE of SIC and a 17% reduction in the IIEE of SIE. In terms of sea ice extent, FIOESM projected a reasonable minimum time and amount in mid-September; however, CFS failed to project both. Additional comparison with subseasonal to seasonal (S2S) models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases.

Reduced Arctic sea ice extent during the mid-Pliocene Warm Period concurrent with increased Atlantic-climate regime

Quantifying the contribution of poleward oceanic heat transport to the Arctic Ocean is important for making future sea ice and climate predictions. To highlight its potential importance in a warmer world, we present a new record of water-mass exchange between the Atlantic and the Arctic Oceans using the authigenic neodymium isotopic composition of marine sediments from the Fram Strait during the past ∼3.4 to 2.6 Ma. In this study, we target the mid-Pliocene Warm Period (mPWP: 3.264–3.025 Ma) of the Pliocene epoch, the most recent geological analogue for future climate change. We complement our semi-quantitative water mass exchange reconstruction with estimates of spring sea ice concentration based on source-specific biomarkers. Our estimates of volume transport of warm waters into the Arctic Ocean suggest long-term secular changes from the lowest during the Marine Isotope Stage M2 “glacial” (3.312–3.264 Ma), to near complete “Atlantification” of the Eurasian sector of the Arctic Ocean during the mPWP. Orbital forcing is found to be the dominant controlling factor for modulating northward volume transport of Atlantic-derived water masses, with an associated reduction in Arctic spring sea ice concentration of ∼30–35%. Current generation models often produce diverging results, however, and have not yet been validated against proxy data in northern high latitude settings during the mPWP. Our new results of northward volume transport and sea ice extent therefore provide much needed input for validation of current generation models aimed at improving the robustness of future climate modeling in the Arctic.

Snow Topography on Undeformed Arctic Sea Ice Captured by an Idealized “Snow Dune” Model

AbstractOur ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity (albedo), and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. In this paper, we develop a simple model of premelt snow topography that accurately describes snow cover of flat, undeformed Arctic sea ice on several study sites for which data were available. The model considers a surface that is a sum of randomly sized and placed “snow dunes” represented as Gaussian mounds. This model generalizes the “void model” of Popović et al. (2018, https://doi.org/10.1103/PhysRevLett.120.148701) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the premelt snow topography. We show that the model snow depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage evolution during an early stage of pond formation. We also formulate a criterion for ice to remain pond‐free throughout the summer. Results from our model could be directly included in large‐scale models, thereby improving our understanding of energy balance on sea ice and allowing for more reliable predictions of Arctic sea ice in a future climate.

An Internal Atmospheric Process Determining Summertime Arctic Sea Ice Melting in the Next Three Decades: Lessons Learned from Five Large Ensembles and Multiple CMIP5 Climate Simulations

AbstractArctic sea ice melting processes in summer due to internal atmospheric variability have recently received considerable attention. A regional barotropic atmospheric process over Greenland and the Arctic Ocean in summer (June–August), featuring either a year-to-year change or a low-frequency trend toward geopotential height rise, has been identified as an essential contributor to September sea ice loss, in both observations and the CESM1 Large Ensemble (CESM-LE) of simulations. This local melting is further found to be sensitive to remote sea surface temperature (SST) variability in the east-central tropical Pacific Ocean. Here, we utilize five available large “initial condition” Earth system model ensembles and 31 CMIP5 models’ preindustrial control simulations to show that the same atmospheric process, resembling the observed one and the one found in the CESM-LE, also dominates internal sea ice variability in summer on interannual to interdecadal time scales in preindustrial, historical, and future scenarios, regardless of the modeling environment. However, all models exhibit limitations in replicating the magnitude of the observed local atmosphere–sea ice coupling and its sensitivity to remote tropical SST variability in the past four decades. These biases call for caution in the interpretation of existing models’ simulations and fresh thinking about models’ credibility in simulating interactions of sea ice variability with the Arctic and global climate systems. Further efforts toward identifying the causes of these model limitations may provide implications for alleviating the biases and improving interannual- and decadal-time-scale sea ice prediction and future sea ice projection.

Analyzing the impact of CryoSat-2 ice thickness initialization on seasonal Arctic Sea Ice prediction

AbstractTwin 5-month seasonal forecast experiments are performed to predict the September 2018 mean and minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M km2 (5.27 M km2) vs the ensemble forecasts initialized with CS2 ice thickness that had an error of 0.40 M km2 (4.99 M km2), a 43% reduction in error. The September mean integrated ice edge error shows a 18% improvement for the Pan-Arctic with the CS2 data vs the control forecasts. Comparison against upward looking sonar ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of forecasts show ice free conditions as observed at moorings A and D.

Monitoring of the physical and mechanical state of sea ice and short-term prediction of extreme ice phenomena

The article presents the results of studies that complement each other. New methods of instrumental studies of the physical and mechanical characteristics of ice and ice cover are considered.There is briefly described the complex system to developed in the AARI (Arctic and Antarctic research Institute) to determine the strength characteristics of ice formations in natural conditions. The results of determining the ice strength in wells (local strength) at the research station of AARI “Ice Base Cape of Baranov” are presented. The coefficient of comparison of local strength and strength of samples under uniaxial compression is obtained. This allows to determine the ice strength without sampling and testing of samples. On a large experimental material, linear and quadratic approximations for local ice strength were obtained. The influence of the indenter penetration rate on the local ice strength is studied.There is considered the application of the contact remote method for monitoring the dynamic state of the ice cover in order to obtain new data for creating a method of predicting the phenomena of compression and destruction of sea ice in real time.To study the large-scale mechanics of ice during dynamic processes in the air — ice — water system, a modular-block system for ice cover monitoring was developed. The layout of the system was tested in Arctic expeditions.The developed complex system for determining the strength characteristics of ice formations in natural conditions and the modular-block system for monitoring the state of the ice cover complement each other, suggest their further development and improvement, provide wide opportunities for ice research.

A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

AbstractThe decline of Arctic sea ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically driven SIM export and negative feedbacks from sea ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice thickness observations collected after melt onset are particularly critical for summer Arctic sea ice predictions.

Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations

AbstractTo increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short‐term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat‐2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System.

Using a skillful statistical model to predict September sea ice covering Arctic shipping routes

The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the mid-latitudes but also brings more opportunities for Arctic shipping routes and polar resource exploration, both of which motivate us to further understand causes of sea-ice variations and to obtain more accurate estimates of sea-ice cover in the future. Here, a novel data-driven method, the causal effect networks algorithm, is applied to identify the direct precursors of September sea-ice extent covering the Northern Sea Route and Transpolar Sea Route at different lead times so that statistical models can be constructed for sea-ice prediction. The whole study area was also divided into two parts: the northern region covered by multiyear ice and the southern region covered by seasonal ice. The forecast models of September sea-ice extent in the whole study area (TSIE) and southern region (SSIE) at lead times of 1–4 months can explain over 65% and 79% of the variances, respectively, but the forecast skill of sea-ice extent in the northern region (NSIE) is limited at a lead time of 1 month. At lead times of 1–4 months, local sea-ice concentration and sea-ice thickness have a larger influence on September TSIE and SSIE than other teleconnection factors. When the lead time is more than 4 months, the surface meridional wind anomaly from northern Europe in the preceding autumn or early winter is dominant for September TSIE variations but is comparable to thermodynamic factors for NSIE and SSIE. We suggest that this study provides a complementary approach for predicting regional sea ice and is helpful in evaluating and improving climate models.

Going with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observations

Abstract. In recent decades, Arctic sea ice has shifted toward a younger, thinner, seasonal ice regime. Studying and understanding this “new” Arctic will be the focus of a year-long ship campaign beginning in autumn 2019. Lagrangian tracking of sea ice floes in the Community Earth System Model Large Ensemble (CESM-LE) during representative “perennial” and “seasonal” time periods allows for understanding of the conditions that a floe could experience throughout the calendar year. These model tracks, put into context a single year of observations, provide guidance on how observations can optimally shape model development, and how climate models could be used in future campaign planning. The modeled floe tracks show a range of possible trajectories, though a Transpolar Drift trajectory is most likely. There is also a small but emerging possibility of high-risk tracks, including possible melt of the floe before the end of a calendar year. We find that a Lagrangian approach is essential in order to correctly compare the seasonal cycle of sea ice conditions between point-based observations and a model. Because of high variability in the melt season sea ice conditions, we recommend in situ sampling over a large range of ice conditions for a more complete understanding of how ice type and surface conditions affect the observed processes. We find that sea ice predictability emerges rapidly during the autumn freeze-up and anticipate that process-based observations during this period may help elucidate the processes leading to this change in predictability.

Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model

AbstractThis paper describes and evaluates the assimilation component of a seamless sea ice prediction system, which is developed based on the fully coupled Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research Climate Model (AWI‐CM, v1.1). Its ocean/ice component with unstructured‐mesh discretization and smoothly varying spatial resolution enables seamless sea ice prediction across a wide range of space and time scales. The model is complemented with the Parallel Data Assimilation Framework to assimilate observations in the ocean/ice component with an Ensemble Kalman Filter. The focus here is on the data assimilation of the prediction system. First, the performance of the system is tested in a perfect‐model setting with synthetic observations. The system exhibits no drift for multivariate assimilation, which is a prerequisite for the robustness of the system. Second, real observational data for sea ice concentration, thickness, drift, and sea surface temperature are assimilated. The analysis results are evaluated against independent in situ observations and reanalysis data. Further experiments that assimilate different combinations of variables are conducted to understand their individual impacts on the model state. In particular, assimilating sea ice drift improves the sea ice thickness estimate, and assimilating sea surface temperature is able to avert a circulation bias of the free‐running model in the Arctic Ocean at middepth. Finally, we present preliminary results obtained with an extended system where the atmosphere is constrained by nudging toward reanalysis data, revealing challenges that still need to be overcome to adapt the ocean/ice assimilation. We consider this system a prototype on the way toward strongly coupled data assimilation across all model components.

Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model

The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has comparable regional SIE skill in winter due to the improved upper ocean heat content. The relatively low skill of summer SIE in NorCPM suggests that SST anomalies are not sufficient to constrain summer SIE variability and further assimilation of sea ice thickness or atmospheric data is expected to increase the skill.

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.

Medium range sea ice prediction in support of Japanese research vessel MIRAI’s expedition cruise in 2018

ABSTRACT The Japanese research vessel MIRAI sailed into the Arctic Ocean through the Bering Strait from 4 to 25 November 2018 to study the atmosphere, ocean, and sea ice of the Chukchi Sea. Early winter dynamics and thermodynamics can cause sea ice conditions to change over short timescales. Heavy ice pressure may build up in the compression of compact ice. MIRAI is an ice-strengthened ship that has to avoid thick sea ice and areas of high sea ice coverage, and therefore precise medium-range forecast of sea ice distribution is key to its safe and efficient navigation. A high-resolution (about 2.5 km) coupled ice–ocean model was used to produce medium-range (10-day) forecasts for the expedition. European Center for Medium-Range Weather Forecast’s atmospheric model high-resolution 10-day forecast was used as forcing data. Forecast skill is measured by ice edge error, which is the average distance between forecast and observed ice edges. Using a threshold of 15% sea ice concentration to indicate the ice edge, the maximum ice edge error in the ice–ocean coupled model in the Chukchi Sea is 16 km, indicating that the model is able to provide 10-day forecasts with sufficient accuracy for the vessel’s operation.

Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction

The Arctic and Antarctic are among the regions most exposed to climate change, but ironically, they are also the ones for which the least observations are available. Climate models have been instrumental in completing the big picture. It is generally accepted that observations feed the development of climate models: parameterizations are designed based on empirically observed relationships, climate model predictions are initialized using observational products, and numerical simulations are evaluated given matching observational datasets. Recent research suggests that the opposite also holds: climate models can feed the development of polar observational networks by indicating the type, location, frequency, and timing of measurements that would be most useful for answering a specific scientific question. Here, we review the foundations of this emerging notion with five cases borrowed from the field of polar prediction with a focus on sea ice (sub-seasonal to centennial time scales). We suggest that climate models, besides their usual purposes, can be used to objectively prioritize future observational needs – if, of course, the limitations of the realism of these models have been recognized. This idea, which has been already extensively exploited in the context of Numerical Weather Prediction, reinforces the notion that observations and models are two sides of the same coin rather than distinct conceptual entities.

The Value of Sustained Ocean Observations for Sea Ice Predictions in the Barents Sea

ABSTRACT Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of observing system experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea surface temperature (SST) satellite observations and subsurface conductivity–temperature–depth (CTD) measurements. The SST data are found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal time scales.

Predictability of Antarctic Sea Ice Edge on Subseasonal Time Scales

AbstractCoupled subseasonal forecast systems with dynamical sea ice have the potential of providing important predictive information in polar regions. Here, we evaluate the ability of operational ensemble prediction systems to predict the location of the sea ice edge in Antarctica. Compared to the Arctic, Antarctica shows on average a 30% lower skill, with only one system remaining more skillful than a climatological benchmark up to ∼30 days ahead. Skill tends to be highest in the west Antarctic sector during the early freezing season. Most of the systems tend to overestimate the sea ice edge extent and fail to capture the onset of the melting season. All the forecast systems exhibit large initial errors. We conclude that subseasonal sea ice predictions could provide marginal support for decision‐making only in selected seasons and regions of the Southern Ocean. However, major progress is possible through investments in model development, forecast initialization and calibration.

Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts duringXuelong's first trans-Arctic Passage in summer 2017

AbstractIn an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather forecasts from the Global Forecast System of the National Centers for Environmental Prediction drive the sea ice–ocean coupled model. The ensemble mean sea-ice forecasts were used to facilitate the Chinese National Arctic Research Expedition in summer 2017. The forecasted sea-ice concentration is evaluated against AMSR2 and Special Sensor Microwave Imager/Sounder sea-ice concentration data. The forecasted sea-ice thickness is compared to the in-situ observations and the Pan-Arctic Ice-Ocean Modeling and Assimilation System. These comparisons show the promising potential of ArcIOPS for operational Arctic sea-ice forecasts. Nevertheless, the forecast bias in the Beaufort Sea calls for a delicate parameter calibration and a better design of the assimilation system.

Radiative Control of the Interannual Variability of Arctic Sea Ice

AbstractOn top of a declining trend driven by global warming, the Arctic sea ice extent (SIE) exhibits considerable interannual variations. In this study, we analyze the interannual anomalies of September SIE in relation to the surface radiation anomalies. We find that the accumulation of radiation energy in the early months (June, July, and August) very well explains the September SIE variability (R2 = 0.81). In Particular, strong correlations are found between September SIE and June radiation anomalies, which in the shortwave is due to cloud and surface albedo changes and in the longwave due to atmospheric warming. The results show that monitoring the radiation anomalies affords a potential means to improve the prediction of the late summer sea ice.

Grid size dependency of short-term sea ice forecast and its evaluation during extreme Arctic cyclone in August 2016

Recently, cyclones are quite active and dramatically influence the sea ice distribution in the Arctic region. Therefore, precise prediction of sea ice distribution during the cyclones is crucial for safe and efficient navigation in the Arctic Ocean. A high-resolution (about 2.5 km) ice–ocean coupled model is developed for forecasting the short-term sea ice distribution along the Arctic sea routes. Since a commercial ship navigates to avoid the sea ice as much as possible, a factor to score the forecast skill is considered to be ice edge error which is an averaged distance between forecasted and observed ice edges. First, we discuss the influence of horizontal grid resolution on short-term sea ice predictions during May to November 2015. The model grid resolution is varied from 2.5 km, 5 km, 10 km and 15 km. This grid dependency study suggests that the 2.5 km resolution model predicts the ice edge accurately compared to the satellite observations. Second, the primary experiment was run from 2 August 2016 to 7 August 2016 under the extreme cyclone in the Laptev Sea using ensemble predictions. The ensembles are constructed by using forecasted atmospheric forcing data sets from THORPEX Interactive Grand Global Ensemble (TIGGE) project and European Center for Medium-Range Weather Forecast Interim (ERA-Interim) reanalysis data. The ensemble ice edge errors are computed from 2 August 2016 to 7 August 2016. The maximum forecast skill of ensemble average ice edge error in the ice–ocean coupled model is 11.74 ± 0.54 km with the threshold of 15% ice concentration for the AMSR2 and model predicted ice edges. It can be said that the present model of 2.5 km grids satisfies the ship crew requirement of around 10 km ice edge error for 5-day forecast.