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Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 d. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts. [ABSTRACT FROM AUTHOR]

Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physical-based models still have limited accuracy, statistical post-processing techniques (often called calibration) can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 days. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts, and 29 % lower than forecasts based on persistence of the sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different type of predictors (observations, sea ice and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts. [ABSTRACT FROM AUTHOR]

Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physical-based models still have limited accuracy, statistical post-processing techniques (often called calibration) can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 days. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts, and 29 % lower than forecasts based on persistence of the sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different type of predictors (observations, sea ice and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts. [ABSTRACT FROM AUTHOR]

As a consequence of a diminishing sea ice cover in the Arctic, activity is on the rise. The position of the sea ice edge, which is generally taken to define the extent of the ice cover, changes in response to dynamic and thermodynamic processes. Forecasts for sea ice expansion on synoptic timescales due to an advancing ice edge will provide information that can be of significance for open ocean operations in polar regions. However, the value of this information depends on the quality of the forecasts. Here, we present methods for examining the quality of forecasted sea ice expansion on sub-seasonal timescales and the geographic location where the largest expansions are expected from the forecast results. The algorithm is simple to implement, and an examination of 2 years of model results and accompanying observations demonstrates the usefulness of the analysis. [ABSTRACT FROM AUTHOR]

With the improving skill of sea ice forecasts, verification methods are becoming increasingly important to inform model developers and users. In this study, the Modified Hausdorff Distance (MHD), the Spatial Probability Score (SPS), and a variation of the SPS are compared in order to assess their performances for evaluating the ice edge position in the new European Centre for Medium‐Range Weather Forecasts seasonal forecasts (SEAS5). On average, the SEAS5 forecasts outperform a climatological reference during about 3 weeks using the MHD, and during about 5 weeks using the SPS. Furthermore, our results show that the MHD is more sensitive than the SPS to the presence of isolated sea ice patches. Moreover, the variation of the SPS introduced here is not seasonally dependent (contrary to the original SPS) and can be interpreted as a distance error of the ice edge position, which is a potentially relevant information for end users. Key Points: Three verification methods for evaluating the ice edge position are compared in the context of seasonal forecastingVarious characteristics of the verification methods have been identified, which should help future forecast evaluationsPredictive skill of a new seasonal prediction system is assessed using various verification methods [ABSTRACT FROM AUTHOR]

The ice edge is a simple quantity in the form of a line that can be derived from a spatially varying sea ice concentration field. Due to its long history and relevance for operations in the Arctic, the position of the ice edge should be an essential element in any system that is designed to monitor or provide forecasts for the physical state of the Arctic Ocean and adjacent ocean regions. Users of monitoring and forecast products for sea ice must be provided with complementary information on the expected accuracy of the data or model results. Such information is traditionally available as a set of metrics that provide an assessment of the information quality. In this study we provide a survey of metrics that are presently included in the product quality assessment of the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Marine Forecasting Center sea ice edge position forecast. We show that when ice edge results from different products are compared, mismatching results for polynya and local freezing at the coasts of continents and archipelagos have a large impact on the quality assessment. Such situations, which occur regularly in the products we examine, have not been properly acknowledged when sets of metrics for the quality of ice edge position results are constructed. We examine the quality of ice edge forecasts using a total of 15 metrics for the ice edge position. These metrics are analysed in synthetic examples, as well as in selected cases of actual forecasts, and finally for a full year of weekly forecast bulletins. Using necessity and simplicity of information as a guideline, we recommend using a set of four metrics that sheds light on the various aspects of product quality that we consider. Moreover, any user is expected to be interested in a limited part of the geographical domain, so metrics derived as domain-wide integrated quantities may be of limited value. Consequently, we recommend that metrics also be made available for an appropriate set of sub-domains. Furthermore, we find that the metrics decorrelation timescales are much longer than the present forecast range. Hence, our final recommendation is to include depictions of gridded mismatching ice edge positions using maps for the integrated ice edge error. [ABSTRACT FROM AUTHOR]

The ice edge is a simple quantity in the form of a line that can be derived from a spatially varying sea ice concentration field. Due to its long history and relevance for operations in the Arctic, the position of the ice edge should be an essential element in any system that is designed to monitor or provide forecasts for the physical state of the Arctic Ocean and adjacent ocean regions. Like for all components of monitoring and forecast products, users need to complement information about the ice edge position with the expected accuracy of the data or model results. Such information is traditionally available as a set of metrics that provide a concentrated assessment of the information quality. In this study we provide a survey of metrics that are presently included in the product quality assessment of the CMEMS Arctic Marine Forecasting Center sea-ice edge position forecast. We show that when ice edge results from different products are compared, mismatching results for polynya and local freezing at the coasts of continents and archipelagos have a large impact on the quality assessment. Such situations, which occur regularly in the products we examine, have not previously properly been acknowledged when a set of metrics for the quality of ice edge position results have been constructed. We examine the quality of ice edge forecasts using a total of 17 metrics for the ice edge position. These metrics are analyzed in synthetic examples, in selected cases of actual forecasts, and for a full year of weekly forecast bulletins. Using necessity and simplicity of information as a guideline, we recommend using a set of four metrics that sheds light on the various aspects of product quality that we consider. Moreover, any user is expected to be interested in a limited part of the geographical domain, so metrics derived as domain-wide integrated quantities may be of limited value. Consequently, we recommend that metrics are also made available for appropriate set of subdomains. Furthermore, we find that the metrics' decorrelation time scales are much longer than the present forecast range. Hence our final recommendation is to include depictions of gridded mismatching of ice edge positions using maps for the integrated ice edge error. [ABSTRACT FROM AUTHOR]

The Norwegian government has decided that the aquaculture industry shall grow, provided that the growth is environmentally sustainable. Sustainability is scored based on the mortality of wild salmonids caused by the parasitic salmon lice. Salmon lice infestation pressure has traditionally been monitored through catching wild sea trout and Arctic char using nets or traps or by trawling after Atlantic salmon postsmolts. However, due to that the Norwegian mainland coastline is nearly 25 000 km, complementary methods that may be used in order to give complete results are needed. We have therefore developed an operational salmon lice model, which calculates the infestation pressure all along the coast in near real-time based on a hydrodynamical ocean model and a salmon lice particle tracking model. The hydrodynamic model generally shows a negative temperature bias and a positive salinity bias compared to observations. The modeled salmon lice dispersion correlates with measured lice on wild salmonids caught using traps or nets. This allows for using two complementary data sources in order to determine the infestation pressure of lice originating from fish farms on wild salmonids, and thereby provide an improved monitoring system for assessing risk and sustainability which forms the basis for knowledge-based advice to management authorities. [ABSTRACT FROM AUTHOR]

Global atmosphere-ocean general circulation models are the tool by which projections for climate changes due to radiative forcing scenarios have been produced. Further, regional atmospheric downscaling of the global models may be applied in order to evaluate the details in, e.g., temperature and precipitation patterns. Similarly, detailed regional information is needed in order to assess the implications of future climate change for the marine ecosystems. However, regional results for climate change in the ocean are sparse. We present the results for the circulation and hydrography of the Barents Sea from the ocean component of two global models and from a corresponding pair of regional model configurations. The global models used are the GISS AOM and the NCAR CCSM3. The ROMS ocean model is used for the regional downscaling of these results (ROMS-G and ROMS-N configurations, respectively). This investigation was undertaken in order to shed light on two questions that are essential in the context of regional ocean projections: (1) How should a regional model be set up in order to take advantage of the results from global projections; (2) What limits to quality in the results of regional models are imposed by the quality of global models? We approached the first question by initializing the ocean model in the control simulation by a realistic ocean analysis and specifying air-sea fluxes according to the results from the global models. For the projection simulation, the global models' oceanic anomalies from their control simulation results were added upon initialization. Regarding the second question, the present set of simulations includes regional downscalings of the present-day climate as well as projected climate change. Thus, we study separately how downscaling changes the results in the control climate case, and how scenario results are changed. For the present-day climate, we find that downscaling reduces the differences in the Barents Sea between the original global models. Furthermore, the downscaled results are closer to observations. On the other hand, the downscaled results from the scenario simulations are significantly different: while the heat transport into the Barents Sea and the salinity distribution change modestly from control to scenario with ROMS-G, in ROMS-N the heat transport is much larger in the scenario simulation, and the water masses become much less saline. The lack of robustness in the results from the scenario simulations leads us to conclude that the results for the regional oceanic response to changes in the radiative forcing depend on the choice of AOGCM and is not settled. Consequently, the effect of climate change on the marine ecosystem of the Barents Sea is anything but certain. [ABSTRACT FROM AUTHOR]

We investigate trajectory forecasting as an application of ocean circulation ensemble modeling. The ensemble simulations are performed weekly, starting with assimilation of data for various variables from multiple sensors on a range of observational platforms. The ensemble is constructed from 100 members, and member no. 1 is designed as a standard (deterministic) simulation, providing us with a benchmark for the study. We demonstrate the value of the ensemble approach by validating simulated trajectories using data from ocean surface drifting buoys. We find that the ensemble average trajectories are generally closer to the observed trajectories than the corresponding results from a deterministic forecast. We also investigate an alternative model in which velocity perturbations are added to the deterministic results and ensemble mean results, by a first-order stochastic process. The parameters of the stochastic model are tuned to match the dispersion of the ensemble approach. Search areas from the stochastic model give a higher hit ratio of the observations than the results based on the ensemble. However, we find that this is a consequence of a positive skew of the area distribution of the convex hulls of the ensemble trajectory end points. [ABSTRACT FROM AUTHOR]

The autumn and early winter atmospheric response to the record-low Arctic sea ice extent at the end of summer 2007 is examined in ensemble hindcasts with prescribed sea ice extent, made with the European Centre for Medium-Range Weather Forecasts state-of-the-art coupled ocean-atmosphere seasonal forecast model. Robust, warm anomalies over the Pacific and Siberian sectors of the Arctic, as high as 10°C at the surface, are found in October and November. A regime change occurs by December, characterized by weaker temperatures anomalies extending through the troposphere. Geopotential anomalies extend from the surface up to the stratosphere, associated to deeper Aleutian and Icelandic Lows. While the upper-level jet is weakened and shifted southward over the continents, it is intensified over both oceanic sectors, especially over the Pacific Ocean. On the American and Eurasian continents, intensified surface Highs are associated with anomalous advection of cold (warm) polar air on their eastern (western) sides, bringing cooler temperatures along the Pacific coast of Asia and Northeastern North America. Transient eddy activity is reduced over Eurasia, intensified over the entrance and exit regions of the Pacific and Atlantic storm tracks, in broad qualitative agreement with the upper-level wind anomalies. Potential predictability calculations indicate a strong influence of sea ice upon surface temperatures over the Arctic in autumn, but also along the Pacific coast of Asia in December. When the observed sea ice extent from 2007 is prescribed throughout the autumn, a higher correlation of surface temperatures with meteorological re-analyses is found at high latitudes from October until mid-November. This further emphasises the relevance of sea ice for seasonal forecasting in the Arctic region, in the autumn. [ABSTRACT FROM AUTHOR]

Global coupled climate models are generally capable of reproducing the observed trends in the globally averaged atmospheric temperature. However, the global models do not perform as well on regional scales. Here, we present results from a 20-year, high-resolution ocean model experiment for the Atlantic and Arctic Oceans. The atmospheric forcing is taken from the final 20 years of a twentieth-century control run with a coupled atmosphere–ocean general circulation model. The ocean model results from the regional ocean model are validated using observations of hydrography from repeat cruises in the Barents Sea. Validation is performed for average quantities and for probability distributions in space and time. The validation results reveal that, though the regional model is forced by a coupled global model that has a noticeable sea ice bias in the Barents Sea, the hydrography and its variability are reproduced with an encouraging quality. We attribute this improvement to the realistic transport of warm, salty waters into the Barents Sea in the regional model. These lateral fluxes in the ocean are severely underestimated by the global model. The added value with the regional model that we have documented here lends hope to advance the quality of oceanic climate change impact studies. [ABSTRACT FROM AUTHOR]

A theoretical model for the near-surface velocity profile in the presence of breaking waves is presented. Momentum is accumulated by growing waves and is released upon wave breaking. In effect, such a transition is a process involving a time-dependent surface stress acting on the mean current. In this paper, conventional theory for the Stokes drift is expanded to fourth-order accuracy in wave steepness. It is shown that the higher-order terms lead to an enhancement of the surface Stokes drift and a slight retardation of the Stokes volume flux. Furthermore, the results from the wave theory are used to obtain a bulk parameterization of momentum exchange during the process of wave breaking. The mean currents are then obtained by application of a variation of the “level 2.5” turbulence closure theory of Mellor and Yamada. When compared with the traditional approach of a constant surface stress, the mean Eulerian current exhibits a weak enhancement in the near-surface region, compensated by a negative shift deeper in the water column. However, it is found that the results of Craig and Banner and the results of Craig are not significantly affected by the present theory. Hence, this study helps to explain why the Craig and Banner model agrees well with observations when a realistic, time-varying surface stress acts on the drift currents. [ABSTRACT FROM AUTHOR]

Generation and propagation of eddies in the coastal regions of the eastern Gulf of Alaska are examined based on ouput from a numerical ocean model. Results from a 1/8° six-layer isopycnal, wind-forced Pacific basin model are examined within the Gulf of Alaska during the 14-yr period starting in January 1981. Interannual variability in the upper ocean coastal circulation in the Gulf of Alaska is linked to the El Niño–Southern Oscillation phenomenon in the tropical Pacific Ocean, via coastal Kelvin waves and atmospheric teleconnections. El Niño events destabilize the Alaska Current by enhancement of the velocity shear in the vertical. The instability ultimately results in the formation of multiple strong anticyclonic eddies along the coast, which slowly propagate into the Gulf of Alaska where they can survive for more than 1 yr. A typical value for the diameters of the anticyclonic eddies is 200 km in the data and in the model. These eddies are strongly baroclinic, with a typical value for the velocity differences between layers 1 and 2 of 15 cm s[sup -1] . El Viejo (La Niña) events generally reduce eddy formation. [ABSTRACT FROM AUTHOR]