This study discusses recent advances in modeling waves in sea ice in the U.S. Navy’s regional modeling system. It is applied in the marginal seas of the eastern Arctic Ocean, including the Barents Sea, Kara Sea, parts of the Greenland Sea, Norwegian Sea, and waters north of Svalbard. The focus is to assess the skills of two formulations of wave attenuation by sea ice used operationally in WAVEWATCH III. Both are derived from large field datasets, one from the Arctic and the other from the Antarctic. The new model (IC4M9) describes wave attenuation depending on the ice thickness in association with the dependence on wave frequency, while the earlier default scheme (IC4M6) omits the dependence on ice thickness. The modeling results are evaluated against the satellite wave observations from SWIM/CFOSAT and the buoy measurements from the Svalbard Marginal Ice Zone 2024 Campaign (SvalMIZ-24). The comparisons with SWIM data validate the wave model skill in regions of open water or with light ice coverage. When evaluated against the SvalMIZ-24 data, the statistical performance of IC4M9 is substantially better than that of IC4M6, showing the influence of ice thickness on waves in the MIZ. Moreover, diagnosing systematic errors in the predictions by IC4M9, we find that the ice thickness field provided by the sea ice model CICE to the wave model is biased high in the MIZ, thus penalizing the performance of IC4M9 while not affecting the model IC4M6, which depends on frequency only.

Propagating waves on the surface of the ocean can be represented as a stochastic process, whose statistics are characterized by a spectrum. Measuring the wave spectrum, and quantities derived from the spectrum, are reviewed here. Observation begins by sensing some property of the sea surface over space and/or time. Visual observations, collected routinely since the mid 18th century, comprise the longerest running wave record. Measurement methods in the nearshore are advancing, including traditional methods using pressure and acoustic sensing, but also newer methods such as distributed acoustic sensing and lidar. Detailed, small scale wave physics can now be explored with measurement techniques using light, including stereo-imaging and polarimatry. The decrease in size, cost, and power consumption of microelectronics has propagated through to ocean wave instrumentation, most notably in wave buoys. Global networks of freely drifting miniature wave buoys offer novel observational power. Remote sensing techniques based on radar and lidar continue to evolve, and are widely deployed from land and on ships, aircraft, autonomous vehicles, and satellites. Spaceborne altimeters form one of the most important records of wave height, and a suite of suite of new spaceborne sensors are observing directional spectra across the globe with sampling akin to traditional altimetry. Aircraft and autonomous systems are providing strategic sampling capabilities, whether for detailed process studies or accessing extreme storm environments. The quality and quantity of ocean wave measurements has never been greater. This review will help you make sense of it all.

We study the importance of surface characteristics when forecasting near-surface variables with a data-driven weather prediction model. To target the challenge of predicting small-scale weather conditions at high resolution, we introduce a range of surface descriptors in the training of a state-of-the-art data-driven model. The input data includes surface descriptors inherited from the numerical weather prediction model used to produce the training dataset and topographic neighbourhood indices. We found that errors of 2-metre temperature and 10-metre wind speed forecasts were reduced by 1.9

Reliable estimates of Earth system conditions are important for weather forecasting, hydrological modelling and their downstream applications. Both real-time prediction systems and historical reanalyses use a combination of observations and physical laws embedded in numerical models to generate gapless and accurate estimates of weather, climate and hydrological conditions. Data assimilation systems merge information from model estimates and observations in an objective way, accounting for their respective uncertainties. In this work we present a regional reanalysis system, focusing on the land surface component. The system uses a multi-layer snow model together with the ensemble-based Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme. The system is run for a 4 year period over the European Arctic, assimilating in situ snow depth observations. Evaluation of the new snow depth analysis showed reduced errors compared to existing products and positive impact of the data assimilation over the domain. Furthermore, a significant difference in total accumulated snow water was seen over the domain, implying a potential impact on downstream hydrological applications. The ensemble correlations between the total snow depth and the multivariate control vector indicated that the ensemble was able to represent snow compaction processes. The LETKF is thus able to account for processes which are often neglected in snow depth data assimilation. The system presented in this study allows for future extensions, including other types of observations and analyses beyond snow variables.

Ocean wave models are critical for weather and climate forecasting, and accurate in-situ wave observations are essential for validating and improving these models. Open-source, community-driven buoys have democratized wave observations via telemetry in recent years, but these systems transmit only limited amounts of data. Full high-frequency time series, required to study detailed wave physics, can still in most cases only be collected in situ using data loggers. Yet open-source, low-cost logger solutions remain scarce compared to their telemetry-enabled counterparts. Here we present the Openlogartemis Wave Logger (OWL-v2026), an open-source, low-cost, easy-to-build, high-performance logger for wave data measurements. The OWL-v2026 is built from off-the-shelf components from the maker community, requiring only through-hole soldering for assembly, and totals approximately 220USD per unit. Custom firmware enables high-frequency, low-jitter logging of six-axis inertial measurement unit (IMU) data at 208 or 416Hz, and GNSS position and Doppler velocity at 10Hz, with Pulse Per Second (PPS) synchronization for accurate absolute UTC timestamping. We have successfully validated continuous logging over more than 10 days at 208Hz, a power consumption of approximately 80mA (approximately 20 days of autonomy with three D-cell lithium batteries), and absolute UTC timestamp accuracy typically better than 10ms. Though the OWL-v2026 is a purely technical contribution, it has the potential to substantially expand the availability and affordability of high-frequency in-situ wave time series, similar to how the OpenMetBuoy (OMB) (Rabault 2022) expanded the availability of telemetry-enabled wave observations and helped spark new developments in low-cost open-source buoys.

Abstract Propagating waves on the ocean surface can be represented as a stochastic process whose statistics are characterized by a spectrum. This paper reviews methods for measuring the wave spectrum and related quantities. Observations begin by sensing fluid dynamical properties of the sea surface over space and/or time. Visual observations, collected routinely since the mid‐18th century, comprise the longest‐running wave record. Nearshore measurement methods continue to advance, including traditional pressure and acoustic sensing as well as newer technologies like distributed acoustic sensing and LiDAR. Detailed small‐scale wave physics can now be explored with measurement techniques using light, including stereo‐imaging and polarimetry. Reductions in the size, cost, and power consumption of microelectronics have propagated through ocean wave instrumentation, most notably in wave buoys. Global networks of freely drifting miniature wave buoys offer novel observational capabilities. Remote sensing techniques based on radar and LiDAR continue to evolve and are widely deployed from land, ships, aircraft, autonomous vehicles, and satellites. Spaceborne altimeters form one of the most important records of wave height, and new spaceborne sensors now observe directional spectra globally with sampling akin to traditional altimetry. Aircraft and autonomous systems provide strategic sampling capabilities for detailed process studies and access to extreme storm environments. The quality and quantity of ocean wave measurements have never been greater. This review aims to help make sense of it all.

The Marginal Ice Zone (MIZ) forms a critical transition region between the ocean and sea ice cover, as it protects the close ice further in from the effect of the steepest and most energetic open ocean waves. As waves propagate through the MIZ, they become exponentially attenuated. Unfortunately, the associated attenuation coefficient is difficult to accurately estimate and model, and there are still large uncertainties around which attenuation mechanisms dominate depending on the conditions. This makes it difficult to predict waves in ice attenuation, as well as sea ice breakup and dynamics. Here, we report in situ observations of strongly modulated waves in ice amplitude, with a modulation period of around 12 h. We show that simple explanations, such as changes in the incoming open water waves or the direct effect of tides and currents and bathymetry on the propagating waves, cannot explain the observed modulation. Therefore, the wave height modulation observed in the ice comes from a modulation of the waves in ice attenuation coefficient. We gather evidence that sea ice convergence and divergence is likely the factor driving this modulation in the attenuation coefficient, through its influence on the ice “closedness”. This implies that the level of sea ice “closedness” needs to be taken into account by future dissipation parameterizations.

The coupling of weather, sea-ice, ocean, and wave forecasting systems has been a research priority for improving Arctic prediction capabilities. However, the complexity of the underlying physical processes and the difficulty of obtaining observations on representative spatial and temporal scales present significant challenges, particularly in the Marginal Ice Zone (MIZ). The primary objective of the Svalbard Marginal Ice Zone Campaign 2024 (SvalMIZ-24) was to establish a network of observations with a spatial distribution that enables a representative comparison between in situ measurements and gridded model data. The key variables that were measured are air and surface temperatures, sea-ice drift, and wave energy spectra. Within the main observation period, a persistent cold air outbreak as well as a warm air intrusion event, coinciding with the formation of an intense wave system propagating into the MIZ was captured. This dataset provides valuable insights into atmosphere-ice-ocean interactions in the MIZ and serves as a resource for future studies, model validation, and intercomparison efforts aimed at improving Arctic forecasting systems.

There has been a steady increase in marine activity throughout the Arctic Ocean during the last few decades, and maritime end users are requesting skilful high-resolution sea ice forecasts to ensure operational safety. Different studies have demonstrated the effectiveness of utilizing computationally lightweight deep learning models to predict sea ice properties in the Arctic. In this study, we utilize operational atmospheric forecasts, ice charts, and sea ice concentration passive microwave observations as predictors to train a deep learning model with future ice charts as ground truth. The developed deep learning forecasting system predicts regional ice charts covering parts of the East Greenland and Barents seas at 1 km resolution for 1-3 d lead time. We validate the deep learning system performance by evaluating the position of forecasted sea ice concentration contours at different concentration thresholds. It is shown that the deep learning forecasting system achieves a lower error for several sea ice concentration contours when compared against baseline forecasts (persistence forecasts, sea ice free drift, and a linear trend) and two state-of-the-art dynamical sea ice forecasting systems (neXtSIM and Barents-2.5) for all considered lead times and seasons.

There is an increasing need for reliable short-term sea ice forecasts that can support maritime operations in polar regions. While numerous studies have shown the potential of machine learning for sea ice forecasting, there are currently only a few operational data-driven sea ice prediction systems. Here, we introduce MET-AICE, a prediction system providing sea ice concentration forecasts for the next 10 d in the European Arctic. To our knowledge, it is the first operational data-driven prediction system designed for short-term sea ice forecasting. MET-AICE has been trained to predict sea ice concentration observations from the Advanced Microwave Scanning Radiometer 2 (AMSR2) at 5 km resolution. After one year of operation, we show that MET-AICE considerably outperforms persistence of AMSR2 observations (errors about 30 % lower on average), as well as forecasts from several dynamical models such as TOPAZ5, Barents-2.5 km and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System.

The propagation of waves through the marginal ice zone (MIZ) and deeper into pack ice is a key phenomenon that influences the breakup and drift of sea ice. When waves in ice propagate through a solid, non-cracked, thick enough sea ice cover, significant flexural elastic effects can be present in the dispersion relation. This results in a dispersion relation that opens up for 3-wave interactions, also known as wave triads. Here, we report the observation of high-frequency spectral peaks in the power spectral density of waves in ice spectra. We show, in two timeseries datasets, that the presence of these high-frequency peaks is accompanied by high values for the spectral bicoherence. This is a signature that the high-frequency peak is phase-locked with frequency components in the main spectral energy peak, and a necessary condition for nonlinear coupling to take place. Moreover, we show for a timeseries dataset that includes several closely located sensors that the dispersion relation recovered from a cross-spectrum analysis is compatible with the possible existence of wave triads at the same frequencies for which the bicoherence peak is observed. In addition to these observations in timeseries datasets, we show that similar high-frequency peaks are observed from additional, independent datasets of waves in ice power spectrum densities transmitted over iridium from autonomous buoys. These results suggest that nonlinear energy transfers between wave in ice spectral components are likely to occur in some waves and sea ice conditions. This may enable redistribution of energy from weakly damped low-frequency waves to more strongly attenuated higher-frequency spectral components, which can contribute to energy dissipation in the ice.

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.

The Marginal Ice Zone (MIZ) forms a critical transition region between the ocean and sea ice cover as it protects the close ice further in from the effect of the steepest and most energetic open ocean waves. As waves propagate through the MIZ, they get exponentially attenuated. Unfortunately, the associated attenuation coefficient is difficult to accurately estimate and model, and there are still large uncertainties around which attenuation mechanisms dominate depending on the conditions. This makes it challenging to predict waves in ice attenuation, as well as the effective impact of open ocean waves propagating into the MIZ on sea ice breakup and dynamics. Here, we report in-situ observations of strongly modulated waves-in-ice amplitude, with a modulation period of around 12 hours. We show that simple explanations, such as changes in the incoming open water waves, or the effect of tides and currents and bathymetry, cannot explain for the observed modulation. Therefore, the significant wave height modulation observed in the ice most likely comes from a modulation of the waves-in-ice attenuation coefficient. To explain this, we conjecture that one or several waves-in-ice attenuation mechanisms are periodically modulated and switched on and off in the area of interest. We gather evidence that sea ice convergence and divergence may be the factor driving this change in the waves in ice attenuation mechanisms and attenuation coefficient, for example by modulating the intensity of floe-floe interaction mechanisms. Since this conclusion is obtained, at least partially, by elimination, we acknowledge that additional measurements will be needed to provide a positive proof of our conjecture.

The coupling of weather, sea-ice, ocean, and wave forecasting systems has been a long-standing research focus to improve Arctic forecasting system and their realism and is also a priority of international initiatives such as the WMO research project PCAPS. The goal of the Svalbard Marginal Ice Zone 2024 Campaign was to observe and better understand the complex interplay between atmosphere, waves, and sea-ice in the winter Marginal Ice Zone (MIZ) in order to advance the predictive skill of coupled Arctic forecasting systems. The main objective has been to set up a network of observations with a spatial distribution that allows for a representative comparison between in situ observations and gridded model data. The observed variables include air and surface temperature, sea-ice drift, and wave energy spectra. With the support of the Norwegian Coast Guard, we participated in the research cruise with KV Svalbard from 4. April - 21.April 2024. In total 34 buoys were deployed in the Marginal Ice Zone north of the Svalbard Archipelago. The first part of the report describes the instruments and their calibration (Section 2), and the second part briefly describes the weather, sea ice, and wave conditions during the campaign.

Previous research indicates that forecast uncertainty can, in certain formats and decision contexts, provide actionable insights that help users in their decision-making. However, how to best disseminate forecast uncertainty, which factors affect successful uptake, and how forecast uncertainty transforms into better decision-making remains an ongoing topic for discussion in both academic and operational contexts. Interpreting and using visualizations of forecast uncertainty are not straightforward, and choosing how to represent uncertainty in forecast products should be dependent on the specific audience in mind. We present findings from an interactive stakeholder workshop that aimed to advance context-based insights on the usability of graphical representations of forecast uncertainty in the field of maritime operations. The workshop involved participants from various maritime sectors, including cruise tourism, fisheries, government, private forecast service providers, and research/academia. Geographically situated in Norway, the workshop employed sea spray icing as a use case for various decision scenario exercises, using both fixed probability and fixed threshold formats, supplemented with temporal ensemble diagrams. Accumulated operational expertise and characteristics of the forecast information itself, such as color coding and different forms of forecast uncertainty visualizations, were found to affect perceptions of decisionmaking quality. Findings can inform codesign processes of translating ensemble forecasts into usable and useful public and commercial forecast information services. The collaborative nature of the workshop facilitated knowledge sharing and coproduction between forecast providers and users. Overall, the study highlights the importance of incorporating methodological approaches that consider the complex and dynamic operational contexts of ensemble-based forecast provision, communication, and use.

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GNSS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms.

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms.

Understanding recent and future changes of extreme precipitation is essential for climate change adaptation. Here, we use 3800 extreme precipitation events produced by an ensemble seasonal prediction system. The ensemble represents the climate from 1981 to 2018 and we analyse 3-day maximum precipitation events in September–October–November for the west coast of Norway. Two dominant atmospheric patterns, described by an empirical orthogonal function (EOF) analysis, are related to the results of the extreme value statistics. The principal components of the second and third mode of EOFs have significant trends over the last 40 years, but with an opposing impact on the return values of extreme precipitation. This explains the observed stationarity of extreme precipitation over recent decades at the west coast of Norway, which was also found in previous studies. The second mode of EOFs also shows a relation to the sea-ice coverage in the Barents and Kara Seas, which suggests a connection between the decline of sea-ice to the changes in the atmospheric pattern.

The Arctic’s extreme environmental conditions and remoteness make it a complex and dynamic environment for maritime operators. We find that Arctic shipping has grown by 7% per year over the past decade, despite the hazardous weather and sea-ice conditions that pose risks to vessels operating in the region. As a result of a strong increase in winter sailing, the time ships operate in these extreme conditions has even tripled. To mitigate maritime risks, the Polar Code has been introduced. Among other things, it regulates Arctic shipping by specifying hazardous conditions with a sea-ice classification scheme and design temperature threshold. However, we argue that the Polar Code needs refinement through the integration of maritime warning systems and a broader description of hazardous conditions. This is supported by an analysis of shipping activity patterns in severe sea-spray icing conditions and a discussion of a recent sea-ice induced incident along the Northern Sea Route.