Nansen International Environmental and Remote Sensing Center

Operating in the polar regions is growing increasingly important due to expanding research needs, emerging economic opportunities and efforts to protect national sovereignty.Ships operating in the Arctic and Antarctic face heightened risks and more severe consequences in the event of an accident due to sea ice, icebergs, harsh sea states, low visibility and extreme temperatures. These hazards are compounded by sparse infrastructures and remoteness, with immediate assistances not readily available during emergencies.While climate change is quickly reducing the amount of sea ice, this does not necessarily translate to a reduction in risk in the short-term. On the contrary, uncertainly due to changing sea ice conditions, and an increase in icebergs due to melting glaciers, can increase risk.The operational sea ice community provides information to lessen risk to life, property and the environment and to improve operational efficiency. That information is used by ships to avoid or navigate through sea ice, by ship operators in planning polar voyages, and by policy makers to assess the impact of climate change on future decisions regarding polar operations. Beyond safety, these services also increase operational efficiency by enabling optimized routing, reduced fuel consumption, and shorter transit times.The information provided by the operational sea ice community comes from in-situ measurements, satellite earth observation data, and sophisticated models of the atmosphere, oceans, sea ice, and icebergs. These data streams have historically been assembled and interpreted by highly trained human analysts. Given the rapid increase in the amount of data that needs to be analyzed and the constraints on workforces due to government fiscal reductions, their work is increasingly being assisted by artificial intelligence. Furthermore, because sea ice is highly dynamic and can change within a matter of hours, automation and AI are indispensable for providing real-time and forecast information.The operational sea ice workflows have many of the attributes of an Earth System Digital Twin:Utilization of detailed digital models of relevant earth systems (atmosphere, ocean, sea ice),Continuous incorporation of near-real-time data from in-situ sensors and earth observation satellites,Use of artificial intelligence and machine learning,Generation of predictive models and forecasts, andProvision of advance analytics and decision support tools to allow end-users to optimize their choices.In fact, much of the information both used and generated by the operational sea ice community is available in Destination Earth, the initiative of the European Commission to develop a digital twin of the Earth.This presentation will examine the provision of operational sea ice information as an example of the application of digital twins to provide actionable insights on climate adaptation and disaster risk reduction. It will present current capabilities, AI-enabled workflows, and lessons learned from operational implementation, with a focus on supporting safe and sustainable polar shipping.

Europe suffers great financial loss and loss of life every year due to extreme events, particularly heat waves, flooding, droughts, and wildfires. The impacts of these events are increasing with both the increasing exposure of society and the increasing intensity and frequency of the events themselves under a warming climate. Accurate projections of the future changes in extreme events are vital for those stakeholders responsible for preparing the European cities to withstand future extreme events. They are also highly valuable to many industries which are heavily exposed to the impacts of extreme events, including insurance, construction, agriculture, health and energy. However, any adaptation requires information that is tailored to the needs and workflow of the decision makers.In the EU-Impetus4Change project (I4C), we worked with stakeholders from four cities across Europe to select hazard indicators that are relevant to their ongoing adaptation work. The cities of Paris, Prague, Barcelona, and Bergen, were selected because they represent a wide range of climates across Europe and because they provide a sample of the different types of hazardous events faced by most European cities. The selected indicators focus primarily on extreme temperatures and precipitation hazards, though some relate to other sectors including energy and human health. The indicators have been calculated in 67 Euro-CORDEX simulations covering 120 years from 1980 to 2100 at a horizontal resolution of 0.11°. They have also been calculated in various convection permitting simulations of 10-year time slices in the current, mid-century and end of century, with a horizontal resolution of 3 km. This talk will present highlights from the analysis of this unique dataset, show the projected changes in these stakeholder-relevant indicators across different Global Warming Levels (GWLs), explore the biases compared to reanalysis, and examine the improvements of the convection permitting simulations compared to the lower resolution Euro-CORDEX simulations. The full dataset of these indices is planned to be made openly available through an online, user-friendly toolkit as part of the ongoing I4C project.

Abstract Climate change is intensifying the frequency and severity of environmental hazards, with distinct impacts in urban and rural areas. Cities can experience amplified risks due to the urban heat island effect and increased exposure to extremes. We analyze climate extreme indices using Euro-CORDEX regional climate models that include urban representations, focusing on 40 cities and their surrounding rural areas. Our findings highlight that city-scale hazards intensify with warming across the domain, while precipitation responses are strongly regional. Mediterranean cities exhibit robust drying and longer dry spells, whereas central and northern European cities experience the strongest and most widespread increases in heavy-precipitation extremes. Urban–rural contrasts strengthen several hazard metrics, particularly minimum-temperature extremes, with the strongest signals found in inland cities, underscoring the need for targeted adaptation measures. Understanding how global warming impacts these hazard indices is crucial for developing climate-resilient policies and strategies tailored to both urban and rural settings.

Winter storms in the Beaufort Sea can trigger large-scale sea ice breakup, opening leads and enhancing ice mobility. These events can influence the mixed layer through various processes, like brine rejection and shear-driven mixing. However, their rel

<strong class="journal-contentHeaderColor">Abstract.</strong> Systematic errors in dynamical climate models remain a significant challenge to accurate climate predictions, particularly when modeling the nonlinear coupling between the atmosphere and oceans. Despite notable advances in dynamical climate modeling that have improved our understanding of climate variability, these systematic errors can still degrade predictive skills. In this study, we adopt a twin experiment framework with a reduced-order coupled atmosphere-ocean model to explore the utility of machine learning in mitigating these errors. Specifically, we train a data-driven model on data assimilation increments to learn and emulate the underlying dynamical model error, which is then integrated with the dynamical model to form a hybrid system. Comparison experiments show that the hybrid model consistently outperforms the standalone dynamical model in predicting atmospheric and oceanic variables. Further investigation using hybrid models that correct only atmospheric or only oceanic errors reveals that atmospheric corrections are essential for improving short-term forecasts, while concurrently addressing both atmospheric and oceanic errors yields superior performance in long-term climate prediction.

Abstract Today, the global mean sea level(GMSL) stands ~20cm higher than at the beginning of the last century, and the rate of sea-level rise has been accelerating in recent decades. Even a slight, globally uniform sea-level rise can notably impact atmospheric and oceanic circulations at climatic and potentially synoptic scales. However, the extent to which sea-level rise will influence extreme weather remains largely unknown. Here, we specifically focused on East Asia and conducted experiments to investigate the effects of GMSL rise. Our experiments demonstrate that GMSL rise, even in tens of centimeters, promotes stronger and more frequent extreme cold events. This effect is attributed to weakened mid-latitude westerly winds and increased occurrence of blocking events over Eurasia. Our study presents the first evidence that GMSL rise will modify synoptic systems and intensify extreme events. Both coastal and inland countries are exposed to threats arising from GMSL rise.

Аннотация. Статья посвящена использованию методов ассимиляции данных для воспроизведения полярных циклонов атмосферной моделью WRF. Метод четырехмерной ассимиляции данных (FDDA) позволяет ассимилировать данные атмосферного реанализа ERA5, а также спутниковые наблюдения скаттерометров ASCAT. Всего было проведено три численных эксперимента, в которых показано влияние каждого компонента ассимиляции на получаемое поле ветра в одном и том же полярном циклоне. Полученные результаты показывают, что используемый метод ассимиляции данных улучшает оценку максимальных скоростей ветра в циклоне и предоставляет возможность более детально проследить его развитие. Ключевые слова: полярный циклон, численное моделирование, ассимиляция данных, релаксационные методы ассимиляции, Северо-Европейский бассейн. Благодарности: работа выполнена в рамках проекта Российского научного фонда № 22-27-00701.

<strong class="journal-contentHeaderColor">Abstract.</strong> Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The aim of the AutoICE Challenge was to encourage the creation of models capable of mapping sea ice automatically from spaceborne Synthetic Aperture Radar (SAR) imagery using deep learning while inspiring participants to move towards multiple sea ice parameter model retrieval instead of the current focus on a single sea ice parameter, such as concentration. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple ice parameters with convolutional neural network and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results.