Atlantification—the northward inflow of anomalous waters and biota from the Atlantic into the polar basins—has wide-ranging climatological ramifications. Sustained observations demonstrated that, contrary to the global climate model projections, atlantification has already advanced into the Amerasian Basin of the Arctic Ocean, having a significant impact on the physical and ecological components of the climate system. The primary example is the rapidly diminishing sea ice in the Siberian Arctic Ocean (SAO), which is caused by the weakened ocean stratification and amplified heat fluxes. These sea ice thickness anomalies caused by atlantification persist across the Arctic region and are prevalent along the entirety of the Transpolar Drift. Furthermore, we observe the transition of the central SAO to conditions resembling those in the eastern SAO 5-7 years ago and the emergence of a powerful ocean-heat/ice-albedo feedback, which accelerates sea-ice losses. The eastern SAO is still strongly stratified but collaborative international observations demonstrate that the atlantification-driven shoaling of warm, salty, and nutrient-rich intermediate waters already has important ecological consequences there. Disentangling the role of atlantification in multiple and complex high-latitude changes should be a priority in future modeling and observational efforts.
How to cite: Polyakov, I.: Atlantification advances into the Amerasian Basin of the Arctic Ocean , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20584, https://doi.org/10.5194/egusphere-egu25-20584, 2025.
The Arctic Ocean has increasingly drawn widespread attention in global climate change system. However, due to the high-latitude air-sea characteristics and the seasonal distribution of sea ice, the on-site marine environment surveys are more challenging than other oceans.
To understand the ice‒sea thermal dynamic processes, we built the in-situ observation dataset based on a series of international in-situ observation plans carried out in the Arctic Ocean and Chinese Arctic Research Expedition. With the support of polar icebreakers Xuelong and Xuelong-2, China has carried out a series of scientific investigations in Arctic Ocean for special phenomena, and accumulated many first-hand in-situ observations.
We used quality control and data processing methods to analyze and re-arrange the data mentioned above and obtained nearly a million thermohaline profiles from1983 to 2023. Meanwhile, a monthly climatology dataset is established with a horizontal resolution of 0.25×0.25° and 57 vertical layers. The datasets can serve as a standard reference for future observation data quality control, and can also be used to correct the thermohaline results of existing ice-ocean coupled models.
In order to evaluate the quality of the in-situ observations dataset, we selected typical water exchange areas for water mass analysis and partial thermohaline profile analysis，the result shows a significant seasonal variation and has a high quality and effectively reflects the overall hydrological characteristics of the Arctic Ocean. Meanwhile we compared the climatology datasets with WOA18, and find out there is clearly positive feedback by using Chinese Arctic Research Expedition data in the climatology datasets we built. And the thermohaline has stronger continuity and more stable structure. In the key of Chinese Arctic Research Expedition area, the analysis can reflect the high temperature Pacific water flowing into the Arctic Ocean, with a clear meridional temperature stratification, and temperature gradually decreasing from south to north.
Evaluating Ocean Heat Content (OHC) with in-situ observations climatology datasets show that the climatology dataset reflects the accurate state of the OHC, and can be used to verify and evaluate the OHC calculated from different model.
Next step, for studying the thermohaline structure of the Arctic ocean, we will use AI models for training with reanalysis data to get the prediction field by using the observation datasets we built.
How to cite: Wu, X. and Li, J.: Construction and Evaluation of In-situ Observation Dataset and Its Climatology in Arctic Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2085, https://doi.org/10.5194/egusphere-egu25-2085, 2025.
The stratification of the Arctic Ocean plays a central role in regulating the impact of climate change on the Arctic. Though the stratification in the eastern Eurasian Basin halocline is known to have weakened since the 2000s, the variability over the full AW depth range in the whole Eurasian Basin has been little explored.
Our analysis aims to combine available in-situ observations to characterize the regional changes in stratification in the Eurasian Arctic Ocean over the past four decades. We find that, in both the Nansen and Amundsen basins, the variability of the temperature and salinity is most pronounced in the thermocline that separates the Atlantic Water (AW) core from the stratified halocline. This variability is affected by both warm and salty pulses entering through the Fram Strait, and by long-term trends. Positive temperature and salinity anomalies in the thermocline are associated with a destratification of the thermocline down to the AW core. In these layers, the stratification is estimated to have decreased by up to 50% across the Eurasian Arctic over the past 40 years, implying the possibility of enhanced vertical salt and heat fluxes up to the base of the halocline. In contrast, the stratification of the halocline has remained approximately constant or increased. Using a conceptual advective-diffusive model which takes into account the impact of stratification changes on vertical diffusion, we further show that the observed structure of changes is well reproduced by vertical diffusion of anomalies travelling from the Fram Strait around the Eurasian Basin. Our approach, using clustering techniques to divide the Eurasian Basin into several regions with coherent temperature, salinity and stratification profiles, provides new insights on the regional evolution of the Eurasian Arctic stratification, in particular in regions where few long-term studies are available like the Amundsen Basin.
How to cite: Challet, F., Herbaut, C., Houssais, M.-N., and Meneghello, G.: Large-scale destratification in the Eurasian Basin thermocline driving Atlantic Water shoaling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3536, https://doi.org/10.5194/egusphere-egu25-3536, 2025.
The Arctic Ocean plays an important role in the global climate system as it acts, for example, as major reservoir of anthropogenic carbon. Despite its global significance, data on physical parameters and tracers in the Arctic Ocean are still sparse and thus carbon inventory estimates only weakly constrained, for which insights into Arctic air-sea-ice gas exchange and ventilation need to be enhanced. Noble gases, with their biological and chemical inertness and constant atmospheric abundance history, fill this gap, as their concentrations in water are set by the conditions of last atmospheric contact. In light of this, water samples taken during the Synoptic Arctic Survey (SAS) expedition to the Central Arctic Ocean with the Swedish icebreaker Oden in summer 2021 (SAS-Oden 2021) at six stations from the surface to the seafloor were analyzed for their noble gas content. This first application of the full set of the five stable noble gases (helium, neon, argon, krypton and xenon) to the Arctic Ocean marks a new step towards a comprehensive understanding of Arctic Ocean dynamics.
The measured profiles show a strong influence of rapid cooling, excess air injection and brine rejection from sea ice formation, which affect the light and heavy noble gases differently, depending on their size, solubility and diffusivity. Building upon work from groundwater hydrology and extensions to cave calcites, as well as previous ocean applications of noble gases, the concepts of recharge temperatures, excess air terms and ice fractions or freezing rates are transferred to the Arctic Ocean, enabling the development of new parameterizations of the air-sea-ice exchange processes. We present two “static” model approaches, differing in the sea ice parameterization, and a “dynamic” mixed reactor-type model for two limits (steady state and quasi-steady state), resulting in different parameterizations of rapid cooling. The fit results from a least-squares regression for all four models are able to reproduce the measured concentrations both accurately and precisely and thus allow for predictions for other gases. In our study, these are the anthropogenic transient tracers sulfur hexafluoride (SF6) and dichlorodifluoromethane (CFC-12), which were also measured during the SAS-Oden 2021 expedition and are used to determine water ages, a task for which the intitial surface saturations need to be known. We suggest a relative oversaturation of around 6% of SF6 to CFC-12 due to the deviating impact of excess air, compatible with previous estimates from noble gas measurements.
How to cite: Hubner, C.-M., Scott, S., Arck, Y., and Aeschbach, W.: Using Noble Gases to Constrain Parameterizations of Arctic Air-Sea-Ice Gas Exchange Processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4579, https://doi.org/10.5194/egusphere-egu25-4579, 2025.
Fengyun satellites have now developed the capability to retrieve multiple polar sea ice parameters based on active and passive microwave payloads. This includes the operational production and release of four types of polar sea ice products, including the FY-3 MWRI radiometer sea ice concentration, the FY-3E WindRAD scatterometer sea ice edge and type, and the FY-3 GNOS-R sea ice thickness. The monitoring capabilities of Fengyun satellites in the polar regions are continuously improving. This study will systematically introduce the inversion and validation of polar sea ice parameters mentioned above, focusing on the research of sea ice edge and type inversion from the WindRAD scatterometer, which is the world's first dual-frequency, dual-polarization, fan-beam rotating scanning system. The release and application of operational sea ice parameter products from Fengyun satellites can further enhance the polar sea ice monitoring capabilities and provide a scientific and reliable new data source for research related to polar and global climate change, such as climate numerical models and the monitoring of extreme climate events.
How to cite: Zhai, X., Tian, S., Yin, C., Huang, K., Cao, G., Zheng, Z., Shang, J., Wu, S., Chen, L., and Hu, X.: Research Progress and Applications of Polar Sea Ice Products Based on Multi-Source Remote Sensing Payloads of Fengyun Satellites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7556, https://doi.org/10.5194/egusphere-egu25-7556, 2025.
The annual sea ice minimum extent in the Arctic Ocean has decreased almost two-fold since the advent of satellite observations in the 1970s, leaving more open water before the fall freeze-up. Here, we leverage a combined dataset from the 2022 NASA Salinity and Stratification at the Sea Ice Edge (SASSIE) field program to elucidate the central hypothesis that drove SASSIE: Does surface salinity stratification due to sea ice melt, precipitation, and riverine inputs lead to changes in the rates or extent of autumnal sea ice advance? The SASSIE study region in the Beaufort Sea is stratified both by melting sea ice in the summer and riverine discharge. We leverage measurements of oxygen isotopes as well as colored dissolved organic matter (CDOM) to trace the origins of fresher water at the surface.
In addition to an in-depth analysis of in situ data, we use the General Ocean Turbulence Model (GOTM) for individual profiles as well as the Regional Ocean Modeling System (ROMS) initialized and forced with observations from the SASSIE field campaign. These observations include temperature and salinity from the salinity snake instrument at 1-2cm depth, shipborne thermosalinograph (4m) and underway conductivity-temperature-depth (uCTD) measurements (5-100m), acoustic Doppler current profiler (ADCP) data, as well as meteorological and net heat flux observations. In realistically forced runs, we re-create the observations during the month-long cruise. We then modify the stratification to both increase and decrease salinity stratification to assess the importance of salinity stratification on the autumnal sea ice advance. We compare these model outputs to satellite-derived freeze-up data as well as in situ observations from autonomous platforms in the area. Preliminary results show a strong control of salinity on rapid sea ice advance, in which areas that are highly stratified freeze significantly faster than areas of deeper or weaker stratification.
Based on this hypothesis, we present a novel way of modelling the autumnal Arctic Sea Ice advance using a Convolutional Long-Short-Term Memory (LSTM) Neural Network model. In this machine learning approach, we demonstrate that the inclusion of the experimental merged salinity OISSS v3 dataset derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites significantly improves forecast accuracy of sea ice concentration in our study area, which encompasses the East Siberian, Chukchi, and Beaufort Seas. The model is based on 8 years of training data and tested using 3 years of evaluation data. Using this 60-day forecast, we show that the spatial forecasting pattern of sea ice concentration is significantly improved. This is further illustrated in an ablation study, in which we find sea surface salinity to be the 4th most important predictive term after sea surface temperature, net heat flux, and sea ice concentration.
Through these studies, we show the connection between the terrestrial water cycle, oceanic freshwater fluxes, and sea ice formation in the Arctic, and present a novel technique of sea ice prediction that will become increasingly useful as the Arctic becomes more ice free.
How to cite: Schanze, J., Springer, S., Anderson, J., Town, M., Lim, E. Q., Treadwell, D., Zhou, Z., Zhou, S., and Melnichenko, O.: The Effects of Salinity and Stratification on Rapid Sea Ice Advance in the Arctic Ocean , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21413, https://doi.org/10.5194/egusphere-egu25-21413, 2025.
The Arctic sea ice has been rapidly declining due to climate change, with significant impacts on subpolar ocean dynamics and mid-latitude regional weather patterns. However, climate models (e.g., CMIP5 and CMIP6) show a large inter-model spread in projected sea ice changes, often underestimating the observed decline. This discrepancy may result from the poor representation of key ocean heat transport processes in the Arctic Ocean. Using a high-resolution global ocean-sea ice model (NEMO-SI3) with a 1/12° grid, forced at the surface by the Earth System Model UKESM1.1, we explored how atmospheric forcings, boundary currents, energetics, and horizontal/vertical mixing change with the declining Arctic sea ice from 1990 to 2100 under the SSP3-7.0 scenario. We investigated how these changes in the Arctic Ocean drive upward heat fluxes from Atlantic Water (AW) beneath the halocline to the ocean surface, and quantified their contribution to the ocean surface heat budget in an increasingly energetic Arctic. Finally, we demonstrated the critical role of enhanced upward AW heat flux in accelerating sea ice decline under a warming climate. Our study underscores the potential importance of processes linked to Arctic spin-up in the facilitation of heat transfer from the warm, sub-surface Atlantic Water to the cold, fresh Arctic Ocean surface, accelerating sea ice melt and influencing the global climate system.
How to cite: Wei, X., Wilson, C., and Bacon, S.: Arctic spin-up under melting sea ice , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12218, https://doi.org/10.5194/egusphere-egu25-12218, 2025.
State estimates like the Arctic Subpolar gyre sTate Estimate (ASTE, Nguyen et al., 2021) are powerful tools that combine observational data and numerical models to reconstruct the ice and ocean’s physical state over time. Unlike sequential data-assimilated reanalysis products, state estimates minimize misfit to a large set of various observations by adjusting model input and parameters rather than altering the model’s physical state, thereby consistently obeying physical laws and ensuring all source and sink terms can be identified.
In this talk, I will explain the methodology behind a state estimate and present the first release of ASTE, which provides complete estimates of the Arctic sea ice and ocean states spanning 2002-2017 at a spatial resolution of about 15 km. I will highlight how ASTE has informed studies ranging from the analysis of Atlantic Water properties in the Arctic to the characterization of beneficial environmental conditions for high-latitude benthic habitats, and how ASTE’s adjoint model, i.e., the capability of running the model backwards in time to track which processes have influenced a chosen variable, provides a powerful method to unambiguously identify causal connections in the coupled Arctic system.
Towards the next release of ASTE, I will present a study of the impact of tides on Arctic sea ice, based on a higher, 3.5 km resolution version of ASTE that has been run for one full seasonal cycle, in a configuration including and excluding tides. While the study shows an overall decrease in sea ice volume in the presence of tides associated with increased vertical mixing and the upward flux of heat from deeper layers of the Arctic Ocean in line with previous findings, it also reveals an unexpected result, pointing to a new mechanism resulting in delayed sea ice melt in summer.
How to cite: Schulz, K., Nguyen, A., Pillar, H., and Heimbach, P.: The Arctic Subpolar gyre sTate Estimate (ASTE): A Gateway to Understanding Ice-Ocean Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1425, https://doi.org/10.5194/egusphere-egu25-1425, 2025.
Freshwater plays an important role in the Arctic Ocean, where stratification and circulation are dominated by salinity. River runoff is an important piece of the Arctic freshwater budget, and it is changing rapidly with climate change. River runoff into the Arctic Ocean has been increasing in both amount and temperature, a trend which is expected to continue into the future. We look at forcing a state of the art ocean model with future runoff projections for the Arctic Ocean, to understand how this increase in runoff temperature and flow impacts the changing Arctic. Runoff projections are produced using the A-HYPE hydrological model, over the Arctic drainage basin, giving both runoff and river temperature data. These are used to force a regional configuration of the Nucleus for European Modelling of the Ocean (NEMO) framework 4.2, with a nested 1/12 degree Arctic Ocean. As opposed to traditional methods of linearly scaling runoff for future projections, combining hydrological model output with ocean models gives a more complete spatially and temporally varying picture of runoff. Changes in river runoff has implications for sea ice futures, circulation patterns, freshwater storage and release of freshwater to lower latitudes.
How to cite: Weiss-Gibbons, T., Pennelly, C., Stadnyk, T., and Myers, P.: Future Changes in Arctic River Runoff and its Impact on the Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12510, https://doi.org/10.5194/egusphere-egu25-12510, 2025.
Significant uncertainties in projections of various ocean and sea ice variables stem from a variety of sources, including different modeling approaches, imperfect representations of physical processes, and natural variability. Multi-model ensembles like CMIP6 are essential for assessing the range of uncertainty, however they rely on "model democracy," which assumes all models are equally plausible and independent of one-another.
Various constraining and weighting approaches are in use to minimize model uncertainties. Most of these approaches focus on state quantities, often relying solely on historical simulations of the target variable itself as the primary diagnostic. Here, we want to use more process-based diagnostics to incorporate physical mechanisms and interactions that govern the system dynamics. Previous assessments of the historical Arctic's energy budget in CMIP6 have shown tight connections between oceanic heat transports and key Arctic state quantities like sea ice and the ocean's warming rate, with substantial biases prevailing from the ocean to the Arctic surface. Using our new StraitFlux tools, which enable fast and precise calculations of oceanic transports for diverse climate models, we can quite efficiently incorporate oceanic transports into existing model weighting algorithms. By evaluating model performance against observational data and assessing their independence of one-another, we aim to identify and mitigate biases in Arctic projections. We use this approach to weight and constrain key Arctic variables, such as sea ice, for a large ensemble of CMIP6 models. For example, weighting the Arctic September sea ice extent ensemble reduces the spread in the first year of an ice-free Arctic and indicates a general tendency to an earlier ice-free Arctic than when using model democracy. Those results agree very well with past studies using different weighting diagnostics, demonstrating the robustness of the weighting approach.
How to cite: Winkelbauer, S., Mayer, M., and Haimberger, L.: Constraining Arctic Climate Projections: A Process-Based Approach to Model Weighting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9085, https://doi.org/10.5194/egusphere-egu25-9085, 2025.
The Arctic Ocean is a hot spot of climate change, with enhanced warming and freshening of near-surface waters and a rapid decline of sea ice in recent decades. Climate model projections suggest that the Arctic Ocean may be ice-free in summer as early as 2030-2050, accompanied by an intensified seasonal cycle of sea ice characterized by earlier melting and later growth seasons. This transition will enhance interactions between the ocean, atmosphere and sea ice, likely altering the stratification of the Arctic Ocean during summer. The projected retreat of summer sea ice in the coming decades raises the question of how the seasonal cycle of the ocean may change, which is critical in regulating chemical, biological and physical processes in the region. Given the non-uniformity of sea ice loss across the Arctic, pan-Arctic averages fail to capture the spatial variability of these changes. In this study, we analyze 36 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the SSP5-8.5 scenario to characterize regional changes in the Arctic Ocean seasonal cycle in the near future. Our results reveal an intensified seasonal cycle of sea surface temperature and a weakened seasonal cycle of sea surface salinity with significant regional variability and model dependence. Changes at depth are primarily confined to the mixed layer. By analyzing the mixed layer temperature and salinity budget for each region, we identify the key processes driving these changes. These insights enhance our understanding of the evolving seasonal dynamics of the Arctic Ocean and their broader implications in a rapidly changing climate.
How to cite: Le Gloannec, C., Msadek, R., and Lique, C.: The seasonal cycle of the Arctic Ocean in a summer ice-free climate : changes, driving processes and consequences., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10639, https://doi.org/10.5194/egusphere-egu25-10639, 2025.
The near-surface wind speed in the Arctic plays an increasingly critical role in shaping local air-sea interactions and ensuring the safety of trans-Arctic shipping. However, its potential changes under a warming climate and the underlying mechanisms driving these changes remain unclear. By analyzing reanalysis data and model simulations, we demonstrate that Arctic surface wind speed has significantly increased since the 1960s, with the most pronounced acceleration occurring over the Arctic Ocean basins adjacent to the North Atlantic and the North Pacific. Historical simulations from CMIP6 models indicate that this acceleration is primarily driven by greenhouse gas induced warming, which is particularly prominent during the cold seasons. On one hand, the rapid surface warming in the Arctic disrupts the temperature inversion over sea ice, reducing atmospheric stability in the lower troposphere and enhancing thermal turbulence in the Arctic boundary layer. On the other hand, Arctic warming raises the height of the boundary layer, allowing stronger turbulence to mix high-altitude wind speed down to the surface, thereby intensifying near-surface wind speeds. Furthermore, CMIP6 models project a robust increase in Arctic NWS under various warming scenarios throughout the 21st century. This increase is especially prominent near the Kara Sea and the Beaufort Sea, with stronger wind speeds projected under higher SSP scenarios.
How to cite: Deng, K., Liu, W., Yang, S., and Chen, D.: Anthropogenic amplification of the Arctic near-surface wind speed, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4891, https://doi.org/10.5194/egusphere-egu25-4891, 2025.
In the Arctic, where the effects of the changing climate are occurring faster than anywhere else on Earth, warming, sea-ice decline and changes in ocean circulation have already resulted in an overall increase of the marine primary productivity. According to global climate projections, the increased productivity is expected to continue in this region due to greater open-water habitats and larger growing seasons. Significant shifts in phytoplankton composition and an increasingly unstable community structure are also expected through the 21st century in response to climate change. Nevertheless, high uncertainties still exist in future net primary productivity (NPP) and the overall response of phytoplankton to climate change in the Arctic and subarctic regions.
This study assesses the effect of changing physical characteristics in the Nordic and Barents Seas on nutrient distribution and phytoplankton dynamics over the 21st century using the high-resolution NORWegian ECOlogical Model system (NORWECOM.E2E). The results show two distinct pathways of the phytoplankton response, differentiating Arctic conditions (i.e., Barents Sea) and Atlantic conditions (i.e., Nordic Seas). The Barents Sea, a shallow and well-mixed basin with persistent nutrient supply from the deep ocean to the surface, experiences a gradual intensification of the phytoplankton blooms towards the end of the century. This response is consistent with increasing temperatures, sunlight availability due to reduced sea-ice extent and the intensification of the vertical mixing.
In contrast, the Nordic Seas experience an abrupt change in the phytoplankton dynamics, with a sudden shift in the phytoplankton communities from a diatom-dominated to a flagellate-dominated bloom, according to the simulations. The rapid change in phytoplankton bloom dynamics is caused by an interplay between a shallowing mixed layer depth and changing nutrient consumption patterns by phytoplankton. These changes are consistent across climate scenarios SSP2-4.5, SSP3-7.0 and SSP5-8.5. However, the timing and magnitude of the changes vary significantly, with SSP3-7.0 showing the most abrupt changes.
As Arctic conditions continue at an accelerated pace, major implications for local and regional ecosystems are expected. These impacts will, most probably, not be limited to the Arctic region given its crucial role in the Earth’s system. Changes in phytoplankton bloom dynamics have the potential to impact the global carbon cycle by altering primary productivity and carbon export into the deep ocean, ultimately affecting the global climate.
How to cite: Gutierrez-Loza, L. and Lauvset, S. K.: Changes in phytoplankton bloom dynamics in the future Arctic Ocean from a Regional Ecological Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17889, https://doi.org/10.5194/egusphere-egu25-17889, 2025.
The ongoing rapid decline in Arctic sea ice is considered as a tipping element of our climate system. It is exposing a warmer and more acidified ocean directly to the atmosphere, permitting greater light penetration and enhanced exchange of heat, momentum, and gases across the air-sea interface. Earth system models project that these thermal and biogeochemical changes will dramatically perturb Arctic Ocean carbonate chemistry. As one of the consequences, the projections indicate that the seasonal maximum in surface ocean pCO2 generally shifts from winter to summer during this century. Yet, it is unknown whether such biogeochemical changes in the Arctic would be reversible, if we managed to reduce atmospheric carbon dioxide concentrations. Here we analyse the reversibility of Arctic biogeochemistry changes using idealised 1pctCO2-cdr simulations from six earth system models. These model experiments simulate a 140-year period of 1% annual atmospheric CO2 increase (rampup to 4x preindustrial levels), followed by a 140-year period of 1% annual CO2 decrease (rampdown). Our results indicate that the present day pCO2 cycle is largely recovered when atmospheric CO2 returns to preindustrial levels. However, most models exhibit substantial hysteresis, particularly during summer, where surface ocean pCO2 remains more elevated during the rampdown phase relative to the rampup phase (difference in Arctic average up to 60 𝜇atm pCO2 for the same atmospheric CO2 levels). Despite model differences, their projections consistently show pronounced regional variability in the pCO2 hysteresis, with high hysteresis occurring for example in the Nordic Seas and the Barents Sea. Our results indicate that the pCO2 hysteresis is particularly sensitive to sea surface temperature and net primary productivity, both of which show regionally varying hysteresis as well. These findings underscore the complex impacts of Arctic sea ice loss on biogeochemical cycles, emphasising the importance of accounting for hysteresis in CO2 overshoot scenarios and climate mitigation strategies.
How to cite: Köhn, E. E., Kwiatkowski, L., Orr, J. C., Gastineau, G., and Mignot, J.: How reversible are carbonate chemistry changes triggered by future Arctic sea ice loss?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17101, https://doi.org/10.5194/egusphere-egu25-17101, 2025.
The warm and saline Atlantic Water in the Nordic Seas serves as a conduit for poleward oceanic heat transport and plays a crucial role in regulating the Northern Hemisphere climate. However, the impact of mesoscale eddies on this heat transport remains unclear, owing to a lack of in situ observations and numerous ocean modeling challenges. Our study aims to improve the model representation of eddies and investigate their role in oceanic heat transport in the Nordic Seas. Using a novel configuration of the MITgcm ocean-ice model, with a resolution ranging from 1 to 4 km, we analyze 21 years of simulation. We show that oceanic heat transport anomalies are predominantly driven by velocity variations along Norwegian Atlantic Current, while lateral eddies play a significant role in leaking heat westward along a few key pathways, most notably near the Lofoten Escarpment. Further investigation on the linkage between ocean's temporal variability with the atmosphere is underway. Our study emphasizes the significant role of eddies in modulating poleward heat transport toward the Arctic by diverting heat laterally.
How to cite: Jian, D., Zhai, X., Renfrew, I., and Stevens, D.: Oceanic heat transport along the Norwegian Atlantic Current and the role of eddies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-487, https://doi.org/10.5194/egusphere-egu25-487, 2025.
The Arctic Ocean is changing significantly and rapidly in a warming climate. To monitor these changes, it is useful to classify the Arctic Ocean into water masses containing waters of same origin and similar physical and biogeochemical properties. However, there are significant barriers to Arctic Ocean water mass classification: observations of seawater properties are sparsely and heterogeneously sampled in space and time, and traditional water mass classification relies on extensive knowledge of water mass characteristics and circulation and mixing. We propose a tool for estimating relative fractions of Arctic Ocean water masses (0-1) from observations of seawater temperature and salinity, and share the classification tool and water mass dataset. Our estimates of relative fractions of water masses broadly reproduce the spatial and temporal distribution of Arctic Ocean water masses reported in the literature, most notably the key Atlantic Water (AW) and Pacific Water (PW) pathways within the Arctic Ocean and the increasing influence of AW and PW in the Arctic Ocean over the last few decades. Our classification tool and water mass dataset will help improve understanding of Arctic Ocean dynamics and changes, and provides an accessible framework for assessing the accuracy of models in representing Arctic Ocean properties.
How to cite: Oglethorpe, K., Lanham, J., Reiss, R., Boland, E., and Mashayek, A.: A Dataset of Arctic Ocean Water Masses from 40 Years of Hydrographic Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1303, https://doi.org/10.5194/egusphere-egu25-1303, 2025.
Sea ice drift has significant impacts on climate change and navigation safety. Currently, various approaches have been employed to address quantization error and achieve subpixel precision in sea ice drift extraction using maximum cross-correlation (MCC). However, limited research has been conducted to compare these approaches. This study compares the performance of three approaches: image oversampling, subpixel similarity estimation, and the combination of both, for MCC-based Arctic sea ice drift extraction with subpixel precision at different time intervals. The research findings indicate that the combined approach of image oversampling and subpixel similarity estimation outperforms any single approach in terms of the accuracy of extracted sea ice drift. Additionally, this study provides recommended combinations of spatial resolutions (achieved through image oversampling) and subpixel similarity estimation methods for retrieving sea ice drift based on Fengyun-3D (FY-3D) Microwave Radiation Imager (MWRI) data at different time intervals.
How to cite: Wang, X. and Xu, Z.: Sub-Pixel Precision Image Matching for Sea Ice Drift Retrieval Using Maximum Cross-Correlation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3482, https://doi.org/10.5194/egusphere-egu25-3482, 2025.
Fjord ice, that includes both sea and glacier ice, is an important part of the fjord microclimate that impacts e.g. water-atmosphere energy transfer, habitat conditions, ocean wave transformation and coastal processes. It also plays a role in ship and snowmobile operations. Understanding the trends in fjord ice extent, duration and timing aids understanding the impact of changing climate on the magnitude of natural hazards (such as coastal flooding and erosion) and improving future predictions.
Satellite images provide high-frequency large-area information on the state of the fjord ice, with Synthetic Aperture Radar (SAR) imagery being unaffected by polar night and weather conditions. Few studies have attempted automating fjord ice detection from satellite imagery, likely due to problems related to the topography influence on the sea state, mixed land/water pixels, presence of rocks and islands and wave breaking in the nearshore.
This study builds on the recent progress of Johansson et al. (2020) who adapted the semi-automated binary ice/open water classification method of Cristea et al. (2020) to Svalbard fjord environment, and Swirad et al. (2024a) who created a near-daily dataset of binary ice/open water maps at 50 m resolution for Hornsund fjord from the entire Sentinel-1 A/B dataset spanning Oct 2014 – Jun 2023. Swirad et al. (2024a) did not find direct relationships between fjord-scale ice coverage and air and water temperatures. Nonetheless, temporal peaks in ice coverage existed in March for the main basin, April for the inner bays and locally in October. The authors associated these with the arrival of pack ice from the Greenland Sea, formation of in situ fast ice and intensification of tidewater glacier calving, respectively.
Speculating that stronger relationships can be found between climate and ice coverage if fjord ice is unpacked into ‘drift ice’, ‘fast ice’ and ‘glacier ice’ we developed an algorithm that splits the ‘ice’ from the binary classification into the three classes using pixel and polygon properties such as continuity in time, location, size, shape and timing. We then explored relationships between ice, meteorological and hydrographic conditions. The dataset was also extended back to Jan 2012 using RADARSAT-2 imagery (Swirad et al., 2024b).
References:
Cristea, A., van Houtte, J., and Doulgeris, A. P.: Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images, IEEE J. Sel. Top. Appl., 13, 2925–2939, https://doi.org/10.1109/JSTARS.2020.2993067, 2020.
Johansson, A. M., Malnes, E., Gerland, S., Cristea, A., Doulgeris, A. P., Divine, D. V., Pavlova, O., and Lauknes, T. R.: Consistent ice and open water classification combining historical synthetic aperture radar satellite images from ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1A/B, Ann. Glaciol., 61, 40–50, https://doi.org/10.1017/aog.2019.52, 2020.
Swirad, Z. M., Johansson, A. M., and Malnes, E.: Extent, duration and timing of the sea ice cover in Hornsund, Svalbard, from 2014–2023, The Cryosphere, 18, 895–910, https://doi.org/10.5194/tc-18-895-2024, 2024a.
Swirad, Z. M., Johansson, A. M., and Malnes, E.: Ice distribution in Hornsund fjord, Svalbard from RADARSAT-2 (2012-2016) [dataset], PANGAEA, https://doi.org/10.1594/PANGAEA.969031, 2024b.
How to cite: Swirad, Z., Johansson, M., and Malnes, E.: Unpacking fjord ice in Hornsund, Svalbard, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4070, https://doi.org/10.5194/egusphere-egu25-4070, 2025.
Starting from May 2023, a global anomaly event led to the highest recorded sea surface temperature (SST) in history, underscoring the urgency of understanding how warming oceans impact polar and subpolar regions. Against this backdrop, our study focuses on the Sea of Okhotsk, where data from the U.S. National Snow and Ice Data Center and sea surface height measurements revealed an unprecedented, ice-free zone—measuring 50 to 80 kilometers in radius—near the Gulf of Patience (たらいかわん), east of Sakhalin Island, during Feb. – Mar. 2023. This phenomenon stands in stark contrast to observations in previous years and appears closely linked to sea surface height anomalies (SSHA). The role of such localized oceanic features, including eddies, in shaping late-spring sea ice melting patterns is of interest.
During the SOYA cruise in February 2023, National Central University (Taiwan) and Hokkaido University deployed eleven Taiwan-made drifting wave buoys. These buoys captured high-resolution data on waves, ocean currents, and sea temperatures, revealing robust mesoscale ocean eddy activity within the region. This study integrates buoy-based observations, satellite remote sensing, and numerical model outputs to explore the dynamic relationship between mesoscale eddies and the rapid formation of the ice-free zone. It is the aim to investigate how eddies influence springtime sea ice melting and distribution in the Okhotsk Sea. The preliminary findings may have implications for climate modeling, marine ecosystems, and regional socioeconomic activities, and will be shown in detail in the poster.
How to cite: Chien, H., Wang, A.-S., and Lin, L.-C.: Observations of an Emerging Ice-Free Zone in the Sea of Okhotsk during the Spring Sea-Ice Melting Period amid the 2023 Global SST Warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4756, https://doi.org/10.5194/egusphere-egu25-4756, 2025.
Rapid changes in the polar marine environment, driven by climate change, are altering the variability of nutrient and light distribution, with significant impacts on primary producer growth. However, access to polar regions is limited, and satellite data from high-latitude areas are typically available only during the summer, complicating the acquisition of continuous in-situ data. To address this, we collected year-round chlorophyll-a (Chl-a) concentration data in polar regions using a mooring system and compared the results with reanalysis data. Unlike previous satellite-based studies that rely on surface measurements, we applied the annual vertical distribution of Chl-a to the Vertically Generalized Production Model (VGPM) to estimate annual primary production more accurately. Our findings reveal that phytoplankton exhibited a subsurface chlorophyll maximum (SCM) as sea ice retreated, with the SCM layer persisting for approximately four months—contrary to the gradually deepening SCM distribution predicted by model-based reanalysis data. This suggests that light and nutrient conditions within the SCM remained stable, supporting continuous phytoplankton growth. The estimated annual primary production, based on this vertical distribution of Chl-a, was 6.85 gC m−2 yr−1, which is more than ten times higher than estimates based on satellite data alone, highlighting significant underestimation by satellite-based approaches. Furthermore, this value was comparable to the average satellite-derived primary production of surrounding coastal and shelf areas (15.80 ± 10.65 6.85 gC m−2 yr−1). These results emphasize the importance of incorporating vertical distribution of phytoplankton and light in polar marine ecological models to enhance our understanding of carbon cycling and food web dynamics in these regions.
How to cite: Park, J., Ko, E., Lee, Y., and Yang, E. J.: Reassessing primary production in polar ocean: A novel approach using mooring systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5304, https://doi.org/10.5194/egusphere-egu25-5304, 2025.
The presence or absence of sea ice introduces a substantial perturbation to surface‒atmosphere energy exchanges. Comprehending the effect of varying sea ice cover on surface‒atmosphere interactions is an important consideration for understanding the Arctic climate system. The recurring North Water Polynya (NOW) serves as a natural laboratory for isolating cloud responses to a rapid, near-step perturbation in sea ice. In this study, we employed high-resolution Arctic System Reanalysis version 2 (ASRv2) data to estimate turbulent heat fluxes over the NOW and nearby sea ice (NSI) area between 2005/2006 and 2015/2016. The results indicate that the average turbulent heat fluxes in the polynya are about 87% and 86% higher than in the NSI area over the 10 years during the entire duration of the polynya and during polar night, respectively. Enhanced turbulent heat fluxes from the polynya tend to produce more low-level clouds. The relationship between the polynya and low cloud in winter was examined based on Cloud‒Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The low-cloud fraction (0–2 km) was about 7–34% larger over the polynya than the NSI area, and the ice water content below 200 m was about 250%–413% higher over the former than the latter. The correlation between cloud fraction and turbulent heat fluxes in the polynya peaks around the altitude of 200–300 m. These results suggest that the NOW affects the Arctic boundary layer cloudiness and structure in wintertime. Furthermore, higher horizontal resolution reanalysis data can advance our understanding of the cloud-polynya response.
How to cite: Hui, F., Ren, H., Shokr, M., Zhang, T., Zhang, Z., and Cheng, X.: Turbulent heat fluxes in the North Water Polynya and ice estimated based on ASRv2 data and their impact on cloud, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5349, https://doi.org/10.5194/egusphere-egu25-5349, 2025.
The Arctic Ocean is undergoing rapid transformations due to the loss of sea ice, shifts in its heat budget and physical structure, and the “greening” of the polar surface ocean. These changes have profound implications for ocean biogeochemistry, the carbon cycle, and ocean acidification (OA). As part of the U.S. Synoptic Arctic Survey (SAS), we conducted a transect from the Chukchi Sea shelf to the North Pole during late summer 2022, enabling comprehensive sampling of the ocean carbon cycle in the seldom-sampled high Arctic. Discrete samples of Dissolved Inorganic Carbon (DIC) and Total Alkalinity (TA) were collected from CTD-hydrocasts spanning surface to deep waters, complemented by higher-frequency underway measurements of DIC, TA, and pH. These observations establish a critical baseline for tracking future changes in Arctic carbon dynamics, biogeochemistry, and acidification. Additionally, the 2022 US SAS dataset allows for comparison with earlier observations, including the 1994 Arctic Ocean Section (AOS), the 2005 Beringia expedition, and the 2015 GEOTRACES Arctic cruise. Our synthesis reveals significant and ongoing changes in the Arctic Ocean carbon cycle, including: (1) substantial uptake of anthropogenic CO₂; (2) alterations in the driving force for air-sea CO₂ exchange; (3) a decreasing capacity of the Arctic Ocean to absorb atmospheric CO₂; and (4) intensified impacts on surface pH and ocean acidification. These findings underscore the accelerating pace of carbon cycle changes in the high Arctic and highlight the importance of sustained monitoring.
How to cite: Garley, R. and Bates, N.: Arctic Ocean inorganic carbon and acidification changes from 1994 to 2022 across the Chukchi Sea to the North Pole: A US contribution to the International Synoptic Arctic Survey Program, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6782, https://doi.org/10.5194/egusphere-egu25-6782, 2025.
The Arctic is a hot spot of climate change. Sea ice and snow, in particular, act as an insulator that prevent heat exchange between the ocean and the atmosphere and have been an important factor in mitigating temperature increases in the Arctic. However, the reduction of sea ice over the past 40 years has led to an increase in ocean-atmosphere heat exchange, contributing to Arctic Amplification. Despite its importance, obtaining observational data beneath sea ice in the Arctic during winter has been challenging due to the unique conditions of ice coverage, especially in winter. Several studies have been able to make use of recent advances in autonomous instrumentation to calculate wintertime ocean to ice heat flux (OHF). However, there remain considerable discrepancies in OHF estimates, even when examining the same time periods and research areas, primarily due to variations in calculation methods.
In this study, we used observational data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) to calculate OHF from October 2019 to May 2020. The observations were made by Woods Hole Oceanographic Institution Ice-tethered Profilers and Microstructure profilers drifting with sea ice along the Transpolar Drift. Here, we assess the applicability of an OHF parameterization from observational data, relying on the temperature difference between the mixed layer and the freezing temperature.
The results in winter predominantly show negative (downward) OHF. We consider those results physically implausible, and they seem to be related to the ubiquitous presence of supercooled water in the mixed layer. When applying near surface temperature rather than freezing temperature to assess the heat content in the boundary layer, the wintertime OHF values are closed to zero until mid-March 2020. This result is in line with direct (dissipation based) measurements of OHF from the stratified ocean into the mixed layer during the same period. This study, therefore, suggests limitations in the applicability of the OHF parameterization in supercooled conditions. By opting for ocean surface temperature observations from the Arctic winter of 2019-2020, which were consistently lower than the freezing temperature, we anticipate that these refined calculation methods will yield more accurate results for assessing heat flux in future Arctic winters.
From mid-March to early May, the OHF increased significantly, and so did the upward heat flux into the mixed layer. Our results suggest this shift occurred once the sea ice had drifted southward across the Gakkel Ridge toward Fram Strait. Analyzing the hydrographic properties of the upper ocean, we conclude that not only seasonal but also regional changes contributed to this shift.
How to cite: Choi, Y., Kanzow, T., Rabe, B., and Reifenberg, S.: Ocean-to-Ice Heat Flux in the Central Arctic: Results from the MOSAiC Expedition (2019-2020), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6867, https://doi.org/10.5194/egusphere-egu25-6867, 2025.
Arctic eddies are important for mixing and heat exchange between sea ice and ocean. The effect carries over to the ecosystem to cause spatial patterns of primary production up to fish distribution. Strong stratification makes the Rossby radius of eddies on Arctic shelve very small resulting in spatial gradients in eddy sizes over the Arctic. Limited resolution of models in the past has been preventing correct representation. We present eddy statistics in a kilometric Arctic Ocean NEMO-SI3 model, using NEMO version 5.0 with the RK3 advection scheme and the TKE mixing scheme. The sea ice rheology is aEVP. We aim to validate the number of eddies as well as eddy sizes with available data from satellite and moorings. This simulation was done as part of the CANARI project, which includes examination of future sea ice loss impact on mixing and the possibility of accelerated sea ice decline. This work was funded by the Natural Environment Research Council (NERC) project CANARI NE/W004984/1. This work used the ARCHER2 UK National Supercomputing Service (https://www.archer2.ac.uk).
How to cite: Rynders, S., Aksenov, Y., Coward, A., and Harle, J.: First look at Arctic eddies in a kilometric NEMO5 simulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7121, https://doi.org/10.5194/egusphere-egu25-7121, 2025.
Understanding the distribution and variations of sea and glacier ice coverage is critically important for assessing both the impacts and drivers of climate change, particularly in the Arctic. Sea ice responds dynamically to both ocean and atmospheric motion, implying variability on very short timescales which is challenging to monitor. In this study we assess the ability of PlanetScope satellite imagery, offering both high spatial and temporal resolutions, to analyse temporal variability. The study area is Isbjørnhamna-Hansbukta area in north-western Hornsund, Svalbard - a fjord characterised by both in situ formed sea ice (fast ice and drift ice broken from the fast ice e.g. by waves), pack ice drifting into the fjord from south-west with Sørkapp Current that brings cold water masses from Barents Sea, and glacier ice from calving Hansbeen.
We selected cloud-free images over a 4.5 ✕ 4.9 km AOI, large enough to depict the spread of sea ice to ensure accuracy in the analysis. From ten images captured in 2023, we collected sample reflectance data for three categories: thin ice, thick ice, and water. Thin ice in the AOI is typically grey and grey-white sea ice (10-30 cm) as well as brash ice and growlers, while thick ice is often snow-covered young and first-year sea ice (>30cm) as well as bergy bits and icebergs. Using these data, we calculated normalised difference spectral indices for both 8-band and 4-band imagery. Coastal Blue-Green 1 and Blue-Red indices were determined to be the most effective for discriminating between the different categories, and optimum thresholds were identified. Applying these indices and thresholds in QGIS, we generated 233 maps covering the months of March to August for the years 2018 to 2023.
From the initial visual interpretation, the results showed credible classification of the images and revealed continuous seasonal patterns for all years of the study, with minimal ice coverage observed in March, May, and July through August, a peak in sea ice coverage in April, and a resurgence of thin ice in June. However, no observable multi-year trends could be identified from a preliminary analysis of the maps, other than a sharp decline in ice coverage in 2023. Quantitative analysis of the maps allows estimates of the sea and glacier ice extent within the AOI to be made.
This research enhances our understanding of seasonal and interannual sea and glacier ice distribution in the nearshore and coastal zone of Svalbard. These findings have the potential to inform future studies about sea ice distribution, with the PlanetScope Imagery maps to be made publicly available through the Svalbard Integrated Arctic Earth Observing System data portal at the end of the study. Future research will compare the relative advantages of PlanetScope and SAR imagery.
How to cite: Makhotina, E., Rees, G., Swirad, Z., and Tutubalina, O.: Mapping of sea and glacier ice distribution in 2018-2023 in the Hornsund fjord, Svalbard with PlanetScope imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7127, https://doi.org/10.5194/egusphere-egu25-7127, 2025.
The rise in air temperature due to global warming has significantly reduced the extent and thickness of sea ice, a phenomenon with profound implications. Sea ice loss results from complex factors, including changes in heat and momentum fluxes and internal feedbacks within the Arctic air-ocean system. This loss influences atmospheric circulation and mid-latitude weather patterns. Declining sea ice volume increases seasonal variability in ocean-atmosphere heat exchange, emphasizing the need to accurately estimate ocean heat flux at the sea ice base. Ocean heat flux, crucial for sea ice formation and melting, is challenging to measure directly. This study addresses this by using observational data to estimate ocean heat flux through the interplay of conduction (analyzed using Fourier series to reduce noise) and latent heat. The resulting Arctic buoy data map enhances predictions of sea ice dynamics.
How to cite: Hwang, I. and Moon, W.: Ocean heat flux and a buoy data map with noise eliminated, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7857, https://doi.org/10.5194/egusphere-egu25-7857, 2025.
New hydroacoustic measurements combined with old data reveal the widespread occurrence of contourite drift deposits - indicative of persistent strong bottom currents - at rather great water depths in the northern Baltic Sea and Eastern Canadian coastal waters (Foxe Basin, Hudson Bay, Gulf of St. Lawrence). In addition, lag deposits suggest that strong bottom currents temporary eroded sediments most likely during the cold Little Ice Age. For example, the Little Ice Age lag deposits are found to a water depth of ~ 300 m in Foxe Basin and to ~ 150 m in the Baltic Sea. In all ecosystems the depositional environment changed drastically with the onset of climate warming after the Little Ice Age: calm conditions prevailed leading to the accumulation of fine-grained sediments. A possible mechanism to explain the strong bottom currents during the Little Ice Age is an enhanced deep-water formation caused by accelerated convection and/or brine formation (Eastern Canadian waters) during colder winter conditions. Attempts to model the enhanced winter-time deep-water formation / convection remain inconclusive and do not match the hydroacoustic and sedimentological evidence. However, solving this issue is critical as it could allow to, e.g., reconstruct past winter temperatures based on sedimentological grain-size studies. Yet, most proxies used in paleo-oceanographic temperature reconstructions only relate to spring and summer (growing season) conditions. Our results indicate that winter temperature changes (strength and length of sea-ice season) are of critical importance for the depositional environment and bottom water ventilation in the Eastern Canadian and Baltic Sea ecosystems.
How to cite: Moros, M., Kotilainen, A., Neumann, T., Kolling, H., Papenmeier, S., Brembach, K., Lenz, K.-F., De Vernal, A., Lajeunesse, P., St-Onge, G., Kienast, S. S., Damste, J. S., Meier, H. E. M., and Schneider, R.: Strong winter-time deep-water formation during the Little Ice Age in subarctic semi-enclosed formerly glaciated marginal seas (Baltic Sea and Eastern Canadian coastal waters) , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9800, https://doi.org/10.5194/egusphere-egu25-9800, 2025.
The pathways and time scales of Atlantic Water (AW) transport to the Arctic Ocean (AO), and its subsequent return to the North Atlantic, are critical for understanding the ocean’s role in modulating heat, salinity, and the sequestration of anthropogenic trace gases.
To quantify the time scales of AW transport by advective and diffusive processes, we applied the Inverse-Gaussian Transit-Time Distribution (IG-TTD) method, utilizing a suite of radionuclide datasets. The IG-TTD parameters—mean transit time (Γ), representing advection, and width (Δ), characterizing diffusion—were derived from radionuclides such as Iodine-129 (I-129), Technetium-99 (Tc-99), and Uranium-236 (U-236). These radionuclides originate primarily from two European nuclear reprocessing facilities. To complement observational data, idealized tracers from an ocean general circulation model (OGCM) were incorporated, including Boundary Impulse Response (BIR) tracers and dilution tracers. BIR tracers constrained the mixing ratio (Δ/Γ) in the IG-TTD, while the dilution tracer refined source functions for improved accuracy.
Preliminary results indicate a transit time of approximately 25 years from the Iceland-Scotland Ridge to the central Arctic Ocean, with mixing ratios (Δ/Γ) ranging between 0.2 and 0.4—significantly lower than the typical value of ~1 observed for CFCs/SF6 tracers transitioning from surface ventilation to the ocean interior. A dilution factor on the order of 1000 was necessary to scale source functions and avoid unrealistically high mean ages. Transit times showed substantial variability within the same region, depending on radionuclide type and sampling period, highlighting the impact of strong synoptic variability in ocean currents on measurement uncertainties. Additionally, dual-tracer constraints on mixing ratios, comparisons of transit times derived from radionuclides versus ventilation tracers, and assessments against model-simulated BIR tracers are discussed.
How to cite: He, Y., Lin, M., and Jeansson, E.: Estimated Transport of Atlantic Water to the Arctic Ocean Using Observed and Simulated Radionuclides, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13327, https://doi.org/10.5194/egusphere-egu25-13327, 2025.
In the Arctic, ocean surface waves are becoming more energetic. This is due to the larger wind fetch caused by decreased sea ice cover in summer and delayed sea ice formation in fall. These changes, driven by global climate change and regional warming, are projected to be more extreme in the future. Surface gravity waves are a key factor in coastal erosion and flooding, which are already negatively affecting coastlines in the Arctic (Casas-Prat & Wang 2020). Understanding and quantifying surface waves evolution is therefore particularly important for the communities that live along the coasts of the Canadian Arctic Archipelago (CAA), yet it has not been investigated with modeling.
We used the spectral wave model Wavewatch III® (Tolman 1997, 1999a, 2009) to simulate gravity waves formation and propagation for the entire Arctic and the North Atlantic over 2002-2022, using output from a regional 1/4° NEMO simulation. Simulations reveal a positive wave height trend in Baffin Bay and locations near the sea ice margin in the Barents, Kara and East Greenland Seas. A positive trend is found in Baffin Bay from June to October (max 0.25 m/y), where peak wave heights of 4-6 m are also observed during fall, in the second decade of the run. This highlights the importance of combined delay in seasonal sea ice formation and storm activity in the CAA, with storms more likely to produce high waves conditions during fall.
Further ongoing work will: 1) analyze the impact of waves on coastal erosion; 2) project ocean and wave conditions under CMIP6 forcing: the numerical ocean model NEMO, at 1/4° resolution, and a nested grid over the CAA will allow WW3 wave simulations to be projected over 2100.
How to cite: Pochini, E. and Myers, P.: Simulated wave evolution and coastal erosion in the Arctic and the Canadian Arctic Archipelago (2002-2022), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13701, https://doi.org/10.5194/egusphere-egu25-13701, 2025.
How Arctic waters end up in the North Atlantic? We have examined ocean connectivity for neutral density surfaces by developing Montgomery Potential for the NEMO ocean model. The method is coded in Python, enabling calculating geostrophic flow on pseudo-neutral density surfaces. We have analysed global NEMOv4.2 at 1/12 degree runs for the 2008-2021 period for ocean connectivity from the Arctic to the North Atlantic. We have also mapped water-mass pathways by releasing on neutral density surfaces 25.8-28.2 in the Laptev Sea, in the Denmark and Davis Straits, near the Flemish Cap and on the West European Shelf, then by tracking particles forward and backward. The transient times from the Laptev Sea to the Great Banks are of about 6 years; across the Atlantic – another 6 yrs; and the Laptev Sea to the West European Shelf is of about 16 years in total. The model transient times were compared to those from the observed Technetium spread to the Western Barents Sea and to the regions around Greenland. This presented work has been funded from the European Union's project EPOC, EU grant 101059547 and UKRI grant 10038003, EC Horizon Europe project OptimESM “Optimal High Resolution Earth System Models for Exploring Future Climate Changes”, grant 101081193 and UKRI grant 10039429, and from the UK NERC projects LTS-M BIOPOLE (NE/W004933/1), CANARI (NE/W004984/1) and UK LTS-S Atlantic Climate & Environment Strategic Science –ATLANTIS. For the EU projects the work reflects only the authors' view; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains. We acknowledge the use of ARCHER UK National Supercomputing and JASMIN.
How to cite: Aksenov, Y., Rynders, S., Nurser, G. A. J., Megan, A., Kelly, S., and Coward, A.: Arctic to the North Atlantic connectivity using Montgomery Potential on neutral density surfaces, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11030, https://doi.org/10.5194/egusphere-egu25-11030, 2025.
Numerical models serve as an essential tool to investigate the causes and effects of Arctic sea ice changes. Evaluating the simulation capabilities of the most recent CMIP6 models in sea ice volume flux provides references for model applications and improvements. Meanwhile, reliable long-term simulation results of the ice volume flux contribute to a deeper understanding of the sea ice response to global climate change.
In this study, the sea ice volume flux through six Arctic gateways over the past four decades (1979–2014) were estimated in combination of satellite observations of sea ice concentration (SIC) and sea ice motion (SIM) as well as the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) reanalysis sea ice thickness (SIT) data. The simulation capability of 17 CMIP6 historical models for the volume flux through Fram Strait were quantitatively assessed. Sea ice volume flux simulated from the ensemble mean of 17 CMIP6 models demonstrates better performance than that from the individual model, yet IPSL-CM6A-LR and EC-Earth3-Veg-LR outperform the ensemble mean in the annual volume flux, with Taylor scores of 0.86 and 0.50, respectively. CMIP6 models display relatively robust capability in simulating the seasonal variations of volume flux. Among them, CESM2-WACCM performs the best, with a correlation coefficient of 0.96 and a Taylor score of 0.88. Conversely, NESM3 demonstrates the largest deviation from the observation/reanalysis data, with the lowest Taylor score of 0.16. The variability of sea ice volume flux is primarily influenced by SIM and SIT, followed by SIC. The extreme large sea ice export through Fram Strait is linked to the occurrence of anomalously low air temperatures, which in turn promote increased SIC and SIT in the corresponding region. Moreover, the intensified activity of Arctic cyclones and Arctic dipole anomaly could boost the southward sea ice velocity through Fram Strait, which further enhance the sea ice outflow.
How to cite: Ye, Y., Kuang, H., Sun, S., Wang, S., Bi, H., Chen, Z., and Cheng, X.: An assessment of the CMIP6 performance in simulating Arctic sea ice volume flux via Fram Strait, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6241, https://doi.org/10.5194/egusphere-egu25-6241, 2025.
Integrated retrieval using the optimal estimation (OE) method iteratively finds a set of geographical parameters that best match the observations. However, this method becomes more challenging over the ice surface due to the highly sensitive parameters such as sea ice concentration (SIC) and multiyear ice concentration (MYIC). In this study, a new time constraint that captures the distinct temporal characteristics of SIC and MYIC is incorporated into the OE method. The integrated retrievals, using both the original and time-constraint OE method (referred to as OE and OE-Z, respectively), were conducted based on FengYun-3D (FY-3D) microwave radiation imager (MWRI) data. Compared to other radiometer-based SIC and MYIC products, OE-Z outperforms OE, with the correlations increasing from 0.91 to 0.96 for SIC and from 0.41 to 0.49 for MYIC. The time constraint in OE-Z effectively mitigates the anomalous retrievals in SIC and MYIC, resulting in smoother and more reasonable time series than OE. Improvements in SIC and MYIC lead to enhanced simulation of surface microwave emission, thus improving the retrieval of atmospheric parameters. In comparison with the MOSAiC total water vapor (TWV) measurements, the RMSE in OE-Z reduces from 1.72 to 1.66 kg/m2, and the correlation increases from 0.46 to 0.50. The simulated brightness temperature (TB) biases in OE-Z reduce from 0.71 to 0.31 K at 36 GHz and from −8.95 to −7.72 K at 89 GHz. This emphasizes the importance of imposing suitable constraints on highly sensitive parameters in integrated retrieval.
How to cite: Yan, Z., Ye, Y., Heygster, G., Zhang, X., Chen, Z., and Xiao, C.: Integrated Retrieval of Surface and Atmospheric Variables in the Arctic From FY-3D MWRI With a Time-Constraint Optimal Estimation Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2262, https://doi.org/10.5194/egusphere-egu25-2262, 2025.
The Arctic is a complex system in which ocean, sea ice, land, and atmosphere all interact. Poleward energy transport is crucial for climate variability in the Arctic and is controlled by atmospheric transport at the middle-high latitudes. The ocean has been rising non-uniformly under global warming. The future state of the ocean on a regional scale is uncertain. Coupled Model Intercomparison Project Phase 6 (CMIP6) provides different scenarios (SSPs 1.26, 2.45, 5.85) that give insights into this uncertainty across the chosen regions under different global warming thresholds. Here, we show that with every degree change in the global warming threshold, there is a corresponding change in the ocean dynamic sea level (DSL). The Arctic, Irish and Norwegian coasts respond at different scales under the global warming thresholds. This study provides insight into the Irish-Nordic-Arctic Sea's future state, which is necessary for policy formulation and planning.
How to cite: Eresanya, E., McCarthy, G. D., Chinta, V., and Nnamchi, H. C.: Future projection of the Ocean Dynamic Sea Level over the Irish-Nordic-Arctic Seas under different global warming thresholds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2008, https://doi.org/10.5194/egusphere-egu25-2008, 2025.
The Beaufort Gyre is an important feature of the Arctic Ocean. By accumulating or releasing freshwater, it influences ocean properties both within the Arctic and as far as the North Atlantic. Yet, its future remains uncertain: the gyre could strengthen as sea ice declines and allows increased wind stress on the ocean, or weaken along with the Beaufort High pressure system. Here, we provide a first evaluation of the Beaufort Gyre in historical and climate-change simulations from 27 available global climate models. We find that the vast majority of models overestimate the gyre area, strength, and northward extent. After discarding the models with too inaccurate a gyre and its drivers – namely, the sea ice cover and Beaufort High – we quantify changes in the Beaufort Gyre under two emission scenarios: the intermediate SSP2–4.5 and the high-warming SSP5–8.5. By the end of the 21st century, most models simulate a significant decline or even disappearance of the Beaufort Gyre, especially under SSP5–8.5. We show that this decline is mainly driven by a simulated future weakening of the Beaufort High, whose influence on the Beaufort Gyre variations is enhanced by the transition to a thin-ice Arctic. The simulated gyre decline is associated with an expected decrease in freshwater storage, with reduced salinity contrasts between the gyre and both Arctic subsurface waters and freshwater outflow regions. While model biases and unresolved processes remain, such possible stratification changes could shift the Atlantic-Arctic Meridional Overturning Circulation northward.
How to cite: Athanase, M., Köhler, R., Heuzé, C., Lévine, X., and Williams, R.: The Arctic Beaufort Gyre in CMIP6 Models: Present and Future, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5642, https://doi.org/10.5194/egusphere-egu25-5642, 2025.
Seasonal and interannual variations in Arctic Ocean stratification significantly influence the vertical exchange of heat, salt, nutrient fluxes and the surface ice cover. On the seasonal scale, Arctic stratification is mainly influenced by ice melting/freezing processes. We used a one-dimensional (1D) coupled sea ice-ocean model to understand the effects of ice melting/freezing processes on stratification and their feedback on the ice itself. This 1D model can accurately simulate observed seasonal changes in the vertical structure of the upper Arctic Ocean. Then, we prevent the model from releasing meltwater into the ocean or maintaining a constant ice cover during the melting season, in a series of decoupling experiments, which reveal the following points: In summer, meltwater has negative feedback on ice melting by insulating a portion of the solar radiation into the Near Surface Temperature Maximum (NSTM); sea ice changes primarily manifest as the well-known albedo feedback. In winter, meltwater has minimal impact in strongly stratified regions, however, in weakly stratified regions, meltwater promotes freezing by hindering the heat upward mixing from Atlantic warm water (AWW); In regions with less ice cover, if there is no meltwater to counteract the stronger mixing due to the winter atmosphere-ocean energy exchange, the AWW can mix dramatically upwards, and even melt the ice in winter. In contrast, if there is enough ice cover to insulate the atmosphere from the ocean, strong mixing will not occur, even without meltwater. The 1D-model study demonstrates that, as Arctic sea ice diminishes and Atlantification intensifies in the future, the impact of meltwater on the ice-ocean system will become increasingly significant. For multiyear scales, we utilized CIGAR historical ocean reanalysis (1961-2022) data and extensive in situ observations from the Arctic Ocean to investigate the long-term variations in Arctic Ocean stratification. The results show a strong correlation between stratification strength and freshwater content in the Arctic Ocean. However, over the past decade, while the freshwater content in the Beaufort Sea has remained regionally stable, stratification strength has shown a decline. This suggests that, with the retreat of sea ice, atmospheric energy input is becoming increasingly significant in influencing stratification.
How to cite: Zhang, H., Storto, A., Bai, X., and Yang, C.: Impacts of Seasonal and Interannual Sea Ice Changes on Arctic Ocean Stratification, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10970, https://doi.org/10.5194/egusphere-egu25-10970, 2025.
The Arctic Ocean is projected to become ice-free by the middle of the century, accompanied by changes in freshwater input, stratification, and warming of the upper ocean. The marine ecosystem is predominantly influenced by the availability of light and nutrients for phytoplankton, which form the base of the food web. With the projected changes of the physical environment throughout the course of the century, CMIP6 models suggest a general increase in Arctic net primary production. It is anticipated that phytoplankton shift from a light-limited state to nutrient limitation across large areas of the Arctic Ocean, potentially leading to increased exudation of organic carbon into the water column.
We briefly discuss the mechanisms driving the dynamics of organic carbon in the upper Arctic Ocean before focusing on long-term trends in Arctic biogeochemistry projected until 2100. Using an ocean general circulation sea-ice biogeochemistry model based on the IPCC Shared Socio-economic Pathway high-emission scenario SSP3-7.0, we observe regionally varying increases in exuded organic carbon, alongside enhanced formation of particulate organic carbon in the upper water column. These particles can either be transferred from the ocean to the atmosphere, acting as precursors to primary marine organic aerosols, or sink in the water column, contributing to carbon export. Our findings align with other recent studies, showing a shift from light to nutrient limitation in phytoplankton growth, particularly in regions experiencing retreat of the marginal ice zone. Our simulation indicates that diatoms are the primary contributors to organic carbon exudation and subsequent particle aggregation. However, some regions do not exhibit an overall increase in particulate organic carbon due to elevated remineralization rates. Overall, our projection provides an assessment of the impact of changes in the physical environment on phytoplankton dynamics and, consequently, on organic carbon pools in the upper Arctic Ocean. This work is part of the DFG Transregional Collaborative Research Centre 172 on Arctic Amplification.
How to cite: Zeising, M., Oziel, L., and Bracher, A.: Projected Increase of Phytoplankton Carbon Exudation and Particle Formation in the Arctic Ocean until the End of the Century, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18826, https://doi.org/10.5194/egusphere-egu25-18826, 2025.
Arctic sea ice has undergone massive changes in the latest decades. Not only has the ice extent seen a great reduction over the satellite era, sea ice thickness is also strongly altered as a result of changing climate conditions. In this study, we look at sea ice volume in the Arctic and show how its response to increasing temperatures has changed in recent years. Using a Bayesian statistical framework, we look at reanalysis data of sea ice volume and detect changepoints in trends. We have identified an abrupt change in Arctic sea ice volume relative to global mean temperature. Spatial analysis shows that this signal of abrupt change primarily stems from loss of sea ice thickness in the Canadian Basin and Beaufort Gyre region. We compare these findings with CMIP6 Earth system models and find similar behaviour in several models. Further, we have conducted experiments with the NorESM model to better describe the mechanisms of this abrupt change, and to see how the sea ice volume behaves if global warming is later reversed.
How to cite: Steene, R. J. and Rypdal, M.: Changing trends in Arctic sea ice volume, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16814, https://doi.org/10.5194/egusphere-egu25-16814, 2025.
