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The Bering Sea experiences a seasonal sea ice cover, which is important to the biophysical environment found there. A pool of cold bottom water (<2°C) is formed on the shelf each winter as a result of cooling and vertical mixing due to brine rejection during the predominately local sea ice growth. The extent and distribution of this Cold Pool (CP) is largely controlled by the winter extent of sea ice in the Bering Sea, which can vary considerably and recently has been much lower than average. The cold bottom water of the CP is important for food security because it delineates the boundary between arctic and subarctic demersal fish species. A northward retreat of the CP will likely be associated with migration of subarctic species toward the Chukchi Sea. We use the fully-coupled Regional Arctic System Model (RASM) to examine variability of the extent and distribution of the CP and its relation to change in the sea ice cover in the Bering Sea during the period 1980–2018. RASM results confirm the direct correlation between the extent of sea ice and the CP and show a smaller CP as a consequence of realistically simulated recent declines of the sea ice cover in the Bering Sea. In fact, the area of the CP was found to be only 31% of the long-term mean in July of 2018. In addition, we also find that a low ice year is followed by a later diatom bloom, while a heavy ice year is followed by an early diatom bloom. Finally, the RASM probabilistic intra-annual forecast capability is reviewed, based on 31-member ensembles for 2019–2021, for its potential use for prediction of the winter sea ice cover and the subsequent summer CP area in the Bering Sea.

Urban sources, wastewater treatment plants (WWTPs), untreated wastewater (not connected to WWTPs), and especially combined sewer overflow systems (CSS) including stormwater are major pathways for microplastics in the aquatic environment. We compile microplastics emission data for the Baltic Sea region, calculate emissions for each pathway and develop emission scenarios for selected polymer types, namely polyethylene (PE)/polypropylene (PP) and the polyester polyethylene terephthalate (PET). PE/PP and PET differ with respect to their density and can be regarded as representative for large groups of polymers. We consider particles between 20-500 µm with varying shapes. The emission scenarios serve as input for 3D-model simulations, which allow us to estimate transport, behavior and deposition in the Baltic Sea environment. According to our model results, the average residence time of PET and PE/PP in the Baltic Sea water body is about 14 days. Microplastics from urban sources cause average concentrations of 1.4 PE/PP (0.7 PET) particles/m² sea surface (20-500 µm size range) in the Baltic Sea during summer. Average concentrations of PET, resulting from urban sources, at the sea floor are 4 particles/m² sediment surface during summer. Our model approach suggests that accumulation at the shoreline is the major sink for microplastic with annual coastal PE/PP and PET accumulation rates of up to 108 particles/m each near emission hot-spots and in enclosed and semi-closed systems. All concentrations show strong spatial and temporal variability and are linked to high uncertainties. The seasonality of CSS (including stormwater) emissions is assessed in detail. In the south-eastern Baltic, emissions during July and August can be up to 50% of the annual CSS and above 1/3 of the total annual microplastic emissions. The practical consequences especially for monitoring, which should focus on beaches, are discussed. Further, it seems that PET, PE/PP can serve as indicators to assess the state of pollution.

Climate-induced rapid changes in the Arctic Ocean, such as decreasing sea ice extent and increasing water temperature, are altering nutrient and light availability, profoundly impacting primary producer growth. However, access to the high-latitude Arctic Ocean is limited, and satellite data are primarily available only during summer, making continuous in-situ data collection challenging. We collected year-round chlorophyll-a (Chl-a) concentration data in high-latitude regions using a mooring system and performed a comparative analysis with reanalysis data. Unlike previous satellite-based studies, which typically rely on surface measurements, we used the annual vertical distribution of Chl-a. These data were applied to the vertically generalized production model to accurately estimate annual primary production. The moored Chl-a concentration data showed that phytoplankton exhibited a typical subsurface chlorophyll maximum (SCM) layer as sea ice retreated in June. Contrary to the gradually deepening SCM distribution predicted by model-based reanalysis data, the SCM layer persisted for approximately 4 months. This indicates that light and nutrient conditions within the SCM layer remained stable, sustaining continuous phytoplankton growth. Annual primary production, reflecting this vertical distribution of Chl-a concentration, was 6.85 gC m−2 yr−1. This exceeded satellite-based estimates by at least two-fold, highlighting the significant underestimation of primary production by satellite approaches. Estimating primary production while accounting for the vertical distribution of phytoplankton and light is essential for improving ecological models to better understand carbon cycle and food web changes in the Arctic Ocean, with important implications for climate change predictions.

The shallow (~50 m deep), narrow (~85 km wide) Bering Strait is the sole marine link between the Pacific and Arctic Oceans and represents a critical northward throughflow of freshwater, nutrients, and heat into Arctic waters from lower latitudes. The western Bering Strait in particular has long been known to be a region of consistently high primary productivity throughout the spring and summer open-water season. This productivity is sustained through the delivery of high-nutrient AW waters via the northern branch of the bifurcated Bering Slope Current that also causes the Chukchi Sea to the north to be one of the most productive shelves in the Arctic. To investigate recent biological change in the Bering Strait, we compiled a satellite-based time series of chlorophyll-a concentrations derived from Aqua-Moderate Resolution Imaging Spectroradiometer (Aqua-MODIS) calibrated radiances using two algorithms: the OC3m algorithm that was developed at NASA Goddard Space Flight Center (GSFC) and makes use of band ratios and in situ measurements and the CI algorithm that makes use of reflectance differences in conjunction with a model.

Substantial amounts of nutrients and carbon enter the Arctic Ocean from the Pacific Ocean through the Bering Strait, distributed over three main pathways. Water with low salinities and nutrient concentrations takes an eastern route along the Alaskan coast, as Alaskan Coastal Water. A central pathway exhibits intermediate salinity and nutrient concentrations, while the most nutrient-rich water enters the Bering Strait on its western side. Towards the Arctic Ocean, the flow of these water masses is subject to strong topographic steering within the Chukchi Sea with volume transport modulated by the wind field. In this contribution, we use data from several sections crossing Herald Canyon collected in 2008 and 2014 together with numerical modelling to investigate the circulation and transport in the western part of the Chukchi Sea. We find that a substantial fraction of water from the Chukchi Sea enters the East Siberian Sea south of Wrangel Island and circulates in an anticyclonic direction around the island. This water then contributes to the high-nutrient waters of Herald Canyon. The bottom of the canyon has the highest nutrient concentrations, likely as a result of addition from the degradation of organic matter at the sediment surface in the East Siberian Sea. The flux of nutrients (nitrate, phosphate, and silicate) and dissolved inorganic carbon in Bering Summer Water and Winter Water is computed by combining hydrographic and nutrient observations with geostrophic transport referenced to lowered acoustic Doppler current profiler (LADCP) and surface drift data. Even if there are some general similarities between the years, there are differences in both the temperature–salinity and nutrient characteristics. To assess these differences, and also to get a wider temporal and spatial view, numerical modelling results are applied. According to model results, high-frequency variability dominates the flow in Herald Canyon. This leads us to conclude that this region needs to be monitored over a longer time frame to deduce the temporal variability and potential trends.

Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1‐Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1‐Arctic) historical simulations (1950–2014) with the uniform low‐resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large‐scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high‐resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales. Plain Language Summary Earth system models (ESMs) are essential tools for understanding the climate system and projecting future changes, but standard coarse model resolutions often fail to realistically represent regional processes and topography, particularly in the polar regions, while uniform high‐resolution grids are too computationally expensive. To address this, regionally refined meshes (RRMs) have been developed within ESMs to provide high‐resolution simulations in target areas, improving the accuracy of regional climate simulations while maintaining computational efficiency. This study looks at how well the U.S. DOE's RRM model, E3SMv2.1‐Arctic, performs in simulating Arctic climate. The RRM, which focuses on the Arctic region, is compared to a uniform low‐resolution version, as well as to observations and reanalysis data. The RRM does a better job of simulating important Arctic climate features like precipitation, atmospheric circulation, clouds, sea ice and atmospheric rivers compared to the low‐resolution model. However, it shows some seasonal temperature biases, reducing the cold bias in summer but increasing the warm bias in winter. The RRM also underestimates winter sea ice, consistent with the warm winter bias. While the study demonstrates the advantages of using high‐resolution models to better understand Arctic climate changes, it also notes that some biases remain despite the increased resolution. Key Points Evaluation of atmosphere‐land‐ocean‐ice fully coupled E3SM‐Arctic historical simulations with regionally refined meshes for the Arctic E3SM‐Arctic reduces bias in precipitation, clouds and atmospheric rivers due to better topography compared to its low‐resolution counterpart Higher resolution leads to an increased albedo feedback and more accurate sea ice area, but with faster sea ice melting due to a warm bias

The data set presented here consists of Conductivity–Temperature–Depth (CTD) observations collected during 96 research cruises of R/V Oceania across the southern Baltic Sea between 1997 and 2024. The collection comprises towed and vertical station profiles acquired along a repeat transect spanning the Arkona Basin, Bornholm Basin, Słupsk Furrow, and Gdańsk Basin. Acquisition and post-processing procedures include standardized parsing of CNV/TXT files, robust time/position handling, pressure-binning to 1 dbar, median filtering, automated geolocation quality control, and pruning of incomplete profiles. The dataset enables analyses of seasonal to decadal variability in temperature and salinity, inflow propagation, ventilation events, and model validation. Manufacturer specifications for the principal instruments (Guildline 87104, Idronaut OS316/OS316Plus, Sea-Bird SBE49, Sea-Bird SBE19plus) are summarized to inform uncertainty assessment. The dataset is publicly available from IOPAN GeoNetwork (Rak, 2025b; https://doi.org/10.48457/IOPAN.2025.531), while the processing and CF-1.8 NetCDF export scripts are available from Zenodo (Rak, 2025a; https://doi.org/10.5281/zenodo.17814769).

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice edge error, Brier score, and spatial probability score. We find that the CMIP6 multimodel mean captures the mean annual cycle and 1979–2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic subregions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.

When hindcasting wave fields of storm events with state-of-the-art wave models, the quality of the results strongly depends on the meteorological forcing dataset. The wave model will inherit the uncertainty of the atmospheric data, and additional discretization errors will be introduced due to a limited spatial and temporal resolution of the forcing data. In this study, we apply an atmospheric downscaling to (i) add regional details to the wind field, (ii) increase the temporal resolution of the wind fields, (iii) provide a more detailed representation of transient events such as storms and (iv) generate ensembles with perturbed atmospheric conditions, which allows for a flow-dependent and spatio-temporally variable uncertainty estimation. We test different strategies to generate an ensemble hindcast of a relatively strong storm event in February 2002 in the Baltic Sea. The Weather Research and Forecasting (WRF) model used for this purpose is driven by the ECMWF ERA5 reanalysis, and wind fields are passed to the third-generation wave model WAVEWATCH III®. A combination of initial conditions from the ERA5 ensemble of data assimilations and stochastic perturbations during runtime is identified as the most promising strategy. The final aim of the ensemble approach is to quantify the hindcast error, but this approach can also be used to generate alternative representations of historical extreme events to sample the recent climate and to increase the sample size for statistical studies, such as for civil engineering applications for coastal protection studies.

When hindcasting wave fields of storm events with state-of-the-art wave models, the quality of the results strongly depends on the meteorological forcing dataset. The wave model will inherit the uncertainty of the atmospheric data, and additional discretization errors will be introduced due to a limited spatial and temporal resolution of the forcing data. In this study, we apply an atmospheric downscaling to (i) add regional details to the wind field, (ii) increase the temporal resolution of the wind fields, (iii) provide a more detailed representation of transient events such as storms and (iv) generate ensembles with perturbed atmospheric conditions, which allows for a flow-dependent and spatio-temporally variable uncertainty estimation. We test different strategies to generate an ensemble hindcast of a relatively strong storm event in February 2002 in the Baltic Sea. The Weather Research and Forecasting (WRF) model used for this purpose is driven by the ECMWF ERA5 reanalysis, and wind fields are passed to the third-generation wave model WAVEWATCH III.sup.® . A combination of initial conditions from the ERA5 ensemble of data assimilations and stochastic perturbations during runtime is identified as the most promising strategy. The final aim of the ensemble approach is to quantify the hindcast error, but this approach can also be used to generate alternative representations of historical extreme events to sample the recent climate and to increase the sample size for statistical studies, such as for civil engineering applications for coastal protection studies.