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Arctic sea ice loss in response to a warming climate is assessed in 42 models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Sea ice observations show a significant acceleration in the rate of decline commencing near the turn of the 21st century. It is our assertion that state-of-the-art climate models should qualitatively reflect this accelerated trend within the limitations of internal variability and observational uncertainty. Our analysis shows that individual CMIP6 simulations of sea ice depict a wide range of model spread on biases and anomaly trends both across models and among their ensemble members. While the CMIP6 multi-model mean captures the observed sea ice area (SIA) decline relatively well, an individual model’s ability to represent the acceleration in sea ice decline remains a challenge. Seventeen (40%) out of 42 CMIP6 models and 37 (13%) out of the total 286 ensemble members reasonably capture the observed trends and acceleration in SIA decline. In addition, a larger ensemble size appears to increase the odds for a model to include at least one ensemble member skillfully representing the accelerated SIA trends. Simulations of sea ice volume (SIV) show much larger spread and uncertainty than SIA; however, due to limited availability of sea ice thickness data, these are not as well constrained by observations. Finally, we find that models with more ocean heat transport simulate larger sea ice declines, which suggests an emergent constraint in CMIP6 ensembles. This relationship points to the need for better understanding and modeling of ice-ocean interactions, especially with respect to frazil ice growth.

During the 42-year period (1979–2020) of satellite measurements, four major winter (December–March) polynyas have been observed north of Greenland: one in December 1986 and three in the last decade, i.e., February of 2011, 2017, and 2018. The 2018 polynya was unparalleled in its magnitude and duration compared to the three previous events. Given the apparent recent increase in the occurrence of these extreme events, this study aims to examine their evolution and causality, in terms of forced versus natural variability. The limited weather station and remotely sensed sea ice data are analyzed combining with output from the fully coupled Regional Arctic System Model (RASM), including one hindcast and two ensemble simulations. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact (i.e., no significant ice melting) on the evolution of the observed winter open-water episodes in the region. Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss in all four polynyas. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds greater than 10 m s−1) blowing persistently over the study region for at least 2 d or more were required over the study region to mechanically redistribute some of the thickest Arctic sea ice out of the region and thus to create open-water areas (i.e., a latent heat polynya). To assess the role of internal variability versus external forcing of such events, we carried out and examined results from the two RASM ensembles dynamically downscaled with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles (initialized 30 years apart: one in December 1985 and another in December 2015), 17 and 16 winter polynyas were produced north of Greenland, respectively. The frequency of polynya occurrence had no apparent sensitivity to the initial sea ice thickness in the study area pointing to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations, and probabilistic forecasts of Arctic events, such as the ones being investigated here and elsewhere.

Climate observations inform about the past and present state of the climate system. They underpin climate science, feed into policies for adaptation and mitigation, and increase awareness of the impacts of climate change. The Global Climate Observing System (GCOS), a body of the World Meteorological Organization (WMO), assesses the maturity of the required observing system and gives guidance for its development. The Essential Climate Variables (ECVs) are central to GCOS, and the global community must monitor them with the highest standards in the form of Climate Data Records (CDR). Today, a single ECV—the sea ice ECV—encapsulates all aspects of the sea ice environment. In the early 1990s it was a single variable (sea ice concentration) but is today an umbrella for four variables (adding thickness, edge/extent, and drift). In this contribution, we argue that GCOS should from now on consider a set of seven ECVs (sea ice concentration, thickness, snow depth, surface temperature, surface albedo, age, and drift). These seven ECVs are critical and cost effective to monitor with existing satellite Earth observation capability. We advise against placing these new variables under the umbrella of the single sea ice ECV. To start a set of distinct ECVs is indeed critical to avoid adding to the suboptimal situation we experience today and to reconcile the sea ice variables with the practice in other ECV domains.

The Energy Exascale Earth System Model (E3SM) is a state-of-the-science Earth system model (ESM) with the ability to focus horizontal resolution of its multiple components in specific areas. Regionally refined global ESMs are motivated by the need to explicitly resolve, rather than parameterize, relevant physics within the regions of refined resolution, while offering significant computational cost savings relative to the respective cost of configurations with high-resolution (HR) everywhere on the globe. In this paper, we document results from the first Arctic regionally refined E3SM configuration for the ocean and sea-ice components (E3SM-Arctic-OSI), while employing data-based atmosphere, land, and hydrology components. Our aim is an improved representation of the Arctic coupled ocean and sea-ice state, its variability and trends, and the exchanges of mass and property fluxes between the Arctic and the sub-Arctic. We find that E3SM-Arctic-OSI increases the realism of simulated Arctic ocean and sea-ice conditions compared to a similar low-resolution E3SM simulation without the Arctic regional refinement in ocean and sea-ice components (E3SM-LR-OSI). In particular, exchanges through the main Arctic gateways are greatly improved with respect to E3SM-LR-OSI. Other aspects, such as the Arctic freshwater content variability and sea-ice trends, are also satisfactorily simulated. Yet, other features, such as the upper-ocean stratification and the sea-ice thickness distribution, need further improvements, involving either more advanced parameterizations, model tuning, or additional grid refinements. Overall, E3SM-Arctic-OSI offers an improved representation of the Arctic system relative to E3SM-LR-OSI, at a fraction (15 %) of the computational cost of comparable global high-resolution configurations, while permitting exchanges with the lower-latitude oceans that cannot be directly accounted for in Arctic regional models.

17 USC 105 interim-entered record; under review. The article of record as published may be found at https://doi.org/10.5194/os-18-29-2022 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 trans port 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. The science was financially supported by: US National Science Foundation (Grant Number: GEO/PLR ARCSS 575 IAA#1417888), the Department of Energy (DOE) Regional and Global Model Analysis (RGMA), the Swedish Research Council Formas (contract no. 2018-01398), and the Swedish Research Council (contract nos. 621-2006-3240, 621-2010-4084, and 2012-1680). This work was carried out with logistic support from the Knut and Alice Wallenberg Foundation and from Swedish Polar Research Secretariat. The Department of Defense (DOD) High Performance Computer Modernization Program (HPCMP) provided computer resources. This study was also supported by the Russian Scientific Foundation (grant no. # 21-77-580 30001). The science was financially supported by: US National Science Foundation (Grant Number: GEO/PLR ARCSS 575 IAA#1417888), the Department of Energy (DOE) Regional and Global Model Analysis (RGMA), the Swedish Re search Council Formas (contract no. 2018-01398), and the Swedish Research Council (contract nos. 621-2006-3240, 621-2010-4084, and 2012-1680). This work was carried out with logistic support from the Knut and Alice Wallenberg Foundation and from Swedish Polar Research Secretariat. The Department of Defense (DOD) High Performance Computer Modernization Program (HPCMP) provided computer resources. This study was also supported by the Russian Scientific Foundation (grant no. # 21-77-580 30001).

A set of decadal simulations has been completed and evaluated for gains using the Regional Arctic System Model (RASM) to dynamically downscale data from a global Earth system model (ESM) and two atmospheric reanalyses. RASM is a fully coupled atmosphere - land - ocean - sea ice regional Earth system model. Nudging to the forcing data is applied to approximately the top half of the atmosphere. RASM simulations were also completed with a modification to the atmospheric physics for evaluating changes to the modeling system. The results show that for the top half of the atmosphere, the RASM simulations follow closely to that of the forcing data, regardless of the forcing data. The results for the lower half of the atmosphere, as well as the surface, show a clustering of atmospheric state and surface fluxes based on the modeling system. At all levels of the atmosphere the imprint of the weather from the forcing data is present as indicated in the pattern of the monthly and annual means. Biases, in comparison to reanalyses, are evident in the ESM forced simulations for the top half of the atmosphere but are not present in the lower atmosphere. This suggests that bias correction is not needed for fully-coupled dynamical downscaling simulations. While the RASM simulations tended to go to the same mean state for the lower atmosphere, there is a different evolution of the weather across the ensemble of simulations. These differences in the weather result in variances in the sea ice and oceanic states.

17 USC 105 interim-entered record; under review. The article of record as published may be found at http://dx.doi.org/10.1175/JCLI-D-20-0491.1 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. U.S. Navy Department of Energy (DOE) Regional and Global Model Analysis (RGMA) Office of Naval Research (ONR) Arctic and Global Prediction (AGP) National Science Foundation (NSF) Arctic System Science (ARCSS) Ministry of Science and Higher Education in Poland DOE: 89243019SSC0036 DESC0014117 ONR: N0001418WX00364 NSF: IAA1417888 IAA1603602

The Regional Arctic System Model (RASM) has been developed and used for modeling of past to present and predicting future Arctic climate change at time scales from weeks to decades. RASM is a fully coupled ice-ocean-atmosphere-land hydrology model. Its domain covers the pan-Arctic region, with the default atmosphere and land components configured on a 50-km horizontal grid. The ocean and sea ice components are configured on a rotated sphere mesh with the default configuration of 1/12o (~9.3km) in the horizontal space and with 45 ocean vertical layers. As a regional climate model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which are derived either from global atmospheric reanalyses for simulations of the past to present or from global forecasts or from Earth System models (ESMs) for climate projections. The former simulations allow comparison of RASM results with observations in place and time, and their tuning, which is a unique capability not available in global ESMs.Within this framework, RASM has been used every month for the past 3+ years (from January 2019 to present) to dynamically downscale the global intra-annual (i.e., 7-month) operational forecasts from the National Center for Environmental Predictions (NCEP) Climate Forecast System version 2 (CFSv2). Here we present summary results from analysis of RASM predictive skill from these forecasts using the common metrics to quantify model skill in predicting sea ice conditions at time scales from weeks up to 6 months. Examples of possible improvements of RASM predictive skill are discussed, related to optimized parameter space, improved initial conditions and higher spatial resolution. An outlook for up to decadal probabilistic predictions using dynamical downscaling is also discussed.

We present the analysis of global sympagic primary production (PP) from 300 years of pre-industrial and historical simulations of the E3SMv1.1-BGC model. The model includes a novel, eight-element sea ice biogeochemical component, MPAS-Seaice zbgc, which is resolved in three spatial dimensions and uses a vertical transport scheme based on internal brine dynamics. Modeled ice algal chlorophyll-a concentrations and column-integrated values are broadly consistent with observations, though chl-a profile fractions indicate that upper ice communities of the Southern Ocean are underestimated. Simulations of polar integrated sea ice PP support the lower bound in published estimates for both polar regions with mean Arctic values of 7.5 and 15.5 TgC/a in the Southern Ocean. However, comparisons of the polar climate state with observations, using a maximal bound for ice algal growth rates, suggest that the Arctic lower bound is a significant underestimation driven by biases in ocean surface nitrate, and that correction of these biases supports as much as 60.7 TgC/a of net Arctic PP. Simulated Southern Ocean sympagic PP is predominantly light-limited, and regional patterns, particularly in the coastal high production band, are found to be negatively correlated with snow thickness.

Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling capabilities in the Arctic Ocean.

As the sea‐ice modeling community is shifting to advanced numerical frameworks, developing new sea‐ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea‐ice models. Initiated at the Forum for Arctic Modeling and Observational Synthesis, the Sea Ice Rheology Experiment (SIREx) aims at evaluating state‐of‐the‐art sea‐ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea‐ice physics (rheology) and by model configuration. Part 1 of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea‐ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the viscous‐plastic (and the elastic‐viscous‐plastic variant), elastic‐anisotropic‐plastic, and Maxwell‐elasto‐brittle rheologies are compared in a single study. We find that both plastic and brittle sea‐ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi‐fractality. Model configuration (e.g., numerical convergence, atmospheric representation, spatial resolution) and physical parameterizations (e.g., ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea‐ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features in a SIREx Part 2 companion paper. Plain Language Summary: The ice in the Arctic Ocean is not continuous: it is broken into individual pieces of ice (floes). As the winds and ocean currents continually move these ice floes, they get piled up together or pushed away from each other, forming regions of increased ice thickness (ridges) or regions of open water (leads). These leads and ridges (ice deformations) are important features of the Arctic pack ice because they control the amount of energy that can be exchanged between the atmosphere and the ocean. Current climate models cannot simulate individual ice floes and their deformations. Instead, various methods are used to represent the movement and deformation of the Arctic sea‐ice cover. The goal of the Sea Ice Rheology Experiment (SIREx) is to compare these different methods and evaluate the ability of a large number of sea‐ice models to reproduce observed sea‐ice deformations from satellite imagery. SIREx is divided in two parts. In Part 1 (this study), we evaluate how the intensity of ice deformations varies in space and time. In Part 2 (companion paper), we track and evaluate the occurrence of specific deformation features. With this work, we show how to improve sea‐ice models for realistic simulations of sea‐ice deformations. Key Points: Power law scaling and multi‐fractality of deformations in space and time can be achieved by both plastic and brittle sea‐ice rheologies Scaling statistics of simulated sea‐ice deformation fields depend on the model configuration and physical parameterizations Finite‐difference plastic models need to be run at higher resolution than observations to agree with the observed deformation statistics

International audience; With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.

The Energy Exascale Earth System Model (E3SM) is a state-of-the-science Earth system model (ESM) with the ability to focus horizontal resolution of its multiple components in specific areas. Regionally refined global ESMs are motivated by the need to explicitly resolve, rather than parameterize, relevant physics within the regions of refined resolution, while offering significant computational cost savings relative to the respective cost of high-resolution (HR) global configurations. In this paper, we document results from the first Arctic regionally refined E3SM configuration for the ocean and sea-ice components (E3SM-Arctic-OSI), while employing data-based atmosphere, land, and hydrology components. Our aim is an improved representation of the Arctic coupled ocean and sea ice state, its variability and trends, and the exchanges of mass and property fluxes between the Arctic and the Subarctic. We find that E3SM-Arctic-OSI increases the realism of simulated Arctic ocean and sea ice conditions compared to a similar low-resolution E3SM simulation without the Arctic regional refinement in ocean and sea ice components (E3SM-LR-OSI). In particular, exchanges through the main Arctic gateways are greatly improved with respect to E3SM-LR-OSI. Other aspects, such as the Arctic freshwater content variability and sea-ice trends, are also satisfactorily simulated. Yet, other features, such as the upper ocean stratification and the sea-ice thickness distribution, need further improvements, involving either more advanced parameterizations, model tuning, or additional grid refinements. Overall, E3SM-Arctic-OSI offers an improved representation of the Arctic system relative to E3SM-LR-OSI, at a fraction (15 %) of the computational cost of comparable global high-resolution configurations, while permitting exchanges with the lower latitude oceans that can not be directly accounted for in Arctic regional models.

During the 42-year period (1979–2020) of satellite measurements, only three winter polynyas have ever been observed north of Greenland and they all occurred in the last decade, i.e. February of 2011, 2017 and 2018. The 2018 polynya was unparalleled by its magnitude and duration compared to the two previous events. Combined with the limited weather station and remotely-sensed sea ice data, a fully-coupled Regional Arctic System Model (RASM) hindcast simulation was utilized to examine the causality and evolution of these recent extreme events. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact on the development of these winter open water episodes in the study region (i.e., no significant ice melting). Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds of greater than 10 m/s) blowing persistently for at least 2 days or more, were required over the study region to mechanically redistribute some of the thickest sea ice out of the region and thus to create open water areas (a latent heat polynya). In order to assess the role of internal variability versus external forcing of such events, we additionally simulated and examined results from two RASM ensembles forced with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles, initialized 30 years apart, one in December 1985 and another in December 2015, respectively, 17 and 14 winter polynyas were produced over north of Greenland. The frequency of polynya occurrence and no apparent sensitivity to the initial sea ice thickness in the study area point to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations and probabilistic forecast of Arctic events, such as the ones being investigated here and elsewhere.

Simulating sea-ice drift and deformation in the Arctic Ocean is still a challenge because of the multi-scale interaction of sea-ice floes that compose the Arctic sea ice cover. The Sea Ice Rheology...

As the sea-ice modeling community is shifting to advanced numerical frameworks, developing new sea-ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Ar...

USDOE Office of Science (SC), Biological and Environmental Research (BER) U.S. Department of Energy, DOE/SC-ARM-18-005

Oceanic fluxes through Fram Strait may significantly contribute to climate variations in the Arctic. However, their observations are difficult. Here, a 26-year numerical model simulation is used to derive oceanic proxies for interannual variability in heat fluxes through Fram Strait. It is found that variability in the cross-slope gradient of sea surface height (SSH) across the West Spitsbergen Current (WSC) can explain about 90% of the variance of winter and annual mean volume transports of Atlantic water at 79°N. Given the strong covariance between the simulated heat flux in the slope current along Svalbard and the corresponding volume transport, variability of the SSH gradient across the WSC is also found to account for about 80% of the variance of heat flux associated with the northward flow through Fram Strait. Moreover, variations in the SSH gradient across the Arctic Slope Current (ASC) northeast of Svalbard at 31°E explain about 85% of the variance of heat flux there and about 80% of the variance of the net heat flux upstream through Fram Strait. Finally, about 85% and 75% of the variance of the net heat flux through Fram Strait is associated with anomalies of the eastward volume transport and depth-averaged core velocity in the ASC, respectively. These relations indicate that monitoring of the flow in the ASC, even with a single current meter mooring, or of the SSH gradient across this current derived from either in situ or remote measurements may provide useful proxies for the heat import to the Arctic Ocean.

Simulating sea-ice drift and deformation in the Arctic Ocean is still a challenge because of the multi-scale interaction of sea-ice floes that compose the Arctic sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project formed within the Forum of Arctic Modeling and Observational Synthesis (FAMOS) to collect and design skill metrics to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) and provide guidance for modeling small-scale deformation. In this contribution, spatial and temporal properties of LKFs are assessed in 33 simulations of state-of-the-art sea ice models (VP/EVP,EAP, and MEB) and compared to deformation features derived from RADARSAT Geophysical Processor System (RGPS).All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, lifetimes, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric forcing affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2km) the concentration and thickness anomalies along LKFs are large enough to affect air-ice-ocean interaction processes.

During the winter of 2019/2020, as the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project started its work, the Arctic Oscillation (AO) experienced some of its largest shifts, ranging from a highly negative index in November 2019 to an extremely positive index during January–February–March (JFM) 2020. The permanent positive AO phase for the 3 months of JFM 2020 was accompanied by a prevailing positive phase of the Arctic Dipole (AD) pattern. Here we analyze the sea ice thickness (SIT) distribution based on CryoSat-2/SMOS satellite-derived data augmented with results from the hindcast simulation by the fully coupled Regional Arctic System Model (RASM) from November 2019 through March 2020. A notable result of the positive AO phase during JFM 2020 was large SIT anomalies of up to 1.3 m that emerged in the Barents Sea (BS), along the northeastern Canadian coast and in parts of the central Arctic Ocean. These anomalies appear to be driven by nonlinear interactions between thermodynamic and dynamic processes. In particular, in the Barents and Kara seas (BKS), they are a result of enhanced ice growth connected with low-temperature anomalies and the consequence of intensified atmospherically driven sea ice transport and deformations (i.e., ice divergence and shear) in this area. The Davies Strait, the east coast of Greenland and the BS regions are characterized by convergence and divergence changes connected with thinner sea ice at the ice borders along with an enhanced impact of atmospheric wind forcing. Low-pressure anomalies that developed over the eastern Arctic during JFM 2020 increased northerly winds from the cold Arctic Ocean to the BS and accelerated the southward drift of the MOSAiC ice floe. The satellite-derived and simulated sea ice velocity anomalies, which compared well during JFM 2020, indicate a strong acceleration of the Transpolar Drift relative to the mean for the past decade, with intensified speeds of up to 6 km d−1. As a consequence, sea ice transport and deformations driven by atmospheric surface wind forcing accounted for the bulk of the SIT anomalies, especially in January 2020 and February 2020. RASM intra-annual ensemble forecast simulations with 30 ensemble members forced with different atmospheric boundary conditions from 1 November 2019 through 30 April 2020 show a pronounced internal variability in the sea ice volume, driven by thermodynamic ice-growth and ice-melt processes and the impact of dynamic surface winds on sea ice formation and deformation. A comparison of the respective SIT distributions and turbulent heat fluxes during the positive AO phase in JFM 2020 and the negative AO phase in JFM 2010 corroborates the conclusion that winter sea ice conditions in the Arctic Ocean can be significantly altered by AO variability.

The landfast ice (LFI) is an important component of the Arctic environment, especially in regions of shallow shelfs North of Alaska and Siberia. Its presence affects the transfer of energy between the atmosphere and the ocean. Its outer edge continuously interacts with the moving pack ice. One of the mechanisms of LFI formation – grounded ice keels, acting as anchor points – was parametrized in the version 6 of Los Alamos sea ice model (CICE) Consortium. The parametrization is based on the bathymetry data, ice concentration and the mean ice thickness in a grid cell. It enables determination of the critical thickness, required for large ice keels to reach the bottom and calculation of the basal stress. A series of experiments using the Regional Arctic System Model (RASM) with CICEv6 has been conducted. In addition to sea ice model, RASM includes the atmosphere (WRF), ocean (POP), land hydrology (VIC), and river routing scheme (RVIC) components controlled by a flux coupler (CPL). LFI simulations using two different rheologies: elastic-visous-plast (EVP) and elastic-anisotropic-plastic (EAP) have been evaluated in the fully coupled and forced sea ice - ocean configurations. Also, sensitivity studies with varying values of the LFI free parameters have been performed. Results are compared against landfast ice extent data from the National Snow & Ice Data Center. In the optimal configuration, including the basal stress parameterization, the model reproduces observed landfast ice in East Siberian, Laptev Sea, and along the coast of Alaska. However, some areas continue to be problematic – like the Kara Sea where LFI is underestimated and the area around the New Siberian Islands, where landfast ice growth is too high. In the former case, the ice arching might be the major landfast ice formation mechanism there, whereas in the latter case the model internal stress distribution might not be adequate to allow realistic sea ice drift between the islands.

The Arctic climate system is very sensitive to the state of sea ice due to its role in controlling heat and momentum exchanges between the atmosphere and the ocean. However, the representation of sea ice state, its past variability and future projections in modern Earth system models (ESMs) vary widely. One of the reasons for that is strong sensitivity of ESMs to sea ice related varying parameter space. Based on limited observations, those parameters typically have a range of possible values and / or are not constant in space and time, which is a source of model uncertainties. The Regional Arctic System Model (RASM) is a limited-domain fully coupled climate model used in this study to investigate sensitivity of sea ice states to limited set of parameters. It includes the atmospheric (Weather Research and Forecasting; WRF) and land hydrology (Variable Infiltration Capacity; VIC) components sharing a 50-km pan-Arctic grid. The sea ice (the version 6.0 of Los Alamos sea ice model, CICE) and ocean (Parallel Ocean Program, POP) components share a 1/12° pan-Arctic grid. In addition, a river routing scheme (RVIC) is used to represent the freshwater flux from land to ocean. All components are coupled at high frequency via the Community Earth System Model (CESM) coupler version CPL7.We have selected four parameters out of the set evaluated by Urrego-Blanco et al. (2016) and subject to their potential impact on sea ice and coupling across the atmosphere-sea ice-ocean interface. The total of 96 sensitivity simulations have been completed with fully coupled and forced RASM configurations, varying each parameter within its respective acceptable range. Using sea ice volume as a measure of sensitivity, the thermal conductivity of snow (ksno) parameter has produced the most sensitivity, in qualitative agreement with Urrego-Blanco et al. (2016). However, using dynamics related metrics, such as sea ice drift or deformation, other parameters, i.e. controlling the sea ice roughness and frictional energy dissipation, have been shown more important. Finally, different quantitative sensitivities to the same parameter have been diagnosed between fully-coupled and forced RASM simulations, as well as compared to the stand alone sea ice results.

In high-latitude environments such as the Arctic Ocean, phytoplankton growth is strongly constrained by light availability. Because light penetration into the upper ocean is attenuated by snow and ...

The summer polynya along the northern coast of Greenland has been observed only six months later after the winter polynya in 2018, which has prompted concerns about the stability of some of the thickest sea-ice in the Arctic region. This study combines retrospective remotely sensed sea-ice measurements with results from the Regional Arctic System Model (RASM) to examine the causes, effect, and evolution of open-water areas/polynyas in the region.RASM is a limited-domain, fully-coupled climate model, consisting of the atmosphere (Weather Research and Forecasting, WRF3.7), ocean (Los Alamos National Laboratory Parallel Ocean Program, POP2), sea-ice (Community Sea Ice Model, CICE5), land hydrology (Variable Infiltration Capacity, VIC4) and streamflow routing (RVIC) components. The ocean and sea-ice models are configured with the horizontal resolution of 1/12-degree with 45 vertical levels and 5 sea-ice thickness categories, respectively. The atmosphere and land hydrology components are set up on a 50-km grid with 40-vertical levels and 3-soil layers, respectively. The Climate Forecast System Reanalysis (CFSR) and version 2 (CFSv2) output are used as boundary conditions for dynamic downscaling.Analysis of the sea-ice conditions off the coast of northern Greenland revealed that RASM, in agreement with satellite measurements, has simulated five summer polynya events, i.e. in August of 1984, 1985, 2002, 2004 and 2018, over the 39-year period (1980-2018). All these events were primarily dynamically forced, with the thermodynamic forcing playing the secondary, yet still important role. While the thermodynamically driven sea-ice melting exhibited a relatively little year-to-year variability, between 87 km3 and 115 km3, its relative contribution to the total sea-ice loss increased by 2.5 times, from 16% in 1984 to 40% in 2018. This implies that with continuing thinning of sea-ice, increasingly less mechanical forcing may be required to generate and maintain a polynya or open water north of Greenland in summers to come.

The article of record as published may be found at http://dx.doi.org/10.1371/ journal.pone.0266180 The Bering Sea experiences a seasonal sea ice cover, which is important to the biophysical environment found there. A pool of cold bottom water (

The Regional Arctic System Model (RASM) has been developed and used to investigate the past to present evolution of the Arctic climate system and to address increasing demands for Arctic forecasts beyond synoptic time scales. RASM is a fully coupled ice-ocean-atmosphere-land hydrology model configured over the pan-Arctic domain with horizontal resolution of 50 km or 25 km for the atmosphere and land and 9.3 km or 2.4 km for the ocean and sea ice components. As a regional model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which for simulations of the past to present are derived from global atmospheric reanalyses, such as the National Center for Environmental Predictions (NCEP) Coupled Forecast System version 2 and Reanalysis (CFSv2/CFSR). This dynamical downscaling approach allows comparison of RASM results with observations, in place and time, to diagnose and reduce model biases. This in turn allows a unique capability not available in global weather prediction and Earth system models to produce realistic and physically consistent initial conditions for prediction without data assimilation.More recently, we have developed a new capability for an intra-annual (up to 6 months) ensemble prediction of the Arctic sea ice and climate using RASM forced with the routinely produced (every 6 hours) NCEP CFSv2 global 9-month forecasts. RASM intra-annual ensemble forecasts have been initialized on the 1st of each month starting in 2019 with forcing for each ensemble member derived from CSFv2 forecasts, 24-hr apart from the month preceding the initial forecast date. Several key processes and feedbacks will be discussed with regard to their impact on model physics, the representation of initial state and ensemble prediction skill of Arctic sea ice variability at time scales from synoptic to decadal. The skill of RASM ensemble forecasts will be assessed against available satellite observations with reference to reanalysis as well as hindcast data using several metrics, including the standard deviation, root mean square difference, Taylor diagrams and integrated ice-edge error.

The Arctic has warmed more than twice as fast as the global average since the late twentieth century, a phenomenon known as Arctic amplification (AA). Recently, there have been considerable advances in understanding the physical contributions to AA, and progress has been made in understanding the mechanisms that link it to midlatitude weather variability. Observational studies overwhelmingly support that AA is contributing to winter continental cooling. Although some model experiments support the observational evidence, most modelling results show little connection between AA and severe midlatitude weather or suggest the export of excess heating from the Arctic to lower latitudes. Divergent conclusions between model and observational studies, and even intramodel studies, continue to obfuscate a clear understanding of how AA is influencing midlatitude weather.

Each fall, bowhead whales in the Bering-Chukchi-Beaufort (BCB) population migrate westward from summering grounds in the Beaufort Sea through the Chukchi Sea to the northern coast of Chukotka, Russia. Routes whales use when crossing the Chukchi Sea vary by year; in some years, whales migrate directly to the northern coast of Chukotka while in other years, whales may pause migration and linger, presumably to feed, in the central Chukchi Sea. To investigate how whale movements may be related to oceanographic variables we examined bowhead whale habitat selection within the Chukchi Sea in autumn (September--November) at two spatial scales. First, at the landscape scale (i.e. the Chukchi Sea), we compare oceanographic variables (e.g. temperature, salinity, and current velocity) at locations within used and randomly available tracks (i.e. paths of travel) to determine how oceanographic features are associated with where whales cross the Chukchi Sea in autumn. Second, at a local scale, we examine how directed travel or lingering within a whale’s track is associated with oceanographic variables (e.g. temperature, salinity, and current velocity). Whale location data for 24 bowhead whales were paired with oceanographic data from a pan-arctic coupled ice-ocean model for 2006–2009. At the landscape scale, we found that whales generally followed water of Pacific origin characterized by temperatures

The distribution of the Bering-Chukchi-Beaufort Sea population of bowhead whales (Balaena mysticetus) is largely centered in the Chukchi Sea in autumn (September–November), which is also when sea ice is at minimum extent allowing for increased ship traffic and industrial activity. Prior work paired autumn movements of bowhead whales in the Chukchi Sea with simulated hydrographic information and concluded whales followed relatively cold, saline waters of Pacific origin during migration (

The near-surface climate, including the atmosphere, ocean, sea ice, and land state and fluxes, in the initial version of the Regional Arctic System Model (RASM) are presented. The sensitivity of the RASM near-surface climate to changes in atmosphere, ocean, and sea ice parameters and physics is evaluated in four simulations. The near-surface atmospheric circulation is well simulated in all four RASM simulations but biases in surface temperature are caused by biases in downward surface radiative fluxes. Errors in radiative fluxes are due to biases in simulated clouds with different versions of RASM simulating either too much or too little cloud radiative impact over open ocean regions and all versions simulating too little cloud radiative impact over land areas. Cold surface temperature biases in the central Arctic in winter are likely due to too few or too radiatively thin clouds. The precipitation simulated by RASM is sensitive to changes in evaporation that were linked to sea surface temperature biases. Future work will explore changes in model microphysics aimed at minimizing the cloud and radiation biases identified in this work.

The article of record as published may be found at https://doi.org/10.5194/gmd-11-4817-2018 Includes supplementary material The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the interannual and interdecadal variability in the climate of the Arctic region, which global models like CESM cannot do. This multi-institutional work was funded by the U.S. Department of Energy (DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856, DE- SC0005783, and DE-SC0005522), by the U.S. National Science Foundation (PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics and Space Administration (NNX14AM02G). Computing resources were provided via a Chal- lenge Grant from the U.S. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP). This multi-institutional work was funded by the U.S. Department of Energy (DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856, DE- SC0005783, and DE-SC0005522), by the U.S. National Science Foundation (PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics and Space Administration (NNX14AM02G). Computing resources were provided via a Chal- lenge Grant from the U.S. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP).

The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979–2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2°C; however, in the cold seasons there are local biases that exceed 6°C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region.

Strong, mesoscale tip jets and barrier winds that occur along the southeastern Greenland coast have the potential to impact deep convection in the Irminger Sea. The self-organizing map (SOM) training algorithm was used to identify 12 wind patterns that represent the range of winter [November–March (NDJFM)] wind regimes identified in the fully coupled Regional Arctic System Model (RASM) during 1990–2010. For all wind patterns, the ocean loses buoyancy, primarily through the turbulent sensible and latent heat fluxes; haline contributions to buoyancy change were found to be insignificant compared to the thermal contributions. Patterns with westerly winds at the Cape Farewell area had the largest buoyancy loss over the Irminger and Labrador Seas due to large turbulent fluxes from strong winds and the advection of anomalously cold, dry air over the warmer ocean. Similar to observations, RASM simulated typical ocean mixed layer depths (MLD) of approximately 400 m throughout the Irminger basin, with individual years experiencing MLDs of 800 m or greater. The ocean mixed layer deepens over most of the Irminger Sea following wind events with northerly flow, and the deepening is greater for patterns of longer duration. Seasonal deepest MLD is strongly and positively correlated to the frequency of westerly tip jets with northerly flow.

What are the major goals of the project? The overarching science goal of the project is to advance Earth System science by proposing a more realistic representation of physical processes in a Greenland fjord to enable future studies of ice-sheet/ocean interactions and their impact on the Greenland Ice Sheets (GrIS) (in many fjords) and on pan-Arctic climate variability. To achieve this objective, we proposed to build a new, separate, high-resolution ocean model for use in Earth System Models (ESMs) to realistically represent the complex three-dimensional physics of the fjord, its bathymetry, coastlines, and coupling with both ice-shelf and the outer ocean around Greenland using discontinuous Galerkin (DG) methods.

The article of record as published may be found at http://dx.doi.org/10.1002/2017JC013365 The current coarse-resolution global Community Earth System Model (CESM) can reproduce major and large-scale patterns but is still missing some key biogeochemical features in the Arctic Ocean, e.g., low surface nutrients in the Canada Basin. We incorporated the CESM Version 1 ocean biogeochemical code into the Regional Arctic System Model (RASM) and coupled it with a sea-ice algal module to investigate model limitations. Four ice-ocean hindcast cases are compared with various observations: two in a global 18 (40 60 km in the Arctic) grid: G1deg and G1deg-OLD with/without new sea-ice processes incorporated; two on RASM's 1/128 ( 9 km) grid R9km and R9km-NB with/without a subgrid scale brine rejection parameteriza- tion which improves ocean vertical mixing under sea ice. Higher-resolution and new sea-ice processes contrib- uted to lower model errors in sea-ice extent, ice thickness, and ice algae. In the Bering Sea shelf, only higher resolution contributed to lower model errors in salinity, nitrate (NO3), and chlorophyll-a (Chl-a). In the Arctic Basin, model errors in mixed layer depth (MLD) were reduced 36% by brine rejection parameterization, 20% by new sea-ice processes, and 6% by higher resolution. The NO3 concentration biases were caused by both MLD bias and coarse resolution, because of excessive horizontal mixing of high NO3 from the Chukchi Sea into the Canada Basin in coarse resolution models. R9km showed improvements over G1deg on NO3, but not on Chl-a, likely due to light limitation under snow and ice cover in the Arctic Basin.