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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).

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

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.

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 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.

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 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.

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.

Some of the most rapid climate changes on the planet are experienced in the Arctic. In particular, the Arctic has been warming at a quicker pace than any other place on Earth, what is recognized as Arctic Amplification (AA). This warming has been most visibly manifested through a declining perennial sea ice cover, increasing the potential for its transition from the permanent toward a seasonal coverage. Those changes also affect air-sea heat fluxes and amplify ice-albedo feedback, which strongly influences ocean’s absorption of solar radiation. In addition, they also alter the Arctic Ocean acoustical regime, as the thinning sea ice moves faster and deforms easier, while its reduced coverage allows increased momentum transfer from the atmosphere to the upper ocean. This talk will provide an updated overview of the recent changes and trends in the Arctic Ocean of relevance to acoustical oceanography. We will focus on the evolution of the upper ocean stratification and water masses, mesoscale processes, and their linkages to the changing regime of the sea ice cover from multi-year to first-year sea ice. Also, the latest advancements and outstanding challenges in modeling and prediction of Arctic climate change at sub-seasonal to interannual time scales will be discussed.

Within larger ice-free regions of the western Arctic Seas, subject to ongoing trophic cascades induced by past overfishing, as well as to possible future eutrophication of the drainage basins of the Yukon and Mackenzie Rivers, prior very toxic harmful algal blooms (HABs) – first associated with ∼100 human deaths near Sitka, Alaska in 1799 – may soon expand. Blooms of calcareous coccolithophores in the Bering Sea during 1997–1998 were non-toxic harbingers of the subsequent increments of other non-siliceous phytoplankton. But, now saxitoxic dinoflagellates, e.g. Alexandrium tamarense, were instead found by us within the adjacent downstream Chukchi Sea during SBI cruises of 2002 and 2003. A previous complex, coupled biophysical model had been validated earlier by ship-board observations from the Chukchi/Beaufort Seas during the summer of 2002. With inclusion of phosphorus as another chemical state variable to modulate additional competition by recently observed nitrogen-fixers, we now explore here the possible consequences of altered composition of dominant phytoplankton functional groups [diatoms, microflagellates, prymnesiophyte Phaeocystis colonies, coccolithophores, diazotrophs, and dinoflagellates] in relation to increases of the toxic A. tamarense, responding to relaxation of grazing pressure by herbivores north of Bering Strait as part of a continuing trophic cascade. Model formulation was guided by validation observations obtained during 2002–2004 from: cruises of the SBI, CHINARE, and CASES programs; moored arrays in Bering Strait; other RUSALCA cruises around Wrangel Island; and SBI helicopter surveys of the shelf-break regions of the Arctic basin. Our year-long model scenarios during 2002–2003 indicate that post bloom silica-limitation of diatoms, after smaller simulated spring grazing losses, led to subsequent competitive advantages in summer for the coccolithophores, dinoflagellates, and diazotrophs. Immediate top-down control is exerted by imposed grazing pressures of the model’s herbivores and bottom-up control is also effected by light-, nitrate-, ammonium-, silicate-, and phosphate-modulated competition among the six functional groups of the simulated phytoplankton community. Similar to the history of the southern North Sea adjacent to the Rhine River, possible farming of northwestern Alaska and Canada, in conjunction with other human activities of ice retreat and overfishing, may lead to future exacerbations of poisonous phytoplankton. These potential killers include both toxic dinoflagellate and diazotroph HABs, deadly to terrestrial and marine mammals, as well as those of prymnesiophytes, some of which have already foamed beaches, while others have killed fishes of European waters.

Models help researchers understand past and present states as well as predict scenarios of environmental change in the Arctic. The authors analyze results on melting sea-ice from a regional coupled ice-ocean model and demonstrate their robustness independent of timescales for surface temperature and salinity relaxation.

As part of the 2002 Shelf-Basin Interactions (SBI) process study, measurements of the seasonal variation in the export flux of particulate organic carbon (POC) are reported for the upper waters of the Chukchi Sea. POC fluxes were quantified by determination of 234Th/238U disequilibrium and POC/234Th ratios in large ( > 53 μ m ) aggregates collected using in situ pumps. Samples were collected at 35 stations on two cruises, one in predominantly ice-coved conditions during the spring (May 6–June 15) and the other in predominantly open water during the summer (July 17–August 26). Enhanced particle export was observed in the shelf and slope waters, particularly within Barrow Canyon, and there was a marked increase in particle export at all stations during the summer (July–August) relative to the spring (May–June). 234Th-derived POC fluxes exhibit significant seasonal and spatial variability, averaging 2.9 ± 5.3 mmol C m - 2 d - 1 ( range = 0.031 – 22 mmol C m - 2 d - 1 ) in the spring and increasing ∼ 4 -fold in the summer to an average value of 10.5 ± 9.3 mmol C m - 2 d - 1 ( range = 0.79 – 39 mmol C m - 2 d - 1 ) . The fraction of primary production exported from the upper waters increases from ∼ 15 % in the spring to ∼ 32 % in the summer. By comparison, DOC accumulation associated with net community production represented ∼ 6 % of primary production ( ∼ 2 mmol C m - 2 d - 1 ) . The majority of shelf and slope stations indicate a close agreement between POC export and benthic C respiration in the spring, whereas there is an imbalance between POC export and benthic respiration in the summer. The implication is that up to ∼ 20 % of summer production ( ∼ 6 ± 7 mmol C m - 2 d - 1 ) may be seasonally exported off-shelf in this productive shelf/slope region of the Arctic Ocean.

Within the framework of the Arctic Ocean Model Intercomparison Project results from several coupled sea ice–ocean models are compared in order to investigate vertically integrated properties of the Arctic Ocean. Annual means and seasonal ranges of streamfunction, freshwater and heat content are shown. For streamfunction the entire water column is integrated. For heat and freshwater content integration is over the upper 1000 m. The study represents a step toward identifying differences among model approaches and will serve as a base for upcoming studies where all models will be executed with common forcing. In this first stage only readily available outputs are compared, while forcing as well as numerical parameterizations differ. The intercomparison shows streamfunctions differing in pattern and by several Sverdrups in magnitude. Differences occur as well for the seasonal range, where streamfunction is subject to large variability.

Understanding oceanic effects on climate in the Pacific-Arctic region requires knowledge of the mean circulation and its variability in the region. This chapter presents an overview of the mean regional circulation patterns, spatial and temporal variability, critical processes and property fluxes from the northern North Pacific into the western Arctic Ocean, with emphasis on their impact on sea ice. First, results from a high-resolution, pan-Arctic ice-ocean model forced with realistic atmospheric data and observations in the Alaskan Stream, as well as exchanges across the Aleutian Island Passes, are discussed. Next, general ocean circulation in the deep Bering Sea, shelf-basin exchange, and flow across the Bering shelf are investigated. Also, flow across the Chukchi Sea, pathways of Pacific summer water and oceanic forcing of sea ice in the Pacific-Arctic region are analyzed. Finally, we hypothesize that the northward advection of Pacific Water together with the excess oceanic heat that has accumulated below the surface mixed layer in the western Arctic Ocean due to diminishing sea ice cover and subsequent increased solar insolation are critical factors affecting sea ice growth in winter and melt the following year. We argue that process-level understanding and improved model representation of ocean dynamics and ocean-ice-atmosphere interactions in the Pacific-Arctic region are needed to advance knowledge and improve prediction of the accelerated decline of sea ice cover and amplified climate warming in the Arctic.

The Pacific Arctic region (PAR) is experiencing atmospheric changes, rapid seasonal sea ice retreat, seawater warming, regional ocean acidification, along with other environmental changes and biological responses in lower to upper trophic organisms. Both physical and biogeochemical modeling indicate the potential for step-function changes to the overall ecosystem, both under current and in the projected conditions. This volume of synthesis papers was coordinated within the Pacific Arctic Group (PAG), a network of international partners undertaking and facilitating collaborative research in the Pacific influenced Arctic seas and basin. It also serves as a product of activities from the 2007–2008 International Polar Year. The topics range from atmospheric and physical sciences to chemical processing and biological response to changing environmental conditions. Physical and biogeochemical modeling results highlight the need for continued data collection together with interdisciplinary modeling activities to track and forecast the changing ecosystem of the Pacific Arctic in response to climate change.

The article of record as published may be found at https://link.springer.com/chapter/10.1007/978-94-017-8863-2_7 Bering Strait is the only ocean connection between the Pacific and the Arctic. The flow through this narrow and shallow strait links the Pacific and Arctic oceans and impacts oceanic conditions downstream in the Chukchi Sea and the Western Arctic. We present a model synthesis of exchanges through Bering Strait at monthly to decadal time scales, including results from coupled ice-ocean models and observations. Significant quantities of heat and freshwater are delivered annually into the southern Chukchi Sea via Bering Strait. We quantify seasonal signals, along with interannual variability, over the course of 26 years of multiple model integrations. Volume transport and property fluxes are evaluated among several high- resolution model runs and compared with available moored observations. High-resolution models represent the bathymetry better, and may have a more realistic representation of the flow through the strait, although in terms of fluxes and mean properties, this is not always the case. We conclude that, (i) while some of the models used for Arctic studies achieve the correct order of magnitude for fluxes of volume, heat and freshwater, and have significant correlations with observational results, there is still a need for improvement and (ii) higher spatial resolution is needed to resolve features such as the Alaska Coastal Current (ACC). At the same time, additional measurements with better spatial coverage are needed to minimize uncertainties in observed estimates and to constrain models. Bering Strait is the only ocean connection between the Pacific and the Arctic. The flow through this narrow and shallow strait links the Pacific and Arctic oceans and impacts oceanic conditions downstream in the Chukchi Sea and the Western Arctic. We present a model synthesis of exchanges through Bering Strait at monthly to decadal time scales, including results from coupled ice-ocean models and observations. Significant quantities of heat and freshwater are delivered annually into the southern Chukchi Sea via Bering Strait. We quantify seasonal signals, along with interannual variability, over the course of 26 years of multiple model integrations. Volume transport and property fluxes are evaluated among several high- resolution model runs and compared with available moored observations. High-resolution models represent the bathymetry better, and may have a more realistic representation of the flow through the strait, although in terms of fluxes and mean properties, this is not always the case. We conclude that, (i) while some of the models used for Arctic studies achieve the correct order of magnitude for fluxes of volume, heat and freshwater, and have significant correlations with observational results, there is still a need for improvement and (ii) higher spatial resolution is needed to resolve features such as the Alaska Coastal Current (ACC). At the same time, additional measurements with better spatial coverage are needed to minimize uncertainties in observed estimates and to constrain models. Department of Energy Earth System Modeling program (J. C. K and W. M.), National Science Foundation Office of Polar Programs (J. C. K, W. M., M. S., and J. Z.), and the Office of Naval Research (J. C. K and W. M.) for support of this research. We also thank the Arctic Ocean Model Intercomparison Project (J. C. K. and W. M.). At the National Oceanography Centre Southampton (Y.A. and B. d C.) the study was supported by the UK Natural Environment Research Council as a contribution to the Marine Centres’ Strategic Research Programme Oceans 2025. Support for this work was provided (in part) by NSF grants ARC-0632154, ARC-0855748, and the NOAA-RUSALCA program (R. W.). The mooring data used in this study was collected under funding from ONR, NSF, MMS, AOOS and NOAA-RUSALCA (R. W).

At this early stage of modeling marine ecosystems and biogeochemical cycles in the Pacific Arctic Region (PAR), numerous challenges lie ahead. Observational data used for model development and validation remain sparse, especially across seasons and under a variety of environmental conditions. Field data are becoming more available, but at the same time PAR is rapidly changing. Biogeochemical models can provide the means to capture some of these changes. This study introduces and synthesizes ecosystem modeling in PAR by discussing differences in complexity and application of one-dimensional, regional, and global earth system models. Topics include the general structure of ecosystem models and specifics of the combined benthic, pelagic, and ice PAR ecosystems, the importance of model validation, model responses to climate influences (e.g. diminishing sea ice, ocean acidification), and the impacts of circulation and stratification changes on PAR ecosystems and biogeochemical cycling. Examples of modeling studies that help place the region within the context of the Pan-Arctic System are also discussed. We synthesize past and ongoing PAR biogeochemical modeling efforts and briefly touch on decision makers’ use of ecosystem models and on necessary future developments.

Output from six regional sea ice-ocean climate model simulations of the arctic seas is compared to investigate the models' ability to accurately reproduce the observed late winter mean sea surface salinity. The results indicate general agreement within the Nordic seas, strong differences on the arctic continental shelves, and the presence of a climate drift that leads to a high salinity bias in most models within the Beaufort Gyre. The latter is highly sensitive to the wind forcing and to the simulation of freshwater sources on the shelves and elsewhere.

The climatically sensitive zone of the Arctic Ocean lies squarely within the domain of the North Atlantic oscillation (NAO), one of the most robust recurrent modes of atmospheric behavior. However, the specific response of the Arctic to annual and longer-period changes in the NAO is not well understood. Here that response is investigated using a wide range of datasets, but concentrating on the winter season when the forcing is maximal and on the postwar period, which includes the most comprehensive instrumental record. This period also contains the largest recorded low-frequency change in NAO activity—from its most persistent and extreme low index phase in the 1960s to its most persistent and extreme high index phase in the late 1980s/early 1990s. This longperiod shift between contrasting NAO extrema was accompanied, among other changes, by an intensifying storm track through the Nordic Seas, a radical increase in the atmospheric moisture flux convergence and winter precipitation in this sector, an increase in the amount and temperature of the Atlantic water inflow to the Arctic Ocean via both inflow branches (Barents Sea Throughflow and West Spitsbergen Current), a decrease in the late-winter extent of sea ice throughout the European subarctic, and (temporarily at least) an increase in the annual volume flux of ice from the Fram Strait.

The article of record as published may be found at http://dx.doi.org/10.1146/annualreviews.org Arctic sea ice is a key indicator of the state of global climate because of both its sensitivity to warming and its role in amplifying climate change. Accelerated melting of the perennial sea ice cover has occurred since the late 1990s, which is important to the pan-Arctic region, through effects on atmospheric and oceanic circulations, the Greenland ice sheet, snow cover, permafrost, and vegetation. Such changes could have significant ramifications for global sea level, the ocean thermohaline circulation, native coastal communities, and commercial activities, as well as effects on the global surface energy and moisture budgets, atmospheric and oceanic circulations, and geosphere-biosphere feedbacks. However, a system-level understanding of critical Arctic processes and feedbacks is still lacking. To better understand the past and present states and estimate future trajectories of Arctic sea ice and climate, we argue that it Is critical to advance hierarchical regional climate modeling and coordinate it with the design of an integrated Arctic observing system to constrain models.

[1] The Arctic Ocean has experienced an unprecedented reduction in sea ice over the last 3 decades, increasing the potential for greater exchange of gases such as carbon dioxide (CO2) between the atmosphere and the upper ocean. The present study utilizes remotely sensed data on distributions of both sea ice and chlorophyll a, together with modeled temperature and salinity fields, to obtain high-resolution basin-scale estimates of the air-sea flux of CO2 (FCO2) in the Arctic Ocean for the years 1998–2003. Concentrations of dissolved inorganic carbon (DIC) were derived from multiple linear regression relationships with sea surface temperature, salinity, and chlorophyll a. The partial pressure of carbon dioxide (pCO2) in surface waters was computed from DIC and alkalinity, the latter of which varied with salinity. FCO2 was calculated from the air-sea difference in pCO2 and wind speed. Annual FCO2 was highest in the Atlantic-dominated Greenland and Barents sectors due to their lower sea ice cover, although area-normalized FCO2 in these sectors was low. Only the Siberian sector exhibited a significant increase in annual FCO2 during the time of our study, due to a corresponding increase in ice-free water. Overall, the Arctic Ocean was a net atmospheric sink for CO2, with annual FCO2 averaging 118 ± 7 Tg C yr−1 during 1998–2003.

LONG-TERM GOALS: Our research combines state-of-the-art regional modeling of sea ice, ocean, atmosphere and ecosystem to provide a system approach to advance the knowledge and predictive capability of the diverse impacts of changing sea ice cover on the bio-physical marine environment of coastal Alaska and over the larger region of the western Arctic Ocean. The focus of this project on seasonally ice-free Alaskan coasts and shelves is in direct support of the ‘Coastal Effects of a Diminished-ice Arctic Ocean’ and littoral studies of interest to the U.S. Navy. Given the continued warming and summer sea ice cover decrease in the Arctic during the past decades, this research will have broader and long-term impacts by facilitating studies of the potential increased exploration of natural resources along the seasonally ice-free northern Alaskan coasts and shelves and of the use of northern sea routes from the Pacific Ocean to Europe. Such activities will change the strategic importance of the entire pan-Arctic region. The research will allow a better understanding and planning of current and future operational needs in support of the continued US commercial and tactical interests in the region. Award Number: N0001407WR20296

[1] Particulate organic carbon (POC) export fluxes were estimated in the shelf-slope region of the Chukchi Sea using measurements of 234Th−238U disequilibria and the POC/234Th ratio in large (>53-μm) particles. These export fluxes were used in conjunction with rates of primary productivity and benthic carbon respiration to construct a POC budget for this shelf-slope region. Samples were collected along a series of shelf-basin transects in the spring (May–June) and summer (July–August) of 2004. These stations were previously occupied during the ice covered (spring) and open water (summer) seasons of 2002, allowing for an interannual comparison of export flux. In contrast to 2002, when open water POC fluxes were significantly higher than in the ice-covered period, POC export fluxes in 2004 were similar during the spring (average = 19.7 ± 24.8 mmol C m−2 d−1) and summer (average = 20.0 ± 14.5 mmol C m−2 d−1). The high POC fluxes measured during the spring are attributed to a plankton bloom, as evidenced by exceptionally high rates of primary productivity (average = 124.4 ± 88.1 mmol C m−2 d−1). The shelf-slope budget of particulate organic carbon indicates that 10–20% of primary productivity was exported below 50 m but was not consumed during benthic carbon respiration or burial and oxidation in underlying sediments. Furthermore, a water column−sediment budget of 234Th indicates that particulate material is retained in shelf sediments on a seasonal basis.

The article of record as published may be found at https://doi.org/10.1029/2006JC003916 Monthly sea levels from five Arctic Ocean Model Intercomparison Project (AOMIP) models are analyzed and validated against observations in the Arctic Ocean. The AOMIP models are able to simulate variability of sea level reasonably well, but several improvements are needed to reduce model errors. It is suggested that the models will improve if their domains have a minimum depth less than 10 m. It is also recommended to take into account forcing associated with atmospheric loading, fast ice, and volume water fluxes representing Bering Strait inflow and river runoff. Several aspects of sea level variability in the Arctic Ocean are investigated based on updated observed sea level time series. The observed rate of sea level rise corrected for the glacial isostatic adjustment at 9 stations in the Kara, Laptev, and East Siberian seas for 1954–2006 is estimated as 0.250 cm/yr. There is a well pronounced decadal variability in the observed sea level time series. The 5-year running mean sea level signal correlates well with the annual Arctic Oscillation (AO) index and the sea level atmospheric pressure (SLP) at coastal stations and the North Pole. For 1954–2000 all model results reflect this correlation very well, indicating that the long-term model forcing and model reaction to the forcing are correct. Consistent with the influences of AO-driven processes, the sea level in the Arctic Ocean dropped significantly after 1990 and increased after the circulation regime changed from cyclonic to anticyclonic in 1997. In contrast, from 2000 to 2006 the sea level rose despite the stabilization of the AO index at its lowest values after 2000. This research is supported by the National Science Foundation Office of Polar Programs (under cooperative agreements OPP- 0002239 and OPP- 0327664) with the International Arctic Research Center, University of Alaska Fairbanks, and by the Climate Change Prediction Program of the Department of Energy’s Office of Biological and Environmental Research. The development of the UW model is also supported by NASA grants NNG04GB03G and NNG04GH52G and NSF grants OPP-0240916 and OPP-0229429.

The main science hypothesis to be addressed in this project can be formulated as follows. The recently observed dramatic decrease of summer sea ice cover in the western Arctic Ocean is driven by two main, primarily local factors: (1) the oceanic heat advection of summer Pacific Water via the Alaska Coastal Current and (2) the positive ice-albedo feedback. To understand physical processes and air-sea-ice interactions involved in these two driving factors, detailed studies, including historical and new observations and very high-resolution modeling, of the Alaska coastal environment are required. The following five specific science goals are proposed to address those requirements in support of the main hypothesis of this project: 1. Explore feedback processes among sea ice, ocean and atmosphere leading to recent and possible future summer decreases of sea ice cover in the Western Arctic Ocean; 2. Quantify impacts of oceanic and atmospheric forcing on regional sea ice cover and its variability; 3. Determine effects of changing sea ice, ocean dynamics and atmospheric circulation on the Alaskan coastal ecosystem (due to temporal and spatial variability); 4. Establish numerical requirements for an optimal hindcast and prediction of environmental conditions in the Alaskan coastal system; 5. Provide guidance for optimal design of an integrated observing system of the Alaskan coastal environment.

The Arctic is a complex and integral part of the Earth system, influencing the global surface energy and moisture budgets, atmospheric and oceanic circulations, and geosphere-biosphere feedback. Some key influences are linked to the recent changes in the multiyear sea ice cover and the ocean. The ice cover is particularly important because it buffers air-sea heat fluxes and through ice-albedo feedback strongly influences Earth’s absorption of solar radiation, especially by the ocean. Global warming has been most visibly manifested in the Arctic through a declining perennial sea ice cover, which has intensified during the late 1990s and the 2000s, resulting in record minima ice cover in 2007 and in 2012. This talk will provide an overview of the recent states and variability of the Arctic Ocean. We will focus on physical changes of potential relevance to the Arctic acoustics, including the past and present climatological changes, evolution of the upper ocean stratification and water masses, mesoscale processes, including eddies, mixing, coastal and boundary currents, and their linkages to the changing regime of the sea ice cover from multi-year to first-year sea ice. Finally, the latest advancements and outstanding challenges in modeling and prediction of arctic climate change will be discussed.

[1] The impact of the lower-boundary forcing over ocean grid points, namely of sea surface temperature (SST), sea ice fraction, and sea ice thickness, on the mean atmospheric simulation is investigated with an Arctic atmospheric regional climate model. The assessment shows that the sea ice/SST forcing has an impact on the atmospheric simulations. The near-surface air temperature response shows a strong, seasonally dependent sensitivity to sea ice changes. The response is small in summer but significant in winter, and changes in the marginal ice zone have the largest impact on the atmosphere. During winter, the realistic representation of the marginal sea ice zone is important as it contributes to the simulation of regional atmospheric circulation patterns and temperature profiles. Changes in sea ice thickness of the western Arctic lower boundary indicate an Arctic-wide response in the large-scale circulation and seem to have an impact on the troposphere-stratosphere coupling. During summer the direct thermodynamic effect of sea ice changes is small, while the dynamic response is still of importance but smaller than in winter.

Our main objective is to use models of the coupled ice-ocean Arctic environment to understand the past and present sea ice and ocean states and to predict future scenarios of environmental change in the Arctic Ocean. To meet this objective we have developed a coupled ice­ ocean model of the sea ice covered northern hemisphere at 9-km and 45-level grid. The model has been spun up for 48 years. Three 24-year experiments have been completed following the spinup, all forced with realistic 1979-2002 ECMWF data but with a different surface temperature and salinity restoring times. Results from these integrations are compared to each other and to sea ice data available over this period to address the growing need for understanding the recent warming and the subsequent decrease of the Arctic Ice Pack during the late 1990s and 2000s.

[1] Fall case studies of three-dimensional circulation, plankton, and benthos models explored the consequences of interannual changes in ice cover and water motion on carbon/nitrogen cycling by the end of September within the Chukchi/Beaufort Seas. The coupled model scenarios were those of reduced (greater) northward flow, colder (warmer) temperatures, and more (less) extensive ice cover over the preceding ∼60 days of August and September during the negative (positive), anticyclonic (cyclonic) phase of the Arctic Oscillation in 1980 (1989). On the inner Chukchi shelf, stronger flows in 1989 advected nitrate and silicate stocks of Pacific origin ∼130 km farther northwest toward Wrangel Island than in 1980. Yet an increase of the total net photosynthesis by the diatom-dominated phytoplankton community over both shelves in 1989 was mainly the result of less ice cover of the cyclonic period, with a concomittant increase of POC influxes of phytodetritus and fecal pellets to the sediments. In terms of present shelf export, the model's separate pools of ∼65 umol DOC kg−1 and 1 ug chl l−1, or ∼4 umol POC kg−1, at a depth of 60 m above the 2000-m isobath of the Beaufort Sea in September 1989, matched the sum of ∼70 umol TOC kg-−1 sampled there by submarine in September 1997. Accordingly, most of the simulated Chukchi shelf was a weak sink of atmospheric CO2 in both September 1980 and 1989, reflecting a net fall export of particulate and dissolved debris. Within the cyclonic case of strong flows in 1989, a surface pCO2 of 248 uatm was also simulated in September at 155°W on the Beaufort shelf, where ∼250 uatm was measured there in September 2000. Here, farther away from the Pacific source of nutrients for enhanced photosynthesis, the model's estimate of surface sea water fugacity in a weaker flow regime was only 375 uatm of pCO2 at the same location in September 1980, when typically outgassing would have instead prevailed, despite increasing atmospheric pCO2 values, i.e., 356 to 362 uatm of pCO2 were found in Arctic air during 1998 to 2000. Although the northward model transports of water, gases, nutrients, and particulate matter were less during 1980 than in 1989, the simulated relict nutrient fields, left behind by the model's phytoplankton on the outer shelf of the Chukchi Sea, were greater then, as a consequence of their smaller rates of light-limited utilization during less photosynthesis and draw down of CO2. The more important factors, effecting fluxes of CO2 here, may thus be the biological ones of photosynthesis and respiration, rather than the physical ones of temperature and advection. Since the colonial prymnesiophytes of the model also grew better at low light intensities than the diatoms or microflagellates, Phaeocystis won, when all other constraints were similar, in regions of the ice-covered upper euphotic zone, where total stocks were less than 0.5 ug chl l−1, as observed in August 2000. However, increased model fidelity, compared with submarine observations of nutrients along the shelf break of the western Canadian Basin, will require appropriate specification of the persistence of winter initial conditions in Bering Strait. Future reductions of ice cover over deeper regions of these Arctic shelves may result in greater utilization of the untapped winter nutrients by these shade-adapted prymnesiophytes, with less energy transfer to higher trophic levels of their food webs. Altered bases of shelf food webs, rather than increased northward transport of Pacific waters and their constituents, may then have greater consequences for both future element cycling and protein yield of these western Arctic ecosystems.

Dramatic changes in the circulation of sea ice and the upper layers of the Arctic Ocean have been reported during the last decade. Similar variability is modeled using a regional, coupled ice-ocean model. Realistic atmospheric forcing elds for 1979-93 are the only interannual signal prescribed in the model. Our results show large-scale changes in sea ice and oceanic conditions when comparing results for the late 1970s / early 1980s and the 1990s. We hypothesize that these changes are in response to even larger scale atmospheric variability in the Northern Hemisphere that can be de ned as either the Arctic Oscillation or the North Atlantic Oscillation. Agreement between the direction and scale of change in the model and observations, in the absence of interannual forcing from the global ocean thermohaline circulation, suggests that the atmospheric variability by itself is su cient to produce basin-scale changes in the Arctic Ocean and sea ice system.

The freshwater lens covering the surface of the Arctic Ocean is roughly 50 to 150 meters thick and consists of river runoff, sea-ice meltwater, and low-salinity water of Pacific origin imported through Bering Strait. Whereas salinity data provide us with a good picture of the distribution and variability of the total freshwater contained in the Arctic Ocean, they cannot, in general, distinguish between the individual freshwater components. To obtain this information, measurements of additional water mass properties have to be performed.

The polar regions are experiencing major climate and environmental changes due to the combined effects of natural variability and global warming. To address regional Arctic climate processes and their global feedbacks, 53 experts from the United States, Canada, Europe, and Russia gathered for a recent workshop at the Alfred Wegener Institute for Polar and Marine Research, in Potsdam, Germany. The workshop, which was organized by Klaus Dethloff and Annette Rinke, focused on the use of regional models of the Arctic, global coupled climate models, and Arctic impact studies. This article summarizes the main advances and outstanding issues in Arctic modeling that were presented and discussed during the workshop.

The Arctic Ocean is an important component of the global climate system. The processes occurring in the Arctic Ocean affect the rate of deep and bottom water formation in the convective regions of the high North Atlantic and influence ocean circulation across the globe. This fact is highlighted by global climate modeling studies that consistently show the Arctic to be one of the most sensitive regions to climate change. But an identification of the differences among models and model systematic errors in the Arctic Ocean remains unchecked, despite being essential to interpreting the simulation results and their implications for climate variability. For this reason, the Arctic Ocean Model Intercomparison Project (AOMIP), an international effort, was recently established to carry out a thorough analysis of model differences and errors. The geographical focus of this effort is shown in Figure 1.