This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.

The observed retreat and anticipated further decline in Arctic sea ice holds strong climate, environmental, and societal implications. In predicting climate evolution, ensembles of coupled climate models have demonstrated appreciable accuracy in simulating sea-ice area trends throughout the historical period, yet individual climate models still show significant differences in accurately representing the sea-ice thickness distribution. To better understand individual model performance in sea-ice simulation, nine climate models were evaluated in comparison with Arctic satellite and reanalysis-derived sea-ice thickness data, sea-ice area records, and atmospheric reanalysis data of surface wind and air temperature. This assessment found that the simulated spatial distribution of historical sea-ice thickness varies greatly between models and that several key limitations persist among models. Primarily, most models do not capture the thickest regimes of multiyear ice present in the Wandel and Lincoln seas; those that do often possess erroneous positive bias in other regions such as the Laptev Sea or along the Eurasian Arctic Shelf. This analysis provides enhanced understanding of individual model historical simulation performance, which is critical in informing the selection of coupled climate model projections for dependent future modeling efforts.

The global seasonal cycle of energy in Earth's climate system is quantified using observations and reanalyses. After removing long-term trends, net energy entering and exiting the climate system at the top of the atmosphere (TOA) should agree with the sum of energy entering and exiting the ocean, atmosphere, land, and ice over the course of an average year. Achieving such a balanced budget with observations has been challenging. Disagreements have been attributed previously to sparse observations in the high-latitude oceans. However, limiting the local vertical integration of new global ocean heat content estimates to the depth to which seasonal heat energy is stored, rather than integrating to 2000 m everywhere as done previously, allows closure of the global seasonal energy budget within statistical uncertainties. The seasonal cycle of energy storage is largest in the ocean, peaking in April because ocean area is largest in the Southern Hemisphere and the ocean's thermal inertia causes a lag with respect to the austral summer solstice. Seasonal cycles in energy storage in the atmosphere and land are smaller, but peak in July and September, respectively, because there is more land in the Northern Hemisphere, and the land has more thermal inertia than the atmosphere. Global seasonal energy storage by ice is small, so the atmosphere and land partially offset ocean energy storage in the global integral, with their sum matching time-integrated net global TOA energy fluxes over the seasonal cycle within uncertainties, and both peaking in April.

Sea ice, one of the most dominant barriers to Arctic shipping, has decreased dramatically over the past four decades. Arctic maritime transport is hereupon growing in recent years. To produce a long-term assessment of trans-Arctic accessibility, we systematically revisit the daily Arctic navigability with a view to the combined effects of sea ice thickness and concentration throughout the period 1979–2020. The general trends of Navigable Windows (NW) in the Northeast Passage show that the number of navigable days is steadily growing and reached 89±16 days for Open Water (OW) ships and 163±19 days for Polar Class 6 (PC6) ships in the 2010s, despite high interannual and interdecadal variability in the NWs. More consecutive NWs have emerged annually for both OW ships and PC6 ships since 2005 because of the faster sea ice retreat. Since the 1980s, the number of simulated Arctic routes has continuously increased, and optimal navigability exists in these years of record-low sea ice extent (e.g., 2012 and 2020). Summertime navigability in the East Siberian and Laptev Seas, on the other hand, varies dramatically due to changing sea ice conditions. This systematic assessment of Arctic navigability provides a reference for better projecting the future trans-Arctic shipping routes.

The Arctic Ocean has seen a remarkable reduction in sea ice coverage, thickness and age since the 1980s. These changes are most pronounced in the Beaufort Sea, with a transition around 2007 from a regime dominated by multi-year sea ice to one with large expanses of open water during the summer. Using satellite-based observations of sea ice, an atmospheric reanalysis and a coupled ice-ocean model, we show that during the summers of 2020 and 2021, the Beaufort Sea hosted anomalously large concentrations of thick and old ice. We show that ice advection contributed to these anomalies, with 2020 dominated by eastward transport from the Chukchi Sea, and 2021 dominated by transport from the Last Ice Area to the north of Canada and Greenland. Since 2007, cool season (fall, winter, and spring) ice volume transport into the Beaufort Sea accounts for ~45% of the variability in early summer ice volume — a threefold increase from that associated with conditions prior to 2007. This variability is likely to impact marine infrastructure and ecosystems.

Antarctic sea ice is an important component of the Earth system. However, its role in the Earth system is still unclear due to limited Antarctic sea-ice thickness (SIT) data. A reliable sea-ice reanalysis can be useful to study Antarctic SIT and its role in the Earth system. Among various Antarctic sea-ice reanalysis products, the Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output is widely used in the research of Antarctic sea ice. As more Antarctic SIT observations with quality control are being released, a further evaluation of Antarctic SIT from GIOMAS is conducted in this study based on in situ and satellite observations. Generally, though only sea-ice concentration is assimilated, GIOMAS can basically reproduce the observed variability in sea-ice volume and its changes in the trend before and after 2013, indicating that GIOMAS is a good option to study the long-term variation in Antarctic sea ice. However, due to deficiencies in the model and asymmetric changes in SIT caused by assimilation, GIOMAS underestimates Antarctic SIT especially in deformed ice regions, which has an impact on not only the mean state of SIT but also the variability. Thus, besides the further development of the model, assimilating additional sea-ice observations (e.g., SIT and sea-ice drift) with advanced assimilation methods may be conducive to a more accurate estimation of Antarctic SIT.

Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing melt. The spatiotemporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition presented a valuable opportunity to investigate the seasonal evolution of melt ponds through a rich array of atmosphere-ice-ocean measurements across spatial and temporal scales. In this study, we characterize the seasonal behavior and variability in the snow, surface scattering layer, and melt ponds from spring melt to autumn freeze-up using in situ surveys and auxiliary observations. We compare the results to satellite retrievals and output from two models: the Community Earth System Model (CESM2) and the Marginal Ice Zone Modeling and Assimilation System (MIZMAS). During the melt season, the maximum pond coverage and depth were 21% and 22 ± 13 cm, respectively, with distribution and depth corresponding to surface roughness and ice thickness. Compared to observations, both models overestimate melt pond coverage in summer, with maximum values of approximately 41% (MIZMAS) and 51% (CESM2). This overestimation has important implications for accurately simulating albedo feedbacks. During the observed freeze-up, weather events, including rain on snow, caused high-frequency variability in snow depth, while pond coverage and depth remained relatively constant until continuous freezing ensued. Both models accurately simulate the abrupt cessation of melt ponds during freeze-up, but the dates of freeze-up differ. MIZMAS accurately simulates the observed date of freeze-up, while CESM2 simulates freeze-up one-to-two weeks earlier. This work demonstrates areas that warrant future observation-model synthesis for improving the representation of sea-ice processes and properties, which can aid accurate simulations of albedo feedbacks in a warming climate.

Predictability of seasonal sea ice advance in the Chukchi Sea has been investigated in the context of ocean heat transport from the Bering Strait; however, the underlying physical processes have yet to be fully clarified. Using the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS) reanalysis product (1979–2016), we examined seasonal predictability of sea ice advance in early winter (November–December) and its source using canonical correlation analysis. It was found that 2-month leading (September–October) surface heat flux and ocean heat advection is the major predictor for interannual variability of sea ice advance. Surface heat flux is related to the atmospheric cooling process, which has influenced sea ice area in the southeastern Chukchi Sea particularly in the 1980s and 1990s. Anomalous surface heat flux is induced by strong northeasterly winds related to the east Pacific/North Pacific teleconnection pattern. Ocean heat advection, which is related to fluctuation of volume transport in the Bering Strait, leads to decrease in the sea ice area in the northwestern Chukchi Sea. Diagnostic analysis revealed that interannual variability of the Bering Strait volume transport is governed by arrested topographic waves (ATWs) forced by southeasterly wind stress along the shelf of the East Siberian Sea. The contribution of ocean heat flux to sea ice advance has increased since the 2000s; therefore, it is suggested that the major factor influencing interannual variability of sea ice advance in early winter has shifted from atmospheric cooling to ocean heat advection processes.

Ocean-to-ice heat flux (OHF) is important in regulating the variability of sea ice mass balance. Using surface drifting buoy observations, we show that during winter in the Arctic Ocean's Beaufort Gyre region, OHF increased from 0.76 ± 0.05 W/m² over 2006–2012 to 1.63 ± 0.08 W/m² over 2013–2018. We find that this is a result of thinner and less-compact sea ice that promotes enhanced winter ice growth, stronger ocean vertical convection, and subsurface heat entrainment. In contrast, Ekman upwelling declined over the study period, suggesting it had a secondary contribution to OHF changes. The enhanced ice growth creates a cooler, saltier, and deeper ocean surface mixed layer. In addition, the enhanced vertical temperature gradient near the mixed layer base in later years favors stronger entrainment of subsurface heat. OHF and its increase during 2006–2018 were not geographically uniform, with hot spots found in an upwelling region where ice was most seasonally variable.

A model study shows that the decline of Arctic sea ice volume (SIV) slows down during 2007–2020 with increasingly warm atmospheric and oceanic conditions. The slowdown of the SIV decline is because the decrease in ice export from the Arctic exceeds the decrease in net ice production within the Arctic. The relatively strong decrease in ice export occurs when the increase in ice motion is lower than the decrease in SIV. The relatively weak decrease in net ice production is due to strong increases in ice growth as thinner ice grows faster than thicker ice under freezing conditions. The ice growth increases are closely correlated with and benefit from increases in open water creation caused by enhanced ice divergence and shear as thinner ice is easier to deform.

Although considerable work has been done in the Chukchi Sea during summer, much less has been done during other seasons. This has limited our ability to fully understand seasonal cycles and transformations of the Chukchi Sea zooplankton, particularly the key copepod species Calanus glacialis. Abundance and distributions of large zooplankton and of all life stages of C. glacialis in the northeastern Chukchi Sea during May–June 2014 are described. Three main zooplankton communities are identified; "arctic oceanic"” along the Chukchi slope associated with off-shelf water masses; Chukchi Sea "overwintering" associated with cold winter water in the northern part of the study area; and Chukchi Sea "spring" associated with early season summer water in the southern portion of the study area. The overwintering and spring communities were distinguished by the near total absence of younger copepodid (CI–CIII) C. glacialis stages and meroplankton in the overwintering community while older (CV–adult) C. glacialis, amphipods, and chaetognaths were present in both. The distributions of the communities followed the major circulation pathways in the northeastern Chukchi Sea. Water and plankton flooding in from the northern Bering Sea was filling the Chukchi Sea and replenishing the zooplankton communities as the remnants of the overwintering community was being advected northwards and into the Canadian Basin. A conceptual model of the seasonal evolution of C. glacialis populations in the Chukchi Sea, based on the interaction of C. glacialis phenology and advective drivers, enfolds both these spring observations and summer observations from numerous previous studies. Because the flushing time of the Chukchi Sea is shorter than the C. glacialis generation time, the copepod is unable to establish an endemic population in the Chukchi Sea, hence the population there must be renewed annually from the northern Bering Sea.

The Arctic Ocean's Wandel Sea is the easternmost sector of the Last Ice Area, where thick, old sea ice is expected to endure longer than elsewhere. Nevertheless, in August 2020 the area experienced record-low sea ice concentration. Here we use satellite data and sea ice model experiments to determine what caused this record sea ice minimum. In our simulations there was a multi-year sea-ice thinning trend due to climate change. Natural climate variability expressed as wind-forced ice advection and subsequent melt added to this trend. In spring 2020, the Wandel Sea had a mixture of both thin and — unusual for recent years — thick ice, but this thick ice was not sufficiently widespread to prevent the summer sea ice concentration minimum. With continued thinning, more frequent low summer sea ice events are expected. We suggest that the Last Ice Area, an important refuge for ice-dependent species, is less resilient to warming than previously thought.

We investigate how sea ice decline in summer and warmer ocean and surface temperatures in winter affect sea ice growth in the Arctic. Sea ice volume changes are estimated from satellite observations during winter from 2002 to 2019 and partitioned into thermodynamic growth and dynamic volume change. Both components are compared to validated sea ice-ocean models forced by reanalysis data to extend observations back to 1980 and to understand the mechanisms that cause the observed trends and variability. We find that a negative feedback driven by the increasing sea ice retreat in summer yields increasing thermodynamic ice growth during winter in the Arctic marginal seas eastward from the Laptev Sea to the Beaufort Sea. However, in the Barents and Kara Seas, this feedback seems to be overpowered by the impact of increasing oceanic heat flux and air temperatures, resulting in negative trends in thermodynamic ice growth of –2 km³month^-1yr^-1 on average over 2002–2019 derived from satellite observations.

The Distributed Biological Observatory (DBO) was established to detect environmental changes in the Pacific Arctic by regular monitoring of biophysical responses in each of 8 DBO regions. Here we examine the occurrence of bowhead and beluga whale vocalizations in the western Beaufort Sea acquired by acoustic instruments deployed from September 2008 – July 2014 and September 2016 – October 2018 to examine inter-annual variability of these Arctic endemic species in DBO Region 6. Acoustic data were collected on an oceanographic mooring deployed in the Beaufort shelfbreak jet at ~71.4°N, 152.0°W. Spectrograms of acoustic data files were visually examined for the presence or absence of known signals of bowhead and beluga whales. Weekly averages of whale occurrence were compared with outputs of zooplankton, temperature and sea ice from the BIOMAS model to determine if any of these variables influenced whale occurrence. In addition, the dates of acoustic whale passage in the spring and fall were compared to annual sea ice melt-out and freeze-up dates to examine changes in phenology. Neither bowhead nor beluga whale migration times changed significantly in spring, but bowhead whales migrated significantly later in fall from 2008–2018. There were no clear relationships between bowhead whales and the environmental variables, suggesting that the DBO 6 region is a migratory corridor, but not a feeding hotspot, for this species. Surprisingly, beluga whale acoustic presence was related to zooplankton biomass near the mooring, but this is unlikely to be a direct relationship: there are likely interactions of environmental drivers that result in higher occurrence of both modeled zooplankton and belugas in the DBO 6 region. The environmental triggers that drive the migratory phenology of the two Arctic endemic cetacean species likely extend from Bering Sea transport of heat, nutrients and plankton through the Chukchi and into the Beaufort Sea.

Benthic biological hotspots with persistently high macrofaunal biomass exist on the highly advective continental shelf that extends from the northern Bering Sea to the northeast Chukchi Sea. Environmental factors that influence carbon export to the benthos, a key driver for hotspot formation and persistence, remain uncertain. Multiple modeling approaches were used to better understand the combined effects of biological production and physical transport processes on supplying biogenic materials to those biological hotspots. Large data sets of benthic and environmental observations were synthesized, outputs from a pan-arctic ice-ocean-biogeochemical model were analyzed, and particle tracking modeling experiments and statistical analyses were conducted. Two different biophysical mechanisms of biogenic material supply to five benthic hotspot subdomains over a latitudinal range were identified using models and verified using data synthesis. Two hotpots to the south and the north of Bering Strait and the third one in southern Barrow Canyon heavily rely on carbon supplied from upstream biological production. In contrast, the St. Lawrence Island Polynya, southwest of St. Lawrence Island in the northern Bering Sea, and the Northeast Chukchi Sea hotspots are mostly fueled by local production. Spatial statistical modeling of benthic biomass distribution generally recaptured known hotspots but also suggested the likelihood of other probable hotspots in subregions of the biologically productive Gulf of Anadyr and of the topographically controlled Herald Canyon where limited sampling has occurred. The study provides new mechanistic understandings of the oceanographic processes and biophysical interactions that produce organic matter in sea ice and in the water column that subsequently is exported to underlying benthic communities. Combining data synthesis with process-based modeling was critical in understanding the dynamics of these sympagic-pelagic-benthic ecosystems and the potential climate-change-induced ecosystem response in the Pacific Arctic region.

An Arctic sea ice-ocean model is run with three uniform horizontal resolutions (6, 4, and 2 km) and identical sea ice and ocean model parameterizations, including an isotropic viscous-plastic sea ice rheology, a mechanical ice strength parameterization, and an ice ridging parameterization. Driven by the same atmospheric forcing, the three model versions all produce similar spatial patterns and temporal variations of ice thickness and motion fields, resulting in almost identical magnitude and seasonal evolution of total ice volume and mean ice concentration, ice speed, and fractions of ice of various thickness categories over the Arctic Ocean. Increasing model resolution from 6 to 2 km does not significantly improve model performance when compared to NASA IceBridge ice thickness observations. This suggests that the large-scale sea ice properties of the model are insensitive to varying high resolutions within the multifloe scale (2–10 km), and it may be unnecessary to adjust model parameters constantly with increasingly high resolutions. This is also true with models within the aggregate scale (10–75 km), indicating that model parameters used at coarse resolution may be used at high or multiscale resolution. However, even though the three versions all yield similar mean state of sea ice, they differ in representing anisotropic properties of sea ice. While they produce a basic pattern of major sea ice leads similar to satellite observations, their leads are distributed differently in space and time. Without changing model parameters and sea ice spatiotemporal variability, the 2-km resolution model tends to capture more leads than the other two models.

Species distribution models (SDMs) are a widely used tool to estimate and map habitat suitability for wildlife populations. Most studies that model marine mammal density or distributions use oceanographic proxies for marine mammal prey. However, proxies could be a problem for forecasting because the relationships between the proxies and prey may change in a changing climate. We examined the use of model‐derived prey estimates in SDMs using an iconic species, the western Arctic bowhead whale (Balaena mysticetus).

We used Biology Ice Ocean Modeling and Assimilation System (BIOMAS) to simulate ocean conditions important to western Arctic bowhead whales, including important prey species. Using both static and dynamic predictors, we applied Maxent and boosted regression tree (BRT) SDMs to predict bowhead whale habitat suitability on an 8‐day timescale. We compared results from models that used bathymetry with those that used only BIOMAS simulated variables.

The best model included bathymetry and BIOMAS variables. Inclusion of dynamic variables in SDMs produced predictions that reflected temporal dynamics evident from the survey data. Bathymetry was the most influential variable in models that included that variable. Zooplankton was the most important variable for models that did not include bathymetry. Models with bathymetry performed slightly better than models with only BIOMAS derived variables.

Bathymetry and modelled zooplankton were the most important predictor variables in bowhead whale distribution models. Our predictions reflected within‐year variability in bowhead whale habitat suitability. Using modelled prey availability, rather than oceanographic proxies, could be important for forecasting species distributions. Predictor variables used in our study were derived from a biophysical ocean model with demonstrated ability to project future ocean conditions. A natural next step is to use output from our biophysical ocean model to understand the effects of Arctic climate change.

The recent dramatic decline of sea ice in the western Arctic Ocean changes the transfer of momentum across the ice‐ocean boundary layer. The surface stress energy input through the surface geostrophic current in the Beaufort Gyre (BG) based on a numerical model is 0.03 mW/m² in 1992––2004 versus 0.23 mW/m² in 2005–2017. This energy input is primarily concentrated over the southern Canada Basin and the Chukchi Sea. It is 1.38 x 10¹⁶ J in observations versus 4.90 × 10¹⁶ J in the model in the BG during 2003–2014. We find that some well‐known freshwater changes in the BG over 1992–2017 resulted from episodic extrema of energy input in 2007, 2012, and 2016. In particular, most of the energy input in 2007 was transformed into potential energy (57%) which resulted in a new state of freshwater budget. Our study suggests that as of 2016, the BG had not yet reached a saturated freshwater state. Our results provide a way to predict the future changes of BG freshwater content.

The Arctic Ocean's oldest and thickest sea ice lies along the ~2,000 km arc from the western Canadian Arctic Archipelago to the northern coast of Greenland. Climate models suggest that this region will be the last to lose its perennial ice cover, thus providing an important refuge for ice‐dependent species. However, remarkably little is known about the climate or characteristics of the sea ice in this remote and inhospitable region. Here, we use the Pan‐Arctic Ice Ocean Modeling and Assimilation System model to show that the ice cover in the region is very dynamic, with changes occurring at a rate twice that of the Arctic Ocean as a whole. However, there are some differences in the changing nature of the ice cover between the eastern and western regions of the Last Ice Area, which include different timing of the annual minimum in ice thickness as well as distinct ice motion patterns associated with ice thickness extrema.

An ensemble based Sea Ice Seasonal Prediction System (SISPS) is configured towards operationally predicting the Arctic summer sea ice conditions. SISPS runs as a pan-Arctic sea ice–ocean coupled model based on Massachusetts Institute of Technology general circulation model (MITgcm). A 4-month hindcast is carried out by SISPS starting from May 25, 2016. The sea ice–ocean initial fields for each ensemble member are from corresponding restart files from an ensemble data assimilation system that assimilates near-real-time Special Sensor Microwave Imager Sounder (SSMIS) sea ice concentration, Soil Moisture and Ocean Salinity (SMOS) and CryoSat-2 ice thickness. An ensemble of 11 time lagged operational atmospheric forcing from the National Center for Environmental Prediction (NCEP) climate forecast system model version 2 (CFSv2) is used to drive the ice–ocean model. Comparing with the satellite based sea ice observations and reanalysis data, the SISPS prediction shows good agreement in the evolution of sea ice extent and thickness, and performs much better than the CFSv2 operational sea ice prediction. This can be largely attributed to the initial conditions that we used in assimilating the SMOS and CryoSat-2 sea ice thickness data, thereafter reduces the initial model bias in the basin wide sea ice thickness, while in CFSv2 there is no sea ice thickness assimilation. Furthermore, comparisons with sea ice predictions driven by deterministic forcings demonstrate the importance of employing an ensemble approach to capture the large prediction uncertainty in Arctic summer. The sensitivity experiments also show that the sea ice thickness initialization that has a long-term memory plays a more important role than sea ice concentration and sea ice extent initialization on seasonal sea ice prediction. This study shows a good potential to implement Arctic sea ice seasonal prediction using the current configuration of ensemble system.

PIOMAS-20C, an Arctic sea ice reconstruction for 1901–2010, is produced by forcing the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) with ERA-20C atmospheric data. ERA-20C performance over Arctic sea ice is assessed by comparisons with measurements and data from other reanalyses. ERA-20C performs similarly with respect to the annual cycle of downwelling radiation, air temperature, and wind speed compared to reanalyses with more extensive data assimilation such as ERA-Interim and MERRA. PIOMAS-20C sea ice thickness and volume are then compared with in situ and aircraft remote sensing observations for the period of ~1950–2010. Error statistics are similar to those for PIOMAS. We compare the magnitude and patterns of sea ice variability between the first half of the twentieth century (1901–40) and the more recent period (1980–2010), both marked by sea ice decline in the Arctic. The first period contains the so-called early-twentieth-century warming (ETCW; ~1920–40) during which the Atlantic sector saw a significant decline in sea ice volume, but the Pacific sector did not. The sea ice decline over the 1979–2010 period is pan-Arctic and 6 times larger than the net decline during the 1901–40 period. Sea ice volume trends reconstructed solely from surface temperature anomalies are smaller than PIOMAS-20C, suggesting that mechanisms other than warming, such as changes in ice motion and deformation, played a significant role in determining sea ice volume trends during both periods.

Sea ice leads are an important feature in pack ice in the Arctic. Even covered by thin ice, leads can still serve as prime windows for heat exchange between the atmosphere and the ocean, especially in the winter. Lead geometry and distribution in the Arctic have been studied using optical and microwave remote sensing data, but turbulent heat flux over leads has only been measured on-site during a few special expeditions. In this study, we derive turbulent heat flux through leads at different scales using a combination of surface temperature and lead distribution from remote sensing images and meteorological parameters from a reanalysis dataset. First, ice surface temperature (IST) was calculated from Landsat-8 Thermal Infrared Sensor (TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images using a split-window algorithm; then, lead pixels were segmented from colder ice. Heat flux over leads was estimated using two empirical models: bulk aerodynamic formulae and a fetch-limited model with lead width from Landsat-8. Results show that even though the lead area from MODIS is a little larger, the length of leads is underestimated by 72.9% in MODIS data compared to TIRS data due to the inability to resolve small leads. Heat flux estimated from Landsat-8 TIRS data using bulk formulae is 56.70% larger than that from MODIS data. When the fetch-limited model was applied, turbulent heat flux calculated from TIRS data is 32.34% higher than that from bulk formulae. In both cases, small leads accounted for more than a quarter of total heat flux over leads, mainly due to the large area, though the heat flux estimated using the fetch-limited model is 41.39% larger. A greater contribution from small leads can be expected with larger air–ocean temperature differences and stronger winds.

Pacific Winter Water (PWW) enters the western Arctic Ocean from the Chukchi Sea; however, the physical mechanisms that regulate its circulation within the deep basin are still not clear. Here, we investigate the interannual variability of PWW with a comprehensive data set over a decade. We quantify the thickening and expansion of the PWW layer during 2002–2016, as well as its changing pathway. The total volume of PWW in the Beaufort Gyre (BG) region is estimated to have increased from 3.48 ± 0.04 x 10¹⁴ m³ during 2002–2006 to 4.11 ± 0.02 x 10¹⁴ m³ during 2011–2016, an increase of 18%. We find that the deepening rate of the lower bound of PWW is almost double that of its upper bound in the northern Canada Basin, a result of lateral flux convergence of PWW (via lateral advection of PWW from the Chukchi Borderland) in addition to the Ekman pumping. In particular, of the 70‐m deepening of PWW at its lower bound observed over 2003–2011 in the northwestern basin, 43% resulted from lateral flux convergence. We also find a redistribution of PWW in recent years toward the Chukchi Borderland associated with the wind‐driven spin‐up and westward shift of the BG. Finally, we hypothesize that a recently observed increase of lower halocline eddies in the BG might be explained by this redistribution, through a compression mechanism over the Chukchi Borderland.

During late February and early March 2018, an unusual polynya was observed off the north coast of Greenland. This period was also notable for the occurrence of a sudden stratospheric warming. Here we use satellite and in situ data, a reanalysis and an ice‐ocean model to document the evolution of the polynya and its synoptic forcing. We show that its magnitude was unprecedented and that it was associated with the transient response to the sudden stratospheric warming leading to anomalous warm southerly flow in north Greenland. Indeed, regional wind speeds and temperatures were the highest during February going back to the 1960s. There is evidence that the thinning sea ice has increased its wind‐driven mobility. However, we show that the polynya would have developed under thicker ice conditions representative of the late 1970s and that even with the predicted trend toward thinner sea ice, it will only open during enhanced southerly flow.

A melt pond (MP) distribution equation has been developed and incorporated into the Marginal Ice‐Zone Modeling and Assimilation System to simulate Arctic MPs and sea ice over 1979–2016. The equation differs from previous MP models and yet benefits from previous studies for MP parameterizations as well as a range of observations for model calibration. Model results show higher magnitude of MP volume per unit ice area and area fraction in most of the Canada Basin and the East Siberian Sea and lower magnitude in the central Arctic. This is consistent with Moderate Resolution Imaging Spectroradiometer observations, evaluated with Measurements of Earth Data for Environmental Analysis (MEDEA) data, and closely related to top ice melt per unit ice area. The model simulates a decrease in the total Arctic sea ice volume and area, owing to a strong increase in bottom and lateral ice melt. The sea ice decline leads to a strong decrease in the total MP volume and area. However, the Arctic‐averaged MP volume per unit ice area and area fraction show weak, statistically insignificant downward trends, which is linked to the fact that MP water drainage per unit ice area is increasing. It is also linked to the fact that MP volume and area decrease relatively faster than ice area. This suggests that overall the actual MP conditions on ice have changed little in the past decades as the ice cover is retreating in response to Arctic warming, thus consistent with the Moderate Resolution Imaging Spectroradiometer observations that show no clear trend in MP area fraction over 2000–2011.

Life history strategies such as multiyear life cycles, resting stages, and capital breeding allow species to inhabit regions with extreme and fluctuating environmental conditions. One example is the zooplankton species Calanus hyperboreus, whose life history is considered an adaptation to the short and unpredictable growth season in the central Arctic Ocean. This copepod is commonly described as a true Arctic endemic; however, by statistically analyzing compiled observational data, we show that abundances are relatively low and later stages and adults dominate in the central Arctic Ocean basins, indicating expatriation. Combining data analyses with individual‐based modeling and energy requirement estimation, we further demonstrate that while C. hyperboreus can reach higher abundances in areas with greater food availability outside the central Arctic basins, the species' resilience to environmental fluctuations enables the life cycle to be completed in the central Arctic basins. Specifically, the energy level required to reach the first overwintering stage — a prerequisite for successful local production — is likely met in some — but not all — years. This fine balance between success and failure indicates that C. hyperboreus functions as a peripheral population in the central Arctic basins and its abundance will likely increase in areas with improved growth conditions in response to climate change. By illustrating a key Arctic species' resilience to extreme and fluctuating environmental conditions, the results of this study have implications for projections of future biogeography and food web dynamics in the Arctic Ocean, a region experiencing rapid warming and sea ice loss.

Satellite remote sensing provides new insight into the large-scale changes within the Arctic sea ice cover. In this study, satellite-derived sea ice parameters (thickness and age) were explored to investigate age-dependent Arctic sea ice volume changes. Between 2003–2008 (ICESat) and 2011–2015 (CyroSat-2), Arctic Ocean sea ice experienced a net depletion of roughly 4.68 x 10³ km³ during autumn (October–November) and about 87% (or 4.11 x 10³ km³) is caused by the removal in multiyear ice (two years and older). In spring (February–March), the net ice depletion amounts to 1.46 x 10³ km³, with the multiyear ice loss of 3.74 x 10³ km³ and seasonal ice increment of 2.24 x 10³ km³. Among multiyear ice loss, about 74% (autumn) and 93% (spring) of the loss were attributable to the depletion of the oldest ice type (5 years and older). Analyses also affirm that the marvelous volume loss of multiyear ice during cold months (October–May) in 2006/2007 and 2011/2012, along with the low replenishment of perennial ice as noted in the following autumns in 2007 and 2012, plays a major role in leading to a younger Arctic sea ice cover. Consequently, these processes together favors for the overall substantial volume loss observed in the Arctic sea ice cover.

The winter Arctic atmosphere is under the influence of two very different circulation systems: extratropical cyclones travel along the primary North Atlantic storm track from Iceland toward the eastern Arctic, while the western Arctic is characterized by a quasi‐stationary region of high pressure known as the Beaufort High. The winter (January through March) of 2017 featured an anomalous reversal of the normally anticyclonic surface winds and sea ice motion in the western Arctic. This reversal can be traced to a collapse of the Beaufort High as the result of the intrusion of low‐pressure systems from the North Atlantic, along the East Siberian Coast, into the Arctic Basin. Thin sea ice as the result of an extremely warm autumn (October through December) of 2016 contributed to the formation of an anomalous thermal low over the Barents Sea that, along with a northward shift of the tropospheric polar vortex, permitted this intrusion. The collapse of the Beaufort High during the winter of 2017 was associated with simultaneous 2‐sigma sea level pressure, surface wind, and sea ice circulation anomalies in the western Arctic. As the Arctic sea ice continues to thin, such reversals may become more common and impact ocean circulation, sea ice, and biology.

Dramatic changes have occurred in the Arctic Ocean over the past few decades, especially in terms of sea ice loss and ocean warming. Those environmental changes may modify the planktonic ecosystem with changes from lower to upper trophic levels. This study aimed to understand how the biogeographic distribution of a crucial endemic copepod species, Calanus glacialis, may respond to both abiotic (ocean temperature) and biotic (phytoplankton prey) drivers. A copepod individual-based model coupled to an ice-ocean-biogeochemical model was utilized to simulate temperature- and food-dependent life cycle development of C. glacialis annually from 1980 to 2014. Over the 35-year study period, the northern boundaries of modeled diapausing C. glacialis expanded poleward and the annual success rates of C. glacialis individuals attaining diapause in a circumpolar transition zone increased substantially. Those patterns could be explained by a lengthening growth season (during which time food is ample) and shortening critical development time (the period from the first feeding stage N3 to the diapausing stage C4). The biogeographic changes were further linked to large-scale oceanic processes, particularly diminishing sea ice cover, upper ocean warming, and increasing and prolonging food availability, which could have potential consequences to the entire Arctic shelf/slope marine ecosystems.

Seven different methods, with and without including geostrophic currents, were used to explore Ekman dynamics in the western Arctic Ocean for the period 1992–2014. Results show that surface geostrophic currents have been increasing and are much stronger than Ekman layer velocities in recent years (2003–2014) when the oceanic Beaufort Gyre (BG) is spinning up in the region. The new methods that include geostrophic currents result in more realistic Ekman pumping velocities than a previous iterative method that does not consider geostrophic currents and therefore overestimates Ekman pumping velocities by up to 52% in the central area of the BG over the period 2003–2014. When the BG is spinning up as seen in recent years, geostrophic currents become stronger, which tend to modify the ice‐ocean stress and moderate the wind‐driven Ekman convergence in the Canada Basin. This is a mechanism we have identified to play an important and growing role in stabilizing the Ekman convergence and therefore the BG in recent years. This mechanism may be used to explain three scenarios that describe the interplay of changes in wind forcing, sea ice motion, and geostrophic currents that control the variability of the Ekman dynamics in the central BG during 1992–2014. Results also reveal several upwelling regions in the southern and northern Canada Basin and the Chukchi Abyssal Plain which may play a significant role in physical and biological processes in these regions.

A budget of meteoric water (MW = river runoff, net precipitation minus evaporation, and glacial meltwater) over four regions of the Arctic Ocean is constructed using a simple box model, regional precipitation-evaporation estimates from reanalysis data sets, and estimates of import and export fluxes derived from the literature with a focus on the 2003–2008 period. The budget indicates an approximate/slightly positive balance between MW imports and exports (i.e., no change in storage); thus, the observed total freshwater increase observed during this time period likely resulted primarily from changes in non-MW freshwater components (i.e., increases in sea ice melt or Pacific water and/or a decrease in ice export). Further, our analysis indicates that the MW increase observed in the Canada Basin resulted from a spatial redistribution of MW over the Arctic Ocean. Mean residence times for MW were estimated for the Western Arctic (5–7 years), Eastern Arctic (3–4 years), and Lincoln Sea (1–2 years). The MW content over the Siberian shelves was estimated (~14,000 km³) based on a residence time of 3.5 years. The MW content over the entire Arctic Ocean was estimated to be ≥ 44,000 km³. The MW export through Fram Strait consisted mostly of water from the Eastern Arctic (3237 ±â€‰1370 km³ yr^-1) whereas the export through the Canadian Archipelago was nearly equally derived from both the Western Arctic (1182 ±â€‰534 km³ yr^-1) and Lincoln Sea (972 ±â€‰391 km³ yr^-1).

The ability to forecast sea ice (both extent and thickness) and weather conditions are the major factors when it comes to safe marine transportation in the Arctic Ocean. This paper presents findings focusing on sea ice and weather prediction in the Arctic Ocean for navigation purposes, in particular along the Northeast Passage. Based on comparison with the observed sea ice concentrations for validation, the best performing Earth system models from the Intergovernmental Panel on Climate Change (IPCC) program (CMIP5 — Coupled Model Intercomparison Project phase 5) were selected to provide ranges of potential future sea ice conditions. Our results showed that, despite a general tendency toward less sea ice cover in summer, internal variability will still be large and shipping along the Northeast Passage might still be hampered by sea ice blocking narrow passages. This will make sea ice forecasts on shorter time and space scales and Arctic weather prediction even more important.

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

Potential climate drivers of Arctic tundra vegetation productivity are investigated to understand recent greening and browning trends documented by maximum normalized difference vegetation index (NDVI) (MaxNDVI) and time-integrated NDVI (TI-NDVI) for 1982–2015. Over this period, summer sea ice has continued to decline while oceanic heat content has increased. The increases in summer warmth index (SWI) and NDVI have not been uniform over the satellite record. SWI increased from 1982 to the mid-1990s and remained relatively flat from 1998 onwards until a recent upturn. While MaxNDVI displays positive trends from 1982–2015, TI-NDVI increased from 1982 until 2001 and has declined since. The data for the first and second halves of the record were analyzed and compared spatially for changing trends with a focus on the growing season. Negative trends for MaxNDVI and TI-NDVI were more common during 1999–2015 compared to 1982–1998.

Trend analysis within the growing season reveals that sea ice decline was larger in spring for the 1982–1998 period compared to 1999–2015, while fall sea ice decline was larger in the later period. Land surface temperature trends for the 1982–1998 growing season are positive and for 1999–2015 are positive in May–June but weakly negative in July–August. Spring biweekly NDVI trends are positive and significant for 1982–1998, consistent with increasing open water and increased available warmth in spring. MaxNDVI trends for 1999–2015 display significant negative trends in May and the first half of June.

Numerous possible drivers of early growing season NDVI decline coincident with warming temperatures are discussed, including increased standing water, delayed spring snow-melt, winter thaw events, and early snow melt followed by freezing temperatures. Further research is needed to robustly identify drivers of the spring NDVI decline.

To understand the factors causing the interannual variations in the summer retreat of the Beaufort Sea ice edge, Seasonal Ice Zone Reconnaissance Surveys (SIZRS) aboard U.S. Coast Guard Arctic Domain Awareness flights were made monthly from June to October in 2012, 2013, and 2014. The seasonal ice zone (SIZ) is where sea ice melts and reforms annually and encompasses the nominally narrower marginal ice zone (MIZ) where a mix of open-ocean and ice pack processes prevail. Thus, SIZRS provides a regional context for the smaller-scale MIZ processes. Observations with aircraft expendable conductivity–temperature–depth probes reveal a salinity pattern associated with large-scale gyre circulation and the seasonal formation of a shallow (~20 m) fresh layer moving with the ice edge position. Repeat occupations of the SIZRS lines from 72° to 76°N on 140° and 150°W allow a comparison of observed hydrography to atmospheric indices. Using this relationship, the basinwide salinity signals are separated from the fresh layer associated with the ice edge. While this layer extends northward under the ice edge as the melt season progresses, low salinities and warm temperatures appear south of the edge. Within this fresh layer, average salinity is correlated with distance from the ice edge. The salinity observations suggest that the upper-ocean freshening over the summer is dominated by local sea ice melt and vertical mixing. A Price–Weller–Pinkel model analysis reveals that observed changes in heat content and density structure are also consistent with a 1D mixing process.

A 2-km-resolution model of the eastern Bering Sea is developed to capture dynamical processes on the scale of the Rossby radius of deformation on tidal to seasonal time scales. The model spans the region from 178°E to the Alaskan coast and from roughly 50° to 66°N, including the Aleutian Islands in the south and the Bering Strait in the north. The high resolution throughout ensures that the mesoscale dynamics of significant subregions of the domain, such as the Aleutian Island passes, Bering Sea slope, and the shelf canyons, are captured simultaneously without the concern for loss of interconnectivity between regions. Simulations are performed for the ice-free season (June–October) of 2009, with tidal and atmospheric forcing. The model compares favorably with observations from AVHRR and Envisat satellites, Argo drifters, and Bering Sea shelf moorings. The mesoscale dynamics of the mixing and exchange flow through the eastern Aleutian Island passes, which exhibit strong diurnal and two-week variability, are well represented. The two-week oscillation in volume flux through the largest of these passes, Amukta Pass, is found to be out of phase with the transport through the neighboring passes (e.g., Seguam and Samalga passes). Mesoscale structure is also found to be ubiquitous along the mixing front of the cold pool. Structures at the scale of O(20 km) persist and play a role in determining the pattern of erosion of the water mass as the shelf warms and mixes. On the Bering Sea shelf, tidal motions are dominant, and variability on the horizontal scale of the first-mode internal tide develops (O(30 km)) from the shelf break to the onshore edge of the Bering shelf cold pool.

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

To better simulate the seasonal evolution of sea ice in the Arctic, with particular attention to the marginal ice zone, a sea ice model of the distribution of ice thickness, floe size, and enthalpy was implemented into the Pan-arctic IceOcean Modeling and Assimilation System (PIOMAS). Theories on floe size distribution (FSD) and ice thickness distribution (ITD) were coupled in order to explicitly simulate multicategory FSD and ITD distributions simultaneously. The expanded PIOMAS was then used to estimate the seasonal evolution of the Arctic FSD in 2014 when FSD observations are available for model calibration and validation.

Results indicate that the simulated FSD, commonly described equivalently as cumulative floe number distribution (CFND), generally follows a power law across space and time and agrees with the CFND observations derived from TerraSAR-X satellite images. The simulated power-law exponents also correlate with those derived using MODIS images, with a low mean bias of 2%. In the marginal ice zone, the modeled CFND shows a large number of small floes in winter because of stronger winds acting on thin, weak first-year ice in the ice edge region. In mid-spring and summer, the CFND resembles an upper truncated power law, with the largest floes mostly broken into smaller ones; however, the number of small floes is lower than in winter because floes of small sizes or first-year ice are easily melted away. In the ice pack interior there are fewer floes in late fall and winter than in summer because many of the floes are welded together into larger floes in freezing conditions, leading to a relatively flat CFND with low power-law exponents.

The simulated mean floe size averaged over all ice-covered areas shows a clear annual cycle, large in winter and smaller in summer. However, there is no obvious annual cycle of mean floe size averaged over the marginal ice zone. The incorporation of FSD into PIOMAS results in reduced ice thickness, mainly in the marginal ice zone, which improves the simulation of ice extent and yields an earlier ice retreat.

Early ice retreat and ocean warming are changing various facets of the Arctic marine ecosystem, including the biogeographic distribution of marine organisms. Here an endemic copepod species, Calanus glacialis, was used as a model organism, to understand how and why Arctic marine environmental changes may induce biogeographic boundary shifts. A copepod individual-based model was coupled to an ice-ocean-ecosystem model to simulate temperature- and food-dependent copepod life history development. Numerical experiments were conducted for two contrasting years: a relatively cold and normal sea ice year (2001) and a well-known warm year with early ice retreat (2007). Model results agreed with commonly known biogeographic distributions of C. glacialis, which is a shelf/slope species and cannot colonize the vast majority of the central Arctic basins. Individuals along the northern boundaries of this species' distribution were most susceptible to reproduction timing and early food availability (released sea ice algae). In the Beaufort, Chukchi, East Siberian, and Laptev Seas where severe ocean warming and loss of sea ice occurred in summer 2007, relatively early ice retreat, elevated ocean temperature (about 1–2°C higher than 2001), increased phytoplankton food, and prolonged growth season created favorable conditions for C. glacialis development and caused a remarkable poleward expansion of its distribution. From a pan-Arctic perspective, despite the great heterogeneity in the temperature and food regimes, common biogeographic zones were identified from model simulations, thus allowing a better characterization of habitats and prediction of potential future biogeographic boundary shifts.

A two-way nested 4d-variational data assimilation system is implemented in the Eastern Bering Sea (EBS) to investigate changes in circulation and thermodynamic state for a 3.8 year period. Assimilated observations include data from 19 moorings deployed on the shelf and in the Bering Strait, 1705 hydrographic stations occupied during eight surveys, and remotely sensed sea surface temperature and sea surface height (SSH) data. Validation of the presented 4dVar reanalysis against the output of two sequential data-assimilative systems (the Bering Ecosystem Study ice-ocean Modeling and Assimilation System (BESTMAS) and the Arctic Cap Nowcast-Forecast System (ACNFS)) has shown that the product is more consistent with the observed transports in the Bering Strait and in the EBS interior both in terms of their magnitude and time variability. Analysis of the data-optimized solution quantifies a sequence of wind-forced events that resulted in the anomalous heat and freshwater transports through the Bering Strait, including a 28 day long flow reversal that occurred in November 2009 and carried Siberian Coastal Current water down to the Gulf of Anadyr. Lagrangian study of the Arctic-bound Pacific waters indicates the extreme importance of the cross-shelf exchange along the path of the Bering Slope Current and quantifies the spectrum of residence times for the waters entering EBS through Unimak Pass and through Aleutian passages. Residence times in the EBS cold pool are diagnosed to be 2–3 times longer than those in the surrounding waters.

A new planktonic ecosystem model was constructed for the Eastern Bering Sea based on observations from the 2007–2010 BEST/BSIERP (Bering Ecosystem Study/Bering Sea Integrated Ecosystem Research Program) field program. When run with forcing from a data-assimilative ice-ocean hindcast of 1971–2012, the model performs well against observations of spring bloom time evolution (phytoplankton and microzooplankton biomass, growth and grazing rates, and ratios among new, regenerated, and export production). On the southern middle shelf (57°N, station M2), the model replicates the generally inverse relationship between ice-retreat timing and spring bloom timing known from observations, and the simpler direct relationship between the two that has been observed on the northern middle shelf (62°N, station M8). The relationship between simulated mean primary production and mean temperature in spring (15 February to 15 July) is generally positive, although this was found to be an indirect relationship which does not continue to apply across a future projection of temperature and ice cover in the 2040s. At M2, the leading direct controls on total spring primary production are found to be advective and turbulent nutrient supply, suggesting that mesoscale, wind-driven processes—advective transport and storminess—may be crucial to long-term trends in spring primary production in the southeastern Bering Sea, with temperature and ice cover playing only indirect roles. Sensitivity experiments suggest that direct dependence of planktonic growth and metabolic rates on temperature is less significant overall than the other drivers correlated with temperature described above.

Previous observational studies have found increasing primary production (PP) in response to declining sea ice cover in the Arctic Ocean. In this study, under-ice PP was assessed based on three coupled ice-ocean-ecosystem models participating in the Forum for Arctic Modeling and Observational Synthesis (FAMOS) project. All models showed good agreement with under-ice measurements of surface chlorophyll-a concentration and vertically integrated PP rates during the main under-ice production period, from mid-May to September. Further, modeled 30-year (1980–2009) mean values and spatial patterns of sea ice concentration compared well with remote sensing data. Under-ice PP was higher in the Arctic shelf seas than in the Arctic Basin, but ratios of under-ice PP over total PP were spatially correlated with annual mean sea ice concentration, with higher ratios in higher ice concentration regions. Decreases in sea ice from 1980 to 2009 were correlated significantly with increases in total PP and decreases in the under-ice PP/total PP ratio for most of the Arctic, but nonsignificantly related to under-ice PP, especially in marginal ice zones. Total PP within the Arctic Circle increased at an annual rate of between 3.2 and 8.0 Tg C/yr from 1980 to 2009. This increase in total PP was due mainly to a PP increase in open water, including increases in both open water area and PP rate per unit area, and therefore much stronger than the changes in under-ice PP. All models suggested that, on a pan-Arctic scale, the fraction of under-ice PP declined with declining sea ice cover over the last three decades.

Arctic sea ice drift forecasts of 6 h – 9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h – 8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high-resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km x 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast.

We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed –1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

It has been suggested previously that the Atlantic Meridional Overturning Circulation (AMOC) anomaly associated with changes in the North Atlantic Deep Water formation propagates southward with an advection speed north of 34°N. In this study, using Geophysical Fluid Dynamics Laboratory Coupled Model version 2.1 (GFDL CM2.1), we show that this slow southward propagation of the AMOC anomaly is crucial for the evolution and the enhanced decadal predictability of the AMOC fingerprint — the leading mode of upper ocean heat content (UOHC) in the extratropical North Atlantic. A positive AMOC anomaly in northern high latitudes leads to a convergence/divergence of the Atlantic meridional heat transport (MHT) anomaly in the subpolar/Gulf Stream region, thus warming in the subpolar gyre (SPG) and cooling in the Gulf Stream region after several years. Recent decadal prediction studies successfully predicted the observed warm shift in the SPG in the mid-1990s. Our results here provide the physical mechanism for the enhanced decadal prediction skills in the SPG UOHC.

To better describe the state of sea ice in the marginal ice zone (MIZ) with floes of varying thicknesses and sizes, both an ice thickness distribution (ITD) and a floe size distribution (FSD) are needed. In this work, we have developed a FSD theory that is coupled to the ITD theory of Thorndike et al. (1975) in order to explicitly simulate the evolution of FSD and ITD jointly. The FSD theory includes a FSD function and a FSD conservation equation in parallel with the ITD equation. The FSD equation takes into account changes in FSD due to ice advection, thermodynamic growth, and lateral melting. It also includes changes in FSD because of mechanical redistribution of floe size due to ice ridging and, particularly, ice fragmentation induced by stochastic ocean surface waves. The floe size redistribution due to ice fragmentation is based on the assumption that wave-induced breakup is a random process such that when an ice floe is broken, floes of any smaller sizes have an equal opportunity to form, without being either favored or excluded. To focus only on the properties of mechanical floe size redistribution, the FSD theory is implemented in a simplified ITD and FSD sea ice model for idealized numerical experiments. Model results show that the simulated cumulative floe number distribution (CFND) follows a power law as observed by satellites and airborne surveys. The simulated values of the exponent of the power law, with varying levels of ice breakups, are also in the range of the observations. It is found that floe size redistribution and the resulting FSD and mean floe size do not depend on how floe size categories are partitioned over a given floe size range. The ability to explicitly simulate multicategory FSD and ITD together may help to incorporate additional model physics, such as FSD-dependent ice mechanics, surface exchange of heat, mass, and momentum, and wave-ice interactions.

We examine the anthropogenically forced climate response for the 21st century representative concentration pathway (RCP) emission scenarios and their extensions for the period 2101–2500. The experiments were performed with ModelE2, a new version of the NASA Goddard Institute for Space Sciences (GISS) coupled general circulation model that includes three different versions for the atmospheric composition components: a noninteractive version (NINT) with prescribed composition and a tuned aerosol indirect effect (AIE), the TCAD version with fully interactive aerosols, whole-atmosphere chemistry, and the tuned AIE, and the TCADI version which further includes a parameterized first indirect aerosol effect on clouds. Each atmospheric version is coupled to two different ocean general circulation models: the Russell ocean model (GISS-E2-R) and HYCOM (GISS-E2-H). By 2100, global mean warming in the RCP scenarios ranges from 1.0 to 4.5°C relative to 1850%u20131860 mean temperature in the historical simulations. In the RCP2.6 scenario, the surface warming in all simulations stays below a 2°C threshold at the end of the 21st century. For RCP8.5, the range is 3.5%–4.5°C at 2100. Decadally averaged sea ice area changes are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with a larger sensitivity during winter months. By the year 2500, there are complete recoveries of the globally averaged surface air temperature for all versions of the GISS climate model in the low-forcing scenario RCP2.6. TCADI simulations show enhanced warming due to greater sensitivity to CO2, aerosol effects, and greater methane feedbacks, and recovery is much slower in RCP2.6 than with the NINT and TCAD versions. All coupled models have decreases in the Atlantic overturning stream function by 2100. In RCP2.6, there is a complete recovery of the Atlantic overturning stream function by the year 2500 while with scenario RCP8.5, the E2-R climate model produces a complete shutdown of deep water formation in the North Atlantic.

The seasonal evolution of sea ice loss in the Beaufort Sea during 1979–2012 is examined, focusing on differences between eastern and western sectors. Two stages in ice loss are identified: the Day of Opening (DOO) is defined as the spring decrease in ice concentration from its winter maximum below a value of 0.8 areal concentration; the Day of Retreat (DOR) is the summer decrease below 0.15 concentration. We consider three aspects of the subject, i.e., (i) the long-term mean, (ii) long-term linear trends, and (iii) interannual variability. We find that in the mean, DOO occurs earliest in the eastern Beaufort Sea (EBS) owing to easterly winds which act to thin the ice there, relative to the western Beaufort Sea (WBS) where ice has been generally thicker. There is no significant long-term trend in EBS DOO, although WBS DOO is in fact trending toward earlier dates. This means that spatial differences in DOO across the Beaufort Sea have been shrinking over the past 33 years, i.e., these dates are becoming more synchronous, a situation which may impact human and marine mammal activity in the area. Retreat dates are also becoming more synchronous, although with no statistical significance over the studied time period. Finally, we find that in any given year, an increase in monthly mean easterly winds of ~1 m/s during spring is associated with earlier summer DOR of 6–15 days, offering predictive capability with 2–4 months lead time.

Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice%u2013ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981–2010: National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP–U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice–ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R = 0.82). The trend in the total ice volume in September is greatest with MERRA (–4.1 ± 10³ km³ decade^-1) and least with CFSR (–2.7 ± 10³ km³ decade^-1).

A numerical ocean sea-ice model is used to demonstrate that Arctic sea ice retreat affects momentum transfer into the ocean. A thinner and thus weaker ice cover is more easily forced by the wind, which increases the momentum flux. In contrast, increasing open water reduces momentum transfer because the ice surface provides greater drag than the open water surface. We introduce the concept of optimal ice concentration: momentum transfer increases with increasing ice concentration up to a point, beyond which frictional losses by floe interaction damp the transfer. For a common ice internal stress formulation, a concentration of 80–90% yields optimal amplification of momentum flux into the ocean. We study the seasonality and long-term evolution of Arctic Ocean surface stress over the years 1979–2012. Spring and fall feature optimal ice conditions for momentum transfer, but only in fall is the wind forcing at its maximum, yielding a peak basin-mean ocean surface stress of ~0.08 N/m². Since 1979, the basin-wide annual mean ocean surface stress has been increasing by 0.004 N/m²/decade, and since 2000 by 0.006 N/m²/decade. In contrast, summertime ocean surface stress has been decreasing at –0.002 N/m²/decade. These trends are linked to the weakening of the ice cover in fall, winter and spring, and to an increase in open water fraction in summer, i.e., changes in momentum transfer rather than changes in wind forcing. In most areas, the number of days per year with optimal ice concentration is decreasing.

Measurements of ocean bottom pressure (OBP) anomalies from the satellite mission Gravity Recovery and Climate Experiment (GRACE), complemented by information from two ocean models, are used to investigate the variations and distribution of the Arctic Ocean mass from 2002 through 2011. The forcing and dynamics associated with the observed OBP changes are explored. Major findings are the identification of three primary temporal–spatial modes of OBP variability at monthly-to-interannual time scales with the following characteristics. Mode 1 (50% of the variance) is a wintertime basin-coherent Arctic mass change forced by southerly winds through Fram Strait, and to a lesser extent through Bering Strait. These winds generate northward geostrophic current anomalies that increase the mass in the Arctic Ocean. Mode 2 (20%) reveals a mass change along the Siberian shelves, driven by surface Ekman transport and associated with the Arctic Oscillation. Mode 3 (10%) reveals a mass dipole, with mass decreasing in the Chukchi, East Siberian, and Laptev Seas, and mass increasing in the Barents and Kara Seas. During the summer, the mass decrease on the East Siberian shelves is due to the basin-scale anticyclonic atmospheric circulation that removes mass from the shelves via Ekman transport. During the winter, the forcing mechanisms include a large-scale cyclonic atmospheric circulation in the eastern-central Arctic that produces mass divergence into the Canada Basin and the Barents Sea. In addition, strengthening of the Beaufort high tends to remove mass from the East Siberian and Chukchi Seas. Supporting previous modeling results, the month-to-month variability in OBP associated with each mode is predominantly of barotropic character.

A global sea ice–ocean model is used to examine the impact of wind intensification on Antarctic sea ice volume. Based on the NCEP/NCAR reanalysis data, there are increases in surface wind speed (0.13% yr^-1) and convergence (0.66% yr^-1) over the ice-covered areas of the Southern Ocean during the period 1979–2010. Driven by the intensifying winds, the model simulates an increase in sea ice speed, convergence, and shear deformation rate, which produces an increase in ridge ice production in the Southern Ocean (1.1% yr^-1). The increased ridged ice production is mostly in the Weddell, Bellingshausen, Amundsen, and Ross Seas where an increase in wind convergence dominates. The increase in ridging production contributes to an increase in the volume of thick ice (thickness > 2 m) in the Southern Ocean, while the volumes of thin ice (thickness < 1 m) and medium thick ice (1 m < thickness < 2 m) remain unchanged over the period 1979–2010. The increase in thick ice leads to an increase in ice volume in the Southern Ocean, particularly in the southern Weddell Sea where a significant increase in ice concentration is observed. The simulated increase in either the thick ice volume (0.91% yr^-1) or total ice volume (0.46% yr^-1) is significantly greater than other ice parameters (simulated or observed) such as ice extent (0.14–0.21% yr^-1) or ice area fraction (0.24–0.28% yr^-1), suggesting that ice volume is a potentially strong measure of change.

A coupled biophysical model is used to examine the impact of the great Arctic cyclone of early August 2012 on the marine planktonic ecosystem in the Pacific sector of the Arctic Ocean (PSA). Model results indicate that the cyclone influences the marine planktonic ecosystem by enhancing productivity on the shelves of the Chukchi, East Siberian, and Laptev seas during the storm. Although the cyclone's passage in the PSA lasted only a few days, the simulated biological effects on the shelves last 1 month or longer. At some locations on the shelves, primary productivity (PP) increases by up to 90% and phytoplankton biomass by up to 40% in the wake of the cyclone. The increase in zooplankton biomass is up to 18% on 31 August and remains 10% on 15 September, more than 1 month after the storm. In the central PSA, however, model simulations indicate a decrease in PP and plankton biomass. The biological gain on the shelves and loss in the central PSA are linked to two factors. (1) The cyclone enhances mixing in the upper ocean, which increases nutrient availability in the surface waters of the shelves; enhanced mixing in the central PSA does not increase productivity because nutrients there are mostly depleted through summer draw down by the time of the cyclone's passage. (2) The cyclone also induces divergence, resulting from the cyclone's low-pressure system that drives cyclonic sea ice and upper ocean circulation, which transports more plankton biomass onto the shelves from the central PSA. The simulated biological gain on the shelves is greater than the loss in the central PSA, and therefore, the production on average over the entire PSA is increased by the cyclone. Because the gain on the shelves is offset by the loss in the central PSA, the average increase over the entire PSA is moderate and lasts only about 10 days. The generally positive impact of cyclones on the marine ecosystem in the Arctic, particularly on the shelves, is likely to grow with increasing summer cyclone activity if the Arctic continues to warm and the ice cover continues to shrink.

St. Lawrence Island Yupik hunters on St. Lawrence Island, Alaska, take hundreds of Pacific walrus (Odobenus rosmarus divergens) each year. The harvest and associated effort (hunting trips taken), however, are variable from year to year and also from day to day, influenced by physical environmental factors among other variables. We used data from 1996 to 2010 to construct generalized additive models (GAMs) to examine several relationships among the variables. Physical factors explained 18% of the variability in harvest in Savoonga and 25% of the variability in effort; the corresponding figures for Gambell were 24% and 32%. Effort alone explained 63% of the harvest in Savoonga and 59% in Gambell. Physical factors played a relatively smaller role in determining hunting efficiency (walrus taken per hunting trip), explaining 15% of the variability in efficiency in Savoonga and 22% in Gambell, suggesting that physical factors play a larger role in determining whether to hunt than in the outcome of the hunt once undertaken.

Combining physical factors with effort explained 70% of the harvest variability in Savoonga and 66% in Gambell. Although these results indicate that other factors (e.g. fuel prices, socioeconomic conditions) collectively cause a greater share of variability in harvest and effort than ice and wind, at least as indicated by the measures used as predictors in the GAMs, they also suggest that environmental change is also likely to influence future harvest levels, and that climate models that yield appropriately scaled data on ice and wind around St. Lawrence Island may be of use in determining the magnitude and direction of those influences.

Satellite records show a decline in ice extent over more than three decades, with a record minimum in September 2012. Results from the Pan-Arctic Ice-Ocean Modelling and Assimilation system (PIOMAS) suggest that the decline in extent has been accompanied by a decline in volume, but this has not been confirmed by data. Using new data from the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, we generate estimates of ice volume for the winters of 2010/11 and 2011/12. We compare these data with current estimates from PIOMAS and earlier (2003–8) estimates from the National Aeronautics and Space Administration ICESat mission. Between the ICESat and CryoSat-2 periods, the autumn volume declined by 4291 km³ and the winter volume by 1479 km³. This exceeds the decline in ice volume in the central Arctic from the PIOMAS model of 2644 km³ in the autumn, but is less than the 2091 km³ in winter, between the two time periods.

This model study examines the impact of an intense early August cyclone on the 2012 record low Arctic sea ice extent. The cyclone passed when Arctic sea ice was thin and the simulated Arctic ice volume had already declined ~40% from the 2007–2011 mean. The thin sea ice pack and the presence of ocean heat in the near surface temperature maximum layer created conditions that made the ice particularly vulnerable to storms. During the storm, ice volume decreased about twice as fast as usual, owing largely to a quadrupling in bottom melt caused by increased upward ocean heat transport. This increased ocean heat flux was due to enhanced mixing in the oceanic boundary layer, driven by strong winds and rapid ice movement. A comparison with a sensitivity simulation driven by reduced wind speeds during the cyclone indicates that cyclone-enhanced bottom melt strongly reduces ice extent for about two weeks, with a declining effect afterwards. The simulated Arctic sea ice extent minimum in 2012 is reduced by the cyclone, but only by 0.15 x 10⁶ km² (4.4%). Thus without the storm, 2012 would still have produced a record minimum.

Seasonal forecasts of the September 2012 Arctic sea ice thickness and extent are conducted starting from 1 June 2012. An ensemble of forecasts is made with a coupled ice-ocean model. For the first time, observations of the ice thickness are used to correct the initial ice thickness distribution to improve the initial conditions. Data from two airborne campaigns are used: NASA Operation IceBridge and SIZONet. The model was advanced through April and May using reanalysis data from 2012 and for June–September it was forced with reanalysis data from the previous seven summers. The ice extent in the corrected runs averaged lower in the Pacific sector and higher in the Atlantic sector compared to control runs with no corrections. The predicted total ice extent is 4.4 ± 0.5 M km², 0.2 M km² less than that made with the control runs but 0.8 M km² higher than the observed September extent.

Results from a numerical model simulation show significant changes in the dynamic properties of Arctic sea ice during 2007–2011 compared to the 1979–2006 mean. These changes are linked to a 33% reduction in sea ice volume, with decreasing ice concentration, mostly in the marginal seas, and decreasing ice thickness over the entire Arctic, particularly in the western Arctic. The decline in ice volume results in a 37% decrease in ice mechanical strength and 31% in internal ice interaction force, which in turn leads to an increase in ice speed (13%) and deformation rates (17%). The increasing ice speed has the tendency to drive more ice out of the Arctic. However, ice volume export is reduced because the rate of decrease in ice thickness is greater than the rate of increase in ice speed, thus retarding the decline of Arctic sea ice volume. Ice deformation increases the most in fall and least in summer. Thus the effect of changes in ice deformation on the ice cover is likely strong in fall and weak in summer. The increase in ice deformation boosts ridged ice production in parts of the central Arctic near the Canadian Archipelago and Greenland in winter and early spring, but the average ridged ice production is reduced because less ice is available for ridging in most of the marginal seas in fall. The overall decrease in ridged ice production contributes to the demise of thicker, older ice. As the ice cover becomes thinner and weaker, ice motion approaches a state of free drift in summer and beyond and is therefore more susceptible to changes in wind forcing. This is likely to make seasonal or shorter-term forecasts of sea ice edge locations more challenging.

The Marginal Ice Zone (MIZ) intensive field program will employ an array of cutting-edge autonomous platforms to characterize the processes that govern Beaufort Sea MIZ evolution from initial breakup and MIZ formation though the course of the summertime sea ice retreat. Instruments will be deployed on and under the ice prior to initial formation of the MIZ along the Alaska coast, and will continue sampling from open water, across the MIZ, and into full ice cover, as the ice edge retreats northward through the summer. The flexible nature of ice-mounted and mobile, autonomous oceanographic platforms (e.g., gliders and floats) facilitates access to regions of both full ice cover and riskier MIZ regions. This approach exploits the extended endurance of modern autonomous platforms to maintain a persistent presence throughout the entire northward retreat. It also takes advantage of the inherent scalability of these instruments to sample over a broad range of spatial and temporal scales.

We examine the July 2008 to July 2010 circulation over the central Bering Sea shelf using measurements at eight instrumented moorings, hindcast winds and numerical model results. At sub-tidal time scales, the vertically integrated equations of motion show that the cross-shelf balance is primarily geostrophic. The along-shelf balance is also mainly geostrophic, but local accelerations, wind stress and bottom friction account for 10-40% of the momentum balance, depending on season and water depth. The shelf exhibits highly variable flow with small water column average vector mean speeds (< 5 cm s^-1). Mean/peak speeds in summer (3–6 cm s^-1/10–30 cm s^-1) are smaller than in winter and fall (6–12 cm s^-1/30–70 cm s^-1). Low frequency flows (< 1/4 cpd) are horizontally coherent over distances exceeding 200 km. Vertical coherence varies seasonally, degrading with the onset of summer stratification. Because effects of heating and freezing are enhanced in shallow waters, warm summers increase the cross-shelf density gradient and thus enhance northward transport; cold winters with increased ice production and brine rejection increase the (now reversed) cross-shelf density gradient and enhance southward transport. Although the baroclinic velocity is large enough to influence seasonal transports, wind-forced Ekman dynamics are primarily responsible for flow variations. The system changes from strong northward flow (with coastal convergence) to strong southward flow (with coastal divergence) for northerly and easterly winds, respectively. Under northerly and northwesterly winds, nutrient-rich waters flow toward the central shelf from the north and northwest, replacing dilute coastal waters that are carried south and west.

Six AOMIP model simulations are compared with estimates of sea ice thickness derived from pan-arctic satellite freeboard measurements (2004-2008), airborne electromagnetic measurements (2001-2009), ice-draft data from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea (1992- 2008) and from submarines (1975-2000), drill hole data from the Arctic basin, Laptev and East Siberian marginal seas (1982-1986) and coastal stations (1998-2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than ~2 m and underestimate the thickness of ice measured thicker than about ~2 m. In the regions of flat immobile land-fast ice (shallow Siberian Seas with depths less than 25-30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than four times and more than one standard deviation, respectively. The models do not reproduce conditions of fast-ice formation and growth. Instead, the modeled fast-ice is replaced with pack ice which drifts, generates ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the ECCO2 and UW models.

A predictive coastal erosion/shoreline change model has been developed for a small coastal segment near Drew Point, Beaufort Sea, Alaska. This coastal setting has experienced a dramatic increase in erosion since the early 2000's. The bluffs at this site are 3-4 m tall and consist of ice-wedge bounded blocks of fine-grained sediments cemented by ice-rich permafrost and capped with a thin organic layer. The bluffs are typically fronted by a narrow (5 m wide) beach or none at all. During a storm surge, the sea contacts the base of the bluff and a niche is formed through thermal and mechanical erosion. The niche grows both vertically and laterally and eventually undermines the bluff, leading to block failure or collapse. The fallen block is then eroded both thermally and mechanically by waves and currents, which must occur before a new niche forming episode may begin. The erosion model explicitly accounts for and integrates a number of these processes including: (1) storm surge generation due to wind and atmospheric forcing, (2) erosional niche growth due to wave-induced turbulent heat transfer and sediment transport (using the Kobayashi niche erosion model), and (3) thermal and mechanical erosion of the fallen block. The model was calibrated with historic shoreline change data for one time period (1979-2002) and validated with a later time period (2002-2007).

A coupled sea ice–ocean model, combined with observational and reanalysis data, is used to explore the seasonal predictability of the distribution and extent of cold bottom waters on the Bering Sea shelf through numerical simulations or statistical analyses. The model captures the spatiotemporal variability of trawl survey observations of bottom water temperature over the period 1970–2009. Of the various winter air–ice–3ocean parameters considered, the interannual variability of the winter on-shelf heat transport across the Bering Sea shelf break, dominated by changes in ocean flow, is most highly correlated with the interannual variability of the bottom layer properties (bottom temperature, and the distribution and extent of cold bottom waters) in spring–summer. This suggests that the winter heat transport may be the best seasonal predictor of the bottom layer properties. To varying degrees, the winter mean simulated sea surface temperature (SST), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis surface air temperature (SAT), simulated and observed sea ice extent, the Bering Strait outflow, and the Pacific Decadal Oscillation are also significantly correlated with the spring–summer bottom layer properties. This suggests that, with varying skill, they may also be useful for statistical seasonal predictions. Good agreement between observations and results of the coupled ice–ocean model suggests also the possibility of numerical seasonal predictions of the bottom layer properties. The simulated field of bottom layer temperature on the Bering Sea shelf on 31 May is a good predictor of the distribution and extent of cold bottom waters throughout late spring and summer. These variables, both in the model and in reality, do not change significantly from June to October, primarily owing to increased upper ocean stratification in late spring due to ice melt and surface warming, which tends to isolate and preserve the cold bottom waters on the shelf. However, the ocean stratification, and hence the isolation effect, is stronger in cold years than in warm years because more ice is available for melting in spring–summer.

A numerical model is used to investigate the time and space extent of the near-surface temperature maximum (NSTM) of the Canadian Basin of the Arctic Ocean over the years 2000%u20132009. The NSTM is formed from local summertime absorption of solar radiation which, in some circumstances, descends through the fall and early winter to form a warm subsurface layer just below the winter mixed layer. We find that winter survival of this layer is confined largely to the Beaufort Gyre of the Canadian Basin, where Ekman convergence and downwelling push the summer warm layer down below the winter mixing depth. In recent years, summer stratification has increased, downwelling has accelerated, and the NSTM has warmed as the sea ice cover in the Beaufort Gyre has thinned. The result is a strengthening NSTM which contained enough heat by the end of winter 2007/2008 to melt about 20 cm of sea ice. Northwest of Alaska the model also simulates a second, deeper temperature maximum layer that forms from advection of saltier summer Pacific water. However, this layer is difficult to adequately resolve and maintain given the model's resolution.

Uncertainty in the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) Arctic sea ice volume record is characterized. A range of observations and approaches, including in situ ice thickness measurements, ICESat retrieved ice thickness, and model sensitivity studies, yields a conservative estimate for October Arctic ice volume uncertainty of 1.35 x 10^3 km^3 and an uncertainty of the ice volume trend over the 1979-2010 period of 1.0 x 10^3 km^3 decade^-1. A conservative estimate of the trend over this period is ~2.8 x 10^3 km^3 decade^-1. PIOMAS ice thickness estimates agree well with ICESat ice thickness retrievals (<0.1 m mean difference) for the area for which submarine data are available, while difference outside this area are larger. PIOMAS spatial thickness patterns agree well with ICESat thickness estimates with pattern correlations of above 0.8. PIOMAS appears to overestimate thin ice thickness and underestimate thick ice, yielding a smaller downward trend than apparent in reconstructions from observations. PIOMAS ice volume uncertainties and trends are examined in the context of climate change attribution and the declaration of record minima. The distribution of 32 year trends in a preindustrial coupled model simulation shows no trends comparable to those seen in the PIOMAS retrospective, even when the trend uncertainty is accounted for. Attempts to label September minima as new record lows are sensitive to modeling error. However, the September 2010 ice volume anomaly did in fact exceed the previous 2007 minimum by a large enough margin to establish a statistically significant new record.

A sub-monthly mode of non-tidal variability of ocean bottom pressure (OBP) is observed in a 5-year record of deep-sea bottom pressure at the North Pole. OBP records from other regions in the Arctic show that the North Pole non-tidal mass fluctuation is part of a non-propagating basin-coherent variation that is well represented by the ice-ocean model PIOMAS, with a basin-averaged winter-only RMS of 3.3 cm. Wavelet analysis of the modeled OBP shows that the basin-averaged mass variations are non-stationary and only significant during the winter. The basin-averaged OBP is strongly related to the meridional wind component over the Nordic Seas. The ocean response is consistent with episodic wind forcing driving a northward geostrophic slope current. The mass transport anomaly associated with the mode is significant relative to the annual net mean flow.

In this study, we use a numerical sea-ice-ocean model to examine what causes summertime upper ocean warming and sea ice melt during the 21st century in the Arctic Ocean. Our first question is, "What causes the ocean to warm in the Pacific Sector during the summer"? We find that about 80% of total heating over this region comes from ocean surface heat flux, with the remaining 20% originating in ocean lateral heat flux convergence. The latter occurs mostly within a few hundred kilometers of the northwest Alaskan coast. In the summer of 2007, the ocean gained just over twice the amount of heat it did over the average of the previous 7 years. Our second question is, "What causes sea ice to melt in the Pacific Sector during summer"? Our analysis shows that top melt dominates total melt early in the summer, while bottom melt (and in particular, bottom melt due to ocean heat transport) dominates later in the summer as atmospheric heating declines. Bottom melt rates in summer 2007 were 34% higher relative to the previous 7 year average. The modeled partition of top versus bottom melt closely matches observed melt rates obtained by a drifting buoy. Bottom melting contributes about 2/3 of total volume melt but is geographically confined to the Marginal Ice Zone, while top melting contributes a lesser 1/3 of volume melt but occurs over a much broader area of the ice pack.

Numerical experiments are conducted to project arctic sea ice responses to varying levels of future anthropogenic warming and climate variability over 2010–2050. A summer ice-free Arctic Ocean is likely by the mid-2040s if arctic surface air temperature (SAT) increases 4 deg C by 2050 and climate variability is similar to the past relatively warm two decades. If such a SAT increase is reduced by one-half or if a future Arctic experiences a range of SAT fluctuation similar to the past five decades, a summer ice-free Arctic Ocean would be unlikely before 2050. If SAT increases 4 deg C by 2050, summer ice volume decreases to very low levels (10–37% of the 1978–2009 summer mean) as early as 2025 and remains low in the following years, while summer ice extent continues to fluctuate annually. Summer ice volume may be more sensitive to warming while summer ice extent more sensitive to climate variability. The rate of annual mean ice volume decrease relaxes approaching 2050. This is because, while increasing SAT increases summer ice melt, a thinner ice cover increases winter ice growth. A thinner ice cover also results in a reduced ice export, which helps to further slow ice volume loss. Because of enhanced winter ice growth, arctic winter ice extent remains nearly stable and therefore appears to be a less sensitive climate indicator.

We have developed a coupled 3-D pan-Arctic biology/sea ice/ocean model to investigate the impact of declining Arctic sea ice on the marine planktonic ecosystem over 1988–2007. The biophysical model results agree with satellite observations of a generally downward trend in summer sea ice extent during 1988–2007, resulting in an increase in the simulated photosynthetically active radiation (PAR) at the ocean surface and marine primary productivity (PP) in the upper 100 m over open water areas of the Arctic Ocean. The simulated Arctic sea ice thickness has decreased steadily during 1988–2007, leading to an increase in PAR and PP in sea ice-covered areas. The simulated total PAR in all areas of the Arctic Ocean has increased by 43%, from 146 TW in 1988 to 209 TW in 2007; the corresponding total PP has increased by 50%, from 456 Tg C yr^-1 in 1988 to 682 Tg C yr^-1 in 2007. The simulated PAR and PP increases mainly occur in the seasonally and permanently ice-covered Arctic Ocean. In addition to increasing PAR, the decline in sea ice tends to increase the nutrient availability in the euphotic zone by enhancing air-sea momentum transfer, leading to strengthened upwelling and mixing in the water column and therefore increased nutrient input into the upper ocean layers from below. The increasing nutrient availability also contributes to the increase in the simulated PP, even though significant surface nutrient drawdown in summer is simulated. In conjunction with increasing surface absorption of solar radiation and rising surface air temperature, the increasing surface water temperature in the Arctic Ocean peripheral seas further contributes to the increase in PP. As PP has increased, so has the simulated biomass of phytoplankton and zooplankton.

The Chukchi Sea (CS) circulation reconstructed for September 1990 to October 1991 from sea ice and ocean data is presented and analyzed. The core of the observational data used in this study comprises the records from 12 moorings deployed in 1990 and 1991 in U.S. and Russian waters and two hydrographic surveys conducted in the region in the fall of 1990 and 1991. The observations are processed by a two-step data assimilation procedure involving the Pan-Arctic Ice-Ocean Modeling and Assimilation System (employing a nudging algorithm for sea ice data assimilation) and the Semi-implicit Ocean Model [utilizing a conventional four-dimensional variational (4D-var) assimilation technique]. The reconstructed CS circulation is studied to identify pathways and assess residence times of Pacific water in the region; quantify the balances of volume, freshwater, and heat content; and determine the leading dynamical factors configuring the CS circulation.

It is found that in 1990–1991 (high AO index and a cyclonic circulation regime) Pacific water transiting the CS toward the Canada basin followed two major pathways, namely via Herald Canyon (Herald branch of circulation, 0.23 Sv) and between Herald Shoal and Cape Lisburne (central branch of circulation and Alaskan Coastal Current, 0.32 Sv). The annual mean flow through Long Strait was negligible (0.01 Sv). Typical residence time of Pacific water in the region varied between 150 days for waters entering the CS in September and 270 days for waters entering in February/March. Momentum balance analysis reveals that geostrophic balance between barotropic pressure gradient and Coriolis force dominated for most of the year. Baroclinic effects were important for circulation only in the regions with large horizontal salinity gradients associated with the fresh Alaskan and Siberian coastal currents and the Cape Lisburne and Great Siberian polynyas. In the polynyas, the baroclinic effects were due to strong salinification and convection processes associated with sea ice formation.

A coupled sea ice–ocean model is developed to quantify the sea ice response to changes in atmospheric and oceanic forcing in the Bering Sea over the period 1970–2008. The model captures much of the observed spatiotemporal variability of sea ice and sea surface temperature (SST) and the basic features of the upper-ocean circulation in the Bering Sea. Model results suggest that tides affect the spatial redistribution of ice mass by up to 0.1 m or 15% in the central-eastern Bering Sea by modifying ice motion and deformation and ocean flows.

The considerable interannual variability in the pattern and strength of winter northeasterly winds leads to southwestward ice mass advection during January–May, ranging from 0.9 x 10¹² m³ in 1996 to 1.8 x 10¹² m³ in 1976 and averaging 1.4 x 10¹² m³, which is almost twice the January–May mean total ice volume in the Bering Sea. The large-scale southward ice mass advection is constrained by warm surface waters in the south that melt 1.5 x 10¹² m³ of ice in mainly the ice-edge areas during January–May, with substantial interannual variability ranging from 0.94 x 10¹² m³ in 1996 to 2.0 x 10¹² m³ in 1976. Ice mass advection processes also enhance thermodynamic ice growth in the northern Bering Sea by increasing areas of open water and thin ice. Ice growth during January–May is 0.90 x 10¹² m³ in 1996 and 2.1 x 10¹² m³ in 1976, averaging 1.3 x 10¹² m³ over 1970–2008. Thus, the substantial interannual variability of the Bering Sea ice cover is dominated by changes in the wind-driven ice mass advection and the ocean thermal front at the ice edge.

The observed ecological regime shifts in the Bering Sea occurred with significant changes in sea ice, surface air temperature, and SST, which in turn are correlated with the Pacific decadal oscillation over 1970–2008 but not with other climate indices: Arctic Oscillation, North Pacific index, and El Nino–Southern Oscillation. This indicates that the PDO index may most effectively explain the regime shifts in the Bering Sea.

Recent record lows of Arctic summer sea ice extent are found to be triggered by the Arctic atmospheric Dipole Anomaly (DA) pattern. This local, second-leading mode of sea-level pressure (SLP) anomaly in the Arctic produced a strong meridional wind anomaly that drove more sea ice out of the Arctic Ocean from the western to the eastern Arctic into the northern Atlantic during the summers of 1995, 1999, 2002, 2005, and 2007. In the 2007 summer, the DA also enhanced anomalous oceanic heat flux into the Arctic Ocean via Bering Strait, which accelerated bottom and lateral melting of sea ice and amplified the ice–albedo feedback. A coupled ice–ocean model was used to confirm the historical record lows of summer sea ice extent.

Mackenzie River discharge was at a record low in water year (WY) 1995 (October 1994 to September 1995), was near average in WY 1996, and was at a record high in WY 1997. The record high discharge in WY 1997, with above average flow each month, was followed by a record high flow in May 1998, then a sharp decline. Through diagnosing these changing flows and their expression in the Beaufort Sea via synthesis of observations and model output, this study provides insight into the nature of the Arctic's freshwater system.

The low discharge in WY 1995 manifests negative anomalies in P–E and precipitation, recycled summer precipitation, and dry surface conditions immediately prior to the water year. The complex hydrograph for WY 1996 reflects a combination of spring soil moisture recharge, buffering by rising lake levels, positive P–E anomalies in summer, and a massive release of water held in storage by Bennett Dam. The record high discharge in WY 1997 manifests the dual effects of reduced buffering by lakes and positive P–E anomalies for most of the year. With reduced buffering, only modest P–E the following spring led to a record discharge in May 1998. As simulated with a coupled ice–ocean model, the record low discharge in WY 1995 contributed to a negative freshwater anomaly on the Mackenzie shelf lasting throughout the winter of 1995/96. High discharge from July–October 1996 contributed approximately 20% to a positive freshwater anomaly forming in the Beaufort Sea in the autumn of that year. The remainder was associated with reduced autumn/winter ice growth, strong ice melt the previous summer, and positive P–E anomalies over the ocean itself. Starting in autumn 1997 and throughout 1998, the upper ocean became more saline owing to sea ice growth.

The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of –0.57 m decade^-1. Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

Sea ice drift and deformation from coupled ice–ocean models are compared with high-resolution ice motion from the RADARSAT Geophysical Processor System (RGPS). In contrast to buoy drift, the density and extent of the RGPS coverage allows a more extensive assessment and understanding of model simulations at spatial scales from ~10 km to near basin scales and from days to seasonal timescales. This work illustrates the strengths of the RGPS data set as a basis for examining model ice drift and its gradients. As it is not our intent to assess relative performance, we have selected four models with a range of attributes and grid resolution. Model fields are examined in terms of ice drift, export, deformation, deformation-related ice production, and spatial deformation patterns. Even though the models are capable of reproducing large-scale drift patterns, variability among model behavior is high.

When compared to the RGPS kinematics, the characteristics shared by the models are (1) ice drift along coastal Alaska and Siberia is slower, (2) the skill in explaining the time series of regional divergence of the ice cover is poor, and (3) the deformation-related volume production is consistently lower. Attribution of some of these features to specific causes is beyond our current scope because of the complex interplay between model processes, parameters, and forcing. The present work suggests that high-resolution ice drift observations, like those from the RGPS, would be essential for future model developments, improvements, intercomparisons, and especially for evaluation of the small-scale behavior of models with finer grid spacing.

A model study has been conducted of the unprecedented retreat of arctic sea ice in the summer of 2007. It is found that preconditioning, anomalous winds, and ice-albedo feedback are mainly responsible for the retreat. Arctic sea ice in 2007 was preconditioned to radical changes after years of shrinking and thinning in a warm climate. During summer 2007 atmospheric changes strengthened the transpolar drift of sea ice, causing more ice to move out of the Pacific sector and the central Arctic Ocean where the reduction in ice thickness due to ice advection is up to 1.5 m more than usual. Some of the ice exited Fram Strait and some piled up in part of the Canada Basin and along the coast of northern Greenland, leaving behind an unusually large area of thin ice and open water. Thin ice and open water allow more surface solar heating because of a much reduced surface albedo, leading to amplified ice melting. The Arctic Ocean lost additional 10% of its total ice mass in which 70% is due directly to the amplified melting and 30% to the unusual ice advection, causing the unprecedented ice retreat. Arctic sea ice has entered a state of being particularly vulnerable to anomalous atmospheric forcing.

We conduct experiments with an ice-ocean model to answer the question whether and to what degree unusually clear skies during the summer of 2007 contributed to the record sea ice extent minimum in the Arctic Ocean during September of 2007. Anomalously high pressure over the Beaufort Sea during summer 2007 appears associated with a strong negative cloud anomaly. This anomaly is two standard deviations below the 1980–2007 average established from a combination of two different satellite-based records. Cloud anomalies from the MODIS sensor are compared with anomalies from the NCEP/NCAR reanalysis and are found in good agreement in spatial patterns and magnitude. However, these experiments establish that the negative cloud anomaly and increased downwelling shortwave flux from June through August did not contribute substantially to the record sea ice extent minimum. This finding eliminates one aspect of the unusual weather that may have contributed to the record minimum.

Ensemble predictions of arctic sea ice in spring and summer 2008 have been carried out using an ice-ocean model. The ensemble is constructed by using atmospheric forcing from 2001 to 2007 and the September 2007 ice and ocean conditions estimated by the model. The prediction results show that the record low ice cover and the unusually warm ocean surface waters in summer 2007 lead to a substantial reduction in ice thickness in 2008. Up to 1.2 m ice thickness reduction is predicted in a large area of the Canada Basin in both spring and summer of 2008, leading to extraordinarily thin ice in summer 2008. There is a 50% chance that both the Northern Sea Route and the Northwest Passage will be nearly ice free in September 2008. It is not likely there will be another precipitous decline in arctic sea ice extent such as seen in 2007, unless a new atmospheric forcing regime, significantly different from the recent past, occurs.

How well can the extent of arctic sea ice be predicted for lead periods of up to one year? The forecast ability of a linear empirical model is explored. It uses as predictors historical information about the ocean and ice obtained from an ice–ocean model retrospective analysis. The monthly model fields are represented by a correlation-weighted average based on the predicted ice extent. The forecast skill of the procedure is found by fitting the model over subsets of the available data and then making subsequent projections using independent predictor data. The forecast skill, relative to climatology, for predictions of the observed September ice extent for the pan-arctic region is 0.77 for six months lead (from March) and 0.75 for 11 months lead (from October). The ice concentration is the most important variable for the first two months and the ocean temperature of the model layer with a depth of 200 to 270 m is most important for longer lead times. The trend accounts for 76% of the variance of the pan-arctic ice extent, so most of the forecast skill is realized by determining model variables that best represent this trend. For detrended data there is no skill for lead times of 3 months or more. The forecast skill relative to the estimate from the previous year is lower than the climate-relative skill but it is still greater than 0.45 for most lead times. Six-month predictions are also made for each month of the year and regional three-month predictions are made for 45-degree sectors. The ice-ocean model output significantly improves the predictive skill of the forecast model.

Ocean temperature profiles and satellite data have been analyzed for summertime sea surface temperature (SST) and upper ocean heat content variations over the past century, with a focus on the Arctic Ocean peripheral seas. We find that many areas cooled up to –0.5°C per decade during 1930–1965 as the Arctic Oscillation (AO) index generally fell, while these areas warmed during 1965–1995 as the AO index generally rose. Warming is particularly pronounced since 1995, and especially since 2000. Summer 2007 SST anomalies are up to 5°C. The increase in upper ocean summertime warming since 1965 is sufficient to reduce the following winter's ice growth by as much as 0.75 m. Alternatively, this heat may return to the atmosphere before any ice forms, representing a fall freeze-up delay of two weeks to two months. This returned heat might be carried by winds over terrestrial tundra ecosystems, contributing to the local heat budget.

This paper synthesizes a variety of atmospheric and oceanic data to examine the large-scale energy budget of the Arctic. Assessment of the atmospheric budget relies primarily on the ERA-40 reanalysis. The seasonal cycles of vertically integrated atmospheric energy storage and the convergence of energy transport from ERA-40, as evaluated for the polar cap (defined by the 70°N latitude circle), in general compare well with realizations from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis over the period 1979–2001. However, shortcomings in top of atmosphere radiation, as compared to satellite data, and the net surface flux, contribute to large energy budget residuals in ERA-40. The seasonal cycle of atmospheric energy storage is strongly modulated by the net surface flux, which is also the primary driver of seasonal changes in heat storage within the Arctic Ocean. Averaged for an Arctic Ocean domain, the July net surface flux from ERA-40 of –100 W m^-2 (i.e., into the ocean), associated with sea ice melt and oceanic sensible heat gain, exceeds the atmospheric energy transport convergence of 91 W m^-2. During winter (for which budget residuals are large), oceanic sensible heat loss and sea ice growth yield an upward surface flux of 50–60 W m^-2, complemented with an atmospheric energy convergence of 80–90 W m^-2 to provide a net radiation loss to space of 175–180 W m^-2.

An ice-ocean model has been used to investigate the effect of vertical mixing on the circulation of the Atlantic Water layer (AL) in the Arctic Ocean. The motivation of this study comes from the disparate AL circulations in the various models that comprise the Arctic Ocean Model Intercomparison Project (AOMIP). It is found that varying vertical mixing significantly changes the ocean's stratification by altering the vertical distribution of salinity and hence the structure of the arctic halocline. In the Eurasian Basin, the changes in ocean stratification tend to change the strength and depth of the cyclonic AL circulation, but not the basic circulation pattern. In the Canada Basin, however, the changes in ocean stratification are sufficient to alter the direction of the AL circulation. Excessively strong vertical mixing drastically weakens the ocean stratification, leading to an anticyclonic circulation at all depths, including both the AL and the upper layer that consists of the surface mixed layer and the halocline. Overly weak vertical mixing makes the ocean unrealistically stratified, with a fresher and thinner upper layer than observations. This leads to an overly strong anticyclonic circulation in the upper layer and an overly shallow depth at which the underlying cyclonic circulation occurs. By allowing intermediate vertical mixing, the model does not significantly drift away from reality and is in a rather good agreement with observations of the vertical distribution of salinity throughout the Arctic Ocean. This realistic ocean stratification leads to a realistic cyclonic AL circulation in the Canada Basin. In order for arctic ice-ocean models to obtain realistic cyclonic AL circulation in the Canada Basin, it is essential to generate an upward concave-shaped halocline across the basin at certain depths, consistent with observations.

The formation of a low salinity anomaly observed in the southern Beaufort Gyre in fall 1997 is examined, using output from a numerical sea ice – ocean climate model. The anomaly forms from locally reduced fall ice growth and from advection of river water. With regard to the latter, we find anomalous northwestward advection of water from the Mackenzie River delta (MRD) during 1997–1999, which fed a low salinity anomaly that circulated and deepened in the Beaufort Gyre until summer 2002, when it dissipated. The MRD salinity anomaly was especially fresh in 1997 because unusually convergent sea ice the previous summer and fall 1996 suppressed fall ice growth. The model shows a high correlation between advection from the MRD and salinity anomalies in the southern Beaufort Gyre until about 2002, when the correlation weakens as local sea ice melt/growth becomes the dominant forcing.

Ice concentration is a critical parameter of the polar marine environment because of the large effect sea ice has on the surface albedo and heat exchange between the atmosphere and the ocean. Simulations of the energy exchange processes in models would benefit if the ice concentration were represented more accurately. Reanalysis simulations that use historical wind and temperature fields may develop erroneous ice concentration estimates; these can be corrected by using observed ice concentration fields. The ice concentration assimilation presented here is a new method based on nudging the model ice concentration toward the observed concentration in a manner that emphasizes the ice extent and minimizes the effect of observational errors in the interior of the pack. The nudging weight is a nonlinear function of the difference between the model and the observed ice concentration. The simulated ice extent is improved with the assimilation of ice concentration but is not identical to the observed extent. The simulated ice draft is compared to that measured by upward-looking sonars on submarines and moorings. Significant improvements in the ice draft comparisons are obtained with assimilation of ice concentration alone and even more with assimilation of both ice concentration and ice velocity observations.

Model simulations of Arctic sea ice and ocean systems are used to determine the major spatial and temporal modes of variability in the ice thickness. A coupled ice–ocean model is forced with daily NCEP–NCAR reanalysis surface air pressure and surface air temperature fields for the period 1951–2003 with the analysis of the results performed for the 51-yr period 1953–2003. Ice concentration data and ice velocity data (beginning in 1979) are assimilated to further constrain the simulations to match the observed conditions. The simulated ice thins over the study period with the area of greatest thinning in a band from the Laptev Sea across the Pole to Fram Strait. The thinning rate is greatest since 1988. The major spatial modes of variability were determined with empirical orthogonal functions (EOFs) for the ice thickness within the Arctic Ocean. The first three EOFs account for 30%, 18%, and 15%, respectively, of the annual mean ice thickness variance. The first EOF is a nearly basinwide pattern, and the next two are orthogonal lateral modes. Because of the nonstationary nature of the ice thickness time series, significant changes in the modes are found if a shorter period is analyzed. The second and third principal components are well correlated with the Arctic Oscillation. The model results are also used to simulate an observation system and to then determine optimal mooring locations to monitor the basinwide mean ice thickness as well as the spatial and temporal patterns represented in the EOF analysis. The nonstationary aspect of the ice thickness limits the strength of the conclusions that can be drawn.

Recent observations of summer Arctic sea ice over the satellite era show that record or near-record lows for the ice extent occurred in the years 2002–05. To determine the physical processes contributing to these changes in the Arctic pack ice, model results from a regional coupled ice–ocean model have been analyzed. Since 1988 the thickness of the simulated basinwide ice thinned by 1.31 m or 43%. The thinning is greatest along the coast in the sector from the Chukchi Sea to the Beaufort Sea to Greenland.

It is hypothesized that the thinning since 1988 is due to preconditioning, a trigger, and positive feedbacks: 1) the fall, winter, and spring air temperatures over the Arctic Ocean have gradually increased over the last 50 yr, leading to reduced thickness of first-year ice at the start of summer; 2) a temporary shift, starting in 1989, of two principal climate indexes (the Arctic Oscillation and Pacific Decadal Oscillation) caused a flushing of some of the older, thicker ice out of the basin and an increase in the summer open water extent; and 3) the increasing amounts of summer open water allow for increasing absorption of solar radiation, which melts the ice, warms the water, and promotes creation of thinner first-year ice, ice that often entirely melts by the end of the subsequent summer.

Internal thermodynamic changes related to the positive ice–albedo feedback, not external forcing, dominate the thinning processes over the last 16 yr. This feedback continues to drive the thinning after the climate indexes return to near-normal conditions in the late 1990s. The late 1980s and early 1990s could be considered a tipping point during which the ice–ocean system began to enter a new era of thinning ice and increasing summer open water because of positive feedbacks. It remains to be seen if this era will persist or if a sustained cooling period can reverse the processes.

Plastic sea ice rheologies that employ teardrop and parabolic lens yield curves and allow varying biaxial tensile stresses have been developed. These rheologies, together with the previously developed ellipse and Mohr-Coulomb-ellipse rheologies, are implemented in a thickness and enthalpy distribution sea ice model to examine the rheological effect in numerical investigations of arctic climate. The teardrop, lens, and ellipse rheologies obey a normal flow rule and result in a two-peak shear stress distribution. The first peak is at the zero shear stress; the second is near 16,000 N m^-1 for the ellipse and two lens rheologies and near 30,000 N m^-1 for the two teardrop rheologies. The location of the second peak depends on the fatness of the yield curve and the amount of biaxial tensile stress allowed. In contrast, the Mohr-Coulomb-ellipse rheology, based on Coulombic friction failure, does not tend to create the second peak. The incorporation of biaxial tensile stress tends to increase ice thickness in most of the Arctic. A fatter yield curve tends to increase the frequency of large shear stresses. An increased frequency of large shear stresses, in conjunction with the inclusion of biaxial tensile stress, tends to reduce ice speed and ice export, to enhance ice ridging in the Arctic interior, and to reduce ice ridging in the coastal areas, which has a significant impact on arctic spatial ice mass distribution and the total ice budget. The teardrop rheologies reduce spatial bias of modeled ice draft against submarine observations more than others. By changing ice motion, deformation, and thickness the choice of plastic rheology also considerably affects the simulated surface energy exchanges, particularly in the Arctic marginal seas.

Various observations and model results point to an arctic sea ice cover that was extraordinarily thin in the 1990s. This thin ice cover was caused by a strengthened cyclonic circulation of wind and ice and by unusual warmth of springtime air temperatures. Here modeled sea ice volume is decomposed into two components: first, a dynamic or wind-forced response to interannually varying winds but a fixed annual cycle of air temperature and second, a thermally forced solution responding only to interannually varying temperatures. Over the 52-year simulation from 1948 to 1999 these two components have a similar range and variance; the wind-forced component has no substantial trend, but the temperature-forced component has a significant downward trend of –3% per decade. Total ice volume shows a trend of –4% per decade. Export slightly exceeds production over the simulation. Annual export and production can differ from each other and from year to year by ±30%. This behavior seems to characterize an ice cover highly constrained by interannual variations in forcing and not in balance. The bulk (two thirds) of volume loss from the 1960s to the 1990s is a result of a striking thinning of undeformed ice. The remainder of the volume loss is due to thinning of ridged ice and reduced concentrations. The central Arctic Ocean and particularly the East Siberian Sea suffer the greatest losses (of up to 2 m); the ice north of the Canadian archipelago also thinned since the 1960s by ~0.5 m.

A global ice-ocean model shows increasing Atlantic water (AW) inflow at the Iceland-Scotland Ridge (ISR) during 1953–2002. As a result, the Greenland-Iceland-Norwegian (GIN) Sea is gaining more heat and salt from the North Atlantic Ocean, while the latter is being freshened mainly by exporting more salt to the GIN Sea. The exchanges of volume, heat, and freshwater at the Greenland-Scotland Ridge (GSR) are strongly correlated with the North Atlantic Oscillation (NAO) and their positive trend is closely linked to the NAO elevation in recent decades. The model confirms observations of decreasing dense water outflow at the Faroe-Scotland Passage since the 1950s. However, the simulated dense water outflow shows an increase at Denmark Strait, at the Iceland-Faroe Ridge, and at the GSR as a whole, owing to an increase in AW inflow that may cause an increase in AW recirculation and deep water production in the GIN Sea. The increase of the ISR heat inflow since 1965 contributes to continued thinning of the arctic sea ice since 1966. The influence of the heat inflow on arctic sea ice lags 2–3 years, which suppresses ice production even when the NAO temporarily shifts to a negative mode. Because of this delay, the decline of arctic sea ice is likely to continue if the inflow continues to increase and if the NAO does not shift to a sustained negative mode.

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.

Annual mean heat content, referenced to 0°C, in the Canada Basin varies from –3.5 to +1.8 GJ m^-2 among the models, representing both colder and warmer solutions compared to the climatology. Seasonal range is highest in regions with seasonal or no ice cover.

Corresponding freshwater content, referenced to 34.8 ppt, shows differences most obviously in the Beaufort Sea and Canada Basin where maximum values vary between 6 and 24 m for the individual models. Maxima in the seasonal range are related to river inflow.

In the current stage of the project, applied windstress contributes significantly to the differences. However differences due to model resolutions and model parameterizations can already be detected.

Aided by submarine observations of ice thickness for model evaluation, we investigate the effects of assimilating buoy motion data and satellite SSM/I (85 Ghz) ice motion data on simulation of Arctic sea ice. The sea-ice model is a thickness and enthalpy distribution model and is coupled to an ocean model. Ice motion data are assimilated by means of optimal interpolation. Assimilating motion data, particularly from drifting buoys, significantly improves the modeled ice motion, reducing the error to 0.04 m s^-1 from 0.07 m s^-1 and increasing the correlation with observations to 0.90 from 0.66. Without data assimilation, the modeled ice moves too slowly with excessive stoppage. Assimilation leads to more robust ice motion with substantially reduced stoppage, which in turn leads to strengthened ice outflow at Fram Strait and enhanced ice deformation everywhere. Enhanced deformation doubles the production of ridged ice to an Arctic Ocean average of 0.77 m yr^-1, and raises the amount of ridged ice to half the total ice volume per unit area of 2.58 m. Assimilation also significantly alters the spatial distribution of ice mass and brings the modeled ice thickness into better agreement with the thickness observed in four recent submarine cruises, reducing the error to 0.66 m from 0.76 m, and increasing the correlation with observations to 0.65 from 0.45. Buoy data are most effective in reducing model errors because of their small measurement error. SSM/I data, because of their more complete spatial coverage, are helpful in regions with few buoys, particularly in coastal areas. Assimilating both SSM/I and buoy data combines their individual advantages and brings about the best overall model performance in simulating both ice motion and ice thickness.

A parallel ocean and ice model (POIM) in generalized orthogonal curvilinear coordinates has been developed for global climate studies. The POIM couples the Parallel Ocean Program (POP) with a 12-category thickness and enthalpy distribution (TED) sea ice model. Although the POIM aims at modeling the global ocean and sea ice system, the focus of this study is on the presentation, implementation, and evaluation of the TED sea ice model in a generalized coordinate system. The TED sea ice model is a dynamic thermodynamic model that also explicitly simulates sea ice ridging. Using a viscous plastic rheology, the TED model is formulated such that all the metric terms in generalized curvilinear coordinates are retained. Following the POP's structure for parallel computation, the TED model is designed to be run on a variety of computer architectures: parallel, serial, or vector. When run on a computer cluster with 10 parallel processors, the parallel performance of the POIM is close to that of a corresponding POP ocean-only model. Model results show that the POIM captures the major features of sea ice motion, concentration, extent, and thickness in both polar oceans. The results are in reasonably good agreement with buoy observations of ice motion, satellite observations of ice extent, and submarine observations of ice thickness. The model biases are within 8% in Arctic ice motion, within 9% in Arctic ice thickness, and within 14% in ice extent in both hemispheres. The model captures 56% of the variance of ice thickness along the 1993 submarine track in the Arctic. The simulated ridged ice has various thicknesses, up to 20 m in the Arctic and 16 m in the Southern Ocean. Most of the simulated ice is 1–3 m thick in the Arctic and 1–2 m thick in the Southern Ocean. The results indicate that, in the Atlantic–Indian sector of the Southern Ocean, the oceanic heating, mainly due to convective mixing, can readily exceed the atmospheric cooling at the surface in midwinter, thus forming a polynya. The results also indicate that the West Spitzbergen Current is likely to bring considerable oceanic heat (generated by lateral advection and vertical convection) to the Odden ice area in the Greenland Sea, an important factor for an often tongue-shaped ice concentration in that area.

Observations of sea-ice draft from submarine cruises in much of the Arctic Ocean show that the ice cover was unusually thin in the mid-1990s. Here we limit our examination to digitally recorded draft data from eight cruises spanning the years 1987 to 1997 and find a decrease of about 1 m over the 11-year span. Comparisons of our modeled draft with observed draft show good agreement in the temporal change. Comparing average draft over entire cruises, the RMS discrepancy between modeled and observed draft is 0.3 m and the correlation is 0.98. Agreement in the spatial patterns of draft is somewhat lower; the RMS discrepancy of 50-km averages of draft is 0.7 m and the correlation is 0.73. We review reports of interannual variations of ice thickness or volume from other model studies. All models agree that thickness decreased by between 0.6 and 0.9 m from 1987 to 1996. Our model shows a modest recovery in thickness from 1996 to 1999. For the 1950s, 1960s, and 1970s, models tend to disagree on the size and to a lesser extent the timing or phase of interannual variations.

The deformation of sea ice is an important element of the Arctic climate system because of its influence on the ice thickness distribution and on the rates of ice production and melt. New data obtained from the Radarsat Geophysical Processor System (RGPS) using satellite synthetic aperture radar images of the ice offers an opportunity to compare observations of the ice deformation to estimates obtained from models. The RGPS tracks tens of thousands of points, spaced roughly at 10-km intervals, for an entire season in a Lagrangian fashion. The deformation is computed from cells formed by the tracked points, typically at 3-day intervals. We used a coupled ice/ocean model with ice thickness and enthalpy distributions that covers the entire Arctic Ocean with a 40-km grid. Model-only and model-with-data-assimilation runs were analysed. The data assimilation runs were analysed in order to determine the validity of the comparison techniques and to find the comparisons under the best of circumstances, when many buoy measurements are available for assimilation. This step is necessary because the RGPS and model data differ in spatial and temporal sampling characteristics. The assimilated data included buoy motion and Special Sensor Microwave/Imager (SSM/I)-derived ice motion. The Pacific half of the Arctic Basin was analysed for a 10-month period in 1997 and 1998. Comparisons of ice velocity observations to the modelled velocities showed excellent agreement from the model-with-data-assimilation run but poorer agreement for the model-only run. At a scale of 320 km, the deformation from the data assimilation run was in modest agreement with the observations but where many buoys were available for assimilation the agreement was quite good. Both model runs showed poor agreement during summer. Comparisons of the deformation distribution functions suggest why the agreements were poor even though the velocity agreements were good. Decreasing the ice strength parameter in the model improved the deformation comparisons for the model-only runs.

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.

The theory of sea ice thickness distribution developed by Thorndike et al. has been extended to include sea ice enthalpy distribution. The extended theory conserves both ice mass and thermal energy, in the form of the heat stored in the ice, by jointly solving a thickness-distribution equation and an enthalpy-distribution equation. Both equations have been implemented in a one-dimensional dynamic thermodynamic sea-ice model with 12 ice thickness categories following the numerical procedure of Hibler. The implementation of the enthalpy-distribution equation allows the sea-ice model to account for any changes in the ice thermal energy induced by sea ice processes. As a result, the model is able to conserve not only the ice mass but also its thermal energy in the presence of ice advection, growth, melting, and ridging. Conserving ice thermal energy in a thickness-distribution sea ice model improves the prediction of ice growth, summer ice melt in particular, and therefore ice thickness. Inability to conserve the thermal energy by not implementing the enthalpy-distribution equation, compounded with an effect of the surface albedo feedback, causes the model to underestimate ice thickness by up to 11% under various conditions of thermal and mechanical forcing. This indicates the importance of conserving energy in numerical investigations of climate.

Temperatures and albedos derived from satellite imagery are combined with a thermodynamic ice model to estimate thin ice thickness distributions over the Beaufort and the northern Greenland seas. The study shows that thin ice (thinner than 1 m) occupied over half the area in the seasonal ice zones in November and December of 1990 but dropped significantly in April 1991; the Beaufort Shelf showed the largest seasonal change. The aggregate properties of surface salt flux and compressive ice strength, both strongly dependent on the thin end of the ice thickness distribution, were estimated with data from advanced very high resolution radiometer to reveal the spatial and temporal variations over a large scale. The salt flux from growing thin ice was 1–2 orders of magnitude larger on the Beaufort and Greenland Shelves than in the deep basins. On the shelves, flux from thin ice accounted for over 90% of the total surface salt budget. These satellite-derived estimates provided detailed spatial information on salt flux, which can be of great use in studies of surface patterns of salinity forcing and shelf-basin interaction. Also revealed by the satellite data was the wide range in values of compressive strength, which affects how freely the ice cover can deform. Strengths were low in early winter and in seasonal ice zones and nearly doubled in spring. Both thin ice fraction and compressive ice strength estimated from satellite imagery were in good agreement with those simulated by a coupled ice-ocean model.

It is well established that periods of high North Atlantic oscillation (NAO) index are characterized by a weakening of the surface high pressure and surface anticyclone in the Beaufort Sea and the intensification of the cyclonic circulation in the eastern Arctic Ocean. The response of Arctic sea ice to these atmospheric changes has been studied with a thickness distribution sea-ice model coupled to an ocean model. During a period of high NAO, 1989–96, the model shows a substantial reduction of ice advection into the eastern Arctic from the Canada Basin, and an increase of ice export through Fram Strait, both of which tend to deplete thick ice in the eastern Arctic Ocean and enhance it in the western Arctic, in an uneven dipolar pattern we call the East–West Arctic Anomaly Pattern (EWAAP). From the period 1979–88 with a lower-NAO index to the period 1988–96 with a high-NAO index, the simulated ice volume in the eastern Arctic drops by about a quarter, while that in the western Arctic increases by 16%. Overall, the Arctic Ocean loses 6%. The change from 1987 to 1996 is even larger — a loss of some 20% in ice volume for the whole Arctic. Both the model and satellite data show a significant reduction in ice extent in the eastern Arctic and in the Arctic Ocean as a whole.

There are corresponding changes in open water and therefore in ice growth, which tend to moderate the anomaly, and in lateral melting, which tends to enhance the anomaly. During the high NAO and strong EWAAP period, 1989%u201396, the eastern (western) Arctic has more (less) open water and enhanced (reduced) winter ice growth, so ice growth stabilizes the ice cover. On the other hand, the increased (decreased) open water enhances (reduces) summer melt by lowering (increasing) albedo in the eastern (western) Arctic. The nonlinearity of ice%u2013 albedo feedback causes the increased summer melt in the eastern Arctic to dominate the thermodynamic response and to collaborate with the ice advection pattern to enhance the EWAAP during high NAO.

A computationally efficient numerical method is developed for solving sea ice momentum equations that employ a nonlinear viscous-plastic rheology. The method is based on an alternating direction implicit (ADI) technique that involves a direct solution of the momentum equations. This method is therefore more computationally efficient than those employing an iterative procedure in solving the equations. The ADI method for modeling sea ice dynamics is dynamically consistent since it rapidly approaches a viscous-plastic solution described by the sea ice rheology. With different model configurations of varying spatial resolutions and decreasing time step intervals the ADI method converges to the same viscous-plastic solution as another numerical method that uses a line successive relaxation procedure to solve the ice momentum equations. This indicates that the ADI method is also numerically consistent. The approximateness of numerical solutions of sea ice, resulting from coarse model resolutions in time, is addressed. It is found that a significant bias, up to 10% or more, in the solution is likely to occur for a typical but coarse time step interval. This indicates that an assessment of the numerically created bias from a crude time integration may be necessary when model data comparisons are performed. In addition, suggestions are given for selecting appropriate time step intervals to enhance numerical accuracy in model applications.