Sea Ice Concentration Observations Research Articles

An assessment of the CMIP6 performance in simulating Arctic sea ice volume flux via Fram Strait

Numerical models serve as an essential tool to investigate the causes and effects of Arctic sea ice changes. Evaluating the simulation capabilities of the most recent CMIP6 models in sea ice volume flux provides references for model applications and improvements. Meanwhile, reliable long-term simulation results of the ice volume flux contribute to a deeper understanding of the sea ice response to global climate change. In this study, the sea ice volume flux through six Arctic gateways over the past four decades (1979–2014) were estimated in combination of satellite observations of sea ice concentration (SIC) and sea ice motion (SIM) as well as the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) reanalysis sea ice thickness (SIT) data. The simulation capability of 17 CMIP6 historical models for the volume flux through Fram Strait were quantitatively assessed. Sea ice volume flux simulated from the ensemble mean of 17 CMIP6 models demonstrates better performance than that from the individual model, yet IPSL-CM6A-LR and EC-Earth3-Veg-LR outperform the ensemble mean in the annual volume flux, with Taylor scores of 0.86 and 0.50, respectively. CMIP6 models display relatively robust capability in simulating the seasonal variations of volume flux. Among them, CESM2-WACCM performs the best, with a correlation coefficient of 0.96 and a Taylor score of 0.88. Conversely, NESM3 demonstrates the largest deviation from the observation/reanalysis data, with the lowest Taylor score of 0.16. The variability of sea ice volume flux is primarily influenced by SIM and SIT, followed by SIC. The extreme large sea ice export through Fram Strait is linked to the occurrence of anomalously low air temperatures, which in turn promote increased SIC and SIT in the corresponding region. Moreover, the intensified activity of Arctic cyclones and Arctic dipole anomaly could boost the southward sea ice velocity through Fram Strait, which further enhance the sea ice outflow.

Improving short-term sea ice concentration forecasts using deep learning

Abstract. Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 d. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts.

The tasks for bathymetric investigations and cartographic support of the northern sea high latitude routes in the East Siberian Sea

The current state and issues of bathymetric investigations of the East Siberian Sea are examined in this study. The main sources of bathymetric data are characterized. The possibilities to solve these issues in order to improve the efficiency of the oncoming bathymetric investigation in the East Siberian Sea for supporting the urgent practical tasks of the Russian Federation concerning the Northern Sea high latitude routes are discussed. The importance of a new hydrographic investigation through the high latitude routes in the East Siberian Sea in order to provide the possibility of year-round navigation of the large-tonnage Arc7‑class icebreaking LNG carriers is especially emphasized. Compiling of the new coastal electronic navigation charts for the new Northern Sea high latitude routes is stated as one of the main priorities. The lack of coastal electronic navigation charts of the high latitude routes in the East Siberian Sea will present a challenge for the establishing of year-round navigation for high ice-capable Arc7‑class LNG carriers during the coming several years. The drafting of high latitude routes must also consider the seafloor gas venting in the East Siberian Sea. The satellite remote sensing data from passive microwave radiometers and optical sensors using «modis & amsr2» technology provided by the University of Bremen ensures the complementary observations of sea ice concentration in the eastern part of the East Siberian Sea. The re-analysis of data from microwave radiometers AMSR‑2 beginning from 2018 provides the conclusion about the autumn degradation of the Aion ice massif. The analysis of the sea-ice concentration of the Aion ice massif during autumn–winter of 2022, the most severe in the last 5 years, allows us to give a recommendation concerning the new high latitude route in the East Siberian Sea. This route passes along the northern coast of Vrangel island into the Chukchi Sea and it is more efficient for large-tonnage Arc7‑class icebreaking LNG carriers ensuring year-round navigation in the East Siberian Sea. It is stated that hydrographic investigations carried out on the sections of the Northern Sea Route must adhere to the requirements of the International Hydrographic Organization. The urgency of developing the state integrated bathymetric database for the Arctic Ocean including the Arctic Seas data along with the necessity of providing all Russian bathymetric data acquired in the Arctic region to the assigned national centre is discussed. The FKU 280 ZKP VMF is proposed as such national centre. The importance of coastal electronic navigation charts for the high latitude routes in the East Siberian Sea created on the basis of modern technology is emphasized.

Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments

AbstractData assimilation is often viewed as a framework for correcting short‐term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short‐term corrections, or analysis increments, can closely mirror the systematic bias patterns of the dynamical model. In this study, we use convolutional neural networks (CNNs) to learn a mapping from model state variables to analysis increments, in order to showcase the feasibility of a data‐driven model parameterization which can predict state‐dependent model errors. We undertake this problem using an ice‐ocean data assimilation system within the Seamless system for Prediction and EArth system Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory, which assimilates satellite observations of sea ice concentration every 5 days between 1982 and 2017. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are states and tendencies of sea ice concentration, sea‐surface temperature, ice velocities, ice thickness, net shortwave radiation, ice‐surface skin temperature, sea‐surface salinity, as well as a land‐sea mask. We find the CNN is able to make skillful predictions of the increments in both the Arctic and Antarctic and across all seasons, with skill that consistently exceeds that of a climatological increment prediction. This suggests that the CNN could be used to reduce sea ice biases in free‐running SPEAR simulations, either as a sea ice parameterization or an online bias correction tool for numerical sea ice forecasts.

Relevance of warm air intrusions for Arctic satellite sea ice concentration time series

Abstract. Winter warm air intrusions entering the Arctic region can strongly modify the microwave emission of the snow-covered sea ice system due to temperature-induced snow metamorphism and ice crust formations, e.g., after melt–refreeze events. Common microwave radiometer satellite sea ice concentration retrievals are based on empirical models using the snow-covered sea ice emissivity and thus can be influenced by strong warm air intrusions. Here, we carry out a long-term study analyzing 41 years of winter sea ice concentration observations from different algorithms to investigate the impact of warm air intrusions on the retrieved ice concentration. Our results show that three out of four algorithms underestimate the sea ice concentration during (and up to 10 d after) warm air intrusions which increase the 2 m air temperature (daily maximum) above − 5 ∘C. This can lead to sea ice area underestimations in the order of 104 to 105 km2. If the 2 m temperature during the warm air intrusions crosses − 2 ∘C, all algorithms are impacted. Our analysis shows that the strength of these strong warm air intrusions increased in recent years, especially in April. With a further climate change, such warm air intrusions are expected to occur more frequently and earlier in the season, and their influence on sea ice climate data records will become more important.

Spatio-temporal analysis of east greenland polar front

The East Greenland Polar Front (EGPF) is an important front with strong salinity and temperature gradients in the Nordic Seas. It is formed by the interaction between Arctic-origin and Atlantic-origin water. The variations of EGPF are closely linked with sea ice melting and heat content transport associated with North Atlantic water recirculation. For a three-dimensional (3D) daily analysis, we use the global ocean eddy resolution reanalysis product (GLORYS12V1) from 1993 to 2018 to calculate the salinity and temperature horizontal gradient in the upper ocean and obtain the spatiotemporal distribution and intensity characteristics of EGPF. After assessment, the thresholds of the salinity and temperature fronts are set to 0.04 psu/km and 0.09°C/km, respectively. Compared with satellite observations of sea ice concentration, a significant spatial relationship is observed between the main position of EGPF and the ice edge before the sea ice shrinks to the continental shelf sea area. Affected by the freshening of the Arctic-origin water due to the melting of the sea ice, the intensity and area of EGPF show significant seasonal variations. Against the background of global warming, the sea ice area presents an obvious decreasing trend in the Greenland Sea. The melting of sea ice increased annually every summer from 1998 to 2018. The heat content transport of the Atlantic-origin water has also increased in recent years. The 3D characteristics (intensity and volume) of EGPF as salinity and temperature fronts exhibit increasing trends.

The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation

Abstract. The Regional Ocean Modeling System (ROMS), including an ice shelf component, has been applied on a circum-Antarctic domain to derive estimates of ice shelf basal melting. Significant improvements made compared to previous models of this scale are the inclusion of tides and a horizontal spatial resolution of 2 km, which is sufficient to resolve on-shelf heat transport by bathymetric troughs and eddy-scale circulation. We run the model with ocean–atmosphere–sea ice conditions from the year 2007 to represent nominal present-day climate. We force the ocean surface with buoyancy fluxes derived from sea ice concentration observations and wind stress from ERA-Interim atmospheric reanalysis. Boundary conditions are derived from the ECCO2 ocean state estimate; tides are incorporated as sea surface height and barotropic currents at the open boundary. We evaluate model results using satellite-derived estimates of ice shelf melting and established compilations of ocean hydrography. The Whole Antarctic Ocean Model (WAOM v1.0) qualitatively captures the broad scale difference between warm and cold regimes as well as many of the known characteristics of regional ice–ocean interaction. We identify a cold bias for some warm-water ice shelves and a lack of high-salinity shelf water (HSSW) formation. We conclude that further calibration and development of our approach are justified. At its current state, the model is ideal for addressing specific, process-oriented questions, e.g. related to tide-driven ice shelf melting at large scales.

CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization

To study the effectiveness of methods to reduce errors for Arctic Sea ice initialization due to underestimation of background error covariance, an advanced ensemble analysis system has been developed. The system integrates the local ensemble transform Kalman filter (LETKF) with the community ice code (CICE). With a mixed layer ocean model used to compute the sea surface temperature (SST), the experiments on assimilation of observations of sea ice concentration (SIC) have been carried out. Assimilation experiments were performed over a 3-month period from January to March in 1997. The model was sequentially constrained with daily observation data. The effects of observation density, amplification factor for analysis error covariance, and relaxation of disturbance and spread on the results of SIC initialization were studied. It is shown that doubling the density of observation of SIC does not bring significant further improvement on the analysis result; when the ensemble size is doubled, most severe SIC biases in the Labrador, Greenland, Norwegian, and Barents seas are reduced; amplifying the analysis error covariance, relaxing disturbance, and relaxing spread all contribute to improving the reproduction of SIC with amplifying covariance with the largest magnitude.

Warm Atlantic Water Explains Observed Sea Ice Melt Rates North of Svalbard

AbstractWarm Atlantic water (AW) that flows northward along the Svalbard west coast is thought to transport enough heat to melt regional Arctic sea ice effectively. Despite this common assumption, quantitative requirements necessary for AW to directly melt sea ice fast enough under realistic winter conditions are still poorly constrained. Here we use meteorological data, satellite observations of sea ice concentration and drift, and model output to demonstrate that most of the sea ice entering the area over the Yermak Plateau melts within a few weeks. Simulations using the Los Alamos Sea Ice Model (CICE) in a 1‐D vertically resolved configuration under a relatively wide range of in situ observed atmospheric and ocean forcing show a good fit to observations. Simulations require high‐frequency atmospheric forcing data to accurately reproduce vertical heat fluxes between the ice or snow and the atmosphere. Moreover, we switched off hydrostatic equilibrium to properly reproduce ice and snow thickness when observations showed that ice had a negative freeboard, without surface flooding and snow‐ice formation. This modeling shows that realistic melt rates require a combination of warm near‐surface AW and storm‐induced ocean mixing. However, if AW is warmer than usual (>5°C), then lower mixing rates are sufficient. Our results suggest that increased winter storm frequency and increased heat content of the AW may work together in reducing future sea ice cover in the Eurasian basin.

Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset

Abstract. Observations of sea-ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea-ice concentrations. This yields a merged sea-ice concentration product combining the gap-free spatial coverage of the passive microwave sea-ice concentration and the 1 km resolution of the thermal infrared sea-ice concentration. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea-ice concentration product resolves leads at sea-ice concentrations between 60 % and 90 %. They are not resolved by the coarser passive microwave sea-ice concentration product. The benefit of the merged product is most pronounced during the formation of the polynya. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly below 0 ∘C and thus more than 20 ∘C higher than normal. Two estimates of thermodynamic sea-ice growth yield sea-ice thicknesses of 60 and 65 cm at the end of March in the area opened by the polynya. This differed from airborne sea-ice thickness measurements, indicating that sea-ice growth processes in the polynya are complicated by rafting and ridging. A sea-ice volume of 33 km3 was produced thermodynamically.

Skillful spring forecasts of September Arctic sea ice extent using passive microwave sea ice observations

AbstractIn this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March–May, while the SIC forecasts produce the highest skill in June–August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

Sea ice melt onset associated with lead opening during the spring/summer transition near the North Pole

AbstractIn the central Arctic Ocean, autonomous observations of the ocean mixed layer and ice documented the transition from cold spring to early summer in 2011. Ice‐motion measurements using GPS drifters captured three events of lead opening and ice ridge formation in May and June. Satellite sea ice concentration observations suggest that locally observed lead openings were part of a larger‐scale pattern. We clarify how these ice deformation events are linked with the onset of basal sea ice melt, which preceded surface melt by 20 days. Observed basal melt and ocean warming are consistent with the available input of solar radiation into leads, once the advent of mild atmospheric conditions prevents lead refreezing. We use a one‐dimensional numerical simulation incorporating a Local Turbulence Closure scheme to investigate the mechanisms controlling basal melt and upper ocean warming. According to the simulation, a combination of rapid ice motion and increased solar energy input at leads promotes basal ice melt, through enhanced mixing in the upper mixed layer, while slow ice motion during a large lead opening in mid‐June produced a thin, low‐density surface layer. This enhanced stratification near the surface facilitates storage of solar radiation within the thin layer, instead of exchange with deeper layers, leading to further basal ice melt preceding the upper surface melt.

Improving Arctic Sea Ice Prediction Using PIOMAS Initial Sea Ice Thickness in a Coupled Ocean–Atmosphere Model

Abstract Because sea ice thickness is known to influence future patterns of sea ice concentration, it is likely that an improved initialization of sea ice thickness in a coupled ocean–atmosphere model would improve Arctic sea ice cover forecasts. Here, two sea ice thickness datasets as possible candidates for forecast initialization were investigated: the Climate Forecast System Reanalysis (CFSR) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Using Ice, Cloud, and Land Elevation Satellite (ICESat) data, it was shown that the PIOMAS dataset had a more realistic representation of sea ice thickness than CFSR. Subsequently, both March CFSR and PIOMAS sea ice thicknesses were used to initialize hindcasts using the Climate Forecast System, version 2 (CFSv2), model. A second set of model runs was also done in which the original model physics were modified to more physically reasonable settings—namely, increasing the number of marine stratus clouds in the Arctic region and enabling realistic representation of the ice–ocean heat flux. Hindcasts were evaluated using sea ice concentration observations from the National Aeronautics and Space Administration (NASA) Team and Bootstrap algorithms. Results show that using PIOMAS initial sea ice thickness in addition to the physics modifications yielded significant improvement in the prediction of September Arctic sea ice extent along with increased interannual predictive skill. Significant local improvements in sea ice concentration were also seen in distinct regions for the change to PIOMAS initial thickness or the physics adjustments, with the most improvement occurring when these changes were applied concurrently.

Arctic sea ice in the global eddy-permitting ocean reanalysis ORAP5

We discuss the state of Arctic sea ice in the global eddy-permitting ocean reanalysis Ocean ReAnalysis Pilot 5 (ORAP5). Among other innovations, ORAP5 now assimilates observations of sea ice concentration using a univariate 3DVar-FGAT scheme. We focus on the period 1993–2012 and emphasize the evaluation of model performance with respect to recent observations of sea ice thickness. We find that sea ice concentration in ORAP5 is close to assimilated observations, with root mean square analysis residuals of less than 5 % in most regions. However, larger discrepancies exist for the Labrador Sea and east of Greenland during winter owing to biases in the free-running model. Sea ice thickness is evaluated against three different observational data sets that have sufficient spatial and temporal coverage: ICESat, IceBridge and SMOSIce. Large-scale features like the gradient between the thickest ice in the Canadian Arctic and thinner ice in the Siberian Arctic are simulated well by ORAP5. However, some biases remain. Of special note is the model’s tendency to accumulate too thick ice in the Beaufort Gyre. The root mean square error of ORAP5 sea ice thickness with respect to ICESat observations is 1.0 m, which is on par with the well-established PIOMAS model sea ice reconstruction. Interannual variability and trend of sea ice volume in ORAP5 also compare well with PIOMAS and ICESat estimates. We conclude that, notwithstanding a relatively simple sea ice data assimilation scheme, the overall state of Arctic sea ice in ORAP5 is in good agreement with observations and will provide useful initial conditions for predictions.

Environmental determinants of top predator distribution within the dynamic winter pack ice zone of the northern Antarctic Peninsula

Global warming is predicted to reduce the amount of sea ice concentration in polar environments, thus presenting profound changes for populations of seabirds and marine mammals dependent on sea ice. Using data from a shipboard survey during August 2012, I test the hypothesis that relative abundance of seabird and marine mammals reflects environmental variability associated with the dynamic pack ice zone. Using environmental data and observations of sea ice concentration, I quantified an environmental gradient that describes the spatial organization of the dynamic pack ice zone. The relationship of top predators to this environmental gradient revealed three important aspects: (1) an open water and pack ice community is present with some top predator species exhibiting higher abundance associated with moderate sea ice concentration (40–60 %) as opposed to the pack ice edge (10 %), (2) Antarctic fur seals (Arctocephalus gazella) were the most abundant pinniped and they were observed resting on ice floes and foraging within leads and polynyas, and (3) for the most abundant species, spatial regression models indicate that latitude and sea ice concentration (a principal north/south gradient) are the most important environmental determinants. Winter ocean conditions may strongly influence population dynamics of top predators; therefore, information regarding their habitat use during winter is needed for understanding ecosystem dynamics.

Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty

Abstract A new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice–SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.

Estimates of the Southern Ocean general circulation improved by animal‐borne instruments

AbstractOver the last decade, several hundred seals have been equipped with conductivity‐temperature‐depth sensors in the Southern Ocean for both biological and physical oceanographic studies. A calibrated collection of seal‐derived hydrographic data is now available, consisting of more than 165,000 profiles. The value of these hydrographic data within the existing Southern Ocean observing system is demonstrated herein by conducting two state estimation experiments, differing only in the use or not of seal data to constrain the system. Including seal‐derived data substantially modifies the estimated surface mixed‐layer properties and circulation patterns within and south of the Antarctic Circumpolar Current. Agreement with independent satellite observations of sea ice concentration is improved, especially along the East Antarctic shelf. Instrumented animals efficiently reduce a critical observational gap, and their contribution to monitoring polar climate variability will continue to grow as data accuracy and spatial coverage increase.

The mixed layer salinity budget and sea ice in the Southern Ocean

[1] The seasonal variation of the mixed layer salinity budget in the Southern Ocean is evaluated over the latitude range 45°S–62°S using Argo profiling float data, freshwater fluxes (evaporation minus precipitation (E-P)), geostrophic velocity, wind stress, and sea ice concentration observations. The seasonal cycle of the mixed layer salinity is driven by seasonality in E-P, Ekman advection, entrainment, and sea ice. Over large areas, the geostrophic advection and diffusion show smaller contributions to the seasonal variation relative to other terms. The air-sea freshwater flux and Ekman advection in this area generally result in net decreases in salinity, while the entrainment term yields increases. Residual imbalance is consistent with a sea ice effect, whose contribution is evaluated. Sea ice is found to make a significant contribution, growing in importance toward the ice edge.

Latitudinal gradients in sea ice and primary production determine Arctic seabird colony size in Greenland

Sea ice loss will indirectly alter energy transfer through the pelagic food web and ultimately impact apex predators. We quantified spring-time trends in sea ice recession around each of 46 thick-billed murre (Uria lomvia) colonies in west Greenland across 20 degrees of latitude and investigated the magnitude and timing of the associated spring-time primary production. A geographical information system was used to extract satellite-based observations of sea ice concentration from the Nimbus-7 scanning multichannel microwave radiometer (SMMR, 1979-1987) and the Defence Meteorological Satellite Programs Special Sensor Microwave/Imager (SSMI, 1987-2004), and satellite-based observations of chlorophyll a from the moderate resolution imaging spectroradiometer (MODIS: EOS-Terra satellite) in weekly intervals in circular buffers around each colony site (150 km in radius). Rapid recession of high Arctic seasonal ice cover created a temporally predictable primary production bloom and associated trophic cascade in water gradually exposed to solar radiation. This pattern was largely absent from lower latitudes where little to no sea ice resulted in a temporally variable primary production bloom driven by nutrient cycling and upwelling uncoupled to ice. The relationship between the rate and variability of sea ice recession and colony size of thick-billed murres shows that periodical confinement of the trophic cascade at high latitudes determines the carrying capacity for Arctic seabirds during the breeding period.