Seasonal Cycle Of Sea Ice Research Articles

High‐Resolution Thermal Imaging in the Antarctic Marginal Ice Zone: Skin Temperature Heterogeneity and Effects on Heat Fluxes

AbstractInsufficient in situ observations from the Antarctic marginal ice zone (MIZ) limit our understanding and description of relevant mechanical and thermodynamic processes that regulate the seasonal sea ice cycle. Here we present high‐resolution thermal images of the ocean surface and complementary measurements of atmospheric variables that were acquired underway during one austral winter and one austral spring expedition in the Atlantic and Indian sectors of the Southern Ocean. Skin temperature data and ice cover images were used to estimate the partitioning of the heterogeneous surface and calculate the heat fluxes to compare with ERA5 reanalyses. The winter MIZ was composed of different but relatively regularly distributed sea ice types with sharp thermal gradients. The surface‐weighted skin temperature compared well with the reanalyses due to a compensation of errors between the sea ice fraction and the ice floe temperature. These uncertainties determine the dominant source of inaccuracy for heat fluxes as computed from observed variables. In spring, the sea ice type distribution was more irregular, with alternation of sea ice cover and large open water fractions even 400 km from the ice edge. The skin temperature distribution was more homogeneous and did not produce substantial uncertainties in heat fluxes. The discrepancies relative to reanalysis data are however larger than in winter and are attributed to biases in the atmospheric variables, with the downward solar radiation being the most critical.

Underestimation of oceanic carbon uptake in the Arctic Ocean: ice melt as predictor of the sea ice carbon pump

Abstract. The Arctic Ocean is generally undersaturated in CO2 and acts as a net sink of atmospheric CO2. This oceanic uptake is strongly modulated by sea ice, which can prevent air–sea gas exchange and has major impacts on stratification and primary production. Moreover, carbon is stored in sea ice with a ratio of alkalinity to dissolved inorganic carbon that is larger than in seawater. It has been suggested that this storage amplifies the seasonal cycle of seawater pCO2 and leads to an increase in oceanic carbon uptake in seasonally ice-covered regions compared to those that are ice-free. Given the rapidly changing ice scape in the Arctic Ocean, a better understanding of the link between the seasonal cycle of sea ice and oceanic uptake of CO2 is needed. Here, we investigate how the storage of carbon in sea ice affects the air–sea CO2 flux and quantify its dependence on the ratio of alkalinity to inorganic carbon in ice. To this end, we present two independent approaches: a theoretical framework that provides an analytical expression of the amplification of carbon uptake in seasonally ice-covered oceans and a simple parameterization of carbon storage in sea ice implemented in a 1D physical–biogeochemical ocean model. Sensitivity simulations show a linear relation between ice melt and the amplification of seasonal carbon uptake. A 30 % increase in carbon uptake in the Arctic Ocean is estimated compared to ice melt without amplification. Applying this relationship to different future scenarios from an earth system model that does not account for the effect of carbon storage in sea ice suggests that Arctic Ocean carbon uptake is underestimated by 5 % to 15 % in these simulations.

Assessment of Antarctic sea ice area and concentration in Coupled Model Intercomparison Project Phase 5 and Phase 6 models

AbstractSea ice is an important and complex component of the Earth system and is considered a sensitive indicator of climate change. The seasonal sea ice cycle regulates the exchange of heat and salinity, altering the energy balance between high and low latitudes as well as the ocean and atmospheric circulation. The accurate representation of Antarctic sea ice has been considered a hot topic in the climate modelling community and lacks conclusive answers. In this paper, we evaluated the ability of 11 climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) to simulate the sea ice seasonal cycle in Antarctica in terms of area (SIA) and concentration (SIC), as well as the improvements in the most recent models' version, submitted to CMIP6. The results indicated that all models are able to accurately capture the seasonal cycle of the Antarctic SIA, with the minimum (maximum) occurring in February (September). In the Weddell Sea, Amundsen Sea, Bellingshausen Sea, and the Ross Sea, the simulated sea ice concentration revealed a large and systematic bias in February when compared to observations. In September, a large and systematic bias was found nearby the Southern Ocean's northern limit in the Polar Front. Several CMIP6 models exhibited slight improvements on the SIA and SIC estimate over the previous version (CMIP5). All models indicated a significant sea ice loss in the coming years as a response to CO2 forcing. Despite the advancements in the sea ice representation, our findings show that the models are still unable to accurately represent the regional sea ice changes

Turbulent Mixing in a Changing Arctic Ocean

Historically, the Arctic Ocean has been considered an ocean of low variability and weak turbulent mixing. However, the decline in seasonal sea ice cover over the past couple of decades has led to increased coupling between the atmosphere and the ocean, with potential enhancement of turbulent mixing. Here, we review studies that allow identifying energy sources and pathways that lead to turbulent mixing in an increasingly ice-free Arctic Ocean. We find that the evolution of wind-generated, near-inertial oscillations is highly sensitive to the seasonal sea ice cycle, but the response varies greatly between the continental shelves and the abyssal ocean and between the eastern and western ocean basins. There is growing interest in the role of tides and continental shelf waves in driving mixing over sloping topography. Both dissipate through the development of unsteady lee waves. The role eddies play in transporting shelf water into the basins and in supporting mixing has become more apparent as technological advances have permitted higher resolution observations of sea ice retreat. The importance of the dissipation of unsteady lee waves and of eddies in driving mixing highlights the need for parameterizations of these phenomena in regional ocean models and climate simulations.

Compensating Biases and a Noteworthy Success in the CMIP5 Representation of Antarctic Sea Ice Processes

AbstractCoupled Model Intercomparison Project phase 5 (CMIP5) climate models simulate a wide range of historical sea ice areas. Even models with areas close to observed values may contain compensating errors, affecting reliability of their projections. This study focuses on the seasonal cycle of sea ice, including analysis of model concentration budgets. Many models have insufficient autumn ice growth, leading to large winter biases. A subset of models accurately represent sea ice evolution year‐round. However, comparing their winter ice concentration budget to observations reveals a range of behaviors. At least one model has an accurate ice budget, which is only possible due to realistic ice drifts. The CMIP5 generation of model physics and resolution is therefore structurally capable of accurately representing processes in Antarctic sea ice. This implies that substantially improved projections of Antarctic dense ocean water formation and ice sheet melting are possible with appropriate subsetting of existing climate models.

On the Seasonal Cycles Observed at the Continental Slope of the Eastern Eurasian Basin of the Arctic Ocean

AbstractThe Eurasian Basin (EB) of the Arctic Ocean is subject to substantial seasonality. We here use data collected between 2013 and 2015 from six moorings across the continental slope in the eastern EB and identify three domains, each with its own unique seasonal cycle: 1) The upper ocean (<100 m), with seasonal temperature and salinity differences of Δθ = 0.16°C and ΔS = 0.17, is chiefly driven by the seasonal sea ice cycle. 2) The upper-slope domain is characterized by the influence of a hydrographic front that spans the water column around the ~750-m isobath. The domain features a strong temperature and moderate salinity seasonality (Δθ = 1.4°C; ΔS = 0.06), which is traceable down to ~600-m depth. Probable cause of this signal is a combination of along-slope advection of signals by the Arctic Circumpolar Boundary Current, local wind-driven upwelling, and a cross-slope shift of the front. 3) The lower-slope domain, located offshore of the front, with seasonality in temperature and salinity mainly confined to the halocline (Δθ = 0.83°C; ΔS = 0.11; ~100–200 m). This seasonal cycle can be explained by a vertical isopycnal displacement (ΔZ ~ 36 m), arguably as a baroclinic response to sea level changes. Available long-term oceanographic records indicate a recent amplification of the seasonal cycle within the halocline layer, possibly associated with the erosion of the halocline. This reduces the halocline’s ability to isolate the ocean surface layer and sea ice from the underlying Atlantic Water heat with direct implications for the evolution of Arctic sea ice cover and climate.

An Autonomous Approach to Observing the Seasonal Ice Zone in the Western Arctic

The Marginal Ice Zone and Arctic Sea State programs examined the processes that govern evolution of the rapidly changing seasonal ice zone in the Beaufort Sea. Autonomous platforms operating from the ice and within the water column collected measurements across the atmosphere-ice-ocean system and provided the persistence to sample continuously through the springtime retreat and autumn advance of sea ice. Autonomous platforms also allowed operational modalities that reduced the field programs’ logistical requirements. Observations indicate that thermodynamics, especially the radiative balances of the ice-albedo feedback, govern the seasonal cycle of sea ice, with the role of surface waves confined to specific events. Continuous sampling from winter into autumn also reveals the imprint of winter ice conditions and fracturing on summertime floe size distribution. These programs demonstrate effective use of integrated systems of autonomous platforms for persistent, multiscale Arctic observing. Networks of autonomous systems are well suited to capturing the vast scales of variability inherent in the Arctic system.

Estimating Arctic sea-ice freeze-up and break-up from the satellite record: A comparison of different approaches in the Chukchi and Beaufort Seas

1. Abstract The recognized importance of the annual cycle of sea ice in the Arctic to heat budgets, human behavior, and ecosystem functions, requires consistent definitions of such key events in the ice cycle as break-up and freeze-up. An internally consistent and reproducible approach to characterize the timing of these events in the annual sea-ice cycle is described. An algorithm was developed to calculate the start and end dates of freeze-up and break-up and applied to time series of satellite-derived sea-ice concentration from 1979 to 2013. Our approach builds from discussions with sea-ice experts having experience observing and working on the sea ice in the Bering, Chukchi and Beaufort Seas. Applying the algorithm to the 1979–2013 satellite data reveals that freeze-up is delayed by two weeks per decade for the Chukchi coast and one week per decade for the Beaufort coast. For both regions, break-up start is arriving earlier by 5–7 days per decade and break-up end is arriving earlier by 10–12 days per decade. In the Chukchi Sea, “early” break-up is arriving earlier by one month over the 34-year period and alternates with a “late” break-up. The calculated freeze-up and break-up dates provide information helpful to understanding the dynamics of the annual sea-ice cycle and identifying the drivers that modify this cycle. The algorithm presented here, and potential refinements, can help guide future work on changes in the seasonal cycle of sea ice. The sea-ice phenology of freeze-up and break-up that results from our approach is consistent with observations of sea-ice use. It may be applied to advancing our understanding and prediction of the timing of seasonal navigation, availability of ice as a biological habitat, and assessment of numerical models.

Ice platelets below Weddell Sea landfast sea ice

AbstractBasal melt of ice shelves may lead to an accumulation of disc-shaped ice platelets underneath nearby sea ice, to form a sub-ice platelet layer. Here we present the seasonal cycle of sea ice attached to the Ekström Ice Shelf, Antarctica, and the underlying platelet layer in 2012. Ice platelets emerged from the cavity and interacted with the fast-ice cover of Atka Bay as early as June. Episodic accumulations throughout winter and spring led to an average platelet-layer thickness of 4 m by December 2012, with local maxima of up to 10 m. The additional buoyancy partly prevented surface flooding and snow-ice formation, despite a thick snow cover. Subsequent thinning of the platelet layer from December onwards was associated with an inflow of warm surface water. The combination of model studies with observed fast-ice thickness revealed an average ice-volume fraction in the platelet layer of 0.25 ± 0.1. We found that nearly half of the combined solid sea-ice and ice-platelet volume in this area is generated by heat transfer to the ocean rather than to the atmosphere. The total ice-platelet volume underlying Atka Bay fast ice was equivalent to more than one-fifth of the annual basal melt volume under the Ekström Ice Shelf.

Arctic sea ice and atmospheric circulation under the GeoMIP G1 scenario

AbstractWe analyze simulated sea ice changes in eight different Earth System Models that have conducted experiment G1 of the Geoengineering Model Intercomparison Project (GeoMIP). The simulated response of balancing abrupt quadrupling of CO2 (abrupt4xCO2) with reduced shortwave radiation successfully moderates annually averaged Arctic temperature rise to about 1°C, with modest changes in seasonal sea ice cycle compared with the preindustrial control simulations (piControl). Changes in summer and autumn sea ice extent are spatially correlated with temperature patterns but much less in winter and spring seasons. However, there are changes of ±20% in sea ice concentration in all seasons, and these will induce changes in atmospheric circulation patterns. In summer and autumn, the models consistently simulate less sea ice relative to preindustrial simulations in the Beaufort, Chukchi, East Siberian, and Laptev Seas, and some models show increased sea ice in the Barents/Kara Seas region. Sea ice extent increases in the Greenland Sea, particularly in winter and spring and is to some extent associated with changed sea ice drift. Decreased sea ice cover in winter and spring in the Barents Sea is associated with increased cyclonic activity entering this area under G1. In comparison, the abrupt4xCO2 experiment shows almost total sea ice loss in September and strong correlation with regional temperatures in all seasons consistent with open ocean conditions. The tropospheric circulation displays a Pacific North America pattern‐like anomaly with negative phase in G1‐piControl and positive phase under abrupt4xCO2‐piControl.

Origins and Levels of Seasonal Forecast Skill for Sea Ice in Hudson Bay Using Canonical Correlation Analysis

Abstract Canonical correlation analysis (CCA) is used to estimate the levels and sources of seasonal forecast skill for July ice concentration in Hudson Bay over the 1971–2005 period. July is an important transition month in the seasonal cycle of sea ice in Hudson Bay because it is the month when the sea ice clears enough to allow the first passage of ships to the Port of Churchill. Sea surface temperature (quasi global, North Atlantic, and North Pacific), Northern Hemisphere 500-mb geopotential height (z500), sea level pressure (SLP), and regional surface air temperature (SAT) are tested as predictors at 3-, 6-, and 9-month lead times. The model with the highest skill has three predictors—fall North Atlantic SST, fall z500, and fall SAT—and significant tercile forecast skill covering 61% of the Hudson Bay region. The highest skill for a single-predictor model is from fall North Atlantic SST (6-month lead). Fall SST explains 69% of the variance in July ice concentration in Hudson Bay and a possible atmospheric link that accounts for the lagged relationship is presented. CCA diagnostics suggest that changes in the subpolar North Atlantic gyre and the Atlantic multidecadal oscillation (AMO), reflected in sea surface temperature, precedes a deepening/weakening of the winter upper-air ridge northwest of Hudson Bay. Changes in the height of the ridge are reflected in the strength of the winter northwesterly winds over Hudson Bay that have a direct impact on the winter ice thickness distribution; anomalies in winter ice severity are later reflected in the pattern and timing of spring breakup. July ice concentration in Hudson Bay has declined by approximately 20% per decade between 1979 and 2007, and the hypothesized link to the AMO may help explain this significant loss of ice.

Sea ice in the western Antarctic Peninsula region: Spatio-temporal variability from ecological and climate change perspectives

The Antarctic Peninsula region is undergoing rapid change: a warming in winter of almost 6 °C since 1950, the loss of six ice shelves, the retreat of 87% of the marine glaciers, and decreases in winter sea-ice duration. Concurrently, there is evidence of ecosystem change along the western Antarctic Peninsula (wAP). Since the life histories of most polar marine species are synchronized with the seasonal cycle of sea ice, we assess how the seasonal sea-ice cycle is changing in the wAP region. Four new metrics of seasonal sea-ice variability were extracted from spatial maps of satellite derived daily sea-ice concentration: (a) day of advance, (b) day of retreat, (c) the total number of sea-ice days (between day of advance and retreat), and (d) the percent time sea-ice was present (or sea-ice persistence). The spatio-temporal variability describes distinct on-to-offshore and alongshore differences in ice–ocean marine habitats, characterized overall by a longer sea-ice season in coastal regions (6.8–7.9 months) versus a shorter sea-ice season over the shelf (4.1–5.3 months), with on-to-offshore differences increasing south-to-north. Large perturbations in the seasonality of the marine habitat occur in association with ENSO and Southern Annular Mode (SAM) variability. The local atmospheric response to these climate modes is largely a strengthening of the meridional winds during spring-to-autumn, which in turn affect the timing of the sea-ice retreat and subsequent advance. These perturbations are embedded in overall trends towards a later sea-ice advance, earlier retreat and consequently shorter sea-ice season, the impacts of which are expected to affect ecosystem functionality in the wAP region. A suite of ocean–atmosphere–ice interactions are described that are consistent with the amplified warming in late autumn, early winter.

Sensitivity of the Antarctic sea ice distribution to its advection in a general circulation model

A dynamic-thermodynamic sea ice model is used and coupled with an atmospheric general circulation model to simulate the seasonal cycle of the global sea ice distribution. We have run the coupled system and obtain a creditable seasonal simulation of the Antarctic sea ice. To understand the role of ice advection on the seasonal cycle of Antarctic sea ice in the coupled system, results from the thermodynamiconly (T) sea ice model have been compared with those from the dynamic thermodynamic (DT) sea ice model. The seasonal cycle of sea ice differs between the two models. When ice motion is eliminated sea ice becomes more compact and thinner, and sea ice is more extensive in summer. A number of previous studies have examined the effect of ice dynamics on sea ice simulations with prescribed atmospheric conditions. Here experiments have been performed with a fully coupled atmosphere sea ice system and also using prescribed daily atmospheric forcing and monthly mean atmospheric forcing, to examine the differences of the sensitivity of the ice advection between the coupled and forcing models. Similar differences have been observed between DT and T in the forcing models but the magnitude is smaller than in the fully coupled model, and with monthly mean atmospheric forcing the difference is least. These differences highlight the importance of the inclusion of ice advection when undertaking studies using a fully interactive atmosphere sea ice model, or using prescribed daily/monthly atmospheric conditions to force a sea ice model for the Antarctic.

Seasonal Arctic Sea-Ice Simulations With A Prognostic Ice-Ocean Model

The seasonal cycle of sea ice, especially the ice margin location in the East Greenland region, is significantly affected by ocean circulation. The ocean circulation in turn can be altered by ice dynamics which cause large amounts of ice to be transported to the ice margin to be melted, thus stratifying the ocean there. By responding to wind changes, the ice dynamics can also create rapid melting or freezing events which can destabilize the ocean.In an earlier study, Hibler and Bryan (1987) carried out a diagnostic simulation of the Arctic ice-ocean system in which a coupled ice-ocean circulation model was integrated for about five years beginning with mean annual estimates by Levitus (1982) of the observed temperature and salinity fields. As a consequence of this short integration, the mean baroclinic circulation of the ocean deviated little from the initial fields, although seasonal and local effects due to the interactive models were simulated. One particularly interesting result of this study was the presence of fluctuations of oceanic heat flux at the ice margin, which appeared to coincide with strong wind events occurring over a few days which induced periods of freezing.With this diagnostic model, good results for the location of the ice margin were obtained. However, a global budget analysis indicated that the net northward heat transport through the Faero–Shetland passage was not adequate to balance the heat loss to the atmosphere sustained by the ocean in the fifth year. Moreover, a 20-year simulation without diagnostic terms showed a degraduation of the baroclinic fields in the Arctic Basin possibly due to the very weak wind stress used for this particular years's wind forcing, or perhaps due to excessive damping in the ocean due to computational requirements imposed by the coarse grid.As a first step in the development of a higher-resolution fully interactive prognostic model, we have modified this model and carried out two prognostic simulations of the Arctic ice ocean system by employing 50-year integrations. The ocean model used for this study is essentially that of Hibler and Bryan (1987). However, the boundary conditions, atmospheric forcing, and ice model have been changed. In particular, a much more robust wind forcing was obtained by replacing the monthly mean wind fields with a 30-year means in order to obtain a seasonal forcing closer to climatology. With regard to the ice rheology, a cavitating fluid model in spherical coordinates which fully conserves ice mass and air sea heat exchanges was employed. The idea here is to attenuate less of the stress into the ocean so that even though the circulation is somewhat sluggish due to large viscous damping, a reasonable current field for the Arctic Basin might be obtained.Two types of prognostic circulation experiments were carried out with this model using different southern boundary conditions. In one case, a diagnostic relaxation near the boundary as used by Hibler and Bryan (1987) was employed. In this case, heat mass and salt transports through the southern boundary are essentially simulated. In the second case, the net burotropic flow through the Faero-Shetland passage and Denmark Strait were specified with the baroclinic transports partially specified by diagnostic relaxation terms. The results from both these models are analyzed with special attention to the ice edge location and the character of the baroclinic fields in the Arctic Basin.

Seasonal Arctic Sea-Ice Simulations With A Prognostic Ice-Ocean Model

The seasonal cycle of sea ice, especially the ice margin location in the East Greenland region, is significantly affected by ocean circulation. The ocean circulation in turn can be altered by ice dynamics which cause large amounts of ice to be transported to the ice margin to be melted, thus stratifying the ocean there. By responding to wind changes, the ice dynamics can also create rapid melting or freezing events which can destabilize the ocean. In an earlier study, Hibler and Bryan (1987) carried out a diagnostic simulation of the Arctic ice-ocean system in which a coupled ice-ocean circulation model was integrated for about five years beginning with mean annual estimates by Levitus (1982) of the observed temperature and salinity fields. As a consequence of this short integration, the mean baroclinic circulation of the ocean deviated little from the initial fields, although seasonal and local effects due to the interactive models were simulated. One particularly interesting result of this study was the presence of fluctuations of oceanic heat flux at the ice margin, which appeared to coincide with strong wind events occurring over a few days which induced periods of freezing. With this diagnostic model, good results for the location of the ice margin were obtained. However, a global budget analysis indicated that the net northward heat transport through the Faero–Shetland passage was not adequate to balance the heat loss to the atmosphere sustained by the ocean in the fifth year. Moreover, a 20-year simulation without diagnostic terms showed a degraduation of the baroclinic fields in the Arctic Basin possibly due to the very weak wind stress used for this particular years's wind forcing, or perhaps due to excessive damping in the ocean due to computational requirements imposed by the coarse grid. As a first step in the development of a higher-resolution fully interactive prognostic model, we have modified this model and carried out two prognostic simulations of the Arctic ice ocean system by employing 50-year integrations. The ocean model used for this study is essentially that of Hibler and Bryan (1987). However, the boundary conditions, atmospheric forcing, and ice model have been changed. In particular, a much more robust wind forcing was obtained by replacing the monthly mean wind fields with a 30-year means in order to obtain a seasonal forcing closer to climatology. With regard to the ice rheology, a cavitating fluid model in spherical coordinates which fully conserves ice mass and air sea heat exchanges was employed. The idea here is to attenuate less of the stress into the ocean so that even though the circulation is somewhat sluggish due to large viscous damping, a reasonable current field for the Arctic Basin might be obtained. Two types of prognostic circulation experiments were carried out with this model using different southern boundary conditions. In one case, a diagnostic relaxation near the boundary as used by Hibler and Bryan (1987) was employed. In this case, heat mass and salt transports through the southern boundary are essentially simulated. In the second case, the net burotropic flow through the Faero-Shetland passage and Denmark Strait were specified with the baroclinic transports partially specified by diagnostic relaxation terms. The results from both these models are analyzed with special attention to the ice edge location and the character of the baroclinic fields in the Arctic Basin.

Prospects for Describing and Monitoring from Space the Elements of the Seasonal Cycle of Sea Ice

A discussion is presented of the elements of the sea-ice seasonal cycle which are significant in climatic description, and an annual cycle of measured microwave radiance is shown and discussed. Areas are defined where interpretation of spacecraft data could improve the seasonal picture of sea ice including snow cover, surface albedo, and ice production.

Prospects for Describing and Monitoring from Space the Elements of the Seasonal Cycle of Sea Ice

A discussion is presented of the elements of the sea-ice seasonal cycle which are significant in climatic description, and an annual cycle of measured microwave radiance is shown and discussed. Areas are defined where interpretation of spacecraft data could improve the seasonal picture of sea ice including snow cover, surface albedo, and ice production.