Abstract

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.

Abstract

Estimates of sea ice extent based on satellite observations show an increasing Antarctic sea ice cover from 1979 to 2004 even though in situ observations show a prevailing warming trend in both the atmosphere and the ocean. This riddle is explored here using a global multicategory thickness and enthalpy distribution sea ice model coupled to an ocean model. Forced by the NCEP–NCAR reanalysis data, the model simulates an increase of 0.20 × 10¹² m³ yr⁻¹ (1.0% yr⁻¹) in total Antarctic sea ice volume and 0.084 × 10¹² m² yr⁻¹ (0.6% yr⁻¹) in sea ice extent from 1979 to 2004 when the satellite observations show an increase of 0.027 × 10¹² m² yr⁻¹ (0.2% yr⁻¹) in sea ice extent during the same period. The model shows that an increase in surface air temperature and downward longwave radiation results in an increase in the upper-ocean temperature and a decrease in sea ice growth, leading to a decrease in salt rejection from ice, in the upper-ocean salinity, and in the upper-ocean density. The reduced salt rejection and upper-ocean density and the enhanced thermohaline stratification tend to suppress convective overturning, leading to a decrease in the upward ocean heat transport and the ocean heat flux available to melt sea ice. The ice melting from ocean heat flux decreases faster than the ice growth does in the weakly stratified Southern Ocean, leading to an increase in the net ice production and hence an increase in ice mass. This mechanism is the main reason why the Antarctic sea ice has increased in spite of warming conditions both above and below during the period 1979–2004 and the extended period 1948–2004.

Abstract

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.

Abstract

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.

Abstract

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, 1989–96, 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– 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.

Abstract

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 × 10¹² m³ in 1996 to 1.8 × 10¹² m³ in 1976 and averaging 1.4 × 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 × 10¹² m³ of ice in mainly the ice-edge areas during January–May, with substantial interannual variability ranging from 0.94 × 10¹² m³ in 1996 to 2.0 × 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 × 10¹² m³ in 1996 and 2.1 × 10¹² m³ in 1976, averaging 1.3 × 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 Niño–Southern Oscillation. This indicates that the PDO index may most effectively explain the regime shifts in the Bering Sea.

Abstract

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.

Abstract

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.

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.

Abstract

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.