Sea Ice and Heat Budget

Since the Apr 1957 occupation of the first long-term US drifting station on sea ice, much has been learned about the properties of sea ice as a material. Properties of large, composite sheets of natural sea ice are still to be defined, however, because of the need for accurate knowledge of strength, roughness and albedo, among other parameters. In explanation of the ice cover, studies have been made of energy fluxes, radiative fluxes, albedos, wind and water flow profiles in boundary layers, air and ice temperature profiles and evaporation of condensation. There remain unresolved and complex questions concerning the circumstances under which the present ice cover could change

A continuum model of melt pond evolution on Arctic sea ice

[1] During the Northern Hemisphere summer, absorbed solar radiation melts snow and the upper surface of Arctic sea ice to generate meltwater that accumulates in ponds. The melt ponds reduce the albedo of the sea ice cover during the melting season, with a significant impact on the heat and mass budget of the sea ice and the upper ocean. We have developed a model, designed to be suitable for inclusion into a global circulation model (GCM), which simulates the formation and evolution of the melt pond cover. In order to be compatible with existing GCM sea ice models, our melt pond model builds upon the existing theory of the evolution of the sea ice thickness distribution. Since this theory does not describe the topography of the ice cover, which is crucial to determining the location, extent, and depth of individual ponds, we have needed to introduce some assumptions. We describe our model, present calculations and a sensitivity analysis, and discuss our results

Modeling the summertime evolution of sea-ice melt ponds

1] We present a mathematical model describing the summer melting of sea ice. We simulate the evolution of melt ponds and determine area coverage and total surface ablation. The model predictions are tested for sensitivity to the melt rate of unponded ice, enhanced melt rate beneath the melt ponds, vertical seepage, and horizontal permeability. The model is initialized with surface topographies derived from laser altimetry corresponding to first-year sea ice and multiyear sea ice. We predict that there are large differences in the depth of melt ponds and the area of coverage between the two types of ice. We also find that the vertical seepage rate and the melt rate of unponded ice are important in determining the total surface ablation and area covered by melt ponds

The influence of ocean flow on newly forming sea ice

The heat and mass balance of the Arctic Ocean is very sensitive to the growth and decay of sea ice and the interaction between the heat and salt fields in the oceanic boundary layer. The hydraulic roughness of sea ice controls the detailed nature of turbulent fluxes in the boundary layer and hence is an important ingredient in model parameterizations. We describe a novel mechanism for the generation of corrugations of the sea ice–ocean interface, present a mathematical analysis elucidating the mechanism, and present numerical calculations for geophysically relevant conditions. The mechanism relies on brine flows developing in the sea ice due to Bernoulli suction by flow of ocean past the interface. For oceanic shears at the ice interface of 0.2 s−1, we expect the corrugations to form with a wavelength dependent upon the permeability structure of the sea ice which is described herein. The mechanism should be particularly important during sea ice formation in wind-maintained coastal polynyas and in leads. This paper applies our earlier analyses of the fundamental instability to field conditions and extends it to take account of the anisotropic and heterogeneous permeability of sea ice

Combining QSAR and SSD to predict aquatic toxicity and species sensitivity of pyrethroid and organophosphate pesticides

The widespread use of pyrethroid and organophosphate pesticides necessitates accurate toxicity predictions for regulatory compliance. In this study QSAR and SSD models for six pyrethroid and four organophosphate compounds using QSAR Toolbox and SSD Toolbox have been developed. The QSAR models, described by the formula 48 h-EC50 or 96 h-LC50 = x + y * log Kow, were validated for predicting 48 h-EC50 values for acute toxicity and 96 h-LC50 values for acute fish toxicity, meeting criteria of ≥10, ≥0.7, and >0.5. Predicted 48 h-EC50 values for pyrethroids ranged from 3.95 × 10 mg/L (permethrin) to 8.21 × 10 mg/L (fenpropathrin), and 96 h-LC50 values from 3.89 × 10 mg/L (permethrin) to 1.68 × 10 mg/L (metofluthrin). For organophosphates, 48 h-EC50 values ranged from 2.00 × 10 mg/L (carbophenothion) to 3.76 × 10 mg/L (crufomate) and 96 h-LC50 values from 3.81 × 10 mg/L (carbophenothion) to 12.3 mg/L (crufomate). These values show a good agreement with experimental data, though some, like Carbophenothion, overestimated toxicity. HC05 values, indicating hazardous concentrations for 5% of species, range from 0.029 to 0.061 µg/L for pyrethroids and 0.030 to 0.072 µg/L for organophosphates. These values aid in establishing environmental quality standards (EQS). Compared to existing EQS, HC05 values for pyrethroids were less conservative, while those for organophosphates were comparable

A model of melt pond evolution on sea ice

A one-dimensional, thermodynamic, and radiative model of a melt pond on sea ice is presented that explicitly treats the melt pond as an extra phase. A two-stream radiation model, which allows albedo to be determined from bulk optical properties, and a parameterization of the summertime evolution of optical properties, is used. Heat transport within the sea ice is described using an equation describing heat transport in a mushy layer of a binary alloy (salt water). The model is tested by comparison of numerical simulations with SHEBA data and previous modeling. The presence of melt ponds on the sea ice surface is demonstrated to have a significant effect on the heat and mass balance. Sensitivity tests indicate that the maximum melt pond depth is highly sensitive to optical parameters and drainage. INDEX TERMS: 4207 Oceanography: General: Arctic and Antarctic oceanography; 4255 Oceanography: General: Numerical modeling; 4299 Oceanography: General: General or miscellaneous; KEYWORDS: sea ice, melt pond, albedo, Arctic Ocean, radiation model, thermodynami

Loss of sea ice during winter north of Svalbard

Sea ice loss in the Arctic Ocean has up to now been strongest during summer. In contrast, the sea ice concentration north of Svalbard has experienced a larger decline during winter since 1979. The trend in winter ice area loss is close to 10% per decade, and concurrent with a 0.3°C per decade warming of the Atlantic Water entering the Arctic Ocean in this region. Simultaneously, there has been a 2°C per decade warming of winter mean surface air temperature north of Svalbard, which is 20–45% higher than observations on the west coast. Generally, the ice edge north of Svalbard has retreated towards the northeast, along the Atlantic Water pathway. By making reasonable assumptions about the Atlantic Water volume and associated heat transport, we show that the extra oceanic heat brought into the region is likely to have caused the sea ice loss. The reduced sea ice cover leads to more oceanic heat transferred to the atmosphere, suggesting that part of the atmospheric warming is driven by larger open water area. In contrast to significant trends in sea ice concentration, Atlantic Water temperature and air temperature, there is no significant temporal trend in the local winds. Thus, winds have not caused the long-term warming or sea ice loss. However, the dominant winds transport sea ice from the Arctic Ocean into the region north of Svalbard, and the local wind has influence on the year-to-year variability of the ice concentration, which correlates with surface air temperatures, ocean temperatures, as well as the local wind