Sea ice model

The horizontal ice distribution simulated with such a low order ice model resembles the observed distributions of sea ice however, the storage of freshwater in the ice and the formation of a new water mass by freezing with brine release and by melting is neglected. To include these features, the three-level ice model of Winton (2000) is coupled with MOM-3.1 to provide an improved representation of sea ice for long-term simulations. The sea ice is vertically resolved by two ice layers and a snow cover, with different development of thickness and temperature. As shown in Fig. 19.3, this local thermodynamic description yields arealistic simulation of the interannual variation in the thickness and the spatial extent of the ice cover in the Baltic Sea. The transfer of wind momentum to the currents and to surface waves is exponentially damped out if the ice thickness exceeds a critical value, for example, 10 cm, assuming fast ice. 

FIGURE 19.3 Example for the simulated ice thickness distribution (a) in March 1987, and a time series of the area covered by sea ice (b). The symbols denote data of Seina and Palosuo (1996). 

More elaborate models, including the rheology, and the drift of sea ice have to be applied to describe the formation of different ice classes, transports of sea ice, and a forecast potential for ship navigation. Dynamic-thermodynamic sea ice is applied in three-dimensional models of the Baltic Sea by Haapala and Lepparanta (1996), Meier et al., (1999,2(X)2a,b), Lehmann and Hinrichsen (2000), and Schrum et al. (2003). A comprehensive overview of the theory and application of sea ice drift is given by Lepparanta (2005). Models based on the Flexible Model System (FMS), including the Modular Ocean Model (version 4), may also apply a dynamical ice module (Griffies et al., 2004 Balaji, 2004). 

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The model includes a dynamic thermodynamic sea ice model. The dynamics of sea ice are formulated using viscous-plastic rheology [Hibler (1979)]. The thermodynamics relate changes in sea ice thickness to a balance of radiant, turbulent, and oceanic heat fluxes. The effect of snow accumulation on sea ice is included, along... 

Sea ice is represented in the model as a two-dimensional surface covered with a snowpack. Ice advection, rheology and snow cover are calculated from the sea-ice model embedded in MPIOM [Hibler (1979)]. The only source of pollutants for the ice compartment is deposition from the atmosphere. Once pollutants enter the ice compartment they can diffuse into the snow pore space air, dissolve in the interstitial liquid water or adsorb to the ice surface. Together with the sea ice the pollutants undergo advection. Sinks considered for the ice compartment are volatilisation to the atmosphere and release into the ocean with melt water. 

In applications such as the Arctic sea ice model (Chapman et al., 1994) mentioned above, a strategic objective of a preliminary computer experiment is screening finding the important input variables. Screening is not a trivial task because the computer model is typically complex, and the relationships between input variables and output variables are not obvious. A common approach is to approximate the relationship by a statistical surrogate model, which is easier to explore. This is particularly useful when there are many input variables. 

NCAR (1997). The NCAR Community Climate Model CCM3 with NCAR/CSM Sea Ice Model. University Corporation for Atmospheric Research, National Center for Atmospheric Reseeu-ch, and Climate and Global Dynamics Division, http //www.cgd.ucar.edu 80/ccr/bettge/ice. 

Winton, M., 2000. A reformulated three-layer sea ice model. Journal of Atmospheric and Oceanic, Technology, 17 (4), 525-531. 

Hibler, W.D. 1979. A dynamic-thermodynamic sea ice model, J.Phys.Oceanogr., 9, 815-845. 

Kreyscher,M. and M. Harder. 1998. First results of the Sea Ice Model Intercomparison Project (SIMIP), Annals of Glaciology 25, in press. 

Such OGCM modeling also suggests the importance of ice-cover in controlling the amount of Thxs advection (Henderson et al. 1999a). Low particle fluxes beneath sea-ice may lead to low scavenging rates in these areas, particularly where ice cover is permanent. In these areas, °Thxs may be advected to the edge of the ice sheet where it is... 

The previous model setup included an identical horizontal resolution for ocean and atmosphere, and online coupling was implemented in the ECHAM submodel with an exchange rate of 3 hours. In the current model setup atmosphere and ocean are coupled online using the Ocean-Atmosphere-Sea Ice-Soil (OASIS) coupler [Valcke et al (2004)] with a coupling time step of one day. OASIS passes boundary condi-... 

Processes not resolved in MPI-MCTM that also might serve as explanations for discrepancies between model results and observations are degradation of DDT in the ocean and degradation of DDT in the particle-bound state in air. Furthermore, the neglection of sea-ice had been shown to increase the northern hemisphere meridional DDT gradient [Guglielmo (2008)]. 

Over recent years, increased computational power and improved efficiency have allowed significant developments and improvements to be applied to climate models [19], including the improved representation of dynamical processes such as advection [20] and an increase in the horizontal and vertical resolution of models. It has also enabled additional processes to be incorporated in models, particularly the coupling of the atmospheric and ocean components of models, the modelling of aerosols and of land surface and sea ice processes. The parame-terisations of physical processes have also been improved. 

Levis et al. (2003) described an algorithm based on the data of field and laboratory measurements that enables calculation of BVOC emissions being used as a component of the interactive climate model CCSM (Version 2.0) for the atmosphere-ocean-land-sea ice cover system developed by National Center for... 

Over a considerable fraction of the high-latitude global ocean, sea ice forms a boundary between the atmosphere and the ocean, and considerably influences their interaction. The details and consequences of the role of sea ice in the global climate system are still poorly known. Improved knowledge is needed of the broad-scale time-varying distributions of the physical characteristics of sea ice, particularly ice thickness and the overlying snow-cover thickness, in both hemispheres, and the dominant processes of ice formation, modification, decay and transport which influence and determine ice thickness, composition and distribution. We do not know how accurate present model predictions of the sea ice responses to climate change are, since the representation of much of the physics is incomplete in many models, and it will be necessary to improve coupled models considerably to provide this predictive capability. 

In connection with the SHEBA project, the U.S. Department of Energy s Atmosphere Radiation Measurement (ARM) program indicated its intention to develop a Cloud and Radiation Testbed (CART) facility on the North Slope of Alaska. The principal focus of this program will be on atmospheric radiative transport, especially as modified by clouds (such transport impacts the growth and decay of sea ice), as well as testing, validation, and comparison of radiation transfer models in both the ice pack and Arctic coastal environment. 

The most important impacts of sea ice on climate, as revealed through numerical modeling, are the following ... 

As mentioned above, recent attempts to identify the level of atmospheric climate change have been confined to analyses of comparatively long data series on SAT, though smaller volumes of data on changes in sea ice cover extent, vertical temperature profile (radiosonde data), and results of satellite microwave sensing have also been considered (Christy et ai, 1998). However, numerical modeling results show... 

Legendre L. and Krapivin V.F. (1992). Model for vertical structure of phytoplankton community in Arctic regions. Proc. of 7th Int. Symp. on Okhotsk Sea Sea Ice (February 2-5, 1992, Mombetsu, Japan). Okhotsk Sea Cold Ocean Res. Assoc., Mombetsu, pp. 314-316. 

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