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• W2188318198 abstract "primary strength of 3D general circula-tion models (GCMs) is how well theysimulate the coupled interactions be-tween sea-ice, the land surface, the at-mosphere, and the ocean, all of which are essentialfor understanding the climate system’s response toforcing perturbations. However, GCMs have lim-ited spatial and temporal resolution (because of to-tal integration time) and sometimes fail to capturethe fundamentally important processes that affectclimate variability. Moreover, the computationalconstraints on large models restrict the number andlength of sensitivity experiments.Component models, on the other hand, usespecified forcing at the boundaries, and althoughthey can’t study the coupled system’s response,they are easier to interpret and are useful forstudying individual forcing parameters. Re-searchers can also use models of intermediatecomplexity, such as regional ice-ocean coupledmodels, to study certain processes in partially cou-pled modes. Perhaps the best option of all is to usea hierarchy of models—a combination of interme-diate-complexity models, process models, andGCMs—to gain a clearer understanding of howmultiple processes can affect, say, the high-latitudeclimate system.The field of climate variability involves a widerange of spatial and temporal scales. Small spatial-scale processes such as turbulence, mixing, andconvection, for example, affect large-scale ice-ocean-atmosphere circulation patterns, which de-termine the system’s basic state, which in turnaffects small-scale processes. Small spatial-scaleprocesses also typically operate over shortertimescales. Resolving (or parameterizing) the cli-mate system’s smaller-scale features while per-forming long-term integrations on complex GCMsconstitutes the principal challenge for computa-tional scientists interested in the field.In this article, the authors discuss future pro-jections of the Arctic sea-ice cover from sophisti-cated General Circulation Model (GCMs), theuncertainties associated with these projections, andhow the use of simpler component models can helpin the interpretation of complex GCMs." @default.
• W2188318198 created "2016-06-24" @default.
• W2188318198 creator A5057469105 @default.
• W2188318198 date "2007-01-01" @default.
• W2188318198 modified "2023-09-27" @default.
• W2188318198 title "The authors discuss modeling's role in understanding the ice-ocean system, as well as its importance in predicting the future state of Arctic sea-ice. In doing so, this article presents results from a hierarchy of models of different complexity, their strengths and weaknesses, and how they could help forecast the future state of the ice-ocean system." @default.
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• W2188318198 hasPublicationYear "2007" @default.
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