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B. Kochtubajda and E. P. Lozowski


A numerical model has been developed to investigate the sublimation rate of cylindrical dry ice pellets in clear and cloudy air. Experiments conducted in the University of Alberta FROST icing-wind tunnel were used to make comparisons with the model predictions of the sublimation rate. These experiments were carried out in both cold and warm environments. Furthermore, some of the experiments were conducted with the sprays operating in order to determine the effect of a simulated “cloudy” environment on the sublimation rate.

The principal conclusions are: a) despite the use of several simplifying assumptions, the cylindrical model predict the sublimation rates of dry ice pellets to within 20%, when compared with wind tunnel observations, and b) cloudy and saturated conditions at warm temperatures enhance the sublimation rate of dry ice, but cloudy and saturated conditions at cold temperatures do not have an appreciable effect on the sublimation rate of dry ice.

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R. E. Stewart, H. G. Leighton, P. Marsh, G. W. K. Moore, H. Ritchie, W. R. Rouse, E. D. Soulis, G. S. Strong, R. W. Crawford, and B. Kochtubajda

The Mackenzie River is the largest North American source of freshwater for the Arctic Ocean. This basin is subjected to wide fluctuations in its climate and it is currently experiencing a pronounced warming trend. As a major Canadian contribution to the Global Energy and Water Cycle Experiment (GEWEX), the Mackenzie GEWEX Study (MAGS) is focusing on understanding and modeling the fluxes and reservoirs governing the flow of water and energy into and through the climate system of the Mackenzie River Basin. MAGS necessarily involves research into many atmospheric, land surface, and hydrological issues associated with cold climate systems. The overall objectives and scope of MAGS will be presented in this article.

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Energy and Water Cycles in a High-Latitude, North-Flowing River System

Summary of Results from the Mackenzie GEWEX Study—Phase I

W. R. Rouse, E. M. Blyth, R. W. Crawford, J. R. Gyakum, J. R. Janowicz, B. Kochtubajda, H. G. Leighton, P. Marsh, L. Martz, A. Pietroniro, H. Ritchie, W. M. Schertzer, E. D. Soulis, R. E. Stewart, G. S. Strong, and M. K. Woo

The MacKenzie Global Energy and Water Cycle Experiment (GEWEX) Study, Phase 1, seeks to improve understanding of energy and water cycling in the Mackenzie River basin (MRB) and to initiate and test atmospheric, hydrologic, and coupled models that will project the sensitivity of these cycles to climate change and to human activities. Major findings from the study are outlined in this paper. Absorbed solar radiation is a primary driving force of energy and water, and shows dramatic temporal and spatial variability. Cloud amounts feature large diurnal, seasonal, and interannual fluctuations. Seasonality in moisture inputs and outputs is pronounced. Winter in the northern MRB features deep thermal inversions. Snow hydrological processes are very significant in this high-latitude environment and are being successfully modeled for various landscapes. Runoff processes are distinctive in the major terrain units, which is important to overall water cycling. Lakes and wetlands compose much of MRB and are prominent as hydrologic storage systems that must be incorporated into models. Additionally, they are very efficient and variable evaporating systems that are highly sensitive to climate variability. Mountainous high-latitude sub-basins comprise a mosaic of land surfaces with distinct hydrological attributes that act as variable source areas for runoff generation. They also promote leeward cyclonic storm generation. The hard rock terrain of the Canadian Shield exhibits a distinctive energy flux regimen and hydrologic regime. The MRB has been warming dramatically recently, and ice breakup and spring outflow into the Polar Sea has been occurring progressively earlier. This paper presents initial results from coupled atmospheric-hydrologic modeling and delineates distinctive cold region inputs needed for developments in regional and global climate modeling.

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