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Author: Gordon H. Fleming Publisher: ISBN: Category : Sea ice Languages : en Pages : 245
Book Description
A coupled ice-ocean numerical model is developed which improves the simulation of the annual cycle and interannual variations in ice cover in the Arctic. Although the accuracy of the simulated ice concentration is increased, the annual cycle of ice coverage is still exaggerated. Several experiments are conducted to determine the importance of incorporating a fully interactive ocean. Inclusion of a fully prognostic ocean component vice a ten-year mean ocean cycle in the model improves the correlation of simulated ice concentration fields with observed data. This is the case for all regions in the Arctic; for both the annual cycle and interannual variations of the ice cover. In contrast to results using ice models without a fully prognostic ocean component, this model is quite insensitive to changes in the frozen surface albedo. Exceptions are evident where the ocean heat flux into the mixed layer is small and the ice is thin. At the spatial (110 km) and temporal (monthly) scales used here, the heat provided by the ocean appears to be the dominant mechanism controlling the position of the ice edge and the extent of the ice pack. Within the pack, it is the dynamic forcing and, in particular, the wind forcing which controls the ice thickness and thickness distribution. The ocean circulation below the mixed layer appears to position the heat underneath the MIZ. The MIZ is also the region where the ice thickness tends to decrease through divergence. The linkage between the subsurface heat and the thinned ice cover is apparently controlled by conditions at the surface and the resulting response of the mixed layer. Keywords: Theses; Arctic regions; Sea ice; Mathematical models/prediction; Ice forecasting; Ocean models. (cp).
Author: Gordon H. Fleming Publisher: ISBN: Category : Sea ice Languages : en Pages : 245
Book Description
A coupled ice-ocean numerical model is developed which improves the simulation of the annual cycle and interannual variations in ice cover in the Arctic. Although the accuracy of the simulated ice concentration is increased, the annual cycle of ice coverage is still exaggerated. Several experiments are conducted to determine the importance of incorporating a fully interactive ocean. Inclusion of a fully prognostic ocean component vice a ten-year mean ocean cycle in the model improves the correlation of simulated ice concentration fields with observed data. This is the case for all regions in the Arctic; for both the annual cycle and interannual variations of the ice cover. In contrast to results using ice models without a fully prognostic ocean component, this model is quite insensitive to changes in the frozen surface albedo. Exceptions are evident where the ocean heat flux into the mixed layer is small and the ice is thin. At the spatial (110 km) and temporal (monthly) scales used here, the heat provided by the ocean appears to be the dominant mechanism controlling the position of the ice edge and the extent of the ice pack. Within the pack, it is the dynamic forcing and, in particular, the wind forcing which controls the ice thickness and thickness distribution. The ocean circulation below the mixed layer appears to position the heat underneath the MIZ. The MIZ is also the region where the ice thickness tends to decrease through divergence. The linkage between the subsurface heat and the thinned ice cover is apparently controlled by conditions at the surface and the resulting response of the mixed layer. Keywords: Theses; Arctic regions; Sea ice; Mathematical models/prediction; Ice forecasting; Ocean models. (cp).
Author: Gordon H. Fleming Publisher: ISBN: Category : Sea ice Languages : en Pages : 0
Book Description
A coupled ice-ocean numerical model is developed which improves the simulation of the annual cycle and interannual variations in ice cover in the Arctic. Although the accuracy of the simulated ice concentration is increased, the annual cycle of ice coverage is still exaggerated. Several experiments are conducted to determine the importance of incorporating a fully interactive ocean. Inclusion of a fully prognostic ocean component vice a ten-year mean ocean cycle in the model improves the correlation of simulated ice concentration fields with observed data. This is the case for all regions in the Arctic; for both the annual cycle and interannual variations of the ice cover. In contrast to results using ice models without a fully prognostic ocean component, this model is quite insensitive to changes in the frozen surface albedo. Exceptions are evident where the ocean heat flux into the mixed layer is small and the ice is thin. At the spatial (110 km) and temporal (monthly) scales used here, the heat provided by the ocean appears to be the dominant mechanism controlling the position of the ice edge and the extent of the ice pack. Within the pack, it is the dynamic forcing and, in particular, the wind forcing which controls the ice thickness and thickness distribution. The ocean circulation below the mixed layer appears to position the heat underneath the MIZ. The MIZ is also the region where the ice thickness tends to decrease through divergence. The linkage between the subsurface heat and the thinned ice cover is apparently controlled by conditions at the surface and the resulting response of the mixed layer. Keywords: Theses; Arctic regions; Sea ice; Mathematical models/prediction; Ice forecasting; Ocean models. (cp).
Author: National Research Council Publisher: National Academies Press ISBN: 0309265266 Category : Science Languages : en Pages : 93
Book Description
Recent well documented reductions in the thickness and extent of Arctic sea ice cover, which can be linked to the warming climate, are affecting the global climate system and are also affecting the global economic system as marine access to the Arctic region and natural resource development increase. Satellite data show that during each of the past six summers, sea ice cover has shrunk to its smallest in three decades. The composition of the ice is also changing, now containing a higher fraction of thin first-year ice instead of thicker multi-year ice. Understanding and projecting future sea ice conditions is important to a growing number of stakeholders, including local populations, natural resource industries, fishing communities, commercial shippers, marine tourism operators, national security organizations, regulatory agencies, and the scientific research community. However, gaps in understanding the interactions between Arctic sea ice, oceans, and the atmosphere, along with an increasing rate of change in the nature and quantity of sea ice, is hampering accurate predictions. Although modeling has steadily improved, projections by every major modeling group failed to predict the record breaking drop in summer sea ice extent in September 2012. Establishing sustained communication between the user, modeling, and observation communities could help reveal gaps in understanding, help balance the needs and expectations of different stakeholders, and ensure that resources are allocated to address the most pressing sea ice data needs. Seasonal-to-Decadal Predictions of Arctic Sea Ice: Challenges and Strategies explores these topics.
Author: Jacqueline M. Grebmeier Publisher: Springer ISBN: 9401788634 Category : Science Languages : en Pages : 461
Book Description
The Pacific Arctic region is experiencing rapid sea ice retreat, seawater warming, ocean acidification and biological response. Physical and biogeochemical modeling indicates the potential for step-function changes to the overall marine ecosystem. This synthesis book was coordinated within the Pacific Arctic Group, a network of international partners working in the Pacific Arctic. Chapter topics range from atmospheric and physical sciences to chemical processing and biological response to changing environmental conditions. Physical and biogeochemical modeling results highlight the need for data collection and interdisciplinary modeling activities to track and forecast the changing ecosystem of the Pacific Arctic with climate change.
Author: Jerome Weiss Publisher: Springer Science & Business Media ISBN: 940076202X Category : Science Languages : en Pages : 95
Book Description
Sea ice is a major component of polar environments, especially in the Arctic where it covers the entire Arctic Ocean throughout most of the year. However, in the context of climate change, the Arctic sea ice cover has been declining significantly over the last decades, either in terms of its concentration or thickness. The sea ice cover evolution and climate change are strongly coupled through the albedo positive feedback, thus possibly explaining the Arctic amplification of climate warming. In addition to thermodynamics, sea ice kinematics (drift, deformation) appears as an essential factor in the evolution of the ice cover through a reduction of the average ice age (and consequently of the cover's thickness), or ice export out of the Arctic. This is a first motivation for a better understanding of the kinematical and mechanical processes of sea ice. A more upstream, theoretical motivation is a better understanding of the brittle deformation of geophysical objects across a wide range of scales. Indeed, owing to its very strong kinematics, compared e.g. to the Earth’s crust, an unrivaled kinematical data set is available for sea ice from in situ (e.g. drifting buoys) or satellite observations. Here, we review the recent advances in the understanding of sea ice drift, deformation and fracturing obtained from these data. We focus particularly on the scaling properties in time and scale that characterize these processes, and we emphasize the analogies that can be drawn from the deformation of the Earth’s crust. These scaling properties, which are the signature of long-range elastic interactions within the cover, constrain future developments in the modeling of sea ice mechanics. We also show that kinematical and rheological variables such as average velocity, average strain-rate or strength have significantly changed over the last decades, accompanying and actually accelerating the Arctic sea ice decline.
Author: Peter Loewe Publisher: ISBN: Category : Ice Languages : en Pages : 12
Book Description
As a first step in the development of a fully coupled atmospheric ice model including ice dynamics, an Interannual simulation of the Arctic ice cover with GCM simulated surface fluxes and winds is compared to a Control equilibrium simulation using observed atmospheric forcing. For the simulated forcing results form a coarse resolution GCM with specified sea surface temperatures and ice extents, but simulated cloud cover, are used. For the observed data, climatological temperatures and winds (with daily fluctuations added) are employed. Results from a thermodynamic sea ice model subjected to the simulated forcing are also reported for comparison. Main fields as ice thickness, compactness, and drift was well as radiative and wind forcing are analyzed, both in terms of the seasonal cycle and in terms of interannual variability. An analysis of regional mass budget and longitudinal variability of key variables is presented in addition to results covering the full spatial domain. Amplitude and phase of simulated annual cycles of total ice mass and extent are in good agreement with those of the Control cycles; the annual mean thickness being about 2.5m and ice extent in summer being about 50% of the extent in winter. The simulated spatial distribution of ice is however considerably distorted. Large portions of the Arctic Ocean become ice free each summer while marginal seas stay ice covered. This is caused by a wind forcing that is generally simulated too stationary and too strong on account of the GCM's low resolution.
Author: Martin Vancoppenolle Publisher: Presses univ. de Louvain ISBN: 2874631132 Category : Science Languages : en Pages : 229
Book Description
Ice formed from seawater, called sea ice, is both an important actor in and a sensitive indicator of climate change. Covering 7% of the World Ocean, sea ice damps the atmosphere-ocean exchanges of heat, radiation and momentum in polar regions. It also affects the oceanic circulation at a global scale. Recent satellite and submarine observations systems indicate a sharp decrease in the extent and volume of Arctic sea ice over the last 30 years. In addition, climate models project drastic sea ice reductions for the next century, in both hemispheres, with potentially large consequences on climate and ecosystems. Contrary to what is commonly believed, sea ice retains about 25% of the oceanic salt when it forms. As salt cannot lock in the ice crystalline lattice, it accumulates in liquid inclusions of salty water (brine). Under a temperature change, the inclusions freeze or melt and release or absorb huge amounts of latent heat. This affects heat transfer through and storage in sea ice, which may affect the mass balance of sea ice at a global scale. This is the central hypothesis of this work. In order to address this problem, the author develops two sea ice models and assesses their ability to simulate the recent evolution of the sea ice mass balance. Then, the physics of brine uptake and drainage are included in the models and sea ice desalination is investigated. Finally, the impact of sea ice salinity variations on the global sea ice mass balance is studied. The roles of sea ice thermal properties, of ice-ocean salt / fresh water fluxes and of oceanic feedbacks are evaluated. The new salinity module improves the simulation of ice and ocean characteristics compared to observations. Including salinity variations increases ice growth, reduces vertical mixing in the ocean and the ocean-to-ice heat flux. In conclusion, salinity variations should be included in future sea ice models used for climate projections.
Author: Wouter Lefebvre Publisher: Presses univ. de Louvain ISBN: 9782874630958 Category : Science Languages : en Pages : 254
Book Description
The interannual variability of the sea ice in the Southern Ocean and its evolution projected for the end of the 21st century are investigated using observations and different types of models. First of all, none of the known atmospheric modes of variabilit
Author: Gordon H. Fleming
Author: Gordon H. Fleming
Author: Gordon H. Fleming
Author: Gordon H. Fleming
Author: National Research Council
Author: Jacqueline M. Grebmeier
Author: Jerome Weiss
Author: Peter Loewe
Author: 
Author: Martin Vancoppenolle
Author: Wouter Lefebvre