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- Author or Editor: J. Maslanik x
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Abstract
Realistic treatment of sea ice processes in general circulation models is needed to simulate properly global climate and climate change scenarios. As new sea ice treatments become available, it is necessary to evaluate them in terms of their accuracy and computational time. Here, several dynamic ice models are compared using both a 2-category and 28-category ice thickness distribution. Simulations are conducted under normal wind forcing, as well as under increased and decreased wind speeds. It is found that the lack of a shear strength parameterization in the cavitating fluid rheology produces significantly different results in both ice thickness and ice velocity than those produced by an elliptical rheology. Furthermore, use of a 28-category ice thickness distribution amplifies differences in the responses of the various models. While the choice of dynamic model is governed by requirements of accuracy and implementation, it appears that, in terms of both parameterization of physical properties and computational time, the elliptical rheology is well-suited for inclusion in a GCM.
Abstract
Realistic treatment of sea ice processes in general circulation models is needed to simulate properly global climate and climate change scenarios. As new sea ice treatments become available, it is necessary to evaluate them in terms of their accuracy and computational time. Here, several dynamic ice models are compared using both a 2-category and 28-category ice thickness distribution. Simulations are conducted under normal wind forcing, as well as under increased and decreased wind speeds. It is found that the lack of a shear strength parameterization in the cavitating fluid rheology produces significantly different results in both ice thickness and ice velocity than those produced by an elliptical rheology. Furthermore, use of a 28-category ice thickness distribution amplifies differences in the responses of the various models. While the choice of dynamic model is governed by requirements of accuracy and implementation, it appears that, in terms of both parameterization of physical properties and computational time, the elliptical rheology is well-suited for inclusion in a GCM.
Abstract
The characteristics of satellite-derived land-cover data for climate models vary depending on sensor properties and processing options. To better understand the first-order effects of differences in land-cover data on a land surface parameterization scheme (VBATS), stand-alone model runs were performed for two adjacent 2.8° × 2.8° GCM grid cells in Wyoming using land cover from two satellite-derived maps (AVHRR, TM) and a global land-cover dataset commonly used in GCMs.
The dominant cover type by area differed among the datasets for both grid cells. In the western grid cell, these differences resulted in substantially different surface fluxes simulated by VBATS. At spatial resolutions of 0.2° and 0.4°, the two satellite-derived datasets agreed on only 54%–62% of the land-cover types in both grid cells. Despite this disagreement, the VBATS simulated surface fluxes averaged over the grid cell were similar in the eastern grid cell for the two satellite-derived datasets. In the western grid cell, the partitioning of net radiation into sensible and latent heat fluxes was influenced by the dataset prescriptions of land-cover heterogeneity. In particular, the relative proportions of wet cover types (i.e., inland water and irrigated crop) had an effect on this partitioning, emphasizing the importance of accounting for the presence of wet cover types within a GCM grid cell in arid regions.
Spatial aggregation of the satellite-derived datasets reduced the number of land-cover types prescribed for each GCM grid cell. In the western grid cell, the reduction in the number of cover types from 11 to 2 resulted in differences in annual averages of sensible and latent heat fluxes of about 10%. Other simulations involving these datasets suggest that these differences could be reduced if one accounted for the wet cover types. In this respect, fine spatial resolution is required for some cover types, whereas coarser resolution may be adequate for other types. Land-cover classifications for land surface modeling need to be based more on model sensitivities than on traditional vegetation-type schemes.
Abstract
The characteristics of satellite-derived land-cover data for climate models vary depending on sensor properties and processing options. To better understand the first-order effects of differences in land-cover data on a land surface parameterization scheme (VBATS), stand-alone model runs were performed for two adjacent 2.8° × 2.8° GCM grid cells in Wyoming using land cover from two satellite-derived maps (AVHRR, TM) and a global land-cover dataset commonly used in GCMs.
The dominant cover type by area differed among the datasets for both grid cells. In the western grid cell, these differences resulted in substantially different surface fluxes simulated by VBATS. At spatial resolutions of 0.2° and 0.4°, the two satellite-derived datasets agreed on only 54%–62% of the land-cover types in both grid cells. Despite this disagreement, the VBATS simulated surface fluxes averaged over the grid cell were similar in the eastern grid cell for the two satellite-derived datasets. In the western grid cell, the partitioning of net radiation into sensible and latent heat fluxes was influenced by the dataset prescriptions of land-cover heterogeneity. In particular, the relative proportions of wet cover types (i.e., inland water and irrigated crop) had an effect on this partitioning, emphasizing the importance of accounting for the presence of wet cover types within a GCM grid cell in arid regions.
Spatial aggregation of the satellite-derived datasets reduced the number of land-cover types prescribed for each GCM grid cell. In the western grid cell, the reduction in the number of cover types from 11 to 2 resulted in differences in annual averages of sensible and latent heat fluxes of about 10%. Other simulations involving these datasets suggest that these differences could be reduced if one accounted for the wet cover types. In this respect, fine spatial resolution is required for some cover types, whereas coarser resolution may be adequate for other types. Land-cover classifications for land surface modeling need to be based more on model sensitivities than on traditional vegetation-type schemes.
Warming of the arctic climate is having a substantial impact on the Alaskan North Slope coastal region. The warming is associated with increasing amounts of open water in the arctic seas, rising sea level, and thawing permafrost. Coastal geography and increasing development along the coastline are contributing to increased vulnerability of infrastructure, utilities, and supplies of food and gasoline to storms, flooding, and coastal erosion. Secondary impacts of coastal flooding may include harm to animals and their land or sea habitats, if pollutants are released. Further, Inupiat subsistence harvesting of marine sources of food, offshore resource extraction, and marine transportation may be affected. This paper describes a project to understand, support, and enhance the local decision-making process on the North Slope of Alaska on socioeconomic issues that are influenced by warming, climate variability, and extreme weather events.
Warming of the arctic climate is having a substantial impact on the Alaskan North Slope coastal region. The warming is associated with increasing amounts of open water in the arctic seas, rising sea level, and thawing permafrost. Coastal geography and increasing development along the coastline are contributing to increased vulnerability of infrastructure, utilities, and supplies of food and gasoline to storms, flooding, and coastal erosion. Secondary impacts of coastal flooding may include harm to animals and their land or sea habitats, if pollutants are released. Further, Inupiat subsistence harvesting of marine sources of food, offshore resource extraction, and marine transportation may be affected. This paper describes a project to understand, support, and enhance the local decision-making process on the North Slope of Alaska on socioeconomic issues that are influenced by warming, climate variability, and extreme weather events.
Abstract
Simulations of Arctic climate require treatment of land, ocean, ice, and atmospheric processes, and are further complicated by the dynamic nature of the sea ice cover. Here, the ability of a climate system model to simulate conditions over the Arctic Ocean during April–September 1990, a period of anomalous atmospheric circulation and sea ice conditions, is investigated. Differences between observations and model results are used to gain insight into the mechanisms that contributed to the observed record reduction in ice extent in late summer. The coupled model reproduces the general patterns seen in comparison sea level pressure fields in most months, but the discrepancies significantly affect the model’s ability to simulate details of sea ice transport and warm air advection linked to the unusual ice conditions. The use of prescribed sea ice fraction in the climate model yields relatively small changes in the surface energy balance compared to the fully-coupled simulation with dynamic ice cover, but significantly affects atmospheric circulation in spring and late summer. Analyses of observations, coupled model experiments, and stand-alone ice model output suggest a positive feedback between ice dynamics and ice melt that contributed to the ice extent anomaly. The results highlight the importance of regional atmospheric circulation in driving interannual variations in Arctic ice extent, and illustrate the level of model performance needed to simulate such variations.
Abstract
Simulations of Arctic climate require treatment of land, ocean, ice, and atmospheric processes, and are further complicated by the dynamic nature of the sea ice cover. Here, the ability of a climate system model to simulate conditions over the Arctic Ocean during April–September 1990, a period of anomalous atmospheric circulation and sea ice conditions, is investigated. Differences between observations and model results are used to gain insight into the mechanisms that contributed to the observed record reduction in ice extent in late summer. The coupled model reproduces the general patterns seen in comparison sea level pressure fields in most months, but the discrepancies significantly affect the model’s ability to simulate details of sea ice transport and warm air advection linked to the unusual ice conditions. The use of prescribed sea ice fraction in the climate model yields relatively small changes in the surface energy balance compared to the fully-coupled simulation with dynamic ice cover, but significantly affects atmospheric circulation in spring and late summer. Analyses of observations, coupled model experiments, and stand-alone ice model output suggest a positive feedback between ice dynamics and ice melt that contributed to the ice extent anomaly. The results highlight the importance of regional atmospheric circulation in driving interannual variations in Arctic ice extent, and illustrate the level of model performance needed to simulate such variations.
Abstract
The primary goal of the Western Arctic Linkage Experiment (WALE) was to better understand uncertainties of simulated hydrologic and ecosystem dynamics of the western Arctic in the context of 1) uncertainties in the data available to drive the models and 2) different approaches to simulating regional hydrology and ecosystem dynamics. Analyses of datasets on climate available for driving hydrologic and ecosystem models within the western Arctic during the late twentieth century indicate that there are substantial differences among the mean states of datasets for temperature, precipitation, vapor pressure, and radiation variables. Among the studies that examined temporal trends among the alternative climate datasets, there is not much consensus on trends among the datasets. In contrast, monthly and interannual variations of some variables showed some correlation across the datasets. The application of hydrology models driven by alternative climate drivers revealed that the simulation of regional hydrology was sensitive to precipitation and water vapor differences among the driving datasets and that accurate simulation of regional water balance is limited by biases in the forcing data. Satellite-based analyses for the region indicate that vegetation productivity of the region increased during the last two decades of the twentieth century because of earlier spring thaw, and the temporal variability of vegetation productivity simulated by different models from 1980 to 2000 was generally consistent with estimates based on the satellite record for applications driven with alternative climate datasets. However, the magnitude of the fluxes differed by as much as a factor of 2.5 among applications driven with different climate data, and spatial patterns of temporal trends in carbon dynamics were quite different among simulations. Finally, the study identified that the simulation of fire by ecosystem models is particularly sensitive to alternative climate datasets, with little or no fire simulated for some datasets. The results of WALE identify the importance of conducting retrospective analyses prior to coupling hydrology and ecosystem models with climate system models. For applications of hydrology and ecosystem models driven by projections of future climate, the authors recommend a coupling strategy in which future changes from climate model simulations are superimposed on the present mean climate of the most reliable datasets of historical climate.
Abstract
The primary goal of the Western Arctic Linkage Experiment (WALE) was to better understand uncertainties of simulated hydrologic and ecosystem dynamics of the western Arctic in the context of 1) uncertainties in the data available to drive the models and 2) different approaches to simulating regional hydrology and ecosystem dynamics. Analyses of datasets on climate available for driving hydrologic and ecosystem models within the western Arctic during the late twentieth century indicate that there are substantial differences among the mean states of datasets for temperature, precipitation, vapor pressure, and radiation variables. Among the studies that examined temporal trends among the alternative climate datasets, there is not much consensus on trends among the datasets. In contrast, monthly and interannual variations of some variables showed some correlation across the datasets. The application of hydrology models driven by alternative climate drivers revealed that the simulation of regional hydrology was sensitive to precipitation and water vapor differences among the driving datasets and that accurate simulation of regional water balance is limited by biases in the forcing data. Satellite-based analyses for the region indicate that vegetation productivity of the region increased during the last two decades of the twentieth century because of earlier spring thaw, and the temporal variability of vegetation productivity simulated by different models from 1980 to 2000 was generally consistent with estimates based on the satellite record for applications driven with alternative climate datasets. However, the magnitude of the fluxes differed by as much as a factor of 2.5 among applications driven with different climate data, and spatial patterns of temporal trends in carbon dynamics were quite different among simulations. Finally, the study identified that the simulation of fire by ecosystem models is particularly sensitive to alternative climate datasets, with little or no fire simulated for some datasets. The results of WALE identify the importance of conducting retrospective analyses prior to coupling hydrology and ecosystem models with climate system models. For applications of hydrology and ecosystem models driven by projections of future climate, the authors recommend a coupling strategy in which future changes from climate model simulations are superimposed on the present mean climate of the most reliable datasets of historical climate.
