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Abstract
Analysis of weekly sea ice charts produced by the U.S. National Ice Center from 1976 to 2007 indicates large interannual variations in the averaged winter landfast ice extent around the Arctic Basin. During the 32-yr period of the record, landfast ice cover was relatively extensive from the early to mid-1980s but since then has declined in many coastal regions of the Arctic, particularly after the early 1990s. While the Barents, Baltic, and Bering Seas show increases in landfast ice area, the overall change for the Northern Hemisphere is negative, about −12.27 (±2.8) × 103 km2 yr−1, or −7 (±1.5)% decade−1 relative to the long-term mean. Except in a few coastal regions, the seasonal duration of landfast ice is shorter overall, particularly in the Laptev, East Siberian, and Chukchi Seas. The decreased winter landfast ice extent is associated with some notable changes in ice growth and melt patterns, in particular the slowed landfast ice expansion during fall and early winter since 1990. The observed changes in Arctic landfast ice could have profound impacts on the Arctic coasts. The challenge is to understand and project the responses of the whole coastal ecosystem to changing ice cover and Arctic warming.
Abstract
Analysis of weekly sea ice charts produced by the U.S. National Ice Center from 1976 to 2007 indicates large interannual variations in the averaged winter landfast ice extent around the Arctic Basin. During the 32-yr period of the record, landfast ice cover was relatively extensive from the early to mid-1980s but since then has declined in many coastal regions of the Arctic, particularly after the early 1990s. While the Barents, Baltic, and Bering Seas show increases in landfast ice area, the overall change for the Northern Hemisphere is negative, about −12.27 (±2.8) × 103 km2 yr−1, or −7 (±1.5)% decade−1 relative to the long-term mean. Except in a few coastal regions, the seasonal duration of landfast ice is shorter overall, particularly in the Laptev, East Siberian, and Chukchi Seas. The decreased winter landfast ice extent is associated with some notable changes in ice growth and melt patterns, in particular the slowed landfast ice expansion during fall and early winter since 1990. The observed changes in Arctic landfast ice could have profound impacts on the Arctic coasts. The challenge is to understand and project the responses of the whole coastal ecosystem to changing ice cover and Arctic warming.
Abstract
The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska.
Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate.
Abstract
The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska.
Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate.
Abstract
A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial system is responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992–2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinder all-sky temperatures (APP); 4) National Centers for Environmental Prediction– National Center for Atmospheric Research (NCEP–NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effect on the output from hydrologic models, and several different datasets should be used for a robust hydrologic assessment.
Abstract
A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial system is responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992–2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinder all-sky temperatures (APP); 4) National Centers for Environmental Prediction– National Center for Atmospheric Research (NCEP–NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effect on the output from hydrologic models, and several different datasets should be used for a robust hydrologic assessment.
Abstract
Generation and sample applications of an integrated set of remotely sensed products for investigations of Arctic climate are described. Cloud fraction, ice surface temperature, surface albedo, downwelling radiative fluxes, ice motion vectors, and cloud properties such as optical depth, phase, and droplet effective radius are estimated from calibrated and navigated AVHRR 1.1-km imagery of the Arctic Beaufort Sea region for June 1992 through July 1993. The processing strategy and characteristics of the products are reviewed. The utility of this type of multiparameter dataset for modeling applications and process studies is illustrated using simple examples of an albedo parameterization, sensible heat flux calculation, and sea ice advection.
Abstract
Generation and sample applications of an integrated set of remotely sensed products for investigations of Arctic climate are described. Cloud fraction, ice surface temperature, surface albedo, downwelling radiative fluxes, ice motion vectors, and cloud properties such as optical depth, phase, and droplet effective radius are estimated from calibrated and navigated AVHRR 1.1-km imagery of the Arctic Beaufort Sea region for June 1992 through July 1993. The processing strategy and characteristics of the products are reviewed. The utility of this type of multiparameter dataset for modeling applications and process studies is illustrated using simple examples of an albedo parameterization, sensible heat flux calculation, and sea ice advection.
