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June 1995. In the 0.66- μ m visible image ( Fig. 1a ), both clouds, apparent from the shadows that they cast, and frozen ponds appear bright because their reflectances are higher than those of the surrounding arctic tundra. The 11- μ m IR image in Fig. 1b displays that clouds and frozen ponds both appear to be very dark due to their cold temperatures. Using traditional threshold techniques it is almost impossible in such images to discriminate clouds from frozen ponds. In the past, many cloud
June 1995. In the 0.66- μ m visible image ( Fig. 1a ), both clouds, apparent from the shadows that they cast, and frozen ponds appear bright because their reflectances are higher than those of the surrounding arctic tundra. The 11- μ m IR image in Fig. 1b displays that clouds and frozen ponds both appear to be very dark due to their cold temperatures. Using traditional threshold techniques it is almost impossible in such images to discriminate clouds from frozen ponds. In the past, many cloud
(MODIS) and 2) to assess the accuracy of MODIS-derived surface temperatures by comparison with Thermochron-derived surface measurements. Here, we consider three Arctic domains that have different thermal characteristics: 1) snow-covered sea ice, 2) snow-covered tundra in a complex “built environment,” defined as an area with human-made structures and energy-use networks, and 3) snow-covered tundra in a homogeneous environment. Several different types of temperature sensors were used to collect an
(MODIS) and 2) to assess the accuracy of MODIS-derived surface temperatures by comparison with Thermochron-derived surface measurements. Here, we consider three Arctic domains that have different thermal characteristics: 1) snow-covered sea ice, 2) snow-covered tundra in a complex “built environment,” defined as an area with human-made structures and energy-use networks, and 3) snow-covered tundra in a homogeneous environment. Several different types of temperature sensors were used to collect an
. Climate ). Reflected shortwave radiation is controlled by the surface albedo, which in the Arctic is mainly determined by the presence or absence of snow. In the spring, when much of the region is still snow covered, downward shortwave radiation increases rapidly. Much of this radiation is reflected due to the high albedo of the snow-covered land surface. RASM captures the difference in cold season albedos between the taiga and tundra, with typical values of 0.4 and 0.6 respectively ( Fig. 3
. Climate ). Reflected shortwave radiation is controlled by the surface albedo, which in the Arctic is mainly determined by the presence or absence of snow. In the spring, when much of the region is still snow covered, downward shortwave radiation increases rapidly. Much of this radiation is reflected due to the high albedo of the snow-covered land surface. RASM captures the difference in cold season albedos between the taiga and tundra, with typical values of 0.4 and 0.6 respectively ( Fig. 3
the Arctic frontal zone over North America for January, April, July, and October. Based on a trajectory analysis of air masses for July, Bryson (1966) demonstrated that the modal position of the summer Arctic frontal zone over North America coincides closely with Reed and Kunkel’s (1960) analysis as well as the position of the tree line. He postulated that the summer frontal position might be important in determining the distribution of forest versus tundra. However, Bryson also considered the
the Arctic frontal zone over North America for January, April, July, and October. Based on a trajectory analysis of air masses for July, Bryson (1966) demonstrated that the modal position of the summer Arctic frontal zone over North America coincides closely with Reed and Kunkel’s (1960) analysis as well as the position of the tree line. He postulated that the summer frontal position might be important in determining the distribution of forest versus tundra. However, Bryson also considered the
to AVHRR data collected in the Arctic, and tested against ground-based measurements. In section 5 , algorithms to retrieve optical depth and effective radius of liquid water clouds are described, validated using in situ irradiance measurements, and applied to AVHRR images for three different arctic surface types: ocean, tundra, and snow. A summary is provided in section 6 . Satellite and field data in the arctic The National Oceanic and Atmospheric Administration (NOAA) polar orbiter data used
to AVHRR data collected in the Arctic, and tested against ground-based measurements. In section 5 , algorithms to retrieve optical depth and effective radius of liquid water clouds are described, validated using in situ irradiance measurements, and applied to AVHRR images for three different arctic surface types: ocean, tundra, and snow. A summary is provided in section 6 . Satellite and field data in the arctic The National Oceanic and Atmospheric Administration (NOAA) polar orbiter data used
effect decreases evapotranspiration in both hemispheres, particularly in the tropical rain forests (not statistically significant), while the radiative effect increases evapotranspiration in the NH and decreases it in the SH. The radiative effect generally increases evapotranspiration in the Arctic ( p < 0.05), as vegetation expands into the tundra, and decreases it in the Tropics and SH subtropics ( p < 0.05 over the Amazon, South Africa, Australia), resulting from forest dieback. Fixed vegetation
effect decreases evapotranspiration in both hemispheres, particularly in the tropical rain forests (not statistically significant), while the radiative effect increases evapotranspiration in the NH and decreases it in the SH. The radiative effect generally increases evapotranspiration in the Arctic ( p < 0.05), as vegetation expands into the tundra, and decreases it in the Tropics and SH subtropics ( p < 0.05 over the Amazon, South Africa, Australia), resulting from forest dieback. Fixed vegetation
soil. Currently there is no explicit incorporation of moss, lichen, or peat layers in the National Center for Atmospheric Research Land Surface Model (NCAR LSM; Bonan 1996 ) that has been used to investigate land–atmosphere interactions in the Arctic ( Lynch et al. 1999a,b ; Eugster et al. 1997 ). Mosses are ubiquitous in boreal forest and tundra ecosystems, which occupy 14% of the total global land area. In boreal forests, feather mosses ( Hylocomium and Pleurozium spp.) dominate the
soil. Currently there is no explicit incorporation of moss, lichen, or peat layers in the National Center for Atmospheric Research Land Surface Model (NCAR LSM; Bonan 1996 ) that has been used to investigate land–atmosphere interactions in the Arctic ( Lynch et al. 1999a,b ; Eugster et al. 1997 ). Mosses are ubiquitous in boreal forest and tundra ecosystems, which occupy 14% of the total global land area. In boreal forests, feather mosses ( Hylocomium and Pleurozium spp.) dominate the
1. Introduction The increase in global air temperature, which is twice as pronounced in polar regions than in all other parts of the world ( Chylek et al. 2009 ), is changing the distribution of vegetation zones ( Myers-Smith and Hik 2018 ). This change is particularly notable in the forest–tundra ecotone ( Payette et al. 2001 ) at the interface between the boreal forest and the Arctic shrub tundra. This region is characterized by a mosaic of forest patches, usually restricted to humid, wind
1. Introduction The increase in global air temperature, which is twice as pronounced in polar regions than in all other parts of the world ( Chylek et al. 2009 ), is changing the distribution of vegetation zones ( Myers-Smith and Hik 2018 ). This change is particularly notable in the forest–tundra ecotone ( Payette et al. 2001 ) at the interface between the boreal forest and the Arctic shrub tundra. This region is characterized by a mosaic of forest patches, usually restricted to humid, wind
OCXOB~-R1978 MING-KO WOO AND 'PHILIP MARSH 1537Analysis of Error in the Determination of Snow Storage for Small High Arctic Basins~ MING-KO WOO AND PHILIP MARSHDepartment of Geography, McMaster University, Hamilton, Ontario, Canada LSS 4K1(Manuscript received 14 November 1977, in final form 1 February 1978) ABSTRACT Water balance studies in tundra regions
OCXOB~-R1978 MING-KO WOO AND 'PHILIP MARSH 1537Analysis of Error in the Determination of Snow Storage for Small High Arctic Basins~ MING-KO WOO AND PHILIP MARSHDepartment of Geography, McMaster University, Hamilton, Ontario, Canada LSS 4K1(Manuscript received 14 November 1977, in final form 1 February 1978) ABSTRACT Water balance studies in tundra regions
and simulations in the Arctic tundra region have been carried out in various areas. Pomeroy et al. (1997) and Essery et al. (1999) simulated a snow distribution and verified their results with a snow survey in Trail Valley Creek (68°43′N, 130°40′W). According to Pomeroy et al. (1997) and Essery et al. (1999) , vegetation has a strong control on snow accumulation, and taller vegetation such as shrub of sparse forest traps more wind-blown snow. Averaged snow mass in shrub tundra and sparse
and simulations in the Arctic tundra region have been carried out in various areas. Pomeroy et al. (1997) and Essery et al. (1999) simulated a snow distribution and verified their results with a snow survey in Trail Valley Creek (68°43′N, 130°40′W). According to Pomeroy et al. (1997) and Essery et al. (1999) , vegetation has a strong control on snow accumulation, and taller vegetation such as shrub of sparse forest traps more wind-blown snow. Averaged snow mass in shrub tundra and sparse
