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- Author or Editor: Ross D. Brown x
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Abstract
Historical and reconstructed snow cover data from stations in Canada, the United States, the former Soviet Union, and the People’s Republic of China were used to reconstruct monthly snow cover extent (SCE) fluctuations over midlatitudinal (∼40°–60°N) regions of North America (NA) and Eurasia back to the early 1900s using an areal snow index approach. The station distribution over NA allowed SCE to be reconstructed back to 1915 for 6 months (November–April), along with estimates of monthly mean snow water equivalent (SWE) from gridded daily snow depth data. Over Eurasia, SCE was able to be reconstructed back to 1922, but major gaps in the station network limited the approach to 3 months (October, March, and April). The reconstruction provided evidence of a general twentieth century increase in NA SCE, with significant increases in winter (December–February) SWE averaging 3.9% per decade. The results are consistent with an observed increasing trend in winter snow depth over Russia and provide further evidence for systematic increases in precipitation over NH midlatitudes. North American spring snow cover was characterized by rapid decreases during the 1980s and early 1990s with a significant long-term decrease in April SWE averaging 4.4% per decade. Eurasia was characterized by a significant reduction in April SCE over the 1922–97 period associated with a significant spring warming. The snow cover reduction was significant at the hemispheric scale with an estimated average NH SCE loss of 3.1 × 106 km2 (100 yr)−1 associated with significant warming of 1.26°C (100 yr)−1 over NH midlatitudinal land areas (40°–60°N). The computed temperature sensitivity of NH April SCE was −2.04 × 106 km2 °C−1. Since 1950, March SCE decreases have become more important than those in April with significant reductions over both continents averaging 8.5 × 106 km2 (100 yr)−1. March was also observed to have experienced the largest warming during the November–April snow season with significant post-1950 warming trends in both continents averaging 4.1°C (100 yr)−1. The hemisphere-wide elevated March snow cover–temperature response is consistent with the position of the snowline over continental grassland vegetation zones where snow cover is relatively shallow and the potential snow cover area–albedo feedback is large.
Abstract
Historical and reconstructed snow cover data from stations in Canada, the United States, the former Soviet Union, and the People’s Republic of China were used to reconstruct monthly snow cover extent (SCE) fluctuations over midlatitudinal (∼40°–60°N) regions of North America (NA) and Eurasia back to the early 1900s using an areal snow index approach. The station distribution over NA allowed SCE to be reconstructed back to 1915 for 6 months (November–April), along with estimates of monthly mean snow water equivalent (SWE) from gridded daily snow depth data. Over Eurasia, SCE was able to be reconstructed back to 1922, but major gaps in the station network limited the approach to 3 months (October, March, and April). The reconstruction provided evidence of a general twentieth century increase in NA SCE, with significant increases in winter (December–February) SWE averaging 3.9% per decade. The results are consistent with an observed increasing trend in winter snow depth over Russia and provide further evidence for systematic increases in precipitation over NH midlatitudes. North American spring snow cover was characterized by rapid decreases during the 1980s and early 1990s with a significant long-term decrease in April SWE averaging 4.4% per decade. Eurasia was characterized by a significant reduction in April SCE over the 1922–97 period associated with a significant spring warming. The snow cover reduction was significant at the hemispheric scale with an estimated average NH SCE loss of 3.1 × 106 km2 (100 yr)−1 associated with significant warming of 1.26°C (100 yr)−1 over NH midlatitudinal land areas (40°–60°N). The computed temperature sensitivity of NH April SCE was −2.04 × 106 km2 °C−1. Since 1950, March SCE decreases have become more important than those in April with significant reductions over both continents averaging 8.5 × 106 km2 (100 yr)−1. March was also observed to have experienced the largest warming during the November–April snow season with significant post-1950 warming trends in both continents averaging 4.1°C (100 yr)−1. The hemisphere-wide elevated March snow cover–temperature response is consistent with the position of the snowline over continental grassland vegetation zones where snow cover is relatively shallow and the potential snow cover area–albedo feedback is large.
Abstract
Seasonal snow cover information over southern Canada was reconstructed from daily snowfall and maximum temperature data back to 1915 using a simple mass balance approach with snowmelt estimated via a calibrated temperature index method. The reconstruction method was able to account for 70%–80% of the variance in annual snow cover duration (SCD) over most of southern Canada for the 1955–1992 calibration period. The data were used to construct regional SCD anomaly series in four regions spanning the continent. The regional SCD series were characterized by high interannual variability, with most of the variance concentrated at periods less than 5 years. Spring (MAM) snow cover variability was characterized by a prominent spectral peak with a period of approximately 4 years, which appeared to be linked to tropical Pacific sea surface temperature variability.
There was no evidence of statistically significant long-term trends in snow cover in any of the regions, but the data suggested that winter (DJF) snow cover had increased and spring snow cover had decreased over much of southern Canada. One of the most prominent regional features was a systematic decrease in winter and spring snow cover over the prairies since approximately 1970. However, current low snow cover values in this region are still within the expected range of natural variability. Linear combinations of the regional SCD series, including data from the Great Plains, were able to explain 81% and 75% of the variance in North American winter and spring snow covered area (SCA) over the 1972–1992 period. Reconstructed values of SCA back to 1915 suggested that North American winter snow cover has exhibited a gradual increase of 11.0 × 103 km2 yr−1 during much of this century, while spring snow cover has decreased by an average −6.0 × 103 km2 yr−1. These represent rather small changes in SCA (<10% of current mean SCA over a 100-yr period).
Of the several teleconnection indices investigated, the Pacific-North American (PNA) pattern was observed to exert the strongest influence on snow cover variability; the positive phase of the PNA pattern was associated with reduced snow cover in all seasons over western Canada. The influence of ENSO on snow cover variability was found to be highly variable in both time and space, with lag 0 correlations indicating that El Niño was associated with less snow cover over western Canada. These correlations were much weaker than the PNA pattern. The influence of the North Atlantic oscillation pattern was observed to be mainly confined to winter snow cover variations across the eastern United States and southern Ontario.
Abstract
Seasonal snow cover information over southern Canada was reconstructed from daily snowfall and maximum temperature data back to 1915 using a simple mass balance approach with snowmelt estimated via a calibrated temperature index method. The reconstruction method was able to account for 70%–80% of the variance in annual snow cover duration (SCD) over most of southern Canada for the 1955–1992 calibration period. The data were used to construct regional SCD anomaly series in four regions spanning the continent. The regional SCD series were characterized by high interannual variability, with most of the variance concentrated at periods less than 5 years. Spring (MAM) snow cover variability was characterized by a prominent spectral peak with a period of approximately 4 years, which appeared to be linked to tropical Pacific sea surface temperature variability.
There was no evidence of statistically significant long-term trends in snow cover in any of the regions, but the data suggested that winter (DJF) snow cover had increased and spring snow cover had decreased over much of southern Canada. One of the most prominent regional features was a systematic decrease in winter and spring snow cover over the prairies since approximately 1970. However, current low snow cover values in this region are still within the expected range of natural variability. Linear combinations of the regional SCD series, including data from the Great Plains, were able to explain 81% and 75% of the variance in North American winter and spring snow covered area (SCA) over the 1972–1992 period. Reconstructed values of SCA back to 1915 suggested that North American winter snow cover has exhibited a gradual increase of 11.0 × 103 km2 yr−1 during much of this century, while spring snow cover has decreased by an average −6.0 × 103 km2 yr−1. These represent rather small changes in SCA (<10% of current mean SCA over a 100-yr period).
Of the several teleconnection indices investigated, the Pacific-North American (PNA) pattern was observed to exert the strongest influence on snow cover variability; the positive phase of the PNA pattern was associated with reduced snow cover in all seasons over western Canada. The influence of ENSO on snow cover variability was found to be highly variable in both time and space, with lag 0 correlations indicating that El Niño was associated with less snow cover over western Canada. These correlations were much weaker than the PNA pattern. The influence of the North Atlantic oscillation pattern was observed to be mainly confined to winter snow cover variations across the eastern United States and southern Ontario.
Abstract
A snowpack model sensitivity study, observed changes of snow cover in the NOAA satellite dataset, and snow cover simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset are used to provide new insights into the climate response of Northern Hemisphere (NH) snow cover. Under conditions of warming and increasing precipitation that characterizes both observed and projected climate change over much of the NH land area with seasonal snow cover, the sensitivity analysis indicated snow cover duration (SCD) was the snow cover variable exhibiting the strongest climate sensitivity, with sensitivity varying with climate regime and elevation. The highest snow cover–climate sensitivity was found in maritime climates with extensive winter snowfall—for example, the coastal mountains of western North America (NA). Analysis of trends in snow cover duration during the 1966–2007 period of NOAA data showed the largest decreases were concentrated in a zone where seasonal mean air temperatures were in the range of −5° to +5°C that extended around the midlatitudinal coastal margins of the continents. These findings were echoed by the climate models that showed earlier and more widespread decreases in SCD than annual maximum snow water equivalent (SWEmax), with the zone of earliest significant decrease located over the maritime margins of NA and western Europe. The lowest SCD–climate sensitivity was observed in continental interior climates with relatively cold and dry winters, where precipitation plays a greater role in snow cover variability. The sensitivity analysis suggested a potentially complex elevation response of SCD and SWEmax to increasing temperature and precipitation in mountain regions as a result of nonlinear interactions between the duration of the snow season and snow accumulation rates.
Abstract
A snowpack model sensitivity study, observed changes of snow cover in the NOAA satellite dataset, and snow cover simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset are used to provide new insights into the climate response of Northern Hemisphere (NH) snow cover. Under conditions of warming and increasing precipitation that characterizes both observed and projected climate change over much of the NH land area with seasonal snow cover, the sensitivity analysis indicated snow cover duration (SCD) was the snow cover variable exhibiting the strongest climate sensitivity, with sensitivity varying with climate regime and elevation. The highest snow cover–climate sensitivity was found in maritime climates with extensive winter snowfall—for example, the coastal mountains of western North America (NA). Analysis of trends in snow cover duration during the 1966–2007 period of NOAA data showed the largest decreases were concentrated in a zone where seasonal mean air temperatures were in the range of −5° to +5°C that extended around the midlatitudinal coastal margins of the continents. These findings were echoed by the climate models that showed earlier and more widespread decreases in SCD than annual maximum snow water equivalent (SWEmax), with the zone of earliest significant decrease located over the maritime margins of NA and western Europe. The lowest SCD–climate sensitivity was observed in continental interior climates with relatively cold and dry winters, where precipitation plays a greater role in snow cover variability. The sensitivity analysis suggested a potentially complex elevation response of SCD and SWEmax to increasing temperature and precipitation in mountain regions as a result of nonlinear interactions between the duration of the snow season and snow accumulation rates.
Abstract
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Abstract
No abstract available
Ouranos is a nonprofit consortium launched in 2002 with the mandate to provide climate services to its governmental, academic, and private partners. These services have focused on the impacts of climate change in the province of Québec, the identification of vulnerabilities and opportunities associated with the future climate, and the assessment of adaptation options. This paper discusses the experience and insights acquired at Ouranos over the last 10 years in building climate scenarios in support of these impact and adaptation studies. Most of this work is aimed at making climate science intelligible and useful for end users, and the paper describes approaches to developing climate scenarios that are tailored to the needs and level of climate expertise of different user categories. The experience has shown that a group of professionals dedicated to scenario construction and user support is a key element in the delivery of effective climate services.
Ouranos is a nonprofit consortium launched in 2002 with the mandate to provide climate services to its governmental, academic, and private partners. These services have focused on the impacts of climate change in the province of Québec, the identification of vulnerabilities and opportunities associated with the future climate, and the assessment of adaptation options. This paper discusses the experience and insights acquired at Ouranos over the last 10 years in building climate scenarios in support of these impact and adaptation studies. Most of this work is aimed at making climate science intelligible and useful for end users, and the paper describes approaches to developing climate scenarios that are tailored to the needs and level of climate expertise of different user categories. The experience has shown that a group of professionals dedicated to scenario construction and user support is a key element in the delivery of effective climate services.
During stable nighttime periods, large variations in temperature and visibility often occur over short distances in regions of only moderate topography. These are of great practical significance and yet pose major forecasting challenges because of a lack of detailed understanding of the processes involved and because crucial topographic variations are often not resolved in current forecast models. This paper describes a field and numerical modeling campaign, Cold-Air Pooling Experiment (COLPEX), which addresses many of the issues.
The observational campaign was run for 15 months in Shropshire, United Kingdom, in a region of small hills and valleys with typical ridge–valley heights of 75–150 m and valley widths of 1–3 km. The instrumentation consisted of three sites with instrumented flux towers, a Doppler lidar, and a network of 30 simpler meteorological stations. Further instrumentation was deployed during intensive observation periods including radiosonde launches from two sites, a cloud droplet probe, aerosol monitoring equipment, and an instrumented car. Some initial results from the observations are presented illustrating the range of conditions encountered.
The modeling phase of COLPEX includes use of the Met Office Unified Model at 100-m resolution, and some brief results for a simulation of an intensive observation period are presented showing the model capturing a cold-pool event. As well as aiding interpretation of the observations, results from this study are expected to inform the design of future generations of operational forecasting systems
During stable nighttime periods, large variations in temperature and visibility often occur over short distances in regions of only moderate topography. These are of great practical significance and yet pose major forecasting challenges because of a lack of detailed understanding of the processes involved and because crucial topographic variations are often not resolved in current forecast models. This paper describes a field and numerical modeling campaign, Cold-Air Pooling Experiment (COLPEX), which addresses many of the issues.
The observational campaign was run for 15 months in Shropshire, United Kingdom, in a region of small hills and valleys with typical ridge–valley heights of 75–150 m and valley widths of 1–3 km. The instrumentation consisted of three sites with instrumented flux towers, a Doppler lidar, and a network of 30 simpler meteorological stations. Further instrumentation was deployed during intensive observation periods including radiosonde launches from two sites, a cloud droplet probe, aerosol monitoring equipment, and an instrumented car. Some initial results from the observations are presented illustrating the range of conditions encountered.
The modeling phase of COLPEX includes use of the Met Office Unified Model at 100-m resolution, and some brief results for a simulation of an intensive observation period are presented showing the model capturing a cold-pool event. As well as aiding interpretation of the observations, results from this study are expected to inform the design of future generations of operational forecasting systems
