Remote Sensing of 2000–2016 Alpine Spring Snowline Elevation in Dall Sheep Mountain Ranges of Alaska and Western Canada
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Snow Depth on Arctic Sea Ice
1814 JOURNAL OF CLIMATE VOLUME 12 Snow Depth on Arctic Sea Ice STEPHEN G. WARREN,IGNATIUS G. RIGOR, AND NORBERT UNTERSTEINER Department of Atmospheric Sciences, University of Washington, Seattle, Washington VLADIMIR F. R ADIONOV,NIKOLAY N. BRYAZGIN, AND YEVGENIY I. ALEKSANDROV Arctic and Antarctic Research Institute, St. Petersburg, Russia ROGER COLONY International Arctic Research Center, University of Alaska, Fairbanks, Alaska (Manuscript received 5 December 1997, in ®nal form 27 July 1998) ABSTRACT Snow depth and density were measured at Soviet drifting stations on multiyear Arctic sea ice. Measurements were made daily at ®xed stakes at the weather station and once- or thrice-monthly at 10-m intervals on a line beginning about 500 m from the station buildings and extending outward an additional 500 or 1000 m. There were 31 stations, with lifetimes of 1±7 yr. Analyses are performed here for the 37 years 1954±91, during which time at least one station was always reporting. Snow depth at the stakes was sometimes higher than on the lines, and sometimes lower, but no systematic trend of snow depth was detected as a function of distance from the station along the 1000-m lines that would indicate an in¯uence of the station. To determine the seasonal progression of snow depth for each year at each station, priority was given to snow lines if available; otherwise the ®xed stakes were used, with an offset applied if necessary. The ice is mostly free of snow during August. Snow accumulates rapidly in September and October, moderately in November, very slowly in December and January, then moderately again from February to May. -
Comparison of Remote Sensing Extraction Methods for Glacier Firn Line- Considering Urumqi Glacier No.1 As the Experimental Area
E3S Web of Conferences 218, 04024 (2020) https://doi.org/10.1051/e3sconf/202021804024 ISEESE 2020 Comparison of remote sensing extraction methods for glacier firn line- considering Urumqi Glacier No.1 as the experimental area YANJUN ZHAO1, JUN ZHAO1, XIAOYING YUE2and YANQIANG WANG1 1College of Geography and Environmental Science, Northwest Normal University, Lanzhou, China 2State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources/Tien Shan Glaciological Station, Chinese Academy of Sciences, Lanzhou, China Abstract. In mid-latitude glaciers, the altitude of the snowline at the end of the ablating season can be used to indicate the equilibrium line, which can be used as an approximation for it. In this paper, Urumqi Glacier No.1 was selected as the experimental area while Landsat TM/ETM+/OLI images were used to analyze and compare the accuracy as well as applicability of the visual interpretation, Normalized Difference Snow Index, single-band threshold and albedo remote sensing inversion methods for the extraction of the firn lines. The results show that the visual interpretation and the albedo remote sensing inversion methods have strong adaptability, alonger with the high accuracy of the extracted firn line while it is followed by the Normalized Difference Snow Index and the single-band threshold methods. In the year with extremely negative mass balance, the altitude deviation of the firn line extracted by different methods is increased. Except for the years with extremely negative mass balance, the altitude of the firn line at the end of the ablating season has a good indication for the altitude of the balance line. -
Glacier (And Ice Sheet) Mass Balance
Glacier (and ice sheet) Mass Balance The long-term average position of the highest (late summer) firn line ! is termed the Equilibrium Line Altitude (ELA) Firn is old snow How an ice sheet works (roughly): Accumulation zone ablation zone ice land ocean • Net accumulation creates surface slope Why is the NH insolation important for global ice• sheetSurface advance slope causes (Milankovitch ice to flow towards theory)? edges • Accumulation (and mass flow) is balanced by ablation and/or calving Why focus on summertime? Ice sheets are very sensitive to Normal summertime temperatures! • Ice sheet has parabolic shape. • line represents melt zone • small warming increases melt zone (horizontal area) a lot because of shape! Slightly warmer Influence of shape Warmer climate freezing line Normal freezing line ground Furthermore temperature has a powerful influence on melting rate Temperature and Ice Mass Balance Summer Temperature main factor determining ice growth e.g., a warming will Expand ablation area, lengthen melt season, increase the melt rate, and increase proportion of precip falling as rain It may also bring more precip to the region Since ablation rate increases rapidly with increasing temperature – Summer melting controls ice sheet fate* – Orbital timescales - Summer insolation must control ice sheet growth *Not true for Antarctica in near term though, where it ʼs too cold to melt much at surface Temperature and Ice Mass Balance Rule of thumb is that 1C warming causes an additional 1m of melt (see slope of ablation curve at right) -
UNIVERSITY of CALIFORNIA Los Angeles Southern California
UNIVERSITY OF CALIFORNIA Los Angeles Southern California Climate and Vegetation Over the Past 125,000 Years from Lake Sequences in the San Bernardino Mountains A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Geography by Katherine Colby Glover 2016 © Copyright by Katherine Colby Glover 2016 ABSTRACT OF THE DISSERTATION Southern California Climate and Vegetation Over the Past 125,000 Years from Lake Sequences in the San Bernardino Mountains by Katherine Colby Glover Doctor of Philosophy in Geography University of California, Los Angeles, 2016 Professor Glen Michael MacDonald, Chair Long sediment records from offshore and terrestrial basins in California show a history of vegetation and climatic change since the last interglacial (130,000 years BP). Vegetation sensitive to temperature and hydroclimatic change tended to be basin-specific, though the expansion of shrubs and herbs universally signalled arid conditions, and landscpe conversion to steppe. Multi-proxy analyses were conducted on two cores from the Big Bear Valley in the San Bernardino Mountains to reconstruct a 125,000-year history for alpine southern California, at the transition between mediterranean alpine forest and Mojave desert. Age control was based upon radiocarbon and luminescence dating. Loss-on-ignition, magnetic susceptibility, grain size, x-ray fluorescence, pollen, biogenic silica, and charcoal analyses showed that the paleoclimate of the San Bernardino Mountains was highly subject to globally pervasive forcing mechanisms that register in northern hemispheric oceans. Primary productivity in Baldwin Lake during most of its ii history showed a strong correlation to historic fluctuations in local summer solar radiation values. -
Snow Cover and Glacier Change Study in Nepalese Himalaya Using Remote Sensing and Geographic Information System
26 A. B. Shrestha & S. P. Joshi August 2009 Snow Cover and Glacier Change Study in Nepalese Himalaya Using Remote Sensing and Geographic Information System Arun Bhakta Shrestha1 and Sharad Prasad Joshi2 1 International Centre for Integrated Mountain Development, Nepal E-mail: [email protected] 2 Water and Energy Commission Secretariat, Nepal ABSTRACT Snow cover and glaciers in the Himalaya play a major role in the generation of stream flow in south Asia. Various studies have suggested that the glaciers in the Himalaya are in general condition of retreat. The snowline is also found to be retreating. While there are relatively more studies on glaciers fluctuation in the Himalaya, studies on snow cover is relatively sparse. In this study, snow cover and glacier fluctuation in the Nepalese Himalaya were studied using remote sensing techniques and geographic information system. The study was carried out in two spatial scales: catchments scale and national scale. In catchments scale two catchments: Langtang and Khumbu were studied. Intermittent medium resolution satellite imageries (Landsat) were used to study the fluctuation in snow cover and glacier area in the two catchments. In the national scale study coarse resolution (MODIS) imageries were used to derive seasonal variations in snow cover. An indication of decreasing trend in snow cover is shown by this study, although this result needs verification with more data. The snowline elevation is in general higher in Khumbu compared to Langtang. In both catchments, snowline elevation are higher in east, south-east, south and south-west aspects. The areas of snow cover in those aspects are also greater. -
World Map Outline Find and Shade: Andes, Alps, Rockies, Himalayas, Caucasus Mountains, East Africa Mountains, Alaska/Yukon Ranges, Sentinel Range, Sudiman Range
World Map Outline Find and shade: Andes, Alps, Rockies, Himalayas, Caucasus Mountains, East Africa Mountains, Alaska/Yukon Ranges, Sentinel Range, Sudiman Range 1 The World’s Highest Peaks 2 Earth Cross-Section 3 Mountain Climates Fact Sheet • How high a mountain is affects what its climate is like. Moving 300 metres up is the same as moving 350 miles towards one of the poles! • Air pressure also changes as one gains altitude. At the top of Mount Everest (8848 m) the pressure is around 310-360 millibars, compared to around 1013 mb at sea level. • As a result of falling air pressure, rising air expands and cools (although, dry air cools faster than moist air because, as the moist air rises, the water vapour condenses – like in clouds – and this gives off some heat). The higher you are the cooler it gets. That is why we often have snow on mountaintops, even along the equator. • Mountains therefore act as a barrier to moisture-laden winds. Air rising to pass over the mountains cools and the water vapour condenses, turning into either clouds, rain, or if it is cold enough, snow. This is why on one side of a mountain you can experience a wet climate, while on the other side of the same mountain you find an arid one. • A large mountain range can affect the weather of the land beyond it. The Himalayas influence the climate of the rest of India by sheltering it from the cold air mass of central Asia. • In high mountains the first snow may fall several weeks earlier than it does in the surrounding area. -
Outlook on Climate Change Adaptation in the Tropical Andes Mountains
MOUNTAIN ADAPTATION OUTLOOK SERIES Outlook on climate change adaptation in the Tropical Andes mountains 1 Southern Bogota, Colombia photo: cover Front DISCLAIMER The development of this publication has been supported by the United Nations Environment Programme (UNEP) in the context of its inter-regional project “Climate change action in developing countries with fragile mountainous ecosystems from a sub-regional perspective”, which is financially co-supported by the Government Production Team of Austria (Austrian Federal Ministry of Agriculture, Forestry, Tina Schoolmeester, GRID-Arendal Environment and Water Management). Miguel Saravia, CONDESAN Magnus Andresen, GRID-Arendal Julio Postigo, CONDESAN, Universidad del Pacífico Alejandra Valverde, CONDESAN, Pontificia Universidad Católica del Perú Matthias Jurek, GRID-Arendal Björn Alfthan, GRID-Arendal Silvia Giada, UNEP This synthesis publication builds on the main findings and results available on projects and activities that have been conducted. Contributors It is based on available information, such as respective national Angela Soriano, CONDESAN communications by countries to the United Nations Framework Bert de Bievre, CONDESAN Convention on Climate Change (UNFCCC) and peer-reviewed Boris Orlowsky, University of Zurich, Switzerland literature. It is based on review of existing literature and not on new Clever Mafuta, GRID-Arendal scientific results generated through the project. Dirk Hoffmann, Instituto Boliviano de la Montana - BMI Edith Fernandez-Baca, UNDP The contents of this publication do not necessarily reflect the Eva Costas, Ministry of Environment, Ecuador views or policies of UNEP, contributory organizations or any Gabriela Maldonado, CONDESAN governmental authority or institution with which its authors or Harald Egerer, UNEP contributors are affiliated, nor do they imply any endorsement. -
Status of the Cordillera Vilcanota, Including the Quelccaya Ice Cap, Northern Central Andes, Peru
The Cryosphere, 8, 359–376, 2014 Open Access www.the-cryosphere.net/8/359/2014/ doi:10.5194/tc-8-359-2014 The Cryosphere © Author(s) 2014. CC Attribution 3.0 License. Glacial areas, lake areas, and snow lines from 1975 to 2012: status of the Cordillera Vilcanota, including the Quelccaya Ice Cap, northern central Andes, Peru M. N. Hanshaw and B. Bookhagen Department of Geography, University of California, Santa Barbara, CA, USA Correspondence to: M. N. Hanshaw ([email protected]) Received: 14 December 2012 – Published in The Cryosphere Discuss.: 25 February 2013 Revised: 19 December 2013 – Accepted: 10 January 2014 – Published: 3 March 2014 Abstract. Glaciers in the tropical Andes of southern Peru as glacial regions have decreased, 77 % of lakes connected have received limited attention compared to glaciers in to glacial watersheds have either remained stable or shown other regions (both near and far), yet remain of vital im- a roughly synchronous increase in lake area, while 42 % of portance to agriculture, fresh water, and hydropower sup- lakes not connected to glacial watersheds have declined in plies of downstream communities. Little is known about area (58 % have remained stable). Our new and detailed data recent glacial-area changes and how the glaciers in this on glacial and lake areas over 37 years provide an important region respond to climate changes, and, ultimately, how spatiotemporal assessment of climate variability in this area. these changes will affect lake and water supplies. To rem- These data can be integrated into further studies to analyze edy this, we have used 158 multi-spectral satellite images inter-annual glacial and lake-area changes and assess hydro- spanning almost 4 decades, from 1975 to 2012, to ob- logic dependence and consequences for downstream popula- tain glacial- and lake-area outlines for the understudied tions. -
Ice Cores: Unlocking Past Climates Module 1: Climate and Ice
Ice Cores: Unlocking Past Climates Module 1: Climate and Ice Overview Part I of this lesson begins with a video that explains the difference between weather and climate, describes glacier formation, and identifies the types of information that can be found in the glacial record. The video is followed by an exploration of current weather conditions at various locations around the world. The locations represent a variety of climate types and each is located near a glacier or ice sheet. Students then differentiate between weather and climate and examine climate data for their location. In Part II students visit websites to learn what glaciers are and how they are formed. Students conduct additional internet research in Part III to discover what types of substances can be trapped in glaciers. In the final activity students locate a glacier or ice sheet closest to the city they collected weather data for and create a model that tells the story of several years in the life of their glacier. The story includes weather information and information about any natural or human-induced events that contributed materials to the glacier’s record. Content Objectives Students will Differentiate between weather and climate Investigate how glaciers are formed and where they are found Build a glacier model that illustrates several years of the glacial record Grade Level: 5-8 Suggested Time: 2-3 class periods Multimedia Resources [link to video] http://www.wunderground.com/ http://www.weatherbase.com The Life Cycle of a Glacier, http://www.pbs.org/wgbh/nova/vinson/glac-flash.html How Glaciers Work, http://science.howstuffworks.com/glacier.htm Glacier Formation, http://science.howstuffworks.com/glacier1.htm. -
Assessment of Climate Change Effects on Mountain Ecosystems Through a Cross-Site Analysis in the Alps and Apennines
Assessment of climate change effects on mountain ecosystems through a cross-site analysis in the Alps and Apennines. Rogora M.1*a, Frate L.2a, Carranza M.L.2a, Freppaz M.3a, Stanisci A.2a, Bertani I.4, Bottarin R.5, Brambilla A.6, Canullo R.7, Carbognani M.8, Cerrato C.9, Chelli S.7, Cremonese E.10, Cutini M.11, Di Musciano M.12, Erschbamer B.13, Godone D.14, Iocchi M.11, Isabellon M.3,10, Magnani A.15, Mazzola L.16, Morra di Cella U.17, Pauli H.18, Petey M.17, Petriccione B.19, Porro F.20, Psenner R.5, 21, Rossetti G.22, Scotti A.5, Sommaruga R.21, Tappeiner U.5, Theurillat J.-P.23, Tomaselli M.8, Viglietti D.3, Viterbi R.9, Vittoz P.24, Winkler M.18 Matteucci G.25a *Corresponding author: e-mail address: [email protected] (M. Rogora) a joint first authors 1 CNR Institute of Ecosystem Study, Verbania Pallanza, Italy 2 DIBT, Envix-Lab, University of Molise, Pesche (IS), Italy 3 DISAFA, NatRisk, University of Turin, Grugliasco (TO), Turin, Italy 4 Graham Sustainability Institute, University of Michigan, 625 E. Liberty St., Ann Arbor, MI 48104, USA 5 Eurac Research, Institute for Alpine Environment, Bolzano (BZ), Italy 6 Alpine Wilidlife Research Centre, Gran Paradiso National Park, Degioz (AO) 11 Valsavarenche, Italy; Department of Evolutionary Biology and Environmental Studies, University of Zurich. Winterthurerstrasse 190, 8057 Zurich, Switzerland 7 School of Biosciences and Veterinary Medicine, Plant Diversity and Ecosystems Management Unit, University of Camerino (MC) Italy 8 Department of Chemistry, Life Sciences and Environmental Sustainability University of Parma, Parma, Italy 9 Alpine Wilidlife Research Centre, Gran Paradiso National Park, Degioz (AO) 11 Valsavarenche, Italy 10 Environmental Protection Agency of Aosta Valley, ARPA VdA, Climate Change Unit, Aosta, Italy 11 Department of Biology, University of Roma Tre, Viale G. -
Learning About Precipitation Orographic Enhancement from Snow-Course Data Improves Water-Balance Modeling
https://doi.org/10.5194/hess-2020-571 Preprint. Discussion started: 26 November 2020 c Author(s) 2020. CC BY 4.0 License. Learning about precipitation orographic enhancement from snow-course data improves water-balance modeling Francesco Avanzi1, Giulia Ercolani1, Simone Gabellani1, Edoardo Cremonese2, Paolo Pogliotti2, Gianluca Filippa2, Umberto Morra di Cella2,1, Sara Ratto3, Hervè Stevenin3, Marco Cauduro4, and Stefano Juglair4 1CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy 2Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy 3Regione Autonoma Valle d’Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy 4Direzione Operativa Operations, C.V.A. S.p.A., Via Stazione 31, 11024 Châtillon, Italy Correspondence: Francesco Avanzi ([email protected]) Abstract. Precipitation orographic enhancement depends on both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic-forecasting procedures and water management in general. 5 Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged 11,000+ snow-course data upstream two reservoirs in the Western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and consequently improve predictions of a snow-hydrologic modeling chain (Flood-PROOFS). We found that Snow Water Equivalent (SWE) above 3000 m ASL was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m ASL, with gradients up to 1000 1 10 mm w.e. -
Maps of Snow-Cover Probability for the Northern Hemisphere
June 1967 R. R. Dickson and Julian Posey 347 MAPS OF SNOW-COVER PROBABILITY FOR THE NORTHERN HEMISPHERE R.R. DICKSON AND JULIAN POSEY Extended Forecast Division; NMC, Weather Bureau, ESSA, Washington, D.C. ABSTRACT Map analyses are provided depicting the probability of snow-cover 1 inch or more in depth at the end of each month from September through May for the Northern Hemisphere. 1. INTRODUCTION To supplement this primary data source, empirical snow-cover probabilities were computed for the 193 1-50 Recent work dealing with thermodynamic [l] and period for a network of 110 stations in the United States synoptic [8] aspects of long-range forecasting has em- from data included in US. Weather Bureau Station phasized the importance of considering the heat balance Record Books (available on microfilm from the Atmos- of the earth and the atmosphere when dealing with the pheric Sciences Library of ESSA) . Canadian snow-cover long-term evolution of the atmospheric circulation. data were extracted from a recent publication [lo]. This An important factor in such a heat balance is the albedo was augmented for May and September by data from of the earth’s surface and this in turn is critically depend- additional stations during 1940-64, obtained from monthly ent upon the snow-cover distribution. published records [5]. Thus a need exists for broadscale climatic analyses Analyses for China and Korea are based upon data depicting the areal extent of snow cover throughout the published by their respective meteorological offices [9] , [4]. year. While map analyses of average snow depth and Both sources give the a$erage number of days with snow average dates of first and last snowfall are readily available, cover for each month.