Articles | Volume 12, issue 3
https://doi.org/10.5194/tc-12-1027-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-12-1027-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Mar 2018
Research article |
| 23 Mar 2018
Changes in Andes snow cover from MODIS data, 2000–2016
Freddy A. Saavedra
CORRESPONDING AUTHOR
Departamento de Ciencias Geográficas, Facultad de Ciencias Naturales y Exactas, Universidad de Playa Ancha,
Leopoldo Carvallo 270, Playa Ancha, Valparaíso, Chile
Centro de Estudios Avanzados, Universidad de Playa Ancha, Traslaviña 450, Viña del Mar, Chile
Stephanie K. Kampf
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523-1476, USA
Steven R. Fassnacht
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523-1476, USA
Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523-1375, USA
Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA
Geographisches Institut, Georg-August-Universität Göttingen, 37077 Göttingen, Germany
Jason S. Sibold
Department of Anthropology, Colorado State University, Fort Collins, CO, USA
Related authors
Alexis Caro, Thomas Condom, Antoine Rabatel, Nicolas Champollion, Nicolás García, and Freddy Saavedra
The Cryosphere, 18, 2487–2507, https://doi.org/10.5194/tc-18-2487-2024, https://doi.org/10.5194/tc-18-2487-2024, 2024
Short summary
Short summary
The glacier runoff changes are still unknown in most of the Andean catchments, thereby increasing uncertainties in estimating water availability, especially during the dry season. Here, we simulate glacier evolution and related glacier runoff changes across the Andes between 2000 and 2019. Our results indicate a glacier reduction in 93 % of the catchments, leading to a 12 % increase in glacier melt. These results can be downloaded and integrated with discharge measurements in each catchment.
Julio Salcedo-Castro, Antonio Olita, Freddy Saavedra, Gonzalo S. Saldías, Raúl C. Cruz-Gómez, and Cristian D. De la Torre Martínez
Ocean Sci., 19, 1687–1703, https://doi.org/10.5194/os-19-1687-2023, https://doi.org/10.5194/os-19-1687-2023, 2023
Short summary
Short summary
Considering the relevance and impact of river discharges on the coastal environment, it is necessary to understand the processes associated with river plume dynamics in different regions and at different scales. Modeling studies focused on the eastern Pacific coast under the influence of the Humboldt Current are scarce. Here, we conduct for the first time an interannual modeling study of two river plumes off central Chile and discuss their characteristics.
S. R. Fassnacht, M. L. Cherry, N. B. H. Venable, and F. Saavedra
The Cryosphere, 10, 329–339, https://doi.org/10.5194/tc-10-329-2016, https://doi.org/10.5194/tc-10-329-2016, 2016
Short summary
Short summary
We used 60 years of daily meteorological data from 20 stations across the US Northern Great Plains to examine climate trends, focusing on the winter climate. Besides standard climate trends, we computed trends in snowfall amounts, days with precipitation, days with snow, and modelled winter albedo (surface reflectivity). Daily minimum temperatures and days with precipitation increased at most locations, while winter albedo decreased at many stations. There was much spatial variability.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
Short summary
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Alexis Caro, Thomas Condom, Antoine Rabatel, Nicolas Champollion, Nicolás García, and Freddy Saavedra
The Cryosphere, 18, 2487–2507, https://doi.org/10.5194/tc-18-2487-2024, https://doi.org/10.5194/tc-18-2487-2024, 2024
Short summary
Short summary
The glacier runoff changes are still unknown in most of the Andean catchments, thereby increasing uncertainties in estimating water availability, especially during the dry season. Here, we simulate glacier evolution and related glacier runoff changes across the Andes between 2000 and 2019. Our results indicate a glacier reduction in 93 % of the catchments, leading to a 12 % increase in glacier melt. These results can be downloaded and integrated with discharge measurements in each catchment.
Julio Salcedo-Castro, Antonio Olita, Freddy Saavedra, Gonzalo S. Saldías, Raúl C. Cruz-Gómez, and Cristian D. De la Torre Martínez
Ocean Sci., 19, 1687–1703, https://doi.org/10.5194/os-19-1687-2023, https://doi.org/10.5194/os-19-1687-2023, 2023
Short summary
Short summary
Considering the relevance and impact of river discharges on the coastal environment, it is necessary to understand the processes associated with river plume dynamics in different regions and at different scales. Modeling studies focused on the eastern Pacific coast under the influence of the Humboldt Current are scarce. Here, we conduct for the first time an interannual modeling study of two river plumes off central Chile and discuss their characteristics.
Molly E. Tedesche, Erin D. Trochim, Steven R. Fassnacht, and Gabriel J. Wolken
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-143, https://doi.org/10.5194/tc-2022-143, 2022
Publication in TC not foreseen
Short summary
Short summary
Perennial snowfields in the Brooks Range of Alaska are critical for the ecosystem and provide caribou habitat. Caribou are a crucial food source for rural hunters. The purpose of this research is to map perennial snowfield extents using several remote sensing techniques with Sentinel-1 and 2. These include analysis of Synthetic Aperture Radar backscatter change and of optical satellite imagery. Results are compared with field data and appear to effectively detect perennial snowfield locations.
Leonie Kiewiet, Ernesto Trujillo, Andrew Hedrick, Scott Havens, Katherine Hale, Mark Seyfried, Stephanie Kampf, and Sarah E. Godsey
Hydrol. Earth Syst. Sci., 26, 2779–2796, https://doi.org/10.5194/hess-26-2779-2022, https://doi.org/10.5194/hess-26-2779-2022, 2022
Short summary
Short summary
Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how variations in rainfall and snowmelt affect discharge. We found that annual discharge and stream cessation depended on the magnitude and timing of rainfall and snowmelt and on the snowpack melt-out date. This highlights the importance of precipitation timing and emphasizes the need for spatiotemporally distributed simulations of snowpack and rainfall dynamics.
Ryan W. Webb, Keith Jennings, Stefan Finsterle, and Steven R. Fassnacht
The Cryosphere, 15, 1423–1434, https://doi.org/10.5194/tc-15-1423-2021, https://doi.org/10.5194/tc-15-1423-2021, 2021
Short summary
Short summary
We simulate the flow of liquid water through snow and compare results to field experiments. This process is important because it controls how much and how quickly water will reach our streams and rivers in snowy regions. We found that water can flow large distances downslope through the snow even after the snow has stopped melting. Improved modeling of snowmelt processes will allow us to more accurately estimate available water resources, especially under changing climate conditions.
John C. Hammond, Adrian A. Harpold, Sydney Weiss, and Stephanie K. Kampf
Hydrol. Earth Syst. Sci., 23, 3553–3570, https://doi.org/10.5194/hess-23-3553-2019, https://doi.org/10.5194/hess-23-3553-2019, 2019
Short summary
Short summary
Streamflow in high-elevation and high-latitude areas may be vulnerable to snow loss, making it important to quantify how snowmelt and rainfall are divided between soil storage, drainage below plant roots, evapotranspiration and runoff. We examine this separation in different climates and soils using a physically based model. Results show runoff may be reduced with snowpack decline in all climates. The mechanisms responsible help explain recent observations of streamflow sensitivity to snow loss.
Steven R. Fassnacht, Jared T. Heath, Niah B. H. Venable, and Kelly J. Elder
The Cryosphere, 12, 1121–1135, https://doi.org/10.5194/tc-12-1121-2018, https://doi.org/10.5194/tc-12-1121-2018, 2018
Short summary
Short summary
We conducted a series of experiments to determine how snowpack properties change with varying snowmobile traffic. Experiments were initiated at a shallow (30 cm) and deep (120 cm) snow depth at two locations. Except for initiation at 120 cm, snowmobiles significantly changed the density, hardness, ram resistance, and basal layer crystal size. Temperature was not changed. A density change model was developed and tested. The results inform management of lands with snowmobile traffic.
Ryan W. Webb, Steven R. Fassnacht, and Michael N. Gooseff
The Cryosphere, 12, 287–300, https://doi.org/10.5194/tc-12-287-2018, https://doi.org/10.5194/tc-12-287-2018, 2018
Short summary
Short summary
We observed how snowmelt is transported on a hillslope through multiple measurements of snow and soil moisture across a small headwater catchment. We found that snowmelt flows through the snow with less infiltration on north-facing slopes and infiltrates the ground on south-facing slopes. This causes an increase in snow water equivalent at the base of the north-facing slope by as much as 170 %. We present a conceptualization of flow path development to improve future investigations.
Graham A. Sexstone, Steven R. Fassnacht, Juan Ignacio López-Moreno, and Christopher A. Hiemstra
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-188, https://doi.org/10.5194/tc-2016-188, 2016
Revised manuscript has not been submitted
Short summary
Short summary
Seasonal snowpacks vary spatially within mountainous environments and the representation of this variability by modeling can be a challenge. This study uses high-resolution airborne lidar data to evaluate the variability of snow depth within a grid size common for modeling applications. Results suggest that snow depth coefficient of variation is well correlated with ecosystem type, depth of snow, and topography and forest characteristics, and can be parameterized using airborne lidar data.
S. R. Fassnacht, M. L. Cherry, N. B. H. Venable, and F. Saavedra
The Cryosphere, 10, 329–339, https://doi.org/10.5194/tc-10-329-2016, https://doi.org/10.5194/tc-10-329-2016, 2016
Short summary
Short summary
We used 60 years of daily meteorological data from 20 stations across the US Northern Great Plains to examine climate trends, focusing on the winter climate. Besides standard climate trends, we computed trends in snowfall amounts, days with precipitation, days with snow, and modelled winter albedo (surface reflectivity). Daily minimum temperatures and days with precipitation increased at most locations, while winter albedo decreased at many stations. There was much spatial variability.
S. R. Fassnacht and M. Hultstrand
Proc. IAHS, 371, 131–136, https://doi.org/10.5194/piahs-371-131-2015, https://doi.org/10.5194/piahs-371-131-2015, 2015
Short summary
Short summary
Snowpack properties vary over distance. Water resources managers use operational data to estimate streamflow, while scientists use snow data models to understand climate and hydrology. We suggest that there is the individual measurements in a snowcourse be used to address uncertainty. Further, over the long term trends may not be obvious but increasing and decreasing trends can exist over shorter time periods, as seen in Northern Colorado. Such trends mirror global temperature patterns.
R. M. Records, M. Arabi, S. R. Fassnacht, W. G. Duffy, M. Ahmadi, and K. C. Hegewisch
Hydrol. Earth Syst. Sci., 18, 4509–4527, https://doi.org/10.5194/hess-18-4509-2014, https://doi.org/10.5194/hess-18-4509-2014, 2014
Short summary
Short summary
We demonstrate a framework to assess system sensitivity to combined climate and land cover change scenarios. In the western United States study watershed, findings suggest that mid-21st-century nutrient and sediment loads could increase significantly or show little change under no wetland losses, depending on climate scenario, but that the combined impact of climate change and wetland losses on nutrients could be large.
G. Formetta, S. K. Kampf, O. David, and R. Rigon
Geosci. Model Dev., 7, 725–736, https://doi.org/10.5194/gmd-7-725-2014, https://doi.org/10.5194/gmd-7-725-2014, 2014
G. A. Sexstone and S. R. Fassnacht
The Cryosphere, 8, 329–344, https://doi.org/10.5194/tc-8-329-2014, https://doi.org/10.5194/tc-8-329-2014, 2014
Related subject area
Remote Sensing
Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado
Toward long-term monitoring of regional permafrost thaw with satellite interferometric synthetic aperture radar
Improved records of glacier flow instabilities using customized NASA autoRIFT (CautoRIFT) applied to PlanetScope imagery
Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data
The AutoICE Challenge
Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data
Tower-based C-band radar measurements of an alpine snowpack
A study of sea ice topography in the Weddell and Ross seas using dual-polarimetric TanDEM-X imagery
Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models
Mapping surface hoar from near-infrared texture in a laboratory
Sentinel-1 detection of ice slabs on the Greenland Ice Sheet
Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals
Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign
Sea ice transport and replenishment across and within the Canadian Arctic Archipelago, 2016–2022
SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition
Lake ice break-up in Greenland: timing and spatiotemporal variability
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model
Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method
Lead fractions from SAR-derived sea ice divergence during MOSAiC
Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment
Assessing the sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations
Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17
A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography
Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry
Pan-Arctic Sea Ice Concentration from SAR and Passive Microwave
Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach
Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method
Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods
Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
Annual to seasonal glacier mass balance in High Mountain Asia derived from Pléiades stereo images: examples from the Pamir and the Tibetan Plateau
Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada
Co-registration and residual correction of digital elevation models: a comparative study
Morphodynamics of the Mont Blanc glaciers and their recent evolution
Out-of-the-box calving-front detection method using deep learning
Mapping the extent of giant Antarctic icebergs with deep learning
Allometric scaling of retrogressive thaw slumps
Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions
Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow
Brief communication: Identification of tundra topsoil frozen/thawed state from SMAP and GCOM-W1 radiometer measurements using the spectral gradient method
New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data
GLAcier Feature Tracking testkit (GLAFT): a statistically and physically based framework for evaluating glacier velocity products derived from optical satellite image feature tracking
Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments
Relevance of warm air intrusions for Arctic satellite sea ice concentration time series
Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2
Cast shadows reveal changes in glacier surface elevation
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
Short summary
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Taha Sadeghi Chorsi, Franz J. Meyer, and Timothy H. Dixon
The Cryosphere, 18, 3723–3740, https://doi.org/10.5194/tc-18-3723-2024, https://doi.org/10.5194/tc-18-3723-2024, 2024
Short summary
Short summary
The active layer thaws and freezes seasonally. The annual freeze–thaw cycle of the active layer causes significant surface height changes due to the volume difference between ice and liquid water. We estimate the subsidence rate and active-layer thickness (ALT) for part of northern Alaska for summer 2017 to 2022 using interferometric synthetic aperture radar and lidar. ALT estimates range from ~20 cm to larger than 150 cm in area. Subsidence rate varies between close points (2–18 mm per month).
Jukes Liu, Madeline Gendreau, Ellyn Mary Enderlin, and Rainey Aberle
The Cryosphere, 18, 3571–3590, https://doi.org/10.5194/tc-18-3571-2024, https://doi.org/10.5194/tc-18-3571-2024, 2024
Short summary
Short summary
There are sometimes gaps in global glacier velocity records produced using satellite image feature-tracking algorithms during times of rapid glacier acceleration, which hinders the study of glacier flow processes. We present an open-source pipeline for customizing the feature-tracking parameters and for including images from an additional source. We applied it to five glaciers and found that it produced accurate velocity data that supplemented their velocity records during rapid acceleration.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
Short summary
Short summary
Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif Toudal Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando Jose Pena Cantu, Javier Noa Turnes, Jinman Park, Linlin Xu, Katharine Andrea Scott, David Anthony Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil Curtis Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan Nicolaas van Rijn, Jens Jakobsen, Martin Samuel James Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde Brandt Kreiner
The Cryosphere, 18, 3471–3494, https://doi.org/10.5194/tc-18-3471-2024, https://doi.org/10.5194/tc-18-3471-2024, 2024
Short summary
Short summary
The AutoICE challenge encouraged the development of deep learning models to map multiple aspects of sea ice – the amount of sea ice in an area and the age and ice floe size – using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. Professionally drawn operational sea ice charts were used as a reference. A total of 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
Short summary
Short summary
Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
Short summary
Short summary
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
Lanqing Huang and Irena Hajnsek
The Cryosphere, 18, 3117–3140, https://doi.org/10.5194/tc-18-3117-2024, https://doi.org/10.5194/tc-18-3117-2024, 2024
Short summary
Short summary
Interferometric synthetic aperture radar can measure the total freeboard of sea ice but can be biased when radar signals penetrate snow and ice. We develop a new method to retrieve the total freeboard and analyze the regional variation of total freeboard and roughness in the Weddell and Ross seas. We also investigate the statistical behavior of the total freeboard for diverse ice types. The findings enhance the understanding of Antarctic sea ice topography and its dynamics in a changing climate.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
Short summary
Short summary
Surface hoar crystals are snow grains that form when vapor deposits on a snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote-sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with an accuracy upwards of 95 %.
Riley Culberg, Roger J. Michaelides, and Julie Z. Miller
The Cryosphere, 18, 2531–2555, https://doi.org/10.5194/tc-18-2531-2024, https://doi.org/10.5194/tc-18-2531-2024, 2024
Short summary
Short summary
Ice slabs enhance meltwater runoff from the Greenland Ice Sheet. Therefore, it is important to understand their extent and change in extent over time. We present a new method for detecting ice slabs in satellite radar data, which we use to map ice slabs at 500 m resolution across the entire ice sheet in winter 2016–2017. Our results provide better spatial coverage and resolution than previous maps from airborne radar and lay the groundwork for long-term monitoring of ice slabs from space.
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
The Cryosphere, 18, 2473–2486, https://doi.org/10.5194/tc-18-2473-2024, https://doi.org/10.5194/tc-18-2473-2024, 2024
Short summary
Short summary
The total Arctic sea-ice area (SIA), which is an important climate indicator, is routinely monitored with the help of satellite measurements. Uncertainties in observations of sea-ice concentration (SIC) partly cancel out when summed up to the total SIA, but the degree to which this is happening has been unclear. Here we find that the uncertainty daily SIA estimates, based on uncertainties in SIC, are about 300 000 km2. The 2002 to 2017 September decline in SIA is approx. 105 000 ± 9000 km2 a−1.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
The Cryosphere, 18, 2257–2276, https://doi.org/10.5194/tc-18-2257-2024, https://doi.org/10.5194/tc-18-2257-2024, 2024
Short summary
Short summary
We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest and when the Sun was behind the satellite. From this work, we learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
Short summary
Short summary
The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
Short summary
Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Christoph Posch, Jakob Abermann, and Tiago Silva
The Cryosphere, 18, 2035–2059, https://doi.org/10.5194/tc-18-2035-2024, https://doi.org/10.5194/tc-18-2035-2024, 2024
Short summary
Short summary
Radar beams from satellites exhibit reflection differences between water and ice. This condition, as well as the comprehensive coverage and high temporal resolution of the Sentinel-1 satellites, allows automatically detecting the timing of when ice cover of lakes in Greenland disappear. We found that lake ice breaks up 3 d later per 100 m elevation gain and that the average break-up timing varies by ±8 d in 2017–2021, which has major implications for the energy budget of the lakes.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
Short summary
Short summary
Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi
The Cryosphere, 18, 1621–1632, https://doi.org/10.5194/tc-18-1621-2024, https://doi.org/10.5194/tc-18-1621-2024, 2024
Short summary
Short summary
This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
Short summary
Short summary
Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
EGUsphere, https://doi.org/10.5194/egusphere-2024-651, https://doi.org/10.5194/egusphere-2024-651, 2024
Short summary
Short summary
This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
EGUsphere, https://doi.org/10.5194/egusphere-2024-179, https://doi.org/10.5194/egusphere-2024-179, 2024
Short summary
Short summary
Satellite passive microwave observations are crucial for monitoring the Arctic's sea ice and atmosphere. To do this effectively, it's important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we've improved our understanding, identifying four distinct emission types and expanding current knowledge towards higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
Short summary
Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Jérôme Messmer and Alexander Raphael Groos
The Cryosphere, 18, 719–746, https://doi.org/10.5194/tc-18-719-2024, https://doi.org/10.5194/tc-18-719-2024, 2024
Short summary
Short summary
The lower part of mountain glaciers is often covered with debris. Knowing the thickness of the debris is important as it influences the melting and future evolution of the affected glaciers. We have developed an open-source approach to map variations in debris thickness on glaciers using a low-cost drone equipped with a thermal infrared camera. The resulting high-resolution maps of debris surface temperature and thickness enable more accurate monitoring and modelling of debris-covered glaciers.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
Short summary
Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
EGUsphere, https://doi.org/10.5194/egusphere-2024-178, https://doi.org/10.5194/egusphere-2024-178, 2024
Short summary
Short summary
Here, we present ASIP (Automated Sea Ice Products): a new and comprehensive deep learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from Sentinel-1 SAR and AMSR2 passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and passive microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
Short summary
Short summary
In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
Benjamin J. Wallis, Anna E. Hogg, Yikai Zhu, and Andrew Hooper
EGUsphere, https://doi.org/10.5194/egusphere-2023-2874, https://doi.org/10.5194/egusphere-2023-2874, 2024
Short summary
Short summary
The grounding line, where ice begins to float, is an essential variable to understand ice dynamics, but in some locations it can be difficult to measure. Using satellite data and a new method, Wallis et al. measure the grounding line position of glaciers and ice shelves in the Antarctic Peninsula and find retreats of up to 16.3 km have occurred since the last time measurements were made in 1990s.
Yungang Cao, Rumeng Pan, Meng Pan, Ruodan Lei, Puying Du, and Xueqin Bai
The Cryosphere, 18, 153–168, https://doi.org/10.5194/tc-18-153-2024, https://doi.org/10.5194/tc-18-153-2024, 2024
Short summary
Short summary
This study built a glacial lake dataset with 15376 samples in seven types and proposed an automatic method by two-stage (the semantic segmentation network and post-processing) optimizations to detect glacial lakes. The proposed method for glacial lake extraction has achieved the best results so far, in which the F1 score and IoU reached 0.945 and 0.907, respectively. The area of the minimum glacial lake that can be entirely and correctly extracted has been raised to the 100 m2 level.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
Short summary
Short summary
Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023, https://doi.org/10.5194/tc-17-5519-2023, 2023
Short summary
Short summary
To alleviate tedious manual image annotations for training deep learning (DL) models in floe instance segmentation, we employ a classical image processing technique to automatically label floes in images. We then apply a DL semantic method for fast and adaptive floe instance segmentation from high-resolution airborne and satellite images. A post-processing algorithm is also proposed to refine the segmentation and further to derive acceptable floe size distributions at local and global scales.
Daniel Falaschi, Atanu Bhattacharya, Gregoire Guillet, Lei Huang, Owen King, Kriti Mukherjee, Philipp Rastner, Tandong Yao, and Tobias Bolch
The Cryosphere, 17, 5435–5458, https://doi.org/10.5194/tc-17-5435-2023, https://doi.org/10.5194/tc-17-5435-2023, 2023
Short summary
Short summary
Because glaciers are crucial freshwater sources in the lowlands surrounding High Mountain Asia, constraining short-term glacier mass changes is essential. We investigate the potential of state-of-the-art satellite elevation data to measure glacier mass changes in two selected regions. The results demonstrate the ability of our dataset to characterize glacier changes of different magnitudes, allowing for an increase in the number of inaccessible glaciers that can be readily monitored.
Jennika Hammar, Inge Grünberg, Steven V. Kokelj, Jurjen van der Sluijs, and Julia Boike
The Cryosphere, 17, 5357–5372, https://doi.org/10.5194/tc-17-5357-2023, https://doi.org/10.5194/tc-17-5357-2023, 2023
Short summary
Short summary
Roads on permafrost have significant environmental effects. This study assessed the Inuvik to Tuktoyaktuk Highway (ITH) in Canada and its impact on snow accumulation, albedo and snowmelt timing. Our findings revealed that snow accumulation increased by up to 36 m from the road, 12-day earlier snowmelt within 100 m due to reduced albedo, and altered snowmelt patterns in seemingly undisturbed areas. Remote sensing aids in understanding road impacts on permafrost.
Tao Li, Yuanlin Hu, Bin Liu, Liming Jiang, Hansheng Wang, and Xiang Shen
The Cryosphere, 17, 5299–5316, https://doi.org/10.5194/tc-17-5299-2023, https://doi.org/10.5194/tc-17-5299-2023, 2023
Short summary
Short summary
Raw DEMs are often misaligned with each other due to georeferencing errors, and a co-registration process is required before DEM differencing. We present a comparative analysis of the two classical DEM co-registration and three residual correction algorithms. The experimental results show that rotation and scale biases should be considered in DEM co-registration. The new non-parametric regression technique can eliminate the complex systematic errors, which existed in the co-registration results.
Fabrizio Troilo, Niccolò Dematteis, Francesco Zucca, Martin Funk, and Daniele Giordan
EGUsphere, https://doi.org/10.5194/egusphere-2023-2771, https://doi.org/10.5194/egusphere-2023-2771, 2023
Short summary
Short summary
One of the main features of glaciers is their movement along slopes. The study of this process is relevant in many aspects of glaciology. In the present study, optical satellite images of Mont Blanc were processed, obtaining surface velocities of 30 glaciers between 2016–2022. The study highlighted different behaviours and velocity variations that have relationships with the glacier morphology. An unexpected accelerating trend was observed, but its origin needs to be further investigated.
Oskar Herrmann, Nora Gourmelon, Thorsten Seehaus, Andreas Maier, Johannes J. Fürst, Matthias H. Braun, and Vincent Christlein
The Cryosphere, 17, 4957–4977, https://doi.org/10.5194/tc-17-4957-2023, https://doi.org/10.5194/tc-17-4957-2023, 2023
Short summary
Short summary
Delineating calving fronts of marine-terminating glaciers in satellite images is a labour-intensive task. We propose a method based on deep learning that automates this task. We choose a deep learning framework that adapts to any given dataset without needing deep learning expertise. The method is evaluated on a benchmark dataset for calving-front detection and glacier zone segmentation. The framework can beat the benchmark baseline without major modifications.
Anne Braakmann-Folgmann, Andrew Shepherd, David Hogg, and Ella Redmond
The Cryosphere, 17, 4675–4690, https://doi.org/10.5194/tc-17-4675-2023, https://doi.org/10.5194/tc-17-4675-2023, 2023
Short summary
Short summary
In this study, we propose a deep neural network to map the extent of giant Antarctic icebergs in Sentinel-1 images automatically. While each manual delineation requires several minutes, our U-net takes less than 0.01 s. In terms of accuracy, we find that U-net outperforms two standard segmentation techniques (Otsu, k-means) in most metrics and is more robust to challenging scenes with sea ice, coast and other icebergs. The absolute median deviation in iceberg area across 191 images is 4.1 %.
Jurjen van der Sluijs, Steven V. Kokelj, and Jon F. Tunnicliffe
The Cryosphere, 17, 4511–4533, https://doi.org/10.5194/tc-17-4511-2023, https://doi.org/10.5194/tc-17-4511-2023, 2023
Short summary
Short summary
There is an urgent need to obtain size and erosion estimates of climate-driven landslides, such as retrogressive thaw slumps. We evaluated surface interpolation techniques to estimate slump erosional volumes and developed a new inventory method by which the size and activity of these landslides are tracked through time. Models between slump area and volume reveal non-linear intensification, whereby model coefficients improve our understanding of how permafrost landscapes may evolve over time.
Trystan Surawy-Stepney, Anna E. Hogg, Stephen L. Cornford, and David C. Hogg
The Cryosphere, 17, 4421–4445, https://doi.org/10.5194/tc-17-4421-2023, https://doi.org/10.5194/tc-17-4421-2023, 2023
Short summary
Short summary
The presence of crevasses in Antarctica influences how the ice sheet behaves. It is important, therefore, to collect data on the spatial distribution of crevasses and how they are changing. We present a method of mapping crevasses from satellite radar imagery and apply it to 7.5 years of images, covering Antarctica's floating and grounded ice. We develop a method of measuring change in the density of crevasses and quantify increased fracturing in important parts of the West Antarctic Ice Sheet.
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, https://doi.org/10.5194/tc-17-4343-2023, 2023
Short summary
Short summary
Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, https://doi.org/10.5194/tc-17-4363-2023, 2023
Short summary
Short summary
Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
Short summary
Short summary
Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Konstantin Muzalevskiy, Zdenek Ruzicka, Alexandre Roy, Michael Loranty, and Alexander Vasiliev
The Cryosphere, 17, 4155–4164, https://doi.org/10.5194/tc-17-4155-2023, https://doi.org/10.5194/tc-17-4155-2023, 2023
Short summary
Short summary
A new all-weather method for determining the frozen/thawed (FT) state of soils in the Arctic region based on satellite data was proposed. The method is based on multifrequency measurement of brightness temperatures by the SMAP and GCOM-W1/AMSR2 satellites. The created method was tested at sites in Canada, Finland, Russia, and the USA, based on climatic weather station data. The proposed method identifies the FT state of Arctic soils with better accuracy than existing methods.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
Short summary
Short summary
In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Whyjay Zheng, Shashank Bhushan, Maximillian Van Wyk De Vries, William Kochtitzky, David Shean, Luke Copland, Christine Dow, Renette Jones-Ivey, and Fernando Pérez
The Cryosphere, 17, 4063–4078, https://doi.org/10.5194/tc-17-4063-2023, https://doi.org/10.5194/tc-17-4063-2023, 2023
Short summary
Short summary
We design and propose a method that can evaluate the quality of glacier velocity maps. The method includes two numbers that we can calculate for each velocity map. Based on statistics and ice flow physics, velocity maps with numbers close to the recommended values are considered to have good quality. We test the method using the data from Kaskawulsh Glacier, Canada, and release an open-sourced software tool called GLAcier Feature Tracking testkit (GLAFT) to help users assess their velocity maps.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023, https://doi.org/10.5194/tc-17-3867-2023, 2023
Short summary
Short summary
During winter, storms entering the Arctic region can bring warm air into the cold environment. Strong increases in air temperature modify the characteristics of the Arctic snow and ice cover. The Arctic sea ice cover can be monitored by satellites observing the natural emission of the Earth's surface. In this study, we show that during warm air intrusions the change in the snow characteristics influences the satellite-derived sea ice cover, leading to a false reduction of the estimated ice area.
Ellen M. Buckley, Sinéad L. Farrell, Ute C. Herzfeld, Melinda A. Webster, Thomas Trantow, Oliwia N. Baney, Kyle A. Duncan, Huilin Han, and Matthew Lawson
The Cryosphere, 17, 3695–3719, https://doi.org/10.5194/tc-17-3695-2023, https://doi.org/10.5194/tc-17-3695-2023, 2023
Short summary
Short summary
In this study, we use satellite observations to investigate the evolution of melt ponds on the Arctic sea ice surface. We derive melt pond depth from ICESat-2 measurements of the pond surface and bathymetry and melt pond fraction (MPF) from the classification of Sentinel-2 imagery. MPF increases to a peak of 16 % in late June and then decreases, while depth increases steadily. This work demonstrates the ability to track evolving melt conditions in three dimensions throughout the summer.
Monika Pfau, Georg Veh, and Wolfgang Schwanghart
The Cryosphere, 17, 3535–3551, https://doi.org/10.5194/tc-17-3535-2023, https://doi.org/10.5194/tc-17-3535-2023, 2023
Short summary
Short summary
Cast shadows have been a recurring problem in remote sensing of glaciers. We show that the length of shadows from surrounding mountains can be used to detect gains or losses in glacier elevation.
Enze Zhang, Ginny Catania, and Daniel T. Trugman
The Cryosphere, 17, 3485–3503, https://doi.org/10.5194/tc-17-3485-2023, https://doi.org/10.5194/tc-17-3485-2023, 2023
Short summary
Short summary
Glacier termini are essential for studying why glaciers retreat, but they need to be mapped automatically due to the volume of satellite images. Existing automated mapping methods have been limited due to limited automation, lack of quality control, and inadequacy in highly diverse terminus environments. We design a fully automated, deep-learning-based method to produce termini with quality control. We produced 278 239 termini in Greenland and provided a way to deliver new termini regularly.
Cited articles
Adam, J. C., Hamlet, A. F., and Lettenmaier, D. P.: Implications of global
climate change for snowmelt hydrology in the twenty-first century, Hydrol.
Process., 23, 962–972, https://doi.org/10.1002/hyp.7201, 2009.
Aravena, J.-C. and Luckman, B. H.: Spatio-temporal rainfall patterns in
Southern South America, Int. J. Climatol., 29, 2106–2120,
https://doi.org/10.1002/joc.1761, 2009.
Arsenault, K. R., Houser, P. R., and De Lannoy, G. J. M.: Evaluation of the
MODIS snow cover fraction product, Hydrol. Process., 28, 980–998,
https://doi.org/10.1002/hyp.9636, 2014.
Ayala, A., McPhee, J., and Vargas, X.: Altitudinal gradients, midwinter melt,
and wind effects on snow accumulation in semiarid midlatitude Andes under La
Niña conditions, Water Resour. Res., 50, 3589–3594,
https://doi.org/10.1002/2013wr014960, 2014.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438,
303–309, https://doi.org/10.1038/nature04141, 2005.
Barry, R. G.: Mountain Weather and Climate, 3rd Edn., University of Colorado,
Boulder, USA, 2008.
Barry, R. G. and Seimon, A.: Research for Mountain Area Development: Climatic
Fluctuations in the Mountains of the Americas and Their Significance, Ambio,
29, 364–370, https://doi.org/10.1579/0044-7447-29.7.364, 2000.
Berrisford, P., Dee, D. P., Poli, P., Brugge, R., Fielding, K., Fuentes, M.,
Kållberg, P. W., Kobayashi, S., Uppala, S., and Simmons, A.: The
ERA-Interim archive Version 2.0, ERA Report Series, ECMWF, Shinfield Park,
Reading, 23 pp., 2011.
Bradley, R. S.: Projected temperature changes along the American cordillera
and the planned GCOS network, Geophys. Res. Lett., 31, L16210,
https://doi.org/10.1029/2004gl020229, 2004.
Bradley, R. S., Vuille, M., Diaz, H. F., and Vergara, W.: Threats to Water
Supplies in the Tropical Andes, Science, 312, 1755–1756, 2006.
Brown, R. D. and Mote, P. W.: The Response of Northern Hemisphere Snow Cover
to a Changing Climate, J. Climate, 22, 2124–2145,
https://doi.org/10.1175/2008jcli2665.1, 2009.
Brown, R. D. and Robinson, D. A.: Northern Hemisphere spring snow cover
variability and change over 1922–2010 including an assessment of
uncertainty, The Cryosphere, 5, 219–229, https://doi.org/10.5194/tc-5-219-2011, 2011.
Carrasco, J., Osorio, R., and Casassa, G.: Secular trend of the
equilibrium-line altitude on the western side of the southern Andes, derived
from radiosonde and surface observations, J. Glaciol., 54, 538–550,
https://doi.org/10.3189/002214308785837002, 2008.
Cornwell, E., Molotch, N. P., and McPhee, J.: Spatio-temporal variability of
snow water equivalent in the extra-tropical Andes Cordillera from distributed
energy balance modeling and remotely sensed snow cover, Hydrol. Earth Syst.
Sci., 20, 411–430, https://doi.org/10.5194/hess-20-411-2016, 2016.
Cortés, G. and Margulis, S.: Impacts of El Niño and La Niña on
interannual snow accumulation in the Andes: Results from a high-resolution
31 year reanalysis, Geophys. Res. Lett., 44, 6859–6867,
https://doi.org/10.1002/2017gl073826, 2017.
Cortés, G., Vargas, X., and McPhee, J.: Climatic sensitivity of
streamflow timing in the extratropical western Andes Cordillera, J. Hydrol.,
405, 93–109, https://doi.org/10.1016/j.jhydrol.2011.05.013, 2011.
Cortés, G., Girotto, M., and Margulis, S. A.: Analysis of sub-pixel snow
and ice extent over the extratropical Andes using spectral unmixing of
historical Landsat imagery, Remote Sens. Environ., 141, 64–78,
https://doi.org/10.1016/j.rse.2013.10.023, 2014.
Cortés, G., Girotto, M., and Margulis, S.: Snow process estimation over
the extratropical Andes using a data assimilation framework integrating MERRA
data and Landsat imagery, Water Resour. Res., 52, 2582–2600,
https://doi.org/10.1002/2015wr018376, 2016.
Dahe, Q., Shiyin, L., and Peiji, L.: Snow Cover Distribution, Variability,
and Response to Climate Change in Western China, J. Climate, 19, 1820–1833,
https://doi.org/10.1175/JCLI3694.1, 2006.
Dettinger, M. D., Battisti, D. S., Garreaud, R. D., McCabe Jr., G. J., and
Bitz, C. M.: Chapter 1 – Interhemispheric Effects of Interannual and Decadal
ENSO-Like Climate Variations on the Americas A2 – Markgraf, Vera, in:
Interhemispheric Climate Linkages, Academic Press, San Diego, 1–16, 2001.
Falvey, M. and Garreaud, R. D.: Regional cooling in a warming world: Recent
temperature trends in the southeast Pacific and along the west coast of
subtropical South America (1979–2006), J. Geophys. Res., 114, D04102,
https://doi.org/10.1029/2008jd010519, 2009.
Fassnacht, S. R., and Hultstrand, M.: Snowpack variability and trends at
long-term stations in northern Colorado, USA, Proceedings of the
International Association of Hydrological Sciences, 371, 131–136,
https://doi.org/10.5194/piahs-371-131-2015, 2015
Fassnacht, S. R., Cherry, M. L., Venable, N. B. H., and Saavedra, F.: Snow
and albedo climate change impacts across the United States Northern Great
Plains, The Cryosphere, 10, 329–339, https://doi.org/10.5194/tc-10-329-2016, 2016.
Fogt, R. L., Bromwich, D. H., and Hines, K. M.: Understanding the SAM
influence on the South Pacific ENSO teleconnection, Clim. Dynam., 36,
1555–1576, https://doi.org/10.1007/s00382-010-0905-0, 2010.
Foster, J. L., Hall, D. K., Kelly, R. E. J., and Chiu, L.: Seasonal snow
extent and snow mass in South America using SMMR and SSM/I passive microwave
data (1979–2006), Remote Sens. Environ., 113, 291–305,
https://doi.org/10.1016/j.rse.2008.09.010, 2009.
Gafurov, A. and Bárdossy, A.: Cloud removal methodology from MODIS snow
cover product, Hydrol. Earth Syst. Sci., 13, 1361–1373,
https://doi.org/10.5194/hess-13-1361-2009, 2009.
Gao, Y., Lu, N., and Yao, T.: Evaluation of a cloud-gap-filled MODIS daily
snow cover product over the Pacific Northwest USA, J. Hydrol., 404, 157–165,
https://doi.org/10.1016/j.jhydrol.2011.04.026, 2011.
Garreaud, R., Vuille, M., and Clement, A. C.: The climate of the Altiplano:
observed current conditions and mechanisms of past changes, Palaeogeogr.
Palaeocl., 194, 5–22, https://doi.org/10.1016/s0031-0182(03)00269-4, 2003.
Garreaud, R. D.: The Andes climate and weather, Adv. Geosci., 22, 3–11,
https://doi.org/10.5194/adgeo-22-3-2009, 2009.
Garreaud, R. D., Vuille, M., Compagnucci, R., and Marengo, J.: Present-day
South American climate, Palaeogeogr. Palaeocl., 281, 180–195,
https://doi.org/10.1016/j.palaeo.2007.10.032, 2009.
Gascoin, S., Kinnard, C., Ponce, R., Lhermitte, S., MacDonell, S., and
Rabatel, A.: Glacier contribution to streamflow in two headwaters of the
Huasco River, Dry Andes of Chile, The Cryosphere, 5, 1099–1113,
https://doi.org/10.5194/tc-5-1099-2011, 2011.
GCOS: Report of the GCOS Regional Workshop for South America on Improving
Observing System for Climate, GCOS-86, World Meteorological Organization,
149 pp., 2003.
Gillett, N. P., Kell, T. D., and Jones, P. D.: Regional climate impacts of
the Southern Annular Mode, Geophys. Res. Lett., 33, L23704,
https://doi.org/10.1029/2006gl027721, 2006.
Groemping, U. and Matthias, L.: Relative importance of regressors in linear
models, R package version 2.2, https://doi.org/10.1139/gen-2016-0039, 2013.
Grömping, U.: Relative Importance for Linear Regression in R: The Package
relaimpo, J. Stat. Softw., 17, 1–27, https://doi.org/10.18637/jss.v017.i01, 2006.
Hall, D. K., Salomonson, V. V., and Riggs, G. A.: MODIS/Terra Snow Cover
8-Day L3 Global 500 m Grid, Version 5. MOD10A2, National Snow and Ice Data
Center Distributed Active Archive Center, Boulder, Colorado USA, NASA,
https://doi.org/10.5067/C574UGKQQU1T (last access: 1 September 2017), 2006.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K.
J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194,
https://doi.org/10.1016/S0034-4257(02)00095-0, 2002.
Hall, D. K., Riggs, G. A., Foster, J. L., and Kumar, S. V.: Development and
evaluation of a cloud-gap-filled MODIS daily snow-cover product, Remote Sens.
Environ., 114, 496–503, https://doi.org/10.1016/j.rse.2009.10.007, 2010.
Hall, D. K., Foster, J. L., DiGirolamo, N. E., and Riggs, G. A.: Snow cover,
snowmelt timing and stream power in the Wind River Range, Wyoming,
Geomorphology, 137, 87–93, https://doi.org/10.1016/j.geomorph.2010.11.011, 2012.
Hobbs, J. E., Lindesay, J., and Bridgeman, H. A.: Climates of the Southern
Continents. Present, Past and Future, John Wiley and Sons, Chichester, UK,
1999.
Kennedy, M. and Kopp, S.: Understanding Map Projections, Environmental
Systems Research Institute, Inc (ESRI), Redland, USA, 1994.
Khaled, H. H. and Ramachandra, A. R.: A modified Mann-Kendall trend test for
autocorrelated data, J. Hydrol., 204, 182–196,
https://doi.org/10.1016/S0022-1694(97)00125-X, 1998.
Kluver, D. and Leathers, D.: Regionalization of snowfall frequency and trends
over the contiguous United States, Int. J. Climatol., 35, 4348–4358,
https://doi.org/10.1002/joc.4292, 2015.
Knowles, N. and Cayan, D. R.: Elevational Dependence of Projected Hydrologic
Changes in the San Francisco Estuary and Watershed, Climatic Change, 62,
319–336, https://doi.org/10.1023/B:CLIM.0000013696.14308.b9, 2004.
Lawrimore, J. H., Menne, M. J., Gleason, B. E., Williams, C. N., Wuertz, D.
B., Vose, R. S., and Rennie, J.: An overview of the Global Historical
Climatology Network monthly mean temperature data set, version 3, J. Geophys.
Res., 116, D19121, https://doi.org/10.1029/2011JD016187, 2011.
Legates, D. R. and Willmott, C. J.: Mean seasonal and spatial variability in
global surface air temperature, Theor. Appl. Climatol., 41, 11–21,
https://doi.org/10.1007/BF00866198, 1990.
Liu, X., Cheng, Z., Yan, L., and Yin, Z.-Y.: Elevation dependency of recent
and future minimum surface air temperature trends in the Tibetan Plateau and
its surroundings, Global Planet. Change, 68, 164–174,
https://doi.org/10.1016/j.gloplacha.2009.03.017, 2009.
Llamedo, P., Hierro, R., de la Torre, A., and Alexander, P.: ENSO-related
moisture and temperature anomalies over South America derived from GPS radio
occultation profiles, Int. J. Climatol., 37, 268–275,
https://doi.org/10.1002/joc.4702, 2016.
López-Moreno, J. I., Goyette, S., and Beniston, M.: Impact of climate
change on snowpack in the Pyrenees: Horizontal spatial variability and
vertical gradients, J. Hydrol., 374, 384–396,
https://doi.org/10.1016/j.jhydrol.2009.06.049, 2009.
Marshall, G.: Trends in the Southern Annular Mode from Observations and
Reanalyses, J. Climate, 16, 4134–4143,
https://doi.org/10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2,
2003.
Masiokas, M., Villalba, R., Luckman, B., Le Quesne, C., and Aravena, J. C.:
Snowpack variations in the Central Andes of Argentina and Chile, 1951–2005:
Large-scale atmospheric influences and implications for water resources in
the region, J. Climate, 19, 6334-6352, https://doi.org/10.1175/JCLI3969.1, 2006.
Masiokas, M. H., Rivera, A., Espizua, L. E., Villalba, R., Delgado, S., and
Aravena, J. C.: Glacier fluctuations in extratropical South America during
the past 1000years, Palaeogeogr. Palaeocl., 281, 242–268,
https://doi.org/10.1016/j.palaeo.2009.08.006, 2009.
Masiokas, M. H., Villalba, R., Luckman, B. H., and Mauget, S.: Intra- to
Multidecadal Variations of Snowpack and Streamflow Records in the Andes of
Chile and Argentina between 30∘ and 37∘ S,
J. Hydrometeorol., 11, 822–831, https://doi.org/10.1175/2010jhm1191.1, 2010.
Masiokas, M. H., Villalba, R., Christie, D. A., Betman, E., Luckman, B. H.,
Le Quesne, C., Prieto, M. R., and Mauget, S.: Snowpack variations since AD
1150 in the Andes of Chile and Argentina (30∘–37∘ S)
inferred from rainfall, tree-ring and documentary records, J. Geophys. Res.,
117, D05112, https://doi.org/10.1029/2011jd016748, 2012.
Matsuura, K. and Willmott, C.: Terrestrial Air Temperature: 1900–2014
Gridded Monthly Time Series (Version 4.01), available at:
http://climate.geog.udel.edu/~climate/html_pages/download.html (last
access: January 2017), 2015.
McLeod, A. I.: Kendall-package: Kendall correlation and trend tests,
available at: http://www.stats.uwo.ca/faculty/aim (last access:
January 2017), 2011.
Mernild, S. H., Liston, G. E., Hiemstra, C. A., Malmros, J. K., Yde, J. C.,
and McPhee, J.: The Andes Cordillera. Part I: snow distribution, properties,
and trends (1979–2014), Int. J. Climatol., 37, 1680–1698,
https://doi.org/10.1002/joc.4804, 2017.
Meza, F. J.: Recent trends and ENSO influence on droughts in Northern Chile:
An application of the Standardized Precipitation Evapotranspiration Index,
Weather and Climate Extremes, 1, 51–58, https://doi.org/10.1016/j.wace.2013.07.002,
2013.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the
GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2,
Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Moore, C., Kampf, S., Stone, B., and Richer, E.: A GIS-based method for
defining snow zones: Aplication to the Western United States, Geocarto Int.,
3, 62–81, https://doi.org/10.1080/10106049.2014.885089, 2015.
Peduzzi, P., Herold, C., and Silverio, W.: Assessing high altitude glacier
thickness, volume and area changes using field, GIS and remote sensing
techniques: the case of Nevado Coropuna (Peru), The Cryosphere, 4, 313–323,
https://doi.org/10.5194/tc-4-313-2010, 2010.
Pellicciotti, F., Burlando, P., and Van Vliet, K.: Recent trends in
precipitation and streamflow in the Aconcagua River Basin, central Chile, in:
Glacier Mass Balance Changes and Meltwater Discharge, IAHS Publ. 318,
17–38, 2007.
Pellicciotti, F., Ragettli, S., Carenzo, M., and McPhee, J.: Changes of
glaciers in the Andes of Chile and priorities for future work, Sci. Total
Environ., 493, 1197–1210, https://doi.org/10.1016/j.scitotenv.2013.10.055, 2013.
Pepin, N., Bradley, R. S., Diaz, H., Baraer, M., Caceres, E., Forsythe, N.,
Fowler, H., Greenwood, G., Hashmi, M., Liu, X., Miller, J. R., Ning, L.,
Ohmura, A., Palazzi, E., Rangwala, I., Schoner, W., Severskiy, I.,
Shahgedonova, M., Wang, M., Williamsn, S., and Yang, D.: Elevation-dependent
warming in mountain regions of the world, Nat. Clim. Change, 5, 424–430,
https://doi.org/10.1038/nclimate2563, 2015.
Prieto, R., Herrera, R., and Dousel, P.: Interannual oscillations and trend
of snow occurrence in the Andes region since 1885, Australian Meteorogical
Magazine, 50, 164–168, 2001.
Quintana, J. M.: Changes in the rainfall regime along the extratropical west
coast of South America (Chile): 30–43 ∘C, Atmosfera, 25, 1–22,
2012.
Rabatel, A., Francou, B., Soruco, A., Gomez, J., Cáceres, B., Ceballos,
J. L., Basantes, R., Vuille, M., Sicart, J.-E., Huggel, C., Scheel, M.,
Lejeune, Y., Arnaud, Y., Collet, M., Condom, T., Consoli, G., Favier, V.,
Jomelli, V., Galarraga, R., Ginot, P., Maisincho, L., Mendoza, J.,
Ménégoz, M., Ramirez, E., Ribstein, P., Suarez, W., Villacis, M., and
Wagnon, P.: Current state of glaciers in the tropical Andes: a multi-century
perspective on glacier evolution and climate change, The Cryosphere, 7,
81–102, https://doi.org/10.5194/tc-7-81-2013, 2013.
RCoreTeam: R: A language and environment for statistical computing, edited
by: Computing, R. F. f. S., Vienna, Austria, 2013.
Riggs, G. A., Hall, D. K., and Salomonson, V. V.: MODIS snow products user
guide to Collection 5, available at:
https://modis-snow-ice.gsfc.nasa.gov/uploads/siug_c5.pdf (last access:
1 September 2017), 2006.
Rittger, K., Painter, T. H., and Dozier, J.: Assessment of methods for
mapping snow cover from MODIS, Adv. Water Resour., 51, 367–380,
https://doi.org/10.1016/j.advwatres.2012.03.002, 2013.
Rutllant, J. A. and Fuenzalida, H.: Synoptic Aspects of the Central Chile
Rainfall Variability Associated with the Southern Oscillation, Int. J.
Climatol., 11, 63–76, https://doi.org/10.1002/joc.3370110105, 1991.
Saavedra, F. A.: Spatial and Temporal Variability of Snow Cover in the Andes
Mountains and Its influence on Streamflow in Snow Dominant Rivers, PhD
thesis, Geoscience, Colorado State University, Fort Collins, 119 pp., 2016.
Saavedra, F. A., Kampf, S. K., Fassnacht, S. R., and Sibold, J. S.: A Snow
Climatology of the Andes Mountains from MODIS Snow Cover Data, Int.
J. Climatol., 37, 1526–1539, https://doi.org/10.1002/joc.4795, 2017.
Salzmann, N., Huggel, C., Rohrer, M., Silverio, W., Mark, B. G., Burns, P.,
and Portocarrero, C.: Glacier changes and climate trends derived from
multiple sources in the data scarce Cordillera Vilcanota region, southern
Peruvian Andes, The Cryosphere, 7, 103–118, https://doi.org/10.5194/tc-7-103-2013,
2013.
Santos, J. L.: The Impact of El Niño – Southern Oscillation Events on
South America, Adv. Geosci., 6, 221–225, https://doi.org/10.5194/adgeo-6-221-2006,
2006.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau,
J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934,
1968.
Stewart, I. T.: Changes in snowpack and snowmelt runoff for key mountain
regions, Hydrol. Process., 23, 78-94, https://doi.org/10.1002/hyp.7128, 2009.
Theil, H.: A rank-invariant method of linear and polynomial regression
analysis, Mathematic, 53, 386–392, 1950.
Valdés-Pineda, R., Pizarro, R., Valdés, J. B., Carrasco, J. F.,
García-Chevesich, P., and Olivares, C.: Spatio-temporal trends of
precipitation, its aggressiveness and concentration, along the Pacific coast
of South America (36–49∘ S), Hydrolog. Sci. J., 61, 2110–2132,
https://doi.org/10.1080/02626667.2015.1085989, 2015a.
Valdés-Pineda, R., Valdés, J. B., Diaz, H. F., and Pizarro-Tapia, R.:
Analysis of spatio-temporal changes in annual and seasonal precipitation
variability in South America-Chile and related ocean-atmosphere circulation
patterns, Int. J. Climatol., 36, 2979–3001, https://doi.org/10.1002/joc.4532, 2015b.
Vaughan, D. G., Comiso, J. C., Allison, I., Carrasco, J., Kaser, G., Kwok,
R., Mote, P., Murray, T., Paul, F., Ren, J., Rignot, E., Solomina, O.,
Steffen, K., and Zhang, T.: Observations: Cryosphere, in: Climate Change
2013: The Physical Science Basis, Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, New York, USA, 2013.
Venable, N. B. H., Fassnacht, S. R., Adyabadam, G., Tumenjargal, S.,
Fernandez-Gimenez, M., and Batbuyan, B.: Does the length of station record
influence the warming trend that is perceived by Mongolian herders near the
Khangai mountains?, Pirineos. Revista de Ecologia de Montana, 167, 71–88,
https://doi.org/10.3989/Pirineos.2011.166004, 2012.
Vera, C. and Silvestri, G.: Precipitation interannual variability in South
America from the WCRP-CMIP3 multi-model dataset, Clim. Dynam., 32,
1003–1014, https://doi.org/10.1007/s00382-009-0534-7, 2009.
Villalba, R., Lara, A., Masiokas, M. H., Urrutia, R., Luckman, B. H.,
Marshall, G. J., Mundo, I. A., Christie, D. A., Cook, E. R., Neukom, R.,
Allen, K., Fenwick, P., Boninsegna, J. A., Srur, A. M., Morales, M. S.,
Araneo, D., Palmer, J. G., Cuq, E., Aravena, J. C., Holz, A., and LeQuesne,
C.: Unusual Southern Hemisphere tree growth patterns induced by changes in
the Southern Annular Mode, Nat. Geosci., 5, 793–798, https://doi.org/10.1038/ngeo1613,
2012.
Vuille, M. and Ammann, C.: Regional Snowfall Patterns in the High, Arid
Andes, Climatic Change, 36, 413–423, https://doi.org/10.1023/A:1005330802974, 1997.
Vuille, M. and Bradley, R. S.: Mean annual temperature trends and their
vertical structure in the tropical Andes, Geophys. Res. Lett., 27,
3885–3888, https://doi.org/10.1029/2000gl011871, 2000.
Williams, R. and Ferringo, J.: Satellite Image Atlas of
Glaciers of the World, South America, USGS, Washington, USA, 1998.
Willmott, C. J. and Matsuura, K.: Advantages of the mean absolute error (MAE)
over the root mean square error (RMSE) in assessing average model
performance, Clim. Res., 30, 79–82, https://doi.org/10.3354/cr030079, 2005.
Wolter, K. and Timlin, M. S.: Measuring the strength of ENSO events: How does
1997/98 rank?, Weather, 53, 315–324,
https://doi.org/10.1002/j.1477-8696.1998.tb06408.x, 1998.
Woodhouse, C. A.: A 431-Yr Reconstruction of Western Colorado Snowpack from
Tree Rings, J. Climate, 16, 1551–1561, https://doi.org/10.1175/1520-0442-16.10.1551,
2003.
Yi, Y., Kimball, J. S., Jones, L. A., Reichle, R. H., and McDonald, K. C.:
Evaluation of MERRA Land Surface Estimates in Preparation for the Soil
Moisture Active Passive Mission, J. Climate, 24, 3797–3816,
https://doi.org/10.1175/2011jcli4034.1, 2011.
Zamboni, L., Kucharski, F., and Mechoso, C. R.: Seasonal variations of the
links between the interannual variability of South America and the South
Pacific, Clim. Dynam., 38, 2115–2129, https://doi.org/10.1007/s00382-011-1116-z, 2011.
Zhang, K., Kimball, J. S., and Running, S. W.: A review of remote sensing
based actual evapotranspiration estimation, WIREs Water, 3, 834–853,
https://doi.org/10.1002/wat2.1168, 2016.
Short summary
This manuscript presents a large latitude and elevation range analysis for snow trends in the Andes using satellite images (MODIS) snow cover product. The research approach is also significant because it presents a novel strategy for defining trends in snow persistence from remote sensing data, and this allows us to improve understanding of climate change effects on snow in areas with sparse and unevenly ground climate data.
This manuscript presents a large latitude and elevation range analysis for snow trends in the...
