Articles | Volume 17, issue 4
https://doi.org/10.5194/tc-17-1431-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-1431-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessment of Arctic seasonal snow cover rates of change
Climate Research Division, Environment and Climate Change Canada,
Toronto, Canada
Lawrence Mudryk
Climate Research Division, Environment and Climate Change Canada,
Toronto, Canada
Related authors
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, https://doi.org/10.5194/tc-18-4955-2024, 2024
Short summary
Short summary
We look at three commonly used snow depth datasets that are produced through a combination of snow modelling and historical measurements (reanalysis). When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine these inconsistencies and highlight issues. This method indicates that one of the complex datasets should be excluded from further studies.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3169, https://doi.org/10.5194/egusphere-2024-3169, 2024
Short summary
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Measuring the snow mass from radar measurements is possible with information on the snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layer yielding richer information but at increased computational cost. Here, we show the capabilities of a new method to simplify a complex snowpack, while preserving the scattering behavior of the snowpack and conserving the mass.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
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
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 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.
Georgina Jean Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamund Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
EGUsphere, https://doi.org/10.5194/egusphere-2024-1237, https://doi.org/10.5194/egusphere-2024-1237, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of SVS2-Crocus and evaluated using density and SSA measurements at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and SSA were identified. Top performing ensemble members featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift and increase viscosity in basal layers.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
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We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Lawrence Mudryk, Colleen Mortimer, Chris Derksen, Aleksandra Elias Chereque, and Paul Kushner
EGUsphere, https://doi.org/10.5194/egusphere-2023-3014, https://doi.org/10.5194/egusphere-2023-3014, 2024
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We evaluate and rank 23 products that estimate historical snow amounts. The evaluation uses new a set of ground measurements with improved spatial coverage enabling evaluation across both mountain and non-mountain regions. Performance measures vary tremendously across the products: while most perform reasonably in non-mountain regions, accurate representation of snow amounts in mountain regions and of historical trends is much more variable.
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyvich
EGUsphere, https://doi.org/10.5194/egusphere-2023-3013, https://doi.org/10.5194/egusphere-2023-3013, 2024
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Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two different types of measurements – snow courses and airborne gamma SWE estimates – and analyse how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
Short summary
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Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
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Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Lawrence Mudryk, María Santolaria-Otín, Gerhard Krinner, Martin Ménégoz, Chris Derksen, Claire Brutel-Vuilmet, Mike Brady, and Richard Essery
The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, https://doi.org/10.5194/tc-14-2495-2020, 2020
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We analyze how well updated state-of-the-art climate models reproduce observed historical snow cover extent and snow mass and how they project that these quantities will change up to the year 2100. Overall the updated models better represent historical snow extent than previous models, and they simulate stronger historical trends in snow extent and snow mass. They project that spring snow extent will decrease by 8 % for each degree Celsius that the global surface air temperature increases.
Colleen Mortimer, Lawrence Mudryk, Chris Derksen, Kari Luojus, Ross Brown, Richard Kelly, and Marco Tedesco
The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, https://doi.org/10.5194/tc-14-1579-2020, 2020
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Existing stand-alone passive microwave SWE products have markedly different climatological SWE patterns compared to reanalysis-based datasets. The AMSR-E SWE has low spatial and temporal correlations with the four reanalysis-based products evaluated and GlobSnow and perform poorly in comparisons with snow transect data from Finland, Russia, and Canada. There is better agreement with in situ data when multiple SWE products, excluding the stand-alone passive microwave SWE products, are combined.
Nick Rutter, Melody J. Sandells, Chris Derksen, Joshua King, Peter Toose, Leanne Wake, Tom Watts, Richard Essery, Alexandre Roy, Alain Royer, Philip Marsh, Chris Larsen, and Matthew Sturm
The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, https://doi.org/10.5194/tc-13-3045-2019, 2019
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Impact of natural variability in Arctic tundra snow microstructural characteristics on the capacity to estimate snow water equivalent (SWE) from Ku-band radar was assessed. Median values of metrics quantifying snow microstructure adequately characterise differences between snowpack layers. Optimal estimates of SWE required microstructural values slightly less than the measured median but tolerated natural variability for accurate estimation of SWE in shallow snowpacks.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
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Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Lawrence R. Mudryk, Chris Derksen, Stephen Howell, Fred Laliberté, Chad Thackeray, Reinel Sospedra-Alfonso, Vincent Vionnet, Paul J. Kushner, and Ross Brown
The Cryosphere, 12, 1157–1176, https://doi.org/10.5194/tc-12-1157-2018, https://doi.org/10.5194/tc-12-1157-2018, 2018
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This paper presents changes in both snow and sea ice that have occurred over Canada during the recent past and shows climate model estimates for future changes expected to occur by the year 2050. The historical changes of snow and sea ice are generally coherent and consistent with the regional history of temperature and precipitation changes. It is expected that snow and sea ice will continue to decrease in the future, declining by an additional 15–30 % from present day values by the year 2050.
Peter Toose, Alexandre Roy, Frederick Solheim, Chris Derksen, Tom Watts, Alain Royer, and Anne Walker
Geosci. Instrum. Method. Data Syst., 6, 39–51, https://doi.org/10.5194/gi-6-39-2017, https://doi.org/10.5194/gi-6-39-2017, 2017
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Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers used for monitoring essential climate variables. A 385-channel hyperspectral L-band radiometer system was designed with the means to quantify the strength and type of RFI. The compact design makes it ideal for mounting on both surface and airborne platforms to be used for calibrating and validating measurement from spaceborne sensors.
Libo Wang, Peter Toose, Ross Brown, and Chris Derksen
The Cryosphere, 10, 2589–2602, https://doi.org/10.5194/tc-10-2589-2016, https://doi.org/10.5194/tc-10-2589-2016, 2016
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The conventional wisdom is that Arctic warming will result in an increase in the frequency of winter melt events. However, results in this study show little evidence of trends in winter melt frequency over 1988–2013 period. The frequency of winter melt events is strongly influenced by the selection of the start and end dates of winter period, and a fixed-window method for analyzing winter melt events is observed to generate false increasing trends from a shift in the timing of snow cover season.
Tom Watts, Nick Rutter, Peter Toose, Chris Derksen, Melody Sandells, and John Woodward
The Cryosphere, 10, 2069–2074, https://doi.org/10.5194/tc-10-2069-2016, https://doi.org/10.5194/tc-10-2069-2016, 2016
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Ice layers in snowpacks introduce uncertainty in satellite-derived estimates of snow water equivalent, have ecological impacts on plants and animals, and change the thermal and vapour transport properties of the snowpack. Here we present a new field method for measuring the density of ice layers. The method was used in the Arctic and mid-latitudes; the mean measured ice layer density was significantly higher than values typically used in the literature.
Bart van den Hurk, Hyungjun Kim, Gerhard Krinner, Sonia I. Seneviratne, Chris Derksen, Taikan Oki, Hervé Douville, Jeanne Colin, Agnès Ducharne, Frederique Cheruy, Nicholas Viovy, Michael J. Puma, Yoshihide Wada, Weiping Li, Binghao Jia, Andrea Alessandri, Dave M. Lawrence, Graham P. Weedon, Richard Ellis, Stefan Hagemann, Jiafu Mao, Mark G. Flanner, Matteo Zampieri, Stefano Materia, Rachel M. Law, and Justin Sheffield
Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, https://doi.org/10.5194/gmd-9-2809-2016, 2016
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This manuscript describes the setup of the CMIP6 project Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP).
Henna-Reetta Hannula, Juha Lemmetyinen, Anna Kontu, Chris Derksen, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 347–363, https://doi.org/10.5194/gi-5-347-2016, https://doi.org/10.5194/gi-5-347-2016, 2016
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The paper described an extensive in situ data set of bulk snow depth, snow water equivalent, and snow density collected as a support of SnowSAR-2 airborne campaign in northern Finland. The spatial and temporal variability of these snow properties was analyzed in different land cover types. The success of the chosen measurement protocol to provide an accurate reference for the simultaneous SAR data products was analyzed in the context of spatial scale, sample size, and uncertainty.
Stephen E. L. Howell, Frédéric Laliberté, Ron Kwok, Chris Derksen, and Joshua King
The Cryosphere, 10, 1463–1475, https://doi.org/10.5194/tc-10-1463-2016, https://doi.org/10.5194/tc-10-1463-2016, 2016
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The Canadian Ice Service record of observed landfast ice and snow thickness represents one of the longest in the Arctic that spans over 5 decades. We analyze this record to report on long-term trends and variability of ice and snow thickness within the Canadian Arctic Archipelago (CAA). Results indicate a thinning of ice at several sites in the CAA. State-of-the-art climate models still have difficultly capturing observed ice thickness values in the CAA and should be used with caution.
S. E. L. Howell, T. Wohlleben, A. Komarov, L. Pizzolato, and C. Derksen
The Cryosphere, 7, 1753–1768, https://doi.org/10.5194/tc-7-1753-2013, https://doi.org/10.5194/tc-7-1753-2013, 2013
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, https://doi.org/10.5194/tc-18-4955-2024, 2024
Short summary
Short summary
We look at three commonly used snow depth datasets that are produced through a combination of snow modelling and historical measurements (reanalysis). When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine these inconsistencies and highlight issues. This method indicates that one of the complex datasets should be excluded from further studies.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3169, https://doi.org/10.5194/egusphere-2024-3169, 2024
Short summary
Short summary
Measuring the snow mass from radar measurements is possible with information on the snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layer yielding richer information but at increased computational cost. Here, we show the capabilities of a new method to simplify a complex snowpack, while preserving the scattering behavior of the snowpack and conserving the mass.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
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
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 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.
Georgina Jean Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamund Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
EGUsphere, https://doi.org/10.5194/egusphere-2024-1237, https://doi.org/10.5194/egusphere-2024-1237, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of SVS2-Crocus and evaluated using density and SSA measurements at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and SSA were identified. Top performing ensemble members featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift and increase viscosity in basal layers.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
Short summary
Short summary
We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Lawrence Mudryk, Colleen Mortimer, Chris Derksen, Aleksandra Elias Chereque, and Paul Kushner
EGUsphere, https://doi.org/10.5194/egusphere-2023-3014, https://doi.org/10.5194/egusphere-2023-3014, 2024
Short summary
Short summary
We evaluate and rank 23 products that estimate historical snow amounts. The evaluation uses new a set of ground measurements with improved spatial coverage enabling evaluation across both mountain and non-mountain regions. Performance measures vary tremendously across the products: while most perform reasonably in non-mountain regions, accurate representation of snow amounts in mountain regions and of historical trends is much more variable.
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyvich
EGUsphere, https://doi.org/10.5194/egusphere-2023-3013, https://doi.org/10.5194/egusphere-2023-3013, 2024
Short summary
Short summary
Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two different types of measurements – snow courses and airborne gamma SWE estimates – and analyse how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
Short summary
Short summary
Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
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High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
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Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Lawrence Mudryk, María Santolaria-Otín, Gerhard Krinner, Martin Ménégoz, Chris Derksen, Claire Brutel-Vuilmet, Mike Brady, and Richard Essery
The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, https://doi.org/10.5194/tc-14-2495-2020, 2020
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We analyze how well updated state-of-the-art climate models reproduce observed historical snow cover extent and snow mass and how they project that these quantities will change up to the year 2100. Overall the updated models better represent historical snow extent than previous models, and they simulate stronger historical trends in snow extent and snow mass. They project that spring snow extent will decrease by 8 % for each degree Celsius that the global surface air temperature increases.
Colleen Mortimer, Lawrence Mudryk, Chris Derksen, Kari Luojus, Ross Brown, Richard Kelly, and Marco Tedesco
The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, https://doi.org/10.5194/tc-14-1579-2020, 2020
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Existing stand-alone passive microwave SWE products have markedly different climatological SWE patterns compared to reanalysis-based datasets. The AMSR-E SWE has low spatial and temporal correlations with the four reanalysis-based products evaluated and GlobSnow and perform poorly in comparisons with snow transect data from Finland, Russia, and Canada. There is better agreement with in situ data when multiple SWE products, excluding the stand-alone passive microwave SWE products, are combined.
Nick Rutter, Melody J. Sandells, Chris Derksen, Joshua King, Peter Toose, Leanne Wake, Tom Watts, Richard Essery, Alexandre Roy, Alain Royer, Philip Marsh, Chris Larsen, and Matthew Sturm
The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, https://doi.org/10.5194/tc-13-3045-2019, 2019
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Impact of natural variability in Arctic tundra snow microstructural characteristics on the capacity to estimate snow water equivalent (SWE) from Ku-band radar was assessed. Median values of metrics quantifying snow microstructure adequately characterise differences between snowpack layers. Optimal estimates of SWE required microstructural values slightly less than the measured median but tolerated natural variability for accurate estimation of SWE in shallow snowpacks.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
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Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Lawrence R. Mudryk, Chris Derksen, Stephen Howell, Fred Laliberté, Chad Thackeray, Reinel Sospedra-Alfonso, Vincent Vionnet, Paul J. Kushner, and Ross Brown
The Cryosphere, 12, 1157–1176, https://doi.org/10.5194/tc-12-1157-2018, https://doi.org/10.5194/tc-12-1157-2018, 2018
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This paper presents changes in both snow and sea ice that have occurred over Canada during the recent past and shows climate model estimates for future changes expected to occur by the year 2050. The historical changes of snow and sea ice are generally coherent and consistent with the regional history of temperature and precipitation changes. It is expected that snow and sea ice will continue to decrease in the future, declining by an additional 15–30 % from present day values by the year 2050.
Peter Toose, Alexandre Roy, Frederick Solheim, Chris Derksen, Tom Watts, Alain Royer, and Anne Walker
Geosci. Instrum. Method. Data Syst., 6, 39–51, https://doi.org/10.5194/gi-6-39-2017, https://doi.org/10.5194/gi-6-39-2017, 2017
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Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers used for monitoring essential climate variables. A 385-channel hyperspectral L-band radiometer system was designed with the means to quantify the strength and type of RFI. The compact design makes it ideal for mounting on both surface and airborne platforms to be used for calibrating and validating measurement from spaceborne sensors.
Libo Wang, Peter Toose, Ross Brown, and Chris Derksen
The Cryosphere, 10, 2589–2602, https://doi.org/10.5194/tc-10-2589-2016, https://doi.org/10.5194/tc-10-2589-2016, 2016
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The conventional wisdom is that Arctic warming will result in an increase in the frequency of winter melt events. However, results in this study show little evidence of trends in winter melt frequency over 1988–2013 period. The frequency of winter melt events is strongly influenced by the selection of the start and end dates of winter period, and a fixed-window method for analyzing winter melt events is observed to generate false increasing trends from a shift in the timing of snow cover season.
Tom Watts, Nick Rutter, Peter Toose, Chris Derksen, Melody Sandells, and John Woodward
The Cryosphere, 10, 2069–2074, https://doi.org/10.5194/tc-10-2069-2016, https://doi.org/10.5194/tc-10-2069-2016, 2016
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Ice layers in snowpacks introduce uncertainty in satellite-derived estimates of snow water equivalent, have ecological impacts on plants and animals, and change the thermal and vapour transport properties of the snowpack. Here we present a new field method for measuring the density of ice layers. The method was used in the Arctic and mid-latitudes; the mean measured ice layer density was significantly higher than values typically used in the literature.
Bart van den Hurk, Hyungjun Kim, Gerhard Krinner, Sonia I. Seneviratne, Chris Derksen, Taikan Oki, Hervé Douville, Jeanne Colin, Agnès Ducharne, Frederique Cheruy, Nicholas Viovy, Michael J. Puma, Yoshihide Wada, Weiping Li, Binghao Jia, Andrea Alessandri, Dave M. Lawrence, Graham P. Weedon, Richard Ellis, Stefan Hagemann, Jiafu Mao, Mark G. Flanner, Matteo Zampieri, Stefano Materia, Rachel M. Law, and Justin Sheffield
Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, https://doi.org/10.5194/gmd-9-2809-2016, 2016
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This manuscript describes the setup of the CMIP6 project Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP).
Henna-Reetta Hannula, Juha Lemmetyinen, Anna Kontu, Chris Derksen, and Jouni Pulliainen
Geosci. Instrum. Method. Data Syst., 5, 347–363, https://doi.org/10.5194/gi-5-347-2016, https://doi.org/10.5194/gi-5-347-2016, 2016
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The paper described an extensive in situ data set of bulk snow depth, snow water equivalent, and snow density collected as a support of SnowSAR-2 airborne campaign in northern Finland. The spatial and temporal variability of these snow properties was analyzed in different land cover types. The success of the chosen measurement protocol to provide an accurate reference for the simultaneous SAR data products was analyzed in the context of spatial scale, sample size, and uncertainty.
Stephen E. L. Howell, Frédéric Laliberté, Ron Kwok, Chris Derksen, and Joshua King
The Cryosphere, 10, 1463–1475, https://doi.org/10.5194/tc-10-1463-2016, https://doi.org/10.5194/tc-10-1463-2016, 2016
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The Canadian Ice Service record of observed landfast ice and snow thickness represents one of the longest in the Arctic that spans over 5 decades. We analyze this record to report on long-term trends and variability of ice and snow thickness within the Canadian Arctic Archipelago (CAA). Results indicate a thinning of ice at several sites in the CAA. State-of-the-art climate models still have difficultly capturing observed ice thickness values in the CAA and should be used with caution.
S. E. L. Howell, T. Wohlleben, A. Komarov, L. Pizzolato, and C. Derksen
The Cryosphere, 7, 1753–1768, https://doi.org/10.5194/tc-7-1753-2013, https://doi.org/10.5194/tc-7-1753-2013, 2013
Related subject area
Discipline: Snow | Subject: Arctic (e.g. Greenland)
Observed and predicted trends in Icelandic snow conditions for the period 1930–2100
Snow properties at the forest–tundra ecotone: predominance of water vapor fluxes even in deep, moderately cold snowpacks
Spatial patterns of snow distribution in the sub-Arctic
Snowfall and snow accumulation during the MOSAiC winter and spring seasons
Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval
Darri Eythorsson, Sigurdur M. Gardarsson, Andri Gunnarsson, and Oli Gretar Blondal Sveinsson
The Cryosphere, 17, 51–62, https://doi.org/10.5194/tc-17-51-2023, https://doi.org/10.5194/tc-17-51-2023, 2023
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In this study we researched past and predicted snow conditions in Iceland based on manual snow observations recorded in Iceland and compared these with satellite observations. Future snow conditions were predicted through numerical computer modeling based on climate models. The results showed that average snow depth and snow cover frequency have increased over the historical period but are projected to significantly decrease when projected into the future.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
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We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Katrina E. Bennett, Greta Miller, Robert Busey, Min Chen, Emma R. Lathrop, Julian B. Dann, Mara Nutt, Ryan Crumley, Shannon L. Dillard, Baptiste Dafflon, Jitendra Kumar, W. Robert Bolton, Cathy J. Wilson, Colleen M. Iversen, and Stan D. Wullschleger
The Cryosphere, 16, 3269–3293, https://doi.org/10.5194/tc-16-3269-2022, https://doi.org/10.5194/tc-16-3269-2022, 2022
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In the Arctic and sub-Arctic, climate shifts are changing ecosystems, resulting in alterations in snow, shrubs, and permafrost. Thicker snow under shrubs can lead to warmer permafrost because deeper snow will insulate the ground from the cold winter. In this paper, we use modeling to characterize snow to better understand the drivers of snow distribution. Eventually, this work will be used to improve models used to study future changes in Arctic and sub-Arctic snow patterns.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
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Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
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Short summary
We examine Arctic snow cover trends through the lens of climate assessments. We determine the sensitivity of change in snow cover extent to year-over-year increases in time series length, reference period, the use of a statistical methodology to improve inter-dataset agreement, version changes in snow products, and snow product ensemble size. By identifying the sensitivity to the range of choices available to investigators, we increase confidence in reported Arctic snow extent changes.
We examine Arctic snow cover trends through the lens of climate assessments. We determine the...