Editorial Type:
Article
Article Type:
Research Article

Patterns and Trend Analysis of Rain-on-Snow Events using Passive Microwave Satellite Data over the Canadian Arctic Archipelago Since 1987

Vincent Sasseville aCentre d’Applications et Recherches en Télédétection (Cartel), Université de Sherbrooke, Quebec, Quebec, Canada
bCentre d’Études Nordiques, Quebec, Quebec, Canada

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https://orcid.org/0000-0002-2813-7178
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Alexandre Langlois aCentre d’Applications et Recherches en Télédétection (Cartel), Université de Sherbrooke, Quebec, Quebec, Canada
bCentre d’Études Nordiques, Quebec, Quebec, Canada

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Ludovic Brucker cNOAA/NESDIS/Center for Satellite Applications and Research, College Park, Maryland
dU.S. National Ice Center, Suitland, Maryland

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Cheryl Ann Johnson eWildlife Landscape Science Division, Environment and Climate Change Canada, Ottawa, Ontario, Canada

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Online Publication:
08 Feb 2024
Print Publication:
01 Feb 2024
DOI:
https://doi.org/10.1175/JHM-D-22-0218.1
Page(s):
311–324
Restricted access

Abstract

Climate change has a profound effect on Arctic meteorology extreme events, such as rain-on-snow (ROS), which affects surface state variable spatial and temporal variability. Passive microwave satellite images can help detect such events in polar regions where local meteorological and snow information is scarce. In this study, we use a detection algorithm using high-resolution passive microwave data to monitor spatial and temporal variability of ROS over the Canadian Arctic Archipelago from 1987 to 2019. The method is validated using data from several meteorological stations and atmospheric corrections have been applied to the passive microwave dataset. Our approach to detect ROS is based on two methods: 1) over a fixed time period (i.e., 1 November–31 May) throughout the study period and 2) using an a priori detection for snow presence before applying our ROS algorithm (i.e., length of studied winter varies yearly). Event occurrence is analyzed for each winter and separated by island groups of the Canadian Arctic Archipelago. Results show an increase in absolute ROS occurrence, mainly along the coasts, although no statistically significant trends are observed.

Significance Statement

Rain-on-snow (ROS) is known to have significant consequences on vegetation and fauna, especially widespread events. This study aimed to use a recent high-resolution dataset of passive microwave observations to investigate spatial and temporal trends in ROS occurrence in the Arctic. Results show that a global increase in event occurrence can be observed across the arctic.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Vincent Sasseville, vincent.sasseville@usherbrooke.ca

Abstract

Climate change has a profound effect on Arctic meteorology extreme events, such as rain-on-snow (ROS), which affects surface state variable spatial and temporal variability. Passive microwave satellite images can help detect such events in polar regions where local meteorological and snow information is scarce. In this study, we use a detection algorithm using high-resolution passive microwave data to monitor spatial and temporal variability of ROS over the Canadian Arctic Archipelago from 1987 to 2019. The method is validated using data from several meteorological stations and atmospheric corrections have been applied to the passive microwave dataset. Our approach to detect ROS is based on two methods: 1) over a fixed time period (i.e., 1 November–31 May) throughout the study period and 2) using an a priori detection for snow presence before applying our ROS algorithm (i.e., length of studied winter varies yearly). Event occurrence is analyzed for each winter and separated by island groups of the Canadian Arctic Archipelago. Results show an increase in absolute ROS occurrence, mainly along the coasts, although no statistically significant trends are observed.

Significance Statement

Rain-on-snow (ROS) is known to have significant consequences on vegetation and fauna, especially widespread events. This study aimed to use a recent high-resolution dataset of passive microwave observations to investigate spatial and temporal trends in ROS occurrence in the Arctic. Results show that a global increase in event occurrence can be observed across the arctic.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Vincent Sasseville, vincent.sasseville@usherbrooke.ca
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Journal of Hydrometeorology

  • Armstrong, R. L., and M. J. Brodzik, 2002: Hemispheric-scale comparison and evaluation of passive-microwave snow algorithms. Ann. Glaciol., 34, 38–44, https://doi.org/10.3189/172756402781817428.

    • Search Google Scholar
    • Export Citation

  • Bintanja, R., K. van der Wiel, E. C. van der Linden, J. Reusen, L. Bogerd, F. Krikken, and F. M. Selten, 2020: Strong future increases in Arctic precipitation variability linked to poleward moisture transport. Sci. Adv., 6, eaax6869, https://doi.org/10.1126/sciadv.aax6869.

    • Search Google Scholar
    • Export Citation

  • Brodzik, M. J., D. G. Long, M. A. Hardman, A. Paget, and R. Armstrong, 2016: MEaSUREs calibrated enhanced-resolution passive microwave daily ease-grid 2.0 brightness temperature ESDR, version 1. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 22 February 2020, https://doi.org/10.5067/measures/cryosphere/nsidc-0630.001.

  • Comiso, J. C., J. Yang, S. Honjo, and R. A. Krishfield, 2003: Detection of change in the Arctic using satellite and in situ data. J. Geophys. Res., 108, 3384, https://doi.org/10.1029/2002JC001347.

    • Search Google Scholar
    • Export Citation

  • Couture, G., 2022: Analyses spatiotemporelles des conditions de glace de mer et des tendances de formation des polynies de l’Archipel Arctique Canadien. M.S. thesis, Département de géomatique appliquée Faculté des lettres et sciences humaines, Université de Sherbrooke, 89 pp., https://savoirs.usherbrooke.ca/handle/11143/19074.

  • Cullather, R. I., Y.-K. Lim, L. N. Boisvert, L. Brucker, J. N. Lee, and S. M. Nowicki, 2016: Analysis of the warmest Arctic winter, 2015–2016. Geophys. Res. Lett., 43, 10 808–10 816, https://doi.org/10.1002/2016GL071228.

    • Search Google Scholar
    • Export Citation

  • Derksen, C., and R. Brown, 2012: Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections. Geophys. Res. Lett., 39, L19504, https://doi.org/10.1029/2012GL053387.

    • Search Google Scholar
    • Export Citation

  • Derksen, C., and L. Mudryk, 2023: Assessment of Arctic seasonal snow cover rates of change. Cryosphere, 17, 1431–1443, https://doi.org/10.5194/tc-17-1431-2023.

    • Search Google Scholar
    • Export Citation

  • Dolant, C., A. Langlois, B. Montpetit, L. Brucker, A. Roy, and A. Royer, 2016: Development of a rain-on-snow detection algorithm using passive microwave radiometry. Hydrol. Processes, 30, 3184–3196, https://doi.org/10.1002/hyp.10828.

    • Search Google Scholar
    • Export Citation

  • Dolant, C., A. Langlois, L. Brucker, A. Royer, A. Roy, and B. Montpetit, 2018a: Meteorological inventory of rain-on-snow events in the Canadian Arctic Archipelago and satellite detection assessment using passive microwave data. Phys. Geogr., 39, 428–444,https://doi.org/10.1080/02723646.2017.1400339.

    • Search Google Scholar
    • Export Citation

  • Dolant, C., B. Montpetit, A. Langlois, L. Brucker, O. Zolina, C. A. Johnson, A. Royer, and P. Smith, 2018b: Assessment of the barren ground Caribou die-off during winter 2015–2016 using passive microwave observations. Geophys. Res. Lett., 45, 4908–4916, https://doi.org/10.1029/2017GL076752.

    • Search Google Scholar
    • Export Citation

  • Dupont, F., G. Picard, A. Royer, M. Fily, A. Roy, A. Langlois, and N. Champollion, 2014: Modeling the microwave emission of bubbly ice: Applications to blue ice and superimposed ice in the Antarctic and Arctic. IEEE Trans. Geosci. Remote Sens., 52, 6639–6651, https://doi.org/10.1109/TGRS.2014.2299829.

    • Search Google Scholar
    • Export Citation

  • Foster, J. L., D. K. Hall, A. T. C. Chang, and A. Rango, 1984: An overview of passive microwave snow research and results. Rev. Geophys., 22, 195–208, https://doi.org/10.1029/RG022i002p00195.

    • Search Google Scholar
    • Export Citation

  • Gautier, C., 2022: Étude de la qualité d’habitat et des patrons migratoires du caribou de Peary (Rangifer tarandus pearyi) à l’aide d’un modèle thermodynamique de neige, des anomalies de glace de mer et du savoir traditionnel. M.S. thesis, Dépt. de géomatique appliquée Faculté des lettres et sciences humaines, Université de Sherbrooke, 155 pp., https://savoirs.usherbrooke.ca/handle/11143/18415.

  • Grenfell, T. C., and J. Putkonen, 2008: A method for the detection of the severe rain-on-snow event on Banks Island, October 2003, using passive microwave remote sensing. Water Resour. Res., 44, W03425, https://doi.org/10.1029/2007WR005929.

    • Search Google Scholar
    • Export Citation

  • Langlois, A., and Coauthors, 2017: Detection of rain-on-snow (ROS) events and ice layer formation using passive microwave radiometry: A context for Peary caribou habitat in the Canadian Arctic. Remote Sens. Environ., 189, 84–95, https://doi.org/10.1016/j.rse.2016.11.006.

    • Search Google Scholar
    • Export Citation

  • Larue, F., A. Royer, D. De Sève, A. Langlois, A. Roy, and L. Brucker, 2017: Validation of GlobSnow-2 snow water equivalent over eastern Canada. Remote Sens. Environ., 194, 264–277, https://doi.org/10.1016/j.rse.2017.03.027.

    • Search Google Scholar
    • Export Citation

  • Liston, G. E., and C. A. Hiemstra, 2011: The changing cryosphere: Pan-Arctic snow trends (1979–2009). J. Climate, 24, 5691–5712, https://doi.org/10.1175/JCLI-D-11-00081.1.

    • Search Google Scholar
    • Export Citation

  • Marmy, A., N. Salzmann, M. Scherler, and C. Hauck, 2013: Permafrost model sensitivity to seasonal climatic changes and extreme events in mountainous regions. Environ. Res. Lett., 8, 035048, https://doi.org/10.1088/1748-9326/8/3/035048.

    • Search Google Scholar
    • Export Citation

  • Martineau, C., 2020: Couplage du logiciel de modélisation de l’habitat MaxEnt à des simulations du couvert nival pour l’amélioration de la prédiction de présence du caribou de Peary. M.S. thesis, Département de géomatique appliquée Faculté des lettres et sciences humaines, Université de Sherbrooke, 92 pp., https://savoirs.usherbrooke.ca/handle/11143/17006.

  • Montpetit, B., A. Royer, A. Roy, A. Langlois, and C. Derksen, 2013: Snow microwave emission modeling of ice lenses within a snowpack using the microwave emission model for layered snowpacks. IEEE Trans. Geosci. Remote Sens., 51, 4705–4717, https://doi.org/10.1109/TGRS.2013.2250509.

    • Search Google Scholar
    • Export Citation

  • Moon, T. A., M. L. Druckenmiller, and R. L. Thoman, 2021: Arctic report card 2021: Executive summary. NOAA Tech. Rep. OAR ARC 21-01, 4 pp., https://doi.org/10.25923/5s0f-5163.

  • NOAA/ESRL/Physical Sciences Laboratory, 2020: NCEP North American regional reanalysis. NOAA, accessed 10 November 2020, https://psl.noaa.gov/data/gridded/data.narr.html.

  • Picard, G., L. Brucker, A. Roy, F. Dupont, M. Fily, A. Royer, and C. Harlow, 2013: Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative Transfer theory: The DMRT-ML model. Geosci. Model Dev., 6, 1061–1078, https://doi.org/10.5194/gmd-6-1061-2013.

    • Search Google Scholar
    • Export Citation

  • Putkonen, J., T. C. Grenfell, K. Rennert, C. Bitz, P. Jacobson, and D. Russell, 2009: Rain on snow: Little understood killer in the North. Eos, Trans. Amer. Geophys. Union, 90, 221–222, https://doi.org/10.1029/2009EO260002.

    • Search Google Scholar
    • Export Citation

  • Rantanen, M., A. Y. Karpechko, A. Lipponen, K. Nordling, O. Hyvärinen, K. Ruosteenoja, T. Vihma, and A. Laaksonen, 2022: The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3.

    • Search Google Scholar
    • Export Citation

  • Rennert, K. J., G. Roe, J. Putkonen, and C. M. Bitz, 2009: Soil thermal and ecological impacts of rain on snow events in the circumpolar Arctic. J. Climate, 22, 2302–2315, https://doi.org/10.1175/2008JCLI2117.1.

    • Search Google Scholar
    • Export Citation

  • Riseborough, D., N. Shiklomanov, B. Etzelmüller, S. Gruber, and S. Marchenko, 2008: Recent advances in permafrost modelling. Permafrost Periglacial Processes, 19, 137–156, https://doi.org/10.1002/ppp.615.

    • Search Google Scholar
    • Export Citation

  • Roy, A., 2014: Modélisation de l’émission micro-onde hivernale en forêt boréale Canadienne. Ph.D. thesis, Université de Sherbrooke, 242 pp., http://savoirs.usherbrooke.ca/handle/11143/70.

  • Royer, A., F. Domine, A. Roy, A. Langlois, N. Marchand, and G. Davesne, 2021: New northern snowpack classification linked to vegetation cover on a latitudinal mega-transect across northeastern Canada. Écoscience, 28, 225–242, https://doi.org/10.1080/11956860.2021.1898775.

    • Search Google Scholar
    • Export Citation

  • Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis. Global Planet. Change, 77, 85–96, https://doi.org/10.1016/j.gloplacha.2011.03.004.

    • Search Google Scholar
    • Export Citation

  • Shi, J., C. Xiong, and L. Jiang, 2016: Review of snow water equivalent microwave remote sensing. Sci. China Earth Sci., 59, 731–745, https://doi.org/10.1007/s11430-015-5225-0.

    • Search Google Scholar
    • Export Citation

  • Sokolov, A. A., N. A. Sokolova, R. A. Ims, L. Brucker, and D. Ehrich, 2016: Emergent rainy winter warm spells may promote boreal predator expansion into the Arctic. Arctic, 69, 121–129, https://doi.org/10.14430/arctic4559.

    • Search Google Scholar
    • Export Citation

  • Song, M., and J. Liu, 2017: The role of diminishing Arctic sea ice in increased winter snowfall over northern high-latitude continents in a warming environment. Acta Oceanol. Sin., 36, 34–41, https://doi.org/10.1007/s13131-017-1021-3.

    • Search Google Scholar
    • Export Citation

  • Weismüller, J., U. Wollschläger, J. Boike, X. Pan, Q. Yu, and K. Roth, 2011: Modeling the thermal dynamics of the active layer at two contrasting permafrost sites on Svalbard and on the Tibetan Plateau. Cryosphere, 5, 741–757, https://doi.org/10.5194/tc-5-741-2011.

    • Search Google Scholar
    • Export Citation
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