Artificial Trends or Real Changes? Investigating Precipitation Records in Ny-Ålesund, Svalbard

Olivier Champagne aInstitute for Geosciences and Environmental Research, Université Grenoble Alpes/CNRS/Grenoble INP/INRAE/IRD, Grenoble, France

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https://orcid.org/0000-0003-1762-4373
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Olga Zolina aInstitute for Geosciences and Environmental Research, Université Grenoble Alpes/CNRS/Grenoble INP/INRAE/IRD, Grenoble, France
bShirshov Institute of Oceanology RAS, Moscow, Russia

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Jean-Pierre Dedieu aInstitute for Geosciences and Environmental Research, Université Grenoble Alpes/CNRS/Grenoble INP/INRAE/IRD, Grenoble, France

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Mareile Wolff cNorwegian University of Life Science, Ås, Norway
dNorwegian Meteorological Institute, Oslo, Norway

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Hans-Werner Jacobi aInstitute for Geosciences and Environmental Research, Université Grenoble Alpes/CNRS/Grenoble INP/INRAE/IRD, Grenoble, France

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Abstract

The Svalbard archipelago, in the Atlantic–Arctic region, has been affected by a strong increase in precipitation in the last decades, with major potential impacts for the cryosphere, biogeochemical cycles, and the ecosystems. Ny-Ålesund (79°N), in the northwest part of Svalbard, hosts invaluable meteorological records widely used by many researchers. Among the observed parameters, the amount of precipitation is subject to large biases, mainly due to the well-known precipitation gauges undercatch during windy conditions. The purpose of this study is to investigate if the observed trend of precipitation in Ny-Ålesund in the 1975–2022 period was real and how it was impacted by the gauge undercatch. We applied several correction factors developed in the last decades, based on local wind speed and temperature. We forced these corrections with 12-hourly precipitation data from the Ny-Ålesund weather station. Taking the period 1975–2022, the trend of precipitation increased from 3.8 mm yr−1 in the observations to 4.5 mm yr−1 (±0.2) after the corrections, mainly due to enhanced snowfall in November–January months. Taking the most recent 40-yr period (1983–2022), the amount of precipitation still increased by 3.8 mm yr−1 in the observations, but only by 2.6 mm yr−1 (±0.5) after the corrections. The recent observed trend of precipitation stays large due to an increase of wet snowfall and rainfall that are measured more efficiently by the precipitation gauge. This result shows the need of applying correction factors when using precipitation gauge data, especially in regions exhibiting large interannual changes of weather conditions.

Significance Statement

The purpose of this study is to investigate if the observed trend of precipitation in Ny-Ålesund in the 1975–2022 period was real and how it was impacted by the gauge undercatch. The results show that the observed trend of precipitation was overestimated when calculated in the most recent 40-yr period (1983–2022). This overestimation was large due to an increase with time of wet snowfall and rainfall that were measured more efficiently by the precipitation gauge. This result shows the need of applying corrections factors when using precipitation gauge data, especially in regions exhibiting large interannual changes of weather conditions.

© 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: Olivier Champagne, olivier.champagne@univ-grenoble-alpes.fr

Abstract

The Svalbard archipelago, in the Atlantic–Arctic region, has been affected by a strong increase in precipitation in the last decades, with major potential impacts for the cryosphere, biogeochemical cycles, and the ecosystems. Ny-Ålesund (79°N), in the northwest part of Svalbard, hosts invaluable meteorological records widely used by many researchers. Among the observed parameters, the amount of precipitation is subject to large biases, mainly due to the well-known precipitation gauges undercatch during windy conditions. The purpose of this study is to investigate if the observed trend of precipitation in Ny-Ålesund in the 1975–2022 period was real and how it was impacted by the gauge undercatch. We applied several correction factors developed in the last decades, based on local wind speed and temperature. We forced these corrections with 12-hourly precipitation data from the Ny-Ålesund weather station. Taking the period 1975–2022, the trend of precipitation increased from 3.8 mm yr−1 in the observations to 4.5 mm yr−1 (±0.2) after the corrections, mainly due to enhanced snowfall in November–January months. Taking the most recent 40-yr period (1983–2022), the amount of precipitation still increased by 3.8 mm yr−1 in the observations, but only by 2.6 mm yr−1 (±0.5) after the corrections. The recent observed trend of precipitation stays large due to an increase of wet snowfall and rainfall that are measured more efficiently by the precipitation gauge. This result shows the need of applying correction factors when using precipitation gauge data, especially in regions exhibiting large interannual changes of weather conditions.

Significance Statement

The purpose of this study is to investigate if the observed trend of precipitation in Ny-Ålesund in the 1975–2022 period was real and how it was impacted by the gauge undercatch. The results show that the observed trend of precipitation was overestimated when calculated in the most recent 40-yr period (1983–2022). This overestimation was large due to an increase with time of wet snowfall and rainfall that were measured more efficiently by the precipitation gauge. This result shows the need of applying corrections factors when using precipitation gauge data, especially in regions exhibiting large interannual changes of weather conditions.

© 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: Olivier Champagne, olivier.champagne@univ-grenoble-alpes.fr

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  • Adam, J. C., and D. P. Lettenmaier, 2003: Adjustment of global gridded precipitation for systematic bias: Global gridded precipitation. J. Geophys. Res., 108, 4257, https://doi.org/10.1029/2002JD002499.

    • Search Google Scholar
    • Export Citation
  • Araźny, A., R. Przybylak, and M. Kejna, 2022: The influence of atmospheric circulation on mean and extreme weather conditions on Kaffiøyra (NW Spitsbergen, Svalbard Archipelago) in the summer seasons 1975–2015. Front. Environ. Sci., 10, 867106, https://doi.org/10.3389/fenvs.2022.867106.

    • Search Google Scholar
    • Export Citation
  • Bjerke, J. W., S. Rune Karlsen, K. Arild Høgda, E. Malnes, J. U. Jepsen, S. Lovibond, D. Vikhamar-Schuler, and H. Tømmervik, 2014: Record-low primary productivity and high plant damage in the Nordic Arctic Region in 2012 caused by multiple weather events and pest outbreaks. Environ. Res. Lett., 9, 084006, https://doi.org/10.1088/1748-9326/9/8/084006.

    • Search Google Scholar
    • Export Citation
  • Dahri, Z. H., E. Moors, F. Ludwig, S. Ahmad, A. Khan, I. Ali, and P. Kabat, 2018: Adjustment of measurement errors to reconcile precipitation distribution in the high-altitude Indus basin. Int. J. Climatol., 38, 38423860, https://doi.org/10.1002/joc.5539.

    • Search Google Scholar
    • Export Citation
  • Descamps, S., and Coauthors, 2017: Climate change impacts on wildlife in a high Arctic Archipelago—Svalbard, Norway. Global Change Biol., 23, 490502, https://doi.org/10.1111/gcb.13381.

    • Search Google Scholar
    • Export Citation
  • Di Mauro, B., and Coauthors, 2019: Saharan dust events in the European Alps: Role in snowmelt and geochemical characterization. Cryosphere, 13, 11471165, https://doi.org/10.5194/tc-13-1147-2019.

    • Search Google Scholar
    • Export Citation
  • Dobler, A., E. J. Førland, and K. Isaksen, 2019: Present and future heavy rainfall statistics for Svalbard: Background-report for climate in Svalbard 2100. NCCS Rep. 3/2019, 29 pp., https://www.met.no/kss/_/attachment/download/c8952215-ed80-4a9b-9c71-30f7c217e014:6e09169e7d8916e12d71991b51931e46105e1ea3/nccs-report-3-2019-final.pdf.

  • Domine, F., G. Lackner, D. Sarrazin, M. Poirier, and M. Belke-Brea, 2021: Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models. Earth Syst. Sci. Data, 13, 43314348, https://doi.org/10.5194/essd-13-4331-2021.

    • Search Google Scholar
    • Export Citation
  • Eckerstorfer, M., and H. H. Christiansen, 2011: Topographical and meteorological control on snow avalanching in the Longyearbyen area, central Svalbard 2006–2009. Geomorphology, 134, 186196, https://doi.org/10.1016/j.geomorph.2011.07.001.

    • Search Google Scholar
    • Export Citation
  • Ehsani, M. R., and A. Behrangi, 2022: A comparison of correction factors for the systematic gauge-measurement errors to improve the global land precipitation estimate. J. Hydrol., 610, 127884, https://doi.org/10.1016/j.jhydrol.2022.127884.

    • Search Google Scholar
    • Export Citation
  • Forbes, B. C., and Coauthors, 2016: Sea ice, rain-on-snow and tundra reindeer nomadism in Arctic Russia. Biol. Lett., 12, 20160466, https://doi.org/10.1098/rsbl.2016.0466.

    • Search Google Scholar
    • Export Citation
  • Førland, E. J., and I. Hanssen-Bauer, 2000: Increased precipitation in the Norwegian Arctic: True or false? Climatic Change, 46, 485509, https://doi.org/10.1023/A:1005613304674.

    • Search Google Scholar
    • Export Citation
  • Førland, E. J., and I. Hanssen-Bauer, 2003: Past and future climate variations in the Norwegian Arctic: Overview and novel analyses. Polar Res., 22, 113124, https://doi.org/10.3402/polar.v22i2.6450.

    • Search Google Scholar
    • Export Citation
  • Førland, E. J., and Coauthors, 1996: Manual for operational correction of Nordic precipitation data. DNMI Rep. 24/96, 72 pp., https://www.met.no/publikasjoner/met-report/met-report-1996/_/attachment/download/ea2cb006-688a-408f-a60c-9f6306843cc0:e16a138129a1d1896cff764ab3eb2cc42aefb160/MET-report-24-1996.pdf.

  • Førland, E. J., R. Benestad, I. Hanssen-Bauer, J. E. Haugen, and T. E. Skaugen, 2011: Temperature and precipitation development at Svalbard 1900–2100. Adv. Meteor., 2011, 893790, https://doi.org/10.1155/2011/893790.

    • Search Google Scholar
    • Export Citation
  • Førland, E. J., K. Isaksen, J. Lutz, I. Hanssen-Bauer, T. V. Schuler, A. Dobler, H. M. Gjelten, and D. Vikhamar-Schuler, 2020: Measured and modeled historical precipitation trends for Svalbard. J. Hydrometeor., 21, 12791296, https://doi.org/10.1175/JHM-D-19-0252.1.

    • Search Google Scholar
    • Export Citation
  • Fuchs, T., J. Rapp, F. Rubel, and B. Rudolf, 2001: Correction of synoptic precipitation observations due to systematic measuring errors with special regard to precipitation phases. Phys. Chem. Earth, 26B, 689693, https://doi.org/10.1016/S1464-1909(01)00070-3.

    • Search Google Scholar
    • Export Citation
  • Gugerli, R., M. Gabella, M. Huss, and N. Salzmann, 2020: Can weather radars be used to estimate snow accumulation on Alpine Glaciers? An evaluation based on glaciological surveys. J. Hydrometeor., 21, 29432962, https://doi.org/10.1175/JHM-D-20-0112.1.

    • Search Google Scholar
    • Export Citation
  • Hansen, B. B., and Coauthors, 2014: Warmer and wetter winters: Characteristics and implications of an extreme weather event in the high Arctic. Environ. Res. Lett., 9, 114021, https://doi.org/10.1088/1748-9326/9/11/114021.

    • Search Google Scholar
    • Export Citation
  • Hanssen-Bauer, I., E. J. Førland, and P. O. Nordli, 1996: Measured and true precipitations at Svalbard. Norwegian Meteorological Institute Rep. 31/96 KLIMA, 50 pp., https://www.met.no/publikasjoner/met-report/met-report-1996/_/attachment/download/384542de-1466-4987-b3ff-8c69767b9f2c:016c399a34d2c64618f450ba4819241e0759057e/MET-report-31-1996.pdf.

  • Hanssen-Bauer, I., E. J. Førland, H. Hisdal, S. Mayer, A. B. Sandø, and A. Sorteberg, 2019: Climate in Svalbard in 2100—A knowledge base for climate adaptation. NCCS Rep. 1/2019, 208 pp., https://doi.org/10.25607/OBP-888.

  • Helbig, M., J. Boike, M. Langer, P. Schreiber, B. R. K. Runkle, and L. Kutzbach, 2013: Spatial and seasonal variability of polygonal tundra water balance: Lena River Delta, Northern Siberia (Russia). Hydrogeol. J., 21, 133147, https://doi.org/10.1007/s10040-012-0933-4.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Indreiten, M., and C. Svarstad, 2016: The Longyearbyen fatal avalanche accident 19th December 2015, Svalbard—Lessons learned from avalanche rescue inside a settlement. Proc. Int. Snow Science Workshop 2016, Breckenridge, CO, Montana State University, 356–362, https://arc.lib.montana.edu/snow-science/objects/ISSW16_O16.01.pdf.

  • Jacobi, H.-W., S. Morin, and J. W. Bottenheim, 2010: Observation of widespread depletion of ozone in the springtime boundary layer of the central Arctic linked to mesoscale synoptic conditions. J. Geophys. Res., 115, D17302, https://doi.org/10.1029/2010JD013940.

    • Search Google Scholar
    • Export Citation
  • Jacobi, H.-W., F. Obleitner, S. Da Costa, P. Ginot, K. Eleftheriadis, W. Aas, and M. Zanatta, 2019: Deposition of ionic species and black carbon to the Arctic snowpack: Combining snow pit observations with modeling. Atmos. Chem. Phys., 19, 10 36110 377, https://doi.org/10.5194/acp-19-10361-2019.

    • Search Google Scholar
    • Export Citation
  • Kirkham, J. D., and Coauthors, 2019: Near real-time measurement of snow water equivalent in the Nepal Himalayas. Front. Earth Sci., 7, 177, https://doi.org/10.3389/feart.2019.00177.

    • Search Google Scholar
    • Export Citation
  • Kochendorfer, J., and Coauthors, 2017: Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE. Hydrol. Earth Syst. Sci., 21, 35253542, https://doi.org/10.5194/hess-21-3525-2017.

    • Search Google Scholar
    • Export Citation
  • Køltzow, M., B. Casati, T. Haiden, and T. Valkonen, 2020: Verification of solid precipitation forecasts from numerical weather prediction models in Norway. Wea. Forecasting, 35, 22792292, https://doi.org/10.1175/WAF-D-20-0060.1.

    • Search Google Scholar
    • Export Citation
  • Lackner, G., F. Domine, D. F. Nadeau, A.-C. Parent, F. Anctil, M. Lafaysse, and M. Dumont, 2022: On the energy budget of a low-Arctic snowpack. Cryosphere, 16, 127142, https://doi.org/10.5194/tc-16-127-2022.

    • Search Google Scholar
    • Export Citation
  • Lehmann, E. L., and H. J. M. D’Abrera, 2006: Nonparametrics: Statistical Methods Based on Ranks. 1st ed. Springer, 463 pp.

  • Lemieux, J.-M., and Coauthors, 2020: Groundwater dynamics within a watershed in the discontinuous permafrost zone near Umiujaq (Nunavik, Canada). Hydrogeol. J., 28, 833851, https://doi.org/10.1007/s10040-020-02110-4.

    • Search Google Scholar
    • Export Citation
  • Li, Y., K. Wang, G. Wu, and Y. Mao, 2023: Effects of wind-induced error on the climatology and trends of observed precipitation in China from 1960 to 2018. J. Hydrometeor., 24, 10551067, https://doi.org/10.1175/JHM-D-22-0153.1.

    • Search Google Scholar
    • Export Citation
  • López-Moreno, J. I., J. Boike, A. Sanchez-Lorenzo, and J. W. Pomeroy, 2016: Impact of climate warming on snow processes in Ny-Ålesund, a polar maritime site at Svalbard. Global Planet. Change, 146, 1021, https://doi.org/10.1016/j.gloplacha.2016.09.006.

    • Search Google Scholar
    • Export Citation
  • Łupikasza, E. B., and K. Cielecka-Nowak, 2020: Changing probabilities of days with snow and rain in the Atlantic sector of the Arctic under the current warming trend. J. Climate, 33, 25092532, https://doi.org/10.1175/JCLI-D-19-0384.1.

    • Search Google Scholar
    • Export Citation
  • Łupikasza, E. B., and Coauthors, 2019: The role of winter rain in the glacial system on Svalbard. Water, 11, 334, https://doi.org/10.3390/w11020334.

    • Search Google Scholar
    • Export Citation
  • Maturilli, M., I. Hanssen-Bauer, R. Neuber, M. Rex, and K. Edvardsen, 2019: The atmosphere above Ny-Ålesund: Climate and global warming, ozone and surface UV radiation. The Ecosystem of Kongsfjorden, Svalbard, H. Hop and C. Wiencke, Eds., Advances in Polar Ecology, Vol. 2, Springer, 23–46.

  • Migała, K., E. Łupikasza, M. Osuch, M. Opała-Owczarek, and P. Owczarek, 2024: Linking drought indices to atmospheric circulation in Svalbard, in the Atlantic sector of the high Arctic. Sci. Rep., 14, 2160, https://doi.org/10.1038/s41598-024-51869-z.

    • Search Google Scholar
    • Export Citation
  • Morgner, E., B. Elberling, D. Strebel, and E. J. Cooper, 2010: The importance of winter in annual ecosystem respiration in the high Arctic: Effects of snow depth in two vegetation types. Polar Res., 29, 5874, https://doi.org/10.1111/j.1751-8369.2010.00151.x.

    • Search Google Scholar
    • Export Citation
  • Morin, S., Y. Lejeune, B. Lesaffre, J.-M. Panel, D. Poncet, P. David, and M. Sudul, 2012: An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models. Earth Syst. Sci. Data, 4, 1321, https://doi.org/10.5194/essd-4-13-2012.

    • Search Google Scholar
    • Export Citation
  • Neu, U., and Coauthors, 2013: IMILAST: A community effort to intercompare extratropical cyclone detection and tracking algorithms. Bull. Amer. Meteor. Soc., 94, 529547, https://doi.org/10.1175/BAMS-D-11-00154.1.

    • Search Google Scholar
    • Export Citation
  • Niwano, M., J. E. Box, A. Wehrlé, B. Vandecrux, W. T. Colgan, and J. Cappelen, 2021a: Rainfall on the Greenland ice sheet: Present‐day climatology from a high‐resolution non‐hydrostatic polar regional climate model. Geophys. Res. Lett., 48, e2021GL092942, https://doi.org/10.1029/2021GL092942.

    • Search Google Scholar
    • Export Citation
  • Niwano, M., M. Kajino, T. Kajikawa, T. Aoki, Y. Kodama, T. Tanikawa, and S. Matoba, 2021b: Quantifying relative contributions of light‐absorbing particles from domestic and foreign sources on snow melt at Sapporo, Japan during the 2011–2012 winter. Geophys. Res. Lett., 48, e2021GL093940, https://doi.org/10.1029/2021GL093940.

    • Search Google Scholar
    • Export Citation
  • Norwegian Meteorological Institute, 2023: Frost API. Accessed 12 June 2023, https://frost.met.no/index.html.

  • Nowak, A., and Coauthors, 2021: From land to fjords: The review of Svalbard hydrology from 1970 to 2019 (SvalHydro). SESS Rep., 26 pp., https://doi.org/10.5281/zenodo.4294063.

  • Nuncio, M., S. Chatterjee, K. Satheesan, S. N. Chenoli, and M. P. Subeesh, 2020: Temperature and precipitation during winter in NyÅlesund, Svalbard and possible tropical linkages. Tellus, 72A, 1746604, https://doi.org/10.1080/16000870.2020.1746604.

    • Search Google Scholar
    • Export Citation
  • Osuch, M., and T. Wawrzyniak, 2017: Inter- and intra-annual changes in air temperature and precipitation in western Spitsbergen. Int. J. Climatol., 37, 30823097, https://doi.org/10.1002/joc.4901.

    • Search Google Scholar
    • Export Citation
  • Osuch, M., T. Wawrzyniak, and E. Łepkowska, 2022: Changes in the flow regime of High Arctic catchments with different stages of glaciation, SW Spitsbergen. Sci. Total Environ., 817, 152924, https://doi.org/10.1016/j.scitotenv.2022.152924.

    • Search Google Scholar
    • Export Citation
  • Papritz, L., F. Aemisegger, and H. Wernli, 2021: Sources and transport pathways of precipitating waters in cold-season deep North Atlantic cyclones. J. Atmos. Sci., 78, 33493368, https://doi.org/10.1175/JAS-D-21-0105.1.

    • Search Google Scholar
    • Export Citation
  • Pavlovskii, I., M. Hayashi, and D. Itenfisu, 2019: Midwinter melts in the Canadian prairies: Energy balance and hydrological effects. Hydrol. Earth Syst. Sci., 23, 18671883, https://doi.org/10.5194/hess-23-1867-2019.

    • Search Google Scholar
    • Export Citation
  • Pedersen, Å. Ø., and Coauthors, 2022: Five decades of terrestrial and freshwater research at Ny-Ålesund, Svalbard. Polar Res., 41, 6310, https://doi.org/10.33265/polar.v41.6310.

    • Search Google Scholar
    • Export Citation
  • Peeters, B., and Coauthors, 2019: Spatiotemporal patterns of rain-on-snow and basal ice in high Arctic Svalbard: Detection of a climate-cryosphere regime shift. Environ. Res. Lett., 14, 015002, https://doi.org/10.1088/1748-9326/aaefb3.

    • Search Google Scholar
    • Export Citation
  • Pilguj, N., L. Kolendowicz, M. Kryza, K. Migała, and B. Czernecki, 2019: Temporal changes in wind conditions at Svalbard for the years 1986–2015. Geogr. Ann., 101A, 136156, https://doi.org/10.1080/04353676.2019.1572973.

    • Search Google Scholar
    • Export Citation
  • Pramanik, A., J. Kohler, T. V. Schuler, W. van Pelt, and L. Cohen, 2019: Comparison of snow accumulation events on two high-Arctic glaciers to model-derived and observed precipitation. Polar Res., 38, 3364, https://doi.org/10.33265/polar.v38.3364.

    • Search Google Scholar
    • Export Citation
  • Previdi, M., K. L. Smith, and L. M. Polvani, 2021: Arctic amplification of climate change: A review of underlying mechanisms. Environ. Res. Lett., 16, 093003, https://doi.org/10.1088/1748-9326/ac1c29.

    • Search Google Scholar
    • Export Citation
  • Ramírez, B. H., A. J. Teuling, L. Ganzeveld, Z. Hegger, and R. Leemans, 2017: Tropical Montane cloud forests: Hydrometeorological variability in three neighbouring catchments with different forest cover. J. Hydrol., 552, 151167, https://doi.org/10.1016/j.jhydrol.2017.06.023.

    • 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, 23022315, https://doi.org/10.1175/2008JCLI2117.1.

    • Search Google Scholar
    • Export Citation
  • Rinke, A., M. Maturilli, R. M. Graham, H. Matthes, D. Handorf, L. Cohen, S. R. Hudson, and J. C. Moore, 2017: Extreme cyclone events in the Arctic: Wintertime variability and trends. Environ. Res. Lett., 12, 094006, https://doi.org/10.1088/1748-9326/aa7def.

    • Search Google Scholar
    • Export Citation
  • Rudeva, I., and I. Simmonds, 2015: Variability and trends of global atmospheric frontal activity and links with large-scale modes of variability. J. Climate, 28, 33113330, https://doi.org/10.1175/JCLI-D-14-00458.1.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., A. D. Crawford, and A. P. Barrett, 2015: Extreme daily precipitation events at Spitsbergen, an Arctic island. Int. J. Climatol., 35, 45744588, https://doi.org/10.1002/joc.4308.

    • Search Google Scholar
    • Export Citation
  • Tang, Q., and D. P. Lettenmaier, 2012: 21st century runoff sensitivities of major global river basins. Geophys. Res. Lett., 39, L06403, https://doi.org/10.1029/2011GL050834.

    • Search Google Scholar
    • Export Citation
  • Taskinen, A., and K. Söderholm, 2016: Operational correction of daily precipitation measurements in Finland. Boreal Environ. Res., 21, 124.

    • Search Google Scholar
    • Export Citation
  • van Pelt, W. J. J., V. A. Pohjola, and C. H. Reijmer, 2016: The changing impact of snow conditions and refreezing on the mass balance of an idealized Svalbard glacier. Front. Earth Sci., 4, 102, https://doi.org/10.3389/feart.2016.00102.

    • Search Google Scholar
    • Export Citation
  • Viceto, C., I. V. Gorodetskaya, A. Rinke, M. Maturilli, A. Rocha, and S. Crewell, 2022: Atmospheric rivers and associated precipitation patterns during the ACLOUD and PASCAL campaigns near Svalbard (May–June 2017): Case studies using observations, reanalyses, and a regional climate model. Atmos. Chem. Phys., 22, 441463, https://doi.org/10.5194/acp-22-441-2022.

    • Search Google Scholar
    • Export Citation
  • Vincent, W. F., 2020: Arctic climate change: Local impacts, global consequences, and policy implications. The Palgrave Handbook of Arctic Policy and Politics, K. S. Coates and C. Holroyd, Eds., Springer, 507–526.

  • Wang, X. L., H. Xu, B. Qian, Y. Feng, and E. Mekis, 2017: Adjusted daily rainfall and snowfall data for Canada. Atmos.–Ocean, 55, 155168, https://doi.org/10.1080/07055900.2017.1342163.

    • Search Google Scholar
    • Export Citation
  • Wickström, S., M. O. Jonassen, J. J. Cassano, and T. Vihma, 2020: Present temperature, precipitation, and rain‐on‐snow climate in Svalbard. J. Geophys. Res. Atmos., 125, e2019JD032155, https://doi.org/10.1029/2019JD032155.

    • Search Google Scholar
    • Export Citation
  • Wolff, M. A., K. Isaksen, A. Petersen-Øverleir, K. Ødemark, T. Reitan, and R. Brækkan, 2015: Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: Results of a Norwegian field study. Hydrol. Earth Syst. Sci., 19, 951967, https://doi.org/10.5194/hess-19-951-2015.

    • Search Google Scholar
    • Export Citation
  • Yang, D., D. Kane, Z. Zhang, D. Legates, and B. Goodison, 2005: Bias corrections of long-term (1973–2004) daily precipitation data over the northern regions. Geophys. Res. Lett., 32, L19501, https://doi.org/10.1029/2005GL024057.

    • Search Google Scholar
    • Export Citation
  • Zahn, M., M. Akperov, A. Rinke, F. Feser, and I. I. Mokhov, 2018: Trends of cyclone characteristics in the Arctic and their patterns from different reanalysis data. J. Geophys. Res. Atmos., 123, 27372751, https://doi.org/10.1002/2017JD027439.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., L. Gao, J. Chen, L. Zhao, J. Zhao, Y. Qiao, and J. Shi, 2022: Comprehensive evaluation of mainstream gridded precipitation datasets in the cold season across the Tibetan Plateau. J. Hydrol., 43, 101186, https://doi.org/10.1016/j.ejrh.2022.101186.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., Y. Ren, G. Ren, and G. Wang, 2020: Precipitation trends over Mainland China from 1961–2016 after removal of measurement biases. J. Geophys. Res. Atmos., 125, e2019JD031728, https://doi.org/10.1029/2019JD031728.

    • Search Google Scholar
    • Export Citation
  • Zhao, Y., R. Chen, C. Han, L. Wang, S. Guo, and J. Liu, 2021: Correcting precipitation measurements made with Geonor T-200B weighing gauges near the August-one ice cap in the Qilian Mountains, Northwest China. J. Hydrometeor., 22, 19731985, https://doi.org/10.1175/JHM-D-20-0271.1.

    • Search Google Scholar
    • Export Citation
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