Articles | Volume 18, issue 1
https://doi.org/10.5194/tc-18-403-2024
© Author(s) 2024. 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-18-403-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jan 2024
Research article |
| 29 Jan 2024
| 29 Jan 2024
Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes
Vilna Tyystjärvi
CORRESPONDING AUTHOR
Climate System Research, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Pekka Niittynen
Department of Biological and Environmental Science, University of Jyväskylä, PL 35, 40014 Jyväskylä, Finland
Julia Kemppinen
The Geography Research Unit, University of Oulu, P.O. Box 8000, 90014 Oulu, Finland
Miska Luoto
Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Tuuli Rissanen
Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Juha Aalto
Weather and Climate Change Impact Research, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
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Cited articles
Aalto, J., Pirinen, P., and Jylhä, K.: New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate, J. Geophys. Res.-Atmos., 121, 3807–3823, https://doi.org/10.1002/2015JD024651, 2016. a, b
Aalto, J., Riihimäki, H., Meineri, E., Hylander, K., and Luoto, M.: Revealing topoclimatic heterogeneity using meteorological station data, Int. J. Climatol., 37, 544–556, https://doi.org/10.1002/joc.5020, 2017. a, b
Aalto, J., Scherrer, D., Lenoir, J., Guisan, A., and Luoto, M.: Biogeophysical controls on soil-atmosphere thermal differences: implications on warming Arctic ecosystems, Environ. Res. Lett., 13, 074003, https://doi.org/10.1088/1748-9326/aac83e, 2018. a, b
Ashcroft, M. B. and Gollan, J. R.: Fine-resolution (25 m) topoclimatic grids of near-surface (5 cm) extreme temperatures and humidities across various habitats in a large (200 x 300 km) and diverse region, Int. J. Climatol., 32, 2134–2148, https://doi.org/10.1002/joc.2428, 2012. a
Barry, R. G. and Blanken, P. D.: Microclimate and local climate, Cambridge University Press, New York, https://doi.org/10.1017/CBO9781316535981, 2016. a, b
Bennett, K. E., Miller, G., Busey, R., Chen, M., Lathrop, E. R., Dann, J. B., Nutt, M., Crumley, R., Dillard, S. L., Dafflon, B., Kumar, J., Bolton, W. R., Wilson, C. J., Iversen, C. M., and Wullschleger, S. D.: Spatial patterns of snow distribution in the sub-Arctic, The Cryosphere, 16, 3269–3293, https://doi.org/10.5194/tc-16-3269-2022, 2022. a
Bintanja, R. and Andry, O.: Towards a rain-dominated Arctic, Nat. Clim. Change, 7, 263–267, https://doi.org/10.1038/nclimate3240, 2017. a
Blume-Werry, G., Kreyling, J., Laudon, H., and Milbau, A.: Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants, J. Ecol., 104, 1638–1648, https://doi.org/10.1111/1365-2745.12636, 2016. a
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating snow-cover trends from space, Nat. Clim. Change, 8, 924–928, https://doi.org/10.1038/s41558-018-0318-3, 2018. a
Brown, P. J. and DeGaetano, A. T.: A paradox of cooling winter soil surface temperatures in a warming northeastern United States, Agr. Forest Meteorol., 151, 947–956, https://doi.org/10.1016/j.agrformet.2011.02.014, 2011. a
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. a
Cartwright, K., Hopkinson, C., Kienzle, S., and Rood, S. B.: Evaluation of temporal consistency of snow depth drivers of a Rocky Mountain watershed in southern Alberta, Hydrol. Process., 34, 4996–5012, https://doi.org/10.1002/hyp.13920, 2020. a
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991-2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015. a, b
Daly, C., Conklin, D. R., and Unsworth, M. H.: Local atmospheric decoupling in complex topography alters climate change impacts, Int. J. Climatol., 30, 1857–1864, https://doi.org/10.1002/joc.2007, 2010. a, b
De Frenne, P., Lenoir, J., Luoto, M., Scheffers, B. R., Zellweger, F., Aalto, J., Ashcroft, M. B., Christiansen, D. M., Decocq, G., De Pauw, K., Govaert, S., Greiser, C., Gril, E., Hampe, A., Jucker, T., Klinges, D. H., Koelemeijer, I. A., Lembrechts, J. J., Marrec, R., Meeussen, C., Ogée, J., Tyystjärvi, V., Vangansbeke, P., and Hylander, K.: Forest microclimates and climate change: Importance, drivers and future research agenda, Glob. Change Biol., 27, 2279–2297, https://doi.org/10.1111/gcb.15569, 2021. a, b, c, d
Domine, F., Barrere, M., and Sarrazin, D.: Seasonal evolution of the effective thermal conductivity of the snow and the soil in high Arctic herb tundra at Bylot Island, Canada, The Cryosphere, 10, 2573–2588, https://doi.org/10.5194/tc-10-2573-2016, 2016. a
Domine, F., Belke-Brea, M., Sarrazin, D., Arnaud, L., Barrere, M., and Poirier, M.: Soil moisture, wind speed and depth hoar formation in the Arctic snowpack, J. Glaciol., 64, 990–1002, https://doi.org/10.1017/jog.2018.89, 2018. a
Ellis, C. R., Pomeroy, J. W., Essery, R. L., and Link, T. E.: Effects of needleleaf forest cover on radiation and snowmelt dynamics in the Canadian Rocky Mountains, Can. J. Forest Res., 41, 608–620, https://doi.org/10.1139/X10-227, 2011. a, b, c
Essery, R. and Pomeroy, J.: Vegetation and topographic control of wind-blown snow distributions in distributed and aggregated simulations for an Arctic tundra basin, J. Hydrometeorol., 5, 735–744, https://doi.org/10.1175/1525-7541(2004)005<0735:VATCOW>2.0.CO;2, 2004. a, b
Farbrot, H., Hipp, T. F., Etzelmüller, B., Isaksen, K., Ødegård, R. S., Schuler, T. V., and Humlum, O.: Air and Ground Temperature Variations Observed along Elevation and Continentality Gradients in Southern Norway, Permafrost Periglac., 22, 343–360, https://doi.org/10.1002/ppp.733, 2011. a, b, c, d
Finnish Meteorological Insitute: Lumitilastot, https://www.ilmatieteenlaitos.fi/lumitilastot (last access: 7 July 2022), 2022. a
Gisnås, K., Westermann, S., Schuler, T. V., Litherland, T., Isaksen, K., Boike, J., and Etzelmüller, B.: A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover, The Cryosphere, 8, 2063–2074, https://doi.org/10.5194/tc-8-2063-2014, 2014. a, b, c
Grace, J. B., Anderson, T. M., Olff, H., and Scheiner, S. M.: On the specification of structural equation models for ecological systems, Ecol. Monogr., 80, 67–87, https://doi.org/10.1890/09-0464.1, 2010. a
Groffman, P. M., Driscoll, C. T., Fahey, T. J., Hardy, J. P., Fitzhugh, R. D., and Tierney, G. L.: Colder soils in a warmer world: a snow manipulation study in a northern hardwood forest ecosystem, Biogeochemistry, 56, 135–150, https://doi.org/10.1023/A:1013039830323, 2001. a
Grundstein, A., Todhunter, P., and Mote, T.: Snowpack control over the thermal offset of air and soil temperatures in eastern North Dakota, Geophys. Res. Lett., 32, L08503, https://doi.org/10.1029/2005GL022532, 2005. a, b, c
Haei, M., Öquist, M. G., Kreyling, J., Ilstedt, U., and Laudon, H.: Winter climate controls soil carbon dynamics during summer in boreal forests, Environ. Res. Lett., 8, 024017, https://doi.org/10.1088/1748-9326/8/2/024017, 2013. a, b
Hedstrom, N. and Pomeroy, J.: Measurements and modelling of snow interception in the boreal forest, Hydrol. Process., 12, 1611–1625, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4, 1998. a, b
Jokinen, P., Pirinen, P., Kaukoranta, J.-P., Kangas, A., Alenius, P., Eriksson, P., Johansson, M., and Wilkman, S.: Tilastoja Suomen ilmastosta ja merestä 1991–2020, Reports 2021:8, Ilmatieteen laitos – Finnish Meteorological Institute, Helsinki, https://doi.org/10.35614/isbn.9789523361485, 2021. a
Kankaanpää, T., Skov, K., Abrego, N., Lund, M., Schmidt, N. M., and Roslin, T.: Spatiotemporal snowmelt patterns within a high Arctic landscape, with implications for flora and fauna, Arct. Antarct. Alp. Res., 50, e1415624, https://doi.org/10.1080/15230430.2017.1415624, 2018. a
Kellomäki, S., Maajärvi, M., Strandman, H., Kilpeläinen, A., and Peltola, H.: Model computations on the climate change effects on snow cover, soil moisture and soil frost in the boreal conditions over Finland, Silva Fenn., 44, 213–233, https://doi.org/10.14214/sf.455, 2010. a, b
Kelsey, K. C., Pedersen, S. H., Leffler, A. J., Sexton, J. O., Feng, M., and Welker, J. M.: Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities, Glob. Change Biol., 27, 1572–1586, https://doi.org/10.1111/gcb.15505, 2021. a, b
Kohler, J. and Aanes, R.: Effect of Winter Snow and Ground-Icing on a Svalbard Reindeer Population: Results of a Simple Snowpack Model, Arct. Antarct. Alp. Res., 36, 333–341, https://doi.org/10.1657/1523-0430(2004)036[0333:EOWSAG]2.0.CO;2, 2004. a
Koivusalo, H. and Kokkonen, T.: Snow processes in a forest clearing and in a coniferous forest, J. Hydrol., 262, 145–164, https://doi.org/10.1016/S0022-1694(02)00031-8, 2002. a, b
Larsen, K. S., Grogan, P., Jonasson, S., and Michelsen, A.: Respiration and Microbial Dynamics in Two Subarctic Ecosystems during Winter and Spring Thaw: Effects of Increased Snow Depth, Arct. Antarct. Alp. Res., 39, 268–276, https://doi.org/10.1657/1523-0430(2007)39[268:RAMDIT]2.0.CO;2, 2007. a
Latimer, C. E. and Zuckerberg, B.: Forest fragmentation alters winter microclimates and microrefugia in human-modified landscapes, Ecography, 40, 158–170, https://doi.org/10.1111/ecog.02551, 2017. a, b
Lefcheck, J. S.: piecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics, Methods Ecol. Evol., 7, 573–579, https://doi.org/10.1111/2041-210X.12512, 2016. a, b
López-Moreno, J. I., Revuelto, J., Alonso-González, E., Sanmiguel-Vallelado, A., Fassnacht, S. R., Deems, J., and Moran-Tejeda, E.: Using very long-range terrestrial laser scanner to analyze the temporal consistency of the snowpack distribution in a high mountain environment, J. Mountain Sci., 14, 823–842, https://doi.org/10.1007/s11629-016-4086-0, 2017. a
Luomaranta, A., Aalto, J., and Jylhä, K.: Snow cover trends in Finland over 1961–2014 based on gridded snow depth observations, Int. J. Climatol., 39, 3147–3159, https://doi.org/10.1002/joc.6007, 2019. a, b
Mazzotti, G., Webster, C., Quéno, L., Cluzet, B., and Jonas, T.: Canopy structure, topography, and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests, Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, 2023. a, b, c
Metsäkeskus: Paikkatieteoaineistot, https://www.metsakeskus.fi/fi/avoin-metsa-ja-luontotieto/aineistot-paikkatieto-ohjelmille/paikkatietoaineistot (last access: 1 February 2023), 2023. a
Musselman, K. N. and Pomeroy, J. W.: Estimation of Needleleaf Canopy and Trunk Temperatures and Longwave Contribution to Melting Snow, J. Hydrometeorol., 18, 555–572, https://doi.org/10.1175/JHM-D-16-0111.1, 2017. a
Nicholas C., P., Martin K., S., and Liam D., R.: Quantification of the cold-air pool in Kevo Valley, Finnish Lapland, Weather, 64, 60–67, https://doi.org/10.1002/wea.260, 2009. a
NLS: National Land Survey of Finland: Laser scanning data, National Land Survey of Finland [data set], https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/laser-scanning-data (last access: 1 February 2023), 2023. a
Nobrega, S. and Grogan, P.: Deeper snow enhances winter respiration from both plant-associated and bulk soil carbon pools in birch hummock tundra, Ecosystems, 10, 419–431, https://doi.org/10.1007/s10021-007-9033-z, 2007. a
Opedal, Ø. H., Armbruster, W. S., and Graae, B. J.: Linking small-scale topography with microclimate, plant species diversity and intra-specific trait variation in an alpine landscape, Plant Ecol. Divers., 8, 305–315, https://doi.org/10.1080/17550874.2014.987330, 2015. a
Outcalt, S. I., Nelson, F. E., and Hinkel, K. M.: The zero-curtain effect: Heat and mass transfer across an isothermal region in freezing soil, Water Resour. Res., 26, 1509–1516, https://doi.org/10.1029/WR026i007p01509, 1990. a
Pauli, J. N., Zuckerberg, B., Whiteman, J. P., and Porter, W.: The subnivium: a deteriorating seasonal refugium, Front. Ecol. Environ., 11, 260–267, https://doi.org/10.1890/120222, 2013. a
poniitty: poniitty/MICROCLIMATES: Zenodo-ready (v0.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.10558454, 2024. a, b
Post, E., Alley, R. B., Christensen, T. R., Macias-Fauria, M., Forbes, B. C., Gooseff, M. N., Iler, A., Kerby, J. T., Laidre, K. L., Mann, M. E., Olofsson, J., Stroeve, J. C., Ulmer, F., Virginia, R. A., and Wang, M.: The polar regions in a 2 C warmer world, Sci. Adv., 5, eaaw9883, https://doi.org/10.1126/sciadv.aaw9883, 2019. a
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: 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, 2022. a
Renaud, V., Innes, J., Dobbertin, M., and Rebetez, M.: Comparison between open-site and below-canopy climatic conditions in Switzerland for different types of forests over 10 years (1998–2007), Theor. Appl. Climatol., 105, 119–127, https://doi.org/10.1007/s00704-010-0361-0, 2011. a
Revuelto, J., López-Moreno, J. I., Azorin-Molina, C., and Vicente-Serrano, S. M.: Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence, The Cryosphere, 8, 1989–2006, https://doi.org/10.5194/tc-8-1989-2014, 2014. a
Rissanen, T., Aalto, A., Kainulainen, H., Kauppi, O., Niittynen, P., Soininen, J., and Luoto, M.: Local snow and fluvial conditions drive taxonomic, functional and phylogenetic plant diversity in tundra, Oikos, 2023, e09998, https://doi.org/10.1111/oik.09998, 2023. a
Roussel, J.-R., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R., Meador, A. S., Bourdon, J.-F., de Boissieu, F., and Achim, A.: lidR: An R package for analysis of Airborne Laser Scanning (ALS) data, Remote Sens. Environ., 251, 112061, https://doi.org/10.1016/j.rse.2020.112061, 2020. a
Ruosteenoja, K., Markkanen, T., and Räisänen, J.: Thermal seasons in northern Europe in projected future climate, Int. J. Climatol., 40, 4444–4462, https://doi.org/10.1002/joc.6466, 2020. a
Sanders-DeMott, R. and Templer, P. H.: What about winter? Integrating the missing season into climate change experiments in seasonally snow covered ecosystems, Methods in Ecol. Evol., 8, 1183–1191, https://doi.org/10.1111/2041-210X.12780, 2017. a, b
Schmidt, S., Weber, B., and Winiger, M.: Analyses of seasonal snow disappearance in an alpine valley from micro-to meso-scale (Loetschental, Switzerland), Hydrol. Process., 23, 1041–1051, https://doi.org/10.1002/hyp.7205, 2009. a
Semenchuk, P. R., Christiansen, C. T., Grogan, P., Elberling, B., and Cooper, E. J.: Long-term experimentally deepened snow decreases growing-season respiration in a low-and high-arctic tundra ecosystem, J. Geophys. Res.-Biogeo., 121, 1236–1248, https://doi.org/10.1002/2015JG003251, 2016. a
Sturm, M. and Wagner, A. M.: Using repeated patterns in snow distribution modeling: An Arctic example, Water Resour. Res., 46, W12549, https://doi.org/10.1029/2010WR009434, 2010. a
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal conductivity of seasonal snow, J. Glaciol., 43, 26–41, https://doi.org/10.3189/S0022143000002781, 1997. a
Tierney, G. L., Fahey, T. J., Groffman, P. M., Hardy, J. P., Fitzhugh, R. D., and Driscoll, C. T.: Soil freezing alters fine root dynamics in a northern hardwood forest, Biogeochemistry, 56, 175–190, https://doi.org/10.1023/A:1013072519889, 2001. a, b
Tikkanen, M.: Climate, in: The physical geography of Fennoscandia, edited by: Seppälä, M., Oxford University Press, https://doi.org/10.1017/S003224740623599X, 2005. a
Wild, J., Kopeckỳ, M., Macek, M., Šanda, M., Jankovec, J., and Haase, T.: Climate at ecologically relevant scales: A new temperature and soil moisture logger for long-term microclimate measurement, Agr. Forest Meteorol., 268, 40–47, https://doi.org/10.1016/j.agrformet.2018.12.018, 2019. a
Winkler, R. D. and Moore, R. D.: Variability in snow accumulation patterns within forest stands on the interior plateau of British Columbia, Canada, Hydrol. Process., 20, 3683–3695, https://doi.org/10.1002/hyp.6382, 2006. a
Winstral, A. and Marks, D.: Simulating wind fields and snow redistribution using terrain-based parameters to model snow accumulation and melt over a semi-arid mountain catchment, Hydrol. Process., 16, 3585–3603, https://doi.org/10.1002/hyp.1238, 2002. a
Short summary
At high latitudes, winter ground surface temperatures are strongly controlled by seasonal snow cover and its spatial variation. Here, we measured surface temperatures and snow cover duration in 441 study sites in tundra and boreal regions. Our results show large variations in how much surface temperatures in winter vary depending on the landscape and its impact on snow cover. These results emphasise the importance of understanding microclimates and their drivers under changing winter conditions.
At high latitudes, winter ground surface temperatures are strongly controlled by seasonal snow...
