Intense sea-effect snowfall case on the western coast of Finland
Taru Olsson
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Tuuli Perttula
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Anna Luomaranta
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Related authors
Anna Rutgersson, Erik Kjellström, Jari Haapala, Martin Stendel, Irina Danilovich, Martin Drews, Kirsti Jylhä, Pentti Kujala, Xiaoli Guo Larsén, Kirsten Halsnæs, Ilari Lehtonen, Anna Luomaranta, Erik Nilsson, Taru Olsson, Jani Särkkä, Laura Tuomi, and Norbert Wasmund
Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, https://doi.org/10.5194/esd-13-251-2022, 2022
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A natural hazard is a naturally occurring extreme event with a negative effect on people, society, or the environment; major events in the study area include wind storms, extreme waves, high and low sea level, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. In the future, an increase in sea level, extreme precipitation, heat waves, and phytoplankton blooms is expected, and a decrease in cold spells and severe ice winters is anticipated.
Taru Olsson, Anna Luomaranta, Kirsti Jylhä, Julia Jeworrek, Tuuli Perttula, Christian Dieterich, Lichuan Wu, Anna Rutgersson, and Antti Mäkelä
Adv. Sci. Res., 17, 87–104, https://doi.org/10.5194/asr-17-87-2020, https://doi.org/10.5194/asr-17-87-2020, 2020
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Statistics of the frequency and intensity of snow bands affecting the Finnish coast during years 2000–2010 was conducted. A set of criteria for meteorological variables favoring the formation of the snow bands were applied to regional climate model (RCA4) data. We found on average three days per year with favorable conditions for coastal sea-effect snowfall. The heaviest convective snowfall events were detected most frequently over the southern coastline.
Noora Veijalainen, Juho Jakkila, Taru Olsson, Leif Backman, Bertel Vehviläinen, and Jussi Kaurola
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-602, https://doi.org/10.5194/hess-2017-602, 2017
Revised manuscript not accepted
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Climate change impacts on floods in Finland were estimated on several locations. Regional climate model data was bias corrected and then used as input of a hydrological model and the function of the bias correction was evaluated. The bias correction improved the simulation of floods, but some scenarios are still unable to match the observed hydrology adequately. The changes in floods by 2070–2099 vary in different regions in Finland depending on season and the main flood producing mechanism.
T. Olsson, J. Jakkila, N. Veijalainen, L. Backman, J. Kaurola, and B. Vehviläinen
Hydrol. Earth Syst. Sci., 19, 3217–3238, https://doi.org/10.5194/hess-19-3217-2015, https://doi.org/10.5194/hess-19-3217-2015, 2015
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With most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data and produces more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data in Finland. However, if the biases in the mean or the standard deviation of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections.
A. Virkkula, J. Levula, T. Pohja, P. P. Aalto, P. Keronen, S. Schobesberger, C. B. Clements, L. Pirjola, A.-J. Kieloaho, L. Kulmala, H. Aaltonen, J. Patokoski, J. Pumpanen, J. Rinne, T. Ruuskanen, M. Pihlatie, H. E. Manninen, V. Aaltonen, H. Junninen, T. Petäjä, J. Backman, M. Dal Maso, T. Nieminen, T. Olsson, T. Grönholm, J. Aalto, T. H. Virtanen, M. Kajos, V.-M. Kerminen, D. M. Schultz, J. Kukkonen, M. Sofiev, G. De Leeuw, J. Bäck, P. Hari, and M. Kulmala
Atmos. Chem. Phys., 14, 4473–4502, https://doi.org/10.5194/acp-14-4473-2014, https://doi.org/10.5194/acp-14-4473-2014, 2014
Natalia Korhonen, Otto Hyvärinen, Virpi Kollanus, Timo Lanki, Juha Jokisalo, Risto Kosonen, David S. Richardson, and Kirsti Jylhä
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-75, https://doi.org/10.5194/nhess-2024-75, 2024
Preprint under review for NHESS
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The skill of hindcasts of the European Centre for Medium-Range Weather Forecasts in forecasting heat wave days (periods with the 5-day moving average temperature being above its local summer 90th percentile) over Europe 1 to 4 weeks ahead is examined. The heat wave days forecasts show potential in warning of heat risk in 1–2 weeks in advance, and enhanced accuracy in forecasting prolonged heat waves, in lead times of up to 3 weeks, when the heat wave had initiated prior to the forecast issuance.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
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We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Anna Rutgersson, Erik Kjellström, Jari Haapala, Martin Stendel, Irina Danilovich, Martin Drews, Kirsti Jylhä, Pentti Kujala, Xiaoli Guo Larsén, Kirsten Halsnæs, Ilari Lehtonen, Anna Luomaranta, Erik Nilsson, Taru Olsson, Jani Särkkä, Laura Tuomi, and Norbert Wasmund
Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, https://doi.org/10.5194/esd-13-251-2022, 2022
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A natural hazard is a naturally occurring extreme event with a negative effect on people, society, or the environment; major events in the study area include wind storms, extreme waves, high and low sea level, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. In the future, an increase in sea level, extreme precipitation, heat waves, and phytoplankton blooms is expected, and a decrease in cold spells and severe ice winters is anticipated.
Mika Rantanen, Kirsti Jylhä, Jani Särkkä, Jani Räihä, and Ulpu Leijala
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-314, https://doi.org/10.5194/nhess-2021-314, 2021
Revised manuscript not accepted
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Using sea level and precipitation observations, we analysed the meteorological characteristics of days when heavy precipitation and high sea level occur simultaneously in Finland. We found that around 5 % of all heavy precipitation and high sea level events on the Finnish coast are so called compound events when they both occur simultaneously, and these events were associated with close passages of mid-latitude cyclones. Our results act as a basis for compound flooding research in Finland.
Taru Olsson, Anna Luomaranta, Kirsti Jylhä, Julia Jeworrek, Tuuli Perttula, Christian Dieterich, Lichuan Wu, Anna Rutgersson, and Antti Mäkelä
Adv. Sci. Res., 17, 87–104, https://doi.org/10.5194/asr-17-87-2020, https://doi.org/10.5194/asr-17-87-2020, 2020
Short summary
Short summary
Statistics of the frequency and intensity of snow bands affecting the Finnish coast during years 2000–2010 was conducted. A set of criteria for meteorological variables favoring the formation of the snow bands were applied to regional climate model (RCA4) data. We found on average three days per year with favorable conditions for coastal sea-effect snowfall. The heaviest convective snowfall events were detected most frequently over the southern coastline.
Noora Veijalainen, Juho Jakkila, Taru Olsson, Leif Backman, Bertel Vehviläinen, and Jussi Kaurola
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-602, https://doi.org/10.5194/hess-2017-602, 2017
Revised manuscript not accepted
Short summary
Short summary
Climate change impacts on floods in Finland were estimated on several locations. Regional climate model data was bias corrected and then used as input of a hydrological model and the function of the bias correction was evaluated. The bias correction improved the simulation of floods, but some scenarios are still unable to match the observed hydrology adequately. The changes in floods by 2070–2099 vary in different regions in Finland depending on season and the main flood producing mechanism.
Matti Kämäräinen, Otto Hyvärinen, Kirsti Jylhä, Andrea Vajda, Simo Neiglick, Jaakko Nuottokari, and Hilppa Gregow
Nat. Hazards Earth Syst. Sci., 17, 243–259, https://doi.org/10.5194/nhess-17-243-2017, https://doi.org/10.5194/nhess-17-243-2017, 2017
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Freezing rain is a high-impact wintertime weather phenomenon. The direct damage it causes to critical infrastructure (transportation, communication and energy) and forestry can be substantial. In this work a method for estimating the occurrence of freezing rain was evaluated and used to derive the climatology. The method was able to accurately reproduce the observed, spatially aggregated annual variability. The highest frequencies of freezing rain were found in eastern and central Europe.
T. Olsson, J. Jakkila, N. Veijalainen, L. Backman, J. Kaurola, and B. Vehviläinen
Hydrol. Earth Syst. Sci., 19, 3217–3238, https://doi.org/10.5194/hess-19-3217-2015, https://doi.org/10.5194/hess-19-3217-2015, 2015
Short summary
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With most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data and produces more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data in Finland. However, if the biases in the mean or the standard deviation of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections.
H. Gregow, P. Poli, H. M. Mäkelä, K. Jylhä, A. K. Kaiser-Weiss, A. Obregon, D. G. H. Tan, S. Kekki, and F. Kaspar
Adv. Sci. Res., 12, 63–67, https://doi.org/10.5194/asr-12-63-2015, https://doi.org/10.5194/asr-12-63-2015, 2015
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Many users of climate information are unaware of the availability of reanalysis feedback data and input observations, and uptake of feedback data is rather low. The most important factors limiting the use of this data is that the users feel that there is no easy interface to get the data or they do not find it at all. The relevant communities should invest resources to develop tools and provide training to bridge the gap between current capabilities and comprehensive exploitation of the data.
P. Räisänen, A. Luomaranta, H. Järvinen, M. Takala, K. Jylhä, O. N. Bulygina, K. Luojus, A. Riihelä, A. Laaksonen, J. Koskinen, and J. Pulliainen
Geosci. Model Dev., 7, 3037–3057, https://doi.org/10.5194/gmd-7-3037-2014, https://doi.org/10.5194/gmd-7-3037-2014, 2014
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Snowmelt influences greatly the climatic conditions in spring. This study evaluates the timing of springtime end of snowmelt in the ECHAM5 model. A key finding is that, in much of northern Eurasia, snow disappears too early in ECHAM5, in spite of a slight cold bias in spring. This points to the need for a more comprehensive treatment of the surface energy budget. In particular, the surface temperature for the snow-covered and snow-free parts of a climate model grid cell should be separated.
A. Virkkula, J. Levula, T. Pohja, P. P. Aalto, P. Keronen, S. Schobesberger, C. B. Clements, L. Pirjola, A.-J. Kieloaho, L. Kulmala, H. Aaltonen, J. Patokoski, J. Pumpanen, J. Rinne, T. Ruuskanen, M. Pihlatie, H. E. Manninen, V. Aaltonen, H. Junninen, T. Petäjä, J. Backman, M. Dal Maso, T. Nieminen, T. Olsson, T. Grönholm, J. Aalto, T. H. Virtanen, M. Kajos, V.-M. Kerminen, D. M. Schultz, J. Kukkonen, M. Sofiev, G. De Leeuw, J. Bäck, P. Hari, and M. Kulmala
Atmos. Chem. Phys., 14, 4473–4502, https://doi.org/10.5194/acp-14-4473-2014, https://doi.org/10.5194/acp-14-4473-2014, 2014
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Short summary
A new national daily snowfall record was measured in Finland in January 2016 when it snowed 73 cm in less than a day at a small town on the western coast of Finland. The area of the most intense snowfall was very small, which is common in convective precipitation. In this work we used hourly weather radar images to identify the sea-effect snowfall case and found that a weather prediction model worked quite well in simulating the snowbands.
A new national daily snowfall record was measured in Finland in January 2016 when it snowed...