Evolution of observed and modelled temperatures in Finland in 1901–2018 and potential dynamical reasons for the differences
Abstract Observed monthly and annual mean temperatures in Finland in 1901–2018 were compared with simulations performed with 28 global climate models (GCMs), and dynamical factors behind the emerging differences were studied by regression analysis. Observational temperatures were extracted from high...
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crwiley:10.1002/joc.7024 2024-06-02T08:11:57+00:00 Evolution of observed and modelled temperatures in Finland in 1901–2018 and potential dynamical reasons for the differences Ruosteenoja, Kimmo Räisänen, Jouni Academy of Finland 2021 http://dx.doi.org/10.1002/joc.7024 https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7024 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/joc.7024 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7024 en eng Wiley http://creativecommons.org/licenses/by/4.0/ International Journal of Climatology volume 41, issue 5, page 3374-3390 ISSN 0899-8418 1097-0088 journal-article 2021 crwiley https://doi.org/10.1002/joc.7024 2024-05-03T10:55:01Z Abstract Observed monthly and annual mean temperatures in Finland in 1901–2018 were compared with simulations performed with 28 global climate models (GCMs), and dynamical factors behind the emerging differences were studied by regression analysis. Observational temperatures were extracted from high‐quality kriging analyses specifically tailored for Finland. Considering the entire time interval, the increase in the annual multi‐GCM mean temperature agrees well with the observed warming, even though observations exhibit substantial inter‐decadal fluctuations. After 2000, the mean temperatures have been higher than during any period in the 20th century. In the baseline regression model, the 10 leading EOFs of the European—Northeast Atlantic sea‐level pressure (SLP) field were used to explain differences between the GCM‐mean and observed evolution of temperature. The regression model is able to reduce the mean squared difference of the temporally‐smoothed temperature by 58%. The performance is highest in winter and summer and lowest in April. For a sensitivity assessment, multiple alternative regression models were tested, for example, one using the local SLP, geostrophic wind and vorticity as predictors. These models mostly showed somewhat inferior performance. We specifically explored the trends of monthly temperatures during 1961–2018, a period considerably affected by anthropogenic emissions. Compared with the multi‐GCM mean, warming proved to be negligible in June, fairly slow in October and quite rapid in December. All these features were explained rather nicely by dynamical factors. Accordingly, the deviations of the observed regional temperature trends from the multi‐GCM mean largely appear to be related to internal variability. Article in Journal/Newspaper Northeast Atlantic Wiley Online Library International Journal of Climatology 41 5 3374 3390 |
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Wiley Online Library |
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English |
description |
Abstract Observed monthly and annual mean temperatures in Finland in 1901–2018 were compared with simulations performed with 28 global climate models (GCMs), and dynamical factors behind the emerging differences were studied by regression analysis. Observational temperatures were extracted from high‐quality kriging analyses specifically tailored for Finland. Considering the entire time interval, the increase in the annual multi‐GCM mean temperature agrees well with the observed warming, even though observations exhibit substantial inter‐decadal fluctuations. After 2000, the mean temperatures have been higher than during any period in the 20th century. In the baseline regression model, the 10 leading EOFs of the European—Northeast Atlantic sea‐level pressure (SLP) field were used to explain differences between the GCM‐mean and observed evolution of temperature. The regression model is able to reduce the mean squared difference of the temporally‐smoothed temperature by 58%. The performance is highest in winter and summer and lowest in April. For a sensitivity assessment, multiple alternative regression models were tested, for example, one using the local SLP, geostrophic wind and vorticity as predictors. These models mostly showed somewhat inferior performance. We specifically explored the trends of monthly temperatures during 1961–2018, a period considerably affected by anthropogenic emissions. Compared with the multi‐GCM mean, warming proved to be negligible in June, fairly slow in October and quite rapid in December. All these features were explained rather nicely by dynamical factors. Accordingly, the deviations of the observed regional temperature trends from the multi‐GCM mean largely appear to be related to internal variability. |
author2 |
Academy of Finland |
format |
Article in Journal/Newspaper |
author |
Ruosteenoja, Kimmo Räisänen, Jouni |
spellingShingle |
Ruosteenoja, Kimmo Räisänen, Jouni Evolution of observed and modelled temperatures in Finland in 1901–2018 and potential dynamical reasons for the differences |
author_facet |
Ruosteenoja, Kimmo Räisänen, Jouni |
author_sort |
Ruosteenoja, Kimmo |
title |
Evolution of observed and modelled temperatures in Finland in 1901–2018 and potential dynamical reasons for the differences |
title_short |
Evolution of observed and modelled temperatures in Finland in 1901–2018 and potential dynamical reasons for the differences |
title_full |
Evolution of observed and modelled temperatures in Finland in 1901–2018 and potential dynamical reasons for the differences |
title_fullStr |
Evolution of observed and modelled temperatures in Finland in 1901–2018 and potential dynamical reasons for the differences |
title_full_unstemmed |
Evolution of observed and modelled temperatures in Finland in 1901–2018 and potential dynamical reasons for the differences |
title_sort |
evolution of observed and modelled temperatures in finland in 1901–2018 and potential dynamical reasons for the differences |
publisher |
Wiley |
publishDate |
2021 |
url |
http://dx.doi.org/10.1002/joc.7024 https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7024 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/joc.7024 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7024 |
genre |
Northeast Atlantic |
genre_facet |
Northeast Atlantic |
op_source |
International Journal of Climatology volume 41, issue 5, page 3374-3390 ISSN 0899-8418 1097-0088 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/joc.7024 |
container_title |
International Journal of Climatology |
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41 |
container_issue |
5 |
container_start_page |
3374 |
op_container_end_page |
3390 |
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1800758256030711808 |