Non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in Finland, 1961–2016
There is an urgent need to understand and predict how extreme precipitation events (EPEs) will change at high latitudes, both for local climate change adaptation plans and risk mitigation and as a potential proxy “anticipating” the impact of climate change elsewhere in the world. This paper illustra...
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crwiley:10.1002/joc.5867 2024-06-23T07:50:14+00:00 Non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in Finland, 1961–2016 Pedretti, Daniele Irannezhad, Masoud Suomen Kulttuurirahasto 2018 http://dx.doi.org/10.1002/joc.5867 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjoc.5867 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5867 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor International Journal of Climatology volume 39, issue 2, page 1128-1143 ISSN 0899-8418 1097-0088 journal-article 2018 crwiley https://doi.org/10.1002/joc.5867 2024-06-13T04:21:02Z There is an urgent need to understand and predict how extreme precipitation events (EPEs) will change at high latitudes, both for local climate change adaptation plans and risk mitigation and as a potential proxy “anticipating” the impact of climate change elsewhere in the world. This paper illustrates that a combination of non‐stationary modelling approaches can be adopted to evaluate trends in EPEs under uncertainty. A large database of daily rainfall events from 281 sparsely distributed weather stations in Finland between 1961 and 2016 was analysed. Among the tested methods, Poisson distributions provided a powerful method to evaluate the impacts of multiple physical covariates, including temperature and atmospheric circulation patterns (ACPs), on the resulting trends. The analysis demonstrates that non‐stationarity is statistically valid for the majority of observations, independently of their location in the country and the season of the year. However, subsampling can severely hinder the statistical validity of the trends, which can be easily confused with random noise and therefore complicate the decision‐making processes regarding long‐term planning. Scaling effects have a strong impact on the estimates of non‐stationary parameters, as homogenizing the data in space and time reduces the statistical validity of the trends. Trends in EPE statistics (mean, 90 and 99% percentiles) and best‐fitted Generalized Pareto parameters in the tails of the distributions appear to be stronger when approaching the Polar region (Lapland) than away from it, consistent with the Arctic amplification of climate change. ACPs are key covariates in physically explaining these trends. In particular, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) can explain statistically significant increases in extreme precipitation in Lapland, Bothnian and South regions of Finland, particularly during summer and fall seasons. Article in Journal/Newspaper Arctic Climate change North Atlantic North Atlantic oscillation Lapland Wiley Online Library Arctic International Journal of Climatology 39 2 1128 1143 |
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Wiley Online Library |
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English |
description |
There is an urgent need to understand and predict how extreme precipitation events (EPEs) will change at high latitudes, both for local climate change adaptation plans and risk mitigation and as a potential proxy “anticipating” the impact of climate change elsewhere in the world. This paper illustrates that a combination of non‐stationary modelling approaches can be adopted to evaluate trends in EPEs under uncertainty. A large database of daily rainfall events from 281 sparsely distributed weather stations in Finland between 1961 and 2016 was analysed. Among the tested methods, Poisson distributions provided a powerful method to evaluate the impacts of multiple physical covariates, including temperature and atmospheric circulation patterns (ACPs), on the resulting trends. The analysis demonstrates that non‐stationarity is statistically valid for the majority of observations, independently of their location in the country and the season of the year. However, subsampling can severely hinder the statistical validity of the trends, which can be easily confused with random noise and therefore complicate the decision‐making processes regarding long‐term planning. Scaling effects have a strong impact on the estimates of non‐stationary parameters, as homogenizing the data in space and time reduces the statistical validity of the trends. Trends in EPE statistics (mean, 90 and 99% percentiles) and best‐fitted Generalized Pareto parameters in the tails of the distributions appear to be stronger when approaching the Polar region (Lapland) than away from it, consistent with the Arctic amplification of climate change. ACPs are key covariates in physically explaining these trends. In particular, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) can explain statistically significant increases in extreme precipitation in Lapland, Bothnian and South regions of Finland, particularly during summer and fall seasons. |
author2 |
Suomen Kulttuurirahasto |
format |
Article in Journal/Newspaper |
author |
Pedretti, Daniele Irannezhad, Masoud |
spellingShingle |
Pedretti, Daniele Irannezhad, Masoud Non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in Finland, 1961–2016 |
author_facet |
Pedretti, Daniele Irannezhad, Masoud |
author_sort |
Pedretti, Daniele |
title |
Non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in Finland, 1961–2016 |
title_short |
Non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in Finland, 1961–2016 |
title_full |
Non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in Finland, 1961–2016 |
title_fullStr |
Non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in Finland, 1961–2016 |
title_full_unstemmed |
Non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in Finland, 1961–2016 |
title_sort |
non‐stationary peaks‐over‐threshold analysis of extreme precipitation events in finland, 1961–2016 |
publisher |
Wiley |
publishDate |
2018 |
url |
http://dx.doi.org/10.1002/joc.5867 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjoc.5867 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5867 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change North Atlantic North Atlantic oscillation Lapland |
genre_facet |
Arctic Climate change North Atlantic North Atlantic oscillation Lapland |
op_source |
International Journal of Climatology volume 39, issue 2, page 1128-1143 ISSN 0899-8418 1097-0088 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/joc.5867 |
container_title |
International Journal of Climatology |
container_volume |
39 |
container_issue |
2 |
container_start_page |
1128 |
op_container_end_page |
1143 |
_version_ |
1802641114200539136 |