Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes
Sample sizes of observed climate extremes are typically too small to reliably constrain return period estimates when there is non-stationary behaviour. To increase the historical record 100-fold, we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and...
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ftloughboroughun:oai:figshare.com:article/13139330 2023-05-15T18:29:48+02:00 Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes Timo Kelder M. Müller L.J. Slater Tim Marjoribanks Robert Wilby C. Prudhomme P. Bohlinger L. Ferranti T. Nipen 2020-11-27T00:00:00Z https://figshare.com/articles/journal_contribution/Using_UNSEEN_trends_to_detect_decadal_changes_in_100-year_precipitation_extremes/13139330 unknown 2134/13139330.v1 https://figshare.com/articles/journal_contribution/Using_UNSEEN_trends_to_detect_decadal_changes_in_100-year_precipitation_extremes/13139330 CC BY 4.0 CC-BY Uncategorized Atmospheric science Climate change Environmental impact Hydrology Text Journal contribution 2020 ftloughboroughun 2022-01-01T19:15:59Z Sample sizes of observed climate extremes are typically too small to reliably constrain return period estimates when there is non-stationary behaviour. To increase the historical record 100-fold, we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead times from the ECMWF seasonal prediction system SEAS5. We fit the GEV distribution to the UNSEEN ensemble with a time covariate to facilitate detection of changes in 100-year precipitation values over a period of 35 years (1981–2015). Applying UNSEEN trends to 3-day precipitation extremes over Western Norway substantially reduces uncertainties compared to estimates based on the observed record and returns no significant linear trend over time. For Svalbard, UNSEEN trends suggests there is a significant rise in precipitation extremes, such that the 100-year event estimated in 1981 occurs with a return period of around 40 years in 2015. We propose a suite of methods to evaluate UNSEEN and highlight paths for further developing UNSEEN trends to investigate non-stationarities in climate extremes. Other Non-Article Part of Journal/Newspaper Svalbard Loughborough University: Figshare Norway Svalbard |
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Open Polar |
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Loughborough University: Figshare |
op_collection_id |
ftloughboroughun |
language |
unknown |
topic |
Uncategorized Atmospheric science Climate change Environmental impact Hydrology |
spellingShingle |
Uncategorized Atmospheric science Climate change Environmental impact Hydrology Timo Kelder M. Müller L.J. Slater Tim Marjoribanks Robert Wilby C. Prudhomme P. Bohlinger L. Ferranti T. Nipen Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes |
topic_facet |
Uncategorized Atmospheric science Climate change Environmental impact Hydrology |
description |
Sample sizes of observed climate extremes are typically too small to reliably constrain return period estimates when there is non-stationary behaviour. To increase the historical record 100-fold, we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead times from the ECMWF seasonal prediction system SEAS5. We fit the GEV distribution to the UNSEEN ensemble with a time covariate to facilitate detection of changes in 100-year precipitation values over a period of 35 years (1981–2015). Applying UNSEEN trends to 3-day precipitation extremes over Western Norway substantially reduces uncertainties compared to estimates based on the observed record and returns no significant linear trend over time. For Svalbard, UNSEEN trends suggests there is a significant rise in precipitation extremes, such that the 100-year event estimated in 1981 occurs with a return period of around 40 years in 2015. We propose a suite of methods to evaluate UNSEEN and highlight paths for further developing UNSEEN trends to investigate non-stationarities in climate extremes. |
format |
Other Non-Article Part of Journal/Newspaper |
author |
Timo Kelder M. Müller L.J. Slater Tim Marjoribanks Robert Wilby C. Prudhomme P. Bohlinger L. Ferranti T. Nipen |
author_facet |
Timo Kelder M. Müller L.J. Slater Tim Marjoribanks Robert Wilby C. Prudhomme P. Bohlinger L. Ferranti T. Nipen |
author_sort |
Timo Kelder |
title |
Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes |
title_short |
Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes |
title_full |
Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes |
title_fullStr |
Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes |
title_full_unstemmed |
Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes |
title_sort |
using unseen trends to detect decadal changes in 100-year precipitation extremes |
publishDate |
2020 |
url |
https://figshare.com/articles/journal_contribution/Using_UNSEEN_trends_to_detect_decadal_changes_in_100-year_precipitation_extremes/13139330 |
geographic |
Norway Svalbard |
geographic_facet |
Norway Svalbard |
genre |
Svalbard |
genre_facet |
Svalbard |
op_relation |
2134/13139330.v1 https://figshare.com/articles/journal_contribution/Using_UNSEEN_trends_to_detect_decadal_changes_in_100-year_precipitation_extremes/13139330 |
op_rights |
CC BY 4.0 |
op_rightsnorm |
CC-BY |
_version_ |
1766213189617319936 |