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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Online Access: | http://nora.nerc.ac.uk/id/eprint/529184/ https://nora.nerc.ac.uk/id/eprint/529184/1/N529184JA.pdf https://doi.org/10.1038/s41612-020-00149-4 |
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ftnerc:oai:nora.nerc.ac.uk:529184 2023-05-15T18:29:48+02:00 Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes Kelder, T. Müller, M. Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, C. Bohlinger, P. Ferranti, L. Nipen, T. 2020-11-27 text http://nora.nerc.ac.uk/id/eprint/529184/ https://nora.nerc.ac.uk/id/eprint/529184/1/N529184JA.pdf https://doi.org/10.1038/s41612-020-00149-4 en eng Springer Nature https://nora.nerc.ac.uk/id/eprint/529184/1/N529184JA.pdf Kelder, T.; Müller, M.; Slater, L.J.; Marjoribanks, T.I.; Wilby, R.L.; Prudhomme, C.; Bohlinger, P.; Ferranti, L.; Nipen, T. 2020 Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes. npj Climate and Atmospheric Science, 3, 47. https://doi.org/10.1038/s41612-020-00149-4 <https://doi.org/10.1038/s41612-020-00149-4> cc_by_4 CC-BY Hydrology Meteorology and Climatology Atmospheric Sciences Publication - Article PeerReviewed 2020 ftnerc https://doi.org/10.1038/s41612-020-00149-4 2023-02-04T19:51:30Z 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. Article in Journal/Newspaper Svalbard Natural Environment Research Council: NERC Open Research Archive Svalbard Norway npj Climate and Atmospheric Science 3 1 |
institution |
Open Polar |
collection |
Natural Environment Research Council: NERC Open Research Archive |
op_collection_id |
ftnerc |
language |
English |
topic |
Hydrology Meteorology and Climatology Atmospheric Sciences |
spellingShingle |
Hydrology Meteorology and Climatology Atmospheric Sciences Kelder, T. Müller, M. Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, C. Bohlinger, P. Ferranti, L. Nipen, T. Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes |
topic_facet |
Hydrology Meteorology and Climatology Atmospheric Sciences |
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 |
Article in Journal/Newspaper |
author |
Kelder, T. Müller, M. Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, C. Bohlinger, P. Ferranti, L. Nipen, T. |
author_facet |
Kelder, T. Müller, M. Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, C. Bohlinger, P. Ferranti, L. Nipen, T. |
author_sort |
Kelder, T. |
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 |
publisher |
Springer Nature |
publishDate |
2020 |
url |
http://nora.nerc.ac.uk/id/eprint/529184/ https://nora.nerc.ac.uk/id/eprint/529184/1/N529184JA.pdf https://doi.org/10.1038/s41612-020-00149-4 |
geographic |
Svalbard Norway |
geographic_facet |
Svalbard Norway |
genre |
Svalbard |
genre_facet |
Svalbard |
op_relation |
https://nora.nerc.ac.uk/id/eprint/529184/1/N529184JA.pdf Kelder, T.; Müller, M.; Slater, L.J.; Marjoribanks, T.I.; Wilby, R.L.; Prudhomme, C.; Bohlinger, P.; Ferranti, L.; Nipen, T. 2020 Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes. npj Climate and Atmospheric Science, 3, 47. https://doi.org/10.1038/s41612-020-00149-4 <https://doi.org/10.1038/s41612-020-00149-4> |
op_rights |
cc_by_4 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41612-020-00149-4 |
container_title |
npj Climate and Atmospheric Science |
container_volume |
3 |
container_issue |
1 |
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1766213187801186304 |