Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes
Abstract 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 me...
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ftoslouniv:oai:www.duo.uio.no:10852/83033 2023-05-15T18:29:48+02:00 Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes Kelder, T. Müller, Malte Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, Christel Bohlinger, Patrik Ferranti, L. Nipen, Thomas Nils 2021-01-20T17:02:12Z http://hdl.handle.net/10852/83033 http://urn.nb.no/URN:NBN:no-85810 https://doi.org/10.1038/s41612-020-00149-4 EN eng http://urn.nb.no/URN:NBN:no-85810 Kelder, T. Müller, Malte Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, Christel Bohlinger, Patrik Ferranti, L. Nipen, Thomas Nils . Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes. npj Climate and Atmospheric Science. 2020, 3 http://hdl.handle.net/10852/83033 1875865 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=npj Climate and Atmospheric Science&rft.volume=3&rft.spage=&rft.date=2020 npj Climate and Atmospheric Science 3 1 13 https://doi.org/10.1038/s41612-020-00149-4 URN:NBN:no-85810 Fulltext https://www.duo.uio.no/bitstream/handle/10852/83033/2/s41612-020-00149-4.pdf Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ CC-BY 2397-3722 Journal article Tidsskriftartikkel Peer reviewed PublishedVersion 2021 ftoslouniv https://doi.org/10.1038/s41612-020-00149-4 2021-02-10T23:31:29Z Abstract 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 Universitet i Oslo: Digitale utgivelser ved UiO (DUO) Norway Svalbard npj Climate and Atmospheric Science 3 1 |
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Open Polar |
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Universitet i Oslo: Digitale utgivelser ved UiO (DUO) |
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ftoslouniv |
language |
English |
description |
Abstract 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, Malte Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, Christel Bohlinger, Patrik Ferranti, L. Nipen, Thomas Nils |
spellingShingle |
Kelder, T. Müller, Malte Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, Christel Bohlinger, Patrik Ferranti, L. Nipen, Thomas Nils Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes |
author_facet |
Kelder, T. Müller, Malte Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, Christel Bohlinger, Patrik Ferranti, L. Nipen, Thomas Nils |
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 |
publishDate |
2021 |
url |
http://hdl.handle.net/10852/83033 http://urn.nb.no/URN:NBN:no-85810 https://doi.org/10.1038/s41612-020-00149-4 |
geographic |
Norway Svalbard |
geographic_facet |
Norway Svalbard |
genre |
Svalbard |
genre_facet |
Svalbard |
op_source |
2397-3722 |
op_relation |
http://urn.nb.no/URN:NBN:no-85810 Kelder, T. Müller, Malte Slater, L.J. Marjoribanks, T.I. Wilby, R.L. Prudhomme, Christel Bohlinger, Patrik Ferranti, L. Nipen, Thomas Nils . Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes. npj Climate and Atmospheric Science. 2020, 3 http://hdl.handle.net/10852/83033 1875865 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=npj Climate and Atmospheric Science&rft.volume=3&rft.spage=&rft.date=2020 npj Climate and Atmospheric Science 3 1 13 https://doi.org/10.1038/s41612-020-00149-4 URN:NBN:no-85810 Fulltext https://www.duo.uio.no/bitstream/handle/10852/83033/2/s41612-020-00149-4.pdf |
op_rights |
Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ |
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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1766213181197254656 |