Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes
To study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However...
Published in: | Quarterly Journal of the Royal Meteorological Society |
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Wiley
2021
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Online Access: | https://doi.org/10.1002/qj.4213 https://ora.ox.ac.uk/objects/uuid:7eb2ee45-e9f2-4284-a018-5814fbccec37 |
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ftuloxford:oai:ora.ox.ac.uk:uuid:7eb2ee45-e9f2-4284-a018-5814fbccec37 2023-05-15T17:36:22+02:00 Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes Falkena, S de Wiljes, J Weisheimer, A Shepherd, T 2021-11-16 https://doi.org/10.1002/qj.4213 https://ora.ox.ac.uk/objects/uuid:7eb2ee45-e9f2-4284-a018-5814fbccec37 eng eng Wiley doi:10.1002/qj.4213 https://ora.ox.ac.uk/objects/uuid:7eb2ee45-e9f2-4284-a018-5814fbccec37 https://doi.org/10.1002/qj.4213 info:eu-repo/semantics/openAccess CC Attribution (CC BY) CC-BY Journal article 2021 ftuloxford https://doi.org/10.1002/qj.4213 2022-06-28T20:16:32Z To study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However, the regime representation is itself affected by noise within the atmosphere, which can make it difficult to detect robust signals. To this end we employ a regularised k-means clustering algorithm to better identify the signal in a model ensemble. The approach allows for the identification of six regimes for the wintertime Euro-Atlantic sector and leads to more pronounced regime dynamics, compared to results without regularisation, both overall and on sub-seasonal and interannual timescales. We find that sub-seasonal variability in the regime occurrence rates is mainly explained by changes in the seasonal cycle of the mean climatology. On interannual timescales relations between the occurrence rates of the regimes and the El Ni˜no Southern Oscillation (ENSO) are identified. The use of six regimes captures a more detailed response of the circulation to ENSO compared to the common use of four regimes. Predictable signals in occurrence rate on interannual timescales are found for the two zonal flow regimes, namely a regime consisting of a negative geopotential height anomaly over the Norwegian Sea and Scandinavia, and the positive phase of the NAO. The signal strength for these regimes is comparable between observations and model, in contrast to that of the NAO-index where the signal strength in the observations is underestimated by a factor of two in the model. Our regime analysis suggests that this signal-to-noise problem for the NAO-index is primarily related to those atmospheric flow patterns associated with the negative NAO-index as we find poor predictability for the corresponding NAO− regime. Article in Journal/Newspaper North Atlantic Norwegian Sea ORA - Oxford University Research Archive Norwegian Sea Quarterly Journal of the Royal Meteorological Society 148 742 434 453 |
institution |
Open Polar |
collection |
ORA - Oxford University Research Archive |
op_collection_id |
ftuloxford |
language |
English |
description |
To study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However, the regime representation is itself affected by noise within the atmosphere, which can make it difficult to detect robust signals. To this end we employ a regularised k-means clustering algorithm to better identify the signal in a model ensemble. The approach allows for the identification of six regimes for the wintertime Euro-Atlantic sector and leads to more pronounced regime dynamics, compared to results without regularisation, both overall and on sub-seasonal and interannual timescales. We find that sub-seasonal variability in the regime occurrence rates is mainly explained by changes in the seasonal cycle of the mean climatology. On interannual timescales relations between the occurrence rates of the regimes and the El Ni˜no Southern Oscillation (ENSO) are identified. The use of six regimes captures a more detailed response of the circulation to ENSO compared to the common use of four regimes. Predictable signals in occurrence rate on interannual timescales are found for the two zonal flow regimes, namely a regime consisting of a negative geopotential height anomaly over the Norwegian Sea and Scandinavia, and the positive phase of the NAO. The signal strength for these regimes is comparable between observations and model, in contrast to that of the NAO-index where the signal strength in the observations is underestimated by a factor of two in the model. Our regime analysis suggests that this signal-to-noise problem for the NAO-index is primarily related to those atmospheric flow patterns associated with the negative NAO-index as we find poor predictability for the corresponding NAO− regime. |
format |
Article in Journal/Newspaper |
author |
Falkena, S de Wiljes, J Weisheimer, A Shepherd, T |
spellingShingle |
Falkena, S de Wiljes, J Weisheimer, A Shepherd, T Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes |
author_facet |
Falkena, S de Wiljes, J Weisheimer, A Shepherd, T |
author_sort |
Falkena, S |
title |
Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes |
title_short |
Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes |
title_full |
Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes |
title_fullStr |
Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes |
title_full_unstemmed |
Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes |
title_sort |
detection of interannual ensemble forecast signals over the north atlantic and europe using atmospheric circulation regimes |
publisher |
Wiley |
publishDate |
2021 |
url |
https://doi.org/10.1002/qj.4213 https://ora.ox.ac.uk/objects/uuid:7eb2ee45-e9f2-4284-a018-5814fbccec37 |
geographic |
Norwegian Sea |
geographic_facet |
Norwegian Sea |
genre |
North Atlantic Norwegian Sea |
genre_facet |
North Atlantic Norwegian Sea |
op_relation |
doi:10.1002/qj.4213 https://ora.ox.ac.uk/objects/uuid:7eb2ee45-e9f2-4284-a018-5814fbccec37 https://doi.org/10.1002/qj.4213 |
op_rights |
info:eu-repo/semantics/openAccess CC Attribution (CC BY) |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1002/qj.4213 |
container_title |
Quarterly Journal of the Royal Meteorological Society |
container_volume |
148 |
container_issue |
742 |
container_start_page |
434 |
op_container_end_page |
453 |
_version_ |
1766135839909216256 |