State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting

Existing methods for diagnosing predictability in climate indices often make a number of unjustified assumptions about the climate system that can lead to misleading conclusions. We present a flexible family of state-space models capable of separating the effects of external forcing on inter-annual...

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Main Authors: Sansom, Philip G., Stephenson, David B., Williamson, Daniel B.
Format: Report
Language:unknown
Published: arXiv 2018
Subjects:
Applications stat.AP
FOS Computer and information sciences
North Atlantic
North Atlantic oscillation
Online Access:https://dx.doi.org/10.48550/arxiv.1807.02671
https://arxiv.org/abs/1807.02671
id ftdatacite:10.48550/arxiv.1807.02671
record_format openpolar
spelling ftdatacite:10.48550/arxiv.1807.02671 2023-05-15T17:29:21+02:00 State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting Sansom, Philip G. Stephenson, David B. Williamson, Daniel B. 2018 https://dx.doi.org/10.48550/arxiv.1807.02671 https://arxiv.org/abs/1807.02671 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Applications stat.AP FOS Computer and information sciences Preprint Article article CreativeWork 2018 ftdatacite https://doi.org/10.48550/arxiv.1807.02671 2022-04-01T09:09:01Z Existing methods for diagnosing predictability in climate indices often make a number of unjustified assumptions about the climate system that can lead to misleading conclusions. We present a flexible family of state-space models capable of separating the effects of external forcing on inter-annual time scales, from long-term trends and decadal variability, short term weather noise, observational errors and changes in autocorrelation. Standard potential predictability models only estimate the fraction of the total variance in the index attributable to external forcing. In addition, our methodology allows us to partition individual seasonal means into forced, slow, fast and error components. Changes in the predictable signal within the season can also be estimated. The model can also be used in forecast mode to assess both intra- and inter-seasonal predictability. We apply the proposed methodology to a North Atlantic Oscillation index for the years 1948-2017. Around 60% of the inter-annual variance in the December-January-February mean North Atlantic Oscillation is attributable to external forcing, and 8% to trends on longer time-scales. In some years, the external forcing remains relatively constant throughout the winter season, in others it changes during the season. Skillful statistical forecasts of the December-January-February mean North Atlantic Oscillation are possible from the end of November onward and predictability extends into March. Statistical forecasts of the December-January-February mean achieve a correlation with the observations of 0.48. : 16 pages, 10 figures Report North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Applications stat.AP
FOS Computer and information sciences
spellingShingle Applications stat.AP
FOS Computer and information sciences
Sansom, Philip G.
Stephenson, David B.
Williamson, Daniel B.
State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting
topic_facet Applications stat.AP
FOS Computer and information sciences
description Existing methods for diagnosing predictability in climate indices often make a number of unjustified assumptions about the climate system that can lead to misleading conclusions. We present a flexible family of state-space models capable of separating the effects of external forcing on inter-annual time scales, from long-term trends and decadal variability, short term weather noise, observational errors and changes in autocorrelation. Standard potential predictability models only estimate the fraction of the total variance in the index attributable to external forcing. In addition, our methodology allows us to partition individual seasonal means into forced, slow, fast and error components. Changes in the predictable signal within the season can also be estimated. The model can also be used in forecast mode to assess both intra- and inter-seasonal predictability. We apply the proposed methodology to a North Atlantic Oscillation index for the years 1948-2017. Around 60% of the inter-annual variance in the December-January-February mean North Atlantic Oscillation is attributable to external forcing, and 8% to trends on longer time-scales. In some years, the external forcing remains relatively constant throughout the winter season, in others it changes during the season. Skillful statistical forecasts of the December-January-February mean North Atlantic Oscillation are possible from the end of November onward and predictability extends into March. Statistical forecasts of the December-January-February mean achieve a correlation with the observations of 0.48. : 16 pages, 10 figures
format Report
author Sansom, Philip G.
Stephenson, David B.
Williamson, Daniel B.
author_facet Sansom, Philip G.
Stephenson, David B.
Williamson, Daniel B.
author_sort Sansom, Philip G.
title State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting
title_short State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting
title_full State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting
title_fullStr State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting
title_full_unstemmed State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting
title_sort state-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting
publisher arXiv
publishDate 2018
url https://dx.doi.org/10.48550/arxiv.1807.02671
https://arxiv.org/abs/1807.02671
genre North Atlantic
North Atlantic oscillation
genre_facet North Atlantic
North Atlantic oscillation
op_rights arXiv.org perpetual, non-exclusive license
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
op_doi https://doi.org/10.48550/arxiv.1807.02671
_version_ 1766123262434082816

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