Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach

Seasonal forecasts of winter North Atlantic atmospheric variability have until recently shown little skill. Here we present a new technique for developing both linear and non‐linear statistical forecasts of the winter North Atlantic Oscillation (NAO) based on complex systems modelling, which has bee...

Full description

Bibliographic Details
Main Authors: Hall, R.J., Wei, H.L., Hanna, E.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2019
Subjects:
Online Access:https://eprints.whiterose.ac.uk/147010/
https://eprints.whiterose.ac.uk/147010/1/North%20Atlantic%20Seasonal%20Forecasting%20%28QJRMS%20accepted%20manuscript%29.pdf
id ftleedsuniv:oai:eprints.whiterose.ac.uk:147010
record_format openpolar
spelling ftleedsuniv:oai:eprints.whiterose.ac.uk:147010 2023-05-15T15:39:05+02:00 Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach Hall, R.J. Wei, H.L. Hanna, E. 2019-09-05 text https://eprints.whiterose.ac.uk/147010/ https://eprints.whiterose.ac.uk/147010/1/North%20Atlantic%20Seasonal%20Forecasting%20%28QJRMS%20accepted%20manuscript%29.pdf en eng Wiley https://eprints.whiterose.ac.uk/147010/1/North%20Atlantic%20Seasonal%20Forecasting%20%28QJRMS%20accepted%20manuscript%29.pdf Hall, R.J., Wei, H.L. orcid.org/0000-0002-4704-7346 and Hanna, E. (2019) Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach. Quarterly Journal of the Royal Meteorological Society, 145 (723). pp. 2568-2585. ISSN 0035-9009 Article PeerReviewed 2019 ftleedsuniv 2023-01-30T22:19:35Z Seasonal forecasts of winter North Atlantic atmospheric variability have until recently shown little skill. Here we present a new technique for developing both linear and non‐linear statistical forecasts of the winter North Atlantic Oscillation (NAO) based on complex systems modelling, which has been widely used in a range of fields, but generally not in climate research. Our polynomial NARMAX models demonstrate considerable skill in out‐of‐sample forecasts and their performance is superior to that of linear models, albeit with small sample sizes. Predictors can be readily identified and this has the potential to inform the next generation of dynamical models and models allow for the incorporation of non‐linearities in interactions between predictors and atmospheric variability. In general there is more skill in forecasts developed over a shorter training period from 1980 compared with an equivalent forecast using training data from 1956. This latter point may relate to decreased inherent predictability in the period 1955‐1980, a wider range of available predictors since 1980 and/or reduced data quality in the earlier period and is consistent with previously identified decadal variability of the NAO. A number of predictors such as sea‐level pressure over the Barents Sea, and a clear tropical signal are commonly selected by both linear and polynomial NARMAX models. Tropical signals are modulated by higher latitude boundary conditions. Both approaches can be extended to developing probabilistic forecasts and to other seasons and indices of atmospheric variability such as the East Atlantic pattern and jet stream metrics. Article in Journal/Newspaper Barents Sea North Atlantic North Atlantic oscillation White Rose Research Online (Universities of Leeds, Sheffield & York) Barents Sea
institution Open Polar
collection White Rose Research Online (Universities of Leeds, Sheffield & York)
op_collection_id ftleedsuniv
language English
description Seasonal forecasts of winter North Atlantic atmospheric variability have until recently shown little skill. Here we present a new technique for developing both linear and non‐linear statistical forecasts of the winter North Atlantic Oscillation (NAO) based on complex systems modelling, which has been widely used in a range of fields, but generally not in climate research. Our polynomial NARMAX models demonstrate considerable skill in out‐of‐sample forecasts and their performance is superior to that of linear models, albeit with small sample sizes. Predictors can be readily identified and this has the potential to inform the next generation of dynamical models and models allow for the incorporation of non‐linearities in interactions between predictors and atmospheric variability. In general there is more skill in forecasts developed over a shorter training period from 1980 compared with an equivalent forecast using training data from 1956. This latter point may relate to decreased inherent predictability in the period 1955‐1980, a wider range of available predictors since 1980 and/or reduced data quality in the earlier period and is consistent with previously identified decadal variability of the NAO. A number of predictors such as sea‐level pressure over the Barents Sea, and a clear tropical signal are commonly selected by both linear and polynomial NARMAX models. Tropical signals are modulated by higher latitude boundary conditions. Both approaches can be extended to developing probabilistic forecasts and to other seasons and indices of atmospheric variability such as the East Atlantic pattern and jet stream metrics.
format Article in Journal/Newspaper
author Hall, R.J.
Wei, H.L.
Hanna, E.
spellingShingle Hall, R.J.
Wei, H.L.
Hanna, E.
Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach
author_facet Hall, R.J.
Wei, H.L.
Hanna, E.
author_sort Hall, R.J.
title Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach
title_short Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach
title_full Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach
title_fullStr Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach
title_full_unstemmed Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach
title_sort complex systems modelling for statistical forecasting of winter north atlantic atmospheric variability: a new approach
publisher Wiley
publishDate 2019
url https://eprints.whiterose.ac.uk/147010/
https://eprints.whiterose.ac.uk/147010/1/North%20Atlantic%20Seasonal%20Forecasting%20%28QJRMS%20accepted%20manuscript%29.pdf
geographic Barents Sea
geographic_facet Barents Sea
genre Barents Sea
North Atlantic
North Atlantic oscillation
genre_facet Barents Sea
North Atlantic
North Atlantic oscillation
op_relation https://eprints.whiterose.ac.uk/147010/1/North%20Atlantic%20Seasonal%20Forecasting%20%28QJRMS%20accepted%20manuscript%29.pdf
Hall, R.J., Wei, H.L. orcid.org/0000-0002-4704-7346 and Hanna, E. (2019) Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach. Quarterly Journal of the Royal Meteorological Society, 145 (723). pp. 2568-2585. ISSN 0035-9009
_version_ 1766370511675195392