How predictable are the Arctic and North Atlantic Oscillations? Exploring the Variability and Predictability of the Northern Hemisphere
The North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) describe the dominant part of the variability in the Northern Hemisphere extratropical troposphere. Due to the strong connection of these patterns with surface climate, recent years have shown an increased interest and an increasin...
| Published in: | Journal of Climate |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
AMS (American Meteorological Society)
2018
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| Subjects: | |
| Online Access: | https://oceanrep.geomar.de/id/eprint/40162/ https://oceanrep.geomar.de/id/eprint/40162/2/jcli-d-17-0226.1-1.pdf https://oceanrep.geomar.de/id/eprint/40162/13/jcli-d-17-0226.1.pdf https://doi.org/10.1175/JCLI-D-17-0226.1 |
| Summary: | The North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) describe the dominant part of the variability in the Northern Hemisphere extratropical troposphere. Due to the strong connection of these patterns with surface climate, recent years have shown an increased interest and an increasing skill in forecasting them. However, it is unclear what the intrinsic limits of short-term predictability for the NAO and AO patterns are. This study compares the variability and predictability of both patterns, using a range of data and index computation methods for the daily NAO/AO indices. Small deviations from Gaussianity are found and characteristic decorrelation time scales of around one week. In the analysis of the Lyapunov spectrum it is found that predictability is not significantly different between the AO and NAO or between reanalysis products. Differences exist however between the indices based on EOF analysis, which exhibit predictability time scales around 12 - 16 days, and the station-based indices, exhibiting a longer predictability of 18 - 20 days. Both of these time scales indicate predictability beyond that currently obtained in ensemble prediction models for short-term predictability. Additional longer-term predictability for these patterns may be gained through local feedbacks and remote forcing mechanisms for particular atmospheric conditions. |
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