Publication Date:
2022-05-26
Description:
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Athanasiadis, P. J., Yeager, S., Kwon, Y. O., Bellucci, A., Smith, D. W., & Tibaldi, S. Decadal predictability of North Atlantic blocking and the NAO. Npj Climate and Atmospheric Science, 3(1), (2020): 20, doi:10.1038/s41612-020-0120-6.
Description:
Can multi-annual variations in the frequency of North Atlantic atmospheric blocking and mid-latitude circulation regimes be skilfully predicted? Recent advances in seasonal forecasting have shown that mid-latitude climate variability does exhibit significant predictability. However, atmospheric predictability has generally been found to be quite limited on multi-annual timescales. New decadal prediction experiments from NCAR are found to exhibit remarkable skill in reproducing the observed multi-annual variations of wintertime blocking frequency over the North Atlantic and of the North Atlantic Oscillation (NAO) itself. This is partly due to the large ensemble size that allows the predictable component of the atmospheric variability to emerge from the background chaotic component. The predictable atmospheric anomalies represent a forced response to oceanic low-frequency variability that strongly resembles the Atlantic Multi-decadal Variability (AMV), correctly reproduced in the decadal hindcasts thanks to realistic ocean initialization and ocean dynamics. The occurrence of blocking in certain areas of the Euro-Atlantic domain determines the concurrent circulation regime and the phase of known teleconnections, such as the NAO, consequently affecting the stormtrack and the frequency and intensity of extreme weather events. Therefore, skilfully predicting the decadal fluctuations of blocking frequency and the NAO may be used in statistical predictions of near-term climate anomalies, and it provides a strong indication that impactful climate anomalies may also be predictable with improved dynamical models.
Description:
This study received support by the Blue-Action project (European Union’s Horizon 2020 research and innovation program, #727852). A.B. was supported by the H2020 EUCP (grant no. GA 776613) project. S.Y. acknowledges the support of National Science Foundation (NSF) grants OPP-1737377 and OCE-1243015. NCAR is a major facility sponsored by NSF under Cooperative Agreement No. 1852977. The CESM-DPLE was generated using computational resources provided by the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant for Cheyenne (https://doi.org/10.5065/D6RX99HX) that was awarded by NCAR’s Computational and Information Systems Laboratory. Y.-O.K. was supported by the DOE Regional and Global Model Analysis Program (DE-SC0019492), and the NSF Arctic Natural Science Program (OPP-1736738) and Climate and Large-scale Dynamics Program (AGS-1355339).
Repository Name:
Woods Hole Open Access Server
Type:
Article
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