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  North Atlantic climate far more predictable than models imply

Smith, D., Scaife, A., Eade, R., Athanasiadis, P., Bellucci, A., Bethke, I., et al. (2020). North Atlantic climate far more predictable than models imply. Nature, 583, 796-800. doi:10.1038/s41586-020-2525-0.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-DDC1-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-DDC4-4
Genre: Journal Article

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Smith, D.M., Author
Scaife, A.A., Author
Eade, R., Author
Athanasiadis, P., Author
Bellucci, A., Author
Bethke, I., Author
Bilbao, R., Author
Borchert, L.F., Author
Caron, L.-P., Author
Counillon, F., Author
Danabasoglu, G., Author
Delworth, T., Author
Doblas-Reyes, F.J., Author
Dunstone, N.J., Author
Estella-Perez, V., Author
Flavoni, S., Author
Hermanson, L., Author
Keenlyside, N., Author
Kharin, V., Author
Kimoto, M., Author
Merryfield, W.J., AuthorMignot, J., AuthorMochizuki, T., AuthorModali, K., AuthorMonerie, P.-A., AuthorMüller, Wolfgang A.1, Author              Nicolí, D., AuthorOrtega, P., AuthorPankatz, K., AuthorPohlmann, Holger1, Author              Robson, J., AuthorRuggieri, P., AuthorSospedra-Alfonso, R., AuthorSwingedouw, D., AuthorWang, Y., AuthorWild, S., AuthorYeager, S., AuthorYang, X., AuthorZhang, L., Author more..
Affiliations:
1Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society, ou_1479671              

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Free keywords: atmospheric circulation; climate change; climate modeling; error analysis; North Atlantic Oscillation; regional climate; signal-to-noise ratio; uncertainty analysis, article; climate change; Europe; North America; North Atlantic oscillation; prediction; signal noise ratio; simulation; uncertainty; winter, Atlantic Ocean; Atlantic Ocean (North); Europe; North America
 Abstract: Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1–3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7–9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. © 2020, Crown.

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Language(s): eng - English
 Dates: 2019-122020-05-012020-07-292020-07-30
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41586-020-2525-0
 Degree: -

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Project name : EUCP
Grant ID : 776613
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : MSCA-COFUND
Grant ID : 754433
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : Blue-Action
Grant ID : 727852
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Nature
Source Genre: Journal
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Publ. Info: Nature Research
Pages: - Volume / Issue: 583 Sequence Number: - Start / End Page: 796 - 800 Identifier: ISSN: 00280836