North Atlantic climate far more predictable than models imply

D. M. Smith (), A. A. Scaife, R. Eade, P. Athanasiadis, A. Bellucci, I. Bethke, R. Bilbao, L. F. Borchert, L.-P. Caron, F. Counillon, G. Danabasoglu, T. Delworth, F. J. Doblas-Reyes, N. J. Dunstone, V. Estella-Perez, S. Flavoni, L. Hermanson, N. Keenlyside, V. Kharin, M. Kimoto, W. J. Merryfield, J. Mignot, T. Mochizuki, K. Modali, P.-A. Monerie, W. A. Müller, D. Nicolí, P. Ortega, K. Pankatz, H. Pohlmann, J. Robson, P. Ruggieri, R. Sospedra-Alfonso, D. Swingedouw, Y. Wang, S. Wild, S. Yeager, X. Yang and L. Zhang
Additional contact information
D. M. Smith: Met Office Hadley Centre
A. A. Scaife: Met Office Hadley Centre
R. Eade: Met Office Hadley Centre
P. Athanasiadis: Centro Euro-Mediterraneo sui Cambiamenti Climatici
A. Bellucci: Centro Euro-Mediterraneo sui Cambiamenti Climatici
I. Bethke: University of Bergen and Bjerknes Centre for Climate Research
R. Bilbao: Barcelona Supercomputing Center
L. F. Borchert: Sorbonne Universités, LOCEAN Laboratory, Institut Pierre Simon Laplace (IPSL)
L.-P. Caron: Barcelona Supercomputing Center
F. Counillon: University of Bergen and Bjerknes Centre for Climate Research
G. Danabasoglu: National Center for Atmospheric Research
T. Delworth: Princeton University
F. J. Doblas-Reyes: Barcelona Supercomputing Center
N. J. Dunstone: Met Office Hadley Centre
V. Estella-Perez: Sorbonne Universités, LOCEAN Laboratory, Institut Pierre Simon Laplace (IPSL)
S. Flavoni: Sorbonne Universités, LOCEAN Laboratory, Institut Pierre Simon Laplace (IPSL)
L. Hermanson: Met Office Hadley Centre
N. Keenlyside: University of Bergen and Bjerknes Centre for Climate Research
V. Kharin: Environment and Climate Change Canada
M. Kimoto: University of Tokyo
W. J. Merryfield: Environment and Climate Change Canada
J. Mignot: Sorbonne Universités, LOCEAN Laboratory, Institut Pierre Simon Laplace (IPSL)
T. Mochizuki: Kyushu University
K. Modali: Max-Planck-Institut für Meteorologie
P.-A. Monerie: University of Reading
W. A. Müller: Max-Planck-Institut für Meteorologie
D. Nicolí: Centro Euro-Mediterraneo sui Cambiamenti Climatici
P. Ortega: Barcelona Supercomputing Center
K. Pankatz: Deutscher Wetterdienst
H. Pohlmann: Max-Planck-Institut für Meteorologie
J. Robson: University of Reading
P. Ruggieri: Centro Euro-Mediterraneo sui Cambiamenti Climatici
R. Sospedra-Alfonso: Environment and Climate Change Canada
D. Swingedouw: Université de Bordeaux
Y. Wang: Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research
S. Wild: Barcelona Supercomputing Center
S. Yeager: National Center for Atmospheric Research
X. Yang: Princeton University
L. Zhang: Princeton University

Nature, 2020, vol. 583, issue 7818, 796-800

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41586-020-2525-0

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