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Journal Article

Structural decomposition of decadal climate prediction errors: A Bayesian approach

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Modali,  Kameswarrao
Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Fulltext (public)

s41598-017-13144-2.pdf
(Publisher version), 4MB

Supplementary Material (public)

41598_2017_13144_MOESM2_ESM.xls
(Supplementary material), 116KB

41598_2017_13144_MOESM3_ESM.avi
(Supplementary material), 29MB

Citation

Zanchettin, D., Gaetan, C., Arisido, M. W., Modali, K., Toniazzo, T., Keenlyside, N., et al. (2017). Structural decomposition of decadal climate prediction errors: A Bayesian approach. Scientific Reports, 7: 12862. doi:10.1038/s41598-017-13144-2.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002E-0E62-8
Abstract
Decadal climate predictions use initialized coupled model simulations that are typically affected by a drift toward a biased climatology determined by systematic model errors. Model drifts thus reflect a fundamental source of uncertainty in decadal climate predictions. However, their analysis has so far relied on ad-hoc assessments of empirical and subjective character. Here, we define the climate model drift as a dynamical process rather than a descriptive diagnostic. A unified statistical Bayesian framework is proposed where a state-space model is used to decompose systematic decadal climate prediction errors into an initial drift, seasonally varying climatological biases and additional effects of co-varying climate processes. An application to tropical and south Atlantic sea-surface temperatures illustrates how the method allows to evaluate and elucidate dynamic interdependencies between drift, biases, hindcast residuals and background climate. Our approach thus offers a methodology for objective, quantitative and explanatory error estimation in climate predictions.