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

Zanchettin, D., Gaetan, C., Arisido, M., Modali, K., Toniazzo, T., Keenlyside, N., et al. (2017). Structural decomposition of decadal climate prediction errors: A Bayesian approach. SCIENTIFIC REPORTS, 7(1) [10.1038/s41598-017-13144-2].

Structural decomposition of decadal climate prediction errors: A Bayesian approach

Arisido, MW;
2017

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
Articolo in rivista - Articolo scientifico
NA
English
2017
7
1
12862
none
Zanchettin, D., Gaetan, C., Arisido, M., Modali, K., Toniazzo, T., Keenlyside, N., et al. (2017). Structural decomposition of decadal climate prediction errors: A Bayesian approach. SCIENTIFIC REPORTS, 7(1) [10.1038/s41598-017-13144-2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/229500
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