Structural decomposition of decadal climate prediction errors: A Bayesian approach
Zanchettin, D.; Gaetan, C.; Arisido, M.W.; Modali, K.; Toniazzo, T.; Keenlyside, N.; Rubino, A. (2017). Structural decomposition of decadal climate prediction errors: A Bayesian approach. NPG Scientific Reports 7(1): 11 pp. https://dx.doi.org/10.1038/s41598-017-13144-2
In: Scientific Reports (Nature Publishing Group). Nature Publishing Group: London. ISSN 2045-2322; e-ISSN 2045-2322, meer
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| Auteurs | | Top |
- Zanchettin, D.
- Gaetan, C.
- Arisido, M.W.
- Modali, K.
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- Toniazzo, T.
- Keenlyside, N.
- Rubino, A.
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| 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. |
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