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Structural decomposition of decadal climate prediction errors: A Bayesian approach. | LitMetric

AI Article Synopsis

  • Decadal climate predictions often struggle with model drift, which leads to biased results due to systematic errors in the climate models.
  • This study proposes a new statistical Bayesian framework that treats climate model drift as a dynamic process, using a state-space model to break down prediction errors into distinct components.
  • The approach enhances understanding of relationships between model drift, biases, and other climate factors, allowing for more objective and precise error estimation in climate predictions.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5634475PMC
http://dx.doi.org/10.1038/s41598-017-13144-2DOI Listing

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