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.
Highlights
The multiannual forecast horizon inherent to decadal climate prediction requires that the complexity and uncertainties arising from the interaction between the climate response to external forcing and the evolution of internal modes of climate variability are accounted for[1,2]
The posterior marginal distributions, i.e., the posterior distributions of the individual systematic error components obtained by the structural decomposition, characterize three major drift phases: an initial strong warming (β(t) > 0) in the first two hindcast years, which peaks at ~4 °C; a subsequent progressive weak cooling (β(t) < 0), which extends into the 7th hindcast year; and a transition into climatological bias conditions (β(t) ≈ 0) quantified as a bias of 3.12 [3.00, 3.23] °C – estimated as median and 5th–95th percentile range of δ(t = 90 ...120)
The major novelty of our approach is that the proposed state-space model allows for an explicit statistical estimation of the temporal evolution of major systematic error components, including drift, climatological bias and seasonal biases
Summary
The multiannual forecast horizon inherent to decadal climate prediction requires that the complexity and uncertainties arising from the interaction between the climate response to external forcing and the evolution of internal modes of climate variability are accounted for[1,2]. Decadal climate forecasts based on full-field initialization unavoidably include a growing systematic error, which corresponds with the adjustment of the simulations from the assimilated state drawn from the observed climatology towards a state consistent with the biased climatology of the model[2]. This signal is commonly referred to as climate model drift[6]. As discriminant effects are non-observable, a state-space model is built in which the state vector includes all unobserved elements and the transition matrix describes their individual dynamics (see the methods section for details)
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