Abstract
Abstract. Near-term climate predictions such as multi-year to decadal forecasts are increasingly being used to guide adaptation measures and building of resilience. To ensure the utility of multi-member probabilistic predictions, inherent systematic errors of the prediction system must be corrected or at least reduced. In this context, decadal climate predictions have further characteristic features, such as the long-term horizon, the lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typical pairs of hindcasts and observations are available to estimate calibration parameters. With DeFoReSt (Decadal Climate Forecast Recalibration Strategy), Pasternack et al. (2018) proposed a parametric post-processing approach to tackle these problems. The original approach of DeFoReSt assumes third-order polynomials in lead time to capture conditional and unconditional biases, second order for dispersion and first order for start time dependency. In this study, we propose not to restrict orders a priori but use a systematic model selection strategy to obtain model orders from the data based on non-homogeneous boosting. The introduced boosted recalibration estimates the coefficients of the statistical model, while the most relevant predictors are selected automatically by keeping the coefficients of the less important predictors to zero. Through toy model simulations with differently constructed systematic errors, we show the advantages of boosted recalibration over DeFoReSt. Finally, we apply boosted recalibration and DeFoReSt to decadal surface temperature forecasts from the German initiative Mittelfristige Klimaprognosen (MiKlip) prototype system. We show that boosted recalibration performs equally as well as DeFoReSt and yet offers a greater flexibility.
Highlights
Decadal climate predictions of initialized forecasts focus on describing the climate variability for the coming years
Despite the progress being made in decadal climate forecasting, such forecasts still suffer from considerable systematic errors like unconditional and conditional biases and ensemble over- or underdispersion
This is likely due to DeFoReSt assuming third-order polynomials in lead time to capture conditional and unconditional biases and second order for dispersion, and it does not account for systematic errors based on higher orders
Summary
Decadal climate predictions of initialized forecasts focus on describing the climate variability for the coming years. DeFoReSt with third- and/or second-order polynomials turned out in past applications to be beneficial for both full-field initialized decadal predictions (Pasternack et al, 2018) and anomaly initialized counterparts (Paxian et al, 2018), as well as decadal regional predictions (Feldmann et al, 2019), it is worth challenging the a priori assumption by using a systematic model selection strategy In this context, full-field initializations show larger drifts in comparison to anomaly initializations even though drift of the latter is not negligible, when taking initialization time dependency into account (Kruschke et al, 2015). 3 describes the decadal forecast recalibration strategy DeFoReSt and introduces boosted recalibration, an extension to higher-order polynomials, parameter estimation with non-homogeneous boosting and cross-validation for model selection.
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