Abstract
Abstract. The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1–5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6–10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.
Highlights
Interest in longer-term climate predictions in the range of about 10 years is growing
Skilful modelling and good initialisation of these components is essential (Keenlyside et al, 2008); (iii) predictability must come from the large-scale processes and interactions, such as those of the Atlantic multidecadal oscillation (AMO), El Niño–Southern Oscillation (ENSO), quasi-biennial oscillation (QBO), and North Atlantic oscillation (NAO), which must be captured by the global models; (iv) assuming the models capture the effects of external forcing, prediction means, essentially, prediction of long-term internal variability
Concerning the reliability, it can clearly be seen that the CCLM spread is much larger than the MPI-ESM-LR spread and covers the observations in most cases
Summary
Interest in longer-term climate predictions in the range of about 10 years is growing. Skilful modelling and good initialisation of these components is essential (Keenlyside et al, 2008); (iii) predictability must come from the large-scale processes and interactions, such as those of the Atlantic multidecadal oscillation (AMO), El Niño–Southern Oscillation (ENSO), QBO, and NAO, which must be captured by the global models; (iv) assuming the models capture the effects of external forcing (especially concentration changes of greenhouse gases), prediction means, essentially, prediction of long-term (decadal) internal variability. Since both deterministic and stochastic processes contribute to internal variability, ensembles of simulations are required.
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