A systematic analysis of the performance of the IPSL-CM5A-LR model for decadal temperature predictions over Europe

Reliable climate predictions over a time-horizon of 1-10 year are crucial for stakeholders and policymakers, as it is the time span for relevant decisions of public and private for infrastructures and other business planning. This promoted, about a decade ago, the development of a new family of climate model: the Decadal Climate Predictions (DCP). Similarly to climate projections, the DCP consists in forced simulations of climate, but initialised from a specific observed climatic state, which potentially represents an added value. Being a relatively new branch of climate modelling the effective application of DCP to impact analysis supporting operational adaptation measures is still conditional on their evaluation.

Here we contribute to this evaluation by exploring the performance of the IPSL-CM5A-LR DCP system in predicting the air temperature over Europe.  Our assessment of the potentiality of the DCP system follows two main steps: (1) the comparison between the simulated large-scale air temperature from hindcasts and the observations from mid-1900 to present day, i.e. NOAA-20CR dataset, which defines a prediction skill, calculated through both the Anomaly Correlation Coefficient (ACC) and the Root Mean Square Error (RMSE); (2) the detection of the “windows of opportunity”, i.e. specific conditions under which the DCP performs better. The exploration of the windows of opportunity stems from a systematic detection that evaluates the DCP skills for each combination of periods, lead times and seasons. Our analysis involves both raw simulations and de-biased simulations, i.e. outputs data that have been adjusted through the quantile-quantile method.

Our results evidence a significant added value over most of Europe with respect to non-initialised historical simulations.  Significant skill scores have been generally found over the Mediterranean sector of Europe and UK, while the performance over the rest of Europe results rather conditional on the season and on the period considered. The best predicted months appear to be those between spring and autumn, while low skills have been found for winter months. Also, the predictions appear to be more performant after the ’80, when a rapid warming signal characterised the temperature over Europe: this shift is well reproduced in the initialised simulations. Finally, skill anomalies between raw and debiased outputs are generally minimal. Nevertheless, debiased data show an overall higher RMSE skill, while ACC skill appears to be slightly higher in winter and slightly lower in summer. These findings may be useful for the exploitation of the IPSL DCP for near-term timescale impact analysis over Europe. Also, our systematic approach for the exploration of the windows of opportunity may be at the base of similar investigations applied to other DCP systems.