SORS: Improving decadal climate predictions by ocean ensemble dispersion filtering (EDF) and an efficient systematic evaluation framework (Freva)
Objectives
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Abstract: Decadal climate predictions have the objective to predict the development of the climate for the following years to decades. Numerical Earth system models are initialized with observational values, similar to the methodology applied in weather forecasting. Additionally, they are forced by boundary conditions, like greenhouse gas scenarios, to project the long term development. This talk investigates decadal climate predictions with Earth system models and their further improvement with new strategies in research and technologies.
A modern decadal prediction system is evaluated and investigated for sources of potential skill. Hence a systematic evaluation strategy is developed. It contains the assessment of accuracy of the ensemble mean and the ensemble spread, and compares decadal experiments with climatology, observations, and climate projections. The entire assessment is performed within a novel evaluation system called Free Evaluation System Framework (Freva). This system is designed to complement climate modeling by a systematic and efficient assessment. Freva serves as a resource-efficient process framework between the data generation and its evaluation, to detect climate research potential. Freva runs on high performance computers to handle customizable evaluation systems of research projects, institutes or universities - to connect scientists. It combines different software technologies into one common hybrid infrastructure, including all features present in the shell and web environment.
A new prediction technique called ’Ensemble Dispersion Filter’ is developed. It exploits two important climate prediction paradigms: the ocean’s heat capacity and the advantage of the ensemble mean. The Ensemble Dispersion Filter averages the ocean temperatures of the ensemble members every three months, uses this ensemble mean as a restart condition for each member, and further executes the prediction. The evaluation by the new verification framework Freva shows that the Ensemble Dispersion Filter results in a significant improvement in the predictive skill compared to the unfiltered reference system. Even in comparison with prediction systems of a larger ensemble size and higher resolution, the Ensemble Dispersion Filter system performs better. In particular, the prediction of the global average temperature of the forecast years 2 to 5 shows a significant skill improvement.
1 fona-miklip.de, 2 www-miklip.dkrz.de, 3 fona-miklip.de, 4 Kadow et al. 2017, 5 cmip-eval.dkrz.de, 6 freva.met.fu-berlin.de
Speakers
Christopher Kadow, expert in climate modeling, prediction, and verification.