FRONTIER focuses on providing a fundamental breakthrough on how climate model data is generated and analysed, so that we can provide more reliable climate information using less data and computer resources, and thereby accelerating time to science discovery by orders of magnitude.
In FRONTIER, we aim to quantitatively assess the simulation of high
impact weather events in regional climate models, reduce the number of performance metrics for more efficient analysis, and constrain the size of climate ensembles through a novel approach called Design of Experiment (DoE)-based ensemble. We believe that the next frontier of regional climate modeling is not in producing more data, but in producing more information through a targeted reduction of the data volume and by increasing its representativeness. This will make a substantial contribution towards Sustainable Development Goal (SDG) 13 "Climate action" by directly influencing the production of representative and skilful climate information. The methodological approach of FRONTIER is based on:
(a) Developing novel process-based model analysis metrics in Lagrangian space to identify optimal model resolutions to capture societally relevant processes
(b) Developing a new reduced set of performance metrics using Big Data methods to simplify and improve efficiency in data analysis
(c) Exploring a new approach to multi-model ensembles, which we call Design of Experiment (DoE)-based ensemble, and contrast it with the current 'ensemble of opportunity'.
In FRONTIER, we believe that the next frontier of regional climate modeling is not in producing more data, but in producing less (more representative) information and improving efficiency in data analysis. Hence, we propose three underpinning frontier questions:
1) can the RCM added value be better detected in a Lagrangian framework?
2) can the number of performance metrics be reduced?
3) can the ensemble of opportunity be replaced by something better?