The increasing volume of climate model data makes the use of traditional analysis tool impractical. This hinders the discovery of new crucial knowledge for society. COLUMBIA initiates a multidisciplinary research activity that will result in a cutting-edge customizable methodology that aims to facilitate rigorous evaluation of large ensemble of Earth system models. The innovative assessment tool will be based on combining Machine Learning with robust statistics and multiple process-oriented assessments, and aims to filter 'good' models from the rest and therefore increasing our confidence when synthesizing future climate change projections. The proposed work is well timed as the process for upcoming IPCC-AR6 has just began and the first batch of new Earth system model simulations will become available in early 2018. The proposed integrated evaluation method is at the forefront of international efforts in understanding sources of model uncertainty and producing optimized future climate projections. The project outcome will therefore have a broad international relevance for both climate modeling and observational community. Customizing existing Machine Learning techniques for climate science will also brings new opportunities and values to the computer science community. New scientific knowledge gained from the project will provide critical support for the climate science community as well important inputs for the IPCC-AR6. A unique close collaboration between climate modellers and computational scientists exists within Uni Research and the Bjerknes Centre, ensuring seamless collaboration in developing the new method. Ultimately, method developed in COLUMBIA will produce a new generation of optimized climate change projections that will give the best advice to policy makers and help society to find the best adaption strategy for climate change.
Project leader: Jerry Tjiputra
Institution: NORCE Miljø/Klima VESTLAND