Contact persons: Trude Storelvmo, Kirstin Krüger
Keywords: Weather, marine, and climate extremes
Research group: Meteorology and Oceanography (MetOs)
Department of Geosciences
The occurrence of climate extremes has increased since the 1950s with dramatic consequences for society, infrastructure and industry. Our understanding and prediction of extreme events is often limited by short sample sizes, the non-stationarity of the climate system, and the complex interactions within the Earth System. Novel approaches are needed to combine large amounts of observational and model data in order to better understand causal relationships within the Earth System for advanced extreme predictions. Extreme climate events (such as, heat waves, droughts, extreme precipitation, severe storms, polar lows, tropical cyclones, volcanic eruptions impact) in the earth system of the past, present, and future climate can be studied. The project can focus on atmospheric or oceanic compound events (i.e. coincident extremes in more than one climate variable) with “low likelihood, high impact”.
The following approaches can be included (but are not limited to):
- Utilising generative machine learning approaches to increase sample size, harvest large model ensemble data sets, and utilize unsupervised machine learning methods to find causal relationships.
- Utilizing observational-based and model data sets to train deep neural networks for advanced extreme predictions.
- Emulation and downscaling of NorESM Earth system model results using machine learning, high resolution observations and other modelling data.
Projects will be defined in consultation with the successful candidates, but the focus on extreme events using computational approaches will be overarching. The use of NorESM or MET Norway’s weather/ocean prediction model combined with observational data will be seen as an advantage.
External partner:
- Norwegian Meteorological Institute (met.no)