What has I4C been reading over the past few months?
What could be better than lounging in a nice place with a good read? In this series, the I4C consortium members share their best findings with you. Discover them below and we hope you find some inspiration!
Cécile Caillaud is a researcher in the regional climate team at CNRS-Météo-France. She has recently enjoyed reading the “The Multi-Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation”.
Cécile is part of Work Package 3, and is interested in high resolution modelling and extreme precipitation. After setting up a protocol and common domains with the other members of the research team, Cécile is now in charge of performing and then analysing simulations with the CNRM-AROME high resolution climate model (2.5 km) on the ALPX-3 domain. Her recommended reading highlights a way to perform a process-based understanding of extreme precipitation with a Lagrangian approach that could be applied both to evaluate climate model simulations and to characterise and understand future climate projections.
Stephen Outten works at the Nansen Centre and Bjerknes Centre, with a background in atmospheric dynamics and expertise in studying extreme weather events. Recently he read various studies on new statistical and machine learning techniques for studying extreme precipitation events.
In the I4C project, Stephen leads Work Package 4 which focuses on Near Term Hazard Assessment. He recommends reading the work of Hu and Ayyub 2019. This paper showcases the uses of Machine Learning for temporal downscaling of short-term extreme precipitation events, and serves as an interesting twist on the more common spatial downscaling of events. Stephan also recommends the study by Jiang et al. 2020 which implements the eigendecomposition of a matrix that describes pairwise extremal dependence. Stephen underlines that studies like these are all indicative of the new avenues continuously opening up for the study of extreme events.
Pablo Ortega is based at the Barcelona Supercomputing Center, in the role of co-leader of the research group on Climate Variability and Change. For Impetus4Change, he is involved in Work Package 2 focused on the improvement of climate predictions and co-leads Work Package 5 on the forecast blending methodologies.
Pablo has recently read two papers, namely on “Reduced Southern Ocean warming enhances global skill and signal-to-noise in an eddy-resolving decadal prediction system” and “Exceptional multi-year prediction skill of the Kuroshio Extension in the CESM high-resolution decadal prediction system”. These two papers present the first results of a pioneering decadal prediction system based on the climate model CESM, the first of its kind performed at eddy-resolving scales in the ocean. This system shows critical worldwide improvements enabled by enhancements in process representation, which partly correct some lingering issues that affect the quality and usability of state-of-the-art decadal climate predictions. Pablo foresees that the inspiring results presented in both papers will have the domino effect to encourage prediction centres to redefine their mid to long-term development plans, underpinning climate prediction research worldwide.