2026
April 2026: Thomas attended EGU, presenting a poster entitled "Learning Compound Climate Extremes: Generative AI for Hot–Dry Event Risk".
March 2026: Ioana co-authored a paper in Climate Dynamics: "Role of weather noise in El Niño-southern oscillation variability and prediction"
February 2026: Ioana, Chen and Matt taught on the NCAS Introduction to Atmospheric Science course in Leeds for graduate students. They covered topics including machine learning and climate, climate variability and radiative forcing.
2025
December 2025: The group attended the inaugral Scotland Climate Science meeting at the School of Earth and Environmental Sciences.
November 2025: The group hosted a delegation of senior staff from the Chinese Meteorological Administration
November 2025: Chen Lu joined NCAS St Andrews as a research fellow. Welcome Chen!
October 2025: Thomas and Max Joined the group as PhD students. Welcome Thomas and Max!
September 2025: Ioana and Matt attended the CANARI annual meeting at the British Antarctic Survey in Cambridge and both gave talks.
May 2025: Matt wrote a popular science article with Dr Simon Lee on the recent dry spring and how this may or may not have been linked to climate change. Read it here. He also gave an interview for ITV news about the weather pattern causing the hot and dry spring this year. You can watch the interview here.
April 2025: Ioana attended the workshop "The Way Forward: Future Directions for AI/ML in Earth System Science", co-organized by the National Centre for Atmospheric Research (NCAR) and the Mediterranean Center for Climate Change (CMCC). She also presented her work at EGU.
April 2025: Ioana (in-person) and Matt (remotely) attended a summer school on model hierarchies (from dynamical to AI models) hosted by the Centre for Theoretical Physics (ICTP) in Trieste, Italy and Ioana gave a talk.
2024
July 2024: Ioana published a paper in Geophysical Research Letters: "A machine learning-based approach to quantify ENSO sources of predictability." The study uses a deep learning approach to show that near-surface wind is a key contributor to the predictability of the El-Nino Southern Oscillation. This finding could help predict ENSO, which plays a significant role in extreme weather, far in advance of its impacts.