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Developing models and techniques to produce regional climate information for climate change impacts and adaptation assessments.

The primary tool used in this work is the regional climate model, a higher resolution limited area version of a global atmospheric model. It simulates high-resolution climate skilfully through its improved resolution of a regional physiography and atmospheric motions. Work is undertaken to assess

Artificial intelligence (AI) and machine learning (ML) have demonstrated potential for their application in weather forecasting, the crossovers with climate science suggests that similar progress is possible in climate modelling.

Climate models are numerical representations of the Earth system (including components such as the atmosphere, ocean and land) that are used to explore long-term changes to the underlying statistical distributions that govern day-to-day weather. Developments in climate models have typically come

when: “The colour of the new green leaves is just visible between the scales of the swollen or elongated bud” (https://www. woodlandtrust.org.uk/visiting-woods/natures-calendar/). Phenological records, when combined with climate observations, provide long-term indicators of how plants and animals

. In addition to the main site, three outlying screens were erected to help record the micro-climate of the area during the Antarctic winter. Further to these base observations, still more were made on the ‘‘sledging’’ journeys away from base to either explore specific geographic areas or when in depots

AI4 Climate explores and applies cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) techniques to advance climate science and deliver improved climate information more efficiently.

AI4 Climate is funded by the UK Government’s Department for Science, Innovation and Technology (DSIT) through the International Science Partnerships Fund (ISPF) and sits within the Met Office’s National Capability AI (NCAI) Programme.  The NCAI Programme demonstrates our commitment to embedding AI

). PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies. Bulletin of the American Meteorological Society, 96(1), 69–83. https://doi.org/10.1175/BAMS-D-13-00068.1 Bai, L., Shi, C., Li, L., Yang, Y., & Wu, J. (2018). Accuracy

Office and the Met Office logo are registered trademarks. © Crown copyright 2021, Met Office 01604 FOOD SECURITY PACK – Future Climate - Northeast Farming Region Current drought risk Drought is the dominant climate risk in the NFR. Climate models show that the observational record (blue line

Verification (capabilities and outputs) Dynamics research 42 Post processing (Gridded, Site specific, climatological record) Impact modelling Observation based research Observations systems research Weather Science IT Informatics Atmospheric dispersion Science partnerships Ocean forecasting Climate

attribution study using © Crown copyright 2024, Met Office Page 5 of 34 HadGEM3-A (Ciavarella et al., 2018) to assess the changing chance of observing the record high UK May and Spring (March-April-May) temperatures recorded in 2024. To facilitate a rapid study, the attribution study uses a single climate