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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. Vision and purpose AI4 Climate aims to integrate AI/ML methods

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

Office 2014 26 Historical Weather Data Weather Observations The Met Office holds an extensive archive of weather observations from thousands of different locations around the UK and globally. We hold original manuscripts dating back to 1860 and have digitised climate records from around 1960

of different locations around the UK and globally. We hold original manuscripts dating back to 1860 and have digitised climate records from around 1960 for a wide variety of weather variables to meet your individual business needs. These include the following: • Precipitation • Air temperature

that provides weather and climate information to enable the general public and specialist users (i.e. academia) to research and understand the science and history of meteorology and ensures compliance with the Public Records Act 1958. The Met Office should ensure that the weather story is the same

in developed countries than for countries in the global south. Confidence in climate attribution analysis relies on high quality observational records, climate models’ abilities to simulate a particular type of event, and scientific understanding of how natural variability and climate change may influence

with the previous generation, although they simulate global patterns of climate and climate change with greater fidelity. Despite the recent pause in the global mean surface temperature rise, the upper ranges of TCR and ECS derived from extended observational records, and specifically including

Vol. 69: 129–141, 2016 doi: 10.3354/cr01395 CLIMATE RESEARCH Clim Res Published online June 27 Effects of climate change on the yield of winter wheat in the eastern Mediterranean and Middle East K. Constantinidou 1, *, P. Hadjinicolaou 1 , G. Zittis 1 , J. Lelieveld 1,2 1 Energy Environment

Our climate monitoring and attribution scientists

Nick Rayner Nick leads the Climate Monitoring and Attribution team. She is an expert in the development of observed climate data sets. Dr Peter Stott Peter is a Science Fellow in the attribution of climate change to anthropogenic and natural causes. Dr Chris Atkinson Chris works on the development