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Quantifying and reducing uncertainty in climate change, through understanding and improving the representation of key processes.

This area of research involves the design, building, evaluation and improvement of climate models informed by knowledge of the mechanisms of past, present and future climates. The scientific focus is on gaining understanding and improving the representation of key processes that are critical for climate variability and change on global and regional scales. Related pages Climate change scientists  

Our research into climate variability and predictability is aimed at improving the skill of the Met Office monthly to decadal forecasts.

Some physical processes that affect the climate system, such as El Niño and the global oceanic circulation, provide potential sources of climate predictability from a month to decades ahead. Our work involves the study of these processes in order to improve their representation in our prediction

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

). 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

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

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

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