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Impacts of climate variability

Description of research and applications of the impacts of climate variability on monthly to seasonal timescales.

Predictions and climate model output often refer to large-scale phenomena (e.g. ENSO, NAO) or give information on large-area averages. The variables for which predictions are made are most often meteorological (e.g. temperature, rainfall). Users' needs are typically related to their economic

UK climate extremes

the day-time maximum. In either situation this makes a comparison with the daily records invalid. We do not quote highest/lowest maximum/minimum day-time and night-time records separately. The main reason is that manual climate stations only report daily 0900-0900 UTC maximum and minimum temps

Seasonal Climate Outlooks

What is the Seasonal Climate Outlook? Following the El Nino event in 2015, the Met Office worked with the UK Government’s Foreign, Commonwealth and Development Office (FCDO) and the University of Reading to design a new service which would provide insights into the upcoming season and enable more

pioneers_scott-bae-1910_1913_2023.pdf

. 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

mo-phenology-supplement-v4.pdf

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

arrcc_carissa_ws4_observational_datasets-v2.pdf

). 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

AI4 Climate: Harnessing artificial intelligence to transform climate science

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

CSSP-food security.indd

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

rapidattributionsummary_may2024_v2.pdf

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

public-weather-service-customer-supplier-agreement-2025-30-website.pdf

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

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