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Understanding climate change
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
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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
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Upscaling Climate Services
As part of the UK Climate Resilience (UKCR) programme, the Met Office has compiled an approach for upscaling climate services. This page introduces climate services providers to this resource.
this means for climate services. We have reviewed existing upscaling literature and resources, and adapted themes and concepts from them to produce an upscaling approach for climate services. We have tested this in three case studies with different services and service providers. A toolkit, to aid
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Climate monitoring and attribution
Developing observational data; monitoring and interpreting climate variations and change.
Climate information and statistics, based on many types of surface, atmospheric and marine measurements, are produced on national to global scales. Climate models are used to attribute causes of past climate change that are seen within the observations. The datasets produced by our scientists are also used by other science areas. Scientific users throughout the world access the data and statistics via the HadObs website.
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The Climate Security team
Providing advice on the impact of climate variability and change for security.
Climate science has made huge progress in understanding the dynamics of climate variability and change over the last few decades, with climate models being a valuable tool for understanding the future climate. However, there remains a gap between the type of information climate projections provide
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Climate change and health
Skip to main content Weather & climate Research programmes Services About us Careers Menu Search site Search Back Weather & climate Everything you need to know about the forecast, and making the most of the weather. Find a forecast Warnings & advice Warnings & advice UK weather warnings UK Storm
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State of the UK Climate
Annual publication which provides an up-to-date assessment of the UK climate.
The 2025 report is published as a special issue of the International Journal of Climatology, which is the Royal Meteorological Society journal of climate science and provides a review of the UK’s climate and significant meteorological events of the year. Latest report State of the UK Climate 2025
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Seasonal and climate models
Configurations of the Unified Model for seasonal, decadal and centennial climate predictions run at the Met Office.
These are usually lower resolution than the models used for day to day weather forecasting, and include ocean and sea-ice components coupled to the atmosphere model in order to represent the full coupled climate system. Additional processes associated with atmospheric chemistry and the ecosystem
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The future of climate modelling
Climate modelling at the Met Office
As faster supercomputers with more processing power are developed, harnessing this power and speed for the benefit of improving climate projections is the dream of climate scientists. The reality is there will never be enough speed or capability to infinitely improve climate models in all aspects
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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