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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

Climate Data Challenge hackathon series During the first half of 2021 the Met Office and Met Office Academic Partnership (MOAP) universities led a series of virtual hackathon events with the aim of using a variety of skill sets and data products to tackle challenges related to climate change

Our climate change scientists

Dr Tim Andrews Tim is a climate scientist working on forcing, feedbacks and heat uptake in the climate system. Dr Alejandro Bodas-Salcedo Alejandro works on developing and assessing the Met Office Hadley Centre's climate models. Dr Rob Chadwick Rob looks at changes in the global water cycle related

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

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.

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

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

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

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