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AI in climate science

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

metoffice_climatechange_deeperdiscovery_interpreting-climate-models.pdf

Deeper discovery Interpreting climate models 1 2 3 If your group is new to climate change, introduce the concept. Explain that climate change is the long-term shift in average weather patterns across the world. Since the mid-1800s, humans have contributed to the release of carbon dioxide and other

Climate Risk Reports

Climate Risk Reports

Human activities have unequivocally caused global warming, and this is affecting weather and climate extremes in every region of the world, disproportionately affecting the most vulnerable (IPCC AR6 SYR A1, A2). Action to adapt and prevent climate change cannot wait. Improved understanding

Climate, cryosphere and oceans

Improving our understanding of the role of the oceans and the cryosphere (ice) in the climate system.

Key aims Improving ocean and ice modelling capability. Providing advice to government regarding climate mitigation. Understanding how the oceans, sea-ice and land-ice could be affected by climate change and how these changes could feed back onto the climate system.  

upscaling-toolkit-introduction_and_stage1.pdf

• Institutionalisation address them • The innovation is tested and improved in collaboration with stakeholder groups requested climate service of the new normal is lobbied for, making it part of legal or climate services frameworks, for example Record any notes related to these additional

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

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

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

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

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

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