Global warming continues at an alarming rate, presenting unprecedented challenges to society that require urgent, science-led mitigation and adaptation. Earth system models (ESMs) are essential tools for projecting climate change, providing important information to decision makers. However, confidence in predicted climate change is undermined by a number of uncertainties; (i) ESMs disagree on how much the Earth will warm for a given increase in atmospheric carbon dioxide (CO2) (Earth’s equilibrium climate sensitivity); (ii) how much emitted CO2 will stay in the atmosphere to warm the planet (half the CO2 emitted by humans has been absorbed by the land and ocean) and (iii) how much excess heat in the Earth system will enter the ocean interior, delaying surface warming (~90 % of the heat in the Earth system goes into the ocean). Central to these uncertainties are poorly understood, and poorly modelled, Earth system feedbacks, in particular cloud feedbacks, carbon cycle feedbacks and ocean heat uptake. Poor representation of these phenomena degrades the accuracy of ESM projections, with implicationsfor anticipating future climate extremes and societal impacts. We aim to improve the representation of these feedbacks in ESMs, reducing uncertainty in global warming projections. We propose a multidisciplinary approach, focused on “learning” how to accurately describe processes underpinning these feedbacks, through a fusion of observations with advanced machine learning (ML) and artificial intelligence (AI). Such data and approaches, constrained by the laws of physics, will deliver a step change in the accuracy of Earth system models. AI4PEX will place Europe at the forefront of a revolution in Earth system modelling, leading to increased accuracy of climate change projections and superior support for implementation of the Paris Climate Agreement and the European Green Deal.

TipESM brings together scientists from a range of disciplines to deliver a step change in our understanding of climate tipping points in the Earth system, including their impact on ecosystems and society, combined with a set of early warning indicators and safe future emission pathways that minimise the risk of exceeding such tipping points. TipESM assembles the latest Earth System Models (ESMs), including recent improvements to key processes: ice sheets, vegetation and land use, permafrost, marine and terrestrial biogeochemistry. In cooperation with the WCRP/Future-Earth project TIPMIP, TipESM will organise an international collaboration to design and realise a common ESM experiment protocol that will facilitate analysis of the likelihood of occurrence, and potential reversibility, of tipping elements at different levels and duration of global warming. These experiments, will be combined with more project-specific ESM experiments, designed to investigate interactions and feedbacks across the Earth system. Based on the TipESM experiments, existing simulations and observations, we will investigate tipping points, their driving processes, potential early warning signals and cascading effects across the climate, ecosystems and society. Including the most important components of the Earth system in our ESMs will also allow TipESM to identify potentially unknown tipping elements, their precursors and impacts. TipESM brings together expertise from climate science and climate impactsto investigate both the role of gradual climate change for tipping in individual ecosystems and society, and the impact of crossing specific climate tipping points for society, ecosystems, and biodiversity. Project findings will be synthesised into a tipping points risk register. New knowledge and data from TipESM will be regularly communicated to a broad range of researchcommunities, policymakers and the public, contributing to a prepared and resilient society.

ACACIA is an ambitious interdisciplinary alliance to enhance the resilience of at-risk communities in Sub-Saharan Africa to climate impacts. Focusing on floods in the Greater Horn of Africa and floods and tropical cyclones in Madagascar, we seek to improve the ways climate services are produced, disseminated and used for making short-term and long-term decisions to diminish climate risk. Our strategy is to mitigate five obstacles to climate adaptation: 1) Temporal and spatial mismatches and lack of relevance of climate services; 2) Capacity to implement coping strategies and access to climate services; 3) Governance barriers, including fragmentation of responsibility; 4) Climate change, which can make existing coping strategies obsolete; 5) Lack of evidence on the socioeconomic impacts of climate services. Our consortium is highly multidisciplinary. Social science is strongly represented in our cross-cutting co-production activities, which involve working with peer communities consisting of actors from multiple sectors including policymakers, and community work with 100 vulnerable villages in Madagascar. Our rigorous assessment of the added value of climate services-based interventions in these villages will also be led by social scientists. Climate scientists will steer the co-production of national and regional operational early warning systems and work to enhance the skill of a subseasonal forecast model system. All the partners will be involved in an extensive training and capacity-building programme, which targets vulnerable communities, consortium members including national and regional meteorological services. Our consortium has 14 partners, with strong representation from Africa. It involves actors from all parts of the climate services ecosystem. The reason for mobilising such a broad alliance is to ensure that services and protocols are developed locally, which enables our outcomes to be sustained beyond the lifetime of ACACIA.

Climate change increases the frequency and severity of extreme weather events, such as storms, heatwaves and droughts. Such events can have devastating societal impacts, and it is becoming increasingly clear that the most impactful disasters are often the result of a complex interplay of multiple physical and societal drivers. Climate attribution, which examines the causal links between extreme events, natural variability, and anthropogenic climate change, can help to unravel this complexity and thereby promote societal preparedness and awareness for climate change impacts. The COMPASS project aims to develop a harmonised, yet flexible, methodological framework for climate and impact attribution of various hazard types. COMPASS will go beyond the current frameworks by bridging the gap from the attribution of single-driver extremes to the attribution of more complex extremes (that is compound, sequences and cascading hazard events) and enabling a shift from a hazard-centred analysis to an impact-centred perspective. Main novelties include event-based hazard and impact modelling using a multi-scale approach, the use of weather type analysis for better understanding the physical drivers that give rise compound extremes, and the use of contextualized storylines to communicate attribution results. The framework will be validated and applied to a set of use cases that cover historical extremes for various hazard types and impact context as well as extreme events happening during the project. COMPASS will lay the scientific foundation for the operational deployment as part of the Copernicus Climate Change Services. The project will create a modular and scalable framework for on-the fly analysis, and thus transferable to other extremes and regions. To promote uptake of the project’s results, data, methods and tools will be made openly available, a web-based demonstrator will showcase the results of the use cases, and clear guideline for attribution will be developed.

The climate system is changing rapidly and some regions have seen increases in extremes beyond what is expected from climate model simulations. To support targeted climate adaptation strategies, EXPECT will enable trustworthy assessments and predictions of regional climate change including extremes by developing a prototype operational capability for integrated attribution and prediction of climate. This ambitious goal is closely aligned with the WCRP Lighthouse Activity on Explaining and Predicting Earth System Change. EXPECT will identify and quantify the mechanisms by which physical processes govern regional climatic changes, including extremes, on inter annual to multi-decadal time scales. It will do so by exploiting newly available climate simulations and Earth Observations (EOs), and by combining machine learning (ML) with physical methods. The research will target fundamental knowledge gaps related to atmospheric circulation and land-atmosphere interactions, which represent major limitations in current climate predictions and projections, and in particular in understanding changes in European summer extremes. To underpin the research, and benefitting the wider research community, EXPECT will develop tools to efficiently analyse a variety of large data sets in combination that are hosted in different repositories across institutions. This will facilitate the exploitation of recent investments into high-resolution climate models and EO data. EXPECT will further build data science capacity for the scientifically robust, efficient and reproducible analysis of the massive data assets, including novel ML approaches, and provide training for the climate science community and the next generation of researchers in particular. EXPECT will thus deliver significant scientific and technological advances for society and the climate science community that will last well beyond the project, in support of WCRP’s strategic objectives.