Significant reductions in anthropogenic emissions and increases in CO2 sinks are needed to meet the 1.5 centigrades threshold for global warming set out in the Paris Agreement and reach the climate-neutrality goal of the European Green Deal by 2050. The CO2 sink provided by forests, including old-growth forests, partially offsets the rise in anthropogenic CO2 emissions, providing a large-scale buffer to climate change. Depending on their characteristics and local circumstances, such as management practices or ecosystem services, forests may range from net CO2 sinks to sources. The project 'OPTimising FORest management decisions for a low-carbon, climate resilient future in Europe (OptFor-EU)' will build a Decision Support System (DSS) to provide forest managers and other relevant stakeholders with tailored options for optimising decarbonisation and other Forest Ecosystem Services (FES) across Europe. Based on exploitation of existing data sources, use of novel Essential Forest Mitigation Indicators and relationships between climate drivers, forest responses and ecosystem services, OptFor-EU has five specific objectives: (1) Provide an improved characterisation of the Forest-Climate Nexus and FES; (2) Utilise end-user focused process modelling; (3) Empower forest end-users to make informed decisions to enhance forest resilience and decarbonisation; (4) Provide a novel DSS service; and (5) Bridging different EU strategic priorities, robust science, and stakeholders in the forest and forest-based sectors. Based on a supply-demand approach, the methodology combines an iterative process of data consolidation, modelling, and co-development of solutions alongside forest managers and other practice stakeholders in all European Forest Types. The DSS will be designed and tested at 8 case study areas, to provide a ready-to-use service, near to operational (TRL7) at European level, while a user adoption and up-take plan will maximise the societal and business impact.

Plant pests and pathogens damage agricultural production and endanger food security. Their control relies heavily on the use of synthetic insecticides, leading to a negative environmental impact. Developing new methods for pest and pathogen control is therefore essential to safeguard human health and meet the challenge of increasing crop yields, while reducing the use of chemical pesticides. The overarching objective of the NextGenBioPest project is to meet this need by delivering novel and improved products, methods, and practices for the rational control of the most difficult-to-manage arthropod pests and pathogens, with substantially reduced pesticide use. The project will provide a new toolkit for plant protection in key vegetable and fruit crops including diagnostics for pest and pathogen identification and incrimination, novel Biological Control Agents and methods to augment their performance in the field, RNA-based pesticides, Low Risk/Green chemicals, plant resistance inducers and innovative agronomic and ecological practices. These innovations will be integrated with existing approaches, to achieve effective, environment friendly and sustainable crop protection. They will be validated in large field studies, with both their efficiency and socioeconomic impact assessed. Demonstration fields, extensive training and modern targeted communication channels, will enable the appropriate dissemination and uptake of the outcomes to the stakeholders and end users. Data protection and commercialization strategies will ensure their exploitation. These goals will be achieved by integrating leading institutional and industrial partners with drivers of pest control programs. The multidisciplinary and multi-actor team will exploit their diverse expertise, access to extensive preliminary data and resources, and strong networks, to meet the project objectives and ensure the knowledge and tools generated deliver economic, ecological and societal impact.

OptimESM will develop a novel generation of Earth system models (ESMs), combining high-resolution with an unprecedented representation of key physical and biogeochemical processes. These models will be used to deliver cutting-edge and policy-relevant knowledge around the consequences of reaching or exceeding different levels of global warming, including the risk of rapid change in key Earth system phenomena and the regional impacts arising both from the level of global warming and the occurrence of abrupt changes. OptimESM will realise these goals by bringing together four ESM groups with Integrated Assessment Modelling teams, as well as experts in model evaluation, Earth system processes, machine learning, climate impacts and science communication. OptimESM will further develop new policy-relevant emission and land use scenarios, including ones that realise the Paris Agreement, and others that temporarily or permanently overshoot the Paris Agreement targets. Using these scenarios, OptimESM will deliver long-term projections that will increase our understanding of the risk for triggering potential tipping points in phenomena such as, ice sheets, sea ice, ocean circulation, marine ecosystems, permafrost, and terrestrial ecosystems. OptimESM will further our understanding of the processes controlling such tipping points, attribute the risk of exceeding various tipping points to the level of global warming, and develop a range of techniques to forewarn the occurrence of tipping points in the real world. Artificial Intelligence (AI-) methods for statistical downscaling will be developed and applied to improve our understanding of the effect of long-term global change and tipping points on regional climate, particularly extreme events. New knowledge and data from OptimESM will be actively communicated to other disciplines, such as the impacts and policy research communities, as well as the general public. This knowledge will provide a solid foundation for actionable science-based policies.

To develop and embed new capabilities in machine algorithms, and remote sensing analysis, which will enable an integrated, automated insurance rebuild cost estimate for residential and commercial properties.

Aerosol-cloud interactions (ACI) remain the largest source of uncertainty in past, present, and future radiative forcing, impeding credible climate projections. ACI effects are expected to change dramatically as we enter a post-fossil world, characterized by strong reductions in anthropogenic aerosol emissions but with increasingly larger impacts from natural aerosols. Although we expect cleaner clouds compared to today, ACI in this post-fossil state may considerably differ from preindustrial conditions, owing to shifts in climate and changes in sources region characteristics. CleanCloud will address the major gaps impeding robust ACI assessments, improve their representation in current and next generation kilometer-scale climate models, quantify and understand their regional and temporal effects, and how they will evolve in the transition to the post-fossil regime. To accomplish this, CleanCloud will 1) carry out targeted field experiments in European climate hotspots; 2) develop state-of-the-art algorithms and analysis tools to obtain new proxies and diagnostics for key ACI-related processes; 3) contribute to the calibration and validation of upcoming satellite missions in coordination with the satellite community; 4) improve and better constrain kilometer- and large-scale climate models using advanced machine learning, data assimilation and model calibration, confronting perturbed physics ensembles with existing and new satellite and in-situ data; and 5) assess the role of aerosols in the life cycle of convective systems, focusing on precipitation formation and the impacts on the hydrological cycle, and 6) enhance the exploitation of data centres, measurement programs, international campaigns, laboratory studies, and models. With these, CleanCloud will profoundly strengthen European Research on climate change, significantly contribute to upcoming climate assessments, and benefit society through models that enable improved weather and seasonal predictions