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HORIZON 2020 was the European Commission’s Framework Programme for Research and Innovation between 2014 and 2020. With a budget of nearly €80 billion, it was the largest financial instrument for research and innovation ever in the European Union. Its predecessor, the 7th Framework Programme for Research and Technological Development (FP7), for the period 2007–2013, had a budget of just over €50 billion. Almost 35 500 projects received funding within Horizon 2020 (H2020). Of these, Swedish organisations participated in a little more than 10%. Linköping University (LiU) participated in 117 projects within H2020. Based on number of participations and funding received, Linköping University positioned itself as 9th in Sweden, re­ceiving just over €51 million in EU contribution. The purpose of this report is mainly to provide a general overview of the research projects funded by Horizon 2020 at Linköping University. The statistics have been collected from the European Commission’s reporting platform Horizon Dashboard, the European Commission’s Funding & Tenders Portal, and the LiU Grants Office Horizon 2020 database.

Cities are experiencing unprecedented climate impacts related to increasing temperatures, which vary within a city due to the heterogenous nature of urban environments. Adapting urban areas to heat requires efforts on multiple levels from urban governance, spatial planning and design to adapting everyday activities. This paper presents the prototype of a pedestrian routing tool to support citizens in navigating urban heat, and the results of tests and interviews with 24 practitioners and experts in Portugal and Sweden. The study aims to assess how and to what extent a navigation tool on urban heat could support urban climate risk management, and to evaluate the potential of the tool to support everyday adaptation and increase citizen engagement. We explore what functionality and additional information would be required to make the tool useful and relevant for different user groups. Results indicate that (i) climate services that fit in your pocket increase access to climate information and have potential to guide everyday adaptation practices; and (ii) applications need to be contextualized and tailored to match the needs and decision contexts of the user through integration of relevant information or tools.

The study set out to investigate how the experience of creating a map-based participatory system might help identify what is needed to support the production of relevant volunteered geographic information (VGI) about urban areas exposed to impacts of adverse weather events in Trondheim, Norway. This article details the systematic approach used to collect VGI, starting from the active engagement of end users during the design and development process of the CitizenSensing participatory system, through using the system in two VGI campaigns, up to the examination of the collected data. Although the VGI examination identified exposed areas in Trondheim, for instance, those that are likely to accumulate surface water from heavy rains or meltwater, the experience gained from the use of the CitizenSensing system helped to identify some critical points regarding the production of relevant VGI. Potential practical implications justify the need for VGI. For instance, in the case of Trondheim, relevant VGI may result in better planned municipal interventions regarding city infrastructure for pedestrians, cyclists and drivers, increased public awareness and access to local knowledge about areas exposed to inundation. The study also confirmed the need for adequate system components for VGI vetting and exploration in the post-collection stage to obtain a comprehensive insight into collected VGI. Funding: project Citizen Sensing-Urban Climate Resilience through Participatory Risk Management Systems, ERA4CS, an ERA-NET by JPIClimate; FCT (Portugal) [ERA4CS/0001/2016]; FORMAS (Sweden) [2017-01719]; NWO (The Netherlands) [438.17.805]; RCN (Norway) [274192, 321002]; European Union [690462] Citzensensing

AbstractWith a focus on oceans, we collaborated across ecological, social and legal disciplines to respond to the United Nations call for transformation in the ‘2030 Agenda for Sustainable Development’. We developed a set of 13 principles that strategically and critically connect transformative ocean research to transformative ocean governance (complementing the UN Decade for Ocean Science). We used a rigorous, iterative and transparent consensus-building approach to define the principles, which can interact in supporting, neutral or sometimes conflicting ways. We recommend that the principles could be applied as a comprehensive set and discuss how to learn from their interactions, particularly those that reveal hidden tensions. The principles can bring and keep together partnerships for innovative ocean action. This action must respond to the many calls to reform current ocean-use practices which are based on economic growth models that have perpetuated inequities and fuelled conflict and environmental decline.

This editorial serves as an extended introduction to the Special Issue on Technologies for Risk Mitigation and Support of Impaired Drivers. It gives the context to recent advances in assisted and automated driving and the new challenges that arise when modern technology meets human users. The Special Issue focuses on the development of robust sensors and detection algorithms for driver state monitoring of fatigue, stress, and inattention, and on the development of personalized multimodal, user-oriented, and adaptive information, warning, actuation, and handover strategies. A summary of more recent developments serves as a motivation for each article that follows.

As the sea-ice modeling community is shifting to advanced numerical frameworks, developing new sea-ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea-ice models. Initiated at the Forum for Arctic Modeling and Observational Synthesis, the Sea Ice Rheology Experiment (SIREx) aims at evaluating state-of-the-art sea-ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea-ice physics (rheology) and by model configuration. Part 1 of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea-ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the viscous-plastic (and the elastic-viscous-plastic variant), elastic-anisotropic-plastic, and Maxwell-elasto-brittle rheologies are compared in a single study. We find that both plastic and brittle sea-ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi-fractality. Model configuration (e.g., numerical convergence, atmospheric representation, spatial resolution) and physical parameterizations (e.g., ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea-ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features in a SIREx Part 2 companion paper. International audience

Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small‐scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state‐of‐the‐art sea ice models and compared to deformation features derived from the RADARSAT Geophysical Processor System. All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric boundary condition affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2 km) the concentration and thickness anomalies along LKFs are large enough to affect air‐ice‐ocean interaction processes. Plain Language Summary: Winds and ocean currents continuously move and deform the sea ice cover of the Arctic Ocean. The deformation eventually breaks an initially closed ice cover into many individual floes, piles up floes, and creates open water. The distribution of ice floes and open water between them is important for climate research, because ice reflects more light and energy back to the atmosphere than open water, so that less ice and more open water leads to warmer oceans. Current climate models cannot simulate sea ice as individual floes. Instead, a variety of methods is used to represent the movement and deformation of the sea ice cover. The Sea Ice Rheology Experiment (SIREx) compares these different methods and assesses the deformation of sea ice in 36 numerical simulations. We identify and track deformation features in the ice cover, which are distinct narrow areas where the ice is breaking or piling up. Comparing specific spatial and temporal properties of these features, for example, the different amounts of fractured ice in specific regions, or the duration of individual deformation events, to satellite observations provides information about the realism of the simulations. From this comparison, we can learn how to improve sea ice models for more realistic simulations of sea ice deformation. Key Points: All models simulate linear kinematic features (LKFs), but none accurately reproduces all LKF statistics. Resolved LKFs are affected strongest by spatial and temporal resolution of model grid and atmospheric forcing and rheology. Accurate scaling of deformation rates is a proxy only for realistic LKF numbers but not for any other LKF static. Gouvernement du Canada, Natural Sciences and Engineering Research Council of Canada (NSERC) http://dx.doi.org/10.13039/501100000038 Innovation Fund Denmark and the Horizon 2020 Framework Programme of the European Union Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659 U.S. Department of Energy Regional and Global Model Analysis program German Helmholtz Climate Initiative REKLIM (Regional Climate Change) Environment and Climate Change Canada Grants & Contributions program Office of Naval Research Arctic and Global Prediction program National Science Foundation Arctic System Science program National centre for Climate Research, SALIENSEAS, ERA4CS https://zenodo.org/communities/sirex HYCOM NOPP DOE