The problem: Building climate change resilience necessarily means building urban resilience. Africa's future is dominated by a rapidly increasing urban population with complicated demographic, economic, political, spatial and infrastructural transitions. This creates complex climate vulnerabilities of critical consequence in the co-dependent city-regions. Climate change substantially complicates the trajectories of African development, exacerbated by climate information that is poorly attuned to the needs of African decision makers. Critical gaps are how climate processes interact at the temporal and spatial scales that matter for decision making, limited institutional capacity to develop and then act on climate information, and inadequate means, methods, and structures to bridge the divides. Current modalities in climate services are largely supply driven and rarely begin with the multiplicity of climate sensitive development challenges. There is a dominant need to address this disconnect at the urban scale, yet climate research in Africa is poorly configured to respond, and the spatial scale and thematic foci are not well attuned to urban problems. Most climate-related policies and development strategies focus at the national scale and are sectorally based, resulting in a poor fit to the vital urban environments with their tightly interlocking place-based systems. Response: FRACTAL's aim is to advance scientific knowledge about regional climate responses to anthropogenic forcings, enhance the integration of this knowledge into decision making at the co-dependent city-region scale, and thus enable responsible development pathways. We focus on city-region scales of climate information and decision making. Informed by the literature, guided by co-exploration with decision makers, we concentrate on two key cross-cutting issues: Water and Energy, and secondarily their influence on food security. We work within and across disciplinary boundaries (transdisciplinarity) and develop all aspects of the research process in collaboration with user groups (co-exploration).The project functions through three interconnected work packages focused on three Tier 1 cities (Windhoek, Maputo and Lusaka), a secondary focus on three Tier 2 cities (Blantyre, Gaborone and Harare), and two self-funded partner cities (Cape Town and eThekwini). Work Package 1 (WP1) is an ongoing and sustained activity operating as a learning laboratory for pilot studies to link research from WP2 and 3 to a real world iterative dialogue and decision process. WP1 frames, informs, and steers the research questions of WP2 and 3, and so centres all research on needs for responsible development pathways of city-region systems. WP2 addresses the decision making space in cities; the political, economic, technical and social determinants of decision making, and seeks to understand the opportunities for better incorporation of climate information into local decision making contexts. WP3, the majority effort, focuses on advancing understanding of the physical climate processes that govern the regional system, both as observed and simulated. This knowledge grounds the development of robust and scale relevant climate information, and the related analysis and communication. This is steered explicitly by WP1's perspective of urban climate change risk, resilience, impacts, and decisions for adaptation and development. The project will frame a new paradigm for user-informed, knowledge-based decisions to develop pathways to resilience for the majority population. It will provide a step change in understanding the cross-scale climate processes that drive change and so enable enhanced uptake of climate information in near to medium-term decision making. The project legacy will include improved scientific capacity and collaboration, provide transferable knowledge to enhance decision making on the African continent, and in this make significant contribution to academic disciplines.

Mesoscale Convective Systems (MCSs) are among the most powerful storms in the world, and in many places are the dominant cause of hazards such as high winds, lightning, flash flooding and tornadoes. Across the tropics, MCSs account for 80% of extreme rainfall days. They result from thunderstorms that organize into a single large complex hundreds of km wide, which travel across the land for hours, in some cases days, causing extraordinary downpours along their path. They are particularly prevalent in certain "hotspot" regions including Northern Argentina and India, West and Central Africa, and the US Great Plains, where a combination of warm, moist air and favourable winds support their development. In these hotspot regions, an understanding of how MCSs will change as the world warms is urgently needed in order to build climate-resilient homes, roads, bridges and dams. Conventional climate models lack sufficient spatial resolution to realistically simulate these storms. There has however been a revolution in high-resolution climate models over the past 5 years, enabled by increasing computer power. New "Convection Permitting Models" (CPMs) can represent MCSs and are starting to deliver improved predictions and better understanding of how MCSs respond to their environment. We know that spatial patterns in vegetation and soil humidity affect air temperature, moisture and wind flows, and that these changes can affect where (or indeed whether) a powerful MCS develops. For example, contrasts between tropical forests and deserts control surface temperature differences across the continents, creating MCS hotspot regions through favourable wind conditions. Those surface temperature differences are already increasing due to global warming, and have been implicated in a tripling of the most intense West African MCSs over just 35 years, contributing to a dramatic rise in flash flooding there. We also know that the land surface affects individual MCS tracks. Evidence, again from West Africa, shows that MCSs are steered away from the saturated soils created by previous storms. This feedback makes predicting the track of a hazardous storm easier, though we do not know how strong the effect is in other regions of the world. This project will focus on how MCSs are affected by patterns of soil moisture and vegetation, through analysis of both satellite observations and CPMs. The work will discover how strong land effects are across the different hotspots of the world, and what processes are key to determining that strength. Experiments with a CPM will identify the surface patch sizes, ranging from 10s to many 100s km, which have the biggest impact on MCSs. Satellite data will be analysed to detect how MCS intensity and lifetime have been affected in regions with recent land use change (e.g. irrigation, deforestation, urbanisation). The work will explore how, as the world warms, and contrasts between wet and dry areas get stronger, the feedback between soil moisture patchiness and MCSs is changing. This matters because the feedback may amplify trends towards more extreme rain and longer gaps between storms. Identified observation-based relationships between the land and MCSs will also be used to scrutinise theoretical understanding and to evaluate the fast-emerging next generation of CPMs. This will include analysis of the world's first year-long global simulation from a land-atmosphere-ocean CPM able to capture the kilometre-scale motions within MCSs. Overall, the project will make substantial advances in understanding of how the land affects this powerful type of storm, with observations and model studies from across the world. The results will provide underpinning knowledge to improve prediction of storm hazards, informing decision making across weather to climate change time-scales.

The goal of ACRoBEAR is to predict and understand health risks from wildfire air pollution and natural-focal disease at high latitudes, under rapid Arctic climate change, and resilience and adaptability of communities across the region to these risks. This will be achieved through integrating satellite and in-situ observations, modelling, health data and knowledge, and community knowledge and stakeholder dialogue.The Arctic has warmed rapidly over recent decades, at around twice the rate of global mean temperature increases, resulting in rapid changes to the high latitude Earth system. Changes in the high latitude terrestrial environment include observed increases in temperature extremes and precipitation patterns, which are leading to increasing trends in boreal wildfire and changes in the distribution of disease-carrying vectors, with evidence for emerging interactions between these changing risks. Recent years (including 2019) have seen unprecedented fire activity at Arctic latitudes, leading to unhealthy air quality in high latitude towns and cities. Vector-borne disease occurrence in these regions is also changing in response to rapid changes in temperature and moisture. Moreover, fire activity is intrinsically linked to changes in vector-borne disease risk through changing the habitat conditions for vectors and their hosts. Environmental, social, and governance factors specific to high latitudes hamper our current ability to understand community resilience and response to these changing risks. ACRoBEAR will tackle these urgent issues in the most rapidly warming region of the planet. To address these research challenges, ACRoBEAR brings together a diverse, international, interdisciplinary team of world-leading research groups and collaborators. The project will benefit from two-way dialogue with community groups and stakeholders throughout, across three key regions (Alaska, Eastern Siberia, Sweden). These groups will take an active part in co-design of specific research deliverables, and contribute local and indigenous knowledge to the development of new understanding within the project. ACRoBEAR aims to connect natural science with local community and stakeholder priorities, and to integrate natural science with local community knowledge and understanding. The ACRoBEAR team comprises world-leading experts in air pollution, climate science, natural-focal disease, social science and governance, landscape fire science, and health science, from across four European countries, Russia, and the United States. The unique interdisciplinary team will allow an end-to-end state-of-the art assessment of community resilience to changes in risk due to wildfire and natural-focal disease at high latitudes as a result of rapid Arctic warming. The planned workflow exploits cross-disciplinary collaboration and knowledge transfer to deliver integrated outcomes. ACRoBEAR will benefit a broad range of local and national-level stakeholders, including local communities, government, health and forestry agencies, and local and national policy makers. ACRoBEAR will deliver substantial impact on local communities, policy makers and health agencies in Arctic nations. Impact will result from providing new understanding to enable implementation of robust measures for mitigating harmful health impacts due to changes in high latitude wildfire and natural-focal disease and development of policy options to enable adaptation and increase resilience, tailored to regional communities and governance structures. The key legacy impact will be a series of web-based data tools and resources, carefully tailored to community and stakeholder needs via continual two-way dialogue throughout the project.

Globally averaged surface air temperature (SAT) during the 20th and 21st centuries displays a gradual warming and superimposed year-to-year and decadal-scale fluctuations. The upward trend contains the climate response to an anthropogenic increase of heat-trapping atmospheric greenhouse gases. The temperature ups and downs around the trend - that are particularly pronounced in the Arctic - mostly reflect natural variability. Natural climate variations are of two types, internal and external. The former is produced by the climate system itself, e.g. due to variations in ocean circulation. An example of the latter is solar-induced climate variability. Decadal-scale variability is of large societal relevance. It is observed, for example, in Atlantic hurricane activity, Sahel rainfall, Indian and East Asian Monsoons, Eurasian winter coldness and in the Arctic SAT and sea ice. The understanding and skillful prediction of decadal-scale climate variability that modulates the regional occurrence of extreme weather events will be of enormous societal and economic benefit. InterDec is an international initiative aiming at understanding the origin of decadal-scale climate variability in different regions of the world and the linkages between them by using observational data sets and through coordinated multi-model experiments. How can a decadal-scale climate anomaly in one region influence very distant areas of the planet? This can happen through atmospheric or oceanic teleconnections. Fast signal communication between different latitudinal belts within days or weeks is possible through atmospheric teleconnection, whereas communication through oceanic pathways is much slower requiring years to decades or even longer. Understanding these processes will enhance decadal climate prediction of both mean climate variations and associated trends in regional extreme events. Scientists from different European countries, from China and Japan will closely collaborate to disentangle the secrets of the inter-relations of decadal-scale variability around the globe.

By modifying the amount of solar radiation absorbed at the land surface, bright snow and dark forests have strong influences on weather and climate; either a decrease in snow cover or an increase in forest cover, which shades underlying snow, increases the absorption of radiation and warms the overlying air. Computer models for weather forecasting and climate prediction thus have to take these effects into account by calculating the changing mass of snow on the ground and interactions of radiation with forest canopies. Such models generally have coarse resolutions ranging from kilometres to hundreds of kilometres. Forest cover cannot be expected to be continuous over such large distances; instead, northern landscapes are mosaics of evergreen and deciduous forests, clearings, bogs and lakes. Snow can be removed from open areas by wind, shaded by surrounding vegetation or sublimated from forest canopies without ever reaching the ground, and these processes which influence patterns of snow cover depend on the size of the openings, the structure of the vegetation and weather conditions. Snow itself influences patterns of vegetation cover by supplying water, insulating plants and soil from cold winter temperatures and storing nutrients. The aim of this project is to develop better methods for representing interactions between snow, vegetation and the atmosphere in models that, for practical applications, cannot resolve important scales in the patterns of these interactions. We will gather information on distributions of snow, vegetation and radiation during two field experiments at sites in the arctic: one in Sweden and the other in Finland. These sites have been chosen because they have long records of weather and snow conditions, easy access, good maps of vegetation cover from satellites and aircraft and landscapes ranging from sparse deciduous forests to dense coniferous forests that are typical of much larger areas. Using 28 radiometers, and moving them several times during the course of each experiment, will allow us to measure the highly variable patterns of radiation at the snow surface in forests. Information from the field experiments will be used in developing and testing a range of models. To reach the scales of interest, we will begin with a model that explicitly resolves individual trees and work up through models with progressively coarser resolutions, testing the models at each stage against each other and in comparison with observations. The ultimate objective is a model that will be better able to make use of landscape information in predicting the absorption of radiation at the surface and the accumulation and melt of snow. We will work in close consultation with project partners at climate modelling and forecasting centres to ensure that our activities are directed towards outcomes that will meet their requirements.