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Lagos, Nigeria is rapidly urbanising and is one of the fastest growing cities in the world, with a population increasing at almost 500,000 people per year. Yet the impacts on Lagos’s local climate via its urban heat island (UHI) have not been well explored. Considering the tropics already have year-round high temperatures and humidity, small changes are very likely to tip these regions over heat-health thresholds. Using a well-established model, but with an extended investigation of uncertainty, we explore the impact of Lagos’s recent urbanisation on its UHI. Following a multi-physics evaluation, our simulations, against the background of an unusually warm period in February 2016 (where temperatures regularly exceeded 36°C), show a 0.44°C ensemble-time-mean increase in night-time UHI intensity between 1984–2016. The true scale of the impact is seen spatially where the area in which ensemble-time-mean UHIs exceeding 1°C were found to increase steeply from 254 km2 in 1984 to 1572 km2 in 2016. The rate of warming within Lagos will undoubtedly have a high impact due to the size of the population (12+ million) already at risk from excess heat. Significant warming and modifications to atmospheric boundary-layer heights are also found in rural areas downwind, directly caused by the city. However, there is limited long-term climate monitoring in Lagos or many similarly expanding cities, particularly in the tropics. As such, our modelling can only be an indication of this impact of urbanisation, and we highlight the urgent need to deploy instrumentation.

The Weather Research and Forecasting (WRF) community model is widely used to explore cross‐scale atmospheric features. Although WRF uncertainty studies exist, these usually involve ensembles where different physics options are selected (e.g., the boundary layer scheme) or adjusting individual parameters. Uncertainty from perturbing initial conditions, which generates internal model variability (IMV), has rarely been considered. Moreover, many off‐line WRF research studies generate conclusions based on a single model run without addressing any form of uncertainty. To demonstrate the importance of IMV, or noise, we present a 4‐month case study of summer 2018 over London, UK, using a 244‐member initial condition ensemble. Simply by changing the model start time, a median 2‐m temperature range or IMV of 1.2 °C was found (occasionally exceeding 8 °C). During our analysis, episodes of high and low IMV were found for all variables explored, explained by a relationship with the boundary condition data. Periods of slower wind speed input contained increased IMV, and vice versa, which we hypothesis is related to how strongly the boundary conditions influence the nested region. We also show the importance of IMV effects for the uncertainty of derived variables like the urban heat island, whose median variation in magnitude is 1 °C. Finally, a realistic ensemble size to capture the majority of WRF IMV is also estimated, essential considering the high computational overheads (244 members equaled 140,000 CPU hours). We envisage that highlighting considerable IMV in this repeatable manner will help advance best practices for the WRF and wider regional climate modeling community.

With peak oil behind us, nuclear generation capacity dwindling, and increasingly daunting looking carbon emissions targets, we are moving to a world where we must consider transitioning to renewable energy sources. Renewables are time varying and their inherent unpredictability must challenge our everyday assumptions around energy availability-leading, we believe, to an emphasis on 'supply' rather than 'demand'. Using a range of methods including action research, participatory design and technology mediated enquiry, we report on our work in partnership with the community of Tiree as an exemplar of this future. Tiree is the outermost of the Scottish Inner Hebridesa remote island on the edge of the national electricity grid with a precarious grip on energy-here we uncover the role of renewables and the resilience of a community in moving away from traditional energy provision. We offer opportunities for designing ICT to support supply driven practices in this context, and a simple framework for exploiting under and over supply.