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.

Lagos, Nigeria, is rapidly urbanizing and is one of the fastest-growing cities in the world, with a population that is 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 that 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 urbanization on its UHI. Following a multiphysics evaluation, our simulations, against the background of an unusually warm period in February 2016 (during which temperatures regularly exceeded 36°C), show a 0.44°C ensemble-time-mean increase in nighttime UHI intensity between 1984 and 2016. The true scale of the impact is seen spatially as the area over which ensemble-time-mean UHIs exceed 1°C was 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 because of 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 modeling can only be an indication of this impact of urbanization, and we highlight the urgent need to deploy instrumentation.

Urbanisation is changing the climate of the world we live in. In Great Britain (GB) 5.8% of the total land area is covered by artificial surfaces, increasing from 4.3% in 1975. Aside from associated loss of farmland, biodiversity and a range of ecosystem services, changing to urban form warms the Earth’s surface: the urban heat island (UHI) effect. Standard estimates of temperature changes do not account for urbanisation (i.e. use of rural-only stations or removal of urban bias in observations), meaning that anthropogenic modifications to the land surface may be causing the surface-level atmosphere to warm quicker than those estimates suggest. Using observations from a high-density urban monitoring network, we show that locally this warming (instantaneously) may be over 8 °C. Based on the relationships between UHI intensity, urban fraction and wind speed in this network, we create a statistical model and use it to estimate the current daily-mean urban warming across GB to be 0.04 °C [0.02 °C –0.06 °C]. Despite this climate contribution appearing small (94% of GB’s land cover for the time-being is still rural), we show that half of GB’s population currently live in areas with average daily-mean warming ∼0.4 °C. Under heatwave conditions our high estimates show 40% of GB’s population may experience over a 1 °C daily-mean UHI. Furthermore, simply due to urbanisation (1975–2014) we estimate GB is warming at a rate equivalent and in addition to 3.4% [1.9%–5.0%] of the observed surface-level warming calculated from background stations. In the fastest urbanising region, South East GB, we find that these warming rates are up to three times faster. The methodology is straightforward and can be readily extended to other countries or updated as future land cover data becomes available.

Weather and climate networks traditionally follow rigorous siting guidelines, with individual stations located away from frost hollows, trees or urban areas. However, the diverse nature of the UK landscape suggests that the feasibility of siting stations that are truly representative of regional climate and free from distorting local effects is increasingly difficult. Whilst the urban heat island is a well-studied phenomenon and usually accounted for, the effect of warm urban air advected downwind is rarely considered, particularly at rural stations adjacent to urban areas. Until recently, urban heat advection (UHA) was viewed as an urban boundary-layer process through the formation of an urban plume that rises above the surface as it is advected. However, these dynamic UHA effects are shown to also have an impact on surface observations. Results show a significant difference in temperatures anomalies (\(p\,< \,0.001\)) between observations taken downwind of urban and rural areas. For example, urban heat advection from small urbanized areas (\(\sim \)1\(\,\hbox {km}^{2}\)) under low cloud cover and wind speeds of 2–3\(\,\hbox {m}\,\hbox {s}^{-1}\) is found to increase mean nocturnal air temperatures by 0.6\(\,^{\circ }\hbox {C}\) at a horizontal distance of 0.5 km. Fundamentally, these UHA results highlight the importance of careful interpretation of long-term temperature data taken near small urban areas.

Urban heat advection (UHA) can extend the climatic impact of a city into the surrounding countryside. This may lead to an intensification of already well‐documented urban heat island (UHI) impacts on health and infrastructure, and challenge the representativeness of long‐term reference temperature records taken near urban areas. However, previous UHA studies have been unable to accurately quantify surface‐level UHA due to challenges arising from complex urban land‐use patterns. To address this, the numerical Weather Research and Forecasting (WRF) mesoscale model coupled with the Building Energy Parameterization urban canopy scheme is used to simulate meteorological fields for idealized land‐use cases. Hypothetical square cities (up to 32 km in size) are simulated for a year's period. A time‐mean 2‐m temperature field (representing the canopy UHI) shows that the mean UHI intensity (up to 4.3 °C [SD 1.7 °C]), wind speed <3.9 m/s) is linearly related to the logarithm of city size. This finding, entirely derived from numerical modelling, is consistent with the log‐linear relationships previously found in the observational data of many cities in the world. A UHA methodology was then applied to the temperature fields to separate UHA from the UHI, with up to 2.9 °C (SD 1.7 °C) of UHA found downwind of the largest city size. For this hypothetical city size, an UHA intensity of 0.5 °C is found up to 24‐km downwind from the urban boundary. In addition, the UHA‐distance profiles along the central horizontal transect for various urban sizes are found to follow a scaling rule as a good approximation. As a result, the findings of this paper can be used as a starting point for climate impact assessments for areas surrounding urban areas without the need for complex, computation‐intensive simulations.