Observations of urban boundary layer structure during a strong urban heat island event

Abstract

It has long been known that the urban surface energy balance is different to that of a rural surface, and that heating of the urban surface after sunset gives rise to the Urban Heat Island (UHI). Less well known is how flow and turbulence structure above the urban surface are changed during different phases of the urban boundary layer (UBL). This paper presents new observations above both an urban and rural surface and investigates how much UBL structure deviates from classical behaviour. A 5-day, low wind, cloudless, high pressure period over London, UK, was chosen for analysis, during which there was a strong UHI. Boundary layer evolution for both sites was determined by the diurnal cycle in sensible heat flux, with an extended decay period of approximately 4 h for the convective UBL. This is referred to as the "Urban Convective Island" as the surrounding rural area was already stable at this time. Mixing height magnitude depended on the combination of regional temperature profiles and surface temperature. Given the daytime UHI intensity of 1.5°C, combined with multiple inversions in the temperature profile, urban and rural mixing heights underwent opposite trends over the period, resulting in a factor of three height difference by the fifth day. Nocturnal jets undergoing inertial oscillations were observed aloft in the urban wind profile as soon as the rural boundary layer became stable: clear jet maxima over the urban surface only emerged once the UBL had become stable. This was due to mixing during the Urban Convective Island reducing shear. Analysis of turbulent moments (variance, skewness and kurtosis) showed "upside-down" boundary layer characteristics on some mornings during initial rapid growth of the convective UBL. During the "Urban Convective Island" phase, turbulence structure still resembled a classical convective boundary layer but with some influence from shear aloft, depending on jet strength. These results demonstrate that appropriate choice of Doppler lidar scan patterns can give detailed profiles of UBL flow. Insights drawn from the observations have implications for accuracy of boundary conditions when simulating urban flow and dispersion, as the UBL is clearly the result of processes driven not only by local surface conditions but also regional atmospheric structure.

Analysis of the potential of near-ground measurements of CO<sub>2</sub> and CH<sub>4</sub> in London, UK, for the monitoring of city-scale emissions using an atmospheric transport model

“…This problem was acknowledged in other GHG transport studies (Denning et al, 1999;Geels et al, 2007;Lac et al, 2013). It is possible that this is exacerbated here because of the London urban heat island, which is significant overnight (Barlow et al, 2014;Bohnenstengel et al, 2015), while the model meteorological forcing did not include an urban parameterisation of the heat fluxes.…”

“…The amplitude of the mean diurnal cycle in mixing layer height (Fig. 3a) is approximately 1500 m, typical of summer convective conditions in an urban area (Barlow et al, 2014).…”

“…The limited sampling rate of the lidar was accounted for using a spectral correction method described in Barlow et al (2014) and Hogan et al (2009). Mixing heights were calculated based on a threshold value of the vertical velocity variance, which was perturbed between 0.080 and 0.121 m 2 s −1 , to check the sensitivity of calculated mixing heights to the threshold, which is an approximate parameter for this computation.…”

“…Mixing heights were calculated based on a threshold value of the vertical velocity variance, which was perturbed between 0.080 and 0.121 m 2 s −1 , to check the sensitivity of calculated mixing heights to the threshold, which is an approximate parameter for this computation. Mean, median, and 5th and 95th percentile values were calculated for each hour based on these perturbations, and account for both measurement and method uncertainties (Barlow et al, 2014;Bohnenstengel et al, 2015). Based on the 5th and 95th percentile data averaged across all data for each hour, estimated measurement and method uncertainty was between 53 and 299 m throughout the daily cycle, with the highest uncertainties usually overnight.…”

Understanding in situ ozone production in the summertime through radical observations and modelling studies during the Clean air for London project (ClearfLo)

“…For species measured more frequently, data were averaged to 15 minute intervals, whilst those measured at a lower time resolution were interpolated. The loss of all non-constrained, model generated, species by deposition or mixing was 10 represented as a first order loss rate equivalent to 1 cm s -1 in a boundary layer depth which varied from ~300 m during the night to 1800 m in the afternoon (estimated from vertical velocity variance (Barlow et al, 2015)) leading to lifetimes of ~8 hrs during the night and ~ 50 hrs during the afternoons.…”