• Energy Research

Sensible heat fluxes (QH) are determined using scintillometry and eddy covariance over a suburban area. Two large aperture scintillometers provide spatially integrated fluxes across path lengths of 2.8 km and 5.5 km over Swindon, UK. The shorter scintillometer path spans newly built residential areas and has an approximate source area of 2-4 km2, whilst the long path extends from the rural outskirts to the town centre and has a source area of around 5-10 km2. These large-scale heat fluxes are compared with local-scale eddy covariance measurements. Clear seasonal trends are revealed by the long duration of this dataset and variability in monthly QH is related to the meteorological conditions. At shorter time scales the response of QH to solar radiation often gives rise to close agreement between the measurements, but during times of rapidly changing cloud cover spatial differences in the net radiation (Q*) coincide with greater differences between heat fluxes. For clear days QH lags Q*, thus the ratio of QH to Q* increases throughout the day. In summer the observed energy partitioning is related to the vegetation fraction through use of a footprint model. The results demonstrate the value of scintillometry for integrating surface heterogeneity and offer improved understanding of the influence of anthropogenic materials on surface-atmosphere interactions.

This dataset includes the final one-year data of building anthropogenic heat (QF,B), building energy consumption (QEC) and change in storage heat flux due to human activities (ΔS), which is calculated through parametric building energy modeling. 

Urban canopy models are essential tools for forecasting weather and air quality in cities. However, they require many surface parameters, which are uncertain and can reduce model performance if inappropriately prescribed. Here, we evaluate the model sensitivity of the single‐layer urban canopy model (SLUCM) in the Weather Research and Forecasting (WRF) model to surface parameters in two different configurations, one coupled to the overlying atmosphere (on‐line) in a 1D configuration and one without coupling (off‐line). A two‐day summertime period in London is used as a case study, with clear skies and low wind speeds. Our sensitivity tests indicate that the SLUCM reacts differently when coupled to the atmosphere. For certain surface parameters, atmospheric feedback effects can outweigh the variations caused by surface parameter settings. Hence, in order to fully understand the model sensitivity, atmospheric feedback should be considered.

Abstract. Buildings are a major source of anthropogenic heat emissions, impacting energy use and human health in cities. The difference in magnitude and time lag between building energy consumption and building anthropogenic heat emission is poorly quantified. Energy consumption (QEC) is a widely used proxy for the anthropogenic heat flux from buildings (QF,B). Here we revisit the latter's definition. If QF,B is the heat emission to the outdoor environment from human activities within buildings, we can derive it from the changes in energy balance fluxes between occupied and unoccupied buildings. Our derivation shows that the difference between QEC and QF,B is attributable to a change in the storage heat flux induced by human activities (ΔSo-uo) (i.e. QF,B=QEC-ΔSo-uo). Using building energy simulations (EnergyPlus) we calculate the energy balance fluxes for a simplified isolated building (obtaining QF,B, QEC, ΔSo-uo) with different occupancy states. The non-negligible differences in diurnal patterns between QF,B and QEC are caused by thermal storage (e.g. hourly QF,B to QEC ratios vary between −2.72 and 5.13 within a year in Beijing, China). Negative QF,B can occur as human activities can reduce heat emission from a building but this is associated with a large storage heat flux. Building operations (e.g. opening windows, use of space heating and cooling system) modify the QF,B by affecting not only QEC but also the ΔSo-uo diurnal profile. Air temperature and solar radiation are critical meteorological factors explaining day-to-day variability of QF,B. Our new approach could be used to provide data for future parameterisations of both anthropogenic heat flux and storage heat fluxes from buildings. It is evident that storage heat fluxes in cities could also be impacted by occupant behaviour.