Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

Anthropogenic heat flux (QF) may be significant in cities, especially under low solar irradiance and at night. It is of interest to many practitioners including meteorologists, city planners and climatologists. QF estimates at fine temporal and spatial resolution can be derived from models that use varying amounts of empirical data. This study compares simple and detailed models in a European megacity (London) at 500 m spatial resolution. The simple model (LQF) uses spatially resolved population data and national energy statistics. The detailed model (GQF) additionally uses local energy, road network and workday population data. The Fractions Skill Score (FSS) and bias are used to rate the skill with which the simple model reproduces the spatial patterns and magnitudes of QF, and its sub-components, from the detailed model. LQF skill was consistently good across 90% of the city, away from the centre and major roads. The remaining 10% contained elevated emissions and B hot spots ^ representing 30 – 40% of the total city-wide energy. This structure was lost because it requires workday population, spatially resolved building energy consumption and/or road network data. Daily total building and traffic energy consumption estimates from national data were within ± 40% of local values. Progressively coarser spatial resolutions to 5 km improved skill for total Q F , but important features (hot spots, transport network) were lost at all resolutions when residential population controlled spatial variations. The results demonstrate that simple QF models should be applied with conservative spatial resolution in cities that, like London, exhibit time-varying energy use patterns

Urban signals in high-resolution weather and climate simulations: role of urban land-surface characterisation

Two urban schemes within the Joint UK Land Environment Simulator (JULES) are evaluated offline against multi-year flux observations in the densely built-up city centre of London and in suburban Swindon (UK): (i) the 1-tile slab model, used in climate simulations, (ii) the 2-tile canopy model MORUSES (Met Office–Reading Urban Surface Exchange Scheme), used for numerical weather pre- diction over the UK. Offline, both models perform better at the suburban site, where differences between the urban schemes are less pronounced due to larger vegetation fractions. At both sites, the outgoing short- and longwave radiation is more accurately represented than the turbulent heat fluxes. The seasonal varia- tions of model skill are large in London, where the sensible heat flux in autumn and winter is strongly under-predicted if the large city-centre magnitudes of anthro- pogenic heat emissions are not represented. The delayed timing of the sensible heat flux in the 1-tile model in London results in large negative bias in the morning. The partitioning of the urban surface into canyon and roof in MORUSES improves this as the roof-tile is modelled with a very low thermal inertia, but phase and amplitude of the gridbox-averaged flux critically depend on accurate knowledge of the plan-area fractions of streets and buildings. Not representing non-urban land- cover (e.g. vegetation, inland water) in London results in severely under-predicted latent heat fluxes. Control runs demonstrate that the skill of both models can be greatly improved by providing accurate land-cover and morphology information and using representative anthropogenic heat emissions, which is essential if the model output is intended to inform integrated urban services

Seasonal climatic effects and feedbacks of anthropogenic heat release due to global energy consumption with CAM5

Anthropogenic heat release (AHR) is the heat generated in global energy consumption, which has not been considered in global climate models generally. The global high-resolution AHR from 1992 to 2013, which is estimated by using the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) satellite data, is implemented into the Community Atmosphere Model version 5 (CAM5). The seasonal climatic effects and possible feedbacks of AHR are examined in this study. The modeling results show that AHR increases the global annual mean surface temperature and land surface temperature by 0.02 ± 0.01 K (1σ uncertainty) and 0.05 ± 0.02 K (1σ uncertainty), respectively. The global climatic effect of AHR varies with season: with a stronger climatic effect in the boreal winter leading to global mean land surface temperature increases by 0.10 ± 0.01 K (1σ uncertainty). In the selected regions (40°N–60°N, 0°E–45°E) of Central and Western Europe the average surface temperature increases by 0.46 K in the boreal summer, and in the selected regions (45°N–75°N, 30°E–140°E) of northern Eurasia the average surface temperature increases by 0.83 K in the boreal winter. AHR changes the height and thermodynamic structure of the global planetary boundary layer, as well as the stability of the lower troposphere, which affects the global atmospheric circulation and low cloud fraction. In addition, at the surface both the shortwave radiation flux in the boreal summer and the down-welling longwave flux in the boreal winter change signifi- cantly, as a result of the change in low clouds caused by the effect of AHR. This study suggests a possible new mechanism of AHR effect on global climate through changing the global low-cloud fraction, which is crucial for global energy balance, by modifying the thermodynamic structure and stability of the lower troposphere. Thus this study improves our understanding of the global climate change caused by human activities