Global to city scale urban anthropogenic heat flux: model and variability

Abstract

ABSTRACT:The large scale urban consumption of energy (LUCY) model simulates all components of anthropogenic heat flux (Q F ) from the global to individual city scale at 2.5 × 2.5 arc-minute resolution. This includes a database of different working patterns and public holidays, vehicle use and energy consumption in each country. The databases can be edited to include specific diurnal and seasonal vehicle and energy consumption patterns, local holidays and flows of people within a city. If better information about individual cities is available within this (open-source) database, then the accuracy of this model can only improve, to provide the community data from global-scale climate modelling or the individual city scale in the future. The results show that Q F varied widely through the year, through the day, between countries and urban areas. An assessment of the heat emissions estimated revealed that they are reasonably close to those produced by a global model and a number of small-scale city models, so results from LUCY can be used with a degree of confidence. From LUCY, the global mean urban Q F has a diurnal range of 0.7-3.6 W m −2 , and is greater on weekdays than weekends. The heat release from building is the largest contributor (89-96%), to heat emissions globally. Differences between months are greatest in the middle of the day (up to 1 W m −2 at 1 pm). December to February, the coldest months in the Northern Hemisphere, have the highest heat emissions. July and August are at the higher end. The least Q F is emitted in May. The highest individual grid cell heat fluxes in urban areas were located in

The Impact of Energy Consumption on the Surface Urban Heat Island in China’s 32 Major Cities

“…The seasonal variability of energy consumption was not addressed in this study, due to the lack of seasonal data. As a matter of fact, both energy consumption and SUHII vary with the season [19,26]. If we assumed that energy consumption does not change across the seasons, but that SUHII varies with the season, the urban-suburban difference in ECI had a stronger positive correlation with SUHII during the nighttime in the summer (June-August) (R = 0.57, p < 0.01), followed by autumn (September-November) (R = 0.52, p < 0.01), spring (March-May) (R = 0.48, p < 0.01), and winter (December-February) (R = 0.48, p < 0.01).…”

“…Most studies have estimated the spatial distribution of energy consumption using the inventory-based approach, which can be further divided into bottom-up and top-down approaches. With the bottomup inventory approach, the seasonal and diurnal variability of anthropogenic heat flux are estimated, based on the aggregate of detailed energy consumption data, e.g., flows of vehicles, and the number of buildings and people in each grid point [16,19]. However, this approach places very high demands on the statistical energy consumption data.…”

Changes in regional meteorology induced by anthropogenic heat and their impacts on air quality in South China

“…The top-down energy inventory method, which predicts AH emissions based on the statistics data of energy consumption, is the most common approach and widely used all over the world (Sailor and Lu, 2004;Flanner, 2009;Hamilton et al, 2009;Lee et al, 2009;Allen et al, 2011;Iamarino et al, 2012;Quah and Roth, 2012;Chen et al, 2014a) as well as in China (Chen et al, 2012;Xie et al, 2015Xie et al, , 2016Lu et al, 2016). On the basis of the previous studies, AH fluxes over the area between (101 • E, 16 • N) and (119 • E, 26 • N) in 1990, 1995, 2000, 2005, 2010 and 2014 are calculated in this study by the following equation:…”

Multi‐site evaluation of an urban land‐surface model: intra‐urban heterogeneity, seasonality and parameter complexity requirements

“…The modelled fluxes for the first 10 minutes of each hour are used for evaluation against the hourly observations. The observed fluxes of turbulent sensible heat (Q H ) and net all-wave radiation (Q * ) are adjusted, as described in , to account for anthropogenic heat contribution (Q F ) using an estimate from the globally applicable Large-scale Urban Consumption of energY model (LUCY: Allen et al, 2011) at each location. The size of the Q F contribution for each site is reported in their Table II).…”