As near-surface air temperatures increase due to global climate change, urban environments tend to experience even higher air temperatures than the surrounding suburban areas---a phenomenon commonly known as the urban heat island (UHI) effect. Among various factors contributing to UHI, Anthropogenic Heat Emission (AHE), heat fluxes arising from human consumption of energy, is the most obvious yet commonly misrepresented in numerical investigations of atmospheric environments. A more accurate estimation of AHE is required for both regional climate modeling and global climate change studies. Previous studies have tried to estimate the intensity of AHE at both regional and global scale, via either bottom-up or top-down approaches. The former one relies on detailed datasets of local land use and energy consumption statistics, being able to provide high-resolution AHE profiles but limited to regional scale. On the other hand, the latter one estimates spatial and temporal distribution of AHE from the global datasets of bulk energy consumption, population density and temperature, with a global coverage but low resolution. Given the problem, we propose a top-down method for estimating a global distribution of AHE along with our recently constructed database with a high spatial resolution of 30 arc-seconds and temporal resolution of 1 hour.
2. Methodology
Annual average AHE was derived from human metabolic heating and country-level total primary energy consumption, which was further divided into three components based on consumer sectors. The first and second components were heat loss and heat emissions from industrial and agriculture sectors equally distributed throughout the country and populated areas, respectively. The third component comprised the sum of emissions from commercial, residential, and transportation sectors (CRT). Bulk AHE from the CRT was proportionally distributed using a global population dataset adjusted by radiance-calibrated nighttime lights. This adjustment aims to solve the problem that available dataset of population only reflect residential population and thus cannot adequately represent urban anthropogenic activities during the daytime. In the adjustment, linear relationship was estimated between nighttime lights and population density for each city, and then outliers of population density, which are detected by interquartile statistics, were adjusted based on the level of nighttime light.
To estimate monthly fluctuations of AHE, an investigation on how energy consumption varies with air temperature was conducted for a number of American and Japanese cities. Two linear correlations between monthly energy consumption and monthly mean temperature were found to the left and right of balance point temperature of 20°C. Using the left and right gradients, we derived a local sensitivity of energy consumption to monthly mean temperature. Relationship between the local sensitivity and the local annual mean temperature was investigated to obtain empirical equations that could be used for estimating the monthly-varying AHE globally from any given monthly temperature. Finally, AHE diurnal profiles were created using an hourly weighting factor, comprising four patterns depending on the local monthly temperature, which is obtained from an hourly AHE dataset of Tokyo Metropolis developed via bottom-up approach (Moriwaki et al., 2008).
3. Result and discussion
A global AHE database was constructed for the year 2013 (see attached figure). We compared our database with existing AHE datasets such as: the large scale urban consumption of energy (LUCY) model (Allen et al. 2010), GreaterQF model (Iamarino et al., 2011) and anthropogenic heating profiles in 61 US cities (Sailor et al., 2015). Comparisons between our proposed AHE and other existing AHE datasets revealed that the population input with high resolution and partitioning of energy consumption into different sectors improved the source location and intensity of the heat emissions. A common problem of AHE underestimation at central urban areas existing in previous top-down models was significantly mitigated by the nighttime lights adjustment. A strong agreement in the monthly profiles of AHE between our database and other bottom-up datasets further proved the validity of the sensitivity introduced in this study. Regional statistics of AHE of 29 largest urban agglomerations highlighted that the heat emission from CRT sectors is the main contributor to the total AHE at the city level, whereas the share of metabolic heating varied closely depending on the level of economic development in the city. Globally, peak AHE values were found to occur between December and February, while the lowest values appeared around June to August. Despite of the small global average AHE (0.13 W/m2), a number of individual pixels with extremely high values between 100 to 500 W/m2 were found particularly in the US and East Asia.
4. Summary
A new database of AHE at the global scale with a high spatial resolution (30 arc-seconds) is proposed. The database can be potentially useful for climate change experts, urban climatologists, policy-makers and urban planners. In particular, we expect that incorporation of our proposed AHE into weather models such as the commonly used Weather Research and Forecasting (WRF) model will provide a more realistic representation of urban areas around the world.
5. Acknowledgment:
This research was supported by the Environment Research and Technology Development Fund (S-14) of the Ministry of the Environment, Japan.
6. Reference
[1] Allen, L., Lindberg, F. and Grimmond, C.S.B. (2010) ‘Global to city scale urban anthropogenic heat flux: Model and variability’, International Journal of Climatology, 31(13), pp. 1990–2005. doi: 10.1002/joc.2210.
[2] Iamarino, M., Beevers, S. and Grimmond, C.S.B. (2011) ‘High-resolution (space, time) anthropogenic heat emissions: London 1970-2025’, International Journal of Climatology, 32(11), pp. 1754–1767. doi: 10.1002/joc.2390.
[3] Moriwaki, R., Kanda, M., Senoo, H., Hagishima, A. and Kinouchi, T. (2008) ‘Anthropogenic water vapor emissions in Tokyo’, Water Resources Research, 44(11), p. n/a–n/a. doi: 10.1029/2007wr006624.
[4] Sailor, D.J., Georgescu, M., Milne, J.M. and Hart, M.A. (2015) ‘Development of a national anthropogenic heating database with an extrapolation for international cities’, Atmospheric Environment, 118, pp. 7–18. doi: 10.1016/j.atmosenv.2015.07.016
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