Observing the surface radiation and energy balance, carbon dioxide and methane fluxes over the city centre of Amsterdam

2020 ◽  
Author(s):  
Gert-Jan Steeneveld ◽  
Sophie van der Horst ◽  
Bert Heusinkveld
Keyword(s):  
Carbon Dioxide ◽  
Heat Flux ◽  
Energy Balance ◽  
Surface Radiation ◽  
Anthropogenic Heat ◽  
City Centre ◽  
Gas Fluxes ◽  
Anthropogenic Heat Flux ◽  
Greenhouse Gas Fluxes ◽  
The City

<p>Cities largely affect boundary-layer climates due to complex surface structures, pollutant emissions, and anthropogenic heat release. As urban populations are expanding worldwide, insight is required into the urban surface radiation and energy balance and urban greenhouse gas fluxes. However, little long-term flux measurement records are available for dense city centres. We present one year (June 2018 - May 2019) of flux observations taken at a 40-meters tower in the city centre of Amsterdam. We analyse the diurnal and seasonal variation of the turbulent and greenhouse gas fluxes, and we estimate the flux footprint to gain insight in flux variation with wind direction. Also, anthropogenic heat flux and storage fluxes are estimated from emission inventories and the objective hysteresis model respectively. This analysis shows that, especially during the winter, the sum of the sensible and latent heat flux exceeds the net radiation. Thus, the storage flux and anthropogenic heat flux are significant energy providers. Also, we find a surprisingly good surface energy balance closure, especially during summer. To achieve annual energy closure, the sensible heat and latent heat flux require an increase of 13%. Moreover, we find that the measured carbon dioxide flux (45 kg CO<sub>2</sub> m<sup>-2</sup> y<sup>-1</sup>) is close to bottom-up source quantification (47 kg CO<sub>2</sub> m<sup>-2</sup> y<sup>-1</sup>). For some wind directions, the agreement is better than for others. In addition, we show that the annual methane emission is slightly higher than the emission found in Florence and London. Yet the methane source partitioning in Amsterdam remains open for more research.</p>

scholarly journals Evaluation of the Town Energy Balance Model in Cold and Snowy Conditions during the Montreal Urban Snow Experiment 2005

2010 ◽  
Vol 49 (3) ◽  
pp. 346-362 ◽  
Author(s):  
A. Lemonsu ◽  
S. Bélair ◽  
J. Mailhot ◽  
S. Leroyer
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Transition Period ◽  
Sensible Heat Flux ◽  
Heat Fluxes ◽  
Surface Radiation ◽  
Anthropogenic Heat ◽  
Balance Model ◽  
Spatial And Temporal Variability ◽  
The Town

Abstract Using the Montreal Urban Snow Experiment (MUSE) 2005 database, surface radiation and energy exchanges are simulated in offline mode with the Town Energy Balance (TEB) and the Interactions between Soil, Biosphere, and Atmosphere (ISBA) parameterizations over a heavily populated residential area of Montreal, Quebec, Canada, during the winter–spring transition period (from March to April 2005). The comparison of simulations with flux measurements indicates that the system performs well when roads and alleys are snow covered. In contrast, the storage heat flux is largely underestimated in favor of the sensible heat flux at the end of the period when snow is melted. An evaluation and an improvement of TEB’s snow parameterization have also been conducted by using snow property measurements taken during intensive observational periods. Snow density, depth, and albedo are correctly simulated by TEB for alleys where snow cover is relatively homogeneous. Results are not as good for the evolution of snow on roads, which is more challenging because of spatial and temporal variability related to human activity. An analysis of the residual term of the energy budget—including contributions of snowmelt, heat storage, and anthropogenic heat—is performed by using modeling results and observations. It is found that snowmelt and anthropogenic heat fluxes are reasonably well represented by TEB–ISBA, whereas storage heat flux is underestimated.

scholarly journals Revising the definition of anthropogenic heat flux from buildings: role of human activities and building storage heat flux

2021 ◽  
Author(s):  
Yiqing Liu ◽  
Zhiwen Luo ◽  
Sue Grimmond
Keyword(s):  
Heat Flux ◽  
Energy Consumption ◽  
Energy Balance ◽  
Human Activities ◽  
Building Energy ◽  
Heat Emission ◽  
Heat Fluxes ◽  
Anthropogenic Heat ◽  
Anthropogenic Heat Flux ◽  
The Difference

Abstract. Buildings are a major source of anthropogenic heat emissions, impacting energy use and human health in cities. The difference between building energy consumption and building anthropogenic heat emission magnitudes and time lag and are 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 the difference between QEC and QF,B is attributable to a change in the storage heat flux induced by human activities (∆So-uo) (i.e., QF,B = QEC − ∆So-uo). Using building energy simulations (EnergyPlus) we calculate the energy balance fluxes for a simplified isolated building (obtaining QF,B, QEC, ∆So-uo) with different occupancy states. The non-negligible differences in diurnal patterns between QF,B and QEC 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 building but are associated with a large storage heat flux. Building operations (e.g., open windows, use of HVAC system) modify the QF,B by affecting not only QEC but also the ∆So-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 may also be impacted by occupant behaviour.

scholarly journals A Partition Modeling for Anthropogenic Heat Flux Mapping in China

2019 ◽  
Vol 11 (9) ◽  
pp. 1132 ◽  
Author(s):  
Shasha Wang ◽  
Deyong Hu ◽  
Shanshan Chen ◽  
Chen Yu
Keyword(s):  
Climate Change ◽  
Heat Flux ◽  
Spatial Distribution ◽  
High Resolution ◽  
Urban Areas ◽  
Thermal Environment ◽  
Modeling Method ◽  
Anthropogenic Heat ◽  
Regional Characteristics ◽  
Anthropogenic Heat Flux

Anthropogenic heat (AH) generated by human activities has a major impact on urban and regional climate. Accurately estimating anthropogenic heat is of great significance for studies on urban thermal environment and climate change. In this study, a gridded anthropogenic heat flux (AHF) estimation scheme was constructed based on socio-economic data, energy-consumption data, and multi-source remote sensing data using a partition modeling method, which takes into account the regional characteristics of AH emission caused by the differences in regional development levels. The refined AHF mapping in China was realized with a high resolution of 500 m. The results show that the spatial distribution of AHF has obvious regional characteristics in China. Compared with the AHF in provinces, the AHF in Shanghai is the highest which reaches 12.56 W·m−2, followed by Tianjin, Beijing, and Jiangsu. The AHF values are 5.92 W·m−2, 3.35 W·m−2, and 3.10 W·m−2, respectively. As can be seen from the mapping results of refined AHF, the high-value AHF aggregation areas are mainly distributed in north China, east China, and south China. The high-value AHF in urban areas is concentrated in 50–200 W·m−2, and maximum AHF in Shenzhen urban center reaches 267 W·m−2. Further, compared with other high resolution AHF products, it can be found that the AHF results in this study have higher spatial heterogeneity, which can better characterize the emission characteristics of AHF in the region. The spatial pattern of the AHF estimation results correspond to the distribution of building density, population, and industry zone. The high-value AHF areas are mainly distributed in airports, railway stations, industry areas, and commercial centers. It can thus be seen that the AHF estimation models constructed by the partition modeling method can well realize the estimation of large-scale AHF and the results can effectively express the detailed spatial distribution of AHF in local areas. These results can provide technical ideas and data support for studies on surface energy balance and urban climate change.

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

2020 ◽  
Vol 142 (1-2) ◽  
pp. 701-728
Author(s):  
Denise Hertwig ◽  
Sue Grimmond ◽  
Margaret A. Hendry ◽  
Beth Saunders ◽  
Zhengda Wang ◽  
...  
Keyword(s):  
Heat Flux ◽  
Land Cover ◽  
Sensible Heat Flux ◽  
Urban Land ◽  
Heat Fluxes ◽  
Anthropogenic Heat ◽  
Urban Surface ◽  
City Centre ◽  
Sensible Heat ◽  
Climate Simulations

Abstract 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 prediction 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 variations 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 anthropogenic 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 grid box-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.

ANthropogenic heat FLUX estimation from Space

2017 ◽  
Author(s):  
Nektarios Chrysoulakis ◽  
Mattia Marconcini ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
C.S.B Grimmong ◽  
Christian Feigenwinter ◽  
...  
Keyword(s):  
Heat Flux ◽  
Anthropogenic Heat ◽  
Heat Flux Estimation ◽  
Anthropogenic Heat Flux ◽  
Flux Estimation
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