Simulation of the radiative effect of haze on urban hydrological cycle using reanalysis data in Beijing

Kokkonen, Tom; Grimmond, Sue; Murto, Sonja; Liu, Huizhi; Sundström, Anu-Maija; Järvi, Leena

doi:10.5194/acp-2018-1226

Cited by 3 publications

(3 citation statements)

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“…The advantage of SUEWS when compared to other urban land surface schemes is that it contains a parameterization for downward long‐wave radiation (Loridan et al, ), and it does not have separate tiles for urban and vegetation surface fractions, but rather a dynamic interaction between the surface types is allowed. SUEWS has demonstrated good performance against hydrological observations, and EC‐measured turbulent sensible and latent heat fluxes in several cities in Europe, North America, and Asia (e.g., Alexander et al, ; Ao et al, ; Järvi et al, , ; Kokkonen et al, , ; Ward et al, ).…”

Section: Model Descriptionmentioning

confidence: 99%

Spatial Modeling of Local‐Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki

et al. 2019

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There is a growing need to simulate the effect of urban planning on both local climate and greenhouse gas emissions. Here, a new urban surface carbon dioxide (CO 2 ) flux module for the Surface Urban Energy and Water Balance Scheme is described and evaluated using eddy covariance observations at two sites in Helsinki in 2012. The spatial variability and magnitude of local-scale anthropogenic and biogenic CO 2 flux components at high spatial (250 m × 250 m) and temporal (hourly) resolution are examined by combining high-resolution (down to 2 m) airborne lidar-derived land use data and mobility data to account for people's movement. Urban effects are included in the biogenic components parameterized using urban eddy covariance and chamber observations. Surface Urban Energy and Water Balance Scheme reproduces the seasonal and diurnal variability of the CO 2 flux well. Annual totals deviate 3% from observations in the city center and 2% in a suburban location. In the latter, traffic is the dominant CO 2 source but summertime vegetation partly offsets traffic-related emissions. In the city center, emissions from traffic and human metabolism dominate and the vegetation effect is minor due to the low proportion of vegetation surface cover (22%). Within central Helsinki, human metabolism accounts for 39% of the net local-scale emissions and together with road traffic is to a large extent responsible for the spatial variability of the emissions. Annually, the biogenic emissions and sinks are in near balance and thus the effect of vegetation on the carbon balance is small in this high-latitude city.

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Section: Model Descriptionmentioning

confidence: 99%

Spatial Modeling of Local‐Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki

et al. 2019

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“…Additionally, Cao et al (2016) found a strong correlation between high concentrations of particulate matter over urban areas and the nocturnal UHI for several megacities in China due to enhanced atmospheric absorption and re-emission of LW radiation back to the surface, reducing the evening cooling rate. The ADMS-Urban radiation model does not account for nocturnal warming effects from haze pollution, which has been shown to change surface energy and water partitioning in Beijing (Kokkonen et al, 2019).…”

Section: Case Surface Parameters Ahesmentioning

confidence: 99%

Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood scale

et al. 2021

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Abstract. Information on the spatiotemporal characteristics of Beijing's urban–rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (∼ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both local climate zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ∼ 4.5 ∘C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher-resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

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Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood-scale

Biggart¹,

Stocker²,

Doherty³

et al. 2020

Preprint

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Abstract. Information on the spatiotemporal characteristics of Beijing's urban-rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (~ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both Local Climate Zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ~ 4.5 °C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.

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Simulation of the radiative effect of haze on urban hydrological cycle using reanalysis data in Beijing

Cited by 3 publications

References 23 publications

Spatial Modeling of Local‐Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki

Spatial Modeling of Local‐Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki

Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood scale

Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood-scale

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