Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4551-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4551-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
10 Aug 2023
Model evaluation paper |
| 10 Aug 2023
| 10 Aug 2023
Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance
Yingqi Zheng
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, Finland
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100029, China
Minttu Havu
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, Finland
Huizhi Liu
CORRESPONDING AUTHOR
Department of Atmospheric Sciences, Yunnan University, Kunming, 650091, China
Xueling Cheng
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100029, China
Yifan Wen
School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
Hei Shing Lee
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, Finland
Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, 00560, Finland
Joyson Ahongshangbam
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, Finland
Leena Järvi
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, Finland
Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, 00560, Finland
Related authors
No articles found.
Sasu Karttunen, Matthias Sühring, Ewan O'Connor, and Leena Järvi
Geosci. Model Dev., 18, 5725–5757, https://doi.org/10.5194/gmd-18-5725-2025, https://doi.org/10.5194/gmd-18-5725-2025, 2025
Short summary
Short summary
This paper presents PALM-SLUrb, a single-layer urban canopy model for the PALM model system, designed to simulate urban–atmosphere interactions without resolving flow around individual buildings. The model is described in detail and evaluated against grid-resolved urban canopy simulations, demonstrating its ability to model urban surfaces accurately. By bridging the gap between computational efficiency and physical detail, PALM-SLUrb broadens PALM's potential for urban climate research.
Stavros Stagakis, Dominik Brunner, Junwei Li, Leif Backman, Anni Karvonen, Lionel Constantin, Leena Järvi, Minttu Havu, Jia Chen, Sophie Emberger, and Liisa Kulmala
Biogeosciences, 22, 2133–2161, https://doi.org/10.5194/bg-22-2133-2025, https://doi.org/10.5194/bg-22-2133-2025, 2025
Short summary
Short summary
The balance between CO2 uptake and emissions from urban green areas is still not well understood. This study evaluated for the first time the urban park CO2 exchange simulations with four different types of biosphere model by comparing them with observations. Even though some advantages and disadvantages of the different model types were identified, there was no strong evidence that more complex models performed better than simple ones.
Laura Thölix, Leif Backman, Minttu Havu, Esko Karvinen, Jesse Soininen, Justine Trémeau, Olli Nevalainen, Joyson Ahongshangbam, Leena Järvi, and Liisa Kulmala
Biogeosciences, 22, 725–749, https://doi.org/10.5194/bg-22-725-2025, https://doi.org/10.5194/bg-22-725-2025, 2025
Short summary
Short summary
Cities aim for carbon neutrality and seek to understand urban vegetation's role as a carbon sink. Direct measurements are challenging, so models are used to estimate the urban carbon cycle. We evaluated model performance at estimating carbon sequestration in lawns, park trees, and urban forests in Helsinki, Finland. Models captured seasonal and annual variations well. Trees had higher sequestration rates than lawns, and irrigation often enhanced carbon sinks.
Zijun Zhang, Weiqi Xu, Yi Zhang, Wei Zhou, Xiangyu Xu, Aodong Du, Yinzhou Zhang, Hongqin Qiao, Ye Kuang, Xiaole Pan, Zifa Wang, Xueling Cheng, Lanzhong Liu, Qingyan Fu, Douglas R. Worsnop, Jie Li, and Yele Sun
Atmos. Chem. Phys., 24, 8473–8488, https://doi.org/10.5194/acp-24-8473-2024, https://doi.org/10.5194/acp-24-8473-2024, 2024
Short summary
Short summary
We investigated aerosol composition and sources and the interaction between secondary organic aerosol (SOA) and clouds at a regional mountain site in southeastern China. Clouds efficiently scavenge more oxidized SOA; however, cloud evaporation leads to the production of less oxidized SOA. The unexpectedly high presence of nitrate in aerosol particles indicates that nitrate formed in polluted areas has undergone interactions with clouds, significantly influencing the regional background site.
Esko Karvinen, Leif Backman, Leena Järvi, and Liisa Kulmala
SOIL, 10, 381–406, https://doi.org/10.5194/soil-10-381-2024, https://doi.org/10.5194/soil-10-381-2024, 2024
Short summary
Short summary
We measured and modelled soil respiration, a key part of the biogenic carbon cycle, in different urban green space types to assess its dynamics in urban areas. We discovered surprisingly similar soil respiration across the green space types despite differences in some of its drivers and that irrigation of green spaces notably elevates soil respiration. Our results encourage further research on the topic and especially on the role of irrigation in controlling urban soil respiration.
Joyson Ahongshangbam, Liisa Kulmala, Jesse Soininen, Yasmin Frühauf, Esko Karvinen, Yann Salmon, Anna Lintunen, Anni Karvonen, and Leena Järvi
Biogeosciences, 20, 4455–4475, https://doi.org/10.5194/bg-20-4455-2023, https://doi.org/10.5194/bg-20-4455-2023, 2023
Short summary
Short summary
Urban vegetation is important for removing urban CO2 emissions and cooling. We studied the response of urban trees' functions (photosynthesis and transpiration) to a heatwave and drought at four urban green areas in the city of Helsinki. We found that tree water use was increased during heatwave and drought periods, but there was no change in the photosynthesis rates. The heat and drought conditions were severe at the local scale but were not excessive enough to restrict urban trees' functions.
Jani Strömberg, Xiaoyu Li, Mona Kurppa, Heino Kuuluvainen, Liisa Pirjola, and Leena Järvi
Atmos. Chem. Phys., 23, 9347–9364, https://doi.org/10.5194/acp-23-9347-2023, https://doi.org/10.5194/acp-23-9347-2023, 2023
Short summary
Short summary
We conclude that with low wind speeds, solar radiation has a larger decreasing effect (53 %) on pollutant concentrations than aerosol processes (18 %). Additionally, our results showed that with solar radiation included, pollutant concentrations were closer to observations (−13 %) than with only aerosol processes (+98 %). This has implications when planning simulations under calm conditions such as in our case and when deciding whether or not simulations need to include these processes.
Shengyue Li, Shuxiao Wang, Qingru Wu, Yanning Zhang, Daiwei Ouyang, Haotian Zheng, Licong Han, Xionghui Qiu, Yifan Wen, Min Liu, Yueqi Jiang, Dejia Yin, Kaiyun Liu, Bin Zhao, Shaojun Zhang, Ye Wu, and Jiming Hao
Earth Syst. Sci. Data, 15, 2279–2294, https://doi.org/10.5194/essd-15-2279-2023, https://doi.org/10.5194/essd-15-2279-2023, 2023
Short summary
Short summary
This study compiled China's emission inventory of air pollutants and CO2 during 2005–2021 (ABaCAS-EI v2.0) based on unified emission-source framework. The emission trends and its drivers are analyzed. Key sectors and regions with higher synergistic reduction potential of air pollutants and CO2 are identified. Future control measures are suggested. The dataset and analyses provide insights into the synergistic reduction of air pollutants and CO2 emissions for China and other developing countries.
Yifan Wen, Shaojun Zhang, Ye Wu, and Jiming Hao
Atmos. Chem. Phys., 23, 3819–3828, https://doi.org/10.5194/acp-23-3819-2023, https://doi.org/10.5194/acp-23-3819-2023, 2023
Short summary
Short summary
This study established a high-resolution vehicular NH3 emission inventory for mainland China to quantify the absolute value and relative importance of on-road NH3 emissions for different regions, seasons and population densities. Our results indicate that the significant role of on-road NH3 emissions in populated urban areas may have been underappreciated, suggesting the control of vehicular NH3 emission can be a feasible and cost-effective way of mitigating haze pollution in urban areas.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
Short summary
Short summary
We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Yamei Shao, Huizhi Liu, Qun Du, Yang Liu, Jihua Sun, and Yaohui Li
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-131, https://doi.org/10.5194/bg-2022-131, 2022
Manuscript not accepted for further review
Short summary
Short summary
The effects of sky conditions on ecosystem productivity over wetlands received little attention. Based on eddy covariance measurements during 2016–2020, we explored the impact of sky conditions on net ecosystem productivity (NEP) over an alpine marsh wetland in southwest China. We found diffuse radiation played a critical role in the variations of NEP, and gloomier sky condition was conducive to increasing apparent quantum yield and NEP.
Viktoria F. Sofieva, Risto Hänninen, Mikhail Sofiev, Monika Szeląg, Hei Shing Lee, Johanna Tamminen, and Christian Retscher
Atmos. Meas. Tech., 15, 3193–3212, https://doi.org/10.5194/amt-15-3193-2022, https://doi.org/10.5194/amt-15-3193-2022, 2022
Short summary
Short summary
We present tropospheric ozone column datasets that have been created using combinations of total ozone column from OMI and TROPOMI with stratospheric ozone column datasets from several available limb-viewing instruments (MLS, OSIRIS, MIPAS, SCIAMACHY, OMPS-LP, GOMOS). The main results are (i) several methodological developments, (ii) new tropospheric ozone column datasets from OMI and TROPOMI, and (iii) a new high-resolution dataset of ozone profiles from limb satellite instruments.
Minttu Havu, Liisa Kulmala, Pasi Kolari, Timo Vesala, Anu Riikonen, and Leena Järvi
Biogeosciences, 19, 2121–2143, https://doi.org/10.5194/bg-19-2121-2022, https://doi.org/10.5194/bg-19-2121-2022, 2022
Short summary
Short summary
The carbon sequestration potential of two street tree species and the soil beneath them was quantified with the urban land surface model SUEWS and the soil carbon model Yasso. The street tree plantings turned into a modest sink of carbon from the atmosphere after 14 years. Overall, the results indicate the importance of soil in urban carbon sequestration estimations, as soil respiration exceeded the carbon uptake in the early phase, due to the high initial carbon loss from the soil.
Sasu Karttunen, Ewan O'Connor, Olli Peltola, and Leena Järvi
Atmos. Meas. Tech., 15, 2417–2432, https://doi.org/10.5194/amt-15-2417-2022, https://doi.org/10.5194/amt-15-2417-2022, 2022
Short summary
Short summary
To study the complex structure of the lowest tens of metres of atmosphere in urban areas, measurement methods with great spatial and temporal coverage are needed. In our study, we analyse measurements with a promising and relatively new method, distributed temperature sensing, capable of providing detailed information on the near-surface atmosphere. We present multiple ways to utilise these kinds of measurements, as well as important considerations for planning new studies using the method.
Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki
Geosci. Model Dev., 14, 7411–7424, https://doi.org/10.5194/gmd-14-7411-2021, https://doi.org/10.5194/gmd-14-7411-2021, 2021
Short summary
Short summary
This study aims to replicate computationally expensive high-resolution large-eddy simulations (LESs) with regression models to simulate urban air quality and pollutant dispersion. The model development, including feature selection, model training and cross-validation, and detection of concept drift, has been described in detail. Of the models applied, log-linear regression shows the best performance. A regression model can replace LES unless high accuracy is needed.
Viktoria F. Sofieva, Hei Shing Lee, Johanna Tamminen, Christophe Lerot, Fabian Romahn, and Diego G. Loyola
Atmos. Meas. Tech., 14, 2993–3002, https://doi.org/10.5194/amt-14-2993-2021, https://doi.org/10.5194/amt-14-2993-2021, 2021
Short summary
Short summary
Our paper discusses the structure function method, which allows validation of random uncertainties in the data and, at the same time, probing of the small-scale natural variability. We applied this method to the clear-sky total ozone measurements by TROPOMI Sentinel-5P satellite instrument and found that the TROPOMI random error estimation is adequate. The discussed method is a powerful tool, which can be used in various applications.
Mona Kurppa, Pontus Roldin, Jani Strömberg, Anna Balling, Sasu Karttunen, Heino Kuuluvainen, Jarkko V. Niemi, Liisa Pirjola, Topi Rönkkö, Hilkka Timonen, Antti Hellsten, and Leena Järvi
Geosci. Model Dev., 13, 5663–5685, https://doi.org/10.5194/gmd-13-5663-2020, https://doi.org/10.5194/gmd-13-5663-2020, 2020
Short summary
Short summary
High-resolution modelling is needed to solve the aerosol concentrations in a complex urban area. Here, the performance of an aerosol module within the PALM model to simulate the detailed horizontal and vertical distribution of aerosol particles is studied. Further, sensitivity to the meteorological and aerosol boundary conditions is assessed using both model and observation data. The horizontal distribution is sensitive to the wind speed and stability, and the vertical to the wind direction.
Cited articles
Alexander, P. J., Bechtel, B., Chow, W. T., Fealy, R., and Mills, G.:
Linking urban climate classification with an urban energy and water budget model: Multi–site and multi–seasonal evaluation, Urban Climate, 17, 196–215, https://doi.org/10.1016/j.uclim.2016.08.003, 2016. a
Alvarez, R. and Weilenmann, M.:
Effect of low ambient temperature on fuel consumption and pollutant and CO2 emissions of hybrid electric vehicles in real-world conditions, Fuel, 97, 119–124, https://doi.org/10.1016/j.fuel.2012.01.022, 2012. a
Ao, X., Grimmond, C., Liu, D., Han, Z., Hu, P., Wang, Y., Zhen, X., and Tan, J.:
Radiation fluxes in a business district of Shanghai, China, J. Appl. Meteorol. Clim., 55, 2451–2468, https://doi.org/10.1175/jamc-d-16-0082.1, 2016. a, b
Ao, X., Grimmond, C., Ward, H., Gabey, A., Tan, J., Yang, X.-Q., Liu, D., Zhi, X., Liu, H., and Zhang, N.:
Evaluation of the Surface Urban Energy and Water balance Scheme (SUEWS) at a dense urban site in Shanghai: Sensitivity to anthropogenic heat and irrigation, J. Hydrometeorol., 19, 1983–2005, https://doi.org/10.1175/jhm-d-18-0057.1, 2018. a, b, c
Awal, M., Ohta, T., Matsumoto, K., Toba, T., Daikoku, K., Hattori, S., Hiyama, T., and Park, H.:
Comparing the carbon sequestration capacity of temperate deciduous forests between urban and rural landscapes in central Japan, Urban For. Urban Gree., 9, 261–270, https://doi.org/10.1016/j.ufug.2010.01.007, 2010. a
Best, M. and Grimmond, C.:
Key conclusions of the first international urban land surface model comparison project, B. Am. Meteorol. Soc., 96, 805–819, https://doi.org/10.1175/bams-d-14-00122.1, 2015. a
Best, M. and Grimmond, C.:
Modeling the partitioning of turbulent fluxes at urban sites with varying vegetation cover, J. Hydrometeorol., 17, 2537–2553, https://doi.org/10.1175/jhm-d-15-0126.1, 2016. a
Björkegren, A. and Grimmond, C.:
Net carbon dioxide emissions from central London, Urban Climate, 23, 131–158, https://doi.org/10.1016/j.uclim.2016.10.002, 2018. a, b
Boegh, E., Soegaard, H., Broge, N., Hasager, C., Jensen, N., Schelde, K., and Thomsen, A.:
Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture, Remote Sens. Environ., 81, 179–193, https://doi.org/10.1016/s0034-4257(01)00342-x, 2002. a
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., and Miao, S.:
The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems, Int. J. Climatol., 31, 273–288, https://doi.org/10.1002/joc.2158, 2011. a
Chen, S., Chen, Z., and Zhang, Z.:
The canopy stomatal conductance characteristics of Pinus tabulaeformis and Acer truncatum and their environmental responses in the mountain area of Beijing, Chinese Journal of Plant Ecology, 45, 1329–1340, https://doi.org/10.17521/cjpe.2021.0198, 2021 (in Chinese). a
Cheng, X., Liu, X., Liu, Y., and Hu, F.:
Characteristics of CO2 concentration and flux in the Beijing urban area, J. Geophys. Res.-Atmos., 123, 1785–1801, https://doi.org/10.1002/2017jd027409, 2018. a, b, c
Christen, A., Coops, N., Crawford, B., Kellett, R., Liss, K., Olchovski, I., Tooke, T., Van Der Laan, M., and Voogt, J.:
Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmos. Environ., 45, 6057–6069, https://doi.org/10.1016/j.atmosenv.2011.07.040, 2011. a, b, c, d
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., and Arain, M. A.:
Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites, Agr. Forest Meteorol., 301, 108350, https://doi.org/10.1016/j.agrformet.2021.108350, 2021. a
Crawford, B. and Christen, A.:
Spatial source attribution of measured urban eddy covariance CO2 fluxes, Theor. Appl. Climatol., 119, 733–755, https://doi.org/10.1007/s00704-014-1124-0, 2015. a
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.:
Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis, Version 2.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.20d54e34 (last access: 11 May 2022), 2021. a, b
Cui, Y., Zhang, W., Wang, C., Streets, D. G., Xu, Y., Du, M., and Lin, J.:
Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage, Appl. Energ., 241, 245–256, https://doi.org/10.1016/j.apenergy.2019.03.060, 2019. a, b, c
Demuzere, M., Harshan, S., Järvi, L., Roth, M., Grimmond, C., Masson, V., Oleson, K., Velasco, E., and Wouters, H.:
Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city, Q. J. Roy. Meteor. Soc., 143, 1581–1596, https://doi.org/10.1002/qj.3028, 2017. a
Dou, J., Grimmond, S., Cheng, Z., Miao, S., Feng, D., and Liao, M.:
Summertime surface energy balance fluxes at two Beijing sites, Int. J. Climatol., 39, 2793–2810, https://doi.org/10.1002/joc.5989, 2019. a
Du, M., Wang, X., Peng, C., Shan, Y., Chen, H., Wang, M., and Zhu, Q.:
Quantification and scenario analysis of CO2 emissions from the central heating supply system in China from 2006 to 2025, Appl. Energ., 225, 869–875, https://doi.org/10.1016/j.apenergy.2018.05.064, 2018. a
Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G., Ceulemans, R., Clement, R., and Dolman, H.:
Gap filling strategies for defensible annual sums of net ecosystem exchange, Agr. Forest Meteorol., 107, 43–69, https://doi.org/10.1016/j.agrformet.2006.03.003, 2001. a
Fiorella, R. P., Bares, R., Lin, J. C., Ehleringer, J. R., and Bowen, G. J.:
Detection and variability of combustion-derived vapor in an urban basin, Atmos. Chem. Phys., 18, 8529–8547, https://doi.org/10.5194/acp-18-8529-2018, 2018. a
Foken, T. and Wichura, B.:
Tools for quality assessment of surface-based flux measurements, Agr. Forest Meteorol., 78, 83–105, https://doi.org/10.1016/0168-1923(95)02248-1, 1996. a
Fontaras, G., Zacharof, N.-G., and Ciuffo, B.:
Fuel consumption and CO2 emissions from passenger cars in Europe–Laboratory versus real-world emissions, Prog. Energ. Combust., 60, 97–131, https://doi.org/10.1016/j.pecs.2016.12.004, 2017. a
Friedl, M. and Sulla-Menashe, D.:
MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V006, USGS [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006 (last access: 19 April 2022), 2019. a
Gong, P., Chen, B., Li, X., Liu, H., Wang, J., Bai, Y., Chen, J., Chen, X., Fang, L., and Feng, S.:
Mapping essential urban land use categories in China (EULUC-China): Preliminary results for 2018, Sci. Bull., 65, 182–187, https://doi.org/10.1016/j.scib.2019.12.007, 2020. a
Grimmond, C. and Oke, T.:
Heat storage in urban areas: Local-scale observations and evaluation of a simple model, J. Appl. Meteorol., 38, 922–940, https://doi.org/10.1175/1520-0450(1999)038<0922:HSIUAL>2.0.CO;2, 1999. a
Grimmond, C. and Oke, T.:
Progress in measuring and observing the urban atmosphere, Theor. Appl. Climatol., 84, 3–22, https://doi.org/10.1007/s00704-005-0140-5, 2006. a
Grimmond, C. S. B., Blackett, M., Best, M., Barlow, J., Baik, J., Belcher, S., Bohnenstengel, S., Calmet, I., Chen, F., and Dandou, A.:
The International Urban Energy Balance Models Comparison Project: First Results from Phase 1, J. Appl. Meteorol. Clim., 49, 1268–1292, https://doi.org/10.1175/2010jamc2354.1, 2010. a
Hansen, N., Auger, A., Ros, R., Finck, S., and Pošík, P.:
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009, in: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, Association for Computing Machinery, New York, NY, USA, 1689–1696, https://doi.org/10.1145/1830761.1830790, 2010. a
Havu, M., Lee, H. S., Soininen, J., and Järvi, L.:
Spatial variability of biogenic CO2 flux in Helsinki in 2020 (Version 1), Zenodo [data set], https://doi.org/10.5281/zenodo.7198140, 2022b. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Huete, A.:
A comparison of vegetation indices over a global set of TM images for EOS-MODIS, Remote Sens. Environ., 59, 440–451, https://doi.org/10.1016/s0034-4257(96)00112-5, 1997. a
Järvi, L., Grimmond, C. S. B., Taka, M., Nordbo, A., Setälä, H., and Strachan, I. B.:
Development of the Surface Urban Energy and Water Balance Scheme (SUEWS) for cold climate cities, Geosci. Model Dev., 7, 1691–1711, https://doi.org/10.5194/gmd-7-1691-2014, 2014. a, b, c, d
Järvi, L., Grimmond, C., McFadden, J., Christen, A., Strachan, I., Taka, M., Warsta, L., and Heimann, M.:
Warming effects on the urban hydrology in cold climate regions, Sci. Rep.-UK, 7, 1–8, https://doi.org/10.1038/s41598-017-05733-y, 2017. a
Järvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., Heikinheimo, V., Kolari, P., Riikonen, A., and Grimmond, C. S. B.:
Spatial modeling of local-scale biogenic and anthropogenic carbon dioxide emissions in Helsinki, J. Geophys. Res.-Atmos., 124, 8363–8384, https://doi.org/10.1029/2018jd029576, 2019. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Jiang, X., Zhang, C., Gao, H., and Miao, S.:
Impacts of urban albedo change on urban heat island in Beijing-A case study, Acta Meteorol. Sin., 65, 301–307, https://doi.org/10.1002/jrs.1570, 2007 (in Chinese). a
Johansson, E. and Emmanuel, R.:
The influence of urban design on outdoor thermal comfort in the hot, humid city of Colombo, Sri Lanka, Int. J. Biometeorol., 51, 119–133, https://doi.org/10.1007/s00484-006-0047-6, 2006. a
Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., Oleson, K., Kouznetsov, R., Masson, V., and Järvi, L.:
Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki, Q. J. Roy. Meteor. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016. a
Kokkonen, T., Grimmond, C. S. B., Räty, O., Ward, H., Christen, A., Oke, T., Kotthaus, S., and Järvi, L.:
Sensitivity of Surface Urban Energy and Water Balance Scheme (SUEWS) to downscaling of reanalysis forcing data, Urban Climate, 23, 36–52, https://doi.org/10.1016/j.uclim.2017.05.001, 2018. a
Konopka, J., Heusinger, J., and Weber, S.:
Extensive Urban Green Roof Shows Consistent Annual Net Uptake of Carbon as Documented by 5 Years of Eddy-Covariance Flux Measurements, J. Geophys. Res.-Biogeo., 126, e2020JG005879, https://doi.org/10.1029/2020jg005879, 2021. a
Laine, A., Riutta, T., Juutinen, S., Väliranta, M., and Tuittila, E.-S.:
Acknowledging the spatial heterogeneity in modelling/reconstructing carbon dioxide exchange in a northern aapa mire, Ecol. Model., 220, 2646–2655, https://doi.org/10.1016/j.ecolmodel.2009.06.047, 2009. a
Liu, S., Pang, H., Zhang, N., Xing, M., Wu, S., and Hou, S.:
Temporal variations of the contribution of combustion-derived water vapor to urban humidity during winter in Xi'an, China, Sci. Total Environ., 830, 154711, https://doi.org/10.2139/ssrn.3994594, 2022. a
Liu, Y., Liu, H., Du, Q., and Xu, L.:
Multi-level CO2 fluxes over Beijing megacity with the eddy covariance method, Atmospheric and Oceanic Science Letters, 14, 100079, https://doi.org/10.1016/j.aosl.2021.100079, 2021. a
Loridan, T., Grimmond, C., Offerle, B. D., Young, D. T., Smith, T. E., Järvi, L., and Lindberg, F.:
Local-scale urban meteorological parameterization scheme (LUMPS): longwave radiation parameterization and seasonality-related developments, J. Appl. Meteorol. Clim., 50, 185–202, https://doi.org/10.1175/2010jamc2474.1, 2011. a, b
Lu, P., Yu, Q., Liu, J., and Lee, X.:
Advance of tree-flowering dates in response to urban climate change, Agr. Forest Meteorol., 138, 120–131, https://doi.org/10.1016/j.agrformet.2006.04.002, 2006. a
Luo, Z., Sun, O. J., Ge, Q., Xu, W., and Zheng, J.:
Phenological responses of plants to climate change in an urban environment, Ecol. Res., 22, 507–514, https://doi.org/10.1007/s11284-006-0044-6, 2007. a
Ma, J.:
The study on urban forest structure and eco-service in the Sixth Ring Road of Beijing, Thesis, Chinese Academy of Forestry, Beijing, https://doi.org/10.27625/d.cnki.gzlky.2019.000083, 2019. a
Ma, J., Jia, B., Zhang, W., Liu, X., Li, X., and Liu, J.:
The characteristics of urban forest structure within the Sixth Ring Road of Beijing, Chinese Journal of Ecology, 38, 2318–2325, https://doi.org/10.13292/j.1000-4890.201908.035, 2019. a
Marcotullio, P. J., Sarzynski, A., Albrecht, J., Schulz, N., and Garcia, J.:
The geography of global urban greenhouse gas emissions: An exploratory analysis, Climatic Change, 121, 621–634, https://doi.org/10.1007/s10584-013-0977-z, 2013. a
Masek, J., Vermote, E., Saleous, N., Wolfe, R., Hall, F., Huemmrich, K., Gao, F., Kutler, J., and Lim, T.-K.:
A Landsat Surface Reflectance Dataset for North America, 1990–2000, IEEE Geosci. Remote S., 3, 68–72, https://doi.org/10.1109/lgrs.2005.857030, 2006. a, b
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.:
The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a
Miao, S., Dou, J., Chen, F., Li, J., and Li, A.:
Analysis of observations on the urban surface energy balance in Beijing, Science China Earth Sciences, 55, 1881–1890, https://doi.org/10.1007/s11430-012-4411-6, 2012. a
Moriwaki, R. and Kanda, M.:
Seasonal and diurnal fluxes of radiation, heat, water vapor, and carbon dioxide over a suburban area, J. Appl. Meteorol., 43, 1700–1710, https://doi.org/10.1175/jam2153.1, 2004. a, b, c
Nordbo, A., Karsisto, P., Matikainen, L., Wood, C. R., and Järvi, L.:
Urban surface cover determined with airborne lidar at 2 m resolution–implications for surface energy balance modelling, Urban Climate, 13, 52–72, https://doi.org/10.1016/j.uclim.2015.05.004, 2015. a
Offerle, B., Grimmond, C., and Oke, T. R.:
Parameterization of net all-wave radiation for urban areas, J. Appl. Meteorol. Clim., 42, 1157–1173, https://doi.org/10.1175/1520-0450(2003)042<1157:ponarf>2.0.co;2, 2003. a, b
Oke, T.:
The Heat Island of the Urban Boundary Layer: Characteristics, Causes and Effects, Springer, Dordrecht, https://doi.org/10.1007/978-94-017-3686-2_5, pp. 81–107, 1995. a
Omidvar, H., Sun, T., Grimmond, S., Bilesbach, D., Black, A., Chen, J., Duan, Z., Gao, Z., Iwata, H., and McFadden, J. P.:
Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset, Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, 2022. a, b, c, d
Pataki, D. E., Carreiro, M. M., Cherrier, J., Grulke, N. E., Jennings, V., Pincetl, S., Pouyat, R. V., Whitlow, T. H., and Zipperer, W. C.:
Coupling biogeochemical cycles in urban environments: ecosystem services, green solutions, and misconceptions, Front. Ecol. Environ., 9, 27–36, https://doi.org/10.1890/090220, 2011. a
Peters, E. B. and McFadden, J. P.:
Continuous measurements of net CO2 exchange by vegetation and soils in a suburban landscape, J. Geophys. Res.-Biogeo., 117, G03005, https://doi.org/10.1029/2011jg001933, 2012. a
Prairie, Y. T. and Duarte, C. M.:
Direct and indirect metabolic CO2 release by humanity, Biogeosciences, 4, 215–217, https://doi.org/10.5194/bg-4-215-2007, 2007. a
Qian, Y., Chakraborty, T., Li, J., Li, D., He, C., Sarangi, C., Chen, F., Yang, X., and Leung, L. R.:
Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions, Adv. Atmos. Sci., 39, 819–860, https://doi.org/10.1007/s00376-021-1371-9, 2022. a
Rebmann, C., Göckede, M., Foken, T., Aubinet, M., Aurela, M., Berbigier, P., Bernhofer, C., Buchmann, N., Carrara, A., and Cescatti, A.:
Quality analysis applied on eddy covariance measurements at complex forest sites using footprint modelling, Theor. Appl. Climatol., 80, 121–141, https://doi.org/10.1007/s00704-004-0095-y, 2005. a
Salmon, O. E., Shepson, P. B., Ren, X., Marquardt Collow, A. B., Miller, M. A., Carlton, A. G., Cambaliza, M. O., Heimburger, A., Morgan, K. L., and Fuentes, J. D.:
Urban emissions of water vapor in winter, J. Geophys. Res.-Atmos., 122, 9467–9484, https://doi.org/10.1002/2016jd026074, 2017. a
Sarangi, C., Tripathi, S., Qian, Y., Kumar, S., and Ruby Leung, L.:
Aerosol and urban land use effect on rainfall around cities in Indo–Gangetic Basin from observations and cloud resolving model simulations, J. Geophys. Res.-Atmos., 123, 3645–3667, https://doi.org/10.1002/2017jd028004, 2018. a
Song, Y., Li, F., Wang, X., Xu, C., Zhang, J., Liu, X., and Zhang, H.:
The effects of urban impervious surfaces on eco-physiological characteristics of Ginkgo biloba: A case study from Beijing, China, Urban For. Urban Gree., 14, 1102–1109, https://doi.org/10.1016/j.ufug.2015.10.008, 2015. a
Stagakis, S., Feigenwinter, C., Vogt, R., and Kalberer, M.:
A high-resolution monitoring approach of urban CO2 fluxes. Part 1 – bottom-up model development, Sci. Total Environ., 160216, https://doi.org/10.2139/ssrn.4172744, 2022. a, b
Sun, T. and Grimmond, S.:
A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration, Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, 2019. a
Tan, J., Zheng, Y., Tang, X., Guo, C., Li, L., Song, G., Zhen, X., Yuan, D., Kalkstein, A. J., and Li, F.:
The urban heat island and its impact on heat waves and human health in Shanghai, Int. J. Biometeorol., 54, 75–84, https://doi.org/10.1007/s00484-009-0256-x, 2010. a
United Nations Department of Economic and Social Affairs:
World Urbanization Prospects: The 2018 Revision, United Nations, https://doi.org/10.18356/b9e995fe-en, 2019. a
Velasco, E. and Roth, M.:
Cities as net sources of CO2: Review of atmospheric CO2 exchange in urban environments measured by eddy covariance technique, Geography Compass, 4, 1238–1259, https://doi.org/10.1111/j.1749-8198.2010.00384.x, 2010. a
Wang, L., Fan, S., Hu, F., Miao, S., Yang, A., Li, Y., Liu, J., Liu, C., Chen, S., Ho, H. C., Duan, Z., Gao, Z., and Yang, Y.:
Vertical gradient variations in radiation budget and heat fluxes in the urban boundary layer: A comparison study between polluted and clean air episodes in Beijing during winter, J. Geophys. Res.-Atmos., 125, e2020JD032478, https://doi.org/10.1029/2020jd032478, 2020. a
Wang, Y., Li, G., Di, N., Clothier, B., Duan, J., Li, D., Jia, L., Xi, B., and Ma, F.:
Leaf phenology variation within the canopy and its relationship with the transpiration of Populus tomentosa under plantation conditions, Forests, 9, 603, https://doi.org/10.3390/f9100603, 2018. a
Wang, Y., Chang, Q., and Li, X.:
Promoting sustainable carbon sequestration of plants in urban greenspace by planting design: A case study in parks of Beijing, Urban For. Urban Gree., 64, 127291, https://doi.org/10.1016/j.ufug.2021.127291, 2021. a
Ward, H. C., Evans, J. G., and Grimmond, C. S. B.:
Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmos. Chem. Phys., 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013. a
Watts, N., Adger, W. N., Agnolucci, P., Blackstock, J., Byass, P., Cai, W., Chaytor, S., Colbourn, T., Collins, M., and Cooper, A.:
Health and climate change: policy responses to protect public health, Lancet, 386, 1861–1914, https://doi.org/10.1016/S0140-6736(15)60854-6, 2015. a
Wen, Y., Zhang, S., Zhang, J., Bao, S., Wu, X., Yang, D., and Wu, Y.:
Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data, Appl. Energ., 260, 114357, https://doi.org/10.1016/j.apenergy.2019.114357, 2020. a, b
Wen, Y., Wu, R., Zhou, Z., Zhang, S., Yang, S., Wallington, T. J., Shen, W., Tan, Q., Deng, Y., and Wu, Y.:
A data-driven method of traffic emissions mapping with land use random forest models, Appl. Energ., 305, 117916, https://doi.org/10.1016/j.apenergy.2021.117916, 2022. a, b
Xu, Y., Li, S., Yuan, X., and Feng, Z.:
Emission characteristics of biogenic volatile compounds (BVOCs) from common greening tree species in northern China and their correlations with photosynthetic parameters, Environmental Science, 41, 3518–3526, https://doi.org/10.13227/j.hjkx.202001180, 2020 (in Chinese). a, b
Yang, D., Zhang, S., Niu, T., Wang, Y., Xu, H., Zhang, K. M., and Wu, Y.:
High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets, Atmos. Chem. Phys., 19, 8831–8843, https://doi.org/10.5194/acp-19-8831-2019, 2019. a, b, c
Zhang, D., Gao, J., Tang, D., Wu, X., Shi, J., Chen, J., Peng, Y., Zhang, S., and Wu, Y.:
Switching on auxiliary devices in vehicular fuel efficiency tests can help cut CO2 emissions by millions of tons, One Earth, 4, 135–145, https://doi.org/10.1016/j.oneear.2020.12.010, 2021. a
Zhang, H., Zhou, L., Huang, X., and Zhang, X.:
Decarbonizing a large City's heating system using heat pumps: A case study of Beijing, Energy, 186, 115820, https://doi.org/10.1016/j.energy.2019.07.150, 2019. a, b
Zhang, S., Wu, Y., Liu, H., Huang, R., Yang, L., Li, Z., Fu, L., and Hao, J.:
Real-world fuel consumption and CO2 emissions of urban public buses in Beijing, Appl. Energ., 113, 1645–1655, https://doi.org/10.1016/j.apenergy.2013.09.017, 2014. a, b
Zhang, X., Mi, F., Lu, N., Yan, N., Kuglerova, L., Yuan, S., Peng, Q., and Ma, O. Z.:
Green space water use and its impact on water resources in the capital region of China, Phys. Chem. Earth Pts. A/B/C, 101, 185–194, https://doi.org/10.1016/j.pce.2017.02.001, 2017.
a
Zhang, Y., Yin, P., Li, X., Niu, Q., Wang, Y., Cao, W., Huang, J., Chen, H., Yao, X., and Yu, L.:
The divergent response of vegetation phenology to urbanization: A case study of Beijing city, China, Sci. Total Environ., 803, 150079, https://doi.org/10.1016/j.scitotenv.2021.150079, 2022. a
Zhao, X., Zhou, Y., Chen, W., Li, X., Li, X., and Li, D.:
Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China, GISci. Remote Sens., 58, 717–732, https://doi.org/10.1080/15481603.2021.1935128, 2021. a
Zheng, Y., Liu, H., and Cheng, X.:
Datasets for simulating heat and CO2 fluxes in Beijing using SUEWS V2020b, Zenodo [data set], https://doi.org/10.5281/zenodo.7427360, 2022. a, b, c
Zhu, X., Ni, G., Cong, Z., Sun, T., and Li, D.:
Impacts of surface heterogeneity on dry planetary boundary layers in an urban-rural setting, J. Geophys. Res.-Atmos., 121, 12164–12179, https://doi.org/10.1002/2016jd024982, 2016. a
Short summary
The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated...
