Articles | Volume 22, issue 3
https://doi.org/10.5194/bg-22-725-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-725-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Carbon sequestration in different urban vegetation types in Southern Finland
Climate System Research, Finnish Meteorological Institute, Helsinki, Finland
Leif Backman
Climate System Research, Finnish Meteorological Institute, Helsinki, Finland
Minttu Havu
Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki, Finland
Centre national de recherches météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France
Esko Karvinen
Climate System Research, Finnish Meteorological Institute, Helsinki, Finland
Jesse Soininen
Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki, Finland
Justine Trémeau
Climate System Research, Finnish Meteorological Institute, Helsinki, Finland
Olli Nevalainen
Climate System Research, Finnish Meteorological Institute, Helsinki, Finland
Joyson Ahongshangbam
Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki, Finland
Leena Järvi
Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki, Finland
Helsinki Institute of Sustainability Science (HELSUS), University of Helsinki, Helsinki, Finland
Liisa Kulmala
Climate System Research, Finnish Meteorological Institute, Helsinki, Finland
Related authors
Tommi Ekholm, Nadine-Cyra Freistetter, Aapo Rautiainen, and Laura Thölix
Geosci. Model Dev., 17, 3041–3062, https://doi.org/10.5194/gmd-17-3041-2024, https://doi.org/10.5194/gmd-17-3041-2024, 2024
Short summary
Short summary
CLASH is a numerical model that portrays land allocation between different uses, land carbon stocks, and agricultural and forestry production globally. CLASH can help in examining the role of land use in mitigating climate change, providing food and biogenic raw materials for the economy, and conserving primary ecosystems. Our demonstration with CLASH confirms that reduction of animal-based food, shifting croplands and storing carbon in forests are effective ways to mitigate climate change.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
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.
Henri Kajasilta, Stephanie Gerin, Milla Niiranen, Miika Läpikivi, Maarit Liimatainen, David Kraus, Henriikka Vekuri, Mika Korkiakoski, Liisa Kulmala, Jari Liski, and Julius Vira
EGUsphere, https://doi.org/10.5194/egusphere-2025-4219, https://doi.org/10.5194/egusphere-2025-4219, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We modelled different water table scenarios in drained agricultural peatlands to investigate the impact of water management on greenhouse gas emissions. Our results show that raising the water table reduces emissions, even in fields with thinner peat layers and conservative water management practices. Carbon dioxide emissions were more affected than nitrous oxide emissions. This study sheds light on the role of peatlands in mitigating emissions. Simulations were run using a process-based model.
Aki Tsuruta, Akihiko Kuze, Kei Shiomi, Fumie Kataoka, Nobuhiro Kikuchi, Tuula Aalto, Leif Backman, Ella Kivimäki, Maria K. Tenkanen, Kathryn McKain, Omaira E. García, Frank Hase, Rigel Kivi, Isamu Morino, Hirofumi Ohyama, David F. Pollard, Mahesh K. Sha, Kimberly Strong, Ralf Sussmann, Yao Te, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, Minqiang Zhou, and Hiroshi Suto
Atmos. Chem. Phys., 25, 7829–7862, https://doi.org/10.5194/acp-25-7829-2025, https://doi.org/10.5194/acp-25-7829-2025, 2025
Short summary
Short summary
Satellite data bring invaluable information about greenhouse gas emissions globally. We found that a new type of data from the Greenhouse Gas Observing Satellite (GOSAT), which contains information about methane in the lowest layer of Earth's atmosphere, could provide reliable estimates of recent methane emissions when combined with atmospheric modelling. Therefore, the use of such data is encouraged to improve emission quantification methods and advance our understanding of methane cycles.
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.
Vilna Tyystjärvi, Tiina Markkanen, Leif Backman, Maarit Raivonen, Antti Leppänen, Xuefei Li, Paavo Ojanen, Kari Minkkinen, Roosa Hautala, Mikko Peltoniemi, Jani Anttila, Raija Laiho, Annalea Lohila, Raisa Mäkipää, and Tuula Aalto
Biogeosciences, 21, 5745–5771, https://doi.org/10.5194/bg-21-5745-2024, https://doi.org/10.5194/bg-21-5745-2024, 2024
Short summary
Short summary
Drainage of boreal peatlands strongly influences soil methane fluxes, with important implications for climatic impacts. Here we simulate methane fluxes in forestry-drained and restored peatlands during the 21st century. We found that restoration turned peatlands into a source of methane, but the magnitude varied regionally. In forests, changes in the water table level influenced methane fluxes, and in general, the sink was weaker under rotational forestry compared to continuous cover forestry.
Outi Kinnunen, Leif Backman, Juha Aalto, Tuula Aalto, and Tiina Markkanen
Biogeosciences, 21, 4739–4763, https://doi.org/10.5194/bg-21-4739-2024, https://doi.org/10.5194/bg-21-4739-2024, 2024
Short summary
Short summary
Climate change is expected to increase the risk of forest fires. Ecosystem process model simulations are used to project changes in fire occurrence in Fennoscandia under six climate projections. The findings suggest a longer fire season, more fires, and an increase in burnt area towards the end of the century.
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.
Tommi Ekholm, Nadine-Cyra Freistetter, Aapo Rautiainen, and Laura Thölix
Geosci. Model Dev., 17, 3041–3062, https://doi.org/10.5194/gmd-17-3041-2024, https://doi.org/10.5194/gmd-17-3041-2024, 2024
Short summary
Short summary
CLASH is a numerical model that portrays land allocation between different uses, land carbon stocks, and agricultural and forestry production globally. CLASH can help in examining the role of land use in mitigating climate change, providing food and biogenic raw materials for the economy, and conserving primary ecosystems. Our demonstration with CLASH confirms that reduction of animal-based food, shifting croplands and storing carbon in forests are effective ways to mitigate climate change.
Justine Trémeau, Beñat Olascoaga, Leif Backman, Esko Karvinen, Henriikka Vekuri, and Liisa Kulmala
Biogeosciences, 21, 949–972, https://doi.org/10.5194/bg-21-949-2024, https://doi.org/10.5194/bg-21-949-2024, 2024
Short summary
Short summary
We studied urban lawns and meadows in the Helsinki metropolitan area, Finland. We found that meadows are more resistant to drought events but that they do not increase carbon sequestration compared with lawns. Moreover, the transformation from lawns to meadows did not demonstrate any negative climate effects in terms of greenhouse gas emissions. Even though social and economic aspects also steer urban development, these results can guide planning to consider carbon-smart options.
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.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, https://doi.org/10.5194/gmd-16-4551-2023, 2023
Short summary
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.
Yao Gao, Eleanor J. Burke, Sarah E. Chadburn, Maarit Raivonen, Mika Aurela, Lawrence B. Flanagan, Krzysztof Fortuniak, Elyn Humphreys, Annalea Lohila, Tingting Li, Tiina Markkanen, Olli Nevalainen, Mats B. Nilsson, Włodzimierz Pawlak, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-229, https://doi.org/10.5194/bg-2022-229, 2022
Manuscript not accepted for further review
Short summary
Short summary
We coupled a process-based peatland CH4 emission model HIMMELI with a state-of-art land surface model JULES. The performance of the coupled model was evaluated at six northern wetland sites. The coupled model is considered to be more appropriate in simulating wetland CH4 emission. In order to improve the simulated CH4 emission, the model requires better representation of the peat soil carbon and hydrologic processes in JULES and the methane production and transportation processes in HIMMELI.
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.
Maiju Linkosalmi, Juha-Pekka Tuovinen, Olli Nevalainen, Mikko Peltoniemi, Cemal M. Taniş, Ali N. Arslan, Juuso Rainne, Annalea Lohila, Tuomas Laurila, and Mika Aurela
Biogeosciences, 19, 4747–4765, https://doi.org/10.5194/bg-19-4747-2022, https://doi.org/10.5194/bg-19-4747-2022, 2022
Short summary
Short summary
Vegetation greenness was monitored with digital cameras in three northern peatlands during five growing seasons. The greenness index derived from the images was highest at the most nutrient-rich site. Greenness indicated the main phases of phenology and correlated with CO2 uptake, though this was mainly related to the common seasonal cycle. The cameras and Sentinel-2 satellite showed consistent results, but more frequent satellite data are needed for reliable detection of phenological phases.
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.
Olli Nevalainen, Olli Niemitalo, Istem Fer, Antti Juntunen, Tuomas Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, Liisa Kulmala, Åsa Stam, Otto Kuusela, Stephanie Gerin, Toni Viskari, Julius Vira, Jari Hyväluoma, Juha-Pekka Tuovinen, Annalea Lohila, Tuomas Laurila, Jussi Heinonsalo, Tuula Aalto, Iivari Kunttu, and Jari Liski
Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, https://doi.org/10.5194/gi-11-93-2022, 2022
Short summary
Short summary
Better monitoring of soil carbon sequestration is needed to understand the best carbon farming practices in different soils and climate conditions. We, the Field Observatory Network (FiON), have therefore established a methodology for monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, and modeling. To disseminate our work, we built a website called the Field Observatory (fieldobservatory.org).
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.
Vilma Kangasaho, Aki Tsuruta, Leif Backman, Pyry Mäkinen, Sander Houweling, Arjo Segers, Maarten Krol, Ed Dlugokencky, Sylvia Michel, James White, and Tuula Aalto
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-843, https://doi.org/10.5194/acp-2021-843, 2021
Revised manuscript not accepted
Short summary
Short summary
Understanding the composition of carbon isotopes can help to better understand the changes in methane budgets. This study investigates how methane sources affect the seasonal cycle of the methane carbon-13 isotope during 2000–2012 using an atmospheric transport model. We found that emissions from both anthropogenic and natural sources contribute. The findings raise a need to revise the magnitudes, proportion, and seasonal cycles of anthropogenic sources and northern wetland emissions.
Laura Heimsch, Annalea Lohila, Juha-Pekka Tuovinen, Henriikka Vekuri, Jussi Heinonsalo, Olli Nevalainen, Mika Korkiakoski, Jari Liski, Tuomas Laurila, and Liisa Kulmala
Biogeosciences, 18, 3467–3483, https://doi.org/10.5194/bg-18-3467-2021, https://doi.org/10.5194/bg-18-3467-2021, 2021
Short summary
Short summary
CO2 and H2O fluxes were measured at a newly established eddy covariance site in southern Finland for 2 years from 2018 to 2020. This agricultural grassland site focuses on the conversion from intensive towards more sustainable agricultural management. The first summer experienced prolonged dry periods, and notably larger fluxes were observed in the second summer. The field acted as a net carbon sink during both study years.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski
Geosci. Model Dev., 13, 5959–5971, https://doi.org/10.5194/gmd-13-5959-2020, https://doi.org/10.5194/gmd-13-5959-2020, 2020
Short summary
Short summary
The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.
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
Ahongshangbam, J., Kulmala, L., Soininen, J., Frühauf, Y., Karvinen, E., Salmon, Y., Lintunen, A., Karvonen, A., and Järvi, L.: Sap flow and leaf gas exchange response to a drought and heatwave in urban green spaces in a Nordic city, Biogeosciences, 20, 4455–4475, https://doi.org/10.5194/bg-20-4455-2023, 2023a. a, b, c
Ahongshangbam, J., Kulmala, L., and Järvi, L.: Datasets of sap flow, meteorological, leaf gas measurements in urban green areas in Helsinki (Version v1), Zenodo [data set], https://doi.org/10.5281/zenodo.7525319, 2023b. a
Allaire, S. E., Dufour-L'Arrivée, C., Lafond, J. A., Lalancette, R., and Brodeur, J.: Carbon dioxide emissions by urban turfgrass areas, Can. J. Soil Sci., 88, 529–532, https://doi.org/10.4141/CJSS07043, 2008. a, b
Bergkvist, J., Lagergren, F., Linderson, M.-L. F., Miller, P., Lindeskog, M., and Jönsson, A. M.: Modelling managed forest ecosystems in Sweden: An evaluation from the stand to the regional scale, Ecol. Model., 477, 110253, https://doi.org/10.1016/j.ecolmodel.2022.110253, 2023. a
Berland, A., Shiflett, S. A., Shuster, W. D., Garmestani, A. S., Goddard, H. C., Herrmann, D. L., and Hopton, M. E.: The role of trees in urban stormwater management, Landscape Urban Plan., 162, 167–177, 2017. a
Bezyk, Y., Dorodnikov, M., Grzelka, A., and Wroniszewska, A.: Characteristics of temporal variability of urban ecosystem-atmosphere CO2, CH4, and N2O fluxes, E3S Web Conf., 44, 00013, https://doi.org/10.1051/e3sconf/20184400013, 2018. a
Böttcher, K., Markkanen, T., Thum, T., Aalto, T., Aurela, M., Reick, C. H., Kolari, P., Arslan, A. N., and Pulliainen, J.: Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations, Remote Sens., 8, 580, https://doi.org/10.3390/rs8070580, 2016. a
Cambou, A., Saby, N. P. A., Hunault, G., Nold, F., Cannavo, P., Schwartz, C., and Vidal-Beaudet, L.: Impact of city historical management on soil organic carbon stocks in Paris (France), J. Soils Sediments, 21, 1038–1052, https://doi.org/10.1007/s11368-020-02869-9, 2021. a
Chapin, F. S., Matson, P. A., and Vitousek, P. M.: Principles of Terrestrial Ecosystem Ecology, Springer, https://doi.org/10.1007/978-1-4419-9504-9, 2011. a
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils, Water Resour. Res., 20, 682–690, https://doi.org/10.1029/WR020i006p00682, 1984. a
Crameri, F., Shephard, G. E., and Heron, P. J.: The misuse of colour in science communication, Nat. Commun., 11, 5444, https://doi.org/10.1038/s41467-020-19160-7, 2020. a
Cuthbert, M. O., Rau, G. C., Ekström, M., O'Carroll, D. M., and Bates, A. J.: Global climate-driven trade-offs between the water retention and cooling benefits of urban greening, Nat. Commun., 13, 518, https://doi.org/10.1038/s41467-022-28160-8, 2022. a
Dahlhausen, J., Rötzer, T., Biber, P., van der Maaten-Theunissen, M., and Pretzsch, H.: Urban climate modifies tree growth in Berlin, International J. Biometeorol., 62, 795–808, https://doi.org/10.1007/s00484-017-1481-3, 2018. a
Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon decomposition and feedbacks to climate change, Nature, 440, 165–173, https://doi.org/10.1038/nature04514, 2006. a
Decina, S. M., Hutyra, L. R., Gately, C. K., Getson, J. M., Reinmann, A. B., Short Gianotti, A. G., and Templer, P. H.: Soil respiration contributes substantially to urban carbon fluxes in the greater Boston area, Environ. Pollut., 212, 433–439, https://doi.org/10.1016/j.envpol.2016.01.012, 2016. a
Edmondson, J., Stott, I., Davies, Z., Gaston, K., and Leake, J.: Soil surface temperatures reveal moderation of the urban heat island effect by trees and shrubs, Sci. Rep., 6, 33708,https://doi.org/10.1038/srep33708, 2016. a
Farquhar, G., von Caemmerer, S., and Berry, J.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980. a
Ferrini, F., Fini, A., Mori, J., and Gori, A.: Role of Vegetation as a Mitigating Factor in the Urban Context, Sustainability, 12, 4247, https://doi.org/10.3390/su12104247, 2020. a
GADM: Maps and data, Ver 4.1, https://gadm.org/index.html (last access: 24 February 2023), 2023. a
Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader, J., Böttinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak, K., Gayler, V., Haak, H., Hollweg, H., Ilyina, T., Kinne, S., Kornblueh, L., Matei, D., Mauritsen, T., Mikolajewicz, U., Mueller, W., Notz, D., Pithan, F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur, R., Segschneider, J., Six, K. D., Stockhause, M., Timmreck, C., Wegner, J., Widmann, H., Wieners, K., Claussen, M., Marotzke, J., and Stevens, B.: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5, J. Adv. Model. Earth Sy., 5, 572–597, https://doi.org/10.1002/jame.20038, 2013. a
Goll, D. S., Brovkin, V., Liski, J., Raddatz, T., Thum, T., and Todd-Brown, K. E. O.: Strong dependence of CO2 emissions from anthropogenic land cover change on initial land cover and soil carbon parametrization, Global Biogeochem. Cy., 29, 1511–1523, https://doi.org/10.1002/2014GB004988, 2015. a
Grimmond, C. S. B. and Oke, T. R.: An evapotranspiration-interception model for urban areas, Water Resour. Res., 27, 1739–1755, 1991. a
Hagemann, S. and Stacke, T.: Impact of the soil hydrology scheme on simulated soil moisture memory, Clim. Dynam., 44, 1731–1750, https://doi.org/10.1007/s00382-014-2221-6, 2014. a
Hardiman, B. S., Wang, J. A., Hutyra, L. R., Gately, C. K., Getson, J. M., and Friedl, M. A.: Accounting for urban biogenic fluxes in regional carbon budgets, Sci. Total Environ., 592, 366–372, https://doi.org/10.1016/j.scitotenv.2017.03.028, 2017. a
Hari, P., Aakala, T., Aalto, J., Bäck, J., Hollmén, J., Jõgiste, K., Koupaei, K. K., Kähkönen, M. A., Korpela, M., Kulmala, L., Nikinmaa, E., Pumpanen, J., Salkinoja-Salonen, M., Schiestl-Aalto, P., Simojoki, A., and Havimo, M.: Newtonian boreal forest ecology: The Scots pine ecosystem as an example, PLOS ONE, 12, 1–27, https://doi.org/10.1371/journal.pone.0177927, 2017. a
Havu, M., Kulmala, L., Shing Lee, H., Saranko, O., Soininen, J., Ahongshangbam, J., and Järvi, L.: CO2 uptake of urban vegetation in a warming Nordic city, Urban For. Urban Gree., 94, 128261, https://doi.org/10.1016/j.ufug.2024.128261, 2024. a, b, c
Ilvesniemi, H., Pumpanen, J., Duursma, R., Hari, P., Keronen, P., Kolari, P., Kulmala, M., Mammarella, I., Nikinmaa, E., Rannik, Ü., Pohja, T., Siivola, E., and Vesala, T.: Water balance of a boreal Scots pine forest, Boreal Environ. Res., 15, 375–396, 2010. a
Imhoff, M., Bounoua, L., Ricketts, T., Loucks, C., and Harriss, R.: Global patterns in human consumption of net primary production, Nature, 429, 870–873, https://doi.org/10.1038/nature02619, 2004. a
Inar, H.: Automatic GPP data (Version 1), Finnish Meteorological Institute [data set], https://doi.org/10.57707/FMI-B2SHARE.840B8A856ABF43E18B3FBB329EED5305, 2024. a
Ivashchenko, K., Ananyeva, N., Vasenev, V., Sushko, S., Seleznyova, A., and Kudeyarov, V.: Microbial C-availability and organic matter decomposition in urban soils of megapolis depend on functional zoning, Soil Environ., 38, 31–41, https://doi.org/10.25252/SE/19/61524, 2019. a
Järvi, L.: Daily NEE data from Kumpula Helsinki (Version 1), Finnish Meteorological Institute [data set], https://doi.org/10.57707/FMI-B2SHARE.E638F63A3E6F45EB890E964726154964, 2024. a
Järvi, L., Hannuniemi, H., Hussein, T., Junninen, H., Aalto, P., Hillamo, R., Mäkelä, T., Keronen, P., Siivola, E., Vesala, T., and Kulmala, M.: The urban measurement station SMEAR III: continuous monitoring of air pollution and surface-atmosphere interactions in Helsinki, Finland, Boreal Environ. Res., 14, 86–109, 2009. a, b, c
Järvi, L., Nordbo, A., Junninen, H., Riikonen, A., Moilanen, J., Nikinmaa, E., and Vesala, T.: Seasonal and annual variation of carbon dioxide surface fluxes in Helsinki, Finland, in 2006–2010, Atmos. Chem. Phys., 12, 8475–8489, https://doi.org/10.5194/acp-12-8475-2012, 2012. a, b
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
Järvi, L., Grimmond, C., McFadden, J. P., Christen, A., Strachan, I. B., Taka, M., Warsta, L., and Heimann, M.: Warming effects on the urban hydrology in cold climate regions, Sci. Rep., 7, 5833, 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, 2019. a, b, c, d
Jasek-Kamińska, A., Zimnoch, M., Wachniew, P., and Różański, K.: Urban CO2 Budget: Spatial and Seasonal Variability of CO2 Emissions in Krakow, Poland, Atmosphere, 11, 629, https://doi.org/10.3390/atmos11060629, 2020. a, b
Jokinen, P., Pirinen, P., Kaukoranta, J.-P., Kangas, A., Alenius, P., Eriksson, P., Johansson, M., and Wilkman, S.: Tilastoja Suomen ilmastosta ja merestä 1991–2020, no. 8 in Ilmatieteen laitoksen raportteja, Ilmatieteen laitos, Helsinki, http://hdl.handle.net/10138/336063, 2021. a
Karvinen, E.: Soil respiration, soil carbon, soil temperature, and soil moisture measured in urban green spaces in Helsinki during 2020–2022 (Version 1), Finnish Meteorological Institute [data set], https://doi.org/10.57707/FMI-B2SHARE.F7BA414BFD3642168AC38A95835B06BC, 2023. a
Karvinen, E., Backman, L., Järvi, L., and Kulmala, L.: Soil respiration across a variety of tree-covered urban green spaces in Helsinki, Finland, SOIL, 10, 381–406, https://doi.org/10.5194/soil-10-381-2024, 2024. a, b, c
Kim, S., Sinclair, V. A., Räisänen, J., and Ruuhela, R.: Heat waves in Finland: present and projected summertime extreme temperatures and their associated circulation patterns, Int. J. Climatol., 38, 1393–1408, https://doi.org/10.1002/joc.5253, 2018. a
Knorr, W., Jiang, L., and Arneth, A.: Climate, CO2 and human population impacts on global wildfire emissions, Biogeosciences, 13, 267–282, https://doi.org/10.5194/bg-13-267-2016, 2016. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a
Leuzinger, S., Vogt, R., and Körner, C.: Tree surface temperature in an urban environment, Agr. Forest Meteorol., 150, 56–62, https://doi.org/10.1016/j.agrformet.2009.08.006, 2010. a
Lian, J., Lauvaux, T., Utard, H., Bréon, F.-M., Broquet, G., Ramonet, M., Laurent, O., Albarus, I., Chariot, M., Kotthaus, S., Haeffelin, M., Sanchez, O., Perrussel, O., Denier van der Gon, H. A., Dellaert, S. N. C., and Ciais, P.: Can we use atmospheric CO2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris, Atmos. Chem. Phys., 23, 8823–8835, https://doi.org/10.5194/acp-23-8823-2023, 2023. a
Lindeskog, M., Arneth, A., Bondeau, A., Waha, K., Seaquist, J., Olin, S., and Smith, B.: Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa, Earth Syst. Dynam., 4, 385–407, https://doi.org/10.5194/esd-4-385-2013, 2013. a
Lindeskog, M., Smith, B., Lagergren, F., Sycheva, E., Ficko, A., Pretzsch, H., and Rammig, A.: Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9710): implementation and evaluation of simulations for Europe, Geosci. Model Dev., 14, 6071–6112, https://doi.org/10.5194/gmd-14-6071-2021, 2021. a, b, c
Livesley, S. J., Dougherty, B. J., Smith, A. J., Navaud, D., Wylie, L. J., and Arndt, S. K.: Soil-atmosphere exchange of carbon dioxide, methane and nitrous oxide in urban garden systems: impact of irrigation, fertiliser and mulch, Urban Ecosyst., 13, 273–293, https://doi.org/10.1007/s11252-009-0119-6, 2010. a
Luyssaert, S., Inglima, I., Jung, M., Richardson, A. D., Reichstein, M., Papale, D., Piao, S. L., Schulze, E.-D., Wingate, L., Matteucci, G., Aragao, L., Aubinet, M., Beer, C., Bernhofer, C., Black, K. G., Bonal, D., Bonnefond, J.-M., Chambers, J., Ciais, P., Cook, B., Davis, K. J., Dolman, A. J., Gielen, B., Goulden, M., Grace, J., Granier, A., Grelle, A., Griffis, T., Grünwald, T., Guidolotti, G., Hanson, P. J., Harding, R., Hollinger, D. Y., Hutyra, L. R., Kolari, P., Kruijt, B., Kutsch, W., Lagergren, F., Laurila, T., Law, B. E., Le Maire, G., Lindroth, A., Loustau, D., Malhi, Y., Mateus, J., Migliavacca, M., Misson, L., Montagnani, L., Moncrieff, J., Moors, E., Munger, J. W., Nikinmaa, E., Ollinger, S. V., Pita, G., Rebmann, C., Roupsard, O., Saigusa, N., Sanz, M. J., Seufert, G., Sierra, C., Smith, M.-L., Tang, J., Valentini, R., Vesala, T., and Janssens, I. A.: CO2 balance of boreal, temperate, and tropical forests derived from a global database, Glob. Change Biol., 13, 2509–2537, https://doi.org/10.1111/j.1365-2486.2007.01439.x, 2007. a
Ma, J., Anthoni, P., Olin, S., Rabin, S. S., Bayer, A. D., Xia, L., and Arneth, A.: Estimating the global influence of cover crops on ecosystem service indicators in croplands with the LPJ-GUESS model, Earth's Future, 11, e2022EF003142, https://doi.org/10.1029/2022EF003142, 2023. a
Mahadevan, P., Wofsy, S. C., Matross, D. M., Xiao, X., Dunn, A. L., Lin, J. C., Gerbig, C., Munger, J. W., Chow, V. Y., and Gottlieb, E. W.: A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM), Global Biogeochem. Cy., 22, https://doi.org/10.1029/2006GB002735, 2008. a
Mäkelä, A., Hari, P., Berninger, F., Hänninen, H., and Nikinmaa, E.: Acclimation of photosynthetic capacity in Scots pine to the annual cycle of temperature, Tree Physiol., 24, 369–376, https://doi.org/10.1093/treephys/24.4.369, 2004. a
Marcotullio, P. J., Sarzynski, A., Albrecht, J., Schulz, N., and Garcia, J.: The geography of global urban greenhouse gas emissions: an exploratory analysis, Clim. Change, 121, 1573–1480, https://doi.org/10.1007/s10584-013-0977-z, 2013. a
Martín Belda, D., Anthoni, P., Wårlind, D., Olin, S., Schurgers, G., Tang, J., Smith, B., and Arneth, A.: LPJ-GUESS/LSMv1.0: a next-generation land surface model with high ecological realism, Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, 2022. a
McPherson, E. G., Simpson, J. R., Xiao, Q., and Wu, C.: Million trees Los Angeles canopy cover and benefit assessment, Landscape Urban Plan., 99, 40–50, https://doi.org/10.1016/j.landurbplan.2010.08.011, 2011. a
McPherson, G., Simpson, J. R., Peper, P. J., Maco, S. E., and Xiao, Q.: Municipal forest benefits and costs in five US cities, J. Forest., 103, 411–416, https://doi.org/10.1093/jof/103.8.411, 2005. a
Meineke, E., Youngsteadt, E., Dunn, R. R., and Frank, S. D.: Urban warming reduces aboveground carbon storage, P. R. Soc. B., 283, 1–9, https://doi.org/10.1098/rspb.2016.1574, 2016. a, b
Menzer, O., Meiring, W., Kyriakidis, P. C., and McFadden, J. P.: Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling, Atmos. Environ., 101, 312–327, 2015. a
Miller, J., Lehman, S. J., Verhulst, K. R., Miller, C. E., Duren, R. M., Yadav, V., Newman, S., , and Sloop, C. D.: Large and seasonally varying biospheric CO2 fluxes in the Los Angeles megacity revealed by atmospheric radiocarbon, P. Natl. Acad. Sci. USA, 117, 26681–26687, https://doi.org/10.1073/pnas.2005253117, 2020. a
Mitchell, L. E., Lin, J. C., Hutyra, L. R., and et al.: A multi-city urban atmospheric greenhouse gas measurement data synthesis, Sci. Data, 9, 1–9, https://doi.org/10.1038/s41597-022-01467-3, 2022. a
Moffat, A. M., Papale, D., Reichstein, M., Hollinger, D. Y., Richardson, A. D., Barr, A. G., Beckstein, C., Braswell, B. H., Churkina, G., Desai, A. R., Falge, E., Gove, J. H., Heimann, M., Hui, D., Jarvis, A. J., Kattge, J., Noormets, A., and Stauch, V. J.: Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes, Agr. Forest Meteorol., 147, 209–232, https://doi.org/10.1016/j.agrformet.2007.08.011, 2007. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
Mäki, M., Ryhti, K., Fer, I., Ťupek, B., Vestin, P., Roland, M., Lehner, I., Köster, E., Lehtonen, A., Bäck, J., Heinonsalo, J., Pumpanen, J., and Kulmala, L.: Heterotrophic and rhizospheric respiration in coniferous forest soils along a latitudinal gradient, Agr. Forest Meteorol., 317, 108876, https://doi.org/10.1016/j.agrformet.2022.108876, 2022. a
National Land Survey of Finland: NLS orthophotos, https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/orthophotos (last acess: 3 Marh 2023), 2023a. a
National Land Survey of Finland: Topographic Database, http://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/topographic-database (last access: 7 March 2023), 2023b. a
Nevalainen, O.: ollinevalainen/satellitetools: v1.0.0 (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.5993292, 2022. a
Nevalainen, O., Niemitalo, O., Fer, I., Juntunen, A., Mattila, T., Koskela, O., Kukkamäki, J., Höckerstedt, L., Mäkelä, L., Jarva, P., Heimsch, L., Vekuri, H., Kulmala, L., Stam, Å., Kuusela, O., Gerin, S., Viskari, T., Vira, J., Hyväluoma, J., Tuovinen, J.-P., Lohila, A., Laurila, T., Heinonsalo, J., Aalto, T., Kunttu, I., and Liski, J.: Towards agricultural soil carbon monitoring, reporting, and verification through the Field Observatory Network (FiON), Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, 2022. a, b
Nordbo, A., Järvi, L., and Vesala, T.: Revised eddy covariance flux calculation methodologies–effect on urban energy balance, Tellus, 64, 18184, 2012. a
Nowak, D. J. and Crane, D. E.: The Urban Forest Effects (UFORE) Model: quantifying urban forest structure and functions, in: Integrated tools for natural resources inventories in the 21st century, edited by: Hansen, M. and Burk, T., Gen. Tech. Rep. NC-212. St. Paul, MN, US Dept. of Agriculture, Forest Service, North Central Forest Experiment Station, 212, 714–720, https://doi.org/10.1016/j.wasman.2003.11.007, 2000. a
Nowak, D. J., Greenfield, E. J., Hoehn, R. E., and Lapoint, E.: Carbon storage and sequestration by trees in urban and community areas of the United States, Environ. Pollut., 178, 229–236, https://doi.org/10.1016/j.envpol.2013.03.019, 2013. a
Parton, W., Scurlock, J., Ojima, D., Gilmanov, T., Scholes, R. J., Schimel, D. S., Kirchner, T., Menaut, J.-C., Seastedt, T., García Moya, E., Kamnalrut, A., and Kinyamario, J. I.: Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide, Global Biogeochem. Cy., 7, 785–809, 1993. a
Pouyat, R. V. and Yesilonis, I. D., and Nowak, D. J.: Carbon Storage by Urban Soils in the United States, J. Environ. Qual., 35, 1566–1575, https://doi.org/10.2134/jeq2005.0215, 2006. a
Pumpanen, J., Kulmala, L., Lindén, A., Kolari, P., Nikinmaa, E., and Hari, P.: Seasonal dynamics of autotrophic respiration in boreal forest soil estimated by continuous chamber measurements, Boreal Envirom. Res., 20, 637–650, 2015. a
Rahman, M. A., Stratopoulos, L. M., Moser-Reischl, A., Zölch, T., Häberle, K.-H., Rötzer, T., Pretzsch, H., and Pauleit, S.: Traits of trees for cooling urban heat islands: A meta-analysis, Build. Environ., 170, 106606, 2020. a
Rasheed, M. W., Tang, J., Sarwar, A., Shah, S., Saddique, N., Khan, M. U., Imran Khan, M., Nawaz, S., Shamshiri, R. R., Aziz, M., and Sultan, M.: Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review, Sustainability, 14, 11538, https://doi.org/10.3390/su141811538, 2022. a
Reckien, D., Flacke, J., Dawson, R. J., Heidrich, O., Olazabal, M., Foley, A., Hamann, J. J.-P., Orru, H., Salvia, M., De Gregorio Hurtado, S., Geneletti, D., and Pietrapertosa, F.: Climate change response in Europe: what’s the reality? Analysis of adaptation and mitigation plans from 200 urban areas in 11 countries, Clim. Change, 122, 1573–1480, https://doi.org/10.1007/s10584-013-0989-8, 2014. a
Reick, C. H., Raddatz, T., Brovkin, V., and Gayler, V.: Representation of natural and anthropogenic land cover change in MPI-ESM, J. Adv. Model. Earth Sy., 5, 459–482, https://doi.org/10.1002/jame.20022, 2013. a, b
Reitz, O., Graf, A., Schmidt, M., Ketzler, G., and Leuchner, M.: Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning, J. Geophys. Res.-Biogeo., 126, e2020JG005814, https://doi.org/10.1029/2020JG005814, 2021. a
Rosenzweig, C., Solecki, W., Hammer, S. A., and Mehrotra, S.: Cities lead the way in climate-change action, Nature, 467, 909–911, https://doi.org/10.1038/467909a, 2010. a
Russo, A., Escobedo, F. J., Timilsina, N., Schmitt, A. O., Varela, S., and Zerbe, S.: Assessing urban tree carbon storage and sequestration in Bolzano, Italy, International Journal of Biodiversity Science, Ecosystem Services & Management, 10, 54–70, https://doi.org/10.1080/21513732.2013.873822, 2014. a
Rustad, L. E., Huntington, T. G., and Boone, R. D.: Controls on soil respiration: Implications for climate change, Biogeochemistry, 48, 1–6, https://doi.org/10.1023/A:1006255431298, 2000. a, b
Ryan, M. G.: The enduring mystery of differences between eddy covariance and biometric measurements for ecosystem respiration and net carbon storage in forests, New Phytol., 239, 2060–2063, https://doi.org/10.1111/nph.19105, 2023. a
Ryan, M. G. and Law, B. E.: Interpreting, measuring, and modeling soil respiration, Biogeochemistry, 73, 3–27, https://doi.org/10.1007/s10533-004-5167-7, 2005. a
Sarzhanov, D. A., Vasenev, V. I., Vasenev, I. I., Sotnikova, Y. L., Ryzhkov, O. V., and Morin, T.: Carbon stocks and CO2 emissions of urban and natural soils in Central Chernozemic region of Russia, Catena, 158, 131–140, https://doi.org/10.1016/j.catena.2017.06.021, 2017. a
Schiestl-Aalto, P., Ryhti, K., Mäkelä, A., Peltoniemi, M., Bäck, J., and Kulmala, L.: Analysis of the NSC Storage Dynamics in Tree Organs Reveals the Allocation to Belowground Symbionts in the Framework of Whole Tree Carbon Balance, Frontiers in Forests and Global Change, 2, 1–14, https://doi.org/10.3389/ffgc.2019.00017, 2019. a
Setälä, H. M., Francini, G., Allen, J. A., Hui, N., Jumpponen, A., and Kotze, D. J.: Vegetation Type and Age Drive Changes in Soil Properties, Nitrogen, and Carbon Sequestration in Urban Parks under Cold Climate, Front. Ecol. Evol., 4, https://doi.org/10.3389/fevo.2016.00093, 2016. a
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, 2003. a
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space, Global Ecol. Biogeogr., 10, 621–637, 2001. a
Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Siltberg, J., and Zaehle, S.: Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model, Biogeosciences, 11, 2027–2054, https://doi.org/10.5194/bg-11-2027-2014, 2014. a, b
Soares, A. L., Rego, F. C., McPherson, E., Simpson, J., Peper, P., and Xiao, Q.: Benefits and costs of street trees in Lisbon, Portugal, Urban Forest. Urban Green., 10, 69–78, https://doi.org/10.1016/j.ufug.2010.12.001, 2011. a
Sushko, S., Ananyeva, N., Ivashchenko, K., Vasenev, V., and Kudeyarov, V.: Soil CO2 emission, microbial biomass, and microbial respiration of woody and grassy areas in Moscow (Russia), J. Soils Sediments, 19, 3217–3225, https://doi.org/10.1007/s11368-018-2151-8, 2019. a, b
Tarantino, A., Ridley, A., and Toll, D.: Field Measurement of Suction, Water Content, and Water Permeability, Geotechnical and Geological Engineering, 26, 751–782, https://doi.org/10.1007/s10706-008-9205-4, 2008. a
Thienelt, T. S. and Anderson, D. E.: Estimates of energy partitioning, evapotranspiration, and net ecosystem exchange of CO2 for an urban lawn and a tallgrass prairie in the Denver metropolitan area under contrasting conditions, Urban Ecosyst., 24, 1201–1220, https://doi.org/10.1007/s11252-021-01108-4, 2021. a, b, c
Thölix, L., Backman, L., and Havu, M.: Model results of LAI, GPP, soil respiration, soil temperature, and soil moisture in 2018-2021 and NEE in 2006-2021 in urban green spaces in Helsinki (Version 1), Finnish Meteorological Institute [data set], https://doi.org/10.57707/FMI-B2SHARE.0CB5E547DD2F48DA89C1B690604DD3D0, 2024. a
Trémeau, J., Olascoaga, B., Backman, L., Karvinen, E., Vekuri, H., and Kulmala, L.: Lawns and meadows in urban green space – a comparison from perspectives of greenhouse gases, drought resilience and plant functional types, Biogeosciences, 21, 949–972, https://doi.org/10.5194/bg-21-949-2024, 2024. a, b, c
Trémeau, J., Karvinen, E., and Olascoaga, B.: Fluxes and plant diversity data in urban grasslands, Finnish Meteorological Institute [data set], https://doi.org/10.23728/FMI-B2SHARE.920C1E5F08A74A6D9DFCB3A08CFC6734, 2023. a
Ueyama, M. and Ando, T.: Diurnal, weekly, seasonal, and spatial variabilities in carbon dioxide flux in different urban landscapes in Sakai, Japan, Atmos. Chem. Phys., 16, 14727–14740, https://doi.org/10.5194/acp-16-14727-2016, 2016. a
Vekuri, H., Tuovinen, J., Kulmala, L., Papale, D., Kolari, P., Aurela, M., Laurila, T., Liski, J., and Lohila, A.: A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates, Sci. Rep., 13, 1720, https://doi.org/10.1038/s41598-023-28827-2, 2023. a, b
Vesala, T., Järvi, L., Launiainen, S., Sogachev, A., Rannik, Ü., Mammarella, I., Siivola, E., Keronen, P., Rinne, J., Riikonen, A., and Nikinmaa, E.: Surface–atmosphere interactions over complex urban terrain in Helsinki, Finland, Tellus B, 60, 188–199, https://doi.org/10.3402/tellusb.v60i2.16914, 2008. a, b
Wang, J., Xiang, Z., Wang, W., Chang, W., and Wang, Y.: Impacts of strengthened warming by urban heat island on carbon sequestration of urban ecosystems in a subtropical city of China, Urban Ecosyst., 24, 1165–1177, https://doi.org/10.1007/s11252-021-01104-8, 2021. a
Wei, D., Reinmann, A., Schiferl, L. D., and Commane, R.: High resolution modeling of vegetation reveals large summertime biogenic CO2 fluxes in New York City, Environ. Res. Lett., 17, 124031, https://doi.org/10.1088/1748-9326/aca68f, 2022. a
Weiss, M. and Baret, F.: S2ToolBox Level 2 Products: LAI, FAP AR, FCOVER, Tech. rep., Institut National de la Recherche Agronomique (INRA), Avignon, http://step.esa.int/docs/extra/ATBD_S2ToolBox_V2.0.pdf (last access: 3 February 2025), 2016. a
Wohlfahrt, G., Hammerle, A., Haslwanter, A., Bahn, M., Tappeiner, U., and Cernusca, A.: Seasonal and inter-annual variability of the net ecosystem CO2 exchange of a temperate mountain grassland: Effects of weather and management, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009286, 2008. a
Wohlfahrt, G., Tomelleri, E., and Hammerle, A.: The urban imprint on plant phenology, Nat. Ecol. Evol., 3, 1668–1674, https://doi.org/10.1038/s41559-019-1017-9, 2019. a
Wolf, K. L., Lam, S. T., McKeen, J. K., Richardson, G. R., Van Den Bosch, M., and Bardekjian, A. C.: Urban trees and human health: A scoping review, Int. J. Environ. Res. Pub. He., 17, 4371, 2020. a
Zahn, E., Bou-Zeid, E., Good, S. P., Katul, G. G., Thomas, C. K., Ghannam, K., Smith, J. A., Chamecki, M., Dias, N. L., Fuentes, J. D., Alfieri, J. G., Kwon, H., Caylor, K. K., Gao, Z., Soderberg, K., Bambach, N. E., Hipps, L. E., Prueger, J. H., and Kustas, W. P.: Direct partitioning of eddy-covariance water and carbon dioxide fluxes into ground and plant components, Agr. Forest Meteorol., 315, 108790, https://doi.org/10.1016/j.agrformet.2021.108790, 2022. a
Zapater, M., Bréda, N., Bonal, D., Pardonnet, S., and Granier, A.: Differential response to soil drought among co-occurring broad-leaved tree species growing in a 15- to 25-year-old mixed stand, Ann. For. Sci., 70, 31–39, https://doi.org/10.1007/s13595-012-0233-0, 2013. a
Zheng, Y., Havu, M., Liu, H., Cheng, X., Wen, Y., Lee, H. S., Ahongshangbam, J., and Järvi, L.: Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance, Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, 2023. a
Zirkle, G., Lal, R., and Augustin, B.: Modeling Carbon Sequestration in Home Lawns, HortScience, 46, 808–814, https://doi.org/10.21273/HORTSCI.46.5.808, 2011. a
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.
Cities aim for carbon neutrality and seek to understand urban vegetation's role as a carbon...
Altmetrics
Final-revised paper
Preprint