Urban ecosystems have the potential to provide carbon (C) sequestration; however, the specifics of urban C sequestration and its behaviour under future climate change are poorly quantified. Here, we explore the C sequestration behaviour of five urban ecosystem types (lawn, mesic meadow, dry meadow, park and forest) and two irrigated types (irrigated park and lawn) in Finnish cities under climate change. Our focus was on the role of meteorological drivers and we wanted to understand how weather conditions influence regional differences in the C sequestration of the urban ecosystem types. We utilized the JSBACH model forced with simulated meteorological data between 2006 and 2100 from the CORDEX initiative, downscaled data from the CanESM2, MIROC5 and CNRM-CM5 climate models. The findings indicate that, overall, urban ecosystems with trees exhibit a higher net ecosystem production (NEP) and demonstrate more stable C sequestration in the face of future climate change. In contrast, different grassland systems are smaller C sinks and can become C sources under different climate projections. Irrigated versions of park and lawn had lower C sequestration than their non-irrigated counterparts through the simulation period. We found that changes in annual precipitation and incoming radiation were the predominant climatic drivers of NEP, while changes in temperature had a mixed or weak effect. These findings highlight the importance of long-term planning, management of urban ecosystems, and understanding of the trade-offs between various ecosystem types and services associated with these urban green spaces in future climates.

Urban environments expand in both population and area, stressing the need for means to study and understand urban green spaces (UGS) in a comprehensive manner. Especially tree-covered UGS are central to biogenic carbon (C) sequestration in cities, as well as for the many other ecosystem services they provide. However, many modelling applications used to study them have certain limitations, for instance related to inaccurate representation of tree stand development over time or considering the effects of changing climate. In this study, we examined the applicability of a non-urban forest growth and C balance model PREBASSO to simulating tree-covered UGS at a set of study sites in Southern Finland. We also investigated both the potential of acquiring tree stand composition from airborne laser scanning data and the role of uncertainty in soil conditions in the model results. In doing so, we aimed to address some data limitations typical for urban areas and provide new tools for studying the development of C sequestration of tree-covered UGS in the future. Our results showed that the modelling approach effectively captured the phenology of C sequestration when evaluated against satellite-observed leaf area index time series. However, the model tended to underestimate peak leaf area index and frequently overestimate annual tree height growth. C sequestration was particularly sensitive to the assigned forest site type and to assumptions related to soil C stock and water holding properties. The simulations further indicated that younger forest stands may exhibit higher increase in C sequestration in future conditions. Together, these findings both demonstrated the promise of the assessed model in representing tree-covered UGS and highlighted several avenues for methodological improvement.

Urban vegetation mitigates carbon and provides ecosystem services. Quantifying these benefits relies on land surface models like JSBACH, but high-resolution long-term simulations are computationally heavy and too complex for practical applications. Machine learning emulators offer a computationally efficient alternative. Here, we present daily and monthly emulators for gross primary production (GPP) and net ecosystem exchange (NEE) of CO₂ for different plant functional types (PFTs) in Helsinki: deciduous and coniferous trees, lawn, and crops represented by 50/50 weight of cereal and agricultural grass. The emulators are trained on JSBACH simulations for 1991-2015 and evaluated for 2016-2024. Predictor variables are derived from daily air temperature, precipitation, and shortwave radiation.The emulators are based on gradient boosting models with automated hyperparameter optimization. We trained separate models for each target variable and PFT. To estimate the total value of a target variable for each 50 m × 50 m pixel in Helsinki, we combined PFT specific predictions weighted by the fractional coverage of each vegetation type within the pixel.Emulator performance was high across all plant functional types and for both carbon fluxes. The monthly emulator outperformed the daily emulator consistently, as demonstrated by higher explained variance and lower errors for both GPP and NEE. Although the monthly emulator smoothed out short-term variability, it still reproduced total annual GPP and NEE with a level of accuracy almost matching that of the daily emulator. The two machine learning emulators developed in this study achieved high levels of accuracy, enabling faster simulations than the original land surface model. The daily emulator provided more detailed information on how vegetation responds to different meteorological conditions. In contrast, the monthly emulator was better suited to urban planning, offering fast and reliable information on the carbon sequestration of various PFTs over extended periods, while reducing simulation time by over 95% compared to the daily emulator.

Climate change alters boreal forest dynamics. The risk of boreal forest disturbances are expected to increase by the end of the century compared to the current state. However, projecting the future impacts of climate change on forest disturbances inherently contains uncertainties related to the global climate models. Here, we study the impact of climate change on forest fires and wind damage using ecosystem model (JSBACH) simulations from 1951 to 2100. The simulations are driven by output from three global climate driver models that have been bias-corrected and downscaled (CORDEX EUR-44 domain). The global models from CMIP5 were run under two forcing scenarios, RCP 4.5 and RCP 8.5. To tackle the uncertainty of climate change projections, we use six climate projections. In our simulations the fire season in Fennosscandia is projected to extend in both spring and autumn. The fire season is estimated to lengthen by 20-52 days, starting 10-23 days earlier and ending 10-30 days later, by the end of the century. In general, it is expected that the number of fires and burnt area are projected to increase from the reference period (1981-2010) to the end of the century (2071-2100) due to rising temperatures, despite increases in precipitation. However, the amount and direction of change varies significantly between climate projections and locations. Our preliminary results implicate that the risk for wind damage may change and affect to the number of fires. Wind damage affects the size of litter pools that change the amount of fuel available for fires. Finally, our aim is to study the interaction between forest fires and wind damage in boreal forests.

Many cities seek carbon neutrality and are therefore interested in the sequestration potential of urban vegetation. However, the heterogeneous nature of urban vegetation and environmental conditions limits comprehensive measurement efforts, setting expectations for carbon cycle modelling. In this study, we examined the performance of three models – the Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg (JSBACH), the Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS), and the Surface Urban Energy and Water Balance Scheme (SUEWS) – in estimating carbon sequestration rates in both irrigated and non-irrigated lawns, park trees (Tilia cordata), and urban forests (Betula pendula) in Helsinki, Finland. The test data included observations of various environmental parameters and component fluxes such as soil moisture and temperature, sap flow, leaf area index, photosynthesis, soil respiration, and net ecosystem exchange. Our analysis revealed that these models effectively simulated seasonal and annual variations, as well as the impacts of weather events on carbon fluxes and related factors. However, the validation of the absolute level of modelled fluxes proved difficult due to differences in the scale of the observations and models, particularly for mature trees, and due to the fact that net ecosystem exchange measurements in urban areas include some anthropogenic emissions. Irrigation emerged as a key factor often improving carbon sequestration, while tree-covered areas demonstrated greater carbon sequestration rates compared to lawns on an annual scale. Notably, all models demonstrated similar mean net ecosystem exchange over the urban vegetation sector studied on an annual scale over the study period. However, compared to JSBACH, LPJ-GUESS exhibited higher carbon sequestration rates in tree-covered areas but lower rates in grassland-type areas. All models indicated notable year-to-year differences in annual sequestration rates, but since the same factors, such as temperature and soil moisture, affect processes both assimilating and releasing carbon, connecting the years of high or low carbon sequestration to single meteorological means failed. Overall, this research emphasizes the importance of integrating diverse vegetation types and the impacts of irrigation into urban carbon modelling efforts to inform sustainable urban planning and climate change mitigation strategies.

Quantifying the capacity and dynamics of urban carbon dioxide (CO2) emissions and carbon sequestration is becoming increasingly relevant in the development of integrated monitoring systems for urban greenhouse gas (GHG) emissions. There are multiple challenges in achieving these goals, such as the partitioning of atmospheric measurements of CO2 fluxes to anthropogenic and biospheric processes, the insufficient understanding of urban biospheric processes, and the applicability of existing biosphere models to urban systems. In this study, we applied four biosphere models of varying complexity – diFUME, JSBACH, SUEWS, VPRM – in four urban parks in the city of Zurich and evaluated their performance against in situ measurements collected over almost 2 years on park trees and lawns. In addition, we performed an uncertainty analysis of gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE) of CO2 based on the differences between the estimates of the four models and compared the estimated uncertainties and biospheric fluxes with the monthly anthropogenic CO2 emissions of a wide urban area surrounding the four parks. The results showed that, despite the large differences in model architecture, there was considerable agreement in the seasonal and diurnal GPP, Reco, and NEE estimates. Larger discrepancies between the four models were found for lawn GPP compared to tree GPP, while, for Reco, the differences between lawns and tree areas were similar. On an annual scale, all models agreed, on average, that lawns acted as CO2 sources and tree-covered areas as CO2 sinks during the simulation period, with the exception of diFUME, which simulated both tree and lawn areas as CO2 sources. diFUME and VPRM were more accurate in capturing the onset of the tree leaf growth in spring compared to JSBACH and SUEWS. On the other hand, JSBACH and SUEWS simulated soil water availability more accurately than the satellite-derived water index used by VPRM. The in situ observations revealed a very high spatial variability in lawn Reco across the park areas. All models underestimated the lawn Reco during spring in mowed, sunny locations, whereas the model simulations were closer to the observed Reco in un-mowed, partially shaded locations. The mean monthly uncertainties in biogenic NEE reached 0.8 µmolm-2s-1, which is 10.2 % of the magnitude of the total CO2 balance over the studied area during the month of June. This balance was composed of a mean anthropogenic flux of 8.7 µmolm-2s-1 and a mean biospheric flux of −0.5 µmolm-2s-1. Overall, this study highlights the importance of properly accounting for the biogenic CO2 fluxes and their uncertainties in urban CO2 balance studies, especially during the vegetation growing season, and shows that even simple models, such as VPRM, can adequately simulate the urban biospheric fluxes when appropriately parameterized.

Abstract. Satellite-driven inversions provide valuable information about methane (CH4) fluxes, but the assimilation of total column-averaged dry-air mole fractions of CH4 (XCH4) has been challenging. This study explores, for the first time, the potential of the new lower tropospheric partial column (pXCH4_LT) GOSAT data, retrieved by the Japan Aerospace Exploration Agency (JAXA), to constrain global and regional CH4 fluxes. Using the CarbonTracker Europe-CH4 atmospheric inverse model, we estimated CH4 fluxes between 2016–2019 by assimilating the JAXA/GOSAT pXCH4_LT and XCH4 data and surface CH4 observations, independently of each other. The Northern Hemisphere CH4 fluxes derived from the JAXA/GOSAT pXCH4_LT data were similar to the estimates derived from the surface observations, but was underestimated by about 35 Tg CH4 year-1 (∼6 % of the global total) using the JAXA/GOSAT XCH4 data. For the Southern Hemisphere, the estimates from the both GOSAT inversions were about 15–30 Tg CH4 year-1 higher than that derived from surface data. The evaluations against independent data from the Atmospheric Tomography Mission aircraft campaign showed good agreement in the lower tropospheric CH4 from the inversions using the JAXA/GOSAT pXCH4_LT and surface data. However, the modelled North-South gradients showed significant overestimation in the upper troposphere and stratosphere, possibly due to relatively uniform inter-hemispheric OH distributions that control CH4 sinks. Overall, we found that the use of the JAXA/GOSAT pXCH4_LT data shows considerable potential in constraining global and regional CH4 fluxes, advancing our understanding of the CH4 budget.

As an increasing share of the human population is being clustered in cities, urban areas have swiftly become the epicentres of anthropogenic carbon (C) emissions. Understanding different parts of the biogenic C cycle in urban ecosystems is needed in order to assess the potential to enhance their C stocks as a cost-efficient means to balance the C emissions and mitigate climate change. Here, we conducted a field measurement campaign over three consecutive growing seasons to examine soil respiration carbon dioxide (CO2) fluxes and soil organic carbon (SOC) stocks at four measurement sites in Helsinki, representing different types of tree-covered urban green space commonly found in northern European cities. We expected to find variation in the main drivers of soil respiration – soil temperature, soil moisture, and SOC – as a result of the heterogeneity of urban landscape and that this variation would be reflected in the measured soil respiration rates. In the end, we could see fairly constant statistically significant differences between the sites in terms of soil temperature but only sporadic and seemingly momentary differences in soil moisture and soil respiration. There were also statistically significant differences in SOC stocks: the highest SOC stock was found in inactively managed deciduous urban forest and the lowest under managed streetside lawn with common linden trees. We studied the impacts of the urban heat island (UHI) effect and irrigation on heterotrophic soil respiration with process-based model simulations and found that the variation created by the UHI is relatively minor compared to the increase associated with active irrigation, especially during dry summers. We conclude that, within our study area, the observed variation in soil temperature alone was not enough to cause variation in soil respiration rates between the studied green space types, perhaps because the soil moisture conditions were uniform. Thus, irrigation could potentially be a key factor in altering the soil respiration dynamics in urban green space both within the urban area and in comparison to non-urban ecosystems.

Forest fire dynamics are expected to alter due to climate change. Despite the projected increase in precipitation, rising temperatures will amplify forest fire risk from the present to the end of the century. Here, we analysed changes in fire season, the number of fires, and burnt area in Fennoscandia from 1951 to 2100. Regional simulations from the JSBACH-SPITFIRE ecosystem model (where SPITFIRE stands for SPread and InTensity of FIRE) were performed under two climate change forcing scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5) and three global climate driver models (CanESM2, CNRM-CM5, and MIROC5) with a 0.5 degrees resolution. Simulations were forced by downscaled and bias-corrected EURO-CORDEX data. Generally, as a consequence of the projected longer fire season and drier fuel, the probability of fires is projected to increase. However, changes in fire season, the number of fires, and burnt area are highly dependent on climate projections and location. The fire season is estimated to increase on average from 20 +/- 7 to 52 +/- 12 d, starting from 10 +/- 9 to 23 +/- 11 d earlier and ending from 10 +/- 10 to 30 +/- 16 d later, compared to the reference period (1981-2010), by the end of the century (2071-2100). The results for Finland indicate a change in the number of fires, ranging from -7 +/- 4 % to 98 +/- 56 %, and a change in burnt area, ranging from -19 +/- 24 % to 87 +/- 42 %. These findings contribute to a better understanding of potential changes in the future fire seasons of northern Europe.

Today, city planners design urban futures by considering environmental degradation and climate mitigation. Here, we studied the greenhouse gas fluxes of urban lawns and meadows and linked the observations with plant functional types and soil properties. In eight lawns and eight meadows in the Helsinki metropolitan area, Finland, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes were measured using manual chambers, and plant functional types were recorded. Four of these sites, i.e. an irrigated lawn, an old mesic meadow, a non-irrigated lawn and a young dry meadow, were more intensively studied in 2021–2022. The process-based ecosystem model JSBACH was utilized together with the momentary observations collected approximately every second week on CO2 exchange to quantify the annual carbon (C) balance of these sites. On the remaining sites, we studied the initial dynamics of conversion from lawns to meadows by transforming parts of lawns to meadows in late 2020 and conducting measurements from 2020 to 2022. The mean photosynthetic production (GPP) of the irrigated lawn and mesic meadow was the highest in this study, whereas the dry meadow had the lowest GPP. The studied lawns were stronger C sinks compared to the meadows. However, the net exchange values were uncertain as the soils were not in equilibrium with the vegetation at all sites, which is common for urban habitats, and modelling the heterotrophic emissions was therefore challenging. The conversion from a lawn to a meadow did not affect the fluxes of CH4 and N2O. Moreover, the mesic meadow was more resistant to drought events than the non-irrigated lawn. Lastly, the proportion of herbaceous flowering plants other than grasses was higher in meadows than in lawns. Even though social and economic aspects also steer urban development, these results can guide planning when considering environmentally friendlier green spaces and carbon smartness.

Table S1.Soil properties of the park site (Tilia cordata), urban forest (Betula pendula), irrigated lawn and non-irrigated lawn used in the model simulations.The percentages in the particle size distribution refer to large particles -sand -silt -clay.The soil properties were measured at the park and urban forest.Due to the close connection with the park, the soil properties at the lawns were assumed to be equal to the park. ParkUrban forest Lawns Soil type Sandy loam Sandy loam Sandy loam Bulk density (kg/l) 1.15 1.14 1.15

Large uncertainties persist in quantifying regional CO2 emissions and removals from the land use, land use change, and forestry sector, critical for Finland's climate targets. In support of national greenhouse gas inventories, the scientific community has developed independent top -down and bottom -up approaches to quantify and verify CO2 emissions and removals from biospheric processes. This study merges existing top -down and bottom -up research datasets, including process -based ecosystem models and high -resolution atmospheric inversions, to synthesize available estimates of biospheric CO2 balances from Finnish terrestrial ecosystems. According to the national greenhouse gas inventory, biosphere in Finland removed on average [annual minimum ... maximum] -4.6 [-7.6 ... -1.6] Mt C (of CO2 ) annually between 2012 and 2020. During the same period, regional high -resolution top -down ensemble estimated a mean sink of -12 [-32 ... +2.8] Mt C yr(-1) (deviation pointing to the means of ensemble minimum and maximum) while global top -down ensemble reported a sink closer to inventory with larger deviation between the ensemble members -7.3 [-49 ... +32] Mt C yr(-1) . Corresponding values for regional bottom -up approaches represented by an ensemble of terrestrial ecosystem models closely aligned with the inventory, reporting -4.6 [-21 ... +13] Mt C yr(-1) . The global ecosystem model TRENDY ensemble, with a larger number of ensemble members, estimated an average sink of -9.9 [-30 ... +15] Mt C yr(-1) . Accordingly, we conclude that independent top -down and bottom -up estimates have some consistency in relation to the national greenhouse gas inventory, but at present, large uncertainties found in country -level balances prohibit reliable verification of the inventory.

The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing cryosphere, increasing human activity, and potentially increasing high-latitude methane emissions. Satellite observations from Sentinel-5P TROPOMI provide an unprecedented coverage of a column-averaged dry-air mole fraction of methane (XCH4) in the Arctic, compared to previous missions or in situ measurements. The purpose of this study is to support and enhance the data used for high-latitude research through presenting a systematic evaluation of TROPOMI methane products derived from two different processing algorithms: the operational product (OPER) and the scientific product (WFMD), including the comparison of recent version changes of the products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). One finding is that OPER rpro yields lower XCH4 than WFMD v1.8, the difference increasing towards the highest latitudes. TROPOMI product differences were evaluated with respect to ground-based high-latitude references, including four Fourier Transform Spectrometer in the Total Carbon Column Observing Network (TCCON) and five EM27/SUN instruments in the Collaborative Carbon Column Observing Network (COCCON). The mean TROPOMI–TCCON GGG2020 daily median XCH4 difference was site-dependent and varied for OPER rpro from −0.47 ppb to 22.4 ppb, and for WFMD v1.8 from 1.2 ppb to 19.4 ppb with standard deviations between 13.0 and 20.4 ppb and 12.5–15.0 ppb, respectively. The TROPOMI–COCCON daily median XCH4 difference varied from −26.5 ppb to 5.6 ppb for OPER rpro, with a standard deviation of 14.0–28.7 ppb, and from −5.0 ppb to 17.2 ppb for WFMD v1.8, with a standard deviation of 11.5–13.0 ppb. Although the accuracy and precision of both TROPOMI products are, on average, good compared to the TCCON and COCCON, a persistent seasonal bias in TROPOMI XCH4 (high values in spring; low values in autumn) is found for OPER rpro and is reflected in the higher standard deviation values. A systematic decrease of about 7 ppb was found between TCCON GGG2014 and GGG2020 product update highlighting the importance of also ensuring the reliability of ground-based retrievals. Comparisons to atmospheric profile measurements with AirCore carried out in Sodankylä, Northern Finland, resulted in XCH4 differences comparable to or smaller than those from ground-based remote sensing.

Peatland management practices, such as drainage and restoration, have a strong effect on boreal peatland methane (CH4) fluxes. Furthermore, CH4 fluxes are strongly controlled by local environmental conditions, such as soil hydrology, temperature and vegetation, which are all experiencing considerable changes due to climate change. Both management practices and climate change are expected to influence peatland CH4 fluxes during this century, but the magnitude and net impact of these changes is still insufficiently understood. In this study, we simulated the impacts of two forest management practices, rotational forestry and continuous cover forestry, as well as peatland restoration, on hypothetical forestry-drained peatlands across Finland using the land surface model JSBACH (Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg) coupled with the soil carbon model YASSO and a peatland methane model HIMMELI (Helsinki Model of Methane Buildup and Emission for Peatlands). We further simulated the impacts of climatic warming using two RCP (Representative Concentration Pathway) emission scenarios, RCP2.6 and RCP4.5. We investigated the responses of CH4 fluxes, soil water-table level (WTL), soil temperatures and soil carbon dynamics to changes in management practices and climate. Our results show that management practices have a strong impact on peatland WTLs and CH4 emissions that continues for several decades, with emissions increasing after restoration and clearcutting. Towards the end of the century, WTLs increase slightly, likely due to increasing precipitation. CH4 fluxes have opposing trends in restored and drained peatlands. In restored peatlands, CH4 emissions decrease towards the end of the century following decomposition of harvest residue in the top peat layers despite increasing WTLs, while in drained peatland forests sinks get weaker and occasional emissions become more common, likely due to rising WTLs and soil temperatures. The strength of these trends varies across the country, with CH4 emissions from restored peatlands decreasing more strongly in southern Finland, and forest soil CH4 sinks weakening most in northern Finland.

Abstract. Nowadays, city planners design urban futures by considering climate change and biodiversity loss. Here, we studied the greenhouse gas fluxes of urban lawns and meadows and linked the observations with plant functional diversity and soil properties. CO2, CH4 and N2O fluxes, as well as plant diversity, were measured in eight lawn–meadow pairs in the Helsinki Metropolitan Area, Finland. Among the sites, an irrigated lawn, a non-irrigated lawn, a young dry meadow and an old mesic meadow were intensively studied especially using manual chamber measurements in 2021–2022. The process-based ecosystem model JSBACH was utilized together with the momentary observations on CO2 exchange to quantify the annual C balance of these intensive study sites. On another hand, the initial dynamics of conversion from lawns to meadows were studied with the six other pairs, where measurements were conducted from 2020 to 2022 and the transformation of half of the sites had been performed in late 2020. We found that lawns are clear sink of carbon, whereas the mesic meadow was more resistant to drought events than a non-irrigated lawn. Moreover, according to our results, the conversion from lawn to meadow did not affect the fluxes of CH4 and N2O. Last, the relation between C and N cycle and plant diversity was unclear and would need further investigations.

Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (XCH4) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH4 emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher CH4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates.

Urban green areas can be used as means for sequestering carbon, but also to manage run-off water and to reduce the urban heat island effect. Urban green spaces are often managed, including irrigation, removal of litter and modification of soil. In addition, urban green areas are often subjected to stress factors such as heat, pollution and drought. Here we study various carbon cycle components of urban trees using the JSBACH ecosystem model, focusing on the impact of drought. The study area is Kumpula, a semi-urban area situated in Helsinki, Southern Finland. In 2020 and 2021, an intensive measurement campaign took place involving a park area with tilia trees (Tilia cordata) and an urban forest dominated by Silver birch (Betula pendula). The observations included soil texture, soil temperature and moisture, and soil respiration. We used remote sensing data (Sentinel-2) for the leaf area index. Photosynthesis was measured using leaf cuvettes, and sap flow was measured for birch and lime trees. In addition, we used net ecosystem exchange from eddy covariance measurements at the Kumpula SMEAR III urban measurement station, operated by the University of Helsinki. The meteorological forcing data, to drive the model, was derived from observations at the Kumpula weather observation station, operated by the Finnish Meteorological Institute, and the SMEAR III station.

Managing nature-based solutions (NBS) in urban areas for carbon mitigation and biodiversity outcomes is a global policy challenge, yet little is known about how to both assess and weave diverse knowledge systems and values into carbon-biodiversity trade-off assessments. This paper examines the spatial relationships between biophysical and social values for carbon sequestration potential (measured as carbon dioxide, CO 2 , flux) and biodiversity in Helsinki, Finland, using integrated valuation. The approach combines methods from carbon sequestration modelling, expert scoring approaches to biodiversity assessment and public participation geographic information systems (PPGIS). Results indicate strong spatial associations between biophysical assessment of CO 2 flux and biodiversity priorities, and weaker associations between biophysical and social values. Integration of social and biophysical values leads to multiple pathways for protection of NBS to achieve carbon mitigation and biodiversity outcomes, as well as options for the spatial targeting of education and capacity building programs to areas of local concern.

This study investigates atmospheric δ13CH4 trends, as produced by a global atmospheric transport model using CH4 inversions from CarbonTracker-Europe CH4 for 2000–2020, and compares them to observations. The CH4 inversions include the grouping of the emissions both by δ13CH4 isotopic signatures and process type to investigate the effect, and to estimate the CH4 magnitudes and model CH4 and δ13CH4 trends. In addition to inversion results, simulations of the global atmospheric transport model were performed with modified emissions. The estimated global CH4 trends for oil and gas were found to increase more than coal compared to the priors from 2000–2006 to 2007–2020. Estimated trends for coal emissions at 30∘ N–60∘ N are less than 50% of those from priors. Estimated global CH4 rice emissions trends are opposite to priors, with the largest contribution from the EQ to 60∘ N. The results of this study indicate that optimizing wetland emissions separately produces better agreement with the observed δ13CH4 trend than optimizing all biogenic emissions simultaneously. This study recommends optimizing separately biogenic emissions with similar isotopic signature to wetland emissions. In addition, this study suggests that fossil-based emissions were overestimated by 9% after 2012 and biogenic emissions are underestimated by 8% in the inversion using EDGAR v6.0 as priors.