A model for urban biogenic CO₂ fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)

When estimating fossil fuel carbon dioxide (FFCO2) emissions from observed CO2 concentrations, the accuracy can be hampered by biogenic carbon exchanges during the growing season, even for urban areas where strong fossil fuel emissions are found. While biogenic carbon fluxes have been studied extensively across natural vegetation types, biogenic carbon fluxes within an urban area have been challenging to quantify due to limited observations and differences between urban and rural regions. Here we developed a simple model representation, i.e., Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes (“SMUrF”), that estimates the gross primary production (GPP) and ecosystem respiration (Reco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance (EC) flux data, and ancillary remote-sensing-based products, and we developed algorithms to gap-fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) fluxes are extracted from SMUrF and evaluated against (1) non-gap-filled measurements at 67 EC sites from FLUXNET during 2010–2014 (r>0.7 for most data-rich biomes), (2) independent observations at two urban vegetation and two crop EC sites over Indianapolis from August 2017 to December 2018 (r=0.75), and (3) an urban biospheric model based on fine-grained land cover classification in Los Angeles (r=0.83). Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 emissions over 40 cities and addressed the urban–rural contrast in both the magnitude and timing of CO2 fluxes. To illustrate the application of SMUrF, we used it to interpret a few summertime satellite tracks over four cities and compared the urban–rural gradient in column CO2 (XCO2) anomalies due to NEE against XCO2 enhancements due to FFCO2 emissions. With rapid advances in space-based measurements and increased sampling of SIF and CO2 measurements over urban areas, SMUrF can be useful to inform the biogenic CO2 fluxes over highly vegetated regions during the growing season.

Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia)

Nowadays, different approaches for CO2 anthropogenic emission estimation are applied to control agreements on greenhouse gas reduction. Some methods are based on the inverse modelling of emissions using various measurements and the results of numerical chemistry transport models (CTMs). Since the accuracy and precision of CTMs largely determine errors in the approaches for emission estimation, it is crucial to validate the performance of such models through observations. In the current study, the near-surface CO2 mixing ratio simulated by the CTM Weather Research and Forecasting—Chemistry (WRF-Chem) at a high spatial resolution (3 km) using three different sets of CO2 fluxes (anthropogenic + biogenic fluxes, time-varying and constant anthropogenic emissions) and from Copernicus Atmosphere Monitoring Service (CAMS) datasets have been validated using in situ observations near the Saint Petersburg megacity (Russia) in March and April 2019. It was found that CAMS reanalysis data with a low spatial resolution (1.9 × 3.8°) can match the observations better than CAMS analysis data with a high resolution (0.15 × 0.15°). The CAMS analysis significantly overestimates the observed near-surface CO2 mixing ratio in Peterhof in March and April 2019 (by more than 10 ppm). The best match for the CAMS reanalysis and observations was observed in March, when the wind was predominantly opposite to the Saint Petersburg urbanized area. In contrast, the CAMS analysis fits the observed trend of the mixing ratio variation in April better than the reanalysis with the wind directions from the Saint Petersburg urban zone. Generally, the WRF-Chem predicts the observed temporal variations in the near-surface CO2 reasonably well (mean bias ≈ (−0.3) − (−0.9) ppm, RMSD ≈ 8.7 ppm, correlation coefficient ≈ 0.61 ± 0.04). The WRF-Chem data where anthropogenic and biogenic fluxes were used match the observations a bit better than the WRF-Chem data without biogenic fluxes. The diurnal time variation in the anthropogenic emissions influenced the WRF-Chem data insignificantly. However, in general, the data of all three WRF-Chem model runs give almost the same CO2 temporal variation in Peterhof in March and April 2019. This could be related to the late start of the growing season, which influences biogenic CO2 fluxes, inaccuracies in the estimation of the biogenic fluxes, and the simplified time variation pattern of the CO2 anthropogenic emissions.

High-resolution inversion of fossil fuel emissions and biogenic fluxes over the Paris region during 2019-2020

<p>The study aims to quantify the Paris region’s CO and CO<sub>2</sub> emissions from fossil fuel and biogenic CO<sub>2</sub> fluxes during the spring season (March-May) of 2019-2020, based on a network of six ground-based stations. Hourly CO<sub>2</sub> mixing ratio gradients between the station Saclay (SAC), located in the south-west of Paris region and five other sites in the urban area are used to estimate the 5-day mean daytime budgets of the fossil fuel CO<sub>2</sub> emissions and biogenic fluxes. The inversion relies on the transport model simulations using the Weather Research and Forecasting model at 1 km × 1 km horizontal resolution, combined with 1-km fossil fuel CO<sub>2</sub> emissions from the Origins inventory, and biogenic CO<sub>2</sub> fluxes from the VPRM model. The methodology is based on a Lagrangian particle dispersion model (LPDM) approach that could efficiently determine the sensitivity of downwind mixing ratio changes to upwind sources. The inversion adjusts both fossil fuel emissions and VPRM biogenic CO<sub>2</sub> fluxes using tower observations and transport matrix generated from LPDM hourly footprints. The emission map shows noticeable changes in the central Paris region, whereas the biogenic fluxes do not show any noticeable change after inversion. This can happen if the choice of background station is not representative concerning biogenic fluxes.  The inversion could reduce the uncertainty up to 20% for the fossil fuel emission but the biogenic flux uncertainty does not show a significant difference from the prior. In comparison with the 2019 pattern, the rate of increase in fossil fuel emission after inversion was considerably reduced for 2020 (up to 20-30%). The same pattern is observed in the 5-day total flux time series where the magnitude of posterior fluxes falls below prior fluxes except for the first few days of March, before the lockdown period. This aspect is further analysed in the second part of the study. Analysis of hourly mixing ratios generated from prior and posterior fluxes shows that prior mixing ratios increased as a result of large observed CO<sub>2</sub> gradients. A comparison of diurnal mixing ratios generated from prior and posterior fluxes shows that the mixing ratio gradient of all the sites shows a similar pattern, but the direct observations show an offset in the diurnal pattern. The second part of the study aims to quantify the changes in the CO<sub>2</sub> emission pattern over the Paris region during the recent COVID19 lockdown during 2020. Here, a multisystem comparison is carried out for the Lagrangian-based inversion and Eulerian WRF-CO<sub>2</sub> inversion. Both systems capture the effect of lockdown, with a significant reduction in traffic emissions. To improve the inversion and to reduce the uncertainty, the third part of the study uses a gridded CO/CO<sub>2</sub> mole fraction ratio to further constrain anthropogenic CO<sub>2</sub> emissions. Our study shows that It is an added advantage to assimilate CO mixing ratios alongside CO<sub>2</sub> to increase the accuracy of anthropogenic carbon estimates.</p>

The influence of near-field fluxes on seasonal carbon dioxide enhancements: results from the Indianapolis Flux Experiment (INFLUX)

Background Networks of tower-based CO2 mole fraction sensors have been deployed by various groups in and around cities across the world to quantify anthropogenic CO2 emissions from metropolitan areas. A critical aspect in these approaches is the separation of atmospheric signatures from distant sources and sinks (i.e., the background) from local emissions and biogenic fluxes. We examined CO2 enhancements compared to forested and agricultural background towers in Indianapolis, Indiana, USA, as a function of season and compared them to modeled results, as a part of the Indianapolis Flux (INFLUX) project. Results At the INFLUX urban tower sites, daytime growing season enhancement on a monthly timescale was up to 4.3–6.5 ppm, 2.6 times as large as those in the dormant season, on average. The enhancement differed significantly depending on choice of background and time of year, being 2.8 ppm higher in June and 1.8 ppm lower in August using a forested background tower compared to an agricultural background tower. A prediction based on land cover and observed CO2 fluxes showed that differences in phenology and drawdown intensities drove measured differences in enhancements. Forward modelled CO2 enhancements using fossil fuel and biogenic fluxes indicated growing season model-data mismatch of 1.1 ± 1.7 ppm for the agricultural background and 2.1 ± 0.5 ppm for the forested background, corresponding to 25–29% of the modelled CO2 enhancements. The model-data total CO2 mismatch during the dormant season was low, − 0.1 ± 0.5 ppm. Conclusions Because growing season biogenic fluxes at the background towers are large, the urban enhancements must be disentangled from the biogenic signal, and growing season increases in CO2 enhancement could be misinterpreted as increased anthropogenic fluxes if the background ecosystem CO2 drawdown is not considered. The magnitude and timing of enhancements depend on the land cover type and net fluxes surrounding each background tower, so a simple box model is not appropriate for interpretation of these data. Quantification of the seasonality and magnitude of the biological fluxes in the study region using high-resolution and detailed biogenic models is necessary for the interpretation of tower-based urban CO2 networks for cities with significant vegetation.

The influence of near-field fluxes on seasonal carbon dioxide enhancements: Results from the Indianapolis Flux Experiment (INFLUX)

BackgroundNetworks of tower-based CO2 mole fraction sensors have been deployed in and around cities across the world to quantify anthropogenic CO2 emissions from metropolitan areas. A critical aspect in these approaches is the separation of atmospheric signatures from distant sources and sinks (i.e., the background) from local emissions and biogenic fluxes. We examined CO2 enhancements compared to forested and agricultural background towers in Indianapolis, Indiana, USA, as a function of season and compared them to modeled results, as a part of the Indianapolis Flux (INFLUX) project.ResultsAt the INFLUX urban tower sites, daytime growing season enhancement on a monthly timescale was up to 4.3 – 6.5 ppm, 2.6 times as large as those in the dormant season, on average. The enhancement differed significantly depending on choice of background and time of year, being 2.8 ppm higher in June and 1.8 ppm lower in August using a forested background tower compared to an agricultural background tower. A prediction based on land cover and observed CO2 fluxes showed that differences in phenology and drawdown intensities drove measured differences in enhancements. Forward modelled CO2 enhancements using fossil fuel and biogenic fluxes indicated growing season model-data mismatch of 1.1 ± 1.7 ppm for the agricultural background and 2.1 ± 0.5 ppm for the forested background, corresponding to 25 – 29 % of the modelled CO2 enhancements. The model-data total CO2 mismatch during the dormant season was low, – 0.1 ± 0.5 ppm. ConclusionsBecause growing season biogenic fluxes at the background towers are large, the urban enhancements must be disentangled from the biogenic signal, and growing season increases in CO2 enhancement could be misinterpreted as increased anthropogenic fluxes if the background ecosystem CO2 drawdown is not considered. The magnitude and timing of enhancements depend on the land cover type and net fluxes surrounding each background tower, so a simple box model is not appropriate for interpretation of these data. Quantification of the seasonality and magnitude of the biological fluxes in the study region using high-resolution and detailed biogenic models is necessary for the interpretation of tower-based urban CO2 networks for cities with significant vegetation.

A Model for Urban Biogenic CO₂ Fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)

When estimating fossil fuel carbon dioxide (FFCO2) emissions from observed CO2 concentrations, the accuracy can be hampered by biogenic carbon exchanges during the growing season even for urban areas where strong fossil fuel emissions are found. While biogenic carbon fluxes have been studied extensively across natural vegetation types, biogenic carbon fluxes within an urban area have been challenging to quantify due to limited observations and differences between urban versus rural regions. Here we developed a simple model representation, i.e., Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes ("SMUrF"), that estimates the gross primary production (GPP) and ecosystem respiration (Reco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance flux data, and additional remote sensing-based products, and developed algorithms to gap fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) are extracted from SMUrF and evaluated against 1) non-gapfilled measurements at 67 eddy-covariance (EC) sites from FLUXNET during 2010–2014 (r > 0.7 for most data-rich biomes), 2) independent observations at two urban vegetation and two crop EC sites over Indianapolis from Aug 2017 to Dec 2018 (r = 0.75), and 3) an urban biospheric model based on fine-grained land cover classification within Los Angeles (r = 0.83). Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 emissions over 40 cities and addressed the urban-rural contrast regarding both the magnitude and timing of CO2 fluxes. By examining a few summertime satellite tracks over four cities, we found that the urban-rural gradient in column CO2 (XCO2) anomalies due to NEE can sometimes reach ~ 0.5 ppm and be close to XCO2 enhancements due to FFCO2 emissions. With rapid advances in space-based measurements and increased sampling of SIF and CO2 measurements over urban areas, SMUrF can be useful for informing the biogenic CO2 fluxes over highly vegetated regions during the growing season.

90Sr and 137Cs inventories in the depots and biogenic fluxes of the typical forest stands in the Chernobyl exclusion zone

The paper reports results of the study of depots and biogenic fluxes of 90Sr and 137Cs in the typical coniferous (Scots pine) and deciduous (Silver birch) forest ecosystems of the Chernobyl Exclusion Zone during 2016 - 2018. Data on activity concentrations and shares of the total activity of the studied radionuclides in the components of aboveground and underground biomass and their vertical distributions by 10 cm layers of the soil profile up to a depth of 1 m are presented. The downward and upward fluxes of 90Sr and 137Cs activity (including the processes of their deposition as a result of growth and formation biomass) are calculated in annual terms. Significantly higher 90Sr mobility in elements of forest ecosystems than 137Cs is confirmed. The estimated flux values for the investigated forest areas indicate a gradual further increase in the share of these radionuclides in the aboveground biomass components (up to 0.9 %·year-1 from the total activity in forest ecosystems) owing to the increase of organic matter stocks.

The Influence of Near-field Fluxes on Seasonal Carbon Dioxide Enhancements: Results From the Indianapolis Flux Experiment (INFLUX)

BackgroundNetworks of tower-based CO2 mole fraction sensors have been deployed in and around cities across the world to quantify anthropogenic CO2 emissions from metropolitan areas. A critical aspect in these approaches is the separation of atmospheric signatures from distant sources and sinks (i.e., the background) from local emissions and biogenic fluxes. We examined CO2 enhancements compared to forested and agricultural background towers in Indianapolis, Indiana, USA, as a function of season and compared them to modeled results, as a part of the Indianapolis Flux (INFLUX) project.ResultsAt the INFLUX urban tower sites, daytime growing season enhancement on a monthly timescale was up to 4.3 – 6.5 ppm, 2.6 times as large as those in the dormant season, on average. The enhancement differed significantly depending on choice of background and time of year, being 2.8 ppm higher in June and 1.8 ppm lower in August using a forested background tower compared to an agricultural background tower. A prediction based on land cover and observed CO2 fluxes showed that differences in phenology and drawdown intensities drove measured differences in enhancements. Forward modelled CO2 enhancements using fossil fuel and biogenic fluxes indicated growing season model-data mismatch of 1.1 ± 1.7 ppm for the agricultural background and 2.1 ± 0.5 ppm for the forested background, corresponding to 25 – 29 % of the modelled CO2 enhancements. The model-data total CO2 mismatch during the dormant season was low, – 0.1 ± 0.5 ppm. ConclusionsBecause growing season biogenic fluxes at the background towers are large, the urban enhancements must be disentangled from the biogenic signal, and growing season increases in CO2 enhancement could be misinterpreted as increased anthropogenic fluxes if the background ecosystem CO2 drawdown is not considered. The magnitude and timing of enhancements depend on the land cover type and net fluxes surrounding each background tower, so a simple box model is not appropriate for interpretation of these data. Quantification of the seasonality and magnitude of the biological fluxes in the study region using high-resolution and detailed biogenic models is necessary for the interpretation of tower-based urban CO2 networks for cities with significant vegetation.