The fluxes of carbon dioxide (CO2) to and from vegetation can be significant on a regional scale. It is therefore important to understand the biogenic fluxes of CO2 in order to quantify local carbon ...budgets. The Greenbelt of Ontario is a protected region of cropland and natural vegetation surrounding the Greater Toronto and Hamilton Area (GTHA) in Ontario, Canada. Recently, changes were proposed to the Greenbelt, including the removal of 2,995 ha (7,400 acres) of protected land to be replaced with housing. In this study, we estimate the biogenic CO2 fluxes of the entire Greenbelt as well as the areas that were proposed for removal by using a modified version of the Solar-induced fluorescence for Modeling Urban biogenic Fluxes vegetation model. We find that, on average, the entire Greenbelt has a net sequestration of 9.9 ± 6.4 TgCO2 each year, where the uncertainty represents half of the interannual variability plus error from the individual years, for the years 2018–2020. The net amount of CO2 absorbed by the Greenbelt is roughly equivalent to a fifth of the annual human-made emissions reported for the entire GTHA. The areas proposed for removal are found to have a net sequestration of 0.0061–0.031 TgCO2 annually. During construction, these lands will remain barren, and the soil will continue to emit CO2, thus changing the area from a net sink to a net source of CO2. For a 3- to 5-year construction period, this soil efflux would result in net ecosystem emissions of 0.314 ± 0.078 TgCO2, in addition to the net sequestration lost by removing the original vegetation (−0.077 ± 0.035 TgCO2). This results in a net difference in biogenic CO2 fluxes of 0.390 ± 0.083 TgCO2, which is equivalent to the average CO2 emissions of roughly 85,000 gasoline passenger vehicles over the course of a year. In addition to biogenic fluxes, there will be CO2 emissions associated with the construction of the proposed single-family housing developments as well as larger per capita emissions associated with low-density housing compared to creating higher density housing using less land.
Using co-located space-based measurements of carbon dioxide (CO2) from the Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3 (OCO-2/3) and carbon monoxide (CO) and nitrogen dioxide ...(NO2) from the TROPOspheric Monitoring Instrument (TROPOMI), we calculate total column enhancements for observations influenced by anthropogenic emissions from urban regions relative to clean background values. We apply this method to observations taken over or downwind of 27 large (population of >1 million) urban areas from around the world. Enhancement ratios between species are calculated and compared to emissions ratios derived from four globally gridded anthropogenic emissions inventories. We find that these global inventories underestimate CO emissions in many North American and European cities relative to our observed enhancement ratios, while smaller differences were found for NO2 emissions. We further demonstrate that the calculation and intercomparison of enhancement ratios of multiple tracers can help to identify the underlying biases leading to disagreement between observations and inventories. Additionally, we use high-resolution CO2 inventories for two cities (Los Angeles and Indianapolis) to estimate emissions of CO and NO2 using our calculated enhancement ratios and find good agreement with both a previous modelling study for the megacity of Los Angeles and California Air Resources Board (CARB) inventory estimates.
We use observations of boundary layer methane from the SEAC4RS aircraft campaign over the Southeast US in August–September 2013 to estimate methane emissions in that region through an inverse ...analysis with up to 0.25∘×0.3125∘ (25×25 km2) resolution and with full error characterization. The Southeast US is a major source region for methane including large contributions from oil and gas production and wetlands. Our inversion uses state-of-the-art emission inventories as prior estimates, including a gridded version of the anthropogenic EPA Greenhouse Gas Inventory and the mean of the WetCHARTs ensemble for wetlands. Inversion results are independently verified by comparison with surface (NOAA/ESRL) and column (TCCON) methane observations. Our posterior estimates for the Southeast US are 12.8±0.9 Tga-1 for anthropogenic sources (no significant change from the gridded EPA inventory) and9.4±0.8 Tga-1 for wetlands (27 % decrease from the mean in the WetCHARTs ensemble). The largest source of error in the WetCHARTs wetlands ensemble is the land cover map specification of wetland areal extent. Our results support the accuracy of the EPA anthropogenic inventory on a regional scale but there are significant local discrepancies for oil and gas production fields, suggesting that emission factors are more variable than assumed in the EPA inventory.
The Global Methane Budget 2000-2012 Saunois, Marielle; Bousquet, Philippe; Poulter, Benjamin ...
Earth system science data,
12/2016, Letnik:
8, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a ...stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (approximately biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modeling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations).For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH4 yr(exp -1), range 540-568. About 60 of global emissions are anthropogenic (range 50-65%). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH4 yr(exp -1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (approximately 64% of the global budget, less than 30deg N) as compared to mid (approximately 32%, 30-60deg N) and high northern latitudes (approximately 4%, 60-90deg N). Top-down inversions consistently infer lower emissions in China (approximately 58 TgCH4 yr(exp -1), range 51-72, minus14% ) and higher emissions in Africa (86 TgCH4 yr(exp -1), range 73-108, plus 19% ) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_ METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.
Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic ...greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometres to a few kilometres. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high-resolution fluxes that are more diffuse at lower resolutions. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modelling error with resolution is not linear, making it difficult to quantify the improvement beyond the tested resolutions. Finally, we show that the high-resolution simulations are useful for the assessment of the small-scale variability of CO2 which cannot be represented in coarser-resolution models. These representativeness errors need to be considered when assimilating in situ data and high-resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO2. For these reasons, the high-resolution CO2 simulations provided by the CAMS in real time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.
Atmospheric carbon dioxide (CO2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is,
therefore, a pressing need to understand the rate at which
CO2 accumulates ...in the atmosphere, including the interannual variations
(IAVs) in this rate. IAV in the CO2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric
CO2, and IAV is tied to climatic variations that may provide insights
into long-term carbon–climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our
understanding of atmospheric CO2 IAV since the satellite can measure
over remote terrestrial regions and the open ocean, where traditional in situ CO2 monitoring is difficult, providing better spatial coverage compared
to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO2 mole fraction (XCO2) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all
latitudes, the OCO-2-detected XCO2 IAV shows a clear relationship with El Niño–Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO2 IAV
time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the
Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations
can be used reliably to estimate IAV. Furthermore, the extensive spatial
coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities
for revealing small IAV signals despite sources of noise and error that are
inherent to remote-sensing datasets.
Atmospheric carbon dioxide (CO.sub.2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at which CO.sub.2 ...accumulates in the atmosphere, including the interannual variations (IAVs) in this rate. IAV in the CO.sub.2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric CO.sub.2, and IAV is tied to climatic variations that may provide insights into long-term carbon-climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO.sub.2 IAV since the satellite can measure over remote terrestrial regions and the open ocean, where traditional in situ CO.sub.2 monitoring is difficult, providing better spatial coverage compared to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO.sub.2 mole fraction (XCO.sub.2) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2-detected XCO.sub.2 IAV shows a clear relationship with El Niño-Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO.sub.2 IAV time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations can be used reliably to estimate IAV. Furthermore, the extensive spatial coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities for revealing small IAV signals despite sources of noise and error that are inherent to remote-sensing datasets.
Simultaneous measurements of
O
3
, HCl,
N
2
O
, and
CH
4
were recorded by two infrared Fourier transform spectrometers of differing resolution (0.004 and
0.02
cm
-
1
) over a period of four months in ...the summer of 2005. These coincident observations were made at the Toronto Atmospheric Observatory, a complementary site of the Network for the Detection of Atmospheric Composition Change, and provide one of the longest records of simultaneously recorded ground-based infrared spectra to date. Retrievals performed on the spectra utilized the SFIT2 optimal estimation algorithm with HITRAN 2004 spectroscopic parameters. The influence of instrument resolution was considered in relation to the respective averaging kernels, with the predicted influence of multiplicative bias agreeing well with the observed influence for the stratospheric species. The retrieved column amounts correlated well for the stratospheric gases (
R
2
>
0.6
) but poorer correlations were observed for the well-mixed tropospheric species that were investigated. The median column differences observed by the instruments are
-
1.7
%
and 2.7% in two different micro-windows of
O
3
, 2.2% for HCl,
-
0.36
%
for
N
2
O
, and 3.7% for
CH
4
.
We report new short‐wave infrared (SWIR) column retrievals of atmospheric methane (XCH4) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal ...variations with correlative ground‐based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3‐D GEOS‐Chem chemistry transport model. GOSAT XCH4 retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site‐dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single‐sounding precision between 0.4–0.8%. We find a latitudinal‐dependent difference between the XCH4 retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT XCH4 retrievals agree well with the GEOS‐Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed XCH4 are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis.
Key Points
CH4 is now successfully being retrieved from GOSAT satellite
Validation against ground‐based data shows good agreement
Excellent agreement to model simulations as a first step towards inversions
Atmospheric carbon dioxide (CO2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at whichCO2 accumulates ...in the atmosphere, including the interannual variations (IAVs) in this rate. IAV in the CO2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmosphericCO2, and IAV is tied to climatic variations that may provide insights into long-term carbon–climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO2 IAV since the satellite can measure over remote terrestrial regions and the open ocean, where traditional in situ CO2 monitoring is difficult, providing better spatial coverage compared to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO2 mole fraction (XCO2) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2-detected XCO2 IAV shows a clear relationship with El Niño–Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO2 IAV time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations can be used reliably to estimate IAV. Furthermore, the extensive spatial coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities for revealing small IAV signals despite sources of noise and error that are inherent to remote-sensing datasets.
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