56 research outputs found

    Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions

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    Nitrous oxide (N_2O) is a potent long‐lived greenhouse gas (GHG) and the strongest current emissions of global anthropogenic stratospheric ozone depletion weighted by its ozone depletion potential. In California, N_2O is the third largest contributor to the state's anthropogenic GHG emission inventory, though no study has quantified its statewide annual emissions through top‐down inverse modeling. Here we present the first annual (2013–2014) statewide top‐down estimates of anthropogenic N_2O emissions. Utilizing continuous N_2O observations from six sites across California in a hierarchical Bayesian inversion, we estimate that annual anthropogenic emissions are 1.5–2.5 times (at 95% confidence) the state inventory (41 Gg N_2O in 2014). Without mitigation, this estimate represents 4–7% of total GHG emissions assuming that other reported GHG emissions are reasonably correct. This suggests that control of N_2O could be an important component in meeting California's emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO_2 equivalent) by 2030 and 2050, respectively. Our seasonality analysis suggests that emissions are similar across seasons within posterior uncertainties. Future work is needed to provide source attribution for subregions and further characterization of seasonal variability

    Atmospheric observation-based estimation of fossil fuel CO_2 emissions from regions of central and southern California

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    Combustion of fossil fuel is the dominant source of greenhouse gas emissions to the atmosphere in California. Here, we describe radiocarbon (^(14)CO_2) measurements and atmospheric inverse modeling to estimate fossil fuel CO_2 (ffCO_2) emissions for 2009–2012 from a site in central California, and for June 2013–May 2014 from two sites in southern California. A priori predicted ffCO_2 mixing ratios are computed based on regional atmospheric transport model (WRF-STILT) footprints and an hourly ffCO_2 prior emission map (Vulcan 2.2). Regional inversions using observations from the central California site suggest that emissions from the San Francisco Bay Area (SFBA) are higher in winter and lower in summer. Taking all years together, the average of a total of fifteen 3-month inversions from 2009 to 2012 suggests ffCO_2 emissions from SFBA were within 6 ± 35% of the a priori estimate for that region, where posterior emission uncertainties are reported as 95% confidence intervals. Results for four 3-month inversions using measurements in Los Angeles South Coast Air Basin (SoCAB) during June 2013–May 2014 suggest that emissions in SoCAB are within 13 ± 28% of the a priori estimate for that region, with marginal detection of any seasonality. While emissions from the SFBA and SoCAB urban regions (containing ~50% of prior emissions from California) are constrained by the observations, emissions from the remaining regions are less constrained, suggesting that additional observations will be valuable to more accurately estimate total ffCO_2 emissions from California as a whole

    Investigating Alaskan Methane and Carbon Dioxide Fluxes Using Measurements from the CARVE Tower

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    Northern high-latitude carbon sources and sinks, including those resulting from degrading permafrost, are thought to be sensitive to the rapidly warming climate. Because the near-surface atmosphere integrates surface fluxes over large ( ∌ 500–1000 km) scales, atmospheric monitoring of carbon dioxide (CO2) and methane (CH4) mole fractions in the daytime mixed layer is a promising method for detecting change in the carbon cycle throughout boreal Alaska. Here we use CO2 and CH4 measurements from a NOAA tower 17 km north of Fairbanks, AK, established as part of NASA\u27s Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), to investigate regional fluxes of CO2 and CH4 for 2012–2014. CARVE was designed to use aircraft and surface observations to better understand and quantify the sensitivity of Alaskan carbon fluxes to climate variability. We use high-resolution meteorological fields from the Polar Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport model (hereafter, WRF-STILT), along with the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM), to investigate fluxes of CO2 in boreal Alaska using the tower observations, which are sensitive to large areas of central Alaska. We show that simulated PolarVPRM–WRF-STILT CO2 mole fractions show remarkably good agreement with tower observations, suggesting that the WRF-STILT model represents the meteorology of the region quite well, and that the PolarVPRM flux magnitudes and spatial distribution are generally consistent with CO2 mole fractions observed at the CARVE tower. One exception to this good agreement is that during the fall of all 3 years, PolarVPRM cannot reproduce the observed CO2 respiration. Using the WRF-STILT model, we find that average CH4 fluxes in boreal Alaska are somewhat lower than flux estimates by Chang et al. (2014) over all of Alaska for May–September 2012; we also find that enhancements appear to persist during some wintertime periods, augmenting those observed during the summer and fall. The possibility of significant fall and winter CO2 and CH4 fluxes underscores the need for year-round in situ observations to quantify changes in boreal Alaskan annual carbon balance

    Sources of Carbon Monoxide and Formaldehyde in North America Determined from High-Resolution Atmospheric Data

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    We analyze the North American budget for carbon monoxide using data for CO and formaldehyde concentrations from tall towers and aircraft in a model-data assimilation framework. The Stochastic Time-Inverted Lagrangian Transport model for CO (STILT-CO) determines local to regional-scale CO contributions associated with production from fossil fuel combustion, biomass burning, and oxidation of volatile organic compounds (VOCs) using an ensemble of Lagrangian particles driven by high resolution assimilated meteorology. In many cases, the model demonstrates high fidelity simulations of hourly surface data from tall towers and point measurements from aircraft, with somewhat less satisfactory performance in coastal regions and when CO from large biomass fires in Alaska and the Yukon Territory influence the continental US. Inversions of STILT-CO simulations for CO and formaldehyde show that current inventories of CO emissions from fossil fuel combustion are significantly too high, by almost a factor of three in summer and a factor two in early spring, consistent with recent analyses of data from the INTEX-A aircraft program. Formaldehyde data help to show that sources of CO from oxidation of CH4 and other VOCs represent the dominant sources of CO over North America in summer.Earth and Planetary Science

    Atmospheric observation-based estimation of fossil fuel CO_2 emissions from regions of central and southern California

    Get PDF
    Combustion of fossil fuel is the dominant source of greenhouse gas emissions to the atmosphere in California. Here, we describe radiocarbon (^(14)CO_2) measurements and atmospheric inverse modeling to estimate fossil fuel CO_2 (ffCO_2) emissions for 2009–2012 from a site in central California, and for June 2013–May 2014 from two sites in southern California. A priori predicted ffCO_2 mixing ratios are computed based on regional atmospheric transport model (WRF-STILT) footprints and an hourly ffCO_2 prior emission map (Vulcan 2.2). Regional inversions using observations from the central California site suggest that emissions from the San Francisco Bay Area (SFBA) are higher in winter and lower in summer. Taking all years together, the average of a total of fifteen 3-month inversions from 2009 to 2012 suggests ffCO_2 emissions from SFBA were within 6 ± 35% of the a priori estimate for that region, where posterior emission uncertainties are reported as 95% confidence intervals. Results for four 3-month inversions using measurements in Los Angeles South Coast Air Basin (SoCAB) during June 2013–May 2014 suggest that emissions in SoCAB are within 13 ± 28% of the a priori estimate for that region, with marginal detection of any seasonality. While emissions from the SFBA and SoCAB urban regions (containing ~50% of prior emissions from California) are constrained by the observations, emissions from the remaining regions are less constrained, suggesting that additional observations will be valuable to more accurately estimate total ffCO_2 emissions from California as a whole

    CTDAS-Lagrange v1.0:a high-resolution data assimilation system for regional carbon dioxide observations

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    We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named CTDAS-Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft programmable flask packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system.To estimate the uncertainties in the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical dataset derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model-data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing net ecosystem exchange (NEE) than the multiplicative flux adjustment method, and our sensitivity tests with real observations show that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral BCs and to resolve large biases in the prior biosphere fluxes.Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from -0.92 to -1.26 PgC yr( -1). This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, -0.91 +/- 1.10 PgC yr (-1)) and CarbonTracker Europe (version CTE,2016, -0.91 +/- 0.31 PgC yr(-1)). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain

    CTDAS-Lagrange v1.0 : A high-resolution data assimilation system for regional carbon dioxide observations

    Get PDF
    We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named CTDAS-Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft programmable flask packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties in the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical dataset derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model-data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing net ecosystem exchange (NEE) than the multiplicative flux adjustment method, and our sensitivity tests with real observations show that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral BCs and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from -0.92 to -1.26PgCyr-1. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, -0.91±1.10PgCyr-1) and CarbonTracker Europe (version CTE2016, -0.91±0.31PgCyr-1). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.</p
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