Observation of CH4 and other Non-CO2 Green House Gas Emissions from California

In 2006, California passed the landmark assembly bill AB-32 to reduce California's emissions of greenhouse gases (GHGs) that contribute to global climate change. AB-32 commits California to reduce total GHG emissions to 1990 levels by 2020, a reduction of 25 percent from current levels. To verify that GHG emission reductions are actually taking place, it will be necessary to measure emissions. We describe atmospheric inverse model estimates of GHG emissions obtained from the California Greenhouse Gas Emissions Measurement (CALGEM) project. In collaboration with NOAA, we are measuring the dominant long-lived GHGs at two tall-towers in central California. Here, we present estimates of CH{sub 4} emissions obtained by statistical comparison of measured and predicted atmospheric mixing ratios. The predicted mixing ratios are calculated using spatially resolved a priori CH{sub 4} emissions and surface footprints, that provide a proportional relationship between the surface emissions and the mixing ratio signal at tower locations. The footprints are computed using the Weather Research and Forecast (WRF) coupled to the Stochastic Time-Inverted Lagrangian Transport (STILT) model. Integral to the inverse estimates, we perform a quantitative analysis of errors in atmospheric transport and other factors to provide quantitative uncertainties in estimated emissions. Regressions of modeled and measured mixing ratios suggest that total CH{sub 4} emissions are within 25% of the inventory estimates. A Bayesian source sector analysis obtains posterior scaling factors for CH{sub 4} emissions, indicating that emissions from several of the sources (e.g., landfills, natural gas use, petroleum production, crops, and wetlands) are roughly consistent with inventory estimates, but livestock emissions are significantly higher than the inventory. A Bayesian 'region' analysis is used to identify spatial variations in CH{sub 4} emissions from 13 sub-regions within California. Although, only regions near the tower are significantly constrained by the tower measurements, CH{sub 4} emissions from the south Central Valley appear to be underestimated in a manner consistent with the under-prediction of livestock emissions. Finally, we describe a pseudo-experiment using predicted CH{sub 4} signals to explore the uncertainty reductions that might be obtained if additional measurements were made by a future network of tall-tower stations spread over California. These results show that it should be possible to provide high-accuracy estimates of surface CH{sub 4} emissions for multiple regions as a means to verify future emissions reductions

A Preliminary Study of CO2 Flux Measurements by Lidar

A mechanistic understanding of the global carbon cycle requires quantification of terrestrial ecosystem CO2 fluxes at regional scales. In this paper, we analyze the potential of a Doppler DIAL system to make flux measurements of atmospheric CO2 using the eddy-covariance and boundary layer budget methods and present results from a ground based experiment. The goal of this study is to put CO2 flux point measurements in a mesoscale context. In June 2007, a field experiment combining a 2-m Doppler Heterodyne Differential Absorption Lidar (HDIAL) and in-situ sensors of a 447-m tall tower (WLEF) took place in Wisconsin. The HDIAL measures simultaneously: 1) CO2 mixing ratio, 2) atmosphere structure via aerosol backscatter and 3) radial velocity. We demonstrate how to synthesize these data into regional flux estimates. Lidar-inferred fluxes are compared with eddy-covariance fluxes obtained in-situ at 396m AGL from the tower. In cases where the lidar was not yet able to measure the fluxes with acceptable precision, we discuss possible modifications to improve system performance

Turbulent CO2 Flux Measurements by Lidar: Length Scales, Results and Comparison with In-Situ Sensors

The vertical CO2 flux in the atmospheric boundary layer (ABL) is investigated with a Doppler differential absorption lidar (DIAL). The instrument was operated next to the WLEF instrumented tall tower in Park Falls, Wisconsin during three days and nights in June 2007. Profiles of turbulent CO2 mixing ratio and vertical velocity fluctuations are measured by in-situ sensors and Doppler DIAL. Time and space scales of turbulence are precisely defined in the ABL. The eddy-covariance method is applied to calculate turbulent CO2 flux both by lidar and in-situ sensors. We show preliminary mean lidar CO2 flux measurements in the ABL with a time and space resolution of 6 h and 1500 m respectively. The flux instrumental errors decrease linearly with the standard deviation of the CO2 data, as expected. Although turbulent fluctuations of CO2 are negligible with respect to the mean (0.1 %), we show that the eddy-covariance method can provide 2-h, 150-m range resolved CO2 flux estimates as long as the CO2 mixing ratio instrumental error is no greater than 10 ppm and the vertical velocity error is lower than the natural fluctuations over a time resolution of 10 s

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

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 Inverse Estimates of Methane Emissions from Central California

Methane mixing ratios measured at a tall-tower are compared to model predictions to estimate surface emissions of CH{sub 4} in Central California for October-December 2007 using an inverse technique. Predicted CH{sub 4} mixing ratios are calculated based on spatially resolved a priori CH{sub 4} emissions and simulated atmospheric trajectories. The atmospheric trajectories, along with surface footprints, are computed using the Weather Research and Forecast (WRF) coupled to the Stochastic Time-Inverted Lagrangian Transport (STILT) model. An uncertainty analysis is performed to provide quantitative uncertainties in estimated CH{sub 4} emissions. Three inverse model estimates of CH{sub 4} emissions are reported. First, linear regressions of modeled and measured CH{sub 4} mixing ratios obtain slopes of 0.73 {+-} 0.11 and 1.09 {+-} 0.14 using California specific and Edgar 3.2 emission maps respectively, suggesting that actual CH{sub 4} emissions were about 37 {+-} 21% higher than California specific inventory estimates. Second, a Bayesian 'source' analysis suggests that livestock emissions are 63 {+-} 22% higher than the a priori estimates. Third, a Bayesian 'region' analysis is carried out for CH{sub 4} emissions from 13 sub-regions, which shows that inventory CH{sub 4} emissions from the Central Valley are underestimated and uncertainties in CH{sub 4} emissions are reduced for sub-regions near the tower site, yielding best estimates of flux from those regions consistent with 'source' analysis results. The uncertainty reductions for regions near the tower indicate that a regional network of measurements will be necessary to provide accurate estimates of surface CH{sub 4} emissions for multiple regions

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

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

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

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

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

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

Evaluating consistency between total column CO2 retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article. &nbsp; Feedbacks between the climate system and the carbon cycle represent a key source of uncertainty in model projections of Earth's climate, in part due to our inability to directly measure large-scale biosphere&ndash;atmosphere carbon fluxes. In situ measurements of the CO2 mole fraction from surface flasks, towers, and aircraft are used in inverse models to infer fluxes, but measurement networks remain sparse, with limited or no coverage over large parts of the planet. Satellite retrievals of total column CO2 (XCO2), such as those from NASA's Orbiting Carbon Observatory-2 (OCO-2), can potentially provide unprecedented global information about CO2 spatiotemporal variability. However, for use in inverse modeling, data need to be extremely stable, highly precise, and unbiased to distinguish abundance changes emanating from surface fluxes from those associated with variability in weather. Systematic errors in XCO2 have been identified and, while bias correction algorithms are applied globally, inconsistencies persist at regional and smaller scales that may complicate or confound flux estimation. To evaluate XCO2 retrievals and assess potential biases, we compare OCO-2&nbsp;v10 retrievals with in situ data-constrained XCO2 simulations over North America estimated using surface fluxes and boundary conditions optimized with observations that are rigorously calibrated relative to the World Meteorological Organization X2007 CO2 scale. Systematic errors in simulated atmospheric transport are independently evaluated using unassimilated aircraft and AirCore profiles. We find that the global OCO-2&nbsp;v10 bias correction shifts the distribution of retrievals closer to the simulated XCO2, as intended. Comparisons between bias-corrected and simulated XCO2 reveal differences that vary seasonally. Importantly, the difference between simulations and retrievals is of the same magnitude as the imprint of recent surface flux in the total column. This work demonstrates that systematic errors in OCO-2&nbsp;v10 retrievals of XCO2 over land can be large enough to confound reliable surface flux estimation and that further improvements in retrieval and bias correction techniques are essential. Finally, we show that independent observations, especially vertical profile data, such as those from the National Oceanic and Atmospheric Administration aircraft and AirCore programs are critical for evaluating errors in both satellite retrievals and carbon cycle models.</p

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

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