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Ozone depleting substances (ODSs) controlled by the Montreal Protocol are potent greenhouse gases (GHGs), as are their substitutes, the hydrofluorocarbons (HFCs). Here we provide for the first time a comprehensive estimate of U.S. emissions of ODSs and HFCs based on precise measurements in discrete air samples from across North America and in the remote atmosphere. Derived emissions show spatial and seasonal variations qualitatively consistent with known uses and largely confirm U.S. Environmental Protection Agency (EPA) national emissions inventories for most gases. The measurement-based results further indicate a substantial decline of ODS emissions from 2008 to 2014, equivalent to ~50% of the CO2-equivalent decline in combined emissions of CO2 and all other long-lived GHGs inventoried by the EPA for the same period. Total estimated CO2-equivalent emissions of HFCs were comparable to the sum of ODS emissions in 2014, but can be expected to decline in the future in response to recent policy measures.

U.S. national and regional emissions of HFC-134a are derived for 20082012 based on atmospheric observations from ground and aircraft sites across the U.S. and a newly developed regional inverse model. Synthetic data experiments were first conducted to optimize the model assimilation design and to assess model-data mismatch errors and prior flux error covariances computed using a maximumlikelihood estimation technique. The synthetic data experiments also tested the sensitivity of derived national and regional emissions to a range of assumed prior emissions, with the goal of designing a system that was minimally reliant on the prior. We then explored the influence of additional sources of error in inversions with actual observations, such as those associated with background mole fractions and transport uncertainties. Estimated emissions of HFC-134a range from 52 to 61 Gg yr-1 for the contiguous U.S. during 20082012 for inversions using air transport from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model driven by the 12km resolution meteorogical data from North American Mesoscale Forecast System (NAM12) and all tested combinations of prior emissions and background mole fractions. Estimated emissions for 20082010 were 20% lower when specifying alternative transport from Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by the Weather Research and Forecasting (WRF) meteorology. Our estimates (for HYSPLIT-NAM12) are consistent with annual emissions reported by U.S. Environmental Protection Agency for the full study interval. The results suggest a 1020% drop in U.S. national HFC-134a emission in 2009 coincident with a reduction in transportation-related fossil fuel CO2 emissions, perhaps related to the economic recession. All inversions show seasonal variation in national HFC-134a emissions in all years, with summer emissions greater than winter emissions by 2050%.

Four-dimensional variational data assimilation (4DVAR) is an established data assimilation method that finds the finite-amplitude perturbation that best fits observations consistent with a priori information and model dynamics. The response of a simulated tropical cyclone to specially designed finite perturbations of selected model variables was studied with a modified version of 4DVAR. The usual goal of minimizing data misfits was replaced with a goal of reducing damaging surface winds at the end of six hours of forecast time. For this purpose a property value cost function based on topography was defined. The case studied was a 20-km simulation of a hurricane approaching the Hawaiian Islands. Each prognostic variable in turn--temperature, winds, humidity, vertical velocity, and perturbation pressure--and all prognostic variables at once were used as the control vector for the optimization problem. Of all prognostic variables examined, temperature and the horizontal wind were the most effective at reducing damaging surface winds. The wind-only perturbation was very similar to the wind component of the perturbation calculated when all prognostic variables were used at once. Calculated perturbations had scales of 0.25[degrees]C or 1 m [s.sup.-1], but changes at a few grid points near the center of the storm were an order of magnitude greater. Vertical velocity and humidity perturbations alone were ineffective at reducing damaging winds. The perturbation pressure experiment failed to converge but did substantially reduce the damaging winds.

This paper describes the atmospheric modeling that underlies the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) science analysis, including its meteorological and atmospheric transport components (polar variant of the Weather Research and Forecasting (WRF) and Stochastic Time Inverted Lagrangian Transport (STILT) models), and provides WRF validation for May-October 2012 and March-November 2013 - the first 2 years of the aircraft field campaign. A triply nested computational domain for WRF was chosen so that the innermost domain with 3.3 km grid spacing encompasses the entire mainland of Alaska and enables the substantial orography of the state to be represented by the underlying high-resolution topographic input field. Summary statistics of the WRF model performance on the 3.3 km grid indicate good overall agreement with quality-controlled surface and radiosonde observations. Two-meter temperatures are generally too cold by approximately 1.4 K in 2012 and 1.1 K in 2013, while 2 m dewpoint temperatures are too low (dry) by 0.2 K in 2012 and too high (moist) by 0.6 K in 2013. Wind speeds are biased too low by 0.2 m s-1 in 2012 and 0.3 m s-1 in 2013. Model representation of upper level variables is very good. These measures are comparable to model performance metrics of similar model configurations found in the literature. The high quality of these fine-resolution WRF meteorological fields inspires confidence in their use to drive STILT for the purpose of computing surface influences ("footprints") at commensurably increased resolution. Indeed, footprints generated on a 0.1° grid show increased spatial detail compared with those on the more common 0.5° grid, better allowing for convolution with flux models for carbon dioxide and methane across the heterogeneous Alaskan landscape. Ozone deposition rates computed using STILT footprints indicate good agreement with observations and exhibit realistic seasonal variability, further indicating that WRF-STILT footprints are of high quality and will support accurate estimates of CO2 and CH4 surface-atmosphere fluxes using CARVE observations. [ABSTRACT FROM AUTHOR]

Current knowledge about the European terrestrial biospheric carbon sink, from the Atlantic to the Urals, relies upon bottom-up inventory and surface flux inverse model estimates (e.g. 0.27±0.16 GtC a-1 for 2000-2005 (Schulze et al., 2009), 0.17±0.44 GtC a-1 for 2001-2007 (Peters et al., 2010), 0.45±0.40 GtC a-1 for 2010 (Chevallier et al., 2014), 0.40±0.42 GtC a-1 for 2001-2004 (Peylin et al., 2013)). Inverse models assimilate in situ CO2 atmospheric concentrations measured by surface-based air sampling networks. The intrinsic sparseness of these networks is one reason for the relatively large flux uncertainties (Peters et al., 2010; Bruhwiler et al., 2011). Satellite-based CO2 measurements have the potential to reduce these uncertainties (Miller et al., 2007; Chevallier et al., 2007). Global inversion experiments using independent models and independent GOSAT satellite data products consistently derived a considerably larger European sink (1.0-1.3 GtC a-1 for 09/2009-08/2010 (Basu et al., 2013), 1.2-1.8 GtC a-1 in 2010 (Chevallier et al., 2014)). However, these results have been considered unrealistic due to potential retrieval biases and/or transport errors (Chevallier et al., 2014) or have not been discussed at all (Basu et al., 2013; Takagi et al., 2014). Our analysis comprises a regional inversion approach using STILT (Gerbig et al., 2003; Lin et al., 2003) short-range (days) particle dispersion modelling, rendering it insensitive to large-scale retrieval biases and less sensitive to long-range transport errors. We show that the satellite-derived European terrestrial carbon sink is indeed much larger (1.02±0.30 GtC a-1 in 2010) than previously expected. This is qualitatively consistent among an ensemble of five different inversion set-ups and five independent satellite retrievals (BESD (Reuter et al., 2011) 2003-2010, ACOS (O'Dell et al., 2012) 2010, UoL-FP (Cogan et al., 2012) 2010, RemoTeC (Butz et al., 2011) 2010, and NIES (Yoshida et al., 2013) 2010) using data from two different instruments (SCIAMACHY (Bovensmann et al., 1999) and GOSAT (Kuze et al., 2009)). The difference to in situ based inversions (Peylin et al., 2013), whilst large with respect to the mean reported European carbon sink (0.4 GtC a-1 for 2001-2004), is similar in magnitude to the reported uncertainty (0.42 GtC a-1). The highest gain in information is obtained during the growing season when satellite observation conditions are advantageous, a priori uncertainties are largest, and the surface sink maximises; during the dormant season, the results are dominated by the a priori. Our results provide evidence that the current understanding of the European carbon sink has to be revisited. [ABSTRACT FROM AUTHOR]

Atmospheric inversion models have the potential to quantify CO2 fluxes at regional, sub-continental scales by taking advantage of near-surface CO2 mixing ratio observations collected in areas with high flux variability. This study presents results from a series of regional geostatistical inverse models (GIM) over North America for 2004, and uses them as the basis for an inter-comparison to other inversion studies and estimates from biospheric models collected through the North American Carbon Program Regional and Continental Interim Synthesis. Because the GIM approach does not require explicit prior flux estimates and resolves fluxes at fine spatiotemporal scales (i.e. 1° x 1°, 3-hourly in this study), it avoids temporal and spatial aggregation errors and allows for the recovery of realistic spatial patterns from the atmospheric data relative to previous inversion studies. Results from a GIM inversion using only available atmospheric observations and a fine-scale fossil fuel inventory were used to confirm the quality of the inventory and inversion setup. An inversion additionally including auxiliary variables from the North American Regional Reanalysis found inferred relationships with flux consistent with physiological understanding of the biospheric carbon cycle. Comparison of GIM results with bottom-up biospheric models showed stronger agreement during the growing relative to the dormant season, in part because most of the biospheric models do not fully represent agricultural land-management practices and the fate of both residual biomass and harvested products. Comparison to earlier inversion studies pointed to aggregation errors as a likely source of bias in previous subcontinental scale flux estimates, particularly for inversions that adjust fluxes at the coarsest scales and use atmospheric observations averaged over long periods. Finally, whereas the continental CO2 boundary conditions used in the GIM inversions have a minor impact on spatial patterns, they have a substantial impact on the continental carbon budget, with a difference of 0.8 PgC yr-1 in the total continental flux resulting from the use of two plausible sets of boundary CO2 mixing ratios. Overall, this inter-comparison study helps to assess the state of the science in estimating regional-scale CO2 fluxes, while pointing towards the path forward for improvements in future top-down and bottom-up modeling efforts. [ABSTRACT FROM AUTHOR]

Robust estimates of regional-scale terrestrial CO2 exchange are needed to support carbon management policies and to improve the predictive ability of models representing carbon-climate feedbacks. Large discrepancies remain, however, both among and between CO2 flux estimates from atmospheric inverse models and terrestrial biosphere models. Improved atmospheric inverse models that provide robust estimates at sufficiently fine spatial scales could prove especially useful for monitoring efforts, while also serving as a validation tool for process-based assumptions in terrestrial biosphere models. A growing network of continental sites collecting continuous CO2 measurements provides the information needed to drive such models. This study presents results from a regional geostatistical inversion over North America for 2004, taking advantage of continuous data from the nine sites operational in that year, as well as available flask and aircraft observations. The approach does not require explicit prior flux estimates, resolves fluxes at finer spatiotemporal scales than previous North American inversion studies, and uses a Lagrangian transport model coupled with high-resolution winds (i.e. WRF-STILT) to resolve near-field influences around measurement locations. The estimated fluxes are used in an inter-comparison with other inversion studies and a suite of terrestrial biosphere model estimates collected through the North American Carbon Program Regional and Continental Interim Synthesis. Differences among in- versions are found to be smallest in areas of the continent best-constrained by the atmospheric data, pointing to the value of an expanded measurement network. Aggregation errors in previous coarser-scale inversion studies are likely to explain a portion of the remaining spread. The spatial patterns from a geostatistical inversion that includes auxiliary environmental variables from the North American Regional Reanalysis were similar to those from the median of the biospheric model estimates during the growing season, but diverged more strongly in the dormant season. This could be due to a lack of sensitivity in the inversion during the dormant season, but may also point to a lack of skill in the biospheric models outside of the growing season, particularly in agricultural areas. For the annual continental budget, the boundary conditions used as an input into the inversions were seen to have a substantial impact on the estimated net flux, with a difference of ~0.8 PgCyr-1 associated with results using two different plausible sets of boundary conditions. [ABSTRACT FROM AUTHOR]

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO 2 ) emissions. Here, we detect and estimate decreases in CO 2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO 2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO 2 reduction activities efficiently., (© 2021. The Authors. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

The Ensemble Mars Atmosphere Reanalysis System (EMARS) dataset version 1.0 contains hourly gridded atmospheric variables for the planet Mars, spanning Mars Year (MY) 24 through 33 (1999 through 2017). A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. EMARS uses the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation with the GFDL/NASA Mars Global Climate Model (MGCM). Observations that are assimilated include the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature retrievals. The dataset includes gridded fields of temperature, wind, surface pressure, as well as dust, water ice, CO 2 surface ice and other atmospheric quantities. Reanalyses are useful for both science and engineering studies, including investigations of transient eddies, the polar vortex, thermal tides and dust storms, and during spacecraft operations., (© 2019 The Authors. Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.)

Simulation of the planetary boundary layer (PBL) is key for forecasting air quality and estimating greenhouse gas (GHG) emissions in cities. Here we conducted the first long-term and continuous study of PBL heights (PBLHs) in Boston, MA, using a compact lidar instrument. We developed an image recognition algorithm to estimate PBLHs from the lidar measurements and evaluated simulations of the PBL from seven numerical weather prediction (NWP) model versions, which showed different systematic errors and variability in simulating the PBLHs (discrepancies from -2.5 to 4.0 km). The NWP model with the best overall agreement for the fully developed PBL had R 2 = 0.72 and a bias of only 0.128 km. However, this model predicted a notable number of anomalously high carbon dioxide concentrations at ground stations, because it occasionally significantly underestimated the PBLH. We also developed a novel method that combines lidar data with footprints from a Lagrangian particle dispersion model to identify long-range transport of air pollution in the nocturnal residual layer. Our framework was powerful in evaluating the performance of models used to estimate air pollution and GHG emissions in cities, which is critical to track progress on emission reduction targets and guide effective policies.

Long-term atmospheric CO 2 mole fraction and δ 13 CO 2 observations over North America document persistent responses to the El Niño-Southern Oscillation. We estimate these responses corresponded to 0.61 (0.45 to 0.79) PgC year -1 more North American carbon uptake during El Niño than during La Niña between 2007 and 2015, partially offsetting increases of net tropical biosphere-to-atmosphere carbon flux around El Niño. Anomalies in derived North American net ecosystem exchange (NEE) display strong but opposite correlations with surface air temperature between seasons, while their correlation with water availability was more constant throughout the year, such that water availability is the dominant control on annual NEE variability over North America. These results suggest that increased water availability and favorable temperature conditions (warmer spring and cooler summer) caused enhanced carbon uptake over North America near and during El Niño.

With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO 2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO 2 from five sites in and around Boston were combined with a high-resolution bottom-up CO 2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model-measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO 2 fluxes, and development of a CO 2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO 2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m -2 ·y -1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model-measurement approach demonstrated here, our framework should be able to detect changes in CO 2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas., Competing Interests: The authors declare no conflict of interest.

National-scale emissions of carbon tetrachloride (CCl4) are derived based on inverse modeling of atmospheric observations at multiple sites across the United States from the National Oceanic and Atmospheric Administration's flask air sampling network. We estimate an annual average US emission of 4.0 (2.0-6.5) Gg CCl4 y(-1) during 2008-2012, which is almost two orders of magnitude larger than reported to the US Environmental Protection Agency (EPA) Toxics Release Inventory (TRI) (mean of 0.06 Gg y(-1)) but only 8% (3-22%) of global CCl4 emissions during these years. Emissive regions identified by the observations and consistently shown in all inversion results include the Gulf Coast states, the San Francisco Bay Area in California, and the Denver area in Colorado. Both the observation-derived emissions and the US EPA TRI identified Texas and Louisiana as the largest contributors, accounting for one- to two-thirds of the US national total CCl4 emission during 2008-2012. These results are qualitatively consistent with multiple aircraft and ship surveys conducted in earlier years, which suggested significant enhancements in atmospheric mole fractions measured near Houston and surrounding areas. Furthermore, the emission distribution derived for CCl4 throughout the United States is more consistent with the distribution of industrial activities included in the TRI than with the distribution of other potential CCl4 sources such as uncapped landfills or activities related to population density (e.g., use of chlorine-containing bleach).

Methane emissions from natural gas delivery and end use must be quantified to evaluate the environmental impacts of natural gas and to develop and assess the efficacy of emission reduction strategies. We report natural gas emission rates for 1 y in the urban region of Boston, using a comprehensive atmospheric measurement and modeling framework. Continuous methane observations from four stations are combined with a high-resolution transport model to quantify the regional average emission flux, 18.5 ± 3.7 (95% confidence interval) g CH4 ⋅ m(-2) ⋅ y(-1). Simultaneous observations of atmospheric ethane, compared with the ethane-to-methane ratio in the pipeline gas delivered to the region, demonstrate that natural gas accounted for ∼ 60-100% of methane emissions, depending on season. Using government statistics and geospatial data on natural gas use, we find the average fractional loss rate to the atmosphere from all downstream components of the natural gas system, including transmission, distribution, and end use, was 2.7 ± 0.6% in the Boston urban region, with little seasonal variability. This fraction is notably higher than the 1.1% implied by the most closely comparable emission inventory.

This study quantitatively estimates the spatial distribution of anthropogenic methane sources in the United States by combining comprehensive atmospheric methane observations, extensive spatial datasets, and a high-resolution atmospheric transport model. Results show that current inventories from the US Environmental Protection Agency (EPA) and the Emissions Database for Global Atmospheric Research underestimate methane emissions nationally by a factor of ∼1.5 and ∼1.7, respectively. Our study indicates that emissions due to ruminants and manure are up to twice the magnitude of existing inventories. In addition, the discrepancy in methane source estimates is particularly pronounced in the south-central United States, where we find total emissions are ∼2.7 times greater than in most inventories and account for 24 ± 3% of national emissions. The spatial patterns of our emission fluxes and observed methane-propane correlations indicate that fossil fuel extraction and refining are major contributors (45 ± 13%) in the south-central United States. This result suggests that regional methane emissions due to fossil fuel extraction and processing could be 4.9 ± 2.6 times larger than in EDGAR, the most comprehensive global methane inventory. These results cast doubt on the US EPA's recent decision to downscale its estimate of national natural gas emissions by 25-30%. Overall, we conclude that methane emissions associated with both the animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.

International agreements to limit greenhouse gas emissions require verification to ensure that they are effective and fair. Verification based on direct observation of atmospheric greenhouse gas concentrations will be necessary to demonstrate that estimated emission reductions have been actualized in the atmosphere. Here we assess the capability of ground-based observations and a high-resolution (1.3 km) mesoscale atmospheric transport model to determine a change in greenhouse gas emissions over time from a metropolitan region. We test the method with observations from a network of CO(2) surface monitors in Salt Lake City. Many features of the CO(2) data were simulated with excellent fidelity, although data-model mismatches occurred on hourly timescales due to inadequate simulation of shallow circulations and the precise timing of boundary-layer stratification and destratification. Using two optimization procedures, monthly regional fluxes were constrained to sufficient precision to detect an increase or decrease in emissions of approximately 15% at the 95% confidence level. We argue that integrated column measurements of the urban dome of CO(2) from the ground and/or space are less sensitive than surface point measurements to the redistribution of emitted CO(2) by small-scale processes and thus may allow for more precise trend detection of emissions from urban regions.