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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–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 ( X CO 2 ), 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 X CO 2 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 X CO 2 retrievals and assess potential biases, we compare OCO-2 v10 retrievals with in situ data-constrained X CO 2 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 v10 bias correction shifts the distribution of retrievals closer to the simulated X CO 2 , as intended. Comparisons between bias-corrected and simulated X CO 2 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 v10 retrievals of X CO 2 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.

The number of greenhouse gas (GHG) observing satellites has greatly expanded in recent years, and these new datasets provide an unprecedented constraint on global GHG sources and sinks. However, a continuing challenge for inverse models that are used to estimate these sources and sinks is the sheer number of satellite observations, sometimes in the millions per day. These massive datasets often make it prohibitive to implement inverse modeling calculations and/or assimilate the observations using many types of atmospheric models. Although these satellite datasets are very large, the information content of any single observation is often modest and non-exclusive due to redundancy with neighboring observations and due to measurement noise. In this study, we develop an adaptive approach to reduce the size of satellite datasets using geostatistics. A guiding principle is to reduce the data more in regions with little variability in the observations and less in regions with high variability. We subsequently tune and evaluate the approach using synthetic and real data case studies for North America from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The proposed approach to data reduction yields more accurate CO2 flux estimates than the commonly used method of binning and averaging the satellite data. We further develop a metric for choosing a level of data reduction; we can reduce the satellite dataset to an average of one observation per ∼ 80–140 km for the specific case studies here without substantially compromising the flux estimate, but we find that reducing the data further quickly degrades the accuracy of the estimated fluxes. Overall, the approach developed here could be applied to a range of inverse problems that use very large trace gas datasets.

Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly used approaches because they do not require a single emissions inventory or a bottom–up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using a synthetic CO2 case study based upon NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4×106 unknown fluxes using 9.9×104 synthetic observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximations are, typically, an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.

Responses to COVID‐19 have resulted in unintended reductions of city‐scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 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 CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course‐correct CO2 reduction activities efficiently., Key Points Atmospheric CO2 observations can be used to detect the onset of the COVID‐19 response in Los Angeles and Washington DC/BaltimoreRelative reductions in April 2020 associated with COVID‐19 are ∼30% when compared to emissions in 2018 and 2019Decreases in vehicular traffic do not completely explain observed emissions reductions in both Los Angeles and Washington DC/Baltimore

We developed and evaluated three types of statistical forecasting models (quantitative, probabilistic, and classification) for predicting the maximum daily 8-hour average concentration of ozone based on meteorological and ozone monitoring data for six Texas urban areas from 2009 to 2015. The quantitative and probabilistic forecasting models were generalized additive models (GAMs), whereas the classification forecast used the random forest machine learning method. We found that for the quantitative forecasting models, five of the eight predictors (the day of week, day of the year, water vapor density, wind speed, and previous day’s ozone measurement) were significant at the α = 0.001 level for all urban areas, whereas the other three varied in significance according to the location. The quantitative forecasting for the 2016 ozone season agreed well with the associated measurements (R2 of ~0.70), but it tended to under-predict the ozone level for the days with the highest concentrations. By contrast, the probabilistic forecasting models showed little accuracy in determining the probability of concentrations exceeding policy-relevant thresholds during this season. The success rate for the random forest classification models typically exceeded 75% and would likely increase if the training data sets contained more extreme events.

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. Our analysis uses three lines of evidence with increasing model dependency. The first detects the timing of emissions declines using the variability in atmospheric CO2 observations, the second assesses the continuation of reduced emissions using CO2 enhancements, and the third employs an inverse model to estimate the relative emissions changes in 2020 compared to 2018 and 2019. Emissions declines began in mid-March in both cities. 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, a proxy for vehicular emissions. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines, while the remainder of the emissions reduction remains unexplained. To help diagnose such observed changes in emissions, more reliable, publicly available emission information from all significant sectors needs to be made available. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing urban emissions patterns that can empower cities to course-correct mitigation activities more efficiently.

Methane (CH4) is the second most important greenhouse gas but its emissions from northern regions are still poorly constrained. In this study, we analyze a subset of in situ CH4 aircraft observations made over Alaska during the growing seasons of 2012–2014 as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Net surface CH4 fluxes are estimated using a Lagrangian particle dispersion model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH4 in the mixed layer. We estimate that between May and September, net CH4 emissions from the region of interest were 2.2 ± 0.5 Tg, 1.9 ± 0.4 Tg, and 2.3 ± 0.6 Tg of CH4 for 2012, 2013, and 2014, respectively. If emissions are only attributed to two biogenic eco-regions within our domain, then tundra regions were the predominant source, accounting for over half of the overall budget despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that, overall, CH4 fluxes were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, suggesting deeper methanogenesis in drier soils. Although similar environmental drivers have been found in the past to control CH4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional-scale net CH4 budget.

©2017. American Geophysical Union. All Rights Reserved. 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 CO2and 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.

Global Navigation Satellite System (GNSS) radio occultations (RO) over the last 10 years have proved to be a valuable and essentially unbiased data source for operational global numerical weather prediction. However, the existing sampling coverage is too sparse in both space and time to support forecasting of severe mesoscale weather. In this study, the case study or quick observing system simulation experiment (QuickOSSE) framework is used to quantify the impact of vastly increased numbers of GNSS RO profiles on mesoscale weather analysis and forecasting. The current study focuses on a severe convective weather event that produced both a tornado and flash flooding in Oklahoma on 31 May 2013. The WRF Model is used to compute a realistic and faithful depiction of reality. This 2-km “nature run” (NR) serves as the “truth” in this study. The NR is sampled by two proposed constellations of GNSS RO receivers that would produce 250 thousand and 2.5 million profiles per day globally. These data are then assimilated using WRF and a 24-member, 18-km-resolution, physics-based ensemble Kalman filter. The data assimilation is cycled hourly and makes use of a nonlocal, excess phase observation operator for RO data. The assimilation of greatly increased numbers of RO profiles produces improved analyses, particularly of the lower-tropospheric moisture fields. The forecast results suggest positive impacts on convective initiation. Additional experiments should be conducted for different weather scenarios and with improved OSSE systems.

Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly-used approaches because they do not require a single emissions inventory or a bottom-up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using CO2 observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4 × 106 unknown fluxes using 9.9 × 104 observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximation are typically an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.

North American ecosystems absorb more CO2 from the atmosphere during El Niño than during La Niña periods., Long-term atmospheric CO2 mole fraction and δ13CO2 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.

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 CTE2016, -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.

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 two 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, lending itself better 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.

U.S. national and regional emissions of HFC-134a are derived for 2008-2012 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 maximum likelihood 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 2008-2012 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 2008-2010 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 10-20% 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 20-50%.

Top–down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in combination with the Weather Research and Forecasting (WRF) model in a regional Bayesian synthesis inversion. The regional Lagrangian particle dispersion model framework is well suited to make use of ASCENDS observations to constrain weekly fluxes in North America at a high resolution, in this case at 1° latitude × 1° longitude. We consider random measurement errors only, modeled as a function of the mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the grid scale and weekly resolution, the largest uncertainty reductions, on the order of 50%, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 μm candidate wavelength than for a 2.05 μm wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual biome scale range from ~40% to ~75% across our four instrument design cases and from ~65% to ~85% for the continent as a whole. Tests suggest that the quantitative results are moderately sensitive to assumptions regarding a priori uncertainties and boundary conditions. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr−1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

We determined methane (CH4) emissions from Alaska using airborne measurements from the Carbon Arctic Reservoirs Vulnerability Experiment (CARVE). Atmospheric sampling was conducted between May and September 2012 and analyzed using a customized version of the polar weather research and forecast model linked to a Lagrangian particle dispersion model (stochastic time-inverted Lagrangian transport model). We estimated growing season CH4 fluxes of 8 ± 2 mg CH4⋅m−2⋅d−1 averaged over all of Alaska, corresponding to fluxes from wetlands of 56 − 13 + 22 56−13+22 mg CH4⋅m−2⋅d−1 if we assumed that wetlands are the only source from the land surface (all uncertainties are 95% confidence intervals from a bootstrapping analysis). Fluxes roughly doubled from May to July, then decreased gradually in August and September. Integrated emissions totaled 2.1 ± 0.5 Tg CH4 for Alaska from May to September 2012, close to the average (2.3; a range of 0.7 to 6 Tg CH4) predicted by various land surface models and inversion analyses for the growing season. Methane emissions from boreal Alaska were larger than from the North Slope; the monthly regional flux estimates showed no evidence of enhanced emissions during early spring or late fall, although these bursts may be more localized in time and space than can be detected by our analysis. These results provide an important baseline to which future studies can be compared.

Three widely used Lagrangian particle dispersion models (LPDMs)—the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Stochastic Time-Inverted Lagrangian Transport (STILT), and Flexible Particle (FLEXPART) models—are evaluated with measurements from the controlled tracer-release experiments Cross-Appalachian Tracer Experiment (CAPTEX) and Across North America Tracer Experiment (ANATEX). The LPDMs are run forward in time driven by identical meteorological inputs from the North American Regional Reanalysis (NARR) and several configurations of the Weather Research and Forecasting (WRF) model, and the simulations of tracer concentrations are evaluated against the measurements with a ranking procedure derived from the combination of four statistical parameters. The statistical evaluation reveals that all three LPDMs have comparable skill in simulating the tracer plumes when driven by the same meteorological inputs, indicating that the differences in their formulations play a secondary role. Simulations with HYSPLIT and STILT demonstrate the benefit of using customized hourly WRF fields with 10-km horizontal grid spacing over the use of 3-hourly NARR fields with 32-km horizontal grid spacing. All three LPDMs perform better when the WRF wind fields in the planetary boundary layer are nudged to NARR, with FLEXPART benefitting the most. Case studies indicate that the nudging corrects an overestimate in plume transport speed possibly caused by a positive bias in WRF wind speeds near the surface. All three LPDMs also benefit from the use of time-averaged velocity and convective mass flux fields generated by WRF, but the impact on HYSPLIT and STILT is much greater than on FLEXPART. STILT backward runs perform as well as their forward counterparts, demonstrating this model’s reversibility and its suitability for application to inverse flux estimates.

A recent National Research Council report highlighted the potential utility of atmospheric observations and models for detecting trends in concentrated emissions from localized regions, such as urban areas. The Salt Lake City (SLC), Utah, area was chosen for a pilot study to determine the feasibility of using ground-based sensors to identify trends in anthropogenic urban emissions over a range of time scales (from days to years). The Weather Research and Forecasting model (WRF) was combined with a Lagrangian particle dispersion model and an emission inventory to model carbon dioxide (CO2) concentrations that can be compared with in situ measurements. An accurate representation of atmospheric transport requires a faithful modeling of the meteorological conditions. This study examines in detail the ability of different configurations of WRF to reproduce the observed local and mesoscale circulations, and the diurnal evolution of the planetary boundary layer (PBL) in the SLC area. Observations from the Vertical Transport and Mixing field experiment in 2000 were used to examine the sensitivity of WRF results to changes in the PBL parameterization and to the inclusion of an urban canopy model (UCM). Results show that for urban locations there is a clear benefit from parameterizing the urban canopy for simulation of the PBL and near-surface conditions, particularly for temperature evolution at night. Simulation of near-surface CO2 concentrations for a 2-week period in October 2006 showed that running WRF at high resolution (1.33 km) and with a UCM also improves the simulation of observed increases in CO2 during the early evening.