Atmospheric CO₂ measurements from a dense surface network can help to evaluate terrestrial biosphere model (TBM) simulations of Net Ecosystem Exchange (NEE) with two key benefits. First, gridded CO₂ flux estimates can be evaluated over regional scales, not possible using flux tower observations at discrete locations for model evaluation. Second, TBM ability to explain atmospheric CO₂ fluctuations due to the biosphere can be directly tested, an important objective for anthropogenic emissions monitoring using atmospheric observations. Here, we customize the Vegetation Photosynthesis and Respiration Model (VPRM) for an eastern North American domain with strong biological activity upwind of urban areas. Parameters are optimized using flux tower observations from a historical database with sites in (and near) the domain. In addition, the respiration model (originally a linear function of temperature) is modified to account for impacts of changing foliage, non‐linear temperature, and water stress. Flux estimates from VPRM, the Carnegie‐Ames‐Stanford Approach (CASA) model and the Simple Biosphere Model v4 (SiB4), are convolved with footprints from atmospheric transport models for evaluation with CO₂ observations at 21 towers in the domain, with roughly half of the towers used here for the first time. Results show that the new respiration model in VPRM helps to correct a growing season sink bias in the atmosphere associated with underestimated summertime respiration using the original model with annual parameters. The new VPRM also better explains fine‐scale atmospheric CO₂ variability compared to other TBMs, due to higher resolution diagnostic phenology, the new respiration model, domain‐specific parameters, and high‐quality input data sets.

A credible assessment of a city’s greenhouse gas (GHG) mitigation policies requires a valid account of a city’s emissions. However, questions persist as to whether cities’ ‘self-reported inventories’ (SRIs) are accurate, precise, and consistent enough to track progress toward city mitigation goals. Although useful for broad policy initiatives, city SRIs provide annual snapshots that may have limited use to city managers looking to develop targeted mitigation policies that overlap with other issues like equity, air quality, and human health. An emerging approach from the research community that integrates ‘bottom-up’ hourly, street-level emission data products with ‘top-down’ GHG atmospheric observations have begun to yield production-based (scope 1) GHG estimates that can track changes in emissions at annual and sub-annual timeframes. The use of this integrated approach offers a much-needed assessment of SRIs: the atmospheric observations are tied to international standards and the bottom-up information incorporates multiple overlapping socio-economic data. The emissions are mapped at fine scales which helps link them to attribute information (e.g. fuel types) that can further facilitate mitigation actions. Here, we describe this approach and compare results to the SRI from the City of Indianapolis which shows a yearly difference of 35% in scope 1 emissions. In the City of Baltimore, we show that granular emission information can help address multiple issues, e.g. GHG emissions, air pollution, and inequity, at the sub-zip code scale where many roots and causes for each issue exist. Finally, we show that the incorporation of atmospheric concentrations within an integrated system provides rapid, near-real-time feedback on CO₂ emissions anomalies that can uncover important behavioral and economic relationships. An integrated approach to GHG monitoring, reporting and verification can ensure uniformity, and provide accuracy to city-scale GHG emissions, scalable to states and the nation—ultimately helping cities meet stated ambitions.

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.

As city governments take steps towards establishing emissions reduction targets, the atmospheric research community is increasingly able to assist in tracking emissions reductions. Researchers have established systems for observing atmospheric greenhouse gases in urban areas with the aim of attributing greenhouse gas concentration enhancements (and thus emissions) to the region in question. However, to attribute enhancements to a particular region, one must isolate the component of the observed concentration attributable to fluxes inside the region by removing the background, which is the component due to fluxes outside. In this study, we demonstrate methods to construct several versions of a background for our carbon dioxide and methane observing network in the Washington, DC, and Baltimore, MD, metropolitan region. Some of these versions rely on transport and flux models, while others are based on observations upwind of the domain. First, we evaluate the backgrounds in a synthetic data framework, and then we evaluate against real observations from our urban network. We find that backgrounds based on upwind observations capture the variability better than model-based backgrounds, although care must be taken to avoid bias from biospheric carbon dioxide fluxes near background stations in summer. Model-based backgrounds also perform well when upwind fluxes can be modeled accurately. Our study evaluates different background methods and provides guidance in determining background methodology that can impact the design of urban monitoring networks.

We present and discuss the use of a high‐dimensional computational method for atmospheric inversions that incorporates the space‐time structure of transport and dispersion errors. In urban environments, transport and dispersion errors are largely the result of our inability to capture the true underlying transport of greenhouse gas (GHG) emissions to observational sites. Motivated by the impact of transport model error on estimates of fluxes of GHGs using in situ tower‐based mole‐fraction observations, we specifically address the need to characterize transport error structures in high‐resolution large‐scale inversion models. We do this using parametric covariance functions combined with shrinkage‐based regularization methods within an Ensemble Transform Kalman Filter inversion setup. We devise a synthetic data experiment to compare the impact of transport and dispersion error component of the model‐data mismatch covariance choices on flux retrievals and study the robustness of the method with respect to fewer observational constraints. We demonstrate the analysis in the context of inferring CO₂ fluxes starting with a hypothesized prior in the Washington D.C. /Baltimore area constrained by a synthetic set of tower‐based CO₂ measurements within an observing system simulation experiment framework. This study demonstrates the ability of these simple covariance structures to substantially improve the estimation of fluxes over standard covariance models in flux estimation from urban regions.

Increasing atmospheric CO ₂ measurements in North America, especially in urban areas, may help enable the development of an operational CO ₂ emission monitoring system. However, isolating the fossil fuel emission signal in the atmosphere requires factoring out CO ₂ fluctuations due to the biosphere, especially during the growing season. To help improve simulations of the biosphere, here we customize the Vegetation Photosynthesis and Respiration Model (VPRM) at high-resolution for an eastern North American domain, upwind of coastal cities from Washington D.C. to Boston, MA, optimizing parameters using domain-specific flux tower data from 2001 to the present. We run three versions of VPRM from November 2016 to October 2017 using i) annual (VPRM _ann ) and ii) seasonal parameters (VPRM _seas ), and then iii) modifying the respiration equation to include the Enhanced Vegetation Index (EVI), a squared temperature term and interactions between temperature and water stress (VPRM _new ). VPRM flux estimates are evaluated by comparison with other models (the Carnegie-Ames-Stanford Approach model, or CASA, and the Simple Biosphere Model v4), and with comparison to atmospheric CO ₂ mole fraction data at 21 surface towers. Results show that VPRM _new is relatively unbiased and outperforms all other models in explaining CO ₂ variability from April to October, while VPRM _ann overestimates growing season sinks by underestimating summertime respiration. Despite unknown remaining errors in VPRM _new , and uncertainties associated with other components of the atmospheric CO ₂ comparisons, VPRM _new appears to hold promise for more effectively separating anthropogenic and biospheric signals in atmospheric inversion systems in eastern North America.