Responses to public health threats presented by the global COVID-19 pandemic dramatically altered daily activities in cities around the world, including in the Los Angeles and Washington DC/Baltimore metropolitan areas. Researchers have attempted to determine the extent to which CO 2 emissions were impacted by the pandemic, linking changes in emissions to processes and sectors using different types of activity data and baselines for comparisons (Le Quéré et al., 2020;Liu et al., 2020;Zheng et al., 2020). One study shows that CO 2 emissions declined by 3.9% globally in the first 4 months in 2020, attributing half of this decline to changes in traffic and mobility (Le Quéré et al., 2020). Unlike these studies, which use only activity data to estimate declines, here we also use atmospheric CO 2 observations to detect when and how emissions were impacted, and focus on CO 2 emissions reductions at the city scale.Our analysis relies on high-accuracy atmospheric CO 2 observations from urban networks, building on a recently published study that used lower-accuracy CO 2 sensors to estimate COVID-19 related impacts for the San Francisco Bay area (Turner et al., 2020). Here, we evaluate impacts in two separate metropolitan areas: Los Angeles and Washington DC/Baltimore, allowing for an inter-comparison between two large urban regions. In Los Angeles and Washington DC/Baltimore, traffic congestion and commuting play dominant

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

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.