Comparison of Global Downscaled Versus Bottom‐Up Fossil Fuel CO₂ Emissions at the Urban Scale in Four U.S. Urban Areas

Abstract

Spatiotemporally resolved urban fossil fuel CO2 (FFCO2) emissions are critical to urban carbon cycle research and urban climate policy. Two general scientific approaches have been taken to estimate spatiotemporally explicit urban FFCO2 fluxes, referred to here as “downscaling” and “bottom‐up.” Bottom‐up approaches can specifically characterize the CO2‐emitting infrastructure in cities but are labor‐intensive to build and currently available in few U.S. cities. Downscaling approaches, often available globally, require proxy information to allocate or distribute emissions resulting in additional uncertainty. We present a comparison of a downscaled FFCO2 emission data product (Open‐source Data Inventory for Anthropogenic CO2 (ODIAC)) to a bottom‐up estimate (Hestia) in four U.S. urban areas in an effort to better isolate and understand differences between the approaches. We find whole‐city differences ranging from −1.5% (Los Angeles Basin) to +20.8% (Salt Lake City). At the 1 km × 1 km spatial scale, comparisons reveal a low‐emission limit in ODIAC driven by saturation of the nighttime light spatial proxy. At this resolution, the median difference between the two approaches ranged from 47 to 84% depending upon city with correlations ranging from 0.34 to 0.68. The largest discrepancies were found for large point sources and the on‐road sector, suggesting that downscaled FFCO2 data products could be improved by incorporating independent large point‐source estimates and estimating on‐road sources with a relevant spatial surrogate. Progressively coarsening the spatial resolution improves agreement but greater than approximately 25 km2, there were diminishing returns to agreement suggesting a practical resolution when using downscaled approaches.

Constraining Fossil Fuel CO2 Emissions From Urban Area Using OCO‐2 Observations of Total Column CO2

“…Assessment of prior emissions errors in gridded field is difficult (Andres et al, 2016), which is usually done by comparing emission inventories at an aggregated spatial resolution (Hutchins et al, 2017; Oda et al, 2015). The ODIAC data have been compared to the Hestia emission product (Gurney et al, 2019), which is one of the most accurate and complete emission inventories at the scale of buildings and street segments as evaluated against in situ tower measurements (Lauvaux et al, 2016). At 1‐km × 1‐km spatial scale, the result reveals a low‐emission limit in ODIAC driven by saturation of the nighttime light spatial proxy, and the median difference ranging between 47% and 84%.…”

“…The prior emission uncertainty σ a is set up based on reported estimations in literature, as the ODIAC data product did not provide uncertainty estimates. At annual scales, Gurney et al (2019) investigated the difference between the ODIAC data and a high‐resolution bottom‐up estimate product (Hestia) in four U.S. urban areas, showing the differences of whole‐city emissions ranging from −1.5% to +20.8%. Oda et al (2019) found differences of about 40% by comparing a satellite‐derived annual emissions product to a gridded national inventory at 25‐km resolution.…”

“…For example, urban energy consumption and industrial activity data needed for “bottom‐up” method are reported on a voluntary basis or under climate action activities in only a few cities, for example, those participating the Global Covenant of Mayors (http://www.globalcovenantofmayors.org/) and are usually spatially inexplicit, incomplete, and unverified (Hutyra et al, 2014). Similarly, for inventories developed by disaggregating (“downscaling”) national or regional emissions at fine scales, the uncertainty is large at high spatial and temporal resolutions caused by the disaggregation methods (Janssens‐Maenhout et al, 2012; Kurokawa et al, 2013; Oda & Maksyutov, 2011), resulting in significant discrepancies among different emission inventories (Ackerman & Sundquist, 2008; van der Gon et al, 2011; Gurney et al, 2012, 2019; Hogue et al, 2016; Oda & Maksyutov, 2011; Oda et al, 2018; Turnbull, Karion et al, 2011). The two‐sigma uncertainties of national annual ffCO 2 emissions are estimated to be 2–4% for countries with well‐developed energy statistics and inventories (Rypdal & Winiwarter, 2001) and are at a possible order of 10% for countries with less well‐developed energy data systems (IPCC, 2006).…”

Greenhouse gas observations from the Northeast Corridor tower network

“…These transport and dispersion models are used to interpret observations from both aircraft and tower stations and in atmospheric inverse analyses to estimate fluxes of CO2 and CH4 from the cities of Washington, D.C. and Baltimore, Maryland (Lopez-Coto et al, in prep; Ghosh et al, in prep.). A high-resolution fossil-fuel CO2 inventory, Hestia, 70 is also being developed for this project (Gurney et al, 2012;Gurney et al, 2019).…”

Towards space-borne monitoring of localized CO<sub>2</sub> emissions: an instrument concept and first performance assessment

“…1.1 CO 2 concentration field from the Hestia datasetTo compute a high-resolution three-dimensional field of CO 2 concentrations to be used as input for the radiative transfer simulations, annual estimates of fossil fuel CO 2 emissions for the city of Indianapolis in the year 2015 are used. These data are generated by the Hestia Project(Gurney et al, 2012(Gurney et al, , 2019 where the fossil fuel CO 2 emissions are quantified in urban areas down to the scale of individual buildings and streets using a bottom-up approach. The results for the city of Indianapolis are25 gridded and archived at a spatial resolution of 200 × 200 m 2 .…”