GC11B-1031
Initial Results from an Atmospheric Validation of Urban Greenhouse Gas Budget Estimates for the US Northeast Corridor
Monday, 14 December 2015
Poster Hall (Moscone South)
Thomas Nehrkorn1, Steven C Wofsy2, Lucy Hutyra3, Phil Decola4, William Callahan5, Maryann R Sargent2, Kathryn McKain6, Yanina Barrera2, Taylor Jones2, Conor Gately3, Brady S Hardiman3, Marikate Ellis Mountain7, John Henderson7, George James Collatz8, Crystal L Schaaf3, Charles E Miller9, Amanda Long5, Christopher Sloop5 and Steve Prinzivalli5, (1)Atmospheric and Environmental Research Lexington, Lexington, MA, United States, (2)Harvard University, Cambridge, MA, United States, (3)Boston University, Boston, MA, United States, (4)Sigma Space Corporation, Lanham, MD, United States, (5)Earth Networks Inc., Germantown, MD, United States, (6)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (7)Atmospheric and Environmental Research, Lexington, MA, United States, (8)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (9)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Abstract:
The world’s population is increasingly concentrated in urban areas. Urbanization has a profound impact on carbon dynamics, leading to higher anthropogenic carbon dioxide (CO2) emissions and lower biogenic fluxes. We describe a model-data analysis framework that is designed to validate and improve greenhouse gas (GHG) budget estimates for the US Northeast (Washington DC to Boston) urban corridor. It encompasses an observational network of GHG in-situ measurements (at near surface sites, on tall buildings, and on towers), column amount measurements from ground-based and satellite sensors, miniMPL measurements of the planetary boundary layer structure, a high-resolution emission inventory, and a modeling framework for atmospheric transport and diffusion that is comprised of a mesoscale atmospheric model and a Lagrangian particle dispersion model. We present selected results from different aspects of the modeling-data framework, including emission inventories at high spatio-temporal resolution, verification of meteorological simulation using conventional and novel (e.g., miniMPL) observations, and aspects of the inversion methodology.
