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Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations

Journal Article · · Journal of Geophysical Research: Atmospheres
DOI:https://doi.org/10.1002/2016JD025617· OSTI ID:1364604
 [1];  [2];  [3];  [1];  [1];  [4];  [5];  [6];  [7];  [8];  [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  3. Imperial College London, London (United Kingdom)
  4. Jet Propulsion Lab., Pasadena, CA (United States)
  5. Univ. of California, San Diego, La Jolla, CA (United States)
  6. Colorado State Univ., Fort Collins, CO (United States)
  7. Arizona State Univ., Tucson, AZ (United States)
  8. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Univ. Space Research Assoc., Columbia, MD (United States)
Here, we report simulation experiments estimating the uncertainties in California regional fossil fuel and biosphere CO2 exchanges that might be obtained by using an atmospheric inverse modeling system driven by the combination of ground-based observations of radiocarbon and total CO2, together with column-mean CO2 observations from NASA's Orbiting Carbon Observatory (OCO-2). The work includes an initial examination of statistical uncertainties in prior models for CO2 exchange, in radiocarbon-based fossil fuel CO2 measurements, in OCO-2 measurements, and in a regional atmospheric transport modeling system. Using these nominal assumptions for measurement and model uncertainties, we find that flask measurements of radiocarbon and total CO2 at 10 towers can be used to distinguish between different fossil fuel emission data products for major urban regions of California. We then show that the combination of flask and OCO-2 observations yields posterior uncertainties in monthly-mean fossil fuel emissions of ~5–10%, levels likely useful for policy relevant evaluation of bottom-up fossil fuel emission estimates. Similarly, we find that inversions yield uncertainties in monthly biosphere CO2 exchange of ~6%–12%, depending on season, providing useful information on net carbon uptake in California's forests and agricultural lands. Finally, initial sensitivity analysis suggests that obtaining the above results requires control of systematic biases below approximately 0.5 ppm, placing requirements on accuracy of the atmospheric measurements, background subtraction, and atmospheric transport modeling.
Research Organization:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
Energy Analysis & Environmental Impacts; USDOE
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1364604
Alternate ID(s):
OSTI ID: 1402389
Report Number(s):
LBNL--1007266; ir:1007266
Journal Information:
Journal of Geophysical Research: Atmospheres, Journal Name: Journal of Geophysical Research: Atmospheres Journal Issue: 6 Vol. 122; ISSN 2169-897X
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (10)

Errors and uncertainties in a gridded carbon dioxide emissions inventory journal July 2019
Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon journal March 2019
Assessing fossil fuel CO 2 emissions in California using atmospheric observations and models journal May 2018
Accounting for the vertical distribution of emissions in atmospheric CO2 simulations text January 2019
Southern California megacity CO 2 , CH 4 , and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model journal January 2018
Characterizing uncertainties in atmospheric inversions of fossil fuel CO2 emissions in California journal January 2019
Accounting for the vertical distribution of emissions in atmospheric CO2 simulations journal January 2019
CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations journal January 2018
A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) journal January 2018
CTDAS-Lagrange v1.0: A high-resolution data assimilation system for regional carbon dioxide observations journal December 2017

Figures / Tables (15)