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How f ossil fuel CO 2 uncertainty impacts estimates of carbon exchange and variability

How f ossil fuel CO 2 uncertainty impacts estimates of carbon exchange and variability. Kevin Gurney, Yang Song, Jianhua Huang, Kevin Coltin , Alex Garden

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How f ossil fuel CO 2 uncertainty impacts estimates of carbon exchange and variability

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  1. How fossil fuel CO2 uncertainty impacts estimates of carbon exchange and variability Kevin Gurney, Yang Song, Jianhua Huang, Kevin Coltin, Alex Garden School of Life Sciences/School of Sustainability, Global Institute of Sustainability, Arizona State University, Tempe, AZ, 85281, USA

  2. Introduction • A focus on power plant uncertainty: • More available data, particularly in US • Large proportion of total (~40%) • Examine US and global cases • Examine National inventories - total spread • Descriptive statistical characteristics of spans • Impacts of national span on high resolution fossil fuel CO2 data product (FFDAS) and atmospheric CO2 Inversions

  3. Vulcan www.vulcan.project.asu.edu Gurney et al., Env. Sci & Tech, 2009

  4. See poster 217, Today Hestia

  5. FFDAS+ Uncertainty quantification in all of these data products remains challenging

  6. US Power Plants (almost 40% of US FFCO2) Comparing EPA derived data (CEMs primarily) to DOE/EIA derived data (fuel consumption-based calculation)

  7. US Power Plants continued • Examine hourly data where one can isolate the “methods” used. • CEMs is primary, but there are 6 alternative methods • Require that every hour of a month be 100% single method • Aggregate to month for comparison to DOE/EIA CEMs Mean: -1.8% aka “signed bias”

  8. US Power Plants continued See poster 221, Today • Why? • Are the CEMs measurements biased lo/hi? • Are the Fuel consumption estimates biased hi/lo? • ……Stay tuned….

  9. Global Power Plants • Currently the only available global dataset is CARMA. That includes more than 60,000 power plants worldwide. • CARMA provides plant location and estimated CO2 emission for each power plant. • Statistical model based on WEPP & US data We analyze national publicly disclosed data relative to model prediction to assess accuracy.

  10. Global Power Plants continued To the extent we want to utilize remote platforms to calibrate or monitor large point sources, these distance biases are unacceptable Use of power plants as “standalone” data sources in global FFCO2 estimation requires better accuracy

  11. Global Fossil Fuel CO2 Emission Inventories • CarbonDioxide Information AnalysisCenter (CDIAC) • UN energy data from annual energy surveys • International Energy Agency (IEA) • Annual IEA energy surveys and UN energy data for non-member nations • Sectoral approach (bottom up) vs. Reference approach (top down) • Energy Information Administration (EIA) • EIA’s review of National reports • British Petroleum (BP) • BP’s review of National reports

  12. Macknick’s Harmonized Database Macknick, J. (2011) Energy and CO2 emission data uncertainties. Carbon Manage. 2, 189−205. • Energy Sources • Survey (IEA, CDIAC) vs National Reports (EIA, BP) • Categories • International bunkers, biomass waste, cement production, gas flare… • Conversions • Calorific value • Emission factors • Units • Cubic meters vs feet • Short ton vstonnes • Etc.

  13. Teragrams of carbon Teragrams of carbon Time Mean Percentage span = 7.7% Time Mean Percentage span = 7.6% Year Year World emission unadjusted World emission harmonized (Cement production from CDIAC; Gas flaring from EIA; no wastes, renewables and land-use emissions) ---harmonized data from Macknick, 2011

  14. Implications – atmospheric CO2inversion • Run “hi” and “lo” through TransCom 3 Level 2 inversions • Scales the global fossil total • No change in spatial pattern • RESULTS: • Total Land Uptake for the decade of the 1990’s • “hi”………………………… -1.3 GtC/year • “lo”………………………… -0.89 GtC/year • Total Land Uptake for the decade of the 2000’s • “hi”……………………….. -2.3 GtC/year • “lo”……………………….. -1.8 GtC/year • High resolution inversion underway – implications will be more significant for some global regions.

  15. = “hi” = “lo” = error range

  16. Impact on FFDAS Span as prior uncertainty (diagonal cov matrix only) -200% 0% 200%

  17. Conclusions • Exploration of fossil fuel uncertainty remains challenging • The single largest contributor and sector previously considered the best known – power plant emissions: • US: potentially large error at facility level. Source of bias being investigated. I think we can solve. • CARMA database: requires revision. Underway - VENTUS • Discrepancies among national FFCO2 inventories varies through timeand by country, even after “harmonization” • The median percentage span averaged over countries and time is around 12.5% (mean = 15%) • Impact of the span on fossil fuel CO2 on the inversion results is large: difference exceeds the current “within” inversion error. • Use of this as national uncertainty has significant impact on the FFDAS result • ATMOSPHERIC MEASUREMENTS WILL HELP (OF COURSE), BUT IT ISN’T ENOUGH – THE DATA PRODUCTS MUST IMPROVE

  18. Time mean Percentage Span (%)

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