Kim Mueller , S. Gourdji, V. Yadav , A.E. Andrews, M. Trudeau, G. Petron, D.N. Huntzinger, D. Worthy, W. Munger, M. Fischer, C. Sweeney, B. Stephens, K. Davis, N. Miles, B. Law, M. Gockede & A.M. Michalak Greenhouse2011 Conference Cairns, QLD 8 April, 2011
� Carbon accounting is important for science, politics, business, and the public at large • National databases: Australia – National Carbon Accounting System • Global databases: Carbon Dioxide Information Analysis Center (CDIAC) (Marland, 2010) � There is a need to understand the accuracy of fossil fuel emission estimates The development of reliable emissions inventories through time and by country/region is neither straightforward or quick. This will be a long ‐ term effort, perhaps on order of 5 ‐ 10 years, and will require considerable care to ensure scientific credibility and reliability in terms of the quality of the data. Will Steffen, 2003 Executive Director, International Geosphere ‐ Biosphere Programme 2
� Independent verification of fossil fuel emission inventories important for: • top ‐ down estimates (aka inversions) of biospheric fluxes [Science] • monitoring emission reduction commitments [Policy] � uncertainties in emission databases can be up to 15 ‐ 20% � Different means of assessing the accuracy of inventories: Need to assess emissions inventories from the • developed countries can monitor individual point sources (US ‐ EPA) perspective of atmospheric observations without using • top ‐ down initiatives to assess CO2 pollution from large cities (Mega ‐ expensive measurements or complicated methods: cities project) 1. Help with constraining other natural sources and sinks • use carbon isotopes (C14) or carbon monoxide measurements to 2. Have the potential to validate emissions where direct monitoring is isolate fossil fuel emissions to larger areas difficult 3
� Sites that continuously measure atmospheric CO2
Fossil fuel signal small in magnitude relative to biospheric during height of growing season Question : Is the fossil fuel signal detectable across the entire continent? Throughout the year? Question : Can the biospheric and anthropogenic signals be independently identified? Use a Geostatistical Inversion to try and answer these questions … 5
[CO 2 ] 1. CO 2 Concentration Measurements (mass) 2. Transport Model 3. Flux Estimates (mass/time) TOTAL FLUX = fossil fuel + biospheric fluxes Adapted from Y. Shiga (Umich)
� Geostatistical inverse modeling objective function: Reproducing the Minimize the sum of the 1 1 − − = − − + − − X β X β 1 1 T T y Hs R y Hs s Q s L ( ) ( ) ( ) ( ) s β , measurements squared residuals 2 2 • H (35,000×8,000,000) = Transport information, • s (8,000,000 ×1) = Estimated flux, • y (35,000 ×1) = Concentration measurements • X (8,000,000 ×60) = Auxiliary variables • β (60 ×1) = Estimated regression coefficients • R (35,000 ×35,000) = Model data mismatch covariance • Q (8,000,000 ×8,000,000) = Spatio ‐ temporal covariance matrix 7
^ ^ Geostatistical Inversion acts similar to a multi ‐ linear regression ^ ^ ^ ^ s = β o + β anthro (Emissions) + β bio (Environ. Data) + error
� Use 1° x 1° merged dataset for entire continent • Vulcan 2.0 for continental United States (Gurney et al., 2009) • Night Lights/ CDIAC fossil fuel inventory for Canada & Mexico (Oda & Maksyutov, 2010) – Available for Australasia � Vulcan scaled from 2002 to 2004 using total emissions for continental U.S.
U.S. Energy Information Administration data Compiled by US-EPA
Total Flux (biospheric component only, pre-subtract fossil fuels from observations) Jul y A pr i l Error Total Flux Error Total Flux
� Is the fossil fuel emission signal independent of the biospheric fluxes? • Can calculate a posteriori correlation between β ’s (i.e. ρ ) to assess this Canopy Evapo ‐ Precip. Specific Snow 16 ‐ day cond. trans. humidity cover lag precip Northeast ‐ 0.02 0.03 ‐ 0.04 0.00 0.11 ‐ 0.11 Southeast 0.00 0.02 ‐ 0.01 ‐ 0.19 0.02 ‐ 0.08 Midwest ‐ 0.16 0.06 ‐ 0.02 0.06 0.11 ‐ 0.05 South ‐ central 0.03 0.04 0.06 ‐ 0.29 0.08 0.01 Central plains ‐ 0.18 0.11 ‐ 0.01 0.03 0.18 ‐ 0.07 North Central 0.06 ‐ 0.03 0.01 0.01 0.08 0.02 California & Southwest ‐ 0.02 0.06 0.05 ‐ 0.04 0.13 ‐ 0.03 Pacific Northwest 0.07 ‐ 0.07 0.02 0.01 0.08 ‐ 0.23 Canada 0.02 ‐ 0.01 ‐ 0.04 0.04 0.06 ‐ 0.11
β (2004) β (2008) Northeast 1.18 0.92 Southeast 0.65 1.90 Midwest 1.08 1.35 South central 1.24 1.26 Central plains 2.38 1.70 North central n/a 0.56 California & southwest n/a 1.55 Pacific northwest n/a n/a Alaska n/a n/a Canada 0.61 1.28 Mexico n/a n/a Sampling bias may be inflating β ’s on fossil fuels… 13
Selected fossil fuel variables (in Selected fossil fuel variables (in addition to biospheric): 2004 addition to biospheric): 2008 Region Region Jan Jan Feb Feb Mar Mar Apr Apr May May Jun Jun Jul Jul Aug Aug Sep Sep Oct Oct Nov Nov Dec Dec Northeast U.S. Northeast U.S. X X X X X X X X X X X X Southeast U.S. Southeast U.S. X X X X X X X X X Midwest U.S. Midwest U.S. X X X X X X X X X X X X X X X X X South ‐ central U.S. South ‐ central U.S. X X X X X X X X X X X Central plains U.S. Central plains U.S. X X X X X X X X X X X X X North ‐ central U.S. North ‐ central U.S. X X California & California & X X X X southwest U.S. southwest U.S. Pacific Northwest Pacific Northwest X X X Alaska Alaska X Canada Canada X X Mexico Mexico X X 14
Q : from atmospheric CO 2 measurements in a geostatistical inversion, can we independently identify: • the biospheric and anthropogenic signals? � Yes • the fossil fuel signal for different seasons & regions? � New towers in 2008 help to isolate emissions in more regions relative to 2004 � Still difficult during growing season, except for Midwest in 2008 due to denser measurement network 15
� Before using atmospheric CO 2 to validate fossil fuel inventories or learning more about how to use them for top ‐ down budgeting , need for: • Year ‐ specific emission datasets (i.e. an “operational” Vulcan for the entire continent or globe) • Denser measurement network and additional sites in under ‐ constrained areas and sites away from urban centers OCO2 Simulated Track for 27 th January, 2006 D. Hammerling (Umich) 16
� WRF ‐ STILT: AER, Inc. (Janusz Eluszkiewicz, Thomas Nehrkorn, John Henderson), John Lin, DeyongWen � Data providers: Bill Irving, Andrea Denny � Funders: NASA (ROSES NACP) � Research group at University of Michigan: Abhishek Chatterjee, Dorit Hammerling, Deborah Huntzinger, Dan Obenour, & Yuntao Zhou kimlm@umich.edu sgourdji@umich.edu
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