Salvi Asefi 1 , K. R. Gurney 1 , P. Rayner 2 , Y . Song 1 ,

1. High-resolution global CO2 emissions from fossil fuel inventories for 1992 to 2010 using integrated in-situ and remotely sensed data in a fossil fuel data assimilation system SalviAsefi1, K. R. Gurney1, P. Rayner2, Y. Song1, K. Coltin1, C. D. Elvidge3, K. Baugh3, A. Mcrobert2 1- Arizona State University, School of Life Sciences 2- School of Earth Sciences, University of Melbourne 3- NOAA-NESDIS National Geophysical Data Center

2. accurate global quantification of FFCO2 with high space/time resolution accompanied by uncertainty is a critical need within the carbon cycle science community. • There is a need for functional or process-based quantification. • This provides better space/time resolution (can avail of sector-specific space/time proxies) • Potential for multiple uses (energy analysis, growth morphology) Our answer: Fossil Fuel Data Assimilation (FFDAS) system to create a global high temporal/spatial resolution fossil fuel CO2emission inventory with uncertainties Introduction See Rayner et al., 2010

3. Vulcan Vulcan data product: • Gridded to 10 km x 10 km, hourly, year 2002 • Includes process detail for all sectors of the U.S economy (on-road, non-road, industrial, commercial, residential, cement production, airport, power production, aircraft). ……detailed bottom-up info is rarely available at global scale…………. Other global data products have employed population and nightlights to downscale national emissions. These efforts have begun to use other datasets such as power plants emissions and spatial proxies such as road maps. ODIAC Current FFCO2 emission datasets

4. In contrast to downscaling national emissions we utilize the Fossil Fuel Data Assimilation System (FFDAS) which has a dynamical model at its core………….the Kaya Identity: F = emissions, P = areal population density g = per capita economic activity e= energy intensity of economic activity f= carbon intensity of energy consumption Data assimilation is applied to constrain components of Kaya with a number of observational operators. Advantages of data assimilation to downscaling techniques: • Process-based dynamical model at core • Smoother spatial distribution • The ability to integrate the range of observations • The ability to include prior uncertainty and estimate posterior uncertainties • Ability to perform at different spatial and temporal scales F=Pgef Fossil Fuel Data Assimilation System (FFDAS)

5. National emissions: • National and global FFCO2 are constrained by FFCO2sectoral emissions reported by International Energy Agency IEA and Carbon Dioxide Information and Analysis Center (CDIAC). • Prior uncertaintiesfor national emissions were also objectively estimated and included in FFDAS (see next talk). Per Country CO2 Emissions (CDIAC) Inputs

6. Population: • SEDACglobal gridded population dataset (0.04° resolution, 1995, 2000, 2005 & 2010) combined with LandScanglobal gridded population dataset (30 arc second resolution, 2004, 2006, 2007, 2008, 2010) • Result: population dataset from 1997 to 2010 at 30 arc second resolution. SEDAC population density LandScan population density Germany Germany France France Inputs Spain Spain

7. Nightlights: • Nightlight is a global remote sensing product provided by NOAA-NGDC at 30 arc second resolutions (1992-2010). • However this dataset is subject to instrumental saturation meaning areas of bright nightlights, such as urban coresare often underestimated. • Saturation has been addressed by NGDC and a new unsaturated dataset has been created for five years (1997, 1999, 2003, 2006 and 2010) at 30 arc second resolution. • Linear interpolation applied to estimate unsaturated values for all years from 1997 to 2010. Nightlight (saturated) - 0.1deg Nightlight (unsaturated) - 0.1deg Nightlights Inputs

8. Power plant point sources: • Currently the only available global dataset is CARMA. That includes more than 60000 power plants worldwide. • CARMA provides plant location and estimated CO2 emission for each power plant. • We are finding sizeable biases…….will discuss in next talk Inputs

9. Results represent annual emissions, 1997 - 2010 at the global scale and spatial resolutions of 0.1° x 0.1° (FFDAS v.2) Can produce any resolution – land/sea mask is critical - coastal shuffling. FFDAS fossil fuel emissions in 2010 at 0.1° FFDAS Results

10. FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions FFDAS fossil fuel emissions Year=2001 Year=2002 Year=2003 Year=2009 Year= 2000 Year=1999 Year=1998 Year=2007 Year=2006 Year=2004 Year=2005 Year=2008 Year=2010

11. Comparisonbetween 0.1° resolutions and 0.25° FFCO2 emission 0.1° FFDAS v2 FFCO2 emission 0.25° FFDAS v1 FFDAS Results

12. Inclusion of power plant emission. A major improvement from FFDAS v.1* • Power plants are major global CO2 emission sources (40% of global emissions). No Power plants FFDAS v.1 Power plants included FFDAS v.2 FFDAS Results *(Rayner et al. 2010)

13. Given the importance of power plants to the results (they have no spatial proxy & they are a large component of total)……………. We are building a new power plant CO2 data product: • Improving locations & emissions via national datasets and GE search. • New predictive model utilizing multiple national datasets • Providing uncertainty for each individual power plant Poster 249, Wednesday (in pavilion) Ventus crowd sourcing effort: An interactive website engaging individuals and institutions to help us improve our knowledge of the power plant emissions and locations. Release date: ~March 2013 New power plant data product - Ventus

14. Comparisons with Vulcan Difference map between FFDAS v.2 and Vulcan at 0.1° At 0.5° resolution FFDAS v.1 with VULCAN Correlation =0.74 FFDAS v.2with VULCAN Correlation =0.92 ----------------------------------- At 0.1° resolution FFDAS v.2 with VULCAN Correlation =0.61 • Improvements under development for future versions of FFDAS: • Other observational operators will be included: roads, airports, industrial point sources, aviation routes, impervious surface, etc. • Temporal resolution at hourly timescale using TIMES (Nassar et al.) among others. • Spatial resolutions of 1km and higher

15. Conclusions Data assimilation is powerful approach to building an optimized fossil fuel CO2 emission inventory at regional and global scales. Fossil fuel data assimilation system (FFDAS) approach: • Follows an underlying dynamical model (Kaya identity) that takes into account the relationship between all the elements that contribute to FFCO2 emissions • Enables the use ofprior uncertainties and estimates posterior uncertainties • Has the ability to integrate various layers of observations • Can perform at high temporal & spatial resolutions Integration with Hestia & Vulcan & satellite RS shows promise We have a preliminary data product at annual timestep from 1997 to 2010 at 0.1 degrees resolution Improved data product rolled out in coming months

16. Acknowledgment: This project is supported through NASA grant NNX11AH86G THANK YOU!

17. FFDAS fossil fuel emissions, 2010

18. FFDAS fossil fuel emissions, 2010

19. FFDAS fossil fuel emissions, 2010

20. FFDAS fossil fuel emissions, 2010

21. FFDAS fossil fuel emissions, 2010