High-resolution global CO 2 emissions from fossil fuel inventories for 1992 to 2010 using integrated in-situ and remotely sensed data in a fossil fuel data assimilation system Salvi Asefi 1, K. R. Gurney 1, P. Rayner 2, Y. Song 1, K. Coltin 1, C. D. Elvidge 3, K. Baugh 3, A. Mcrobert 2 1- Arizona State University, School of Life Sciences 2- School of Earth Sciences, University of Melbourne 3- NOAA-NESDIS National Geophysical Data Center

Introduction  accurate global quantification of FFCO 2 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 CO 2 emission inventory with uncertainties See Rayner et al., 2010

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. Vulcan Current FFCO 2 emission datasets ODIAC

Fossil Fuel Data Assimilation System (FFDAS) 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

Inputs National emissions:  National and global FFCO 2 are constrained by FFCO 2 sectoral emissions reported by International Energy Agency IEA and Carbon Dioxide Information and Analysis Center (CDIAC).  Prior uncertainties for national emissions were also objectively estimated and included in FFDAS (see next talk). Per Country CO 2 Emissions (CDIAC)

Inputs Population:  SEDAC global gridded population dataset (0.04° resolution, 1995, 2000, 2005 & 2010) combined with LandScan global 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 France Germany Spain LandScan population density Germany France Spain

Inputs Nightlights:  Nightlight is a global remote sensing product provided by NOAA-NGDC at 30 arc second resolutions ( ).  However this dataset is subject to instrumental saturation meaning areas of bright nightlights, such as urban cores are 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 Nightlights Nightlight (saturated) - 0.1deg Nightlight (unsaturated) - 0.1deg

Inputs Power plant point sources:  Currently the only available global dataset is CARMA. That includes more than power plants worldwide.  CARMA provides plant location and estimated CO 2 emission for each power plant.  We are finding sizeable biases…….will discuss in next talk

FFDAS Results Results represent annual emissions, 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 fossil fuel emissions Year=1998 FFDAS fossil fuel emissions Year=1999 FFDAS fossil fuel emissions Year= 2000 FFDAS fossil fuel emissions Year=2001 FFDAS fossil fuel emissions Year=2002 FFDAS fossil fuel emissions Year=2003 FFDAS fossil fuel emissions Year=2004 FFDAS fossil fuel emissions Year=2005 FFDAS fossil fuel emissions Year=2006 FFDAS fossil fuel emissions Year=2007 FFDAS fossil fuel emissions Year=2008 FFDAS fossil fuel emissions Year=2009 FFDAS fossil fuel emissions Year=2010

FFDAS Results Comparison between 0.1° resolutions and 0.25° FFCO2 emission 0.25° FFDAS v1 FFCO2 emission 0.1° FFDAS v2

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

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 CO 2 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 New power plant data product - Ventus 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 Poster 249, Wednesday (in pavilion)

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 At 0.5° resolution FFDAS v.1 with VULCAN Correlation =0.74 FFDAS v.2 with VULCAN Correlation = At 0.1° resolution FFDAS v.2 with VULCAN Correlation =0.61 Comparisons with Vulcan Difference map between FFDAS v.2 and Vulcan at 0.1°

Data assimilation is powerful approach to building an optimized fossil fuel CO 2 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 FFCO 2 emissions  Enables the use of prior 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 Conclusions

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

FFDAS fossil fuel emissions, 2010