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

Salvi Asefi1, K. R. Gurney1, P. Rayner2, Y. Song1, K. Coltin1, C. D. Elvidge3, K. Baugh3, A. Mcrobert2

1- Arizona State University, School of Life Sciences2- School of Earth Sciences, University of Melbourne3- NOAA-NESDIS National Geophysical Data Center

Introduction

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 CO2 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 FFCO2 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 densityg = 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 FFCO2 are constrained by FFCO2 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 CO2 Emissions (CDIAC)

InputsPopulation: 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 (1992-2010).

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 2010.

Nightlights

Nightlight (saturated) - 0.1degNightlight (unsaturated) - 0.1deg

Inputs

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

FFDAS ResultsResults 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 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 CO2 emission sources (40% of global emissions).

FFDAS Results

No Power plantsFFDAS v.1

Power plants includedFFDAS 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 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

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° resolutionFFDAS v.1 with VULCANCorrelation =0.74

FFDAS v.2 with VULCANCorrelation =0.92

-----------------------------------At 0.1° resolutionFFDAS v.2 with VULCANCorrelation =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 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 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 promiseWe have a preliminary data product at annual timestep from 1997 to 2010 at 0.1 degrees resolutionImproved data product rolled out in coming months

Conclusions

Acknowledgment:This project is supported through NASA grant NNX11AH86G

THANK YOU!

FFDAS fossil fuel emissions, 2010

FFDAS fossil fuel emissions, 2010

FFDAS fossil fuel emissions, 2010

FFDAS fossil fuel emissions, 2010

FFDAS fossil fuel emissions, 2010