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Documentation - CT2011_oi
Biosphere Oceans Observations Fires Fossil Fuel TM5 Nested Model Assimilation
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1.   Introduction
Human beings first influenced the carbon cycle through land-use change. Early humans used fire to control animals and later cleared forest for agriculture. Over the last two centuries, following the industrial and technical revolutions and the world population increase, fossil fuel combustion has become the largest anthropogenic source of CO2. Coal, oil and natural gas combustion are the most common energy sources in both developed and developing countries. Various sectors of the economy rely on fossil fuel combustion: power generation, transportation, residential/commercial building heating, and industrial processes.

In 2008, the world emissions of CO2 from fossil fuel burning, cement manufacturing, and flaring reached 8.7 PgC yr-1 (one PgC=1015 grams of carbon) [Boden et al., 2011] and we estimate the global total emissions for 2009 and 2010 to be 8.6 PgC yr-1 and 9.1 PgC yr-1 respectively [Boden et al., 2011]. The 2010 figure represents a 47% increase over 1990 emissions. The North American (U.S.A, Canada, and Mexico) input of CO2 to the atmosphere from fossil fuel burning was 1.8 PgC in 2008, representing 21% of the global total. North American emissions have remained nearly constant since 2000. On the other hand, emissions from developing economies such as the People's Republic of China have been increasing. The Department of Energy's 2011 International Energy Outlook has projected that the global total source will reach 9.1 PgC yr-1 in 2015 and 11.1 PgC yr-1 in 2030 [DOE].

Despite the recent economic slowdown, which affected developing countries starting in 2008, fossil fuel emissions in many parts of the world continue to increase.

In many flux estimation systems, including CarbonTracker, fossil fuel CO2 emissions are specified. These imposed emissions are not optimized in the estimation framework. Thus, fossil fuel CO2 emissions must be prescribed accurately in order to yield robust flux estimates for the land biosphere and oceans. Fossil fuel emissions estimates we use are available on an annually-integrated global and national basis, and this information needs to be gridded before being incorporated into CarbonTracker. The major uncertainty in this process is distributing the national-annual emissions spatially across a nation and temporally into monthly contributions. In CT2011, two different fossil fuel CO2 emissions datasets were used to help assess the uncertainty in this mapping process. The legacy CarconTracker fossil fuel product ("Miller") has this year been augmented with the "ODIAC" [Oda and Maksyutov, 2011] emissions product. These two datasets share the same global and national emissions for each year, but differ in how those emissions are distributed spatially and temporally.

Figure 1. Spatial distribution of fossil fuel emissions. This is a spatial average of the Miller and ODIAC emissions inventories.


2.   The "Miller" emissions dataset
  • Totals
    The Miller fossil fuel emission inventory is derived from independent global total and spatially-resolved inventories. Annual global total fossil fuel CO2 emissions are from the Carbon Dioxide Information and Analysis Center (CDIAC) [Boden et al. 2011] which extend through 2008. In order to extrapolate these fluxes to 2009 and 2010, we extrapolate using the percentage increase or decrease for each fuel type (solid, liquid, and gas) in each country from the 2011 BP Statistical Review of World Energy for 2009 and 2010.

  • Spatial Distribution
    Miller fossil-fuel CO2 fluxes are spatially distributed in two steps: First, the coarse-scale flux distribution country totals from Boden et al. [2011] are mapped onto a 1°x1° grid. Next, we distribute the country totals within countries according to the spatial patterns from the EDGAR v4.0 inventories [European Commission, 2009], which are annual estimates also at 1°x1° resolution. The CDIAC country-by-country totals, however, only sum to about 95% of the global total. We ascribe the difference to land regions according to the relative pattern of emissions over the globe.

  • Temporal Distribution
    For North America between 30 and 60°N, the Miller system imposes a normalized, annually-invariant, seasonal cycle on emissions. This annual cycle is derived by extracting the first and second harmonics [Thoning et al, 1989] from the Blasing et al. [2005] analysis for the United States. The Blasing analysis has ~10% higher emissions in winter than in summer.

    For Eurasia, a set of seasonal emissions factors from EDGAR, distributed by emissions sector, is used to define fossil fuel seasonality. As in North America, this seasonality is imposed only from 30-60°N. The Eurasian seasonal amplitude is about 25%, significantly larger than that in North America, owing to the absence of a secondary summertime maximum due to air conditioning.

    See Box 1 for the resulting time series of fossil fuel emissions. In order to avoid discontinuities in the fossil fuel emissions between consecutive years, a spline curve that conserves annual totals [Rasmussen 1991] is fit to seasonal emissions in each 1°x1° grid cell.
3.   The "ODIAC" emissions dataset
  • Totals
    The ODIAC fossil fuel emission inventory [Oda and Maksyutov, 2011] is also derived from independent global and country emission estimates from CDIAC, but from the previous year’s estimates [Boden et al. 2010]. Annual country total fossil fuel CO2 emissions from CDIAC which extend through 2007, were extrapolated to 2008, 2009 and 2010 using the BP Statistical Review of World Energy. The difference between the CDIAC global total and country-by-country totals were ascribed to the entire emissions fields. The same adjustment was done for 2009 and 2010 using preliminary 2009 and 2010 estimates by CDIAC.

  • Spatial Distribution
    ODIAC emissions are spatially distributed using many available “proxy data” that explain spatial extent of emissions according to emission types (emissions over land, gas flaring, aviation and marine bunker). Emissions over land were distributed in two steps: First, emissions attributable to power plants were mapped using geographical locations (latitude and longitude) provided by the global power plant data CARMA. Next, the remaining land emissions (i.e. land total minus power plant emissions) were distributed using nightlight imagery collected by U.S. Air Force Defense Meteorological Satellite Project (DMSP) satellites. Emissions from gas flaring were also mapped using nightlight imagery. Emissions from aviation were mapped using flight tracks adopted from UK AERO2k air emission inventory. It should be noted that currently, air traffic emissions are emitted at ground level within CarbonTracker. Emissions from marine bunker fuels are placed entirely in the ocean basins along shipping routes according to patterns from the EDGAR database.

  • Temporal Distribution
    The CDIAC estimates used for mapping emissions in ODIAC only describe how much CO2 was emitted in a given year. To present seasonal changes in emissions, we used the CDIAC 1°x1° monthly fossil fuel emission inventory [Andres et al. 2011]. The CDIAC monthly data utilizes the top 20 emitting countries' fuel (coal, oil and gas) consumption statistics available to estimate seasonal change in emissions. Monthly emission numbers at each pixel were divided by annual total and then a fraction to annual total was obtained. Monthly emissions in the ODIAC inventory were derived by multiplying this fraction by the emission in each grid cell.



Box 1. Comparison of the Miller and ODIAC global fossil fuel emissions estimates


Time series of global fossil fuel emissions. The Miller (green) and ODIAC (tan) estimates are each used by half of the eight inversions in the CT2011 suite, so the CT2011 (black) inventory is effectively an average of Miller and ODIAC. Note that fossil fuel emissions are not optimized in CarbonTracker.



Spatial differences in long-term mean fossil fuel emissions. between the two priors Note that both the Miller and ODIAC emissions inventories use the same country totals, but have different models for spatial distribution of that flux within countries.

  • Uncertainties
    The uncertainty attached to the global total source is of order 5% (2 sigma) until 2007 [Marland, 2008], but the uncertainties for individual regions of the world, and for sub-annual time periods are likely to be larger. Additional uncertainties are further introduced when the emissions are distributed in space and time. In the Miller dataset, the overall Eurasian seasonality is uncertain, but most likely a better representation than assuming no emission seasonality at all. Similarly, the use of the CDIAC monthly emission dataset for modeling seasonality introduces additional uncertainty in ODIAC. The additional uncertainty for the global total in the monthly CDIAC emission, which is solely due to the method for estimating seasonality, is reported as 6.4% [Andres et al. 2011]. As mentioned earlier, fossil fuel emissions are not optimized in the current CarbonTracker system, similar to many similar global carbon data analysis systems.

    Spatial and temporal atmospheric CO2 gradients arise from terrestrial biosphere and fossil-fuel sources. These gradients, which are interpreted by CarbonTracker, are difficult to attribute to one or the other cause. This is because the biospheric and anthropogenic sources are often co-located, especially in the temperate Northern Hemisphere.

    Given that surface CO2 flux due to biospheric activity and oceanic exchange is much more uncertain compared to fossil fuel emissions, CarbonTracker, like most current carbon dioxide data assimilation systems, does not optimize fossil fuel emissions. The contribution of CO2 from fossil fuel burning to observed CO2 mole fractions is considered known. However, for the first time in CarbonTracker, an effort is made to account for some aspects of fossil fuel uncertainty by using two different fossil fuel estimates as detailed above. From a technical point of view, extra land biosphere prior flux uncertainty is included in the system to represent the random errors in fossil fuel emissions. Eventually, fossil fuel emissions could be optimized within CarbonTracker, especially with the addition of 14CO2 observations as constraints.





    3.   Further Reading

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