Due to a current lack of physical measurements at appropriate spatial and
temporal scales, all current global maps and distributions of fossil fuel
carbon dioxide (FFCO2) emissions use one or more proxies to distribute those
emissions. These proxies and distribution schemes introduce additional
uncertainty into these maps. This paper examines the uncertainty associated
with the magnitude of gridded FFCO2 emissions. This uncertainty is gridded at
the same spatial and temporal scales as the mass magnitude maps. This gridded
uncertainty includes uncertainty contributions from the spatial, temporal,
proxy, and magnitude components used to create the magnitude map of FFCO2
emissions. Throughout this process, when assumptions had to be made or expert
judgment employed, the general tendency in most cases was toward
overestimating or increasing the magnitude of uncertainty. The results of the
uncertainty analysis reveal a range of 4–190 %, with an average of
120 % (2
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(
Prior to about the year 1980, the magnitude of fossil fuel carbon dioxide (FFCO2) emissions was the best-known component in the global carbon cycle (Andres et al., 2014). Improving on the best estimate of the magnitude of FFCO2 emissions was sufficient then. Since then, improvements in methodologies, instrumentation, and measurement platforms have improved estimates of the major components of the global carbon cycle (e.g., FFCO2, land use, atmospheric growth, oceanic uptake, and the terrestrial biosphere). This improvement has now reached the point where uncertainty in FFCO2 emissions is now an important quantity to characterize and understand. While uncertainty for each of the major components of the global carbon cycle limits detailed understanding of these components, uncertainty in FFCO2 emissions also impacts our knowledge of the terrestrial biosphere component because its global flux is often calculated as the residual of the other global carbon cycle fluxes. Thus, the magnitude and uncertainty in FFCO2 directly impact the best estimates of the global terrestrial biosphere fluxes. Andres et al. (2014) provided a comprehensive estimate of the uncertainty associated with the global FFCO2 flux. That analysis highlighted two features of the global FFCO2 flux uncertainty: (1) in terms of absolute mass, the mass of uncertain emissions is increasing with time as the total FFCO2 flux is increasing with time (assuming a constant percentage uncertainty), and (2) in terms of relative mass, the percent uncertainty is increasing with time as more FFCO2 emissions are coming from nations with less certain emissions.
Even with the improvements mentioned above, it is not presently possible to directly measure any one component of the global carbon cycle completely and exclusively at significant spatial and temporal scales. Due to process interplay and mixing, direct samples carry the history of global carbon cycle processes within them and oftentimes models are used to deconvolve the effects of these processes on the sample data. This process can lead to a better understanding of the global carbon cycle. One approach to increase knowledge of the global carbon cycle is to sample at finer spatial and temporal scales to better isolate specific components of the global carbon cycle.
This paper examines the FFCO2 component of the global carbon cycle after it is parsed into a grid. Such gridded FFCO2 data are often incorporated into global carbon cycle and global climate (and/or Earth system) models to better understand the interplay amongst various components. Paralleling early efforts in global carbon cycle science where the majority of the effort was concentrated on better estimating the component magnitudes (e.g., FFCO2, land use, atmospheric growth, oceanic uptake, and the terrestrial biosphere), present efforts in gridding FFCO2 emissions are also concentrated on better estimating the flux in each grid cell. These gridding efforts are not trivial in terms of time and data required. Robust estimates of the uncertainty associated with gridded FFCO2 estimates should have at least two major effects: (1) better evaluation of different FFCO2 gridding methodologies to assess whether they give statistically different distributions, and (2) more importantly, allow for further advances in the collective community understanding of global carbon cycle processes, their interplay, and a characterization of change over space and time.
The transfer of carbon from one reservoir to another over a given time
interval can be called a carbon flux. In this paper, the carbon flux
from the fossil fuel reservoir to the atmospheric reservoir through the
processes of combustion will be examined. More specifically, this paper
will pursue a systematic uncertainty analysis that applies to the carbon
flux gridded mass data products (i.e., maps) presented by Andres et
al. (1996), but also could be applied to other maps such as those produced by
Olivier et al. (2005, EDGAR), Gurney et al. (2009, VULCAN), Rayner et al.
(2010, FFDAS), Oda and Maksyutov (2011, ODIAC), and Wang et al. (2013,
PKU-CO
All of these map products attempt to capture the transfer of carbon from the fossil hydrocarbon reservoir to the atmospheric reservoir at varying degrees of spatial and temporal resolution. Each of these map products incorporates different features (i.e., data and schemes) to map FFCO2 emissions in space and time. Since very few measurements exist to accurately plot FFCO2 emissions in space and time, all of these map products utilize various proxies to locate FFCO2 emissions on a two-dimensional surface (i.e., a map) for a given time interval (e.g., a year). These proxies may include population distributions, power plant locations, road and rail networks, traffic counts, nighttime lights, etc..
This uncertainty analysis does not apply to maps such as those produced using
satellite observations (e.g., GOSAT (
The Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National
Laboratory (ORNL), United States (US), FFCO2 time series (Boden et al., 2015)
gives an estimate of FFCO2 emissions from all nations in the world at annual
time steps using the fundamental methods of Marland and Rotty (1984). The
FFCO2 time series is updated periodically with each update adding another
year to the time series as well as revising data in previous years. Over the
years, new dimensions to this basic time series have been produced, including
mapping the emissions at 1
The gridded uncertainty maps will be generated for the years 1950 to the present (i.e., 2013), which is the temporal range of the current global uncertainty analysis (Andres et al., 2014), which, in turn, is temporally limited by the availability of energy data from the United Nations upon which FFCO2 emission calculations are based (Andres et al., 2012). As new data become available from the United Nations, the global uncertainty analysis can be updated and extended, and the gridded uncertainty maps can also be updated and extended. The initial year of the gridded uncertainty maps is limited by the beginning of the global uncertainty analysis, which begins with the year 1950.
As was done with the global uncertainty estimates (Andres et al., 2014),
2
The original intent of this paper was to document the uncertainty in the existing and past CDIAC FFCO2 mass maps. However, in completing the calculations necessary for this paper, it became obvious that the population proxy on which the CDIAC maps rely could be easily and greatly improved. Therefore, this paper also includes a description of the new population proxies that the CDIAC maps now utilize.
Figure 1 is a graphical representation that further clarifies exactly what this paper attempts to accomplish. In Fig. 1, the FFCO2 emissions from a hypothetical country are mapped. The total mass of emissions is identical in the four panels (in this paper, the uncertainty on the country total is not being examined), only the distribution methodology has changed. These different methodologies might represent different spatial proxies (e.g., the CDIAC population proxy), a bottom-up inventory approach (e.g., the VULCAN approach), or a hybrid approach (e.g., point sources and spatial proxies, e.g., ODIAC). Deciding which mapped distribution is best is made difficult by the lack of physical samples of FFCO2 at the spatial and temporal scales of interest. While two such maps can be superimposed and subjected to spatial analyses such as differencing, one gains little insight into an overall superior mapping methodology. This paper aims to supplement the CDIAC maps with similar spatial and temporal scale maps that represent the uncertainty in each map grid cell location. This should facilitate the determination of whether different emission maps are statistically different. More importantly, this should aid those who use these FFCO2 mass maps to better understand, model, and display the data by explicitly showing the uncertainty inherent in the maps.
Hypothetical FFCO2 mass maps for a hypothetical country. The total mass of emissions is identical in the four panels; only the spatial distribution has changed between the panels. This paper aims to aid in the evaluation of such maps by supplying gridded uncertainty information at the same spatial and temporal scales as the emission maps. The scale is in arbitrary units.
Basic CDIAC map creation process. The tabular FFCO2 emission data
are mapped to regions of the world by the 1
The procedure for creating the CDIAC maps of FFCO2 emissions has remained
remarkably stable since first published by Andres et al. (1996). The most
notable changes since that publication have been the update and revision of
data underlying the CDIAC FFCO2 emissions time series and the modification of
the baseline geography map to account for the creation of new political units
(e.g., the unification of Germany in 1990 or the breakup of the
Soviet Union in 1991). Figure 2 shows the basic FFCO2 mass emissions map
creation process. The tabular FFCO2 emission data, by nation, are mapped to
regions of the world using a 1
Prior to this publication, CDIAC used a temporally fixed population proxy to distribute FFCO2 emissions within each country for all years (Andres et al., 1996). While working through the issues associated with this paper, it became clear that methodological improvements to the mapping process would improve the quality of both the magnitude maps and the uncertainty maps. The fixed population map originally reported in Andres et al. (1996) is still utilized for the years 1751–1989 since no better alternative has been identified for these years. Annually varying Global Population of the World (GPWv3, CIESIN and CIAT, 2005) maps are now used for the years 1990–1997. Annually varying LandScan (Dobson et al., 2000) maps are now used for years 1998–2013 and are intended to be used for future years. The two new population data sets are not identical. GPWv3 estimates nighttime population (where people are at night) while LandScan estimates daytime population (where people are during the day). This change in population data sets does induce some variability in the results, but most populated grid cells are less than 10 % different between daytime and nighttime relative populations.
GPWv3 has three base years: 1990, 1995, and 2000. The original 2.5 min data
(approximately 5 km at the equator) were aggregated to the 1
Comparison of the year 1997 GPWv3 population map with CDIAC geography and fixed population maps. The number of water cells is less than 70 % of the total because 4550 ocean cells surrounding Antarctica are labeled as the Antarctic Fisheries, a United-Nations-named unit used to track energy consumption of Southern Ocean fishing fleets. CDIAC considers these Antarctic Fisheries cells as pseudo land cells (i.e., subject to emitting FFCO2). The year 2010 LandScan population map has a similar comparison to the CDIAC geography map (within 3 % in all categories) and population map (within 4 % in all categories). CDIAC, GPWv3, and LandScan population maps all have land cells that are not populated.
Tabular FFCO2 uncertainty assessment example. The plot is for the year 2010 and its key shows the annual uncertainty as a fraction. In parentheses, the monthly uncertainty is shown as a fraction. The two quantities shown have the same spatial extent; they differ only in magnitude. Different years would show slightly different spatial patterns as countries emerge or disappear from the FFCO2 tabular data.
LandScan has maps for the years 1998 to 2012, except for 1999. As with the GPWv3
data, the original 30 s (a distance unit, approximately 1 km at the
equator) data were aggregated to the 1
The main effect of the new annually varying population maps used for the years
1990 to present is the appearance of FFCO2 emissions in grid cells that
previously showed zero population and thus zero emissions. This spread in
FFCO2 emissions for a given country is accompanied by a lowering of the
average FFCO2 emission per grid cell (i.e., the same FFCO2 emission
distributed amongst more grid cells). The new population maps also lead to
some speckling in some map areas that previously appeared more homogeneous in
FFCO2 emission magnitude. Finally, the new population maps increase the range
of FFCO2 emissions displayed at both the lower and higher ends of emissions.
Overall, the maps line up well with each other in geographic extent because the
same underlying 1
All three of the basic input data (i.e., tabular FFCO2 data, geography map,
and population map) contribute uncertainty to the final gridded FFCO2 mass
emissions 1
The underlying FFCO2 tabular data contribute uncertainty to the final gridded FFCO2 mass map. In the case of the CDIAC FFCO2 mass maps, these data are the tabular FFCO2 estimates CDIAC reports for each country of the world, but the discussion here can be applied to all national FFCO2 emissions estimates.
The basic methodology to create the tabular CDIAC FFCO2 data is given in Marland and Rotty (1984). Andres et al. (2012) expand upon this methodology and compare it to three other global FFCO2 tabular data sets. Andres et al. (2014) describe a systematic uncertainty assessment of the CDIAC FFCO2 tabular data. No such similar uncertainty assessment has been published for the three other global FFCO2 tabular data sets. The uncertainty in the tabular FFCO2 data is important as it provides the quantity that is eventually mapped. If the tabular FFCO2 data are uncertain, then the FFCO2 emissions distribution is uncertain.
Figure 3 displays the uncertainty assigned to different countries as described in Andres et al. (2014). The assignment was based upon grouping countries into seven different qualitative classes (Andres et al., 1996) based on similar energy and statistical infrastructures, which were later quantified in Andres et al. (2014). The quantification consisted of determining uncertainties for two of the classes and then doing a linear fit through the rest of the classes. Andres et al. (2014) describe the strengths and weaknesses of this approach. As in Andres et al. (2014), the national FFCO2 uncertainty estimates used in this analysis remain fixed with time. Future versions of this work could utilize changing national FFCO2 uncertainty estimates, but the existence of supporting data to rigorously support changing uncertainty estimates are lacking at this time.
Andres et al. (2011) parse the annual FFCO2 data into monthly FFCO2 data. The
uncertainty associated with this parsing is also described in Andres et
al. (2011). The method for calculating the monthly tabular uncertainty is
independent of the annual uncertainty magnitude. Thus, the magnitude of the
monthly tabular FFCO2 uncertainty is equal to the square root of the sum of
the squares of the annual and monthly uncertainties. The annual uncertainty
is variable and belongs to one of seven classes as seen in the above
paragraph. The monthly uncertainty is constant and at 2
Both the tabular FFCO2 data and the national uncertainties used in this
analysis are for apparent consumption data. Apparent consumption allows for
the estimate of national FFCO2 emissions through the accounting of
production, imports, exports, etc., and thus allows the association of these FFCO2
emissions to geography. Andres et al. (2012) discuss the strengths and
weaknesses of apparent consumption versus production data. Production data
are unsuitable for use in this analysis because their spatial domain is global (in
terms of fuel consumption) and the focus here is on the uncertainty of
1
Figure 3 shows an example of the national FFCO2 uncertainty assessment results. There are 64 uncertainty assessments completed for the annual 1950–2013 time series, each map reflecting the mix of countries that existed in a particular year. Another 64 uncertainty assessments occur for the monthly 1950–2013 time series. The next section discusses the role geography plays in more detail.
The underlying geography map contributes uncertainty to the final gridded
FFCO2 mass map. In the case of the CDIAC FFCO2 mass maps, this geography map
is a 1
The CDIAC geography map is a 1
Raster representation. The left figure shows two hypothetical regions labeled A (purple) and B (yellow). The right figure shows the raster version of this geography where the dominant spatial region in each grid cell on the left becomes the value of the grid cell on the right. Other potential representations include mixed raster and vector (see text for description).
Selected latitudes and the length dimensions of 1
While Fig. 4 is simple in concept, it is illustrative of uncertainty inherent
in raster maps of geography. Many of these sources of uncertainty arise
because of map scale. For example, the Northwest Angle is territory of the
contiguous US that lies entirely north of 49
While the two examples above are largely a function of map scale, political
issues also affect map geography. For example, China and India disagree on
the location of their border at multiple locations. Thus, on maps produced by
each respective nation, the border shifts by more than 1
A final geography uncertainty arises from spatial rescaling as shown in Fig. 5. Here, a finer spatial scale map is rescaled to a coarser grid. A common outcome of this procedure is to name the left coarser grid cell ocean, name the right coarser grid cell land, and move the carbon that was in that left grid cell to the right grid cell. This movement accommodates not having FFCO2 being emitted from an ocean grid cell and maintaining full FFCO2 accounting.
Spatial rescaling issues. The blue area represents ocean and the green area represents land. A hypothetical rescaling from 1 to 5 km is shown. Note that cell C in the finer scale resolution has been recoded to ocean in the coarser resolution. In rescaling FFCO2 mass maps, this recoding is often accompanied by the movement of FFCO2 from cell C to cell D.
Geography contributes uncertainty to the final FFCO2 mass map. Since the identity of an interior grid cell of a large homogeneous political unit is unambiguous (e.g., the geographic center of a country greater than or equal to 3 by 3 grid cells in size), the uncertainty is concentrated around the borders and may be due to map scale issues, political issues, or rescaling, as the examples above illustrated. As the exact map scale changes, the nature of the uncertainty may change, but it does not disappear. The uncertainty in the geography map is important because the map is used to locate the tabular FFCO2 data. If the geography map is uncertain, then the FFCO2 emissions distribution is uncertain.
To assess uncertainty due to the geography map, the algorithm shown in Fig. 6
was used. The central grid cell A is assessed for uncertainty based upon the
values of the surrounding eight grid cells. The simplest case is if all
surrounding eight cells are of the same value as the central cell. In this
case, geography lends 0 % uncertainty to the identity of the central
cell. This is the most common case (63.6 %) in the CDIAC geography
1
Geography map uncertainty is assessed by a 3
Geography map uncertainty assessment examples. The top plot is for the year 1950 and its key shows the uncertainty as a fraction. The bottom plot shows the 1950–2011 differences. A difference plot was shown because only 749 cells (about 1 % of 64 800 total cells) changed value between 1950 and 2011.
This simple approach does exclude enclaves, territories that are completely
surrounded by other territories, which could be problematic in some
locations. For example, the Spanish town of Llívia, for political and
historical reasons, is completely surrounded by French territory. On the
CDIAC 1
On the other end of the spectrum, if no surrounding cells equal the value of
the central cell (e.g., a small island nation), then the uncertainty on the
central cell is 100 %. An example of this situation can be seen in Fig. 4
where there is ambiguity in all of the eight surrounding cells as to whether
the central cell value encroaches on the territory of the surrounding cells.
A worst case scenario for the CDIAC 1
Intermediate between these two end member cases discussed are all other
border configurations. The accompanying table in Fig. 6 gives cell matches
and resulting uncertainties. After assessment of one cell, the 3
Figure 7 shows an example of the geography map uncertainty assessment results. There are 64 uncertainty assessments completed for the 1950–2013 time series, each map reflecting the mix of countries that existed in a particular year. The difference plot is shown in Fig. 7 to highlight some of the changes over time, most notably in Africa, Europe, and Asia. There are no differences between geography map uncertainty for annual and monthly FFCO2 time series.
The population–FFCO2 emissions relationship. Upper panel:
independent data sets of population and FFCO2 emissions are aggregated to
1
Geography map uncertainty can expand internally within nations as individual
states or provinces have local FFCO2 emissions mapped. This has not been
implemented to date in CDIAC 1
The underlying distribution proxy contributes uncertainty to the final gridded FFCO2 mass map. In the case of the CDIAC FFCO2 mass maps, this proxy is a population distribution map, but the discussion here can be applied to all distribution mechanisms.
CDIAC distributes FFCO2 emissions within a country in direct proportion to
the population distribution. In effect, the CDIAC methodology assumes that each
country has fixed per capita FFCO2 emissions across all its territory.
While not the best assumption, it was considered the best available option at
the time the CDIAC 1
The uncertainty in the population map is important because the map is used to perform the within-country FFCO2 emissions distribution. If the population map is uncertain, then the FFCO2 emissions distribution is uncertain. Two issues are of concern here. First, how accurately does the population proxy mirror FFCO2 emissions? Second, since CDIAC uses a fixed population proxy for some years, how has the within-country population distribution changed with time? Both of these issues will be examined in turn.
To address the first concern, the robustness of the population–FFCO2
emissions relationship, the FFCO2 emissions per grid population need to be
examined. The CDIAC 1
Since the CDIAC data are unsuitable to test the population proxy uncertainty,
and since there are insufficient actual measurements of FFCO2 emission rates
at the appropriate spatial and temporal scales, independent population and
FFCO2 emission distributions will be used to assess the population proxy
uncertainty. The population distribution used is the global 30 min (spatial
scale) LandScan data product; it was produced without consideration of FFCO2
emissions. The FFCO2 distribution used is the
The upper panel of Fig. 8 shows the relationship between the independent data
sets of LandScan population and Vulcan FFCO2 emissions for the contiguous US
for the year 2002, the baseline map of the Vulcan emissions. The data axes
have been transformed into natural log scales to allow for easy extraction of
basic statistical parameters (i.e., the linear fit and 95 % confidence
interval). The middle panel shows these same data and statistical parameters
on linear scales. The spread of data around the linear fit shows the
nonlinearity, and thus the nonuniform per capita relationship, of the data.
The initial 2
Population map uncertainty assessment example. The plot is for the year 2011 and its key shows the annual uncertainty as a fraction where 1.75 is 175 % uncertainty. This map was generated by the average relationship seen in the lower panel of Fig. 8.
To reduce the initial 2
The lower panel of Fig. 8 shows this population–FFCO2 emissions 2
The lower panel of Fig. 8 also shows the average 2
It is not expected that the exact population–FFCO2 emissions relationship
shown in the lower panel of Fig. 8 will hold at 0.25, 0.1, and 0.01
The large uncertainty bounds on the carbon–population relationship are
hypothesized to be due to large point sources incorporated in some
1
The middle panel of Fig. 8 also shows some qualities of the population–FFCO2
emissions relationship. First, there are no negative populations. Second,
there are no negative FFCO2 emissions. Third, by definition, the CDIAC FFCO2
mass map locates no FFCO2 emissions where there is zero population. Fourth,
positive FFCO2 emissions are associated with positive populations. The effect
of adding more than one proxy to distribute FFCO2 emissions is to take FFCO2
from one cell and place it in another cell. The result of this redistribution
procedure can increase or decrease the slope of the population–FFCO2
emissions relationship as well as increase or decrease the 2
Figure 9 shows an example of the population map uncertainty assessment results. There are 64 uncertainty assessments completed for the 1950–2013 time series, with each map reflecting the population that existed in a particular year for the given set of countries. These maps were generated by the average relationship seen in the lower panel of Fig. 8. For countries that only occupy one grid cell, their uncertainty was set to zero since the relationship derived in Fig. 8 is not applicable. There are no differences between population map uncertainties for annual and monthly FFCO2 time series.
Figure 9 shows that the majority of the land mass is covered in uncertainties greater than 100 %. This could be used as evidence to argue against using population as a distribution proxy, assuming a better alternative can be found.
To address the second concern, population changes with time, it is assumed
that the annually varying population maps used for the years 1990 to present
capture relative changes and are thus not a concern. However, the pre-1990
years use a fixed population map and this may be of concern. Annual maps of
GPWv3 and LandScan were used to assess the changes in relative population
density within each country on an annual basis. The final result of this
assessment was that population changes with time induce little uncertainty
into the overall FFCO2 distribution globally when a fixed population proxy is
utilized. In specific 1
Figure 10 shows the uncertainty by combining two components: FFCO2 tabular data and geography. This intermediary step is shown because it demonstrates the order of uncertainty (ranging from < 10 to 102 %) that will be associated with all gridded FFCO2 data products that have roots similar to the CDIAC data product. This particular presentation ignores the within-country distribution proxy, only borders and national FFCO2 magnitude are included. The two-component uncertainty shown is the square root of the sum of the squares of the individual components (i.e., Figs. 3 and 7) as each component is independent of the other. Figure 10 does not show many changes temporally (only 809 of 64 800 cells change values from the years 1950 to 2011), but there is much spatial variability within a given year.
Two-component 2
Figure 11 shows the uncertainty by combining all three components: FFCO2 tabular data, geography, and population. This particular presentation includes the within-country distribution proxy, and uncertainties associated with this proxy increase the maximum uncertainty from 102 % (Fig. 10) to 193 %. Other gridded FFCO2 data products will have a different distribution proxy and thus a different absolute uncertainty value. The three-component uncertainty shown is the square root of the sum of the squares of the individual components (i.e., Figs. 3, 7 and 9), as each component is independent of the other. Both of the years Fig. 11 maps, 1950 and 2011, encompass the entire < 20 to < 200 % uncertainty range and show much spatial variability in their respective years. The 2011 map also shows more speckling of uncertainty values in areas that appear more homogeneous in the year 1950 due to the inclusion of the annually varying population proxy.
Thus, this gridded product (i.e., Fig. 11) incorporates all known and deemed-significant uncertainty from the spatial resolution, temporal resolution, and
underlying FFCO2 estimation process. For the years
1950–2013, 64 such maps exist. It is expected that future releases of the annual and monthly
CDIAC 1
The 193 % maximum 2
Three-component 2
CDIAC experience regarding resolution and uncertainty. Here, the focus is on spatial resolution, but CDIAC also noticed a similar relationship in temporal scales going from annual to monthly to daily to hourly. The uncertainty on urban scale maps is largely unknown at present.
For the 2011 1
Not explicitly considered here are autocorrelations of uncertainty in the
combined spatiotemporal domain. For example, if the local power plant is
shut down for maintenance, other power plants located on the same electrical
grid may increase electricity production, and hence FFCO2 emissions, to
maintain overall grid power levels for an electricity demand that is
independent of the local power plant maintenance schedule. In actual cases of
this scenario, of which the authors are aware, the relatively coarse CDIAC
1
Uncertainty generated by using the population map dominates the gridded FFCO2
uncertainty. Population is one proxy used to distribute FFCO2 emissions that
has detail in both time and space. Many of the proxies used by other map
distribution algorithms lack this detail in time and space. Population was
also the only useful global proxy available in 1996 when the CDIAC
1
The linear fit that CDIAC employs for FFCO2 emissions distribution (i.e., the population map) comes with the cost of introducing uncertainty due to the lack of a one-to-one relationship. However, this is true with other proxies because they also lack the one-to-one relationship. It is important to remember why these proxies are utilized: a lack of actual measurements of FFCO2 emission rates at the appropriate spatial and temporal scales. Here, a compromise is introduced into the mapping process: distribution proxies with their associated uncertainties are balanced against computation and data gathering costs. In general, for full global coverage, finer spatial and temporal resolution proxies introduce more uncertainty than coarser spatial and temporal resolution proxies. This higher uncertainty is often rooted in less certain data in all grid cells due to the lack of resources to appropriately monitor all grid cells at the desired spatial and temporal resolutions. This intermingling of spatial and temporal resolution is key. Most high-spatial-resolution proxies are sampled for only short temporal durations or limited spatial extents. Most high-temporal-resolution proxies are sampled for limited spatial extents or limited temporal durations. Figure 12 is a summary of the CDIAC experience regarding resolution and uncertainty. As spatial scales decrease, uncertainty increases. Much effort is now being directed into producing urban scale maps, but their uncertainty at present is largely unknown.
Comparison of INFLUX airplane-based results, Hestia, and the CDIAC
1
This work versus alternative formulation of the gridded map
uncertainty at annual timescales. Minimum, average, maximum, and standard
deviation (SD) of three-component 2
Realizing this simplicity–efficiency compromise and resolution–uncertainty
experience, investigation of alternative FFCO2 distribution strategies may be
worthwhile if they can achieve a lower overall uncertainty. CDIAC has
supported many such alternative distribution efforts in the broader community
in the past and expects to continue to do so in the future. These alternative
distribution strategies need also to be investigated not only for their
initial year of implementation (where most effort is applied), but also in an
honest evaluation of their application across different spatial and temporal
horizons. For example, in the spatial domain, is the quality of the proxy
used to map FFCO2 emissions at 0.1
While there is lack of actual measurements of FFCO2 emission rates at the
appropriate spatial and temporal scales of the CDIAC 1
INFLUX was also aided by a bottom-up inventory, Hestia (Gurney et al., 2012),
which is a detailed building-by-building, street-by-street, hourly FFCO2 emissions
inventory, downscaled from VULCAN. Cambaliza et al. (2014) report Hestia
inventory values for the same dates (Table 3). While there are still
mismatches in temporal and spatial scales at both annual and monthly timescales, the Hestia results fall within the CDIAC 1
Singer et al. (2014) show that for the contiguous US, when large point
sources are removed from the CDIAC 1
A commonly observed human tendency is to underestimate the uncertainties in
our work. Going into this gridded uncertainty assessment, when asked about
the quality of the CDIAC 1
Table 4 also shows the average 2
The uncertainty bounds presented here (e.g., Fig. 11) are large. That may
argue for a new approach to mapping FFCO2 emissions globally. The multi-proxy
approach initially appears promising because large fractions of FFCO2 emissions
can be geolocated with much less spatial uncertainty than the population
proxy provides. However, the databases commonly used to provide the
geolocation usually fail to provide temporal information, making temporal
uncertainty increase, sometimes substantially. Studies like INFLUX also
initially appear promising with their high spatial and temporal resolutions
often accompanied by lower uncertainties than those offered here (e.g.,
Fig. 11). However, INFLUX was a multi-million dollar campaign that gave good
information on one grid cell out of 64 800 (temporally, different data
streams lasted days to years). This approach is too expensive for global
application with current resources. Satellites could offer high spatial and
temporal resolution. However, current technology only senses field-of-view
CO
Going forward, there may be multiple opportunities to improve FFCO2 mass maps by incorporating new data and proxies that were previously unavailable. Besides population, few proxies currently in use have reliable histories longer than a few decades, and thus there may not be many ways to improve the historical record of emissions and their global distribution. Looking forward, existing and new technologies and techniques may provide continuous and detailed space and time data from which to better estimate and map FFCO2 emissions.
Hanging over all of these approaches to mapping FFCO2 emissions are planned, existing, and committed national and international agreements to limit future FFCO2 emissions. How these will be measured, reported, and verified (MRV) remains to be seen. This MRV task becomes only more daunting when uncertainties are used in the MRV process, in addition to the central best estimate of FFCO2 (and other) fluxes affecting the atmosphere (and climate) in which we live.
This paper provides (1) the first, gridded, comprehensive uncertainty estimates of global FFCO2 emissions, (2) a methodology that can be applied to other global FFCO2 mass maps, (3) a reminder to the community that FFCO2 has uncertainty both in tabular mass totals and in map-distributed masses, (4) a beginning for the broader community to statistically compare different FFCO2 distribution maps (once uncertainty evaluations are completed on the other maps) to help determine better FFCO2 distribution algorithms, and (5) the basis for an improved understanding of the global carbon cycle and its components by providing an uncertainty estimate for the CDIAC FFCO2 mass maps that can then be propagated into the rest of the global carbon cycle.
While more detailed proxies (in space, time, or number) may lead to more
visually appealing representations of FFCO2 emissions, that increased detail
often brings increased uncertainty, thus obscuring the perceived
increase in detail. The alternative formulation presented in Table 4 shows
how easy it is to achieve lower reported uncertainties. While uncertainty is
large at the per grid cell basis, Fig. 12 suggests that uncertainty decreases
with aggregation to larger grid cells. While the exact map distribution
mechanism used here – per capita FFCO2 emissions by country – largely
determines the uncertainty associated with the CDIAC 1
Finally, the difficulties encountered during this work should not be taken as deterrents to pursuing this line of research. Rather, they should be embraced as challenges to be overcome by new methods and measurements. While gridded FFCO2 uncertainty maps are not scientifically revolutionary, they will lead to new understanding of the carbon cycle and the climatic system - much in the same way pioneering efforts in quantifying global and national FFCO2 emissions led to new carbon and climate understanding.
The data for this paper are available at the CDIAC website
(
Ray Nassar and an anonymous reviewer provided thoughtful comments and suggestions. Edited by: Q. Zhang Reviewed by: Nassar and one anonymous referee