Introduction
The terrestrial biosphere and oceans play a critical role in the global
carbon cycle by removing approximately 5.1 PgC yr-1 of CO2 out of the
total emitted due to industrial activity and deforestation (Le Quéré
et al., 2013). Quantification of the spatial and temporal patterns of this
removal using atmospheric CO2 inversions is an important approach for
understanding the feedbacks between the carbon cycle and the climate system
(e.g., Gurney et al., 2002). Atmospheric CO2 inversions infer the ocean
and biosphere uptake by solving a set of source–receptor relationships, with
the fossil fuel CO2 emissions acting as either a boundary condition
with no uncertainty or as a “prior” flux for which some adjustment is
allowed in the inversion process (Enting, 2002).
Global fossil fuel CO2 emission data products are now being produced at
spatial resolutions smaller than 10 km and time resolutions that resolve the
diurnal cycle (Rayner et al., 2010; Oda and Maksyutov, 2011; Wang et al.,
2013; Nassar et al., 2013). This, along with the increasing density of
atmospheric CO2 concentration observations, places new emphasis on a
careful examination of the use and uncertainty associated with these
high-resolution fossil fuel CO2 emission data products (Ciais et al.,
2010; Gurney et al., 2005; Peylin et al., 2011; Nassar et al., 2013;
Asefi-Najafabady et al., 2014). For example, Gurney et al. (2005) found a
monthly regional bias of up to 50 % in the biosphere's net carbon
exchange caused by unaccounted variation in fossil fuel emissions. Peylin et
al. (2011) also showed a large response in simulated CO2 concentration
to the spatial and temporal resolution of fossil fuel emissions over Europe.
Similarly, Nassar et al. (2013) confirmed the importance of hourly and weekly
cycles in fossil fuel emissions to simulated CO2 concentration levels.
It is clear from these studies that the specification of the fossil fuel
CO2 emissions is a critical component in efforts that use fossil fuel
emissions either directly or as part of an atmospheric CO2 inversion
process.
In addition to concerns regarding the accuracy of the high-resolution fossil
fuel CO2 emission data products, there are elements of uncertainty in
how they are used within atmospheric tracer transport schemes, either in
forward simulation or inverse mode. Transport models typically distinguish
the surface characteristics of a model grid cell in broad classes such as
land versus water or urban versus rural. These classifications are important
to both the emissions of fossil fuel CO2 (FFCO2) and atmospheric transport above and/or
downwind of particular grid cells. For example, modeled atmospheric transport
processes such as mixing with the planetary boundary layer, convection,
synoptic flow, and even general circulation are influenced by the grid cell
surface characteristics (e.g., surface roughness or energy budget).
Global tracer transport models usually discretize surface grid cells at a
lower resolution than those of fossil fuel CO2 emission data products
produced in recent years and, thus, the emissions need to be aggregated to
the coarser model resolution. In this process, the transport model grid cells
with less than 50 % land geography are usually designated as water
grid cells. Emissions present on the finer FFCO2 grid, resident within
the coarser model water grid cell, are thereby mixed into the atmosphere
according to vertical mixing characteristics of ocean or lake transport
dynamics. This inconsistency between the emissions and transport dynamics
can cause bias both locally and downwind of the errant grid cell(s). This
problem is particularly important for fossil fuel CO2 emissions as they
are notoriously large along coastal margins where population and
infrastructure are dominant.
This study aims to quantify this bias arising from the regridding of fossil
fuel CO2 emissions in global tracer transport simulations. The bias is
defined as spatial distribution and temporal variations of the simulated
CO2 concentration difference driven with two regridded fossil fuel
emission inventories. We do this by constructing two experiments: (1) using
the typical regridding procedure in which emissions are left in grid cells
defined by the majority surface geography and (2) proportionally shifting or
“shuffling” these emissions to neighboring land grid cells to maintain the
spatial integrity of the fossil fuel emissions while avoiding the
emissions–transport inconsistency
Although a similar phenomenon might be expected for inland urban areas where
designation of urban versus rural grid cells may not align with surface
emissions, the global tracer transport models used in this study do not
attempt to resolve transport dynamics over urban versus rural areas.
Thus, we restrict ourselves to the study of the land versus water
misallocation problem.
Section 2 describes the fossil fuel CO2 emission data product used in
the simulations, the atmospheric transport model employed and the adjustment
method used to regrid the emissions. Section 3 presents results highlighting
the difference induced by the shuffling procedure. We examine differences in
emissions and in concentrations, the latter performed at active CO2
monitoring locations for which the shuffling influence is greatest. Section 4 presents our conclusions.
Methods
The impact of fossil fuel CO2 emission regridding is tested here by
examination of simulated CO2 concentration driven by two different
emission fields through an atmospheric transport model. The fossil fuel
CO2 emissions are aggregated from a 0.1∘ × 0.1∘
grid to a 1.25∘ × 1.0∘ transport model grid. One of
these emission fields has the coastal grid cells “shuffled” to correct for
the regridding impact (“experiment”) while the other is left in the
original unshuffled condition (“control”).
Fossil fuel CO2 emissions
Fossil fuel CO2 emissions from the Fossil Fuel Data Assimilation System
(FFDAS) version 2.0 are used as the fossil fuel CO2 emissions in this
study (Asefi-Najafabady et al., 2014). The FFDAS emissions are produced on a
0.1∘ × 0.1∘ grid for every year spanning the 1997 to
2010 time period. We use emissions for 2002 in this study. The FFDAS is a
data assimilation system that estimates the fossil fuel CO2 emissions
at every grid cell by solving a diagnostic model constrained by a series of
spatially explicit observation data sets. The diagnostic model is the Kaya
identity (Rayner et al., 2010), which decomposes emissions into population,
economics, energy and carbon intensity terms. In FFDAS v2.0 the
observational data sets are used to constrain elements in the Kaya
decomposition. The FFDAS uses the remote sensing-based nighttime lights data
product, gridded population and national sector-based fossil fuel CO2
emissions from the International Energy Agency (IEA), and a recently
constructed database of global power plant CO2 emissions (Elvidge et
al., 2009; Asefi-Najafabady et al., 2014).
FFDAS version 2.0 originally estimates fossil fuel CO2 emissions at
0.1∘ and annual resolutions over the globe. From this product, we
have derived a fossil fuel CO2 emission distribution suitable for the
use with our model by dividing the annual amounts in each grid cell by 2920
to obtain emissions that are evenly distributed in time, at the temporal
resolution of our model (i.e., 3 h).
Atmospheric transport model
This study uses a global tracer transport model – the Parameterized Chemical
Transport Model (PCTM) – to simulate the CO2 concentration resulting from
the FFDAS surface emissions (Kawa et al., 2004, 2010). The model uses
dynamical fields from the Modern-Era Retrospective analysis for Research and
Applications (MERRA) (Bosilovich, 2013), which is a NASA reanalysis for the
satellite era using a new version of the Goddard Earth Observing System Data
Assimilation System Version 5 (GEOS-5). The initial data product of GEOS-5
is at 0.7∘ longitude × 0.5∘ latitude with 72 hybrid
vertical levels. Two coarser MERRA products are also produced by aggregating
the high-resolution product to a resolution at 1.25∘ longitude × 1.25∘
latitude or 1.25∘ longitude × 1∘
latitude with 72 hybrid vertical levels (Rienecker et al., 2011; Reichle et
al., 2011; Reichle, 2012). In atmospheric transport simulation and
inversion system, a dynamical consistence problem might be introduced if the
driving meteorology data do not match the transport model grid. However,
this problem does not exist in this study, since the MERRA product used in
this study is on the same grid as PCTM. The model uses a semi-Lagrangian
advection scheme; the subgrid-scale transport includes convection and
boundary layer turbulence processes. The model is run at 1.25∘
longitude × 1.0∘ latitude with 72 hybrid vertical levels. The
vertical mixing profile in PCTM includes two dynamical processes: turbulent
diffusion in the boundary layer and convection. The two processes are
parameterized following the MERRA model – which differentiates the vertical
mixing in the boundary layer over land and ocean by using different surface
heating, radiation, moisture, roughness and other physical factors in the
eddy diffusion coefficient (Kh scheme) (Louis et al., 1982; Lock et al.,
2000; McGrath-Spangler and Molod, 2014). Considering the purpose of this
study, a check of the diffusion coefficients of the MERRA meteorology is
performed. The result shows a significant difference between land and ocean
planetary boundary layers, indicating the existence of different vertical
mixing characteristics between the two boundaries (Fig. 1).
Daily mean diffusion coefficient (KH) at
1.25∘ × 1.0 ∘ for 30 July 2002 at pressure level
about ∼ 950 hpa in MERRA reanalysis. The diffusion coefficient is
determined using a K-diffusion scheme in MERRA modeling.
The simulation is run for 4 years, driven by 2002 MERRA meteorology and
fossil fuel CO2 surface emissions (cycled repeatedly). The MERRA
meteorology has a 3 h time resolution, and a 7.5 min time step is used in
the model simulations. There is no time structure in the fossil fuel
emissions. In the model simulations, tracers are propagated in the atmosphere
to reach a state of equilibrium under the applied forcing. This is achieved
with a 4-year simulation in which the first 3-year period is used for spin-up
and the last year is used for analysis. The PCTM outputs hourly CO2
concentration at every point in the three-dimensional grid. The annual mean
surface CO2 concentration field and hourly time series at
GLOBALVIEW-CO2 monitoring sites are analyzed
(http://www.esrl.noaa.gov/gmd/ccgg/globalview/) (Masarie and Tans,
1995).
Coastal “shuffling”
The FFDAS emissions are regridded from the original 0.1∘ × 0.1∘
resolution to the 1.25∘ longitude × 1.0∘
latitude resolution of the PCTM. The two grids have the same origin, and
hence the coarser grid is overlaid onto the finer grid and the
0.1∘ grid cells are integrated, as needed. In the longitudinal
direction, grid cell boundaries do not align, so area-weighting was used
to distribute emissions.
The PCTM utilizes a gridded land–sea mask that is used to denote the
character of the model surface (land versus ocean/lake). The designation is
based on what constitutes the majority type within each grid cell. In order
to maintain dynamical consistency with the land–sea mask, those grid cells
that are considered ocean/lake by the mask – but contain emissions integrated
from the 0.1∘ degree emissions grid – are treated with a
“shuffling” procedure. These grid cells will have the emitted quantities
transferred to adjacent land grid cells according to weights assigned by the
relative magnitude of those adjacent land grid cells (Fig. 2). The weight is
defined as the ratio of emissions in each of the designated adjacent
grid cells to the sum of their emissions:
wj=Fj/∑i=1NFi,
where wj is the weight of the jth land
grid cell, Fj is its emissions, and N is the
total number of land grid cells to which emissions are transferred. Adjacent
grid cells are defined as those that share a corner with the shuffled cell.
Depiction of the “shuffling” procedure when regridding from a
0.1∘ × 0.1∘ to a
1.25∘ × 1.0∘ model grid. Capital black letters denote
the coarser model grid (1.25∘ × 1.0∘ ). Grid cells
outlined with dashed lines denote the finer model grid
(0.1∘ × 0.1∘ ). Green denotes land, and blue denotes
water. Example emission values and weighting values (w) and the direction
of the allocation are included.
Results and discussion
Emissions difference
The shuffling procedure reallocates emissions along global coastlines, but
the impact on the final CO2 fluxes is most pronounced where there are
large coastal emissions associated with urban areas or large point sources.
Figure 3 shows the difference in surface emissions between the control and
experiment emission fields. The coastal locations with cities or large point
sources exhibit an emissions “dipole”. Positive values reflect the
addition of emissions to land grid cells adjacent to those designated as
ocean in the coarse grid land–sea mask while negative values reflect the
removal of emissions from grid cells designated as ocean.
Difference between experiment and control fossil fuel CO2
emissions. The difference is obtained by subtracting the control from the
experiments. The emission values for some grid cells are not evident because
the grid cells are saturated (beyond the color scale range).
The largest emissions adjustments occur in coastal areas of the US Great
Lakes, coastal Europe, China, India and Japan. The range of the emission
difference varies from -30.3 TgC grid cell-1 yr-1 (-3.39 kgC m-2 yr-1) to
+30.0 TgC grid cell-1 yr-1 (+2.6 kgC m-2 yr-1). To provide context, an
emission difference of 30 TgC grid cell-1 yr-1 is equivalent to ∼ 62 and ∼ 13 % of the annual total carbon emissions for
the Netherlands and Germany in 2002, respectively, but is only limited to a
few grid cells in eastern Asia. Most emission differences in land grid cells
vary between 0.001 TgC grid cell-1 yr-1 (0.0001 kgC m-2 yr-1) and 5.0 TgC grid cell-1 yr-1
(0.056 kgC m-2 yr-1). The summed magnitude of the
emissions that are relocated from ocean to neighboring land grid cells is
674.5 TgC yr-1, which is equivalent to ∼ 10% of the global
total fossil fuel CO2 emissions in 2002.
CO2 concentration difference
The atmospheric CO2 concentration resulting from the control and
experiment simulations offers additional insight into the impact of the
regridding and coastal shuffling (Fig. 4). Similar to the emissions
difference, the simulated CO2 concentrations in the lowest model layer
show differences along coastlines where large urban centers or point sources
are present. In contrast to the emission differences, the response of surface
CO2 concentration reflects not only the immediate local emission impact
but also a downwind impact as the differing concentration fields are
transported by atmospheric motion. A particularly notable example is the
surface CO2 concentration difference downwind of the cluster of large
coastal western European cities, for example, London, Rotterdam, Barcelona
and Rome. Also evident are dipole patterns associated with many of the large
CO2 concentration differences along the coastline driven by the emission
dipole explained in Sect. 3.1, with negative values over ocean grid cells and
positive values over the adjacent land grid cells.
Simulated PCTM surface annual mean surface CO2 concentration
difference (experiment minus control, units: ppm). The * in the figure
denotes existing CO2 monitoring locations where the annual mean CO2
concentration difference exceeds 2 ppm.
The annual mean concentration differences range from -6.60 to +6.54 ppm
at the grid cell scale. These CO2 concentration differences should
be placed in the context of well-known surface concentration gradients such
as the north–south gradient in annual mean CO2 concentration of
∼ 4.0 ppm and Northern Hemisphere longitudinal gradients of
∼ 1.5 ppm (Conway and Tans, 1999). These differences represent
a potential bias in the simulated CO2 signal at, or downwind from,
numerous locations associated with coastal/urban areas, and are the combined
result of the differing emission distribution in the two experiments acted
upon by the atmospheric transport.
Hourly CO2 concentration
Here we examine the simulated CO2 concentration differences at locations
where CO2 concentrations are directly monitored, in an attempt to
provide more guidance to atmospheric CO2 inversion studies that use
these locations as the observational constraint to estimating carbon exchange
between the ocean, land and atmosphere. An examination of the hourly time
series of CO2 concentration in the lowest model layer at GLOBALVIEW
monitoring stations indicates that 169 stations (out of 313 total GLOBALVIEW
stations) show hourly CO2 concentration differences greater than
±0.10 ppm, and 12 of these stations show differences that exceed
±2.0 ppm (Fig. 5). Most of the larger differences are located close to
coastal urban areas and occur at night and the early morning hours. This is
not surprising given the reduction in mixing between the free troposphere and
the planetary boundary layer at these times.
Simulated PCTM surface CO2 concentration difference (experiment
minus control, units: ppm) at the 12 GLOBALVIEW monitoring stations with the
largest concentration difference. (a) Hourly mean CO2
concentration difference; (b) local afternoon mean CO2
concentration difference.
Regional fluxes difference and simulated surface CO2
concentration differences (experiment minus control) and the location of
GLOBALVIEW monitoring site TAP. (a) Flux difference; (b)
concentration difference. Stars mark the location of the TAP site.
The hourly differences at these 12 stations range from -32.1 to +2.50 ppm.
Tae-ahn Peninsula (TAP) has the largest response (-32.1 ppm).
Yonagunijima (YON) and Gosan (GSN) also show large responses, with maximum
differences reaching +5.23 and -4.43 ppm, respectively.
Given the fact that many atmospheric CO2 inversions sample the simulated
and observed CO2 concentration as a local afternoon average, and the
simulated maximum differences found here occur at varying times of day,
greater insight can be gained by examining the simulated differences during
the afternoon. In this case, 38 surface stations show hourly CO2
concentration differences exceeding a magnitude of ±0.10 ppm during the
local afternoon hours from 12:00 to 18:00 (hereafter referred as “local
after noon mean”). Of the 38 stations, 5 (TAP, GSN, SCSN, YON and RYO) have
a local afternoon mean difference ranging between 0.12 and -4.58 ppm
(Fig. 5).
The shift between a positive and negative bias shown in Fig. 5 is owed to the
fact that these coastal sites likely experience onshore and offshore airflow
at different times, and this changes which portion of the local emission
dipole influences the monitoring location. The specific circumstances at the
TAP station are a good example of how the transport acts upon the emission
dipoles to either enhance or diminish the concentration differences seen in
Fig. 6. TAP is a coastal station (36∘43′ N, 126∘07′ E)
located in the Tae-ahn Peninsula (Republic of Korea). This
site is in close proximity to the two cities of Seosan and Taean. TAP is
assigned to an ocean grid cell on the PCTM grid. The emissions on this
grid cell are aggregated to adjacent land grid cells after shuffling process.
The site is thus located in the negative portion of the emission dipole
(emission difference: -24.1 TgC grid cell-1 yr-1) corresponding to the positive
emission portion on adjacent land grid cells, as displayed in Fig. 6a.
Consistently, the TAP site lies in the negative portion of the annual mean
surface CO2 concentration field (-6.60 ppm) opposing to the positive
portion on land (Fig. 6b). Time series of the hourly concentration difference
for the TAP site shows the largest value of about -32.1 ppm occurring on
13 January at 5:00 p.m. local time. PCTM wind fields show low wind
speeds on 12 January (daily mean: < 2 m s-1) and in the
daytime of 13 January (3.5 m s-1) compared to the much higher monthly
mean value (8.4 m s-1). The weak transport during this time period accentuates
the difference between the two experiments by lessening the amount of
horizontal mixing and dispersion of the dipole gradient in this location.
The hourly time series for the TAP site also shows high-frequency behavior
throughout the year, indicating the impact of synoptic-scale atmospheric
transport. Another feature to note is the seasonal pattern in the hourly
CO2 concentration difference time series, with larger absolute
magnitudes appearing at RYO, YON and TAP in the spring and summer,
indicating a seasonal contribution of atmospheric transport to the potential
monitoring station bias. Further examination of the hourly time series also
shows diurnal patterns in all 12 monitoring sites.
Implications for carbon cycle studies
Research in which simulated CO2 concentrations are compared to observed
must consider ways to avoid the potential bias introduced when regridding
high-resolution fossil fuel CO2 emissions to the lower-resolution grids
typical of atmospheric transport models. Atmospheric CO2 inversion
studies are also a good example of research that must overcome this
potential problem. However, we do not consider the impact and uncertainty on
atmospheric inversion in this study, since atmospheric inversions are not
the only purpose for simulations of fossil-fuel-like tracers. Many studies
in atmospheric chemistry have the same need and consequently the same
problem. But the study also does do something of direct use for an
inversion. The fossil fuel is part of the prior flux. So in an atmospheric
inversion this term represents a systematic uncertainty in the mapping of
fossil fuel flux into the prior mismatches (prior simulation of
concentration – observations). It can be seen that the effect is widespread
and large compared to the measurement uncertainty usually used. Thus, this
is enough to demonstrate significance for an inversion.
Utilizing the shuffling procedure outlined here is one way to minimize this
potential bias in the spatial distribution of the fossil fuel CO2
emissions. The goal is to maintain the localization of the large emission
gradients that occur near coastlines due to the preponderance of large
cities and point sources while simultaneously ensuring dynamic consistency
between the emissions and modeled atmospheric transport.
Alternatively, modelers could use data selection procedures to minimize
potential bias when choosing which CO2 concentration observing sites to
compare to simulated results (e.g., Law, 1996). Some inversion model systems
such as NOAA's CarbonTracker model sample only the afternoon daytime
measurements at quasi-continuous stations to avoid times when the model
boundary layer is less reliable (e.g., nighttime) (Peters, et al., 2007).
Eliminating or de-emphasizing (via the assignment of large uncertainty)
atmospheric CO2 monitoring locations that are near, or strongly
influenced by, large fossil fuel CO2 sources can reduce the potential
for the emissions regridding problem. However, given that many global carbon
cycle studies are observationally underconstrained, this choice does come
with potentially large information loss. Given this fact, we recommend the
use of an emissions shuffling procedure.
It also should be pointed out that the fossil fuel emissions from planes and
ships are not included in this study. Airborne emissions are unlikely to be
strongly impacted by this problem since the differences in atmospheric
physics between land and ocean decrease once above the boundary layer. While
emissions from shipping do potentially suffer from this problem, the fraction
subject to misallocation will be small so the total problem is a small
fraction of a small fraction.
Many earth system models avail of “tiling” techniques which can assign
more than one surface characteristic to a grid cell. It should be noted that
the reshuffling simply might transfer errors from one place to another. For
example reshuffling emissions away from an oceanic grid point may leave a
station in that grid cell further from emissions than it really should be.
This is possible of course. This can only been investigated by separating
the transport and relocation effects by using an online model. However, it
is expected that this shuffling method could introduce land–ocean biases,
since fixed fossil sources are almost entirely land-based and putting them
in ocean grid points seems far more likely to introduce land–ocean biases as
the inversion tries to correct a poorly transported signal from the wrong
environment. Generally, without further research testing the sensitivity of
results to this technique, it is unclear to what extent this minimizes the
fossil fuel CO2 emissions regridding problem discussed in this study.
Conclusions
This study tests the sensitivity of simulated CO2 concentration to
regridding of fossil fuel CO2 emissions from a high-resolution grid to a
coarser global atmospheric transport model grid. Two experiments are
conducted. The first regrids from the fine to coarse grid but with no
post-regridding adjustment to those emitting grid cells that inevitably ends
up in the ocean (“control”). The second experiment performs the same
regridding process as the first but moves or “shuffles” the ocean-based
emissions to adjacent land grid cells in a proportional manner. The two
experiments exhibit large fossil fuel CO2 emissions differences in
coastal regions, which range from -30.3 TgC grid cell-1 yr-1
(-3.39 kgC m-2 yr-1) to +30.0 TgC grid
cell-1 yr-1 (+2.6 kgC m-2 yr-1) which, when summed
globally, are equivalent to 10 % of the 2002 global total fossil fuel
CO2 emissions. After transport of these emissions through a global
tracer transport model, these two experiments show simulated CO2
concentration differences along the coastal margin in both the spatial and
temporal domains. The resulting annual mean surface CO2 concentration
difference when examining all surface grid cells varies between -6.60 and
+6.54 ppm. At the hourly level, individual CO2 concentration
differences exceed ±0.10 ppm at 38 monitoring stations, with a maximum
of -32.1 ppm at 1 monitoring location. When examining local afternoon mean
values, which both modeling systems and monitoring protocols emphasize, the
CO2 concentration differences are as large as -4.58 ppm. These
CO2 concentration differences result from the shifted emissions acted
upon by modeled meteorology and can result in biased flux estimation in
atmospheric CO2 inversions which rely on comparison of simulated to
measured CO2. This phenomenon is also potentially important in any study
investigating source–receptor simulations such as those found in air quality
and other trace gas research efforts.
Code availability
The Fortran code to regrid and reallocate the surface fossil fuel emissions
flux to ensure the dynamical consistence between emission and global
transport model is available from the correspondence author
(xia.zhang11@asu.edu).