Introduction
Quantification of the spatial and temporal distribution of carbon sources
and sinks is critical for projecting future atmospheric CO2
concentrations and climate change (Field et al., 2007). Inferring exchanges of CO2 between the atmosphere and the terrestrial biosphere/ocean from
atmospheric CO2 observations, using inverse methods based on
atmospheric transport models, has been an important approach (e.g., Tans et al., 1990;
Enting, 2002; Gurney et al., 2002).
In atmospheric CO2 inversions, fossil fuel CO2 (FFCO2)
emissions are often treated as a known quantity in the system; consequently,
uncertainty in FFCO2 emissions is not considered explicitly and errors
in the distribution of simulated atmospheric FFCO2 are translated into
errors in the terrestrial biospheric flux estimates. This problem has not
been well studied, due mainly to limitations such as the coarse resolution of
traditional FFCO2 inventories, the sparse monitoring of atmospheric
CO2 concentrations, and sub-grid parameterization of atmospheric
transport models. In recent years, significant advances have been made in
increasing the density of atmospheric observations and in the accuracy,
fidelity and resolution of FFCO2 inventories. For example, the network
of atmospheric high-frequency CO2 concentration measurements has grown
over the last decade (NACP project in North America and CarboEurope_IP
project in Europe). Global FFCO2 inventories have been produced at high
resolution in both the space and time domains – these resolve the CO2
emissions at spatial scales smaller than 10 km and with hourly time
resolution (Rayner et al., 2010; Oda and Maksyutov, 2011; Wang et al., 2013;
Nassar et al., 2013; Asefi-Najafabady et al., 2014). These advances provide
information that permits a careful examination of how the high-resolution
FFCO2 emission data products impact the spatial and temporal
distribution of atmospheric CO2 and flux estimates (Ciais et al., 2009;
Gurney et al., 2005; Peylin et al., 2011; Nassar et al., 2013;
Asefi-Najafabady et al., 2014). Further, the development of atmospheric
transport models with increased spatial and temporal resolution makes it
possible to quantify these impacts (e.g., Kawa et al., 2010; Peylin et al.,
2011). Previous literature has reported the uncertainty in related inversion
and forward simulation studies (Gurney et al., 2005; Peylin et al., 2011;
Nassar et al., 2013). For example, Gurney et al. (2005) investigated the
impact of monthly varying FFCO2 emissions on inverted net carbon
exchange and found a monthly bias of up to 50 % in biospheric net fluxes
in some places caused by unaccounted-for variations in fossil fuel emissions.
Peylin et al. (2011) showed a seasonal uncertainty of about 2 ppm in
simulated CO2 concentration associated with uncertainty in the spatial
and temporal variability in FFCO2 emissions over Europe. Similarly,
Nassar et al. (2013) reported the impact of time-varying FFCO2 emissions
on selected geographical regions during wintertime. Previous studies,
however, have focused on only one or two components of the sub-annual
FFCO2 cycles, or else on limited spatial regions or time periods. Thus,
a complete exploration of the space/time influence of all sub-annual
variations in FFCO2 across the globe is needed.
Inversion analysis infers the distribution of sources and sinks of CO2
by reconciling the observed global atmospheric CO2 concentrations at a
network of sampling stations with simulated CO2 concentrations obtained
by driving an atmospheric transport model with an initial estimate of
CO2 fluxes. During this process, the interaction of temporally varying
boundary CO2 fluxes with atmospheric transport/mixing has been shown to
impact the inferred surface CO2 source/sink distribution. For example,
the covariation of seasonal/diurnal biospheric fluxes and seasonal/diurnal
atmospheric transport causes a significant seasonal/diurnal effect (commonly
called the rectifier) on CO2 concentrations, even if the fluxes at each
grid cell average to zero across each time period (e.g., Keeling et al.,
1989; Denning et al., 1995, 1996; Yi et al., 2004; Chen and Chen, 2004; Chan
et al., 2008; Williams et al., 2011). The biospheric rectification is
characterized by a time-mean CO2 spatial concentration gradient, with
the diurnal effect at local to regional scales caused by the interaction of
diurnal biospheric fluxes with the diurnal variation in vertical mixing in
the planetary boundary layer (PBL), and the seasonal rectifier effect at the
global scale resulting from the interaction of seasonal biospheric fluxes
with seasonal atmospheric transport. By contrast, few studies have quantified
the rectification of atmospheric CO2 concentration associated with the
sub-annual variations in FFCO2 fluxes (diurnal, weekly and monthly).
In this paper, we test the sensitivity of simulated global atmospheric
CO2 concentration to sub-annual temporal variations in FFCO2
emissions using a tracer transport model. The sub-annual FFCO2 emission
variability is comprised of three cyclic components: diurnal, weekly, and
seasonal. The resulting surface atmospheric CO2 concentration from
these individual components and their sum are compared to simulated CO2
concentrations driven by a “flat” (temporally invariant) FFCO2
emissions inventory. The impact on the column-integral simulated CO2
concentration is also examined.
The structure of this paper is as follows: Sect. 2 describes the FFCO2 emissions and sub-annual variability, the biospheric fluxes used for
comparison with the FFCO2 emissions, the atmospheric tracer transport
model employed in model simulations, and the methods for analyzing the model
output. In Sect. 3, the results of the flux experiments are presented and
discussed at multiple timescales. Section 4 summarizes the results and
implications of this study.
Methods
In this study, we prescribe five global FFCO2 emission fields that are
introduced into the lowest atmospheric layer of a tracer transport model and
subsequently run for four simulated years. Three years are considered a
spin-up to allow FFCO2 to reach equilibrium through the entire
troposphere. The last year is used for analysis and the FFCO2 mixing
ratio is analyzed globally and at CO2 observing sites.
FFCO2 emissions
The FFCO2 emissions data product, Fossil Fuel Data Assimilation System
(FFDAS) version 2.0, is used as the flux boundary condition for the model
simulations in this study (Asefi-Najafabady et al., 2014). The FFDAS FFCO2 emissions were
estimated using a diagnostic model (the Kaya identity, Kaya and Yokoburi, 1997), constrained by a
series of spatially explicit observational data sets, which decompose
emissions into population, economics, energy, and carbon intensity terms
(Rayner et al., 2010). The observational data sets used in the FFDAS include a remote
sensing-based nighttime lights data product, the LandScan gridded
population data product, 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).
The FFDAS emissions are produced at 0.1∘ × 0.1∘
resolution for the years 1997 to 2010. The emissions for year 2002 are used
in this study. Sub-annual temporal structure is imposed on these annual
emissions based on two additional data sets. Diurnal and weekly cycles are
derived from a global data product referred to as Temporal Improvements for
Modeling Emissions by Scaling (TIMES hereafter) at 0.25∘ × 0.25∘ resolution (Nassar et al., 2013). The monthly temporal cycle is obtained
from the global data product developed by Andres et al. (2011) at a
resolution of 0.1∘ × 0.1∘ and similarly imposed on the
FFDAS emissions. With these temporal structure data sets, five separate
FFCO2 emission fields are created:
A global 0.1∘ × 0.1∘ FFCO2 emission field in
which only the diurnal cycle is represented (“diurnal cycle emissions” – DCE).
This is accomplished by distributing the annual emission total in each grid cell
evenly for every day of the year (divided by 365), and then distributing the daily
total to the 3 h model simulation resolution according to the diurnal
fractions from TIMES.
A global 0.1∘ × 0.1∘ FFCO2 emissions field
in which only the weekly cycle is represented (“weekly cycle emissions” – WCE).
This is accomplished by distributing the annual emissions in each grid cell
evenly for each week of the year (divided by 52) and then distributing the weekly
total according to the day-of-the-week fractions from TIMES.
A global 0.1∘ × 0.1∘ FFCO2 emission field
in which only the monthly cycle is represented (“monthly cycle emissions” – MCE).
This is accomplished by distributing the annual total FFCO2 emissions in
each grid cell according to the monthly fractions from Andres et al. (2011).
To avoid discontinuities at the month boundaries, a cubic spline filter is applied.
A global 0.1∘ × 0.1∘ FFCO2 emission field
that represents all of the sub-annual temporal structure (“all cycle emissions” – ACE).
This is accomplished by applying the MCE, WCE and DCE fractions in succession with
the application of the cubic spline smoother and scaling to ensure conservation of mass.
A global 0.1∘ × 0.1∘ FFCO2 emission field
with no sub-annual temporal structure (“flat emissions” – FE). Hence, the annual
amount in each grid cell is divided by 2920 to obtain evenly distributed
emissions at 3 h model resolution.
To understand the temporal variations in the input FFCO2 emission
fields used in the simulations, we focus attention on areas of the planet
with large FFCO2 emissions, what we refer to as the “large source
regions” (LSRs). These regions are located in the US (30 to
48∘ N, 125 to 70∘ W), western Europe
(40 to 60∘ N, 10∘ W to 40∘ E) and
China (20 to 45∘ N, 105 to
125∘ E).
The DCE FFCO2 emissions over the three LSRs show a diurnal cycle
(Supplement, Fig. S1) that is characterized by smaller emissions at night and
in the early morning vs. larger emissions starting at sunrise and remaining
elevated until just after sunset. The DCE emissions typically reach a minimum
value between midnight and 03:00 local time (LT) and a maximum value at
∼ 15:00 LT. This pattern is expected from the diurnal variations in
human activity, such as waking vs. sleeping hours and work-related activity
cycles (e.g., on-road vehicle “rush” hours, starting and ending most daily
work cycles). We also show the diurnal cycle of PBL height used in this study
(Fig. S1), which shows similar diurnal variation to the diurnal DCE
FFCO2 emissions.
The WCE FFCO2 emissions reflect diminished economic activity on the
weekends vs. the weekdays. For most of the planet, Saturday and Sunday
are the designated weekend days, but in some Middle Eastern countries,
Thursday and Friday constitute the weekend days (Fig. S2).
The MCE FFCO2 emissions reflect the different energy needs in winter
vs. summer: for example, due to space heating of buildings (Fig. S3).
However, the space/time patterns reflect different fossil-fuel-based energy
use across the planet. For example, the FFCO2 emissions in western
Europe are larger in December and January and smaller in July and August.
The US also shows peak emissions in December–January, but with a second peak
in July–August. The summer peak is due to electricity-driven
air-conditioning prevalent in the United States (Gregg et al., 2009). China exhibits an
unusual monthly variation, with the largest FFCO2 emissions in December
followed by a sudden drop in January and February, and then an increasing
trend to December. This has been attributed to uncertainty in the underlying
energy consumption data, discussed in detail in Gregg et al. (2008).
To enable atmospheric transport simulation, the five FFDAS emission fields
were regridded from their original 0.1∘ × 0.1∘ spatial
resolution to the 1.25∘ × 1∘ atmospheric transport model
(see Sect. 2.3) resolution (longitude × latitude). When regridding,
emissions originally emanating from land are often allocated to
water-covered grid cells – an artifact typically encountered along
coastlines when regridding from a fine to coarse resolution. Such a mismatch
can lead to a dynamical inconsistency between the emissions and atmospheric
transport. To avoid this error, we apply the “shuffling” reallocation
method described in Zhang et al. (2014) for all five emissions fields. For
the purposes of atmospheric transport simulations, the emissions derived
from FFDAS for the year 2002 are repeated across all the years in the
atmospheric transport model runs.
Biospheric fluxes
In order to place the impact of the temporal variation in FFCO2
emissions within a larger context, an additional experiment is conducted
driven by terrestrial biospheric carbon fluxes with diurnal and seasonal
variations. The biospheric CO2 flux is a recent version of that used in
the TransCom experiment: CASA model net ecosystem exchange estimates with
“neutral” annual fluxes (e.g., Law, et al., 2008; Peylin et al., 2013;
Randerson et al., 1997) at a 1∘ × 1∘ spatial
resolution and 3-hourly temporal resolution (referred to as “CASA fluxes”
hereafter). The terrestrial biospheric fluxes have a seasonal cycle,
characterized by negative values (carbon uptake from the atmosphere to land)
during the growing season (late spring and summer) vs. positive fluxes
(carbon release from the land to the atmosphere) during the dormant season
(winter and early spring) (Fig. S3). The biospheric fluxes also contain
diurnal variation with typically negative values during the daytime
(dominated by photosynthetic uptake) and positive values during the night
(dominated by respiration) (Fig. S1).
The biospheric fluxes are regridded from the original 1∘ × 1∘ to the 1.25∘ × 1∘ transport model
resolution with the same shuffling method used for the FFCO2 emission
fields.
Transport model
A global tracer transport model, the Parameterized Chemical Transport Model
(PCTM), is used to simulate the FFCO2 concentrations resulting from
each of the five FFCO2 emission fields (Kawa et al., 2004, 2010). The
meteorological fields from the Goddard Earth Observing System Data
Assimilation System Version 5 (GEOS-5) MERRA reanalysis products are used to
drive the atmospheric transport (Reineker et al., 2008). The model uses a semi-Lagragian
advection scheme (Lin and Rood, 1996); the sub-grid-scale transport includes convection
and boundary layer turbulence processes (McGrath-Spangler and Molod, 2014).
The model grid is run at 1.25∘ longitude × 1∘ latitude
with 72 hybrid vertical levels, and produces CO2 concentration output
every hour. The CO2 concentration output from PCTM has been widely used
in comparison with in situ and satellite measurements (Parazoo et al., 2012). It has been shown
that PCTM simulates the diurnal, synoptic, and seasonal variability in
CO2 concentration well (e.g., Kawa et al., 2004, 2010; Law et al., 2008).
A total of six emission cases are run through the PCTM. The GEOS-5
meteorology has a 3 h time resolution and a constant 7.5 min time step
is used in the model simulations.
Analysis methods
In this study, all five FFCO2 simulations use the same meteorology and
the same annual total FFCO2 emissions. The only difference between the
FFCO2 simulations is the sub-annual temporal structure as described in
Sect. 2.1. Hence, the resulting atmospheric FFCO2 concentration
differences are due to the differences in the time structure of the
FFCO2 emissions only. The atmospheric FFCO2 concentration is
examined in two ways: (a) near the surface (at ∼ 998 hPa; in the
bottom layer, which is ∼ 126 m or ∼ 15 hPa thick) and (b) as a
pressure-weighted column integral. In order to understand how the different
cyclic components of the FFCO2 emissions interact with the simulated
atmospheric transport at multiple timescales, we present the simulated
FFCO2 concentration results for the annual mean, and individual
sub-annual cycles for both near-surface and column-integral (diurnal, weekly,
monthly). In addition to global difference maps, concentration differences
between the cyclic and flat FFCO2 emissions are examined at selected
GLOBALVIEW-CO2 monitoring sites
(http://www.esrl.noaa.gov/gmd/ccgg/globalview/co2/co2_intro.html)
(Masarie and Tans, 1995).
The impact of the FFCO2 emissions' sub-annual temporal structure is
defined as the simulated concentration difference between each sub-annually
varying FFCO2 emission field and the FE emission field, when averaged
over specific time cycles:
ΔCit=1N∑k=1N1M∑j=1MCitj,k-1M∑j=1MCif(j,k),
where ΔCit is the mean concentration difference at the
ith grid cell for cyclic emissions, N is the total counts of cycles
over the investigated period, Citj,k is the jth
hourly concentration in the kth cycle at the ith grid cell for
cyclic emissions, M is the total counts of hourly periods for each cyclic
emissions, and Cif(j,k) is the jth hourly concentration in the
kth cycle at the ith grid cell for flat emissions.
Simulated full-day annual mean surface FFCO2 concentration
difference between the time-varying and flat FFCO2 emission fields.
(a) ACE minus FE, (b) DCE minus FE, and (c) MCE minus
FE.
By utilizing Eq. (1), the impact on simulated CO2 concentration is
examined for each individual sub-annual FFCO2 emissions cycle and their
combination. Impacts include
the annual mean full-day concentration difference between each cyclic FFCO2
emission and the flat emission fields, in order to explore FFCO2 emissions
rectification;
the annual mean afternoon (noon to 18:00 LT) concentration difference
between the DCE and FE emission fields, to examine the impact at typical
atmospheric monitoring times;
the annual daily mean concentration difference on weekdays/weekends between
the WCE and FE emission fields, to examine the impact of weekly cycles;
the diurnal amplitude of hourly mean concentration difference over the year
between the DCE and FE emission fields, to examine the impact of diurnal cycles;
the seasonal amplitude of monthly mean concentration difference between MCE
and FE emission fields, to examine the impact of the seasonal cycles.
The amplitude of the simulated concentration differences for DCE and the MCE
simulations is defined as
Camp,it=Cmax,itΔCitj|j=1,M-Cmin,itΔCitj|j=1,M,
where Camp,it is the amplitude at the ith grid cell,
Cmax,it is the maximum of the concentration differences at the
ith grid cell, Cmin,it is the minimum of the concentration
differences at the ith grid cell, ΔCitj is the mean
concentration difference for the jth point of the sub-annual cycle at
the ith grid cell that is defined as Eq. (1), and M is the total points of
the sub-annual cycle.
Results and discussion
The FFCO2 rectifier
Figure 1a shows the annual mean full-day surface FFCO2 concentration
difference between the ACE and FE emission fields (ACE minus FE). Despite
the same annually integrated emissions at each grid cell, the annual mean
surface concentration difference shows nonzero values, suggesting
rectification of the FFCO2 emissions. The largest negative surface
FFCO2 concentration differences (up to -1.35 ppm) are found over the
LSRs, coincident with the largest fossil-fuel-based industrial activity and
energy consumption. Smaller positive surface FFCO2 concentration
differences (up to 0.13 ppm) appear over north and northeastern Europe and
western Siberia. The annual mean surface FFCO2 concentration differences
between the DCE and FE and the MCE and FE are shown in Fig. 1b and c,
respectively. The negative surface FFCO2 concentration differences in
Fig. 1a are primarily driven by the DCE emissions (Fig. 1b) while the
positive differences are primarily driven by the MCE emissions (Fig. 1c). Figure 1a includes the contribution from the WCE emissions, but no
rectification results from this emission cycle at annual scales (Fig. S4).
Over the LSRs, the diurnal FFCO2 emissions are temporally correlated
with the diurnal variation in the PBL (Fig. S1). The emissions are largest
during daytime when the PBL is well mixed, so air with enriched CO2
tends to be transported aloft. By contrast, the smaller nighttime FFCO2
emissions are mixed into a typically shallower and stable PBL, so this
lower-CO2 air is confined closer to the surface. This covariation, when
compared to the same dynamic coupling in the FE field, leads to greater
FFCO2 loss from the surface to the free troposphere in the ACE
simulation, resulting in the negative annual mean surface FFCO2
concentration difference values over the LSRs. The negative DCE
rectification is up to -1.44 ppm at the grid cell scale over the western US
(Fig. 1b). Note that the diurnal FFCO2 rectifier effect shows little
variation across the LSRs, due mainly to the similar diurnal amplitude of
the diurnal emission fields.
The annual mean surface FFCO2 concentration differences between the MCE
and flat FE emissions are largest over the LSRs during the local winter
months and smallest during the local summer months (Fig. S3). This variation
interacts with simultaneous variations in PBL variation. However, distinct
from the diurnal FFCO2 rectification, the seasonal FFCO2
rectification shows positive values (up to 0.23 ppm) for
north and northeastern Europe vs. negative values (up to -0.28 ppm) in
East Asia, and a near-zero signal (no rectification) in the US (Fig. 1c).
The positive rectification obtained in north and northeastern Europe to
Siberia is associated with the coincidence of large wintertime FFCO2
emissions and weak wintertime atmospheric mixing, which tends to trap
CO2-enriched air near the surface. Additionally, the greater vertical
mixing in summertime interacts with the smaller summer FFCO2 emissions, thus distributing more of the CO2-depleted air to the
free troposphere. The limited seasonal rectification in North America vs.
the other LSRs is mainly due to the more complex FFCO2 emissions
seasonality, with peak emissions in both the winter and summer months as
shown previously. Finally, the negative rectification in East Asia is mainly
ascribed to the previously mentioned anomalous monthly FFCO2 emissions in China (increasing trend from January to December) and their
interaction with atmospheric transport. Hence, the CO2-depleted air is
confined to the surface in East Asia by the very small FFCO2 emissions combined with the inactive atmospheric transport in January and
February.
The rectification of the FFCO2 fluxes can be compared to the well-known
biosphere flux rectifier. Surface concentration differences of up to
20.35 ppm at the grid cell scale for the biospheric flux simulation
(Fig. S5) are centered over the tropical land and northern mid- to high
latitudes with much greater spatial extent than found for either the diurnal
or seasonal FFCO2 rectifier. Similar to the FFCO2 rectification,
the biospheric rectifier is a combination of diurnal and seasonal
rectifications (e.g., Denning et al., 1995, 1996; Yi et al., 2004; Chen and
Chen, 2004; Chan et al., 2008; Williams et al., 2011). For the diurnal
biospheric rectification, the daytime net negative CASA fluxes typically
coincide with a well-mixed PBL and greater interaction with the free
troposphere. At night, this flux is typically reversed and mixed into a
shallow PBL, resulting in a positive full-day annual mean surface CO2
concentration due to the greater loss of CO2-depleted air during the
day. In the case of the seasonal biospheric rectifier, the summer net
negative CASA fluxes are mixed into a thicker PBL, resulting in a strong
negative surface perturbation, whereas the winter net positive CASA fluxes
are mixed into a thinner PBL, resulting in a weaker positive perturbation.
The two interactions combine to give a positive annual mean surface CO2
concentration. The above analysis indicates that FFCO2 rectification is
mechanistically similar to biospheric rectification, but the FFCO2
rectifier effect occurs mainly at local-to-regional scales, while the
biosphere rectification is expressed at a larger spatial scale.
Simulated annual mean surface FFCO2 concentration difference
between the DCE and FE FFCO2 emission fields (DCE minus FE), sampled
during the local afternoon (12:00–18:00).
The diurnal amplitude of the FFCO2 surface concentration from
the DCE simulation. (a) The peak-to-peak diurnal amplitude of the
annual mean, hourly concentration difference between the DCE and FE emission
fields (DCE minus FE). (b) Ratio of FFCO2 diurnal amplitude to
the diurnal CO2 amplitude of total FFCO2 and biosphere.
Impact on afternoon sampling
Atmospheric inversion studies of CO2 fluxes using flask and tall tower
atmospheric CO2 measurements require consideration of CO2
concentration sampling times (e.g., Peters et al., 2007; Dang et al., 2011). Given the importance of the simulated
CO2 concentration to the diurnal cycle of FFCO2 emissions, we
sub-sample the DCE FFCO2 simulation output for local afternoon (noon–18:00 LT) conditions, a common sampling time for flask measurement and a chosen
sampling time by inversions to avoid the difficulties associated with
capturing nighttime PBL dynamics. Figure 2 presents the spatial distribution
of the annual mean, afternoon-only surface FFCO2 concentration
difference between the DCE and FE fields. Values vary from -0.21 to
+1.13 ppm, with larger positive values centered over the LSRs. Negative
values are present over regions with low emissions, which is mainly due to
the interaction of small emissions and a stable PBL at nighttime and the
early morning in the DCE experiment compared to the same dynamic in the FE
experiment. The afternoon and 24 h mean signals (Fig. 1b) are of opposite
signs but roughly the same magnitude over the LSRs. This is due to the
afternoon signal being sampled at the time of the largest afternoon
emissions but also contributing the weakest surface signal to the 24 h
diurnal span. The afternoon mean signal indicates that a potential bias
would be incurred by ignoring the diurnal variability in the FFCO2
emissions. It is noteworthy that the afternoon effect mainly occurs at the
local scale, and has a much smaller spatial extent than the full-day diurnal
rectification. This indicates that CO2 monitoring strategies could
minimize the effect of the FFCO2 diurnal cycle when using afternoon
measurements and the measurements can be taken close to large source regions
for studies influenced by the diurnal cycle.
Impact of the diurnal amplitude
The continuous atmospheric CO2 measurements taken by many monitoring
stations can see the complete 24 h coverage of atmospheric CO2 concentration, and can enable the estimate of sub-daily fluxes in
inversion studies using these data (e.g., Law et al., 2008). This motivates
the examination of the diurnal peak-to-peak amplitude of the simulated
concentration, since this parameter includes the overall daily information
of the diurnal FFCO2 concentration.
Figure 3a displays the amplitude of the annual mean diurnal surface
concentration difference between the DCE and FE fields across the globe. The
largest amplitude values are centered over the LSRs, with peak-to-peak values
reaching 9.12 ppm in western US (-117∘ E, 34∘ N). Local
sunrise is the point when the FFCO2 concentrations reach their greatest
difference. At local sunrise, the FE emissions exceed the DCE emissions,
which are small prior to the increase of daytime emitting activity (Fig. S1). When combined with the minimum in vertical mixing and a shallow
nighttime PBL, the resulting FFCO2 concentration difference is negative
(DCE minus FE). Local sunset, by contrast, is the point in the annual mean
diurnal cycle where the differences between the DCE and FE fields are at
their smallest (Fig. S1) and the DCE emissions exceed those of FE. This
combines with the much greater vertical mixing and greater PBL height, and
tends to ameliorate the resulting surface FFCO2 concentration
difference. Hence, the amplitude difference is driven primarily by the
concentration difference at the minima of the diurnal cycle (local sunrise).
To provide context for the magnitude of the FFCO2 diurnal amplitude,
the surface FFCO2 DCE concentration amplitude can be compared to that
resulting from biosphere fluxes. This is shown in Fig. 3b, where the ratio
of FFCO2 amplitude to the total of the FFCO2 and biosphere
amplitudes is presented. Averaged over the LSRs, the diurnal amplitude of
the annual mean FFCO2 concentration accounts for more than 15 % of
the total diurnal amplitude, and this ratio rises as high as 87 % at the
grid cell scale over the LSRs (corresponding to a FFCO2 diurnal
amplitude that is 5 ppm larger than the biospheric amplitude, Fig. 3b). The
diurnal amplitude can be examined seasonally as well. The diurnal FFCO2
amplitude accounts for a larger portion (up to 5 ppm) of the total diurnal
variation than the diurnal biospheric amplitude in winter, when the biosphere
is relatively quiescent and vertical mixing is less vigorous (Fig. S6).
Overall, this result indicates that studies of diurnal atmospheric CO2
should consider the contribution of diurnal FFCO2 emissions, especially
over LSRs and in wintertime.
Seasonal amplitude of the simulated surface FFCO2
concentration. (a) Peak-to-peak seasonal amplitude of simulated
surface FFCO2 concentration difference between the MCE and FE emission
fields (MCE minus FE). (b) Ratio of FFCO2 seasonal amplitude to
the sum of the FFCO2 and biosphere seasonal amplitude.
Impact of the seasonal amplitude
Figure 4 shows the amplitude of monthly CO2 concentration difference
between the MCE and FE (MCE - FE) fluxes. The seasonal amplitude varies from
0.01 to 6.11 ppm, with large signals over the LSRs as seen in previous
figures. Both the magnitude and spatial extent are larger than found in the
diurnal case. The longer periodicity allows more time for an atmospheric
signal to build up and to be advected further from the emission source
regions. The seasonal maxima and minima contribute equally to the amplitude
for all regions (Fig. S7). The seasonal maximum mainly occurs in
December–January, driven by the larger FFCO2 emissions during winter
(Fig. S8). The seasonal minimum exhibits variable timing across the LSRs,
with January for China (up to -3.42 ppm), August/September for the US (-1.09
ppm) and June/July for western Europe (-2.55 ppm). This timing is consistent
with the timing of the smallest FFCO2 emissions over each region (Fig. S8). The seasonal minimum in East Asia is, as has been mentioned, likely an
artifact of the inventory statistics.
The FFCO2 seasonal amplitude can also be compared to the seasonal
biospheric amplitude, for context (Fig. 4b). The biospheric amplitudes are
much larger than the FFCO2 amplitudes at the global scale, except for
specific industrialized source regions in the US, western Europe and East
Asia, where the FFCO2 amplitude accounts for more than 25 % of the
total seasonal amplitude. This result indicates a non-negligible
local-to-regional FFCO2 effect on seasonal amplitude of atmospheric
CO2 concentration.
Impact of the weekly cycle
The impact of the weekly cycle of FFCO2 emissions is demonstrated here
by constructing a mean weekday and mean weekend surface FFCO2
concentration from the difference between the WCE and FE simulations
(Fig. 5). As expected, the surface FFCO2 difference values are centered
over LSRs, with predominantly positive FFCO2 concentration values for
the weekdays and negative values on the weekends. The negative weekend values
are a reflection of the reduced weekend FFCO2 emissions vs. weekday
activity (Nassar et al., 2013). There are a few deviations from this regular
weekday/weekend pattern. First, the different definition of what constitutes
weekend activity is seen over the Middle East, where the weekend is typically
Thursday–Friday vs. Saturday–Sunday in most of the rest of the world. In
contrast to other weekdays, Monday shows positive values only in narrow
portions of East Asia. The other large source regions show negative surface
FFCO2 concentration difference values. This spatial pattern primarily
reflects the residual effect of the lower weekend FFCO2 emissions. This
coherent FFCO2 concentration difference dissipates after 24 h and is
then dominated by the higher weekday FFCO2 emissions. The residual
effect of the larger Friday FFCO2 emissions does not show up clearly in
the simulated weekend FFCO2 concentration (Fig. 5d), due to the fact
that the weekend mean is constructed from 2 days and the residual effect from
effect from Friday is likely negated in the 2-day mean.
Simulated daily mean surface FFCO2 concentration differences
between the WCE and FE emission fields. (a) Monday,
(b) Tuesday and Wednesday, (c) Thursday and Friday, and (d) Saturday and Sunday.
Sampling at monitoring stations
Atmospheric CO2 monitoring locations were originally situated away from
fossil fuel source regions, but as FFCO2 emissions have risen
dramatically over time, they are increasingly influenced by FFCO2
sources. A large number of monitoring stations are situated in strongly
affected areas in temperate North America, western Europe and East Asia that
show a strong diurnal concentration. Noteworthy are the coastal sites close
to the large source regions in the US and western Europe – these show
significant influence from the DCE flux component, despite the fact that
these locations are assumed to represent upwind background CO2. Time
series of daily afternoon-mean CO2 concentration differences demonstrate
this influence (Fig. 6). For the sake of brevity, we focus on two stations:
La Jolla, in the western US (32.9∘ N, 117.3∘ W;
10 m a.s.l.; referred to as LJO), and Lutjewad of the Netherlands
(53.4∘ N, 6.35∘ E; 61 m a.s.l.; referred to as LUTDTA).
The two sites were selected because they are close to LSRs (locations
highlighted in the figure). A strong seasonality of up to 5 ppm for LUTDTA
and up to 3 ppm for LJO is shown in the daily afternoon mean CO2
concentration difference from the ACE simulation. Synoptic variability with
approximately the same magnitude is also evident (Fig. 6b). These seasonal
and synoptic effects are very similar to those presented in Peylin et
al. (2011) at the station scale. Finally, a slight weekly cycle can be seen
in spring and summer at both stations.
The time series can be further understood through examination of the cyclic
FFCO2 flux contributions (Fig. 6c–e). The MCE simulation shows the
largest daily afternoon mean impact on CO2 concentrations (up to 5.5 ppm) vs. smaller values for the WCE (2.2 ppm) and DCE (1.6 ppm). Large
seasonality is shown in the MCE that is caused by the interaction of the
monthly FFCO2 emissions and atmospheric transport. The WCE and DCE
display slight but evident seasonality that is driven mainly by the seasonal
atmospheric transport. Synoptic variability is seen in the MCE (up to 4 ppm)
and DCE (up to 1 ppm). The synoptic-scale effect is comparable to the
results found in Peylin et al. (2011), where a ∼ 5 ppm effect
was found. Also, a weekly cycle is illustrated for the WCE driven by the
weekly FFCO2 emissions. These temporal patterns are common to the
stations with significant response to the time cycle FFCO2 emissions,
but the magnitude is dependent on the local dynamical conditions, transport
patterns and proximity of the site to the FFCO2 sources. LJO shows a
larger impact than LUTDTA in July and August, associated mainly with the
large FFCO2 emissions in summer. Differences are found in the timing of
the synoptic events between the two sites, and the amplitude of the synoptic
variation in the CO2 concentration difference at LUTDTA is roughly
twice that at LJO, which suggests that the synoptic events of atmospheric
transport play an important role in distributing the FFCO2 at LUTDTA.
The simulated surface afternoon mean FFCO2 concentration
difference (12:00–18:00 LT) between the DCE and FE FFCO2 emissions,
and the locations of GlobalView monitoring stations (stars).
(a) Daily afternoon mean FFCO2 concentration differences
between each cyclic FFCO2 emissions field and FE emissions at two
selected GlobalView stations (LJO – gray; LUTDTA – pink); (b) for
all time cycle emissions, (c) for diurnal-only time cycle emission,
(d) for weekly-only time cycle emissions and (e) for
monthly-only time cycle emissions. Solid stars indicate the location of LJO
and LUTDTA.
Column-average concentration
The analysis above indicates significant CO2 concentration response to
sub-annual FFCO2 emission variability near the surface. With the advent
of satellite measurements, as well as the surface-based spectrometers of the
TCCON network, it is important to examine the response of vertically averaged
CO2 concentrations to the FFCO2 emissions. How important is
sub-annual FFCO2 emission variability to the CO2 concentration
seen from space? And what impact do these FFCO2 emission cycles have on
studies that use satellite measurements?
To answer these questions, the same analysis is performed for the simulated
column-integral CO2 concentration for all the cyclic FFCO2 emissions as was performed for the surface. For generality, we have used
simple pressure weighting to compute the column averages, rather than
the vertical weighting appropriate for any particular satellite. Results
indicate weak rectifier effects in the simulated column-integral FFCO2
concentration, with ACE having negative values from -0.02 to -0.06 ppm.
The ACE rectification is centered over large source regions and the MCE
component represents the largest contribution overall, varying from -0.02 to -0.06 ppm (Fig. S9). The DCE exhibits similar rectification
magnitudes varying from -0.02 to -0.04 ppm, but with a response covering
a smaller spatial extent. The MCE rectification reflects the larger vertical
and spatial effect of the monthly FFCO2 emission variability as
compared to the WCE and DCE. Compared to the surface effect, the
column-integral rectification is almost an order of magnitude smaller.
However, note the negative signal in western Europe from MCE, which is opposite
to the positive signal at the surface (Fig. 1). Overall, the sub-annual
FFCO2 emission variability has little effect on all aspects of the
column-integral CO2 concentration.
Conclusions and implication
This study investigates the impact of sub-annual FFCO2 emissions cycles
(diurnal, weekly and monthly) on the simulated CO2 concentration. The
simulated CO2 concentrations are examined at multiple timescales over
the globe as well as at GlobalView monitoring stations. When expressed as
annual means, a FFCO2 rectifier effect is found from the combination of
all cycles, which varies from -1.35 to +0.13 ppm, centered over large
source regions in the northern hemisphere. This is driven by a large
negative diurnal FFCO2 rectification due to the interaction of
large/smaller FFCO2 emissions with vigorous/inactive PBL mixing in
the daytime/nighttime, and a positive seasonal rectification in western
Europe resulting from the covariance of small/larger FFCO2 emissions in
the summertime/wintertime with vigorous/inactive atmospheric transport.
The diurnal FFCO2 emissions are also found to significantly affect the
diurnal variation in simulated CO2 concentrations at the local/regional
scale, driven by the covariance of diurnally varying FFCO2 emissions
and vertical mixing. The impact on the diurnal peak-to-peak amplitude is up
to 9.12 ppm, while the impact on the afternoon mean concentration is as large
as +1.13 ppm at the grid cell scale. The results indicate the importance
of proper temporal sampling when using/interpreting measurements affected by
diurnal FFCO2 emissions (especially those near emission regions). The
small spatial extent of the afternoon effect suggests that measurements can
be taken close to the large source regions when required for studies that
use the afternoon-only measurements.
The monthly FFCO2 variability results in a simulated CO2
concentration seasonal amplitude (up to 6.11 ppm) over large source regions,
caused mainly by the interaction of large/smaller FFCO2 emissions in
wintertime/summertime with inactive/vigorous PBL mixing. Significant spatial
patterns are found at the regional scale, due mainly to the large difference
in the seasonal variations in FFCO2 emissions across the regions.
This result suggests that attention should be given to accurate
representation of seasonal profiles of regional emission inventories,
particularly for large emitters like China. The diurnal response has a more
limited spatial extent than the monthly response and can probably be
disregarded when considering clean air oceanic sites.
The simulated CO2 concentration at the GlobalView stations are found to
be affected by all sub-annual FFCO2 cycles, especially for sites close
to large source regions. These impacts cover multiple timescales, from
diurnal to seasonal, caused by the interaction/combination of the variable
FFCO2 emissions with atmospheric transport. This finding, together with
the above, indicates that current inversion studies that do not incorporate
sub-annually varying FFCO2 emissions could result in biased flux
estimates results due to the FFCO2 rectifier, and that caution should
be taken regarding sampling time and when choosing the locations for new
sites of atmospheric CO2 measurement.
Characterization of the column-average simulated CO2 concentration
suggests a weak impact compared to the surface signal, indicating less
importance than for surface measurements. This also suggests that including
the sub-annual cycles of FFCO2 variability is not as important a
concern for modeling studies using only satellite measurements.