Introduction
Atmospheric measurements of carbon dioxide (CO2) from ground and
airborne platforms have greatly increased our knowledge of the global carbon
cycle. Observations of CO2, including the NOAA Global Greenhouse Gas
Reference Network (GGGRN), initially emphasized ground-based measurements.
These observations, started by C. D. Keeling, have monitored the CO2
trend on both regional and global scales for over 50 years (e.g., Keeling and
Rakestraw, 1960; Tans et al., 1989). In addition, the frequency and spatial
distribution of airborne measurements have increased rapidly in the last two
decades, providing important information about horizontal and vertical
variability of atmospheric CO2 (e.g., Gerbig et al., 2003; Choi et al.,
2008; Biraud et al., 2013). Routine aircraft measurements from the NOAA/ESRL
GGGRN monitor the large-scale distributions of a suite of trace gases,
including CO2, under the influence of continental processes (Sweeney et
al., 2015). A very successful approach has been to employ commercial aircraft
as a platform for CO2 measurements, such as Japan's CONTRAIL
(Comprehensive Observation Network for TRace gases by AIrLiner) project,
which has provided valuable information for CO2 in the high troposphere
and lower stratosphere (Machida et al., 2002, 2008). Vertical profiles of
atmospheric CO2 reflect the combined influences of surface fluxes and
atmospheric mixing. Vertical profiles are particularly useful for evaluating
vertical mixing in atmospheric transport models that are used for inverse
modeling (e.g., Stephens et al., 2007) to derive estimates of regional- to
continental-scale CO2 sources and sinks (e.g., Tans et al., 1990; Gurney
et al., 2002, 2004; Ciais et al., 2010).
While CO2 sources and sinks are well constrained at the global scale by
global mass balance, it remains challenging to accurately resolve CO2
sources and sinks at regional to continental scale, the apportionment of
which depends on relatively minor variations of the observed spatial and
temporal patterns of CO2. When averaging over a few months and longer,
the largest portion of the variations over continents results from
hemispheric-scale terrestrial uptake/emissions (photosynthesis/respiration)
and fossil fuel emissions, while regional net fluxes can make a relatively
small contribution to the signal. For example, a simple mass balance
argument shows that all US CO2 emissions from fossil fuel burning
(∼ 1.4 Pg yr-1) create a total column enhancement of only
0.6 ppm on average in air parcels over the east coast compared to the west
coast and Gulf Coast if we assume an average of 5 days for the winds to
flush the contiguous US (∼ 8 × 1012 m2).
With careful calibration, air handling, and analysis, the uncertainties of
in situ measurements are less than 0.1 ppm. However, in situ observation
networks are sparse in global and regional coverage. Remote sensing data
radically increase the number of observations and capture undersampled
regions. It could have a valuable impact on our understanding of the carbon
cycle. However, both the precision and the potential of even very small
systematic biases in remote sensing measurements need to be carefully
evaluated, especially those that depend on regional and seasonal conditions.
Vertical profiles from in situ CO2 measurements have been used to
evaluate ground-based total column XCO2 (the “X” stands for dry mole
fraction) determinations, such as those from the Total Carbon Column
Observing Network (TCCON) (Washenfelder et al., 2006; Wunch et al., 2010;
Messerschmidt et al., 2011; Tanaka et al., 2012). The uncertainty of TCCON
total column CO2 is reported to be 0.4 ppm (1σ) after
comparison to aircraft measurements (Wunch et al., 2010). Vertical profiles
are also used to evaluate other satellite retrievals of total column
XCO2, such as those from the Tropospheric Emission Spectrometer (TES)
(Kulawik et al., 2013) and the Greenhouse Gases Observing SATellite (GOSAT)
(Inoue et al., 2013, 2016; Saitoh et al., 2016). Satellite retrieval products
have known and unknown biases (due to errors in spectroscopy, viewing
geometry, spatial differences in clouds and aerosols, surface albedo, etc.)
that can result in false horizontal gradients in total column XCO2 for
inverse estimates of sources (Miller et al., 2007; Crisp et al., 2012; Feng
et al., 2016). After correction for known biases, the mean GOSAT total column
CO2 (National Institute for Environmental Studies – NIES – retrievals) biases range between -2.09 and 3.37 ppm
(mean of 0.11 ppm, SD of 1.11 ppm; 20 out of 27 stations show
biases lower than 1 ppm) across different aircraft sites over land when
compared with aircraft-based total column XCO2 (Inoue et al., 2016). The
Orbiting Carbon Observatory 2 (OCO-2) retrieval of total column XCO2 was
estimated to have a mean difference less than 0.5 ppm from TCCON, with rms
differences typically below 1.5 ppm after bias correction (Wunch et al.,
2017). The overall
uncertainty of satellite retrievals is relatively large compared with the
total column XCO2 calculated from in situ measurements. Total column
XCO2 calculated from vertical profiles from the Japanese CONTRAIL
project (Machida et al., 2008) and from the NOAA Carbon Cycle and Greenhouse
Gas aircraft program (Sweeney et al., 2015) complemented with simulated
profiles from a chemistry–transport model above the maximum altitude of the
data have uncertainty less than 1 ppm (Miyamoto et al., 2013). The smaller
uncertainty of the in situ based total column XCO2 suggests that they can
be used to evaluate satellite retrievals of column averaged CO2. Since
aircraft profiles co-located with satellite retrievals are rare, it is useful
to consider the statistics of total column XCO2 fields derived from
repeated aircraft profiles over particular locations.
The effect of satellite column averaging kernels and a priori profiles when
comparing aircraft-based column XCO2 with GOSAT retrievals has been
assessed by Inoue et al. (2013). For the case
considered, application of the averaging kernel and a priori profile to
simulate total column XCO2 was generally within ±0.1 ppm of the
density-weighted total column, suggesting that the averaging kernels can
only account for a small part of the overall uncertainty of the GOSAT total
column XCO2 (Inoue et al., 2013).
Transparent and objective estimates of CO2 sources and sinks derived
from atmospheric measurements are essential for validating emissions
reduction efforts and other mitigation policies, and for lowering the
uncertainties of carbon cycle–climate feedbacks. The latter are major
ambiguities in predicting future climate, such as potential uncontrolled
CH4 and CO2 emissions from warming permafrost in Arctic regions.
Satellite retrievals of total column XCO2 can significantly improve
estimates of sources and sinks only if they are sufficiently precise and
accurate (Houweling et al., 2004; Chevallier et al., 2014), meaning that even
very small systematic errors (biases) must be eliminated. Here, we analyze
the spatial and temporal variability of column CO2 over North America
using well-calibrated CO2 measurements from aircraft and tall towers, and
we use model results from NOAA's CarbonTracker, version CT2015 (Peters et
al., 2007, with updates documented at http://carbontracker.noaa.gov) to
investigate the primary drivers of variability in total column XCO2. The
aircraft data enable direct analysis of column CO2 characteristics,
which is the fundamental step for accurate apportionment of sources and
sinks. This study focuses on long-term averaged column CO2 gradients and
the contributions of different vertical layers to the total column
variability. It can serve as a reference for evaluating regional and seasonal
biases of current and future column CO2 retrievals from both ground and
satellite platforms.
Methods
Aircraft and tall tower sampling
Aircraft sampling in the NOAA GGGRN intends to provide vertical profiles of
long-lived trace gases to capture their seasonal and interannual
variability. The aircraft sampling system consists of 12 borosilicate glass
flasks in each programmable flask package (PFP), a stainless-steel gas
manifold system, and a data logger and control. These flasks (0.7 L each)
are pressurized to obtain 2.2 L of sample air from each target altitude. Air
samples are then shipped back to NOAA/ESRL for carefully calibrated and
quality-controlled measurements. Carbon dioxide is measured using a
nondispersive infrared analyzer. Long-term measurements at ∼ 15 sites are carried out using light aircraft that can reach 8.5 km. Air
samples are collected mostly during late morning to early afternoon, when
the air mass within the planetary boundary layer (PBL) is generally well
mixed, and CO2 enhancement near the ground from plant respiration
during the night has been mixed throughout the boundary layer. Normally, the
aircraft follows a pre-decided route such that most samples are collected
within 0.1∘ of the site location. The sampling frequency varies
from site to site, currently from twice a month to once every 1.5 months.
For more sampling details, quality-control discussions, and an evaluation of
the sampling frequency, please refer to Sweeney et al. (2015). More
information on the aircraft sites can be found at
http://www.esrl.noaa.gov/gmd/ccgg/aircraft/. We estimate the uncertainty of
individual measurements of CO2 in flask air (68 % confidence level)
at 0.08 ppm. However, we have seen evidence of positive biases for samples
collected using older flasks that may contain contaminants. Andrews et al. (2014) reported biases that increased from < 0.1 ppm in 2008 to an
average offset in 2013 of 0.36 ppm. The aircraft sampling protocol was
modified starting in August 2014 to mitigate this bias. For samples
collected prior the protocol change, laboratory tests showed that new/clean
flasks have zero bias, but some older/dirty flasks could have biases of
> 1 ppm. This bias is not consistent among individual flasks and
increases over time (Andrews et al., 2014); the potential bias is hard to
quantify for measurements before August 2014. Thus, the high bias is not
corrected in our study. More recently, low bias has been found in PFP
measurements when the ambient humidity is high, based on comparisons of PFP
measurements with data from in situ analyzers at tall towers. We are working
to understand and quantify this bias, and for this study we have derived a
preliminary correction factor, which shows a linear trend with -1.4 ppm
CO2 offset per 1 % above 1.7 % of ambient water (mole fraction
relative to whole air) content. Only ∼ 4 % of total aircraft
measurements or ∼ 12 % of those below 2 km are impacted by
humidity higher than 1.7 %, for which we have applied corrections before
data analysis. The mean correction applied is 0.53 ± 0.4 (1σ) ppm for the impacted data.
The NOAA tall tower network measures CO2 and other trace gases within
the continental boundary layer. Continuous in situ measurements are
conducted using nondispersive infrared (NDIR) absorption sensors and cavity
ring-down analyzers. The long-term stability of these systems is typically
better than 0.1 ppm for CO2 (Andrews et al., 2014). Most tall
tower sites have more than one air intake height. In this study, continuous
in situ measurements from the highest intake are used to minimize potential
influences from local sources. More information concerning the tower sites
can be found at http://www.esrl.noaa.gov/gmd/ccgg/insitu/. For the column
XCO2 calculation, tower data only from 10:00 to 17:00 local standard time
(LST) on flight days are averaged to one data point per day, as a complement
to vertical profiles within the PBL.
Site description
We analyze data from 19 aircraft sites and 6 tall tower sites during 2004 to
2014 (see Table S1 in the Supplement for a summary of site conditions). After considering the
geographic distribution of these sites in North America, we group them into
eight regions for spatial comparisons (Fig. 1). The northern west (NW) and
southern west (SW) regions represent the inflow area on the west coast of
the US, directly downwind of the Pacific Ocean at both higher elevations. The
northern mid-continent (NM) region represents the boreal forest and
agriculture region in north-central North America. The mid-continent (MC)
region represents a dry landscape due to its high elevation (above 1.5 km on
average) and semi-arid climate. The midwest (MW) region is strongly
influenced by agriculture and temperate forest. The southern mid-continent
(SM) represents the south-central humid temperate region, with inflow from
the Gulf of Mexico during summer. The northeast (NE) region represents the
temperate forest on the northeast coast of the US, which is mostly downwind of
regions to the west above the PBL, and downwind of its southwest regions
within the PBL. The southeast (SE) region represents the warm temperate
region on the southeast coast of the US.
Aircraft, tall tower, and high-elevation/tower sites in the NOAA
GGGRN. The eight boxes define regions that are further discussed for spatial
pattern comparison. See Table S1 for detailed site information.
Smoothing of the reference data and column XCO2 calculation
We use the Mauna Loa Observatory (MLO) as a reference site. MLO is located at
19.536∘ N, 155.576∘ W, and 3397 m above sea level.
Carbon dioxide measurements from this site are widely used to represent
background CO2 in the Northern Hemisphere. For our study, a function
consisting of a quadratic polynomial and four harmonics is fitted to the MLO
data, adopted from the method described by Thoning et al. (1989). Residuals
of the data from this function are smoothed by a low-pass filter with
full-width at half-maximum in the time domain of 1.1 years. The smoothed
residuals are then added back to the polynomial part of the function to
produce the long-term deseasonalized trend. This trend (see Fig. 3) is
subtracted from all aircraft and tall tower measurements. Also, the
CarbonTracker results presented in this study are the differences relative
to observed MLO deseasonalized trend. We use “Δ” to represent
detrended data in the following text and figures. The choice of reference
site is not important for this study, since we focus on examining the
relative seasonal patterns of the detrended spatial and vertical
distributions of CO2 instead of the total changes in CO2
abundance attributed to global surface fluxes.
We calculate partial column average CO2 dry mole fraction using tall
tower and aircraft data, and the total column by adding simulations of high
altitude CO2 (above 330 hPa, ∼ 8 km above sea level) from
CarbonTracker. Since geometric height is available with each aircraft measurement,
either from the onboard Global Positioning
System (GPS) (after 2006) or inferred from the aircraft altimeter or pressure
altitude, we first convert geometric height (in meters) to pressure (in hPa) for the pressure-weighted
column XCO2 calculation. This conversion uses geopotential data from
NOAA/NCEP North American Regional Reanalysis (NARR) (Mesinger et al.,
2006), available at
https://www.esrl.noaa.gov/psd/data/gridded/data.narr.html, in which the
geopotential is a function of latitude, longitude, pressure altitude, and
time. We interpolate the geopotential field vertically to retrieve pressure
and then calculate dry pressure by incorporating specific humidity data from
NARR. Eventually, we use a trapezoidal method to integrate over detrended
vertical profiles for dry-pressure-weighted column averages. For the
long-term averaged column ΔXCO2 calculation, a long-term mean
vertical profile is first constructed for each month by combining 11-year
detrended data together and then averaging data in each 40 hPa vertical bin.
To look at the long-term averaged total column ΔXCO2 from
individual aircraft sites, we combine aircraft data with upper-layer CT2015
simulations.
The NOAA CarbonTracker model assimilates CO2 measurements from surface
sampling networks and tall towers to generate global 3-D fields of
atmospheric CO2 mole fraction. The CarbonTracker model has evolved
significantly since Peters et al. (2007). A detailed description of this model is provided in documents
available at http://carbontracker.noaa.gov. Our study utilizes CarbonTracker
results from the 2015 release (CT2015).
This version provides CO2 mole fraction over North America with
1∘ × 1∘ spatial and 3 h temporal
resolutions, which are analyzed in Sect. 3.2 and 3.3. Total column CO2
calculated from CT2015 global data with 3∘ × 2∘
spatial resolution is also presented in the Supplement. We
have evaluated the performance of CarbonTracker in the upper atmosphere (330 to
0 hPa) by comparing its simulations with in situ measurements from nine AirCore
profiles (Karion et al., 2010) sampled in 2012–2014. AirCore is a
∼ 150 m stainless-steel tube that utilizes changes in ambient
pressure for passive sampling of the vertical profile. The tube is carried
to high altitude by a balloon and it collects a continuous sample as it
descends. It is then measured by an analyzer after it is recovered. More
information about the AirCore system can also be found at
https://www.esrl.noaa.gov/gmd/ccgg/aircore/. All nine AirCore profiles are taken near Southern Great Plains, Oklahoma (SGP) and Briggsdale, Colorado (CAR) (see Fig.1 for site locations.) Figure 2 shows examples of AirCore profiles
compared with CT2015 in the upper atmosphere, which demonstrates good
agreement. We also compare partial column (330 to 0 hPa) averages from the
nine AirCore profiles and CT2015. Results from CT2015 agree generally well with
AirCore, with differences ranging from 0.03 to 1.22 ppm (mean value equals
0.66 ppm), which suggests that CT2015 may have a high bias that could
contribute to 0.66 × 1/3=0.22 ppm overestimation on average to the
total column average. However, AirCore is in the process of rigorous
evaluation; the differences between AirCore and CT2015 are not well
characterized yet, since we only have a limited amount of AirCore data. It is
unclear whether the potential bias of CT2015 in this partial column is
dependent on time or sampling location. Adding a constant bias correction to
all regions will not change the spatial gradients that we focus on in this
study. Thus, no correction is applied when using CT2015 simulations to
represent the upper third of the total column. For uncertainty estimates, we
use a “bootstrap” method that uses random resampling of individual vertical
profiles with restitution (low bias, high humidity was corrected), with 100
Monte Carlo runs for each column average calculation. Uncertainty is then
defined as 1 standard deviation of the 100 Monte Carlo results.
Results and discussions
Seasonal patterns and spatial gradients
CarbonTracker (CT2015) simulations compared with AirCore in situ
measurements in the upper atmosphere. AirCore profiles in the left and right
panels are sampled near CAR and SGP, respectively.
CO2 observations from aircraft (a) and
towers (b). The yellow line in panel (b) illustrates the
deseasonalized trend at the Mauna Loa Observatory (MLO), the same as in (c), in which
the y axis is expanded.
Typically, one aircraft profile contains measurements at 12 different
altitudes. Column ΔXCO2 can be computed for each profile using
the method described in Sect. 2.3 (Fig. S1 in the Supplement). Our aircraft-
and CT2015-based column CO2 at the SGP and LEF
(see Fig. 1 for site locations) sites shows reasonable
agreements with TCCON data retrieved at the Lamont and Park Falls sites
(Washenfelder et al., 2006; Wunch et al., 2009, 2011), respectively
(Fig. S2). Figure 3 shows aircraft (at all altitudes) and tower data (daily
averages for 10:00–17:00 LST data) from all sites used in this study.
Aircraft data above 2 km exhibit much smaller seasonal variations than the
full dataset, because the variations are mainly driven by CO2 sources
and sinks near the Earth's surface. CO2 mole fraction is enhanced in the
shallow wintertime PBL primarily due to reduced plant photosynthesis and
ecosystem respiration combined with slightly increased fossil fuel emissions.
During summer, the PBL is deeper, and depletions within the PBL are due to
strong terrestrial uptake that dominates over emissions especially from
June to August. During the summers of 2010 to 2012, CO2 from aircraft
measurements appears higher than in other years in Fig. 3; however, similar
characteristics are not present in tower data. This apparent difference is
due to a decrease in sampling frequency at several aircraft sites that
resulted in an aliased picture of the full summer signals. Since we focus on
the climatological mean of 11 years of data in our study, this influence is
eliminated by combining 11 years of data together into one “average year”.
Multi-year (2004–2014) average smooth seasonal curves of CO2
relative to the long-term deseasonalized trend at Mauna Loa for different
vertical layers: (a) aircraft and tower data under 2 km – MC is not
presented because only limited data were available due to high surface
elevations (> 1.5 km on average) in this region; (b) aircraft
data from 2 to 5 km; (c) aircraft data from 5 to 8.5 km;
(d) CT2015 model results for layers above 330 hPa (∼ 8.5 km)
to 0 hPa (∼ 80 km).
To investigate the contributions of different altitudes to spatial gradients
between regions, we divided all measurement data into three layers according
to their sampling altitudes: below 2, 2–5, and 5–8.5 km m a.s.l.
(Fig. 4). Smooth seasonal curves are attained from fitting data with four
harmonics using the method described by Thoning et al. (1989). The
peak-to-valley amplitudes of the seasonal cycles below 2 km are the largest
among the three layers for most regions, with a minimum of 10.3 ppm in SM
and a maximum of 25.0 ppm in MW. The seasonal variation amplitudes decrease
to 7.7–11.5 ppm in the 2–5 km layer and further decrease to
7.2–10.0 ppm in the 5–8.5 km layer. We also observe that the seasonal
cycle drawdown occurs later in the layers above 2 km (see Fig. S3, which
provides similar information as Fig. 4, but seasonal curves from different
vertical layers are grouped by regions to facilitate comparisons of the
phases of seasonal cycles). The seasonal CO2 drawdown below 2 km is
mainly influenced by terrestrial photosynthesis, and gradients are due to
local to regional fluxes, with an earlier onset of drawdown in southern
regions than in northern regions. The seasonal cycle aloft is damped and
lagged compared to the PBL, with influences from throughout the Northern
Hemisphere and with spatial gradients likely driven by large-scale transport.
The NW, SW, SM, and SE inflow regions have significant delays of more than
1 month in the 2–5 km layer compared with the surface layer, which is
likely due to the delayed phase of the seasonal cycle in well-mixed air
coming from the oceans. Vertical homogeneity of air over ocean was observed
during the HIAPER Pole-to-Pole Observations (HIPPO) aircraft campaign (Wofsy
et al., 2011; Frankenberg et al., 2016). As air masses are transported
further inland, we observe reduced discrepancies of the timing of CO2
drawdown between surface and upper-layer air (2–5 km), which may be
associated with the increased influence of the land surface in the
mid-troposphere due to strong convection over land. CO2 drawdown in the
5–8.5 km layers also occurs later than in the 2–5 km layers in most
regions; however, differences between these two layers are small. The
declining amplitude and delayed phase of the seasonal cycle with altitude
have been noted often (e.g., Tanaka et al., 1983; Ramonet et al., 2002;
Gerbig et al., 2003; Sweeney et al., 2015). It demonstrates that there is a lot
of important information in the vertical profile that is diminished in
observations of the total column.
We find that the largest horizontal spatial gradients between regions occur
below 2 km during summertime (Fig. 4), with a maximum difference of
∼ 15.5 ppm between MW and SM. SM and SW exhibit
less-pronounced seasonal cycles, which is likely associated with air masses from
the Gulf of Mexico and the Pacific Ocean, respectively, whereas MW exhibits
a deep summer drawdown (amplitude in seasonal cycles) partially as a result
of strong regional forest and crop uptake. Crevoisier et al. (2010) estimated the surface flux over North America using vertical CO2
measurements and average wind vectors, and reported that annually averaged
land carbon fluxes in the western (including SW) and southern regions
(including SM) were neutral. The SE region also demonstrates a less
pronounced seasonal cycle with higher summertime levels compared with other
northern regions, which may be due to the sea-breeze influence in summer
within the PBL. In wintertime, CO2 levels in NE and MW are higher than in
other regions, which result from regional fossil fuel and terrestrial
biogenic emissions combined with transport from the west and south.
Higher-altitude data (above 2 km) exhibit only small spatial gradients. In
the 2–5 km layer, the largest gradient is 4 ppm in summer (Fig. 4b). It
further decreases to less than 3 ppm in the 5–8.5 km layer (Fig. 4c).
Figure 4d shows modeled CO2 mole fractions from CT2015 for the upper
troposphere and above (330 to 0 hPa), which are used to fill in above
the aircraft profiles for calculation of total column ΔXCO2.
Spatial gradients in this layer are less than 0.5 ppm, suggesting that the
top third of the total column has little contribution to the spatial
gradients of the total column.
Long-term mean vertical profiles
To investigate the mean spatial gradients, we first calculate the long-term
mean monthly vertical profiles as described in Sect. 2.3. In addition, each
tower serves as one additional layer in the mean profile. The long-term mean
tower data generally fit well in the vertical profiles from measurements of
aircraft samples (Figs. 5 and 6), suggesting that the biases described
in Sect. 2.1 do not significantly affect the long-term mean. To attain
profiles of the entire atmospheric column, upper layers (330 to 0 hPa) are
filled in by CT2015, and the lowest data point of the measured profile is
extended to ground level, defined by the mean surface elevation in that
region.
Long-term mean (2004–2014) average vertical profiles in
January (a) and August (b) in MW. The error bar shows
1 standard deviation.
Long-term mean (2004–2014) monthly vertical profiles in NM, NW, NE, MW
(by column, from left to right in the upper panel) and in MC, SW, SM, SE
(by column, from left to right in the bottom panel). Blue points were calculated from
observations, red points were calculated from CT2015, and green points were calculated from tower data.
Figure 5 presents two examples of long-term mean profiles with data
variability, which is 1 standard deviation for each 40 hPa bin of
aircraft data or for all flight-day tower data. Variability as large as
20 ppm is seen within the PBL in the MW region in summer, which is due to
strong and heterogeneous surface vegetation uptake and ecosystem respiration
combined with day-to-day changes in wind direction. All long-term mean
monthly vertical profiles are presented in Fig. 6, which shows the mean
temporal and vertical variability of CO2 in each season and further
demonstrates the vertical propagation of seasonal CO2 due to changes of
surface flux. In wintertime, monotonic decrease of CO2 with altitude can
be observed from all regions, in which high PBL CO2 is mainly driven by
surface emissions and reduced vertical mixing (Denning et al.,
1999; Stephens et al.,
2007). Surface CO2 decreases dramatically in the growing season in those
regions influenced by high plant activity, such as the NM and MW regions. For the
summer vertical profiles in the NE and SE regions (east coast of the US), the
CO2 mixing ratio is elevated in the layer under 900 hPa, followed by
significant decreases in the upper layers until 750 hPa, and then increases with
altitude until tropopause (Fig. 6). This is likely a result of sea breeze
influence. Lower-troposphere air from the sea, lacking terrestrial uptake of
CO2, typically has higher CO2 in summer compared with inland air.
Polluted air previously advected offshore can be brought back along with sea
breeze. Without significant vertical mixing over the marine surface, high
levels of pollutants remain in those air masses. The convergence of sea
breeze with prevailing wind moving offshore may create a period with a
stalled frontal structure that can aggregate air pollutants (Banta et al.,
2005). The convective internal boundary layer structure of the sea breeze
system can significantly reduce mixing height (Miller et al., 2003), and also
induces higher CO2 levels. When the sea breeze is not dominant, air
advected from the southwest and west (the land) can also bring in polluted air
with high CO2 since this region is downwind of continental US emissions
(Miller et al., 2012).
Partial column ΔXCO2 and total column ΔXCO2
(a) Partial column ΔXCO2 calculated from
aircraft and tower data; (b) partial column ΔXCO2
calculated from CT2015; (c) total column ΔXCO2
calculated from aircraft and tower data, including the top-layer data from
CT2015.
Seasonal variations of monthly averaged partial column ΔXCO2
(below 330 hPa) demonstrate maximum values in April and minimum values in
August or September (Fig. 7a). The largest amplitude appears in NM, with
peak-to-valley difference up to 13.5 ppm. SW, SM, SE, and MC have similar
amplitudes of 7–8 ppm, smaller than the other three regions. To evaluate the
performance of CT2015 on column ΔXCO2, CT2015 results are
sampled to match the latitude, longitude, altitude, and time of actual
measurements (CarbonTracker Team, 2016). Note that aircraft profiles are not
assimilated in CT2015, so aircraft data are independent of the CT2015 data
assimilation. Figure 7b shows monthly partial columns of ΔXCO2 calculated from CT2015, which demonstrate good agreement with results from
measurements. Only small seasonal biases exist in CT2015, with high bias
occurring mostly in spring and early summer and low bias in September and
October (Fig. S4). The overall differences of monthly partial column ΔXCO2 (CT2015–measurements) mainly fall in the range of -0.64 ppm
(5th percentile) to 0.84 ppm (95th percentile) with a mean
difference of 0.13 ppm. These differences are of similar magnitude to the
uncertainties of partial column ΔXCO2 calculated from the
measurements (Fig. S5). It is clear that CT2015 captures the long-term mean
variations of both phase and amplitude of partial column XCO2
reasonably well when compared with well-calibrated measurements across North
America.
Total column ΔXCO2 is presented in Fig. 7c. In NW, NM,
NE, and MW, seasonal variations of total column ΔXCO2 are
very similar in both phase and amplitude (8–9 ppm peak to valley). For SW,
SM, SE, and MC, amplitudes are ∼ 5.5 ppm. The smallest spatial
gradients occur during May and October, which result in maximum differences
among all regions of only 0.9 and 0.7 ppm, respectively. The largest spatial
gradients occur during June, July, and August, which result in maximum
differences of 2.4, 4.5, and 4.1 ppm, respectively. It is interesting that
the deepest seasonal drawdown is seen in NM, not in MW that
encompasses the very intensive agricultural activities in the US midwest,
which suggests the possibility of strong upwind influence in the NM region.
Transported signals have significant influences on total column CO2.
The summer total column ΔXCO2, represented by the
June–August average from CT2015, has a magnitude that is similar to observations
with differences of no more than 1 ppm (Fig. 8). Based on the seasonal patterns
of total column ΔXCO2 (Fig. 7c) and the summer column ΔXCO2 (Fig. 8), we can separate the eight regions into two groups.
The group with NW, NM, NE, and MW has ∼ 3 ppm stronger
drawdown (larger amplitude) than the group with SW, SM, SE, and MC. For
winter total column ΔXCO2 (December to February average), the
maximum spatial difference is only 1.6 ppm, with the highest total column
ΔXCO2 of 1.2 ppm in NE and the lowest value of -0.3 ppm in MC.
Long-term mean (2004–2014) for June–August partial and total column
ΔXCO2. The error bars represent 1 standard deviation from the
bootstrap uncertainty calculation (see Sect. 2.3).
Influence of large-scale circulation
Figure 9 shows long-term mean summer column ΔXCO2 calculated
from CT2015, together with full column ΔXCO2 from individual
aircraft sites. Note that some aircraft sites have less than 11 years of data
that CT2015 shows in Fig. 9, and only aircraft sites with more than 6 years of
data are presented; the actual values are provided in Table S2. The fact that
total column ΔXCO2 from CT2015 agrees well with aircraft sites
supports the performance of CT2015 on a long-term average basis. The
observations show a similar summer spatial pattern, with lower column ΔXCO2 in the north and northeast regions and higher column ΔXCO2 in the south and southwest regions (Fig. 9a). Scattered hot spots
of high column ΔXCO2 associated with surface emissions from
megacities, or cold spots associated with strong local uptake, are not or
just barely visible in the long-term average column ΔXCO2 map at
1∘ × 1∘ resolution. Instead, the wave-like pattern
of column ΔXCO2 over North America reflects the average
large-scale circulation. To support our hypothesis on the influence of large-scale
circulation, we analyze the long-term mean wind pattern over North America.
We can see that air masses from northwest of the continent bring in low
average column ΔXCO2, while air masses from the south (mainly
the subtropical Pacific Ocean and the Gulf of Mexico) bring in high column
ΔXCO2 (Fig. 9b). The zonal gradients over the continent,
especially north of 40∘ N, also reflect long-term average wind
patterns; southwest wind corresponds to higher column ΔXCO2 over
the western part of the continent until the wind direction shifts to
west–northwest over the eastern part of the continent. This wind pattern
matches well with the geographic division of the over/under -3 ppm areas
colored in green/blue in the column ΔXCO2 map (Fig. 9b).
Figure 9c and d shows partial column averages for the free troposphere
(800–330 hPa) and lower troposphere (below 800 hPa), respectively. The
free troposphere spatial gradient also demonstrates a wave-like pattern. A
previous study on the total column CO2 from the ground-based TCCON found strong correlation between the
midlatitude column CO2 and synoptic-scale variation of potential
temperature (θ, at 700 hPa), a dynamic tracer for adiabatic air
transport (Keppel-Aleks et al., 2012). Thus, they also propose that the
variations in column CO2 are mainly driven by large-scale flux and
transport. Analysis of the interannual variability of the seasonal cycle
amplitudes of column CO2 in the Northern Hemisphere has also found a significant
contribution of large-scale circulations to the north–south gradient (Wunch
et al., 2013).
Long-term mean (2004–2014) for June–August total column ΔXCO2 (in ppm) from CT2015 in 1∘ × 1∘ spatial
resolution with total column ΔXCO2 for 13 individual aircraft
sites in squares (a), and CT2015 column ΔXCO2 overlaid
with pressure-weighted (1000 to 500 hPa) mean wind vectors for the same
period (b). Panels (c) and (d) are similar to
(a), except for the free troposphere (800 to 330 hPa) and lower
troposphere (below 800 hPa), respectively. Note the different color scales.
The strong drawdown over northeast North America in summer is a consequence
of long-range transport of low CO2 from northeast Eurasia, in addition
to regional terrestrial uptake. Sweeney et al. (2015) notes well-mixed
vertical profiles (up to 8 km) of CO2, CO, CH4, N2O, and
SF6 from the THD (Trinidad Head, California), ESP (Estevan Point, BC, Canada),
and PFA (Poker Flat, Alaska; 65.07∘, -147.29∘) sites and suggests that air coming across the Pacific
was strongly influenced by Asian surface fluxes before being vertically
homogenized as it passed over the Pacific Ocean. This well-mixed air forms
an important boundary condition in the column CO2 of air coming into
the North American continent. This was best illustrated at sites like PFA
where the summertime minimum in CO2 significantly preceded maximum
ecosystem uptake of CO2, implying significant influence of transported
air from lower-latitude regions from Asia. We further conduct an experiment
using CarbonTracker to investigate the importance of this effect. A control
run and a “masked run” are conducted for 2010–2012, in which the Eurasian
boreal flux is turned on/off. The MLO CO2 trend from each model
scenario is used as reference background and thus removed before total
column ΔXCO2 calculation. Figure 10 shows the results for
summer 2012, which is an average summer when compared with the 2004–2014
mean pattern (Figs. 9 and 11). The maximum north–south difference
reduces to ∼ 2.5 ppm after we turn off the Eurasian boreal
flux, compared with ∼ 5 ppm from the control run. In both
the control and masked scenarios, the free troposphere partial ΔXCO2 demonstrates similar spatial patterns to total column ΔXCO2 (Fig. S6). This result combined with results from Sweeney et al. (2015) demonstrates that the transport of low CO2 resulting from large
summertime Eurasian boreal uptake has a large contribution on the overall
summer total column CO2 decrease in North America.
Total column ΔXCO2 (in ppm) from the CarbonTracker control
(a) and masked (b, Eurasian boreal flux is masked) runs
for June–August 2012 (3∘ × 2∘ spatial resolution).
MLO trend from each individual scenario is removed before the ΔXCO2 calculation. The same color scale is used as in Fig. 9a. Partial
column ΔXCO2 patterns for the free troposphere (800 to 330 hPa) and
lower troposphere (below 800 hPa) are provided in the Supplement.
Long-term mean (2004–2014) for June–August total column ΔXCO2 (in ppm) from CT2015 (a) in
3∘ × 2∘ spatial resolution, and detail for Europe
overlaid with pressure-weighted (1000 to 500 hPa) mean wind vectors for the
same period (b). The color scale is the same as in Fig. 9a, which
is scaled to reflect 6 ppm difference of XCO2 to compare with satellite
retrievals from Reuter et al. (2014, their Fig. 2a).
A comparison with apparent gradients over Europe
Figure 11 shows the climatological June–August mean modeled global column
ΔXCO2 map in 3∘ × 2∘ spatial
resolution, which presents smooth wave-like patterns. Reuter et al. (2014)
use SCIAMACHY and GOSAT satellite retrievals of column CO2 and inverse
modeling to infer a very large net CO2 uptake flux over the European
region. Column ΔXCO2 from CT2015 (Fig. 11) exhibits a
drastically different summer spatial pattern over Europe compared with the
8-year mean (2003–2010) June through August satellite retrievals
presented by Reuter et al. (2014, their Fig. 2a). The spatial gradient from
CT2015 results in a maximum 3–4 ppm difference and a gradual pattern,
instead of as much as 6 ppm from satellite retrievals. There is no sign of
XCO2 hot spots from surface emissions or removals in the CT2015 spatial
pattern over Europe (Fig. 11), in contrast to several hot spots that are
apparent from the 8-year averaged SCIAMACHY satellite retrievals over
Ireland, the UK, Belgium, the Netherlands, north of Germany, and south of Sweden,
and low spots over Ukraine and Kazakhstan
(Reuter et al., 2014). This SCIAMACHY
retrieval pattern contradicts our understanding of the significant influence
of large-scale transport on column ΔXCO2. Although the
NOAA/ESRL CT2015 (https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/CT2015/)
assimilates fewer observations over Europe than CarbonTracker Europe
(http://www.carbontracker.eu/), both models produced similar fluxes over the
European region (see both websites for detailed fluxes). The 3∘ × 2∘ grid from CT2015 is not likely responsible for a
much smoother pattern for CarbonTracker, compared with the 2∘ × 2∘ grid from satellite retrievals (Reuter et al., 2014).
The North American region on the 3∘ × 2∘ grid
in Fig. 11 shows a similar pattern to the 1∘ × 1∘ grid
in Fig. 9, with similar spatial difference of ∼ 5 ppm. A smoother
spatial distribution should be expected in Europe for the long-term mean
column XCO2 (Fig. 11) due to the influences of dominating west and
southwest winds in summer. We have also evaluated the importance of sampling
bias by sampling CT2015 at the same latitude/longitude/hour (within 1 h) as
in SCIAMACHY Bremen optimal estimation (BESD v02.00.08) data (Reuter et al., 2011). The 8-year mean
pattern shows much smaller gradients (3–4 ppm maximum) without significant
hot/cold spots at the locations of SCIAMACHY (Fig. S7). Although sampling
biases contribute to the unphysical column XCO2 spatial pattern from
SCIAMACHY, they cannot explain the large gradients. When we compare CT2015
directly with SCIAMACHY BESD data, we find up to ∼ 3 ppm differences
over North America and Europe (Fig. S8). Since CT2015 compares well with
calibrated data over North America, we are deeply skeptical about any
sources/sinks inferred from the SCIAMACHY BESD data. A recent study (Feng et
al., 2016) using inverse modeling suggests that satellite retrievals outside
the immediate European region and a small bias of only 0.5 ppm were
sufficient to produce the apparent large carbon sink in the study of Reuter
et al. (2014). This is expected from elementary mass balance considerations
as in Sect. 1. Spatial gradients are the fundamental signals to infer
regional fluxes. Since spatial gradients from CT2015 are realistic, boreal
fluxes inferred by CT2015 should be more trustworthy than fluxes estimated
based on unrealistic spatial patterns. The European carbon sink is still
inconclusive; the discrepancies among different methods and results are
further discussed by Reuter et al. (2017).
Conclusions
Aircraft and tall tower measurements from the NOAA GGGRN provide detailed
information describing the long-term average temporal and spatial variations
of CO2 in the PBL and the free troposphere. These data provide valuable
constraints for evaluating model simulations and satellite retrievals.
Seasonal cycle peak-to-peak amplitudes of CO2 are largest below 2 km,
where those maximum values are about twice those in the vertical layers
above, indicating that most of the information on surface sources and sinks
resides in the continental PBL. Large spatial gradients of CO2 over
North America are observed below 2 km during summer (with a maximum
difference of ∼ 15.5 ppm between MW and SM), while higher-altitude
data (above 2 km) have much smaller contributions to spatial
gradients, with a maximum difference of 4 ppm. The spatial differences of
CO2 in the upper troposphere and above (330 to 0 hPa) are less than
0.5 ppm, according to CT2015. Comparison with AirCore measurements shows
CT2015 performs well at simulating upper tropospheric and lower stratospheric
patterns.
Our long-term mean vertical profiles show that tower data agree well with
aircraft data at similar vertical levels. Partial column ΔXCO2
was calculated from the long-term mean vertical profiles. By comparing the
partial column ΔXCO2 from measurements with those from
CT2015, we verify that CT2015 captures the long-term mean patterns of both
phase and amplitude of partial ΔXCO2.
Large spatial gradients of ΔXCO2 only appeared in summer,
during which time the north and northeast regions had ∼ 3 ppm
stronger drawdowns than the south and southwest regions. Scattered hot spots
of high column ΔXCO2 associated with surface emissions from
megacities, or cold spots associated with strong local uptake, are not or
just barely visible in the long-term average column ΔXCO2.
Instead, the wave-like pattern of column ΔXCO2 over North
America matches well with the average large-scale circulation. A
CarbonTracker experiment to investigate the impact of Eurasian long-range
transport suggests that the large summertime Eurasian boreal flux alone
contributes about half of the north–south column ΔXCO2 gradient across North America. Considering the transported signals from
other upwind regions, including northern Canada, we expect that the
transported signals have the overall largest contribution to the total
column ΔXCO2 spatial gradient.