BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-13-873-2016The 2009–2010 step in atmospheric CO2 interhemispheric differenceFranceyR. J.roger.francey@csiro.auFrederiksenJ. S.CSIRO Oceans & Atmosphere, Aspendale, Victoria,
AustraliaR. J. Francey (roger.francey@csiro.au)17February20161338738856August201511September201511January201626January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://bg.copernicus.org/articles/13/873/2016/bg-13-873-2016.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/13/873/2016/bg-13-873-2016.pdf
The annual average CO2 difference between baseline data from
Mauna Loa and the Southern Hemisphere increased by
∼ 0.8 µmol mol-1 (0.8 ppm) between 2009 and 2010, a
step unprecedented in over 50 years of reliable data. We find no evidence for
coinciding, sufficiently large source and sink changes. A statistical anomaly
is unlikely due to the highly systematic nature of the variation in
observations. An explanation for the step, and the subsequent 5-year
stability in this north–south difference, involves interhemispheric
atmospheric exchange variation. The selected data describing this episode
provide a critical test for studies that employ atmospheric transport models
to interpret global carbon budgets and inform management of anthropogenic
emissions.
Introduction
The record 2009–2010 increase in annual mean CO2 difference between
hemispheres, ΔCN-S, was reported by Francey et al. (2013)
using data from Mauna Loa (mlo; 20∘ N, 156∘ W; altitude
3.4 km) and Cape Grim (cgo; 41∘ S, 145∘ E; 0.2 km) or the
South Pole (spo, 90∘ S, 2.8 km). In the context of seeking an
explanation for decadal differences between the fossil emission trends and
trends in atmospheric CO2 growth rate, Francey et al. (2013) attempted an empirical correction for
reported natural influences on CO2 growth using multiple regression of
reported wild fires, volcanoes, and El Niño–Southern Oscillation (ENSO). None of these reported influences showed statistically
significant anomalous behaviour in the 2009–2010 period.
A Commonwealth Scientific and Industrial Research Organisation (CSIRO)
inversion, which deduces surface fluxes from atmospheric CO2
observations, is based on atmospheric transport described by the Cubic Conformal Atmospheric
Model (CCAM; McGregor and Dix, 2008). This explains the 2009–2010 ΔC
with a 2010 Northern Hemisphere (NH) source in the Asian region, distributed
widely enough to be unverifiable by “bottom-up” methods (R. Law, personal
communication, 2015).
However, Poulter et al. (2014), using a terrestrial biogeochemical model,
atmospheric carbon dioxide inversion, and global carbon budget accounting
methods, suggested that the ΔCmlo-cgo step might be
explained by a record 2011 land carbon sink located in the semi-arid regions
of the Southern Hemisphere (60 % of which was in Australia).
North–south differences and growth rates in CO2 since 1990.
Panel (a) shows, on the left axis, annual average
(January–December) ΔC (ppm) from three programs – CSIRO, NOAA
(mlo–cgo), and SIO (mlo–spo) – plotted mid-year. On the right axis are
reported anthropogenic emissions (dashed line), with the correction suggested
by Francey et al. (2013) (shaded), scaled so that the overall slope is
similar to that from the long-term mlo–spo SIO record. Panel
(b): CSIRO (mlo, cgo, spo) and NOAA (mlo) growth rates,
dC / dt, plotted mid- month. See the “Methods”
section for details.
Furthermore, Patra (2015) demonstrated consistency in 2009–2010 between
their ACTM (atmospheric chemistry transport model) simulations of ΔCmlo-cgo and fluxes obtained from an inversion model. However,
from the limited information available, it seems likely that both the ΔCmlo-cgo and the inversion fluxes are dependent on the same
transport parameterizations and so are not independent.
Patra's comment prompts a question about the
effectiveness of SF6 measurements to “diagnose” model CO2
interhemispheric transport.
In order to address the apparent conflicts, we update CO2 measurements
and search more widely for concurrence with independently determined
parameters, including other trace gas species and atmosphere physical
parameters influencing their distribution.
We are informed by a companion paper examining potential bias in the two
largest terms in a global carbon budget (Francey et al., 2016). This
documents significant reductions in susceptibility to bias in atmospheric
CO2 measurements since the 1990s and expresses concern about the spatial
representation of reported CO2 measurements, e.g. in monthly averaged
data. Mismatch between the inversion model gridscale and the scale of
CO2 representativeness at observing sites can introduce significant
uncertainty in inversion modelling that may act to obscure large-scale
systematic CO2 behaviour.
Updated CO2 data
Inversions of CO2 data effectively interpret CO2 spatial and
temporal differences in terms of surface exchanges. Thus, Fig. 1 illustrates
each type of difference, namely ΔCN-S and dC / dt, in quality
data with maximum spatial representation. Methods to obtain Δ
CN-S and dC / dt from monthly flask data are described in Appendix A.
The updated spatial comparisons of ΔCN-S in Fig. 1a
highlight the largely consistent results from the 1990s using data from flask
samples collected and measured by the CSIRO, by the National Oceanic and
Atmospheric Administration (NOAA; Dlugokencky et al., 2014) and by the
Scripps Institution of Oceanography (SIO; Keeling et al., 2009) networks. For
perspective, a comparison is also made with a linear regression through the
SIO 5-decade ΔCmlo-spo record. This shows an overall
increase, generally attributed to the increase in fossil fuel (FF) emissions
(Boden et al., 2013), which occur predominantly in the NH.
Annual global FF, including the correction suggested by Francey et
al. (2013), are scaled and included to run parallel to the ΔC slope
in Fig. 1a in order to emphasize the unusual magnitude of the 2009–2010
ΔC step. From this perspective the 0.8 ppm step, if it is the result
of an anomalous flux, would equate to an annual 1.6 Pg C (NH) source, one
sufficiently large and rapid that detection by bottom-up studies might be
expected.
North–south CO2 differences using other NH sites from the NOAA
network: NH site minus cgo, using annual average baseline data (calendar
year, plotted mid-year). Latitude and longitude of sites are provided in the
legend.
Also in Fig. 1a, the unusual post-2009 ΔC stability compared to the
earlier record is obvious. Since methodologies have not significantly changed
over this period, it suggests that measurement error is not a factor and the
variability in the pre-2010 ΔCN-S data is not random.
The temporal differences, dC/dt in Fig. 1b, show interannual variability on 3- to 5-year El
Niño–Southern Oscillation (ENSO) timescales. Using CCAM transport to
invert CO2 and δ13CO2 observations, Rayner et al. (2008)
concluded that interannual variability is forced primarily by climate variability on the equatorial land biosphere.
This conclusion is consistent with the observation of limited influence on
ΔC for equatorial exchanges in Fig. 1b, to be discussed further
below. However, the question of the spatial representativeness of the
selected CO2 records is addressed first.
The hemispheric representativeness of baseline data from the mlo and cgo
monitoring sites is supported by a study of aircraft vertical profiles at 12
global sites conducted in maximum convective conditions near midday (Stephens
et al., 2007). The lower levels (< 1–2 km) of all 12 vertical
profiles exhibited seasonal variation resulting from climate influence on
regional surface carbon reservoirs. The amplitudes of the seasonal variation
at mlo and cgo are the least in their respective hemispheres, which aids the
definition of interannual variability at these sites.
Monthly mlo and cgo CO2 time series:
CSIRO data of Fig. 1a are plotted monthly to better examine the onset of the
2009–2010 step (see text).
While the spo data closely track cgo data, and other mid- to high-latitude Southern
Hemisphere (SH) sites in the CSIRO network
(Francey et al., 2013), the situation is less clear for mlo because of NH
heterogeneity and proximity to Asia. A possible recent contributing factor at
mlo may be the geographical susceptibility to rapidly increasing SE Asian
pollution, “rapidly transported to the deep tropics” (Ashfold et al.,
2015). In Fig. 2 we demonstrate similarity in year-to-year changes in ΔC using both Pacific and Atlantic extratropical NH sites from the NOAA
network. The similarity is particularly significant in sign and magnitude for
the two largest observed changes in 2009–2010 and 2002–2003, implying that
especially for these periods, mlo represents NH behaviour.
During 2009–2010, dC / dt show a larger NH ENSO peak, leading that in the SH
by around 6 months, a phase difference not observed for other significant El
Niño peaks in Fig. 1b. This implies either an undetected NH source, or
possibly, rapid changes in interhemispheric (IH) transport.
Poulter (2015) raises the issue of relative timing of the ΔC step and
the response of SH savanna regions to the end of drought. To clarify this we
include Fig. 3, showing CSIRO monthly baseline concentrations at mlo and cgo
through the period. To aid discussion, the seasonal variations are compared
to quadratic fits to the 1992–2014 data for each site, offset by ± the
long-term amplitude of the seasonality: ±3.3 ppm (6.6 ± 0.5 ppm
peak-to-peak) at mlo and ±0.55 ppm (1.10 ± 0.2 ppm p-p) at cgo.
There is a change in the mlo seasonality (the 2009 seasonal amplitude is the
smallest and the 2010 amplitude the largest in this plot) between 2009 and
2010, which is of a sign and magnitude that most easily explains the 0.8 ppm
step in annual average differences, ΔCmlo-cgo. Slightly
lower CO2 in the cgo baseline data in the 2010–2012 period could
possibly be associated with an SH sink. However, the unusually large negative
seasonal excursion from the mean, at the end of the 2009–2010 spring–summer
uptake season, is before the October 2010 to March 2011 record floods in
northern Australia which were identified as a trigger for the savanna
response by Poulter et al. (2014); furthermore, the negative dip is followed
by a near-average positive seasonal excursion in late 2010. Conventional
descriptions of the Cape Grim seasonality have contributions from SH
biosphere, seasonal SH ocean temperature changes, and ∼ 6-month delayed
NH biosphere signals (Law et al., 2006; Stephens et al., 2013); failure of a
delayed NH signal to reach Cape Grim might also contribute to low SH autumn
CO2 at Cape Grim. Nevertheless, a small contribution from an SH
terrestrial sink is difficult to exclude in 2011 and 2012.
This question was further addressed at the 2014 Annual Cape Grim Science
Meeting by Xingjie Lu, Ying-Ping Wang, and Rachel Law (Y.-P. Wang, R. Law,
personal communications, 2014). They used the Community Atmosphere Biosphere
Land Exchange model (CABLE; Law, 2014) to simulate net ecosystem production
anomalies over the 2001 to 2012 period, finding SH anomalies that were mainly
contributed by Argentina and Australia in 2010 and 2011. The timing of the
terrestrial response of Lu, Wang and Law is similar to that of Poulter et al. (2014). Lu, Wang and
Law investigated how the interannual variability in the CABLE biospheric
fluxes affected ΔCmlo-cgo using CO2 response functions
from the CCAM atmospheric model. When the CCAM CO2 response functions
are modified to represent baseline data (at cgo 20–30 % of time with
strong winds over the Southern Ocean), this terrestrial signal is
sufficiently diluted in the large well-mixed troposphere at mid-to-high
southern latitudes to be reduced to insignificance in the reconstructed
ΔCmlo-cgo. With their approval, the relevant CCAM modelling
runs are included in the Supplement. This example highlights a requirement
for high time resolution transport modelling coupled with similar resolution
in the CO2 data if such events are to be correctly attributed.
Finally, independent evidence for the NH origin of the 2009 to 2010 CO2ΔC step comes from a recent analysis of upper-troposphere
measurements for 11 latitude bands between 30∘ N and 30∘ S
(Matsueda et al., 2015) where the step is evident north of 10∘ N.
These authors suggest a role for transport, as well as sources and sinks, to
explain their year-to-year variations in latitudinal differences.
Responses in ΔC and dC / dt to other recent source and sink
anomalies
Before examining a likely role of atmospheric transport in ΔC
variations, we briefly examine Fig. 1 at the times of the major post-1992
independently documented anomalous CO2 source and sink activity: the
1997–1998 Indonesian peat fires, the 2002–2003 NH drought and boreal
wildfires, and the 2008 global financial crisis.
The 1997–1998 Indonesian peat fires correspond to the largest El Niño peak
dC / dt and were estimated as contributing around 1 Pg C (6.5 times the
mean equatorial Asia emissions) to the atmosphere in 1997 (Page et al., 2009;
Giglio et al., 2013). In Fig. 1 there is a small increase in ΔCN-S, with a barely significant larger NH dC / dt peak. A small
response might be explained if the emissions are mixed into both hemispheres.
The possibility that changes in IH mixing may also contribute to
ΔCN-S is discussed below.
While the 2002–2003 ΔCN-S in Fig. 1a is the second largest
year-to-year increase (see also Fig. 2), it is also the largest difference in
dC / dt between the hemispheres. Year 2003 corresponds to reductions in
Europe's primary productivity which, according to Ciais et al. (2005), were “unprecedented during the last
century” and released ∼ 0.5 Pg C yr-1. We add this to the 2003
Global Fire Emissions Database (GFED4) fire emissions in boreal America and
boreal Asia of 0.31 Pg C, 2.5 times the 1997–2013 mean (Giglio et al.,
2013). However, for emissions spread evenly over a full year, a relatively
small ΔC impact is expected since the 2003 NH FF combustion was
∼ 7.5 Pg C compared to < 0.7 Pg C from the non-FF sources.
The global financial crisis (GFC) of 2007–2008 (Peters et al., 2012)
coincides in Fig. 1b with the only occasion when the NH dC / dt ENSO peak is
markedly smaller than that in the SH. While 2008 and 2009 are the two lowest
global fire emission years in the GFED4 database, combined boreal emissions
are near average, favouring the GFC as a more likely explanation for the
dC / dt behaviour. However, it is less clear that relatively low 2008 and
2009 ΔC in Fig. 1a is attributable to the GFC, and a possible
contribution from IH exchange is also examined below.
The equatorial upper-troposphere duct. Panel
(a): correlation over the annual cycle of 1949–2011
upper-tropospheric winds (300 hPa) with the Southern Oscillation Index
(SOI), with strongest correlation in the equatorial Pacific duct. Panel
(b): the difference between open and closed equatorial duct patterns
of Fig. 4d and c, showing similarity to the long-term correlation pattern in
Fig. 4a. Panel (c): July 2009 to June 2010 “closed-duct” pattern
with 300 hPa easterly zonal wind in the equatorial duct. Panel (d):
July 2008 to June 2009 “open-duct” pattern with 300 hPa westerly zonal
winds in the equatorial duct.
Anomalies in annual interhemispheric mixing
Meridional transport and eddy mixing due to large-scale eddy motions are
sources of significant uncertainty in estimations of IH transport (Miyazaki
et al., 2008). Here we examine the role of the opening and closing of the
upper-tropospheric equatorial westerly duct, and associated interhemispheric
Rossby wave propagation, as a contributor to the 2009–2010 ΔCmlo-cgo shift, and other variations, shown in Fig. 1a.
Monthly interhemispheric exchange for CSIRO trace gas species: the
top panel shows monthly uduct (300 hPa, 5∘ N to
5∘ S, 140 to 170∘ W), with red and blue bands indicating El
Niño and La Niña periods respectively. The relative strength and
duration of NH winter (October to April) IH mixing is estimated by Σuduct, plotted in January. The following panels show the relative
interhemispheric exchange fluxes (ΔC ×uduct), due
to Pacific upper-level equatorial turbulence, for different CSIRO flask
species (CO2, CH4, CO, and H2). Black circles indicate 4
months of missing CSIRO flask data from mlo; for CO2, data from these
months are obtained from NOAA records.
Isotopic evidence that interhemispheric CO2 variations are
systematic: the interhemispheric differences Δ13CO2,
represented by Δ(C ×δ13C) plotted against
Δ12CO2 for (a) CSIRO (mlo–cgo), (b) NOAA
(mlo–cgo), and (c) SIO (mlo–spo) flask samples since 1992. (One
2003 NOAA outlier (>5σ), is removed from these plots and
regressions.) The linear regression coefficients and correlation coefficients
(r2) are provided for each data set.
Extratropical NH Rossby waves, including a branch of the Himalayan wave
train, are able to penetrate into the SH when near-equatorial zonal winds are
westerly in the upper-tropospheric duct centred on 140 to 170∘ W and
5∘ N to 5∘ S (Webster and Holton, 1982; Frederiksen and
Webster, 1988; Webster and Chang, 1988). This region is delineated and its
tropospheric relevance revealed in Fig. 4a, showing strongly correlated
upper-tropospheric westerly winds with the Southern Oscillation Index (SOI)
over the full 1949 to 2011 wind reanalysis data set
(http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html).
The wind direction and strength (uduct) in this duct are determined
by seasonal and ENSO sea-surface temperature variations; the
upper-troposphere westerlies are strongest in boreal winter and during La
Niña periods, when they are correlated with proportional increases in
near-equatorial transient kinetic energy (Fig. 6, Frederiksen and Webster,
1988), which facilitates interhemispheric mixing of trace gases. At other
times, including El Niños, the uduct are near zero or easterly,
causing the Rossby wave eddies to be deflected northwards and dissipated in
the equatorial regions, inhibiting interhemispheric exchange.
For the period July 2009 to June 2010, the average 300 hPa equatorial zonal
winds in the duct region were easterly as shown in Fig. 4c, effectively
closing the duct and increasing the build-up of FF CO2 in the NH. The
July 2008 to June 2009 open duct pattern, with westerlies in the duct, is
shown in Fig. 4d. (Appendix C addresses the altitude range involved in this
process. Note also, the meridional wind may make a small contribution to IH
transport in the duct region during this time).
Figure 4b shows the 300 hPa zonal winds for July 2008 to June 2009 (Fig. 4d)
minus those for July 2009 to June 2010 (Fig. 4c), and the pattern bears
strong similarities with the long-term zonal wind versus SOI correlation in
Fig. 4a.
Trace gas interhemispheric exchange through the duct
Interhemispheric exchange of a seasonally varying gas by this process depends
on covariance with uduct and is represented in Fig. 5 by the
product of monthly uduct and ΔC for routinely monitored
CSIRO species C = CO2, CH4, CO, and H2. The direction of a
step in ΔC depends on the magnitude and sign of the trace gas IH
gradient when the duct is open. The seasonality at mlo and cgo for the
different gases is given in the Supplement.
In the top panel monthly uduct is plotted over red and blue shading
representing El Niño and La Niña periods respectively. We add symbols
connected by a solid line that are an integration of the NH winter peaks,
Σuduct (October to April), for a nominal
uduct > 2 m s-1, in order to better compare
year-to-year changes in the strength and duration of the seasonal duct
exchange.
Figure 1 is re-examined in the light of variations in Σuduct.
Of the seven lowest Σuduct in Fig. 5 – 1992, 1995, 1998,
2003, 2005, 2007, and 2010 – six correspond to peak ΔCmlo-cgo in CSIRO data. Differences between laboratories are more
marked before the mid-1990s in Fig. 1a, marking a period of significant
improvement in inter-laboratory quality monitoring (e.g. Masarie et al.,
2001) but also influenced by the major perturbation to the carbon cycle
associated with the 1991 Pinatubo eruption. However, the relationship with
Σuduct is, in the main, supported by SIO and NOAA data.
The two extreme cases of duct closure (Σuduct < 10 m s-1) since 1992 in Fig. 5 are in
1997–98 and 2009–10, showing up as a marked absence of a seasonal IH
exchange (ΔC ×uduct) for CO2, CH4, and
CO. If the Fig. 1a ΔC step in 2009–10 is attributed to duct closure,
then a similar ΔC change might be expected in 1997–98; however, it
is less than half that in 2009–2010. The record CO2 response to the
1997–98 equatorial anomaly, associated with prolonged equatorial peat
combustion (Sect. 3), is a possible explanation for a smaller response. The
next lowest seasonally integrated Σuduct∼ 10 m s-1 in 2003 has the next largest ΔC increase in Figs. 1a and 2, strongly suggesting reduced seasonal IH
transport. This complicates surface flux estimates from the inversion of
CO2 spatial differences by Rayner et al. (2008).
Switching focus to the positive excursions in Σuduct, these
are associated with increased strength of mixing through the open duct.
Compared to previous behaviour, the magnitude of exchange (ΔC ×uduct) immediately after the extended duct closure
from July 2009 to June 2010 is the largest for each gas in Fig. 5, in part
reflecting the fact that the 2010–2011 La Niña corresponds to the most
intense Σuduct since 1990 (top panel Fig. 5). The unusual
species exchange at this time is most marked for CO2 and H2, which
we mainly attribute to the fact that these two gases exhibit the most
significant ΔC trend (CO2 positive, H2 negative) over the 2
decades; also, each has seasonal concentration amplitudes that are the
largest compared to mean annual IH gradients (Supplement).
Through the four “duct-open” periods after 2010, Fig. 1a shows ΔCO2 to be practically constant, a phenomenon difficult to explain with
known source and sink behaviour. During this period Σuduct
monotonically decreases; the constant ΔC might be explained if the
decreasing Σuduct is matched by decreases in the annual
fossil fuel emission increments. Boden et al. (2013) estimate the annual
increments in FF to be 0.5 Pg C in 2010, 0.3 Pg C in 2011, and 0.2 Pg C in
2012, supporting this interpretation.
Isotopic evidence of systematic ΔC variations
While covariance between atmospheric transport and terrestrial biosphere
activity referred to as the “rectifier effect” is an important component in
global carbon budgeting (Denning et al., 1999), it concerns seasonal
variations in the depth of the atmospheric boundary layer rather than the
abrupt upper-atmosphere transport through the duct described by Fig. 5.
Measurements of the stable carbon isotope in atmospheric CO2 have the
potential to clarify the relative importance of modes of atmospheric
behaviour for ΔC. This depends on the fact that an atmospheric
13CO2 anomaly is redistributed in the environment more rapidly than
a 12CO2 anomaly (Tans et al., 1993). This isotopic equilibration
process is facilitated by the large gross turnover of CO2 with oceanic
and terrestrial reservoirs. It can reflect the time that has elapsed since an
emission anomaly occurred and is examined below by comparing monthly with
annually averaged data.
Measurements of the ratio of stable carbon isotopes, 13C /12C,
in atmospheric CO2 are described by a reduced ratio δ13C
expressed in ‰; the 13C content can be conveniently represented
by the product C ×δ13C (see Appendix B).
The dominant hemispheric CO2 emissions are NH FF combustion and forest
respiration. They each contain carbon that has undergone similar
discrimination against the heavier isotope during photosynthesis. These
sources are more depleted in 13C content than other possible sources;
for example, using Lloyd and Farquhar (1994) estimates of global
discrimination relative to ambient atmospheric CO2, forest carbon is
globally ∼ 18 ‰ lighter, savanna grasses are 4 ‰
lighter, and ocean carbon is in close equilibrium. (Note: -18 ‰
equals -1.8 %).
Despite having similar isotopic composition, the imprint of recent forest
exchange and FF emissions on atmospheric δ13C can be different. A
convenient demonstration uses the direct monthly relationship between
δ13C and C (only valid over small ranges of C), which in the NH is
characterized by -0.05 ‰ ppm-1 and, since the seasonal
variation in the SH is small, this relationship exists for monthly NH-SH
Δδ13C. On annual timescales the C and δ13C seasonal
variations are largely cancelled, with negligible contribution to IH
differences, and ΔC changes are dominated by the steadily
accumulating NH FF emissions that have greater opportunity for isotopic
equilibration, which is evidenced over the last 2 decades by the observed
mlo–cgo annual average Δδ13C /ΔC of
-0.027 ± 0.003 ‰ ppm-1.
Significant in the present context, however, over the limited excursion range
of annually averaged CSIRO pre-2010 data NH–SH Δδ13C =-0.050(±0.004)ΔC + 0.062 ‰ (r2= 0.92), identical
to the monthly covariations in Δδ13C and suggesting
involvement of unequilibrated forest CO2.
Francey et al. (2013) reported a synchronous decrease in stable carbon
isotope ratio at the time of the 2009–2010 ΔCO2 increase,
measured in the same flask air samples. Those data are updated in Fig. 6 and
provided in the Supplement.
Figure 6 plots the relative IH spatial changes in 13C, represented by
C ×δ13CNH-C ×δ13CSH,
compared to those in 12C (using CNH-SH, since C is 99 %
12C) in the CSIRO, NOAA, and SIO samples used in Fig. 1a. All three data
sets, and particularly CSIRO, show a linear relationship including the
pre-2010 scatter, the 2009–2010 step, and subsequent data. The slope of the
linear regressions represents the sum of source discrimination and ambient
atmospheric δ13C (Enting et al., 1993; Enting, 2006).
Thus, with the Lloyd and Farquhar estimate of global forest discrimination of
-18 ‰ and an atmospheric value of -8 ‰ (e.g. the
seasonal minimum at mlo in 2009–10), the -26.1 ‰ slope for the
CSIRO data is near the most negative anticipated value, excluding significant
influences of other possible CO2 sources, such as savanna grasses, and
excluding significant isotopic equilibrations that occur on longer than
seasonal timescales, all of which result in less negative slopes. These data
strongly favour a major role for the duct transfer mechanism, for both the
step and prior variability, since it occurs close to the seasonal CO2
peak (δ13C minimum) of NH terrestrial biosphere respiration in
Fig. 5.
Interhemispheric mlo–spo differences from the historic Keeling
CO2 record and uduct: the top panel shows monthly
uduct (300 hPa, 5∘ N to 5∘ S, 140 to
170∘ W), with red and blue bands indicating El Niño and La
Niña periods respectively (left axis). The relative strength and duration
of NH winter (October to April) IH mixing is estimated by Σuduct, plotted in January (right axis). In the bottom panel annual
average mlo–spo ΔC are shown. Red circles indicate occasions when
integrated duct transport is < 5 m s-1, dashed for
> 5 m s-1, and smaller circles (in the top panel) indicate
occasions when brief closures are not followed by La Niña and there is no
detectable ΔC influence.
The relationship is far less well-defined in the NOAA and SIO data with
regression slopes of -20 and -17 ‰, which, while both favouring
C3 sources, do not exclude significant contributions from other sources,
including annually distributed, equilibrated FF CO2. Note, however, that
if the 2009–2010 step was due to savanna grasses, then the post-2010 points
(to the far right) in Fig. 6 would not fit on the 2-decade regressions of any
of the three data sets, since the anticipated slope for savanna exchange is
around -12 ‰.
The NOAA and SIO data exhibit more scatter, with linear regression residual
mean square scatter of 5, 11, and 17 ppm per mil for CSIRO, NOAA, and SIO
plots respectively. A lack of correlation in Δδ13C variations
between the NOAA and SIO suggests that, whatever the IH transport mechanism,
isotopic measurement precision is a more limiting factor in these data sets.
By comparison, as befits an SH focus, precision has been a greater concern in
CSIRO measurement programs, resulting in extensive published quality control
assessments of the CSIRO isotope data since 1992, described in Appendix B,
and supporting our preference for these data.
The intermittent nature of this IH exchange process might be expected to show
up in other species like SF6, used by the modelling community to
diagnose IH transport (Patra, 2015). We address this issue in Appendix C.
Incidentally, the estimate of possible covariance between δ13C and
gross terrestrial primary productivity (Randerson et al., 2002) is likely to
be impacted if a significant portion of IH exchange is via the
upper-atmosphere equatorial duct.
Historic evidence for anomalous interhemispheric CO2 exchange
Figure 7 examines the historic SIO mlo–spo records (Keeling et al., 2009)
for responses to five other extended periods of duct closure since the 1960s.
Working backwards in time, there are seven occasions (circled in the top
panel) when the seasonal Σuduct < 5 m s-1.
The five of these that correspond to an El Niño period closely followed
by a La Niña (or in the case of 1982–1983, a weak La Niña shortly
followed by a stronger one) show prominent peak values in ΔC (circled
bottom panel); the two low Σuduct not coinciding with a
ΔC peak (smaller circles) have relatively brief El Niño periods
not followed by La Niña. While there are two small ΔC peaks prior
to 1970, the ΔC are more susceptible to missing data (particularly at
spo) and measurement bias (Francey et al., 2016), and NCEP data may be less
reliable, so are not considered further here. The 1986–88 event most mirrors
2009–10, being the next largest step, followed by 4 years of relatively
stable ΔC.
We conclude from this that anomalies in the interhemispheric exchange through
the duct have played a significant ongoing role in modifying spatial
differences in CO2 (and other trace species) at the surface. As NH FF
CO2 emissions increase further, the influence is expected to become more
marked in ΔC ×uduct.
Conclusions
Peylin et al. (2013) describe conflict between groups of carbon budgeting
models in locating the major global terrestrial sink, whether mid-northern
latitude or equatorial, and suggest that atmospheric transport
implementations may be involved. We have presented a variety of complementary
evidence, including CO2 isotopes, linking interhemispheric transport
through the Pacific upper-troposphere equatorial duct and the spatial and
temporal difference in measured surface CO2 concentrations. The observed
patterns of CO2 interhemispheric changes are not easily explained by
observed source and sink behaviour. If the parameterizations of transport in
the global carbon budget models do not adequately capture the duct process,
then spatial differences arising from transport are most likely to be
interpreted as variation in terrestrial sinks. It also suggests that the SH
seasonality in CO2 may have been misinterpreted. For example, when the
duct is open, the January to April IH exchange through the duct will offset
the spring minimum CO2 level due to SH terrestrial uptake. The
conventional explanation has a ∼ 6-month delayed exchange arriving in
the SH autumn and enhancing peak SH respiration. Global budgeting of other
trace gas studies (e.g. Locatelli et al., 2013) is also likely to be
impacted.
The observed 2009–2010 changes in CO2 IH difference in particular,
because of the magnitude and also the absence of plausible reported source
and sink changes (in a time of unprecedented monitoring of ecosystem and
ocean exchanges), provide an unusual opportunity to test the implementation
of atmospheric transport in inversion models and help remove current
ambiguities between surface exchanges and transport. More generally, this
requires such models to demonstrate an ability to describe the spatial and
temporal systematic differences in selected high-quality baseline trace gas
records that have well-established large-scale representation, such as the
mlo–cgo records used here.
Trace gas data processing
The analyses for both dC / dt and ΔC are based on monthly average
mixing ratios (or δ13C isotopic ratios) obtained from a smooth
curve through individual flask data (typically four per month) with a
combined harmonic (seasonal) and 80-day smoothing spline (Thoning et al.,
1989). At Cape Grim, selected data represent strong near-surface winds
(> 5 m s-1; 164 m a.s.l.) with trajectories (typically
> 10 days) over the Southern Ocean; at Mauna Loa samples are
collected in moderate downslope winds; South Pole samples are selected to
avoid local (station) contamination. Conventional smoothing splines through
deseasonalized baseline-selected concentration data, with 50 %
attenuation at 22 months, are differentiated to provide dC / dt since
1992; Francey et al. (2016) discuss dC / dt uncertainties. Annually
averaged ∼ 80-day smoothed monthly baseline concentration data are used
to provide ΔC with near-annual time resolution, i.e. potential
ambiguity between seasonality and interannual variation is addressed
differently by dC / dt and ΔC. CSIRO and NOAA data are processed
identically. Scripps atmospheric CO2 data used here are monthly data
that are seasonally adjusted and filled (http://scrippsco2.ucsd.edu/).
(Note: using the spatial differences from individual laboratories effectively
removes most calibration issues that can complicate high-precision
comparisons of data between laboratories).
Laboratory differences in δ13C data
The δ13C in CO2 are a “reduced ratio” of
13C /12C, for sample s and reference r:
δ13Cs=13Cs/12Cs-13Cr/12Cr/13Cr/12Cr.
Mass conservation in 13C is approximated using the product of C and
δ13C (e.g. Tans et al., 1993).
The assumption of independence between C and δ13C measurements
is marginally compromised by the use of the N2O / CO2 ratio to
correct the δ13C for mass spectrometer split resolution (e.g.
Allison and Francey, 2007). The difference in 2009 and 2010 corrections to
Δδ13C is < 0.0007 ‰
compared with the magnitude of ∼ 0.029 ‰
for the 2009–2010 step (C. Allison, personal communication, 2015).
Flask CO2 differences between NOAA and CSIRO same-air comparisons at cgo
since 1992 are 0.11 ± 0.13 ppm (Masarie et al., 2001; Francey et al.,
2016). It is assumed that the mean offset cancels when using mlo–cgo
differences. This implies that the maximum δ13C measurement error
due to flask air contamination should be less than 0.005 ‰.
Exact reasons for the varying quality of δ13C programs in Fig. 6
are not known. However, reduced scatter in the CSIRO program is possibly
related to feedback from regular quality assessment provided by unique method
redundancy (Allison and Francey, 2007). The data in this report involve small
subsamples of chemically dried whole-flask air, from which CO2 is
extracted and analysed using a fully automated Finnigan MAT 602 D mass spectrometer (MS) with MT
Box-C extraction accessory and bracketed by extractions and analysis of cgo
long-lived baseline air standards in high-pressure cylinders. Over most of
the 2 decades, a parallel cgo program involved unique large-sample in situ
extraction of CO2, which is returned and analysed on the same MS but
relative to independently propagated pure CO2 standards.
Despite inadequate support to maintain future quality control in the CSIRO
isotope program, a 2013 thorough quality audit occurred in the context of
comparing recent and 1990s δ13C measurements of ice core air
(Rubino et al., 2013).
Atmospheric transport
In contrast to the situation in Fig. 4c, the average 300 hPa zonal wind for
July 2008 to June 2009, shown in Fig. 4d, has equatorial westerlies between
the date line and 120∘ W. The westerly duct is open and NH
extratropical Rossby waves, including the Himalayan wave train, are able to
penetrate into the SH. Correlation analysis (Frederiksen and Webster, 1988)
indicates increased upper-tropospheric transient kinetic energy near the
Equator with facilitated IH transport of trace gases. Here we have focused on
the 300 hPa level, but our results apply in broad terms to most of the upper
troposphere. In particular, the correlation of the SOI with the zonal wind in
the westerly duct region (Fig. 4a) applies between 500 and 70 hPa, with
similar strength between 300 and 100 hPa and reducing at the upper and lower
levels. Again, the structure of the zonal wind differences of July 2008–June
2009 minus July 2009–June 2010 (Fig. 4b) is largely equivalent
barotropically, with similar strength between 300 and 100 hPa and reducing
at the upper and lower levels. Northern winter (DJF) differences for
2008–2009 minus 2009–2010 are circa twice as strong in the westerly duct
region as in Fig. 4b.
The Patra atmospheric transport modelling (Patra, 2015) relies on measurements of SF6 to support the
transport parameterization. Our early examination of such synthetic species
with respect to the 2009–2010 event was inconclusive. While we can
demonstrate a considerable degree of systematic behaviour in the variation in
baseline monthly CO2 IH differences, by comparison the synthetics were
found to have much larger scatter, though significant precision improvements
have occurred since 2011 (Paul Krummel, personal communication,
2015). Furthermore, over the period of most
concern, we found little agreement between the NOAA HATS SF6 data
(http://www.esrl.noaa.gov/gmd/hats/combined/SF6.html) and equivalent
data from the AGAGE network (https://agage.mit.edu/) in month-to-month
or interannual variability about the long-term increase in IH difference. The
use of past SF6 to calibrate the interhemispheric transport may well be
adequate for the long-term model mean transport but fail to adequately
constrain past irregular periods such as 2009–2010 or similar historic
events.
The Supplement related to this article is available online at doi:10.5194/bg-13-873-2016-supplement.
R. J. Francey proposed this study and provided trace gas
information, and J. S. Frederiksen provided the atmospheric dynamics
information.
Acknowledgements
We are grateful to Anonymous Referee no. 1, Prabir Patra, and Ben Poulter for
their constructive criticism of the discussion paper. The paper relies on the
decades-long commitment by skilled CSIRO GASLAB scientific personnel,
particularly Paul Steele, Ray Langenfelds, Paul Krummel, Colin Allison,
Paul Fraser, and Marcel van der Schoot. Many support staff in GASLAB, Cape
Grim Baseline Air Pollution Station (The Australian Bureau of Meteorology
with CSIRO), and measurement collaborators at NOAA also contribute directly
in this regard. The importance of the historic SIO records cannot be
overstated. Rachel Law provided global, and Ying Ping Wang with Chris Lu
regional, CO2 modelling advice. Ian Enting provided guidance on
interpreting the δ13C spatial gradients. Edited by: S. Zaehle
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