Introduction
Attributing recent changes in the methane budget, and the associated impact
on its growth rate, to specific natural or anthropogenic causes is essential
for climate change mitigation. The impact of climatic variability on methane
emissions is particularly important to assess the potential for CH4
release under future climate scenarios (e.g., from permafrost and wetland
environments, as well as gas hydrates) in a reinforcing feedback. Atmospheric
methane mole fractions [CH4] have increased by 140 % over
pre-industrial levels (MacFarling Meure et al., 2006). The associated increase
in radiative forcing makes CH4 the second most important
anthropogenic greenhouse gas (Shindell et al., 2009). The long-term
[CH4] increase until the late 1990s can be attributed to increasing
emissions from fossil fuel production (Ferretti et al., 2005; Schaefer et
al., 2016), as well as sources from agriculture (enteric fermentation in
livestock, rice production), waste management, and anthropogenic burning (van
Aardenne et al., 2001; Saunois et al., 2016). After a plateau in the early
2000s, [CH4] has been rising again since 2007. Considering recent
reconstructions of methane's dominant atmospheric sink, i.e. the hydroxyl
radical OH, we consider it likely that increasing emissions contribute to
(Rigby et al., 2017), if not dominate (Naus et al., 2018), the
[CH4] rise. If so, the methane source type that varied can be
investigated with measurements of stable carbon isotope ratios in atmospheric
methane (δ13CH4). The latter are influenced by the relative
source contributions from 13C-depleted biogenic, 13C-rich
pyrogenic, and thermogenic methane with intermediate δ13C.
Isotope studies suggest that biogenic methane sources make either a dominant
(Schaefer et al., 2016; Nisbet et al., 2016) or strong (Worden et al., 2017)
contribution to the recent [CH4] rise. Biogenic methane comes
predominantly from wetlands and agriculture. Schaefer et al. (2016) suggested
agriculture as the more likely cause, primarily because satellite data place
the increased emissions in Southeast Asia, India, and China (Houweling et al.,
2014). However, this geographic footprint from an inversion of satellite data
is also consistent with fluxes from one particular wetland emissions model
(Houweling et al., 2014). Other studies also assume a stronger role of
wetlands due to drier conditions during the plateau years (Bousquet et al.,
2006) and higher wetland emissions afterwards, which are attributed to a
switch to predominant La Niña conditions around 2007 (Bousquet et al.,
2011; Nisbet et al., 2016). La Niña is the cold phase of El Niño–Southern Oscillation (ENSO) cycles, which have a strong impact on
precipitation anomalies in tropical regions (Ropelewski and Halpert, 1987;
Lyon and Barnston, 2005) (Fig. 1) that are key source areas for methane
production from wetlands and biomass burning (Kirschke et al., 2013). ENSO
impacts are strongest in the tropics, generally from December to February.
During El Niño (La Niña) events in the December to February period,
it tends to be drier (wetter) in the Indonesian region, north-east Brazil, and
south-eastern Africa, whereas it tends to be wetter (drier) in the southern
USA and Mexico, eastern China and Taiwan, and east-central Africa (Fig. 1).
During El Niño (La Niña) events in the June to August period, it
tends to be drier (wetter) in the Indonesian region, central America, and
India.
Regions of ENSO impacts and monitoring stations used in this study.
The map indicates the locations of the atmospheric monitoring stations on
Ascension Island (ASC), Samoa (SMO), Baring Head (BHD), and Lauder (LAU).
General precipitation anomalies during Northern Hemisphere El Niño
conditions for December–February are taken from
https://www.climate.gov/news-features/featured-images/global-impacts-el-nino-and-la-nina
(last access: 5 December 2017). El Niño dry
regions in June–August are similar for southern Asia and South America;
during La Niña events opposite patterns for wet- and dryness develop in
roughly the same regions.
The generally drier conditions during El Niños suppress global wetland
emissions in models by up to 19 Tg yr-1
in the 1990s (Hodson et al., 2011). Several anthropogenic sources are subject
to the same ENSO forcing and are expected to vary in concert with wetlands
(e.g., rice agriculture, possibly livestock). At the same time, dry El
Niño phases enhance CH4 emissions from both natural and
anthropogenic biomass burning (van der Werf et al., 2006). Wet La Niña
conditions have the opposite effect; summed across the globe they increase
wetland emissions and lower biomass burning CH4. As tropical
wetland fluxes are considerably larger than biomass burning emissions
(Saunois et al., 2017), the expected net effect is a lower [CH4]
growth rate caused by El Niño conditions and a higher one due to La
Niñas. The ENSO impact on δ13CH4 should be more
pronounced than the one on [CH4] because changes in wetland and
biomass burning emissions combine to enrich atmospheric CH4 in
13C during El Niños and deplete it during La Niñas.
Biogenic methanogenesis in wetlands discriminates strongly against
13C and creates methane that is 13C-depleted
(δ13C = -58 ‰ for tropical wetlands) relative
to the plant precursor material (δ13C of -12 ‰ to
-28 ‰ ) and to the combined total of global emissions
(δ13C ∼-53.5 ‰). In contrast, during burning
the isotope ratios of the precursor plant material are essentially conserved
and lead to δ13C ∼-22 ‰ for CH4
emissions from fires (Schwietzke et al., 2016). The simultaneous suppression
of 13C-depleted wetland CH4 and enhancement of very
13C-rich pyrogenic emissions (and vice versa) act in the same
direction on the δ13CH4 of the combined source. The latter
should be detectable in atmospheric δ13CH4 records if the
impact of ENSO on the CH4 cycle is sufficiently large, as is
predicted by the emission anomalies in wetland emission models (Hodson et
al., 2011), reconstructed from satellite observations of burned area (van der
Werf et al., 2010), and observed through variability in hydrogen cyanide
(HCN) (Pumphrey et al., 2018), which is an indicator of biomass burning.
Varying contributions from wetlands dominated by C3 and
C4 plants, which differ in the δ13CH4 of their
emissions, may be part of the ENSO–CH4 signal or work to obscure it
if controlled by other drivers. In general, we assume that
δ13CH4 of the various emission sources has not changed over
the ∼35-year period of our study. Although such changes, correlated to
atmospheric CO2 mole fractions, have been reported to occur over
centuries to millennia in ice core studies (Möller et al., 2013), they
are likely negligible over the short duration and > 20 %
CO2 change of our study period.
Changes in OH have also been suggested as partial or dominant drivers in
recent CH4 trends, both for the onset of the 1999–2006 plateau
(McNorton et al., 2016; Schaefer et al., 2016) and for the post-2007
[CH4] increase (Rigby et al., 2017; Turner et al., 2017). A
chemistry–climate model suggests that ENSO modulates tropical OH (where
hydroxyl levels are highest) via changes in NOx production through lightning,
ozone availability, and specific humidity, as well as emissions of reactive
carbon (Turner et al., 2018). Resulting changes in methane removal could
create their own signal in atmospheric records of [CH4] and
δ13CH4. They could also either reinforce or dampen the
emission impacts discussed above.
We conduct correlation analyses between ENSO variability and [CH4],
as well as δ13CH4 records, to quantify how much ENSO
anomalies in emissions and sinks affect atmospheric CH4.
Specifically, we explore how much of the year-to-year variability in
atmospheric methane can be attributed to ENSO and how large the
ENSO–CH4 signal is in dependence of latitude. We test if recent
trends in methane growth rate can be attributed to wetland emissions
controlled by ENSO dynamics or if agricultural sources are more likely
drivers. ENSO is quantified by four different indices, which are based on
ocean temperature, sea level pressure gradients, and a multivariate
combination. [CH4] and δ13CH4 time series from
four different locations were used, two from stations in the southern tropics
(Samoa, SMO, and Ascension Island, ASC), the southern mid-latitudes (Baring
Head, NZ; BHD), taken as representative of the Southern Hemisphere, and global
average time series of [CH4] and δ13CH4
calculated from a network of global stations (Dlugokencky et al., 2011;
Schaefer et al., 2016). We also investigate ENSO's impact on HCN data
measured in Lauder, NZ (LAU), to quantify the biomass burning contribution
separately. The aim is to detect the impact of ENSO on atmospheric
CH4 on various spatial scales.
Results
Most combinations have r2 values < 0.1 when comparing one
dependent data set to the different ENSO time series (Tables 2–4). In the
following, we only summarize results for the highest r2 for each
dependent time series (across all the nominal, smoothed, and detrended
records for a station). Given that Pearson coefficient and Spearman rank give
comparable results (Tables 3 and 4), we quote the Spearman results, unless
otherwise mentioned. The p values for the Spearman ranks indicate that all
results for r2 > 0.1 are significant (p<0.001),
except for global δ13CH4 correlations, for which no p
values below 0.05 occur. Although the analysis provides r2 values for
lags up to 60 months (Tables 2–4), we consider it likely that lags of
> 3 years indicate spurious correlations, given that individual
ENSO events last 1–2 years and global atmospheric mixing times are on the
order of 1 year. Therefore, we also report the highest r2 for lags
< 3 years in the following sections. For other cases with lags
> 3 years in Tables 2–4, the highest relevant r2 value is
lower than the reported value, which places an upper limit on the influence of ENSO.
Spearman correlation of methane mole fraction with ENSO
variability. Correlations (r2 values) for the Spearman ranking coefficient between
[CH4] time series from various sites and ENSO indices with lag
times (in months) for optimum results. Colour backgrounds indicate
r2 values in 10 % classes. Grey background indicates correlations
with p values > 0.001.
Methane mole fractions show correlations with ENSO of r2 values up to
0.36 at SMO, but only for detrended time series (Table 2). The highest values
are from (detrended) growth rates, which can be more indicative of dynamics
within an ENSO event, rather than its overall emissions impact (Zhang et al.,
2018). For SMO detrended [CH4] series, lag times are fairly
consistent across the various ENSO indices and generally shorter than 1 year.
For other [CH4] records at SMO and ASC, the highest correlations are
r2 < 0.23 and have lags of over 3 years
(r2 < 0.19 for lags < 3 years). The global running
mean [CH4] time series shows r2=0.24 (lag: 4.5 years;
r2=0.04 for lag < 3 years) with the SOI running mean for the
period 1998–2016. However, for the full length of available data, as well as
all BHD records, all correlations are below r2=0.20, with lag times that
are variable, extremely short (zero or 1 month), or over 3 years.
The highest correlations are between HCN total column running means, as well as stratospheric
growth rates, and 12-month running mean ENSO records (Table 3). Here, ENSO
accounts for 30 %–51 % of the observed variability, depending on the
ENSO index. For both total and stratospheric HCN, lag times for maximum
correlation are generally shorter than 1 year and are consistent (≤6
months difference) between the various ENSO indices, with exception of the
EMI.
The δ13CH4 records from the stations SMO, ASC, and BHD all
have r2 values below 0.24 (Table 3). Variability in lag times between
different ENSO indices for the same dependent record is generally high.
None of the global δ13CH4 series produced statistically
robust correlations with ENSO; all p values were higher than 0.05. The
following findings are therefore not relevant for further interpretation. The
highest correlation is between global detrended δ13CH4 and
SOI monthly means with r2=0.37. Global δ13CH4, is the
only parameter for which ENSO monthly means produce higher correlations than the
smoothed (12-month running mean) record. Because the correlation calculation
between annual δ13CH4 and ENSO monthly means is specific
to the month of year, this indicates that global δ13CH4
is more sensitive to the seasonality of ENSO than its IAV. The actual ENSO
influence on global δ13CH4 is shown through correlation
with running ENSO indices, which is highest between nominal
δ13CH4 values and SOI, with Pearson r2=0.25 for
1998–2016. For the period 1992–2016 this value drops to Pearson r2=0.20. The lack of statistical robustness for global δ13CH4-ENSO correlations may stem from the different resolution of the
two sets of time series. In this case, the southern hemispheric record from
BHD may represent the extratropical impact of ENSO on δ13CH4.
Spearman correlation of δ13CH4 and HCN with ENSO
variability. Correlations (r2 values) for the Spearman ranking coefficient between
dependent variables, i.e. δ13CH4 and HCN time series from
various sites, and ENSO indices with lag times (in months) for optimum
results. Colour backgrounds indicate r2 values in 10 % classes. Grey
background indicates correlations with p values > 0.001.
The full BHD record for 1992–2016 gives very similar results as the
1998–2016 subset used for comparison with the other stations (as discussed
above). However, the shorter subset for 1998–2014 produces larger Pearson
r2 values (0.26 for running means and SOI), and for 2001–2014 we find
Pearson r2 values up to 0.38 (growth rate correlated to EMI). These
shorter data sets omit the strong El Niño events of 1998 and/or
2015–2016, which could have been expected to have a strong influence on
methane emissions and consequently δ13CH4.
For none of the stations (including global average) did the detrended
δ13CH4 time series (incl. STL residuals) produce a markedly
stronger correlation with ENSO than any of the other data series from that
station. This is remarkable because ENSO can be expected to have more
influence on IAV than on the long-term trends, which are quite pronounced.
Pearson correlation of δ13CH4 and HCN with ENSO
variability. Correlations (r2 values) for the Pearson correlation coefficient between
dependent variables, i.e. δ13CH4 and HCN time series from
various sites, and ENSO indices with lag times (in months) for optimum
results. Colour backgrounds indicate r2 values in 10 % classes.
Results have not been screened for p values.
Discussion
General causes and caveats for correlations of [CH4], δ13CH4, and HCN with ENSO
Detected correlations between ENSO indices and
[CH4] / δ13CH4 / HCN by themselves do
not prove a causal relationship. However, the underlying mechanisms for a
potential forcing have been presented by van der Werf et al. (2006) for
biomass burning and by Hodson et al. (2011) for wetland CH4
production. Accordingly, a correlation analysis is useful to quantify an
upper limit of variability in the CH4 cycle attributable to ENSO.
Because ENSO simultaneously suppresses wetland CH4 that is more
13C-depleted than the cumulative methane source and enhances
pyrogenic CH4 that is more 13C-enriched (or vice versa),
the two influences partly cancel out the combined emission rates, i.e. their
impact on [CH4]. However, they reinforce each other's impact on
total source δ13CH4. It is possible that biomass burning
and wetland CH4 production have different response times to ENSO
forcing, which would weaken their cumulative impact on δ13CH4. Similarly, a longer atmospheric residence time of CH4
(∼9 years, Prather et al., 2012) over HCN (∼3 months, Li et al.,
2000) and a smaller relative portion of ENSO-sensitive emissions in the
global methane source may lead to dampening effects in the [CH4]
and δ13CH4 variability and hence lower correlation with
ENSO indices compared to HCN. The available records for HCN and
δ13CH4 from ASC and SMO only cover a small number of ENSO
events, which could affect the results. However, when analysing sub-periods of
global and BHD [CH4] and δ13CH4 records, we find
larger correlations for shorter periods, particularly when strong ENSO events
are excluded. This shows that the results are not biased against the
detection of ENSO influences because records are too short. We also note that
all stations measure background air; they are set up to detect broad spatial
and temporal trends and not specific emission events such as an ENSO-triggered plume. However, if ENSO is invoked as a main cause of recent trends
in [CH4] and δ13CH4 (Nisbet et al., 2016) this
should be manifested in sizeable correlations.
Contrasting correlation patterns for [CH4] and δ13CH4 versus HCN
In all [CH4] and δ13CH4 records, ENSO cycles
explain at most about one-third of the
variability in detrended records and less than one-quarter in others. This is
true even for the southern tropics, where ENSO has strong climatic impacts
and where the majority of low-latitude wetland emissions and of biomass
burning emissions originate (Kirschke et al., 2013). Correlations found for
ASC and SMO, which represent this latitude band in our study, exceed those
for the southern mid-latitudes or the global record only by a limited margin
and only for detrended records. Further, inconsistent lag times, lags of more
than 3 years, and higher correlation coefficients for the exclusion of major
ENSO events point to spurious correlations.
In contrast, we find a prominent influence of ENSO on the biomass burning
proxy HCN. ENSO impacts on HCN have been reported before, e.g., by Pumphrey
et al. (2018), who observe suppression of HCN levels during La Niña
events and enhancement during El Niños, particularly in equatorial Asia.
That study found a rather confined geographical impact of El Niño events
with strongly enhanced HCN emissions around Malaysia, Indonesia, and Papua
New Guinea, as well as generally rapid transport eastward and to the
stratosphere. We speculate that the fast, upward transport (although not
observed for all El Niño events) explains why stratospheric growth rates are the most sensitive data set to
ENSO. For the total column, the HCN burden is concentrated in lower
tropospheric levels and may be subjected to more mixing of different air
parcels. According to the results of Pumphrey et al. (2018), data from LAU in
the southern mid-latitudes are outside the region of the strongest HCN
signal. This is also evident in the zonal mean HCN climatologies of Sheese et
al. (2017). However, ENSO accounts for up to 51 % of the variability in
our biomass burning proxy record. One explanation for the lower combined
wetland–pyrogenic δ13CH4 signal is low sensitivity of
wetland CH4 production to ENSO events. This is consistent with
r2 values of 0.12–0.26 between modelled wetland methane emissions
(using different climate data sets as drivers) and MEI as reported by Zhang
et al. (2018). Alternatively, other processes in the CH4 cycle
obscure the ENSO impacts.
Impact of ENSO on methane emission rates
In a correlation analysis by Zhu et al. (2017), ENSO explained 49 % of
IAV in modelled tropical wetland CH4 emissions. This is far higher
than the combined effect with biomass burning on δ13CH4 in
this study, and therefore seems to be an overestimate. Even so, the magnitude
of the modelled emission changes is 6 Tg yr-1 at most. The modelling
study of Hodson et al. (2011) finds slightly larger anomalies in global
wetland emissions due to ENSO with mean reductions of -9±3 Tg yr-1 and mean gains of +8±4 Tg yr-1 for El Niño
and La Niña events, respectively. Pandey et al. (2017) found in a
comprehensive inversion study that the net effect of the strong 2011 La
Niña on tropical and northern extratropical CH4 emissions was a
global increase of +6.6 Tg yr-1. The wetland emission anomalies are
expected to be partly compensated by changes in biomass burning that are of
opposite sign. We are not aware of studies that quantify biomass burning
anomalies for specific ENSO events. Assuming that ENSO is the main control of
biomass burning emissions of CH4, the IAV in the Global Fire
Emissions Database (GFED) data (van der Werf et al., 2010) may serve as an
indication for possible ENSO impacts. In that case, the standard deviation of
2.4 Tg yr-1 for 1998–2014 would approximate the average impact, with
maximum anomalies of up to 4 Tg yr-1. We use these numbers together
with results from Hodson et al. (2011) in the following proof-of-concept
discussions. The combined wetland–biomass burning anomalies are ∼6 Tg yr-1 for average ENSO events and ∼8 Tg yr-1 for
extreme ones, restricted to 1–2-year-long individual events. This is well
short of the sustained increase after 2007 when yearly emissions were ∼20 Tg higher than during the 1999–2006 plateau period and the
9 Tg yr-1 reduction during the 1990s (Schaefer et al., 2016). Previous
findings that modelled tropical (Zhu et al., 2015) and global (Zhang et al.,
2018) wetland CH4 emissions can explain at most 25 % and
14 %, respectively, of the variation in atmospheric methane growth rates
therefore agree with our results that ENSO exerts only a minor control on
atmospheric CH4.
Process-based understanding of ENSO impact on wetlands
A major contribution of ENSO to the recent [CH4] increase is
inconsistent with independent assessments of wetland response, as shown
above, but our findings do not detect any clear minor contribution of ENSO to
[CH4] and δ13CH4 time series, either. Several
reasons may explain the lack of correlation, whereby we assume that wetlands
respond less than proposed. The main ENSO forcing on tropical wetland
CH4 production is thought to be via wetland extent, which is driven
by precipitation (Hodson et al., 2011, and Holmes et al., 2015, in contrast to Zhu et al., 2017, who find
temperature to be dominant). However, a case study in the eastern Amazon
finds that precipitation changes explain only 21 % of wetland
CH4 emission variance during the wet season and 7 % over the
whole year (Basso et al., 2016). The lack of a direct link between
precipitation and wetland CH4 production is also evident in the
large range in output from various wetland models even when forced with the
same meteorological conditions (Melton et al., 2013), although the
disagreement between models could also be due to an incomplete understanding
of influences on the wetland cycle other than precipitation (Turetsky et al.,
2014; Bridgham et al., 2013; Parker et al., 2018). Zhang et al. (2018) report
an evolving response of wetland emissions to El Niños, where an initial
reduction due to decreased wetland extend is counteracted by increased
microbial activity under higher temperatures during the later stages of the
event. A complex response of wetland CH4 production is not only
seen in models, however. The inversion study of Pandey et al. (2017) found a
global increase of +6.6 Tg yr-1 for the strong 2011 La Niña, but
a reduction by -6.1 Tg yr-1 during the 2012 weak La Niña.
Similarly, Liu et al. (2017) found that El Niño conditions produced
opposing weather forcing and carbon cycle responses between various tropical
regions, as well as differing ones between the 1998 and 2015 events. Another
example of this is flooding in the Amazon region during La Niña events,
while flooding in the wetlands of the Paraná region occurs during El
Niños (Parker et al., 2018). Depending on the strength and geographical
expression of the climate anomaly, ENSO may thus cause regional or global
emission anomalies that are opposite to the expected pattern.
Evaluating the consistency of ENSO impacts throughout the record
The atmospheric [CH4] history shows global emission reductions in
the 1990s and increases after 2007 (Schaefer et al., 2016). This would be
consistent with ENSO forcing of the methane cycle, whereby the 1990s were
dominated by drier El Niño periods, whereas the recent years of
predominant La Niña conditions were wetter. Given that the magnitude of
the low-latitude wetland CH4 source exceeds pyrogenic emissions
rates, the expected emissions history would qualitatively match atmospheric
trends. Also, for a short period between 2008 and 2011, Schaefer et al. (2016)
observed the activation of CH4 emissions with an extremely
13C-depleted cumulative δ13CH4 (∼-75 ‰). Such a value on the global scale is hard to match by a
single source type. The cumulative effect of wetland enhancement and fire
suppression forced by the 2008 La Niña event would provide an excellent
explanation. However, the isotopic signal of the emissions reductions in the
1990s should be similar if ENSO forcing was the cause. In contrast, Schaefer
et al. (2016) found that the “lost emissions” during that period are quite
13C-rich and rather indicate a reduction in fossil fuel methane. An
alternative interpretation of these isotope trends by Rice et al. (2016)
requires simultaneous reductions of pyrogenic and biogenic emissions, which
is also inconsistent with the expected ENSO forcing. A consistent match
between ENSO phases and global δ13CH4 is therefore neither
evident in the dominant δ13CH4 trends nor in the
correlation analysis presented in this study.
Using isotopes to attribute emission changes
The impact of an ENSO emissions “perturbation” (i.e. the combined emissions
anomaly of an event) on atmospheric δ13CH4 can be assessed
in isotope mass balance calculations according to
Stotal⋅δtotal=S1⋅δ1+S2⋅δ2+S3⋅δ3,
where, for a given source, S and δ represent emission rate and
δ13CH4, respectively (note that for scenarios discussed
here, S may be negative, i.e., a reduction in emissions). Using generic
isotope source signatures for biogenic, fossil fuel, and pyrogenic methane
emissions from Schwietzke et al. (2016), we find that the average La Niña
perturbations proposed in Sect. 4.3. have an effective δ13CH4 of -79 ‰, with -83 ‰ for extreme ones. As
expected, the combined isotope leverage of wetland enhancement and fire
reductions on the global source is strong, equalling the leverage of much
larger source anomalies (20 Tg yr-1), with lower δ13CH4 of ∼-60 ‰ after 2007 as calculated by Schaefer et
al. (2016). In addition to the assumed 6 Tg yr-1 ENSO perturbation,
another ∼14 Tg yr-1 of emissions with
δ13CH4 =-52 ‰ would be necessary to produce the
observed [CH4] and δ13CH4 trends. The isotope
mass balance then shows that the non-ENSO additional emissions are a roughly
equal mix of fossil fuel and biogenic methane. Noting that the assumption
that all years after 2007 experienced average La Niña conditions is
unrealistic; these findings therefore show the following three points:
(i) ENSO effects alone cannot explain the post-2007 [CH4] rise.
(ii) There was an increase in biogenic sources in addition to ENSO-driven
wetland anomalies. Other wetland variability may have contributed to the rise
(Zhang et al., 2018); given the range in wetland model output (Melton et al.,
2013) this stands to be confirmed by ensemble runs. In the absence of boreal
emission increases (Sweeney et al., 2016), the only biogenic source large
enough to accommodate the required changes is agriculture (Saunois et al.,
2016). (iii) Any ENSO-driven reduction in biomass burning after 2007 allows
for, or requires, growing fossil fuel emissions. The latter has recently been
proposed by Worden et al. (2017), who reconstructed larger biomass burning
reductions after 2007 than recorded by GFED, although without assigning the
reductions to ENSO or other causes.
Role of other methane cycle processes
There is an alternative explanation for the lack of correlation between ENSO
and the methane records. ENSO could affect CH4 emissions from
tropical wetlands and biomass burning as predicted by Hodson et al. (2011)
and van der Werf et al. (2006), respectively, but the resulting isotopic
signal is overwhelmed by other components of the CH4 cycle. Such
influences could be other sources (particularly anthropogenic ones),
variability in atmospheric transport, or changes in CH4 sink
processes. A stronger ENSO signal in southern tropical [CH4] and
δ13CH4 compared to southern mid-latitudes and global
average would be expected for several of these scenarios. This is because
both biomass burning and wetland emissions show strong maxima in the southern
tropics and should be the dominant sources in this latitudinal band (Kirschke
et al., 2013). The detrended [CH4] records from SMO show such a
signal, but one that explains only one-third of the IAV and does not seem to
have significant impact on the trends. Further, we do not find higher ENSO
forcing of the δ13CH4 variability, even in the core region
of its climatic impact. Corbett et al. (2017) show that during La Niña
events high surface temperatures over the western Pacific lead to upward
transport over the Indonesian region (a CH4 source area from
wetlands and rice paddies) and negative CH4 anomalies in the
mid-troposphere (tropical surface air with relatively low [CH4]
replaces air from the Northern Hemisphere with higher [CH4]). This
mechanism would dampen the signal of higher La Niña emissions in surface
records like SMO and ASC. However, the corresponding El Niño anomalies in
mid-tropospheric CH4 over the central Pacific are smaller. This
indicates that central Pacific surface air, where there are no CH4
sources, is closer in [CH4] to mid-tropospheric levels than surface
air from the western Pacific. Unless there were strong longitudinal
differences in mid-tropospheric [CH4], this is inconsistent with a
scenario in which high concentrations of CH4 are generated over the
western Pacific in La Niñas but transported upwards and away from the
surface stations used in this study. On hemispheric or global scales,
transport processes are unlikely to play a strong role, given the short
mixing time of methane relative to its atmospheric turnover.
The low correlations of [CH4] and δ13CH4 with
ENSO rule out a dominant role for ENSO-triggered sink changes in atmospheric
methane records. Removal processes could lead to either amplification or
dampening of source signals. Higher emissions of methane and carbon monoxide
from biomass burning will draw down OH and weaken the sink. Emission factors
from fires for CO are between 10- and 30-fold higher than for CH4
(van der Werf et al., 2017), so that the biomass burning dynamics dominate
the source of reactive carbon, leaving less OH during El Niños and more
during La Niñas to draw down CH4. This would provide a negative
feedback for the emissions' [CH4] signal from ENSO forcing. In
contrast, the feedback on the ENSO emissions' δ13CH4 signal
would be positive due to varying enrichment of 13C–methane through
sink fractionation (less removal leads to less 13C enrichment of
relatively 13C-depleted wetland emissions during La Niñas; more
removal increases the 13C enrichment from biomass burning emissions
during El Niños further). In addition to the reactive carbon effect,
Turner et al. (2018) found a further OH increase during La Niñas due to
higher lightning rates with NOx production. Turner et
al. (2018) could attribute 17 % of OH variability that is forced by
climate cycles (rather than emissions of other atmospheric compounds) to
ENSO. This is a minor part of the variability, but in consequence the
dampening effect on [CH4] and the reinforcing feedback on
δ13CH4 would add further to the reactive carbon feedbacks.
In our correlation results these sink impacts are not apparent, as the
[CH4] correlations for the tropical stations are higher than
δ13CH4 correlations (Tables 2 and 3). Nevertheless, the
OH dynamics provide a possible explanation for the limited ENSO impact on
[CH4] variability and trends. They also make
δ13CH4 a conservative proxy for the influence that ENSO
exerts on tropical methane.
Whether ENSO has less influence on CH4 emissions than assumed or
whether such an impact is overwhelmed by atmospheric removal or other
CH4 cycle processes, our results suggest that global atmospheric
trends in [CH4] and δ13CH4 are dominated by other
components in the methane budget.
Conclusions
To study the impact of natural climate variability on recent trends in
atmospheric methane concentration, we investigated the correlation between
ENSO cycles and records of the mole fractions and stable carbon isotopes of
methane, as well as HCN as a biomass burning indicator. As δ13CH4 is subject to a mutually reinforcing signal from ENSO
suppression of wetland emissions and enhancement of biomass burning
CH4 (or vice versa), as well as positive feedbacks from
OH dynamics, it is particularly suited to study the role of ENSO in the
CH4 cycle.
We find a sizeable effect of ENSO on biomass burning, as indicated by HCN
variability in southern mid-latitudes. In contrast, ENSO explains a smaller
fraction (≤37 %) of [CH4] IAV, even in the southern
tropics, where the expected effect should be greatest. Trends in
[CH4] and δ13CH4 in these latitudes are far less
influenced by ENSO (≤23 %). On hemispheric and global scales the
ENSO signal in the methane records is similarly weak. Our results do not rule
out that ENSO influences CH4 emissions from wetlands and biomass
burning through temperature, enhanced precipitation, or droughts in key
regions, but any such impacts are overwhelmed by OH dynamics or other source
and sink processes. We review literature estimates of ENSO-driven emissions
and find them too small and sporadic to account for the post-2007 rise.
Counteracting OH dynamics are expected to further dampen any influence ENSO
may have on methane growth rates. Our findings suggest that ENSO is not an
important driver for recent global trends in methane, including the
[CH4] plateau and the increase in [CH4] since 2007. The
latter must therefore have different causes. Our results do not rule out that
wetland production is a contributor to the post-2007 [CH4] rise if
driven by environmental controls other than ENSO. This is suggested by an
increase in wetland CH4 production between the periods 2000–2006
and 2006–2014, although with the limited confidence of a single wetland
emissions model (Zhang et al., 2018). The longer the atmospheric
[CH4] and δ13CH4 trends persist, the less
probable processes that impact IAV and short-lived cyclical events like
ENSO as the driver are. Therefore, we consider increasing anthropogenic sources
as the more likely cause of the [CH4] rise. Changes in removal
rates via OH have been suggested as an additional (Rigby et al., 2017) or
alternative (Turner et al., 2017) driver of the increase, but recent work
suggests that sink impacts are not dominant (Naus et al., 2018). There is
evidence for additional methane emissions from agriculture (Wolf et al.,
2017) and from fossil fuel sources (Hausmann et al., 2016); both may
contribute to the current rise in [CH4] (Worden et al., 2017).
Further identification of these processes is necessary to inform climate
change mitigation policies and climate projections.