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. Author manuscript; available in PMC: 2020 Aug 11.
Published in final edited form as: Environ Res Lett. 2018 Jun 27;13(7):074009. doi: 10.1088/1748-9326/aac939

Enhanced response of global wetland methane emissions to the 2015–2016 El Niño-Southern Oscillation event

Zhen Zhang 1,2, Niklaus E Zimmermann 2,3, Leonardo Calle 4, George Hurtt 1, Abhishek Chatterjee 5,6, Benjamin Poulter 5
PMCID: PMC7418631  NIHMSID: NIHMS1504144  PMID: 32788924

Abstract

Wetlands are thought to be the major contributor to interannual variability in the growth rate of atmospheric methane (CH4) with anomalies driven by the influence of the El Niño-Southern Oscillation (ENSO). Yet it remains unclear whether (i) the increase in total global CH4 emissions during El Niño versus La Niña events is from wetlands and (ii) how large the contribution of wetland CH4 emissions is to the interannual variability of atmospheric CH4. We used a terrestrial ecosystem model that includes permafrost and wetland dynamics to estimate CH4 emissions, forced by three separate meteorological reanalyses and one gridded observational climate dataset, to simulate the spatio-temporal dynamics of wetland CH4 emissions from 1980–2016. The simulations show that while wetland CH4 responds with negative annual anomalies during the El Niño events, the instantaneous growth rate of wetland CH4 emissions exhibits complex phase dynamics. We find that wetland CH4 instantaneous growth rates were declined at the onset of the 2015–2016 El Niño event but then increased to a record-high at later stages of the El Niño event (January through May 2016). We also find evidence for a step increase of CH4 emissions by 7.8±1.6 Tg CH4 yr−1 during 2007–2014 compared to the average of 2000–2006 from simulations using meteorological reanalyses, which is equivalent to a ~3.5 ppb yr−1 rise in CH4 concentrations. The step increase is mainly caused by the expansion of wetland area in the tropics (30°S-30°N) due to an enhancement of tropical precipitation as indicated by the suite of the meteorological reanalyses. Our study highlights the role of wetlands, and the complex temporal phasing with ENSO, in driving the variability and trends of atmospheric CH4 concentrations. In addition, the need to account for uncertainty in meteorological forcings is highlighted in addressing the interannual variability and decadal-scale trends of wetland CH4 fluxes.

Introduction

Methane (CH4) is a potent greenhouse gas and has contributed to ~20% of observed warming since pre-industrial times (IPCC, 2013). Atmospheric CH4 concentrations have risen from preindustrial levels of 715 parts per billion (ppb) since the 1800s (Etheridge et al., 1998; MacFarling Meure et al., 2006) to current global concentration of ~1847 ppb, a 2.5-fold increase, primarily driven by anthropogenic activities (Kirschke et al., 2013), e.g. fossil fuel activities, agriculture, and also by the biogeochemical feedbacks of natural processes to climate change (Arneth et al., 2010; Tian et al., 2016; Saunois et al., 2016). However, the variability in the annual growth rate of atmospheric CH4 is strongly related to the climatic sensitivity of biogenic CH4 sources, of which global wetland CH4 comprises 60–80% of natural emissions (Quiquet et al., 2015; Hopcroft et al., 2017) and this large role is likely to persist into the future (Zhang et al., 2017b). Thus, interannual variability in the growth rate of atmospheric CH4 is largely affected by the response of global wetland CH4 emissions to the year-to-year mode of global climate variability such as the El Niño-Southern Oscillation (ENSO). ENSO is one of the largest climate phenomena that drives carbon dynamics and their anomalies across large portions of the globe (Chatterjee et al., 2017).

El Niño, the positive phase of ENSO, influences water- and carbon- fluxes of tropical terrestrial ecosystems through a change in patterns of atmospheric pressure and sea surface temperature (Philander 1990). These changes induce strong warming and reduced precipitation patterns by shifting the Intertropical Convergence Zone southward, causing amplified wildfires (Worden et al., 2013) and reduced wetland areal extent and CH4 emissions (Hodson et al., 2011,_S1_Reference18). Tropical wetlands, which comprise 50–70% of global wetland CH4 emissions (Bousquet et al., 2006), are similarly influenced by the periodic variations of air temperature and precipitation related to ENSO phases (Pison et al., 2013). Atmospheric measurements of CH4 provide evidence that the growth rate of global CH4 concentrations can rise during strong El Niño years (Nisbet et al., 2016; Bousquet et al., 2006), but terrestrial biogeochemical models suggest that tropical and global wetland CH4 emissions are usually found to decrease during El Niño (Hodson et al., 2011; Zhu et al., 2017; Ringeval et al., 2014).

At decadal time scales, the relationship between the annual CH4 growth rate and variability in global wetland CH4 emissions is not fully agreed upon, and the observed pause in the growth rate during 2000–2006 and subsequent return of the growth rate since 2007 (Nisbet et al., 2014) is not fully understood. A recent study suggests that global wetlands have played a limited role during the renewed rise of the growth rate through 2012 (Poulter et al., 2017). However, isotopic measurements indicate that the resumed increase in the growth rate could originate either from biogenic sources (Schwietzke et al., 2016) like tropical wetlands (Nisbet et al., 2016), from agricultural sources (Schaefer et al., 2016), or from the combined effect of decreased biomass burning (Worden et al., 2017) and increased fossil-fuel emissions (Helmig et al., 2016). In addition, simple-box models and more complex atmospheric inversion models can attribute the recent CH4 change to varying hydroxyl radical (OH) concentration, the major CH4 sink in the atmosphere (Turner et al., 2017; Rigby et al., 2017). Our poor understanding of wetland CH4 responses at annual to decadal time scales calls for revisiting the role of relationships between climate forcings and wetland CH4 fluxes to help reconcile top-down and bottom-up methodologies.

Previous El Niño anomalies, in years 1982–1983, 1997–1998, and 2015–2016, had significant impacts on terrestrial ecosystems and these events were considered key drivers of the atmospheric CO2 growth rate variability (Liu et al., 2017). The most recent El Niño event (2015–2016) caused unprecedented warming and extreme drought over most of the Amazonia regions (Jiménez-Muñoz et al., 2016; L’Heureux et al., 2016; Lim et al., 2017; Chatterjee et al., 2017). The occurrence of this extreme El Niño event disrupted regional ecosystems, causing sharp increases in atmospheric CO2 concentrations (Betts et al., 2016) and a doubling of fire-induced emissions in Southeast Asia (Whitburn et al., 2016). The more recent El Niño event may have also contributed to record warming during 2015 and the first third of 2016, with global air temperature at 0.94°C above the 20th century mean annual average (http://www.ncdc.noaa.gov/sotc/global/201613, last access in August 2017). Exactly how much the 2015–2016 ENSO phenomenon has impacted global wetland CH4 emissions and to what extent it has affected the annual growth rate of atmospheric CH4 concentration remains unknown due to challenges in monitoring and modeling.

Here, we analyze the relationship between ENSO phase and wetland CH4 emissions by addressing two main questions: First, how does ENSO, with particular attention to the ENSO event in 2015–2016, affect the interannual variability of CH4 emissions from global wetlands? Second, what are the major mechanisms that link wetland CH4 emissions to the atmospheric increases observed since 2007?

Methods

We use a process-based ecosystem model LPJ-wsl (Lund-Potsdam-Jena model, WSL version) forced with four different meteorological forcings to simulate wetland CH4 emissions from 1980 to 2016. These drivers include one station-based monthly geo-interpolation dataset (CRU) and three meteorological reanalyses products (Table 1). We use multiple climate datasets to investigate uncertainty from meteorological forcing driving simulated atmospheric CH4 concentrations, and hence, to better characterize CH4 variation in response to climate variations.

Table 1.

Model experiment descriptions. Climatic variables T, P, SW, LW, CLD, and WETD represent temperature, precipitation, shortwave radiation, longwave radiation, cloud cover, and wet days respectively.

Run ID number Forcing Temporal Resolution Climatic Variables Time periods
i MERRA2 Daily T, P, SW, LW 1980–2016
ii MERRA2 Monthly T, P, SW, LW* 1980–2016
iii ERA-I Daily T, P, SW, LW 1980–2016
iv ERA-I Monthly T, P, SW, LW 1980–2016
v JRA-55 Daily T, P, SW, LW 1980–2016
vi JRA-55 Monthly T, P, SW, LW* 1980–2016
vii CRU Monthly T, P, CLD, WETD 1901–2016
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*

CLD and WETD are from CRU for comparison

LPJ-wsl (Poulter et al., 2011) is a process-based dynamic global vegetation model (DGVM) developed for studying terrestrial ecosystems, based on an earlier LPJ core model (Sitch et al., 2003). The version of the model applied in this study includes a new hydrology model, TOPMODEL, to determine wetland area and its inter- and intra-annual dynamics (Zhang et al., 2016), a permafrost and dynamic snow model (Wania et al., 2009), and a prognostic wetland CH4 emission model (Hodson et al., 2011), each of which is incorporated into the LPJ-wsl framework with explicit representation of the effects of snow and freeze/thaw cycles on soil temperature and moisture and thus CH4 emissions (Zhang et al., 2016). We apply an empirical model to estimate CH4 emissions in the model which is based on soil respiration, inundated area, and a temperature-based ecosystem emission efficiency (Christensen et al., 1996). Soil respiration is modelled empirically in response to temperature and soil moisture based on an Arrhenius type equation where varying effective activation energies for respiration and a dampening of the temperature sensitivity (Q10) due to acclimation were considered (Sitch et al., 2003). The simulated dynamics of wetland area and CH4 emissions have been evaluated against large-scale observations in previous studies (Hodson et al., 2011; Zhang et al., 2016; Zhang et al., 2017b). Here, we calibrated temperature-modified CH4 emitting factors by scaling simulated global estimates to match 172 Tg CH4 yr−1 in 2004, which was estimated from an independent atmospheric inversion study (Spahni et al., 2011), and is in agreement with independent satellite-based methods from Bloom et al. (2010). We improved inundation estimates by calibrating the TOPMODEL parameter ‘maximum inundation potential’ (Fmax) (Zhang et al., 2016) using an independent inundation dataset (Poulter et al., 2017) that was derived from a satellite-based Surface Water Microwave Product Series (SWAMPS) (Schroeder et al., 2015), an inventory-based dataset Global Lakes and Wetlands Database (GLWD) (Lehner and Döll, 2004), and a regional wetland map derived from satellite retrievals for Amazonia (Hess et al., 2015). To avoid confusion regarding double counting (Thornton et al., 2016), we clarify that our simulated wetland area includes seasonally inundated wetlands, e.g. floodplains, and permanently inundated vegetated wetlands, but excludes rice agriculture, non-vegetated reservoirs, medium to large sized lakes, rivers, and coastal wetlands that are not accounted for by the GLWD.

The climate datasets included the monthly meteorology from the Climate Research Unit (CRU) TS 3.25 (Harris et al., 2014) and three state-of-the-art metrological reanalysis products. The reanalysis products were comprised of 1-hourly reanalysis Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) (Gelaro et al., 2017) from the NASA Global Modeling and Assimilation Office (GMAO), 6-hourly ERA-Interim (ERA-I) (Dee et al., 2011) from the European Centre for Medium-Range Weather Forecasts (ECMWF) data assimilation system and, and lastly, a 6-hourly Japanese 55-year Reanalysis (JRA-55) (Kobayashi et al., 2015) from the Japan Meteorological Agency (JMA). The reanalysis data (total precipitation, 2m air temperature, downward shortwave radiation, and downward longwave radiation) were aggregated to a common daily time-step and downscaled to 0.5° spatial resolution grid using first order conservative interpolation. The soils dataset we used was the Harmonized World Soil Database v1.2 (Nachtergaele et al., 2008) and using pedotransfer functions of the surface soil texture (Cosby et al., 1984) to estimate volumetric water holding capacity. For the monthly CRU data, LPJ-wsl was set up to use a wet-day frequency dataset, a weather generator (Geng et al., 1986) to generate daily precipitations, and a set of simplified equations with monthly cloud cover as input to calculate daily photosynthetically active radiation flux and potential evapotranspiration (Prentice et al., 1993,_S1_Reference42). Additional details of the climate datasets and model experiments are in the Supplementary Material (Table S1). The LPJ-wsl state variables (i.e., carbon in vegetation, litter, and soils) were simulated to reach equilibrium by using a 1000-year spinup, with fire dynamics, and a 398-year spinup for land use change using Land-Use Harmonization dataset (LUHv2) (Hurtt et al. 2011). Spin-up was done using randomly selected climate inputs from 1901–1930 for CRU and 1980–2000 for reanalysis with fixed atmospheric CO2 to the 1860 value. After equilibrium, a transient simulation with fire effects and varying land cover was performed for the years 1901–2016 (for CRU) and 1980–2016 (for reanalysis), forced with changing climate conditions and varying atmospheric CO2 concentration (https://www.esrl.noaa.gov/ccgg/trends, last access at August 2017). The simulations consider gross land-use transitions (with no wood harvest) with primary and secondary lands treated separately (for details, see Arneth et al., 2017), where the soil moisture and soil respiration were calculated by fraction-weighting individual land stands within a grid cell.

We used the Multivariate ENSO index (MEI) for representing the ENSO strength (Wolter and Timlin, 1998). The MEI index represents the first unrotated principal component of the combined, normalized fields of the primary climate variables observed over the tropical Pacific, reflecting a global signal of climate-land-atmosphere interaction for both El Niño and La Niña events. Given that previous studies (Fang et al., 2017; Liu et al., 2017) have shown a hysteresis in the Earth systems response to changes in temperature and precipitation patterns, we carried out a cross-correlation analysis to examine possible time-lag effects of wetland CH4 response to El Niño events.

To test whether annual wetland CH4 anomalies contributed to the growth rate of atmospheric CH4, we compared our results against the annual mean global CH4 growth rate and monthly CH4 trend derived from NOAA/ESRL (https://www.esrl.noaa.gov/gmd/ccgg/flask.php, last access at August 2017). We then used the first derivative of spline-smoothed monthly wetland CH4 anomalies to calculate the wetland CH4 instantaneous growth rate. The time series of CH4 concentration measurements, derived from NOAA cooperative air sampling network, were processed with a curve fitting method (Thoning et al., 1989) that decomposes the full signal into a long-term growth rate fit by a polynomial function, seasonal oscillations by a harmonic function, and a low pass digital filter to retain interannual and short-term variations. From the decomposed signal, we derived component signals such as trend, growth rate, and annual amplitude. The CH4 amplitude of the seasonal cycle from Mauna Loa surface site (MLO: 19.53°N, 155.58°W) in NOAA/ESRL was applied to the analysis as an indicator of the strength of CH4 seasonality in the Northern Tropics, where CH4 amplitude is mainly controlled by OH and fluxes from the land biosphere. Given that wetlands contribute the largest fraction of natural CH4 sources and that the interannual variability of OH is relatively small (Montzka et al., 2011,_S1_Reference33), the changing trends in the CH4 amplitude consequently imply that the variation in the trend is largely affected by changing CH4 dynamics in wetland ecosystems. To test whether the shifting spatio-temporal patterns of simulated wetland CH4 dynamics are consistent with observations, we compared the observed MLO CH4 amplitude with simulated wetland CH4 amplitude, which was calculated as the difference between annual maxima and minima in spline-smoothed monthly wetland CH4 anomalies.

For evaluation of wetland areal changes we used terrestrial water storage (TWS) anomalies, observed by the Gravity Recovery and Climate Experiment (GRACE) satellite measurement, as a proxy for groundwater storage and surface inundation (Bloom et al., 2012; Boening et al., 2012). We used the Level-3 monthly ‘solutions’, version RL05, from Geo Forschung Zentrum (GFZ), the University of Texas Center for Space Research (CSR), and the Jet Propulsion Laboratory (JPL) from April 2002 to December 2016 to analyze the temporal variations of water mass in the tropics. The monthly TWS was multiplied by a spatial grid of scaling coefficients derived from post-processing of GRACE observations (Landerer and Swenson, 2012,_S1_Reference26) to restore the signals attenuated in the processing at small spatial scales. We used the ensemble mean of monthly TWS from three different products in the analysis because the ensemble mean was the most effective in reducing the noise in gravity fields solutions from GRACE data (Sakumura et al., 2014).

Results and Discussion

Long-term response of wetland CH4 to ENSO

The ensemble climate simulations indicate a strong link between ENSO and wetland CH4 emissions, with higher emissions during La Niña and lower emissions during El Niño (Figure 1a). We find significant negative correlations (rMERRA2=−0.51, rERA-I=−0.36, rCRU=−0.45, rJRA-55=−0.35, d.f.=443, p < 0.01) between the ENSO MEI index and monthly wetland CH4 anomalies, regardless of the climate data used in the simulations. This is consistent with findings from bottom-up modeling estimates (Hodson et al., 2011; McNorton et al., 2016; Zhu et al., 2017), atmospheric modeling (Pison et al., 2013; Chen and Prinn, 2006) and satellite observations. For instance, the atmospheric CH4 variations of the mid-troposphere measured by the Infrared Atmospheric Sounding Interferometer (IASI) aboard METOP satellite, and by the Atmospheric Infrared Sounder (AIRS) aboard NASA’s Aqua satellite, also show higher increases in 2007–2008 and 2010–2011 when strong La Niña events occurred (Xiong et al., 2016). Airborne-based estimates of the interannual variability of CH4 fluxes for eastern Amazon Basin also provide ancillary evidence that the CH4 emissions are greatest in 2008, a year of La Niña phase (Basso et al., 2016). Recent satellite observations from the Greenhouse gases Observing SATellite (GOSAT) also suggest large-scale fluctuations in atmospheric CH4 during ENSO events, indicating that wetland CH4 emissions are ~5% higher during La Niña events (Pandey et al., 2017). The increase in CH4 emissions from wetlands during La Niña can be attributed to a large increase in flood extent, primarily over tropical areas (including SE Australia, northern South America, and Southeast Asia) (Boening et al., 2012), whereas the decreases during El Niño are possibly due to drought-induced decreases in flooded area. All of the evidence above suggests a robust negative relationship between annual anomalies of wetland CH4 emissions and ENSO events, i.e., positive anomalies during La Niña and vice versa.

Figure 1.

Figure 1.

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Global anomalies of monthly wetland CH4 emissions (a) and instantaneous growth rates of wetland CH4 emission anomalies from 1980 to 2016 for the Global (b), Tropics (middle, 30°S-30°N) (c), and Northern Hemisphere (bottom, >30°N) (d). The global anomalies of wetland CH4 emissions were calculated relative to monthly average from 1980–2016. The instantaneous growth rate for each simulation is a time derivative of the smoothed monthly CH4 anomalies using spline functions. The Spearman rank correlation coefficients between the multivariate ENSO index (MEI) and monthly wetland anomalies were derived from cross correlation analyses (Figure S1) at 3 month lags (Lag= −3), with different colors corresponding to specific runs. Shaded grey areas represent the strong El Niño phases with MEI strength > 60 according to MEI ranks (https://www.esrl.noaa.gov/psd/enso/mei/rank.html, last access at January 2018).

However, negative anomalies of annual wetland CH4 emissions do not necessarily lead to a decrease in the instantaneous growth rate of wetland CH4 emissions during El Niño. We find that the growth rate of wetland CH4 emissions is initially decreased but then is in a rising phase during the later stages of strong El Niño events. Although, the amplitude of the rising varied depending on which meteorological forcing was used in the simulations (Figure 1b). This is mainly because strong El Niño events drive negative wetland CH4 growth rates at the beginning of the ENSO anomaly, but then the growth rate rapidly recovers to positive values. Despite positive atmospheric methane growth rate correlations with El Niño events, the general decline in wetland area causes declines in wetland CH4 emissions at the beginning of strong El Niño phases. The high temperatures over the tropics strongly increase the CH4 growth rate due to higher soil decomposition rates during the later stages of the 2015–2016 El Niño event. Cross-correlation analyses between the monthly growth rate of wetland CH4 emissions and the MEI index suggest that the peak correlation occurs at a 3-month lag (when ENSO leads ΔCH4/Δt) for the globe. As expected, the timing of wetland response to ENSO varies regionally, where Tropical Asia and Tropical South America exhibit a ~4 month lag and no lag, respectively (Figure S1). The Interannual Variability (IAV) of wetland CH4 emissions is dominated by the Tropics (30°S-30°N) with relatively small contributions from the Northern Hemisphere (Figures 1c, 1d). MERRA2 showed the highest IAV among all four simulations, whereas the CRU-based simulation had the lowest IAV. The rise of wetland CH4 emission growth rate is consistent with the observed spikes of atmospheric CH4 growth rates during strong El Niño events (Nisbet et al., 2016).

Impact of 2015–2016 El Niño on wetland CH4

The amplitude of instantaneous growth in wetland CH4 emissions during the 2015–2016 El Niño was higher than that in the previous periods 1982–1983 and 1997–1998, suggesting an increased sensitivity of wetland CH4 in response to the recent El Niño (Figure 1b). Our results captured the magnitude of this large increase in wetland CH4 emissions with an instantaneous growth rate of ~7.6±1.6 Tg CH4 yr−1 during 2015–2016 El Niño. The meteorological datasets drove instantaneous growth rates that ranged between 9.2 Tg CH4 month−1, 8.6 Tg CH4 yr−1, 7.2 Tg CH4 yr−1, and 5.5 Tg CH4 yr−1 using MERRA2, JRA-55, CRU, and ERA-I, respectively. Although the 2015–2016 El Niño was not as strong as the 1997–1998 El Niño according to the MEI index (~3 in 1997–1998 and ~2.5 in 2015–2016), the combined effect of rising CO2 concentrations and high temperatures most likely amplified the impact, causing 1.8 times the maximum growth rate of CH4 of the 1997–1998 El Niño event (mean growth rate of ~4.2±1.4 Tg CH4 yr−1 for the respective time period).

The spatial distribution of wetland CH4 anomalies demonstrated that the large increases in soil respiration drove the strong growth rate and occurred during the March-April-May (MAM) season in 2016 as a consequence of warming and droughts in the wet seasons (October 2015 - May 2016) (Figure 2). There was a widespread increase in CH4 emissions over western Amazonia, mainly attributed to increased soil respiration. Despite a large decline in wetland extent due to severe drought, significant positive anomalies in CH4 emission peaked across the western Amazonian basin, likely due to high temperatures. Temperature is the primary climatic variable driving the increasing long-term trend in CH4 emissions (Zhang et al., 2017b). However, precipitation is the dominant climatic variable regulating interannual variability in CH4 emissions by altering the inundation extent and creating anaerobic conditions suitable for methanogenesis in the tropics (Zhang et al., 2017b).

Figure 2.

Figure 2.

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Spatial distributions of seasonal ensemble mean anomalies in wetland CH4 emissions (a: eCH4, Unit: g CH4 m−2 mon−1), inundated areas (b: Awet, Unit: %), and heterotrophic respiration (c: Rh, Unit: g C m−2 mon−1) of the greater Amazonia region for the March-April-May season, 2016, where eCH4 shows the highest growth rate during the 2015–2016 ENSO event. The anomalies are calculated as seasonal means during the MAM season of 2016 relative to average over the period of 1980–2016 level, with the uncertainty calculated as one-standard deviation from the four simulations forced by each meteorological dataset.

Wetland CH4 trends between 2000–2006 and post-2007

Using the meteorological reanalysis data, we find evidence for a step increase in global annual wetland emissions between the periods of 2007–2014 relative to that of 2000–2006 (Figure 3a). These simulations suggest that the average annual CH4 emissions from 2007–2014 increased by ~7.8±1.6 Tg CH4 yr−1 compared to the average of 2000–2006, which is equivalent to an increase in the growth rate of up to ~ 3.5 ppb CH4 yr−1 for the post-2007 period, or about half of the observed increase in concentrations. The CRU-based simulation in this study did not show a strong step-increase between these two periods, suggesting only a marginal contribution from wetlands with a 1.5 Tg CH4 yr−1 increase in the post-2007 growth rate. This is consistent with findings from an ensemble modeling experiment using CRU as a forcing dataset, which found no significant increase of global wetland CH4 emissions during the period of renewed atmospheric CH4 growth (Poulter et al., 2017). Another recent atmospheric modeling study, also using CRU as forcing for their prior inputs, likewise suggested that wetlands made only a small contribution to the post-2007 growth at ~1 ppb/yr (McNorton et al., 2016). In contrast to the CRU simulations just listed, all our simulations using meteorological reanalysis data suggest that more than 90% of the increase in the growth rate of wetland CH4 is from the Tropics (Table 2), and mainly due to increases in precipitation across South America, Tropical Africa, and Southeast Asia since 2007. MERRA2-based simulations suggest that the post-2007 rise in global CH4 concentrations primarily comes from South America and Tropical Africa, whereas ERA-I and JRA-55 identify South America as the largest contributor to the CH4 growth rate (Figure S2).

Figure 3.

Figure 3.

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Simulated temporal patterns of CH4 from all model experiments (see details in Table 1). (a) Time series of annual CH4 emissions using climate forcings with daily and monthly temporal resolution. The daily forcings were aggregated to monthly values to evaluate the influence of daily variations of climate variables on CH4 estimations. Solid and dotted lines represent daily and monthly inputs, respectively. The horizontal lines represent averaged annual CH4 emissions for two time periods, 2000–2006 and 2007–2014, with the different colors representing different climate forcings. (b) Comparison between the seasonal cycle of LPJ-wsl simulated monthly CH4 fluxes (solid line) using different climate forcings, with min/maximum range (areal shaded) over the Northern Slope of Alaska for 2012–2014 in comparison to the observed regional CH4 fluxes (dots) estimated from analysis of 15 aircraft flights by the National Aeronautics and Space Administration’s Carbon in Arctic Vulnerability Experiment (CARVE).

Table 2.

Summary of mean annual CH4 emissions of the Tropics (30°S-30°N, denoted as TRO), the Northern Extratropics (denoted as NET), and the Southern Extratropics (denoted SET) for 2000–2006, and 2007–2014 from simulations with daily meteorological forcings MERRA2, ERA-I, and JRA-55 and with a spatial-interpolated climate dataset CRU that is based on interpolations from meteorological stations.

Time period Forcing eCH4 (Tg CH4 yr−1)
TRO NET SET Global
2000–2006 CRU 138.1 32.3 1.8 172.2
MERRA2 136.1 32.5 2.1 170.7
ERA-I 142.3 26.6 1.9 170.9
JRA-55 141.5 29.8 1.8 173.1
2007–2014 CRU 139.1 33.0 1.7 173.8
MERRA2 145.6 32.8 1.9 180.3
ERA-I 148.6 27.0 1.8 177.4
JRA-55 147.7 31.1 1.8 180.6
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The different IAV patterns of CH4 emissions among these simulations suggest considerable uncertainties in CH4 emissions due to climate drivers (Figure 3a). The model experiments demonstrated that the discrepancy originates mainly from different model behavior when using products like CRU and meteorological reanalyses like MERRA2, ERA-I, and JRA-55, regardless of the temporal resolution of climate inputs used (Figure S3). We found only minor differences using a daily or monthly temporal resolution, which likely reduced uncertainties from applying the simulated weather generator and thus show that the weather generator covered the internal climatic variability at monthly scale. The importance of considering uncertainty of climate forcing was also reflected in the representation of the seasonal cycle of CH4 emissions. The comparison of simulated CH4 emissions with independent estimates using an atmospheric model STILT based on CARVE airborne experiments (Zona et al., 2016) suggested a dominant role of climate forcings in capturing CH4 seasonality in arctic regions (Figure 3b). MERRA2, ERA-I, and JRA-55 underestimated the peak CH4 emission in growing season but were able to capture the general seasonal cycle in CH4 emissions for the North Slope of Alaska, while CRU-based estimates failed to reproduce a similar pattern. The seasonal cycle of CH4 emissions was also generally underestimated by most bottom-up models that used CRU climate data in a synthesis modeling experiment (Melton et al., 2013), highlighting the need to better constrain the CH4 emissions by taking into account several datasets that represent climate forcing uncertainty.

Sensitivity of wetland CH4 emissions to ENSO

To further investigate whether the influence of ENSO on global wetland CH4 fluctuation was strengthening, we evaluated the average sensitivity of simulated wetland CH4 emissions and wetland areas in the tropics to ENSO events. To this means we calculated the ratio of the annual anomaly of CH4 emission/wetland area to the annual MEI index for three different time periods, 1980–1999, 2000–2006, and 2007–2016 (Figure 4). We observed a minor change in the sensitivity of CH4 emissions and wetland areas between 1980–1999 and 2000–2006, which suggests a subtle change in the response of global wetland CH4 emissions to increasing global temperatures. However, the sensitivity of the modeled results strongly increased for the period of 2007–2016 relative to the two previous time periods. The sensitivity in CH4 emissions increased by ~200% in MERRA2, ERA-I, and JRA-55, whereas the CRU run resulted in a lower percent increase (42%) compared to the other model experiments. The concurrent increase in the sensitivity of CH4 emissions and wetland areas indicates that the increase of CH4 emissions since 2007 can mainly be attributed to an increased sensitivity of wetland areas, which was driven by the changing precipitation patterns found in meteorological reanalysis products. The GRACE measurement for relative equivalent water storage confirms the large increase for the period of 2007–2014 compared to earlier periods (Figure 5), suggesting that our simulated increases in tropical wetland areas are robust. All of the modeled wetland areas have significant correlations (rMERRA2=0.59, rERA-I=0.59, rCRU=0.56, rJRA-55=0.5, d.f.=176, p <0.01) with GRACE TWS, and suggest a ~150 103 km2 increase in inundation over time period of 2007–2014. This also implies that, despite an observed decline in open waters in the tropics (due to the anthropogenic effect from denser populations and impacts from human activities for the period of 1990s and early 2000s (Prigent et al., 2012)), the enhanced precipitation since 2007 (Sun et al., 2017; Rodell et al., 2018) was primarily related to the ENSO phase over Tropical land, which has affected wetland patterns and CH4 emissions globally.

Figure 4.

Figure 4.

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Sensitivity of (a) wetland CH4 anomalies (Unit: Tg CH4 /yr/MEI) and (b) wetland area anomalies for the tropics (Unit: Mkm2/yr/MEI; Mkm2 = 106 km2) to global ENSO strength for the period of 1980–1999, 2000–2006, and 2007–2016. The sensitivity metric is calculated as the ratio of averaged annual cumulative anomalies of wetland CH4 emissions and wetland areas to the MEI index. Bars represent the modeled sensitivity from experiments with different forcing datasets, and the error bars represent one standard deviation.

Figure 5.

Figure 5.

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Trends of simulated wetland areal anomalies (Unit: Mkm2/month; Mkm2=106 km2) for the tropics (30°S-30°N) compared to area-weighted average terrestrial water storage (TWS; Unit: mm-H2O) from the ensemble mean of GRACE satellite measurement. The wetland anomalies were calculated relative to the monthly mean of 1980–2016, while TWS anomalies were relative to means of the 2004–2009 period. The fitted trends were calculated by smoothing the monthly anomalies with a 12-month moving average. The Spearman rank correlation coefficients between model and TWS are given for each simulation with different climate forcings in corresponding colors.

Relationship between wetland CH4 and atmospheric growth rate

There was a statistically significant (p < 0.10) positive trend in the simulated annual amplitude of wetland CH4 emissions, suggesting an increasingly enhanced sensitivity of wetland CH4 emissions to climate change in recent decades (Figure 6). All model simulations indicated positive trends of the annual amplitude of wetland CH4 emissions with small differences depending on climate forcings. These simulated positive trends are consistent with observed trends in CH4 amplitude at the MLO site, for which MERRA2, ERA-I, and JRA-55 runs were correlated with MLO observations (rMERRA2=0.36, rERA-I=0.42, rCRU=0.29, rJRA-55=0.37, d.f. = 30, p < 0.05) and only CRU-based simulations showed a weak correlation between wetland CH4 emissions and enhanced global CH4 seasonality. These significant correlations suggest relationships between atmospheric CH4 seasonality and natural wetland emissions, despite the major role of OH in determining CH4 seasonality. The increasing trends in CH4 amplitude also imply a high likelihood that there is an underlying shift of CH4 seasonality in wetland ecosystems and this shift in seasonality is likely greatest in tropical regions.

Figure 6.

Figure 6.

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Time series of the seasonal amplitudes of global CH4 fluxes. The seasonal amplitude of CH4 fluxes (dashed dotted line) is calculated as the difference between maxima and minima of simulated monthly CH4 emissions. The dashed black line represents observed peak-to-through seasonal amplitude of atmospheric CH4 concentration at Mauna Loa observational station. The solid lines represent linear fitted long-term trends of the seasonal CH4 cycle with Spearman rank correlation coefficients between models and observed amplitudes listed for each model runs in corresponding colors.

We found a small, but significant, positive correlation between annual wetland CH4 emissions and the annual atmospheric CH4 growth rate in simulations forced by the daily meteorological datasets MERRA2 (r= 0.31, d.f.= 33, p < 0.1), ERA-I (r= 0.36, d.f.=33, p < 0.1), and JRA-55 (r=0.38, d.f.=33, p < 0.05) for the period of 2000–2015, whereas no significant correlation was found in CRU-based runs (r=0.07, d.f.= 33, p>0.75). For the period of 1980–1999, none of the simulations showed a significant correlation with the annual atmospheric CH4 growth rate. The atmospheric CH4 growth rate is not exclusively a result of changes in wetland emissions, but rather due to a combined influence of anthropogenic and natural sources, and also due to a hydroxyl radical sink (Turner et al., 2017; Rigby et al., 2017). Recent studies have reported an increase in annual CH4 emissions from global livestock (Wolf et al., 2017) and an expansion of agricultural areas for rice paddies in Southern Asia (Zhang et al., 2017a), a region where precipitation has largely increased since 2007. Thus, we hypothesize that a combination of tropical wetlands and agricultural sources are likely responsible for the resumed growth rate of atmospheric CH4 concentrations, which is consistent with the depletion in the global isotopic signature in 13CH4 (Schaefer et al., 2016) and with regional measurements of 13CH4 in the Tropics (Nisbet et al., 2016).

Conclusions

We demonstrate that global wetland CH4 emission anomalies are strongly related to ENSO variability using an extended, multi-meteorological ensemble. At sub-annual time-scales, we also found that the instantaneous growth rate of wetland CH4 anomalies was positively correlated with ENSO strengths, which provides an explanation for the observed rise of atmospheric CH4 growth rate during strong El Niño events. The ongoing warming trend, as well as the shifting patterns of global precipitation, has likely had a significant impact on increasing global CH4 interannual variability. The strong El Niño event in 2015–2016, associated with extreme heat and drought over the Amazonian regions, caused record-high growth rates of wetland CH4 emissions compared to the previous three decades. We also found an increasing sensitivity of wetland CH4 emissions to ENSO oscillation since 2007, which we attribute to increases in the areal extent of tropical wetlands from increased precipitation. Our study also highlights the need to account for uncertainty in the climate forcing for estimating wetland CH4 emissions.

Supplementary Material

Table S1
1

Acknowledgements

This study is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF5439 “Quantifying Sources and Sinks in the Global Methane Cycle” to Zhen Zhang, George Hurtt, and Benjamin Poulter. We thank the Competence Center Environment and Sustainability (CCES) project Modeling and Experiments on Land-Surface Interactions with Atmospheric Chemistry and Climate Phase 2 (MAIOLICA2) #42-01, and the National Natural Science Foundation of China (T411391009) for additional funding. We further thank NASA for preparing the MERRA2 dataset, the European Centre for Medium-Range Weather Forecasts for preparing the ERA-Interim dataset, the Japan Meteorological Agency for preparing the Japanese 55-year Reanalysis (JRA-55) dataset, and the Climate Research Unit at the University of East Anglia for providing the CRU dataset. We finally thank the NOAA ESRL Global Monitoring Division for providing CH4 measurements. We thank Sara H. Knox and Katelyn Dolan for constructive comments on the manuscript.

Footnotes

Data availability

The data that support the findings of this study are available upon request, for access please contact Z. Zhang (yuisheng@gmail.com). Atmospheric CH4 concentration datasets were obtained from the NOAA ESRL GMD Carbon Cycle Cooperative Global Air Sampling Network https://www.esrl.noaa.gov/gmd/ccgg/flask.php, last access at August 2017). The annual mean global CH4 growth rate and monthly trend were derived from NOAA/ESRL ((www.esrl.noaa.gov/gmd/ccgg/trends_ch4/). Terrestrial Water Storage products were derived from the GRACE website (https://grace.jpl.nasa.gov/data/get-data/, last accessed on October 2017). We used the multivariate ENSO index (MEI) (https://www.esrl.noaa.gov/psd/enso/mei/, last access at October 2017) as indices for representing ENSO strength.

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