This site uses cookies. By continuing to use this site you agree to our use of cookies. To find out more, see our Privacy and Cookies policy.
Close this notification
Letter The following article is OPEN ACCESS

Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics

, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and

Published 13 September 2017 © 2017 The Author(s). Published by IOP Publishing Ltd
, ,

1748-9326/12/9/094013

Abstract

Increasing atmospheric methane (CH4) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999–2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH4 emissions from wetlands, the largest natural global CH4 source, for 2000–2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000–2012, boreal wetland CH4 emissions increased by 1.2 Tg yr−1 (−0.2–3.5 Tg yr−1), tropical emissions decreased by 0.9 Tg yr−1 (−3.2−1.1 Tg yr−1), yet globally, emissions remained unchanged at 184 ± 22 Tg yr−1. Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH4 emissions have not contributed significantly to the period of renewed atmospheric CH4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH4 emissions, and a decrease in the atmospheric oxidative sink.

Export citation and abstract BibTeX RIS

Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Introduction

The increase of methane (CH4) in the atmosphere is responsible for approximately 20% of the radiative forcing related to contemporary climate change (Ciais et al 2013). Since 1850, atmospheric CH4 concentrations have risen by more than 150%, from a pre-industrial level of 700 ppb to 1834 ppb in 2015, primarily as a result of human activities that include fossil fuel extraction and agriculture practices (Kirschke et al 2013, Ruddiman 2013, Dlugokencky et al 2015, Tian et al 2016). In recent assessments of the global CH4 budget, covering the period 1980 to 2009, natural wetlands were estimated to be the largest but also most uncertain source of CH4, emitting between 177–284 Tg CH4 yr−1 using bottom-up modeling approaches and 142–208 Tg CH4 yr−1 based on top-down atmospheric inversions (Kirschke et al 2013). Wetland emissions now represent about 30% of the total combined natural and anthropogenic sources and are projected to increase and amplify global warming (Stocker et al 2013). The large differences among published source estimates results from difficulties in defining wetland CH4 producing area, uncertainties in biogeochemical modeling of anaerobic sources, oxidative sinks, and from uncertainties in atmospheric inversions (Melton et al 2013, Wania et al 2013, Patra et al 2016).

The atmospheric growth rate of CH4 exhibits complex temporal variability, because (1) the gas has a short perturbation lifetime, ~12 years (Prather et al 2012), compared to longer-lived gases such as CO2 for which emissions accumulate in the atmosphere on centennial to millennial timescales (Ciais et al 2013), and (2) the sources and sinks are diverse and can change rapidly over short time periods (Dlugokencky et al 1999, Bousquet et al 2006). For example, atmospheric CH4 concentrations increased by approximately 12 ± 6 ppb yr−1 during the 1980s (based on observations made by the National Oceanic and Atmospheric Administration's (NOAA) Earth System Research Laboratory (ESRL) at the Mauna Loa Observatory, MLO); however in the 1990s a slowdown in growth was observed (Dlugokencky et al 1999), followed by a stabilization in the atmospheric growth rate of CH4 that began in 1999 and lasted until 2006 (Dlugokencky et al 2009). Starting in 2007 and continuing to 2015, atmospheric CH4 concentrations began to increase once more, at an average rate of 6.4 ppb yr−1, equivalent to 17.8 Tg yr−1, (Dlugokencky et al 2015). The drivers responsible for the CH4 stabilization period remain unclear and may be due to changes in the concentration of atmospheric hydroxyl (OH) radicals, the main oxidative sink for methane (Heimann 2011), but with isotopic evidence also supporting either a reduced contribution from fossil fuel emissions (Aydin et al 2011), or reduced emissions from natural wetlands and rice cultivation (Kai et al 2011). In contrast, the period of renewed atmospheric CH4 growth shows high latitudinal variability (Nisbet et al 2014), and several explanations have been proposed, including a reduction in the OH sink capacity for CH4 (Rigby et al 2008), increases in Arctic and tropical wetland emissions (Dlugokencky et al 2009), increased fossil fuel activities related to hydraulic fracking and natural gas exploitation (Jackson et al 2014, Rice et al 2016, Turner et al 2016), and possible changes in agriculture, in particular livestock production (Herrero et al 2013). Recent observational evidence suggests that the depletion of atmospheric δ13C of CH4 since 2007 supports a storyline for increasing biogenic emissions from agriculture (Schaefer et al 2016) rather than a decreasing thermogenic (fossil fuel related) or pyrogenic (biomass burning) emissions (Ghosh et al 2015).

The role of natural wetlands in the periods of stabilization (1999–2006) and renewed growth (2007–2012) has generally been overlooked in recent global CH4 budgets because these assessments have ended too early, i.e. 1993–2004 in the WETCHIMP ecosystem model ensemble (Melton et al 2013), 1980–2009 in the Kirschke et al (2013) study, and up to 2008 in the Dlugokencky et al (2009) study. An additional constraint has been the limited availability and scope of temporal wetland dynamics datasets, such as the Global Inundation Extent from Multi-Satellites Observations (GIEMS), which presently only covers 1993 to 2007 (Prigent et al 2007, Papa et al 2010) and excludes seasonal or permanent wetlands where surface inundation, or flooding, is not observed. Because a key requirement for wetlands to produce CH4 is by anaerobic soil respiration, where saturated or flooded soil conditions limit oxygen availability and thus create a suitable environment for methanogenesis, the accurate mapping of wetland area is critically important for estimating emissions. We address this issue, and problems related to comprehensively mapping wetland types (Adam et al 2010, Bohn et al 2015), by merging dynamic satellite remote sensing data of surface inundation for the 2000–2012 period (Schroeder et al 2015) with a static inventory of wetlands (Lehner and Doll 2004) following the same definition for natural wetlands used in Matthews and Fung (1987) and Melton et al (2013). These wetland definitions include both permanently and seasonally flooded soils, and include soils with either surface inundation or sub-surface saturation or both. Our definition includes only natural freshwater wetlands that are vegetated, such as peatland systems (bogs and fens) and mineral wetlands (including swamps and marshes), and attempts to avoid double counting of wetland emissions by excluding lakes, rivers, rice cultivation, saline estuaries, salt marshes, and reservoirs, which are typically accounted for as separate CH4 fluxes in global inventories (Saunois et al 2016).

An additional challenge in wetland CH4 emission modeling is that multiple pathways for CH4 production, consumption, and release exist within wetlands; anaerobically produced CH4 is released to the atmosphere after being affected by a combination of processes that include oxidation by methanotrophic bacteria in the soil before diffusion to the atmosphere via plant transport structures known as 'aerenchyma', ebullition or through soil pores. Wetland models represent these biogeochemical and biophysical processes with varying degrees of complexity, with some modeling approaches estimating only the net flux of CH4 as a ratio of CO2 to CH4 production (Christensen et al 1996, Kaplan 2002) and other approaches representing multiple individual processes involved in the production, consumption, and transport of CH4 to the atmosphere (Cao et al 1996, Walter et al 2001, Riley et al 2011, Zürcher et al 2013, Grant et al 2015). The various formulations of model structure, parameterization, and initialization lead to relatively high uncertainties and emphasize the need for an ensemble approach in any comprehensive evaluation of the temporal dynamics and long-term evolution of global wetland CH4 emissions. The use of multiple biogeochemical models also allows for testing hypotheses related to either the climatic sensitivity of methane emissions, versus wetland area, or substrate limitation, for example.

To extend the record of observations, and to consider process-based uncertainties, an ensemble of eleven biogeochemical models that simulate CH4 emissions followed a common protocol (see Methods) to provide monthly integrated global wetland CH4 emissions at 0.5° spatial resolution from 2000–2012. Global wetland area and inundation dynamics were estimated by merging (see Methods) remote sensing based observations of daily surface inundation from the Surface WAter Microwave Product Series (SWAMPS; Schroeder et al 2015) with the static inventory of wetland area from the Global Lakes and Wetlands Database (GLWD; Lehner and Doll 2004). This approach aimed to reduce uncertainties in wetland area estimation using prognostic approaches (Melton et al 2013) and also addressed known issues associated with remote sensing of surface inundation where sub-surface saturation and forested wetlands are poorly detected (Bohn et al 2015). Methane emissions from lakes and rice paddies (Zhang et al 2016a), and soil consumption of atmospheric CH4 (Curry 2007) are excluded from our estimates and included in more a recent multi-sectorial analysis of the global CH4 budget (Saunois et al 2016). The overall objectives of this study were to (i) provide an estimate up to 2012 for global wetland CH4 emissions, (ii) quantify the role of wetlands CH4 emissions on the stabilization period (2000–2006) and the renewed growth period (2007–2012), and (iii) partition the relative role of meteorological drivers, their teleconnections, and wetland area dynamics on wetland CH4 emissions between 2000–2012.

Methods

Wetland area dynamics

To reduce the uncertainty for wetland area dynamics resulting from predictive modeling approaches such as TOPMODEL (Gedney and Cox 2003), we combined remote sensing and inventory data to develop a monthly global wetland area dataset. Current satellite remote sensing of wetlands uses coarse-spatial resolution passive and active microwave sensors, ~25 km2, that observe surface water generally not obscured by vegetation (Bohn et al 2015, Schroeder et al 2015). This includes open water (e.g. lakes, rivers and ocean) as well as surface inundated wetlands comprising mainly of open plant canopies, and thus excludes exposed wetlands with no observable surface flooding as well as surface inundated wetlands beneath closed (forest) canopies. Consequently, whereas ground-based wetland inventories estimate between 8.2 and 10.1 Mkm2 of wetlands globally (Lehner and Doll 2004), remote sensing surface water estimates of wetlands are far lower, i.e. ~6.5 Mkm2 excluding coastal grid regions (Schroeder et al 2015). To develop a comprehensive wetland dynamics dataset, we integrated the Global Lakes and Wetlands Dataset, or GLWD (Lehner and Doll 2004), with the seasonal cycle of surface water inundation from the Surface WAter Microwave Product Series (SWAMPS; Schroeder et al 2015).

The SWAMPS dataset maps fractional surface water dynamics using remote sensing data from multiple passive and active microwave satellite missions using a 28 day screening procedure to mask snow, ice cover, and melting snow. In our analysis, SWAMPS surface inundation was derived from the Special Sensor Microwave Imager version 1 and 2 (SSMI v1/v2), SeaWinds-on-QuickSCAT (QSCAT) from January 2000 to October 2008, and from the European Space Agency Advanced Scatterometer (ASCAT) from November 2008 to December 2012. Land-cover data from MOD12Q1 V004 (Friedl et al 2010) was used to exclude permanent open water (water bodies, rivers, snow/ice) and thus avoid double counting of wetlands, with an additional global FAO land mask (Zobler 1986) applied to remove coastal grid cells where brackish and salt-water wetlands were not considered as a source of methane. The monthly SWAMPS dataset ('fw_28_swe') was re-projected from its native 0.25° EASE grid to a geographic 0.5° rectilinear grid (WGS84) using a conservative remapping interpolation to preserve the original wetland area.

The GLWD Level 3 dataset was first reclassified to remove Classes 1–3, lakes, reservoirs and rivers, and then aggregated by summing wetland area to 0.5° from 30 arc-second resolution. GLWD is commonly used as a benchmark for various remote sensing and wetland mapping activities because it incorporates the highest-quality country-level inventory coverage of wetlands (Peregon et al 2008, Fluet-Chouinard et al 2014, Bohn et al 2015). The integration of SWAMPS and GLWD took place in three phases; first the maximum annual surface water fraction at the per-pixel level (FwMaxx,y) for the 2000–2012 period was compared with GLWD (GLWDmaxx,y) to estimate the relative SWAMPS detection bias (FwMaxCorx,y).

Equation (1)

Second, FwMaxx,y from SWAMPS was adjusted using the FwMaxCorx,y correction factor from equation (1) so that the maximum surface-water fraction from SWAMPS matched the GLWD estimate equation (2). For areas approximately northwest of the Hudson Bay Lowlands, the GLWD classifies the entire region as 'lakes', and so in cases where the merging SWAMPS-GLWD resulted in lower wetland area, the original SWAMPS surface-water values were used equation (3). Seasonal wetlands in desert systems, mapped in the GLWD, were retained in the SWAMPS-GLWD product.

Equation (2)

Equation (3)

Third, the original monthly SWAMPS surface-inundation (Fwx,y,m) was rescaled equation (4) for each year as a fraction of that same year's (uncorrected) maximum inundation, resulting in a unique monthly scalar (0–1) for each year (FwScalarx,y,m). Lastly, FwMaxCorx,y was multiplied by the annual fractional inundation cycle, FwScalarx,y,m (equation 5).

Equation (4)

Equation (5)

The adjusted SWAMPS-GLWD product results in a maximum wetland area of 10.5 Mkm2, and in agreement with the GLWD and other studies (Fluet-Chouinard et al 2014), but the product also maintains the seasonal cycle and inter-annual trends of inundation mapped by SWAMPS. Key wetland areas are retained in the SWAMPS-GLWD in areas such as Amazonia, the Congo Basin, and the Western Siberian Lowlands, which in previous studies have been poorly represented (Bohn et al 2015).

We also conducted a sensitivity test to account for how differences in view angle between the QSCAT and ASCAT instruments might influence trends in surface inundation. SWAMPS accounted for changes in the angle-of-incidence between sensors by applying a time-averaged normalization approach to the backscatter retrievals (Schroeder et al 2015), however sensor-based offsets in grid cells with low surface inundation may affect the trends. We removed low surface-inundation grid cells (defined by their maximum annual value) using a per-pixel threshold of 0.5%, 1%, 2.5% and 5%, and compared the change in methane emissions for each scenario with no filter applied.

Modeling protocol and other driver data

A common simulation protocol was followed by each of the wetland modeling teams (listed in table S1 available at http://stacks.iop.org/ERL/12/094013/mmedia) using standardized climate, atmospheric CO2 and dynamic wetland area data (used to map CH4 producing regions), and also to specify boundary conditions for model spin-up and transient runs. CRU-NCEP v4.0 was used as the meteorology data, which includes long and shortwave radiation, air pressure, specific humidity, total precipitation, air temperature, and wind speed and direction. CRU-NCEP v4.0 combines the higher spatial resolution of CRU TS3.22 (Harris et al 2013) with the higher temporal resolution from the NCEP Reanalysis product (Kanamitsu et al 2002), to produce a meteorological forcing dataset that covers years 1901–2012 at 6 hourly temporal and 0.5 degree spatial resolution, and is used as the climate driver for biogeochemical models included in the annual Global Carbon Project CO2 budget (Le Quéré et al 2015). Global atmospheric CO2 concentrations were provided at an annual resolution for 1860–2012, with data prior to 1958 from ice cores (Joos and Spahni 2008) and after 1958 from the average of NOAA measurements at Mauna Loa (MLO) and the South Pole (SPO) stations.

Models were run to equilibrium during a spin-up phase where the first thirty years of climate data, 1901–1930, were recycled with pre-industrial CO2 concentrations ~276 ppm). Soil texture data was prescribed using model-specific global soil databases such as the Harmonized World Soils Database (FAO/IIASA/ISRIC/ISSCAS/JRC 2012) and with pedo-transfer functions (i.e. Cosby et al 1984) to determine water-holding capacity. Land use (i.e. agriculture or pasture) and land-cover change were not simulated, and CH4 emissions by fire excluded from our analysis. Modeling groups used their default vegetation distributions determined by either a dynamic vegetation model or by prescribed satellite vegetation products (Poulter et al 2015).

Table 1. Wetland methane emissions in Tg CH4 yr−1 for each of the 12 TRANSCOM regions (Gurney et al 2003), with codes as defined in figure 1. The emissions are presented as averaged over the stabilization period (2000–2006), the increasing period (2007–2012) and for 2012. The uncertainty range is estimated as the standard deviation of the wetland CH4 model ensemble (n = 11).

Region 2000–2006 2007–2012 2012
Boreal      
Boreal N America (NABo) 25.1 ± 11.3 26.1 ± 11.8 27.1 ± 12.5
Boreal Eurasia (EuBo) 11 ± 5.3 11 ± 5.2 10.7 ± 5.2
Europe (EURO) 5.7 ± 2.5 5.9 ± 2.6 6.1 ± 2.6
Temperate      
N America (NATe) 16.2 ± 5.6 16.4 ± 5.7 17.6 ± 5.9
S America (SATe) 13.4 ± 3.6 12.1 ± 3.2 11.9 ± 3.4
Eurasia (EUTe) 15.1 ± 7.1 14.8 ± 7.2 14.9 ± 7.4
Tropical      
S America (TrSA) 38.5 ± 9.3 37.4 ± 9.2 36.8 ± 9.1
Asia (TrAs) 22.5 ± 3.7 23.2 ± 3.7 23.9 ± 3.8
Africa (TrAf) 8.4 ± 1.9 8.0 ± 1.7 8.3 ± 1.8
Semi Arid      
N Africa (NAfr) 8.5 ± 3.7 8.8 ± 3.6 8.3 ± 3.3
S Africa (SAfr) 9 ± 1.9 9.2 ± 2 9.2 ± 1.8
Australia (AUST) 2.7 ± 1.5 2.7 ± 1.4 2.6 ± 1.3
Global 184 ± 21.1 183.5 ± 23.1 185.7 ± 23.2

Atmospheric CH4 observations

Atmospheric observations of CH4 were accessed from the NOAA ESRL cooperative air sampling network (Dlugokencky et al 1994). We carried out a comparison of wetland CH4 emissions with atmospheric growth rate data from surface flask measurements at MLO and with the globally averaged marine surface annual mean dataset, which uses selected sites representative of a well-mixed marine boundary layer. In addition to anthropogenic contributions, the growth rate at MLO integrates terrestrial flux processes and has been demonstrated to be useful as a representative station for diagnosing CO2 and CH4 exchange between the biosphere and atmosphere (Fung et al 1991, Dlugokencky et al 1995, Bousquet et al 2006, Wang et al 2014, Meng et al 2015). Annual mean CH4 concentrations from 2000–2012 were first detrended, removing the long-term increase in CH4 concentrations following 2006, because we were interested in evaluating the role of interannual climate variability on CH4 emissions (assuming minimal variability in OH at interannual timescales) and then using a conversion of 2.78 Tg CH4 per ppb to estimate changes in the 'atmospheric burden' (Fung et al 1991). The interannual variability of CH4 concentrations and emissions was then calculated as Yi+Y(i+1) (where Y = year). As could be expected, the variability in the MLO observations was more highly correlated with wetland CH4 emission variability than with the globally averaged observations, where the averaging across multiple, mainly marine, stations across latitudes partly dampens the contribution from land to inter-annual variability. Thus, in the following, only the MLO observations are used to discuss the contribution of the modeled fluxes to atmospheric variability.

Results

Global and regional trends in wetland CH4 emissions (2000–2012)

For the stabilization period (2000–2006), global wetland CH4 emissions were estimated at 184 ± 21 Tg CH4 yr−1, where the uncertainty is estimated as one standard deviation of the model ensemble mean. Wetland emissions remained statistically similar during the period of renewed growth (2007–2012) at 183 ± 23 Tg CH4 yr−1, with a slightly larger value in the last year of analysis, 2012, of 186 ± 23 Tg CH4 yr−1 (table 1). For both time periods, tropical biomes, defined in figure 1(a) as regions 7 to 9, dominated the global flux with representative 2012 emissions, for example, of 69 ± 12 Tg CH4 yr−1, followed by boreal (44 ± 19 Tg CH4 yr−1), temperate (44 ± 10 Tg CH4 yr−1), and sub-tropical biomes (20 ± 5 Tg CH4 yr−1). The global fluxes are consistent with a range of previously published estimates using satellite based approaches, i.e. 170 Tg CH4 yr−1 (Bloom et al 2010), atmospheric inversions, i.e. 149–159 Tg CH4 yr−1 (Ghosh et al 2015) and 165 ± 9 Tg CH4 yr−1 (Bousquet et al 2011), process-based models, i.e. 190 ± 39 Tg CH4 yr−1 (Melton et al 2013) and a combination of inversion and process-model, i.e. 172 Tg CH4 yr−1 (Spahni et al 2011).

Figure 1.

Figure 1. (a) Map of regions, with codes defined in table 1, used to partition globe into boreal (code 1, 2, 3), temperate (code 4, 5, 6), tropical (code 7, 8, 9) and arid biomes (code 10, 11, 12). (b) Change in mean annual wetland methane emissions from the stabilization period to the increasing period. (c) Change in mean annual wetland area from SWAMPS-GLWD, used as the diagnostic model input to the global wetland methane models. Error bars represent standard error.

Standard image High-resolution image Export PowerPoint slide

Between the two time periods (the 2000–2006 'stabilization' and the 2007–2012 'renewed growth' periods), no statistically significant change in the average model ensemble emissions (two-sided Student's t-test; α = 0.1) was found at the global scale or regionally (figure 2(a) and figure 3(a)). Among individual models, the change in global emissions between 2000–2006 and 2007–2012 ranged from a 5.4 Tg CH4 yr−1 decrease for ORCHIDEE to an increase of 4.8 Tg CH4 yr−1 for LPJ-MPI, with an ensemble average change of −0.5 ± 0.9 Tg CH4 yr−1 (figure 1(b)). At the regional scale (figure 1(b)), an increase in boreal wetland CH4 emissions of 1.2 ± 0.3 Tg CH4 yr−1 was found for the ensemble, with only CLM4.5 estimating reduced emissions of 0.2 Tg CH4 yr−1 and with LPJ-MPI providing the largest increase of 3.5 Tg CH4 yr−1. Six of the individual models had statistically significant increasing trends for boreal CH4 emissions (linear regression, p < 0.1) and of these six, all models agreed with an increase in emissions occurring for June−August (JJA) and September−November (SON). A decrease in tropical emissions of 0.9 ± 0.3 Tg CH4 yr−1 between 2000–2006 and 2007–2012 was observed across the model ensemble, with just two models estimating an increase (CLM4.5 and LPJ-MPI, 0.1 and 1.1 Tg CH4 yr−1, respectively) and ORCHIDEE estimating the largest decrease of 3.1 Tg CH4 yr−1. Four of the individual models had a statistically significant (p < 0.1) decrease in tropical CH4 emissions between JJA and SON. Changes in tropical wetland emissions were sensitive to the filtering of the low-surface inundated wetlands, carried out to detect inter-sensor bias, but no statistically significant change was detected (table S3). A decrease in temperate regional emissions of 1.4 ± 0.4 Tg CH4 yr−1 and almost no change in semi-arid emissions (0.4 ± 0.2 Tg CH4 yr−1) was also obtained for the ensemble, but with a larger spread across models than for the boreal and tropical regions.

Figure 2.

Figure 2. Time series for the anomalies of (a) global, (b) boreal, and (c) tropical wetland CH4 emissions, satellite-derived wetland area, and air temperature. Monthly anomalies were estimated relative to their long-term monthly mean (2000–2012). Wetland CH4 emission anomalies are shown for each model (grey lines) and for the model ensemble (see figure 1 in Saunois et al (2016) for anomalies of atmospheric concentrations). A 12 month running mean is applied to the ensemble mean time series (thick line) and forcing data.

Standard image High-resolution image Export PowerPoint slide
Figure 3.

Figure 3. Annual trends, from 2000–2012, in (a) the ensemble averaged CH4 emissions, (b) wetland area, (c) annual precipitation, and (d) air temperature. The black stippling highlights statistically significant trends (p < 0.1). Percent changes are also shown in figure S4.

Standard image High-resolution image Export PowerPoint slide

Climatic and physical drivers of regional CH4 trends

During the 2000–2012 period, a linear regression analysis with climate forcing based on the Climate Research Unit, CRU TS3.22 (Harris et al 2013), masked to match wetland containing grid cells only (as an average over the 2000–2012 time period), revealed variable spatial and seasonal trends in precipitation and air temperature (for annual trends, see figure 3(c) and (d). Total global December–February (DJF) precipitation increased by 2.5 mm yr−1 (p < 0.05) but did not change significantly in other seasons (figure 3(c)). Increasing boreal winter (DJF) precipitation contributed to about half of the annual global increase, 1.4 mm yr−1 (p = 0.1), with other boreal seasons showing no change, tropical DJF precipitation increased by 8.2 mm yr−1 (p = 0.01), and semi-arid DJF precipitation increased by 1.8 mm yr−1 (p = 0.04). Global annual air temperature over wetlands was nearly constant (figure 3(d)), and increased at a rate of 0.02 °C yr−1 in JJA (p < 0.05) and 0.04 °C yr−1 in SON (p < 0.05). The change in global air temperature was mainly due to increasing air temperature in boreal regions with a significant (p < 0.1) rate of increase of 0.06 °C yr−1 from 2000–2012 in SON, and in tropical biomes where air temperature also increased slightly in JJA and SON at a rate of 0.02 °C yr−1 (p < 0.1). Cloud cover (not shown) increased by 0.1% yr−1 (p = 0.05) between March–May (MAM) in the tropics and decreased during boreal MAM by −0.2% yr−1 (p = 0.03).

Average annual maximum global wetland area for the merged SWAMPS-GLWD was 10.5 million km2 (see Methods for comparison with original SWAMPS) and was in agreement with the GLWD inventoried global wetland area (Lehner and Doll 2004) used as the basis for several benchmarking activities (Fluet-Chouinard et al 2014, Bohn et al 2015). Globally and regionally, the SWAMPS-GLWD dataset had a similar seasonal phase (figure S1) for wetland area as GIEMS (R2 > 0.85 for all except the semi-arid region) yet SWAMPS-GLWD had larger seasonal amplitude because of the addition of permanent wetlands from GLWD. The overlapping time period for GIEMS and SWAMPS, years 2000–2007, showed no significant trends globally or regionally for both datasets. Between the CH4 stabilization and renewed growth periods, global mean annual wetland area decreased by 93 000 km2 (2% of average annual wetland area, figure 1(c) and figure 3(b)). At the seasonal scale, a large part of the decrease was explained by negative JJA trends in wetland area, where a statistically significant decrease of 27 000 km2 yr−1 was observed (p < 0.01). Much of the seasonal decrease was explained by statistically significant changes in tropical, temperate, and semi-arid JJA wetland area of −4600, −1400, and −6000 km2 yr−1, respectively, with additional changes in DJF tropical (−3500 km2 yr−1) and semi-arid (−7300 km2 yr−1) wetland area observed. In the boreal regions, wetland area increased by 3000 and 16 400 km2 yr−1 in DJF and SON, respectively (p < 0.01). Overall, a complex pattern of regional and seasonal contributions in declining global wetland area was observed, consistent with decadal and multi-decadal observations of land-water storage and open-water bodies (Dieng et al 2015, Donchyts et al 2016), and with tropical wetland area decreasing (−3.4%) and boreal wetland increasing (1.8%) in area (figures 2(b) and (c)) between the stabilization and the renewed growth periods.

Sensitivity of CH4 emissions to climate and wetland area

A partial correlation analysis was carried out to determine the effect of wetland area and climate on CH4 emissions, and to determine the interaction between local climate and large-scale climatic teleconnections, including the Multivariate El Niño Index (MEI) and the North Atlantic Oscillation (NAO), on regional wetland area dynamics. Partial correlation analysis is an appropriate statistic to provide estimates on the correlation coefficient for a set of variables while simultaneously controlling for their interactions. The resulting partial correlations, r, range from −1 to 1 with absolute values closer to unity reflecting higher explanatory power, either with a negative or positive relationship between the independent and dependent variables. Monthly time series for each variable were correlated for the period 2000–2012 and the data were not detrended beforehand because there were no significant trends detected.

The MEI and NAO represent two major global climatic teleconnections, with the MEI linking Pacific sea surface temperature anomalies (lagged by one month) with a warming and drying in tropical regions in its positive El Niño phase and a wetting of mid-latitude arid regions in its negative La Niña phase (Wolter and Timlin 1993). The MEI is similar in its temporal dynamics to the Oceanic Niño Index that uses sea surface temperature anomalies from the Niño 3.4 region. In contrast, the NAO measures the difference in air pressure between the Icelandic low and Azores high (Barnston and Livezey 1987), reflecting mid-to-high latitude climates, with a positive NAO characterized by above average annual temperature and wet winters in Eastern North America and northern Europe and below-average temperatures in the arctic. In contrast, during the negative NAO phase, cooler and drier than average conditions persist in eastern North America and northern Europe, with warmer than average conditions in the Arctic.

For the model ensemble, variability in global CH4 emissions was most highly correlated with wetland area (r = 0.64), followed by temperature (r = 0.37) and with negligible correlations for precipitation (r = 0.09) and cloud cover (r = 0.11). A two to three month lag between the CH4 emissions response and climate increased the precipitation correlation by a small amount, from 0.09 (with no time lag) to 0.11 with a one month lag. Monthly to seasonal scale lags have also been observed in atmospheric inversion and hydrologic studies (Papa et al 2015, Ribeiro et al 2016, Wilson et al 2016) where transit time of water within a basin and other hydrologic processes, such as evapotranspiration, decouple the more immediate interactions between precipitation and emissions. At the regional scale, wetland area was also the most important variable for CH4 emissions, with a correlation of 0.89 and 0.72 in tropical and temperate regions, respectively. In contrast, for the individual models, global CH4 emission for JULES and LPJ-MPI was more highly correlated with air temperature than with wetland area due to their greater temperature sensitivity than other models, whereas the remaining models were correlated first with wetland area, and then with air temperature followed by smaller precipitation or cloud cover correlations. The ranking of global wetland area as the main driver of CH4 production, followed by temperature and then precipitation was similar for the boreal, tropical, temperate, and arid regions, and consistent with results from a multi-model CH4 sensitivity experiment carried out by Melton et al (2013). Overall, the higher air temperature sensitivity of CH4 emissions was responsible for moderate correlations, with varying time lags (t minus number months, n), with the MEI for boreal (rt−0 = −0.16), tropical (rt−6 = 0.33), and temperate emissions (rt−3 = 0.24), with the NAO also only weakly correlated with the model ensemble for boreal regions (rt−3 = −0.13). At the global scale, the MEI and NAO were most highly correlated with CH4 emissions with a five-month lag, rt−5 = 0.26 and rt−5 = 0.08, respectively, slightly lower than in previously published studies (Bousquet et al 2006, Hodson et al 2011).

Wetland area dynamics from the SWAMPS-GLWD dataset at global and regional scales were moderately correlated with air temperature (masked for wetland grid cells), rt−0 = 0.24 globally, and rt−0 = 0.33 for boreal regions, suggesting surface-flooding increased following seasonal permafrost thaw under warmer temperatures (Schuur et al 2015). Precipitation (also masked for wetland grid cells) was weakly correlated with global wetland area (rt−0 = −0.11), and with wetland area in temperate (rt−0 = 0.18), tropical (rt−0 = −0.12), and arid regions (rt−0 = −0.15). The introduction of time lags (up to +6 months) in the climate variables did not significantly improve the correlations with wetland area except in semi-arid regions where a one month lag increased the precipitation correlation with wetland area (rt−1 = 0.30). These results highlight the importance of incorporating sub-grid cell topographic variation, as well as cell-to-cell interactions, when modeling feedbacks between hydrologic flow paths and surface inundation dynamics. At the global scale, the MEI was positively, albeit weakly, correlated with wetland area (rt−5 = 0.33) mainly because of the relationship with tropical (rt−5 = 0.29) and temperate wetland dynamics (rt−5 = 0.25). Boreal regions were negatively correlated with MEI with a one-month lag (rt−1 = −0.14) and both the MEI and NAO positively correlated with wetland area in temperate regions (rt−0 = 0.27 and rt−0 = 0.15, respectively). The moderate correlations, compared with earlier studies, were partly due to the short time series, where between 2000–2012, the NAO was mainly in negative phase (meaning below-average precipitation in mid-high latitudes, cooler eastern North America and northern European temperatures, and warmer arctic conditions), and no large El Niño events, whereas a record magnitude La Niña lasted from late 2009 to 2011 (Evans and Boyer-Souchet 2012). In addition, our regional definitions may also interfere with the strength of the teleconnection correlations by introducing a mix of biome types with varying climatic responses (Zhang et al 2015).

Discussion

High latitude increases and low latitude decreases in CH4 emissions

From 2000–2012, global wetland emissions appear to have remained stable and with regional increasing and decreasing trends closely compensating for one another, with no net contribution to the observed renewed atmospheric growth rate. By shifting the period of comparison to 2003–2005 and 2010–2012 to evaluate the sensitivity of our definition for the stabilization and renewed growth periods, we find only a slightly larger increase in emissions, from 185 ± 22 Tg CH4 yr−1 to 186 ± 24 Tg CH4 yr−1, an average 1.23 ± 1.1 Tg CH4 increase and also not large enough to explain the 2007 renewed atmospheric CH4 growth rate of ~17 Tg CH4 yr−1. Additionally, the increase between the 2003–2005 and 2010–2012 periods is not robust and almost entirely driven by just one model, which also has the highest temperature sensitivity, LPJ-MPI (12.6 Tg CH4 yr−1 increase). The increase in boreal emissions from 2000–2012 appears to be closely linked to both increasing air temperature and wetland area, with an anomalously warm event in 2007 (Bruhwiler et al 2014). In high latitude regions, evidence for warming air temperature is well documented and the feedbacks between increasing air temperature, sea-ice cover loss, and terrestrial CH4 emissions is becoming increasingly clear (Karl et al 2015, Parmentier et al 2015). Finer-temporal and spatial remote-sensing based analyses are also consistent with the evidence presented here for a net increase in boreal wetland area and CH4 emissions from 2003 to 2011 (Watts et al 2014). Overall, these changes are consistent with field observations (Sweeney et al 2016) and with what could be expected from projected climate change and warming impacts on high latitude systems that link temperature sensitivity as a dominant control on arctic wetland CH4 emissions (Schaefer et al 2011, Chen et al 2015, Schuur et al 2015).

In tropical regions, high interannual variability in precipitation makes detecting decadal scale carbon-cycle trends challenging (Jung et al 2010, Zhang et al 2015). In terms of wetland area dynamics, Papa et al (2010) reported a decrease of 19 600 km2 yr−1 in tropical surface inundation between 1993 and 2005 based on the GIEMS data, and losses of tropical surface inundation appear to have continued through 2012 at a rate of 4000 km2 yr−1 (Schroeder et al 2015). As a consequence, declining tropical wetland CH4 emissions have been found in a range of studies using GIEMS, for example, Meng et al (2015), who found a decline of 1.68 Tg CH4 yr−1 from 1993–2004. However, even with models that used fixed or static, i.e. the GLWD, rather than dynamic areal extent of wetlands, declining tropical wetland CH4 emissions were simulated (Zhu et al 2015), suggesting that trends in climatic drivers that force changes in wetland area may be an equally important constraint on tropical CH4 production. Over Amazonia and the Congo Basins, large consecutive droughts, in 2005 and 2010, combined with regional warming, have resulted in widespread declines in tropical forest canopy greenness (Hilker et al 2014, Zhou et al 2014). These Amazonian droughts are superimposed on an intensification of the hydrologic cycle in the wet season (Gloor et al 2013) rather than an increase in the duration of the wet season throughout the year. Declining precipitation trends between 2010–2012 were observed in Western Amazonia and Eastern Congo, but over Southeast Asia, increases in precipitation were observed (figure 3(c)). Degradation of global wetlands due to human activities is also a large component in declining wetland function (Petrescu et al 2015, Donchyts et al 2016), and losses of tropical wetland area due to drainage are the highest globally, ranging up to 2% per year (Davidson 2014, Papa et al 2015).

Outside of tropical regions, declining wetland area in temperate regions also coincided with long-term drying of soils from 1950–2005 (Mueller and Zhang 2015), however the precipitation trends from 2000–2012 of relevance in this study suggest soil moisture actually increased in this time period (figure 3(c)). Semi-arid regions, i.e. eastern Australia and South America, showed decreasing annual precipitation trends, despite large swings in seasonal precipitation related to a series of strong La Niña events (Boening et al 2012).

Figure 4.

Figure 4. Interannual variability of detrended atmospheric methane concentrations, represented at the atmospheric burden in Tg CH4, as measured at the Mauna Loa Observatory (Tg CH4 yr−1), approximately equivalent to the mean global concentrations, and interannual variability in wetland methane emissions for global, boreal, and tropical wetlands. Bars represent the model ensemble mean (n = 11), and the error bars represent one standard error.

Standard image High-resolution image Export PowerPoint slide

Role of teleconnections on interannual variability of wetland area and CH4 emissions

ENSO has previously been highlighted as a key driver of interannual variability in global wetland CH4 production (Bousquet et al 2006, Hodson et al 2011, Dalsøren et al 2015). Here, we find a possibly lower role for ENSO in driving global wetland CH4 production that is due in part to i) the duration of the brief time series where no strong El Niño was observed and ii) the use of a new integrated wetland–surface–water dataset. Previous analyses have used longer time series, such as Hodson et al (2011), who scaled modeled soil-moisture to wetland area based on a GIEMS calibrated hydrologic model. For the time period 1950–2005 they found a slightly higher global correlation of global CH4 emissions with ENSO, R2 = 0.39 (with a three-month lag) and R2 = 0.56 for the tropics. In addition, using an atmospheric inversion, Bousquet et al (2006) partitioned CH4 emissions to anthropogenic and natural sources for the period 1984–2003. The study concluded that the dominant role of CH4 surface sources was from high interannual variability of wetland area that was synchronized with ENSO. The longer 1950–2005 time period includes a wider range of both positive and negative phase ENSO events, whereas the 2000–2012 period evaluated here includes two major La Niña and only moderate El Niño events.

In addition, many studies have relied on GIEMS surface inundation data to constrain wetland areal dynamics, and have found GIEMS to be highly correlated with ENSO (Prigent et al 2007). While the new SWAMPS-GLWD dataset used here was found to enhance seasonal variation in wetland area, the dataset also partially decoupled the interannual surface-water variability from climate because of the integration of permanently inundated wetlands (with no surface flooding) from GLWD, to address known limitations in microwave remote sensing of wetlands, particularly in forested areas (Bohn et al 2015). We evaluated how the development of the SWAMPS-GLWD wetland dynamics dataset affected interannual variability (IAV) of wetland CH4 emissions by comparing with the detrended observations of atmospheric CH4 variability from MLO. We found the observed IAV at MLO to range from −13 to 22 Tg CH4 yr−1 from 2000 to 2012 (figure 4). In comparison, the IAV of the SWAMPS-GLWD driven wetland model ensemble ranged from −13 to 19 Tg CH4 yr−1, and across models, the range varied from small IAV (−8 to 1 Tg CH4 yr−1 for CTEM) to large IAV (3 to 19 Tg CH4 yr−1 for ORCHIDEE). Compared to observations, the sensitivity of the model ensemble results provide confidence in the use of SWAMPS-GLWD for partially driving a CH4 IAV consistent with previous top-down and isotopic studies, e.g. Bousquet et al (2006), that demonstrate wetland CH4 emissions explain a large portion of the IAV in atmospheric growth (r = 0.46 for the model ensemble, with the individual models ranging from r = 0.2 (TRIPLEX-GHG) to r = 0.6 (SDGVM)). Notably, the contribution of boreal wetlands to global CH4 IAV appears to decline from 2000–2012 relative to an increase from tropical contributions (figure 4), however, overall we found a trend toward decreasing IAV in the observed CH4 growth rate. Wildfires, not considered in this study, can contribute between 10–20 Tg CH4 yr−1 of emission IAV, however no significant trend over time has been observed to date (van der Werf et al 2006, van der Werf et al 2010, Worden et al 2013). Additionally, year-to-year variability in the atmospheric oxidative sink for methane may also affect variability in growth rate anomalies (Rigby et al 2008).

Uncertainties from additional biogenic CH4 sources

The depletion of atmospheric δ13C of CH4 since 2007 presents three scenarios, (i) a change in average biogenic wetland δ13C source signature, (ii) an overall increasing biogenic source, (iii) a decreasing thermogenic or pyrogenic source, or some combination of all (Kirschke et al 2013). Thermogenic, or fossil-fuel related emissions, are unlikely to have decreased in recent years (Bergamaschi et al 2013, Nisbet et al 2014), and recent studies based on isotopic δ13C confirm a large biogenic source (Dlugokencky et al 2011, Rice et al 2016, Schaefer et al 2016). In addition to the wetland types considered in this study, there are several additional sources of biogenic emissions that could contribute to the depletion of atmospheric δ13C. These include river systems (Bastviken et al 2011, Borges et al 2015), lakes (Verpoorter et al 2014, Tan and Zhuang 2015), and agriculture (Leff et al 2004, Chen et al 2013). For example, taken together, river and lake system CH4 emissions are highly uncertain and are estimated to emit as much as 100 ± 50 Tg CH4 yr−1 (Bastviken et al 2011), or the equivalent of ~30%–50% of global wetland emissions, and would require a reassessment of other source terms to close the global methane budget (Saunois et al 2016). These emission hotspots are also geographically distributed across arctic (Walter Anthony et al 2014), temperate (Chen et al 2013) and tropical systems (Borges et al 2015). The temporal response of agricultural CH4 emissions (excluding biomass burning) is poorly understood, yet agriculture accounts for about 30% of total wetland CH4 emissions (Kirschke et al 2013) and is produced from rice cultivation and enteric fermentation of livestock ruminants. These agricultural emissions can change on annual to decadal time scales in response to climate (Li et al 2002), but also in response to farming practices where land management can rapidly respond to socio-economic drivers (Chen et al 2013) and contribute to atmospheric IAV and to long-term trends (Tian et al 2016).

Reducing biogenic source uncertainty

By using a multi-model approach to investigate the temporal trends and spatial patterns in global CH4 emissions, the model uncertainty can be quantified more robustly. Here, the sources of uncertainty can be partitioned to (i) driver data, (ii) model structure, and (iii) parameter uncertainty. By providing a consistent set of climate, atmospheric CO2, and wetland area data, the model spread was reduced from 123 Tg CH4 yr−1 (WETCHIMP, which used a similar model ensemble) to 80 Tg CH4 yr−1 (this study). This reduction highlights that uncertainties in wetland area are almost equally important to our mechanistic understanding of in situ CH4 production and consumption processes. Combining SWAMPS and GLWD led to wetland area estimates consistent with more detailed regional estimates for Amazonia (Wilson et al 2007, Draper et al 2014, Hess et al 2015), southeast Asia (Hooijer et al 2010), and high-latitude systems, such as the Western Siberia Lowlands (Bohn et al 2015, Zhang et al 2016b).

Model structure is another key source of uncertainty (Wania et al 2013, Xu et al 2016a), as illustrated by the range of temperature-emission sensitivities for the current model ensemble. About half of the models used here (JULES, LPJ-wsl, ORCHIDEE, SDGVM, CTEM) were based on the semi-empirical model approach of Christensen et al (1996), whereas the other models (LPX-Bern, LPJ-MPI, CLM4.5, TRIPLEX-GHG) were based on more mechanistic first order approaches based on the framework developed by Walter et al (2001), see table S2 for a summary. One topic of large uncertainty are the oxidative processes that consume CH4 (Ridgwell et al 1999), and that may change over time and alter the CO2:CH4 production ratios used in the semi-empirical approaches (Curry 2007). However, there was no clustering of model structure in terms of global or regional emission trends.

In addition to meteorological and wetland area interannual variability, atmospheric CO2 rose by 24 ppm to 393 ppm from 2000 to 2012 (based on observations from Mauna Loa, MLO). Net primary production in carbon cycle models tends to respond positively to trends in elevated CO2 (Hickler et al 2008), and would be expected to provide a sustained increase in substrate in the form of soil organic carbon for anaerobic processes to produce CH4. A strong CO2 driven response in CH4 emissions was not observed by the ensemble mean because of the high IAV of climate and wetland area that appear to be more limiting for CH4 emissions than substrate. Over longer timescales, i.e. multi-decadal to centennial, a strong CO2 feedback on CH4 emissions is expected, with simulated increases in global emissions of up to 73% ± 49% at 857 ppm CO2 (Melton et al 2013).

Lastly, model parameters are difficult to robustly estimate because CH4 production occurs in complex landscapes where anaerobic soil conditions can be very heterogeneous. To estimate emissions at scales of 50 km2 or larger, where CH4 production may be occurring in small topographic depressions, remains a large challenge (Lara et al 2015, Shi et al 2015). While the area-weighted monthly-average flux estimates for the model ensemble ranged within observations, i.e. from 2.7 to 3.9 g CH4 m−2 month−1 globally, 0.7 to 3.1 g CH4 m−2 month−1 for boreal wetlands (observations 2–4 g CH4 m−2 month−1 in boreal systems (Turetsky et al 2014), and 5.2 to 8.2 g CH4 m−2 month−1 for tropical wetlands (observations 0.1 to 29 g CH4 m−2 month−1 (Sjogersten et al 2014). Benchmarking of process models with flux tower measurements or airborne campaigns remains critical for improving model structure and parameters (Miller et al 2016) and addressing scaling artifacts that may obscure non-linear methane production and consumption processes.

Conclusions

Key findings

Interpreting the interannual and decadal dynamics of the CH4 atmospheric growth rate has presented significant challenges over the past three decades, with the sources and sinks remaining poorly understood (Kirschke et al 2013). Using an ensemble of global wetland models constrained with satellite and inventory based surface inundation and wetland area seasonality and trends, we now provide a comprehensive and updated estimate of the role of wetlands in the recent increase of the atmospheric growth rate that began in 2007. We show that the role of wetlands in the renewed period of atmospheric CH4 growth appears minimal to non-existent, and that:

  • At the global scale, wetland CH4 emissions have remained constant from 2000–2012 at 184 ± 22 Tg g CH4 yr−1 but that significant spatial variability in trends are masked by the global perspective (figure S2 and S3).
  • In boreal regions, increasing CH4 emissions corresponds to increasing wetland area and air temperature, whereas in the tropics, decreasing wetland area and large variability in precipitation has led to decreased emissions.
  • At global and the regional scales defined in our study, the role of climatic teleconnections such as ENSO and the NAO are smaller than what has been reported in previous work; however, we confirm that the IAV of the atmospheric growth rate is largely explained by wetlands.
  • The interannual variability in global wetland emissions is dominated by boreal regions from 2000–2006 and then with increasing contribution from tropical regions possibly coinciding with larger droughts over Amazonia and the Congo Basin (figure 4). However, there has been no consistent shift in the IAV of wetland CH4 emissions over the 2000–2012 time period (figure 4).
  • The range of the modelled interannual variability in global wetland emissions in 2007–2012 is similar to the IAV observed at the MLO station, while it is less than observed for 2000–2006. Therefore, the period 2000–2006 is anomalous not only due to the absent trend in the growth rate of atmospheric CH4 concentrations, but also due to anomalously high IAV not fully explained by natural wetland emissions.
  • Our results, interpreted in the context of a depletion in atmospheric δ13C observed since 2007, suggests that either a shift in δ13C biogenic source signature occurred or other agricultural biogenic sources are required to explain the recent and sustained atmospheric increase in CH4, or that, less likely, a decrease in thermogenic and pyrogenic emission has occurred. This is consistent with recent work of Schaefer et al (2016) who present isotopic evidence suggesting an increasing role of livestock and agriculture in the growth rate of atmospheric CH4.
  • The pattern of increasing high-latitude emissions and possibly decreasing to stable tropical emissions are consistent with climate change projections that forecast a general increase in boreal air temperatures and a decrease in tropical precipitation (Scholze et al 2006). Thus the past decade presents an observational test case for climate and socio economic impact studies on CH4 production (Lawrence et al 2015, Petrescu et al 2015).
  • To reduce uncertainties in wetland dynamics mapping we recommend that (1) multi-platform remote sensing using both radar and optical observations are integrated at higher spatial resolution to resolve issues associated with low-detection probabilities in closed-forest canopy regions, (2) that inter-sensor calibration and effects on inter-annual and seasonal trends are clearly accounted for, and (3) that ground-based wetland inventories are continually updated and made available to benchmark and calibrate remote sensing algorithms, and with clear terminology to avoid double counting.

Acknowledgments

We acknowledge the data providers, especially for updating the gridded climate (CRU) and CO2 and CH4 concentration time series (NOAA ESRL). B.P., P.C., P.B. were supported by the EU FP7 GEOCARBON Programme (283080) and B.P. and Z.Z. were supported on the Swiss CCES MAIOLICA2 project #42V01 and F.J. and R.S. acknowledge support by the Swiss National Science Foundation and the EU FP7 project Past4Future (grant no. 243908). Met Office authors were supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). W.R., C.K., and X.X. were supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy under Contract No. DE-AC2–5CH11231. H.T. and B.Z. were supported by NASA grants (NNX14AF93G; NNX14AO73G) and NSF grants (AGS 1243232, CNH1210360). D.B, D.W, and L.T gratefully acknowledge support by NERC grant (R/131682). J.G.C. thanks the support from the National Environmental Science Program—Earth System and Climate Change Hub. J.G.C. thanks the support from the National Environmental Science Program—Earth System and Climate Change Hub. S.F.A., A.I. and M.S. were supported by the Environment Research and Technology Development Fund (2-1710) of the Ministry of the Environment, Japan, and Environmental Restoration and Conservation Agency. T.K. was supported by the German Ministry of Education and Research (grants no. 03G0836C and 01LP1507B).

Link to supplementary data
Supplementary data (753 KB, PDF)