Abstract
Abstract. Wetlands are the largest and most uncertain natural sources of atmospheric methane (CH4). Several process-based models have been developed to quantify the magnitude and estimate spatial and temporal variations in CH4 emissions from global wetlands. Reliable models are required to estimate global wetland CH4 emissions. This study aimed to test two process-based models, CH4MODwetland and Terrestrial Ecosystem Model (TEM), against the CH4 flux measurements of marsh, swamp, peatland and coastal wetland sites across the world; specifically, model accuracy and generality were evaluated for different wetland types and in different continents, and then the global CH4 emissions from 2000 to 2010 were estimated. Both models showed similar high correlations with the observed seasonal/annual total CH4 emissions, and the regression of the observed versus computed total seasonal/annual CH4 emissions resulted in R2 values of 0.81 and 0.68 for CH4MODwetland and TEM, respectively. The CH4MODwetland produced accurate predictions for marshes, peatlands, swamps and coastal wetlands, with model efficiency (EF) values of 0.22, 0.52, 0.13 and 0.72, respectively. TEM produced good predictions for peatlands and swamps, with EF values of 0.69 and 0.74, respectively, but it could not accurately simulate marshes and coastal wetlands (EF <0). There was a good correlation between the simulated CH4 fluxes and the observed values on most continents. However, CH4MODwetland showed no correlation with the observed values in South America and Africa. TEM showed no correlation with the observations in Europe. The global CH4 emissions for the period 2000–2010 were estimated to be 105.31 ± 2.72 Tg yr−1 by CH4MODwetland and 134.31 ± 0.84 Tg yr−1 by TEM. Both models simulated a similar spatial distribution of CH4 emissions globally and on different continents. Marshes contribute 36 %–39 % of global CH4 emissions. Lakes/rivers and swamps are the second and third greatest contributors, respectively. Other wetland types account for only approximately 20 % of global emissions. Based on the model applicability, if we use the more accurate model, i.e., the one that performs best as evidenced by a higher model efficiency and a lower model bias, to estimate each continent and wetland type, we obtain a new assessment of 116.99–124.74 Tg yr−1 for the global CH4 emissions for the period 2000–2010. Our results imply that performance at a global scale may conceal model uncertainty. Efforts should be made to improve model accuracy for different wetland types and regions, particularly hotspot regions, to reduce the uncertainty in global assessments.
Highlights
Atmospheric methane (CH4) is the second most prevalent human-induced greenhouse gas (GHG) after carbon dioxide (CO2)
Other models are based on more complex land ecosystem models coupled to the CH4 processes module, such as Community Land Model 4 Methane model (CLM4Me), Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE), Sheffield Dynamic Global Vegetation Model (SDGVM) and Terrestrial Ecosystem Model (TEM)
The regression of the observed versus computed total seasonal/annual CH4 emissions by TEM (Fig. 2b) resulted in an R2 of 0.68, with a slope of 0.74 and an intercept of 4.77 g m−2 (n = 58, p < 0.001). These results indicated that the variations in the CH4 emissions between sites and in different years could be delineated by both process-based models
Summary
Atmospheric methane (CH4) is the second most prevalent human-induced greenhouse gas (GHG) after carbon dioxide (CO2). The radiative forcing attributed to CH4 has been re-evaluated by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and was reported to be almost twice as high as the value reported in the Fourth Assessment Report (AR4), with values of 0.97 W m−2 versus 0.48 W m−2, respectively (Myhre et al, 2013) This estimate considers that the emission of CH4 leads to an increase in ozone production, stratospheric water vapor and CO2, which can affect its own lifetime (Boucher et al, 2009; Myhre et al, 2013; Shindell et al, 2012). The causes that drive the variations in growth rate remain unclear due to the uncertainties in estimating CH4 emissions and sinks (Ghosh et al, 2015; Saunois et al, 2016; Nisbet et al, 2019; Dalsøren et al, 2016)
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