Seasonal variability of tropical wetland CH<sub>4</sub> emissions: the role of the methanogen-available carbon pool

Abstract

Abstract. We develop a dynamic methanogen-available carbon model (DMCM) to quantify the role of the methanogenavailable carbon pool in determining the spatial and temporal variability of tropical wetland CH 4 emissions over seasonal timescales. We fit DMCM parameters to satellite observations of CH 4 columns from SCIAMACHY CH 4 and equivalent water height (EWH) from GRACE. Over the Amazon River basin we found substantial seasonal variability of this carbon pool (coefficient of variation = 28 ± 22 %) and a rapid decay constant (φ = 0.017 day −1 ), in agreement with available laboratory measurements, suggesting that plant litter is likely the prominent methanogen carbon source over this region. Using the DMCM we derived global CH 4 emissions for [2003][2004][2005][2006][2007][2008][2009], and determined the resulting seasonal variability of atmospheric CH 4 on a global scale using the GEOS-Chem atmospheric chemistry and transport model. First, we estimated that tropical emissions amounted to 111.1 Tg CH 4 yr −1 , of which 24 % was emitted from Amazon wetlands. We estimated that annual tropical wetland emissions increased by 3.4 Tg CH 4 yr −1 between 2003 and 2009. Second, we found that the model was able to reproduce the observed seasonal lag of CH 4 concentrations peaking 1-3 months before peak EWH values. We also found that our estimates of CH 4 emissions substantially improved the comparison between the model and observed CH 4 surface concentrations (r = 0.9). We anticipate that these new insights from the DMCM represent a fundamental step in parameterising tropical wetland CH 4 emissions and quantifying the seasonal variability and future trends of tropical CH 4 emissions.

Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

“…3 Chang et al (2014) 2.1 ± 0.5 Bloom et al (2012) 111.1 Bousquet et al (2011) 151 ± 10 91 ± 11 Bloom et al (2010) 165 ± 50 91 ± 28 4.9 ± 1.4…”

What are the greenhouse gas observing system requirements for reducing fundamental biogeochemical process uncertainty? Amazon wetland CH<sub>4</sub> emissions as a case study

“…In contrast to our approach, CH 4 flux uncertainty requirements can alternatively be derived by quantifying processbased wetland CH 4 emission model uncertainty (Melton et al, 2013) or by characterizing the CH 4 flux uncertainty stemming from wetland CH 4 model parametric uncertainty (Bloom et al, 2012). An advantage of model-based requirements is the ability to assess CH 4 flux uncertainties associated with the complex interactions between wetland CH 4 processes (e.g., Riley et al, 2011).…”

“…By and large, Amazon wetland CH 4 emissions dominate both the magnitude and uncertainty of global wetland CH 4 emissions (Melton et al, 2013). Estimates of Amazon wetland CH 4 emissions range between 20 and 60 Tg CH 4 year −1 (Fung et al, 1991;Riley et al, 2011;Bloom et al, 2012;Melack et al, 2004), roughly equivalent to 10-30 % of the global wetland CH 4 source. Major uncertainties are also associated with the spatial and temporal variability of CH 4 emissions (Fig.…”

“…Measurement and model-based analyses of Amazon wetland CH 4 emissions provide a range of contradictory estimates on spatial patterns and seasonality (Devol et al, 1990;Riley et al, 2011;Bloom et al, 2012;Melton et al, 2013;Basso et al, 2016) suggesting that the basin-wide process controls on wetland CH 4 emissions remain virtually unknown. Here, our aim is to provide a first-order, model-independent characterization of wetland CH 4 flux resolution and precision requirements based on the basin-wide variations in carbon and hydrological processes.…”

“…(Miyajima et al, 1997), composition (Wania et al, 2010), temporal dynamics (Bloom et al, 2012) and C stocks together drive spatial and temporal variability of carbon limitation on CH 4 production in wetlands. C cycle state variables, including the spatial variability of total biomass (Saatchi et al, 2011;Baccini et al, 2012) and soil carbon (Hiederer and Köchy, 2011), vary at < 1000 km scales.…”