Abstract
Abstract. Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH4) budget, largely due to poorly constrained process controls on CH4 production in waterlogged soils. Process-based estimates of global wetland CH4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH4 emission estimates. Here we construct a global wetland CH4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs version 1.0). Our 0.5° × 0.5° resolution model ensemble is based on satellite-derived surface water extent and precipitation reanalyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature dependence parameterizations for the period 2009–2010; an extended ensemble subset based solely on precipitation and the data-constrained terrestrial carbon cycle analysis is derived for the period 2001–2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare the model against surface measurements of atmospheric CH4; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH4 emissions scenarios. We find that uncertainties in carbon decomposition rates and the wetland extent together account for more than 80 % of the dominant uncertainty in the timing, magnitude and seasonal variability in wetland CH4 emissions, although uncertainty in the temperature CH4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for top-down estimates of wetland CH4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH4 emission uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain the biogeochemical process controls of global wetland CH4 emissions.
Highlights
Methane (CH4) is a potent greenhouse gas with a global warming potential more than 25 times that of CO2 on a 100year time horizon (Myhre et al, 2013)
Global monthly 0.5◦ × 0.5◦ emissions and their associated uncertainty structure span 2009–2010; we evaluate a subset of the model ensemble spanning 2001–2015
We evaluate the full ensemble (FE) and EE wetland CH4 emission means against the World Data Centre for Greenhouse Gases (WDCGG) CH4 measurement sites by incorporating these into the 4◦ × 5◦ resolution GEOS-Chem atmospheric chemical and transport model
Summary
Methane (CH4) is a potent greenhouse gas with a global warming potential more than 25 times that of CO2 on a 100year time horizon (Myhre et al, 2013). From a greenhouse gas balance standpoint, quantifying the global-scale process links between terrestrial carbon cycling and wetland CH4 emissions is crucial to characterizing the combined terrestrial biosphere CO2 and CH4 flux response to climatic variability. The Wetland CH4 Inter-comparison of Models Project (WETCHIMP) model ensemble (Melton et al, 2013) reveals varying levels of spatial and temporal agreement between models; these correlations stem from large-scale patterns in biogeochemical process controls (such as temperature, inundation and carbon cycling). In contrast to a conventional process-based model inter-comparison approach, our wetland CH4 emission ensemble members are derived by exhaustively combining a range of temperature, carbon and wetland extent parameterizations. We formulate a full (2009–2010) and extended (2001– 2015) estimate of wetland CH4 emission magnitude and its associated biogeochemical covariance structure, based on knowledge of the global wetland CH4 source and the primary biogeochemical process controls. We summarize the strengths and limitations of our wetland emissions ensemble and outline its potential applications in global atmospheric inversion frameworks (Sect. 4)
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