A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0)

Abstract

Abstract. Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH 4 ) budget, largely due to poorly constrained process controls on CH 4 production in waterlogged soils. Process-based estimates of global wetland CH 4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH 4 emission estimates. Here we construct a global wetland CH 4 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 CH 4 ; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH 4 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 CH 4 emissions, although uncertainty in the temperature CH 4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH 4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for topdown estimates of wetland CH 4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH 4 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 CH 4 emissions.

High-resolution inversion of methane emissions in the Southeast US using SEAC<sup>4</sup>RS aircraft observations of atmospheric methane: anthropogenic and wetland sources

“…1) for August-September 2013. The prior anthropogenic emissions are from the EPA national inventory for 2012 (EPA, 2016;Maasakkers et al, 2016) and the prior wetland emissions are the means of the WetCHARTs extended ensemble (Bloom et al, 2017). Error bars (one standard deviation) on sectoral emissions are from the prior and posterior error variances of our inversion.…”

“…We can attribute the 0.25 • × 0.3125 • scaling factors from the inversion to specific methane source sectors by using the sector-resolved spatial patterns in the prior emission inventories, as described by but here with the improved anthropogenic source patterns from Maasakkers et al (2016) and wetland emissions from (Bloom et al, 2017). Anthropogenic and wetland sources are well separated spatially in these inventories.…”

“…The WetCHARTs extended ensemble includes 18 wetland methane emission models intended to encompass the uncertainties in estimating wetland emissions (Bloom et al, 2017). The different models (ensemble members) use different datasets for wetland extent fraction A [m 2 wetlands m −2 surface area], heterotrophic respiration rate R [mg C day −1 m −2 of wetland area], temperature-dependent factor q T /10 10 of C respired as CH 4 [mg CH 4 mg −1 C] where T is the surface skin temperature, and global scaling factors s. The wetland methane emission flux E [mg CH 4 m −2 day −1 ] at a time t and location x for each of these members is given by the following equation:…”

“…Values in parentheses are the totals for the region with averaging kernel sensitivities larger than 0.05 (stippled areas in lower left panel). and the mean wetland emissions from the 0.5 • × 0.5 • monthly WetCHARTs extended ensemble for 2013 (Bloom et al, 2017). Figure 1 (top panels) shows the distribution of these prior methane emissions over the inversion domain for August-September 2013.…”

“…The aircraft flights provided extensive boundary layer measurements of methane across the region. We conduct an inverse analysis of the SEAC 4 RS data with the GEOSChem chemical transport model (CTM) at 0.25 • × 0.3125 • resolution, using state-of-the-art prior estimates from the gridded EPA inventory of Maasakkers et al (2016) and the WetCHARTs extended ensemble wetlands inventory of Bloom et al (2017). This allows us to evaluate the EPA inventory with better accuracy and resolution than has been done before and also to gain better understanding of US wetland emissions.…”