Abstract
Abstract. Wetland emissions contribute the largest uncertainties to the current global atmospheric CH4 budget, and how these emissions will change under future climate scenarios is also still poorly understood. Bloom et al. (2017b) developed WetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of CH4 wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs CH4 emission model ensemble against 9 years of high-quality, validated atmospheric CH4 observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric CH4 mixing ratios based on the WetCHARTs emissions and other sources. Across all years and all ensemble members, the observed global seasonal-cycle amplitude is typically underestimated by WetCHARTs by −7.4 ppb, but the correlation coefficient of 0.83 shows that the seasonality is well-produced at a global scale. The Southern Hemisphere has less of a bias (−1.9 ppb) than the Northern Hemisphere (−9.3 ppb), and our findings show that it is typically the North Tropics where this bias is the worst (−11.9 ppb). We find that WetCHARTs generally performs well in reproducing the observed wetland CH4 seasonal cycle for the majority of wetland regions although, for some regions, regardless of the ensemble configuration, WetCHARTs does not reproduce the observed seasonal cycle well. In order to investigate this, we performed detailed analysis of some of the more challenging exemplar regions (Paraná River, Congo, Sudd and Yucatán). Our results show that certain ensemble members are more suited to specific regions, due to either deficiencies in the underlying data driving the model or complexities in representing the processes involved. In particular, incorrect definition of the wetland extent is found to be the most common reason for the discrepancy between the modelled and observed CH4 concentrations. The remaining driving data (i.e. heterotrophic respiration and temperature) are shown to also contribute to the mismatch with observations, with the details differing on a region-by-region basis but generally showing that some degree of temperature dependency is better than none. We conclude that the data-driven approach used by WetCHARTs is well-suited to producing a benchmark ensemble dataset against which to evaluate more complex process-based land surface models that explicitly model the hydrological behaviour of these complex wetland regions.
Highlights
The uncertainty in the emissions from natural wetlands remains the most significant uncertainty in the global CH4 budget (Melton et al, 2013; Kirschke et al, 2013; Saunois et al, 2020)
It is acknowledged (Kleinen et al, 2012; Stocker et al, 2014) that we currently lack sufficient observations to fully constrain estimates of wetland extent produced via land surface process models and that such differences in modelled wetland extent can account for an uncertainty of between 30 % and 40 % in the global wetland CH4 emission estimates (Saunois et al, 2016)
1. identifying whether WetCHARTs is capable of reproducing the observed wetland CH4 seasonal cycle at both the global and regional scale, 2. determining whether we can exploit the ensemble of WetCHARTs data to explain the cause of any discrepancies against observations, 3. improving our understanding of which drivers are the most important for constraining wetland CH4 emissions both spatially and temporally, 4. determining whether WetCHARTs can be used as a suitable benchmark against which to assess more complex process-based land surface models
Summary
The uncertainty in the emissions from natural wetlands remains the most significant uncertainty in the global CH4 budget (Melton et al, 2013; Kirschke et al, 2013; Saunois et al, 2020). Previous studies (Bohn et al, 2015; Poulter et al, 2017) have suggested that it is the uncertainty around wetland extent that is the largest contributor to uncertainties in the total methane emissions, with uncertainties in the climate response driving the interannual variability It is acknowledged (Kleinen et al, 2012; Stocker et al, 2014) that we currently lack sufficient observations to fully constrain estimates of wetland extent produced via land surface process models and that such differences in modelled wetland extent can account for an uncertainty of between 30 % and 40 % in the global wetland CH4 emission estimates (Saunois et al, 2016). 1. identifying whether WetCHARTs is capable of reproducing the observed wetland CH4 seasonal cycle at both the global and regional scale, 2. determining whether we can exploit the ensemble of WetCHARTs data to explain the cause of any discrepancies against observations, 3. improving our understanding of which drivers are the most important for constraining wetland CH4 emissions both spatially and temporally, 4. determining whether WetCHARTs can be used as a suitable benchmark against which to assess more complex process-based land surface models
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