Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations
<p>Wetland emissions contribute the largest uncertainties to the current global atmospheric <span class="inline-formula">CH<sub>4</sub></span> budget, and how these emissions will change under future climate scenarios is also still poorly understood. <span...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-11-01
|
| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/17/5669/2020/bg-17-5669-2020.pdf |
| Summary: | <p>Wetland emissions contribute the largest uncertainties to the current global atmospheric <span class="inline-formula">CH<sub>4</sub></span> budget, and how these emissions will change under future climate scenarios is also still poorly understood. <span class="cit" id="xref_text.1"><a href="#bib1.bibx6">Bloom et al.</a> (<a href="#bib1.bibx6">2017</a><a href="#bib1.bibx6">b</a>)</span> developed WetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of <span class="inline-formula">CH<sub>4</sub></span> wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs <span class="inline-formula">CH<sub>4</sub></span> emission model ensemble against 9 years of high-quality, validated atmospheric <span class="inline-formula">CH<sub>4</sub></span> observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric <span class="inline-formula">CH<sub>4</sub></span> mixing ratios based on the WetCHARTs emissions and other sources.</p>
<p>Across all years and all ensemble members, the observed global seasonal-cycle amplitude is typically underestimated by WetCHARTs by <span class="inline-formula">−</span>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 (<span class="inline-formula">−1.9</span> ppb) than the Northern Hemisphere (<span class="inline-formula">−</span>9.3 ppb), and our findings show that it is typically the North Tropics where this bias is the worst (<span class="inline-formula">−</span>11.9 ppb).</p>
<p>We find that WetCHARTs generally performs well in reproducing the observed wetland <span class="inline-formula">CH<sub>4</sub></span> 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 <span class="inline-formula">CH<sub>4</sub></span> 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.</p>
<p>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.</p> |
|---|---|
| ISSN: | 1726-4170 1726-4189 |