WETCHIMP-WSL: intercomparison of wetland methane emissions models over West Siberia

Abstract

Abstract. Wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of methane emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This risk is particularly relevant at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over the next 100 years. However, global models disagree as to the magnitude and spatial distribution of emissions, due to uncertainties in wetland area and emissions per unit area and a scarcity of in situ observations. Recent intensive field campaigns across the West Siberian Lowland (WSL) make this an ideal region over which to assess the perPublished by Copernicus Publications on behalf of the European Geosciences Union. T. J. Bohn et al.: Intercomparison of wetland methane emissions modelsformance of large-scale process-based wetland models in a high-latitude environment. Here we present the results of a follow-up to the Wetland and Wetland CH 4 Intercomparison of Models Project (WETCHIMP), focused on the West Siberian Lowland (WETCHIMP-WSL). We assessed 21 models and 5 inversions over this domain in terms of total CH 4 emissions, simulated wetland areas, and CH 4 fluxes per unit wetland area and compared these results to an intensive in situ CH 4 flux data set, several wetland maps, and two satellite surface water products. We found that (a) despite the large scatter of individual estimates, 12-year mean estimates of annual total emissions over the WSL from forward models (5.34 ± 0.54 Tg CH 4 yr −1 ), inversions (6.06 ± 1.22 Tg CH 4 yr −1 ), and in situ observations (3.91 ± 1.29 Tg CH 4 yr −1 ) largely agreed; (b) forward models using surface water products alone to estimate wetland areas suffered from severe biases in CH 4 emissions; (c) the interannual time series of models that lacked either soil thermal physics appropriate to the high latitudes or realistic emissions from unsaturated peatlands tended to be dominated by a single environmental driver (inundation or air temperature), unlike those of inversions and more sophisticated forward models; (d) differences in biogeochemical schemes across models had relatively smaller influence over performance; and (e) multiyear or multidecade observational records are crucial for evaluating models' responses to long-term climate change.

The global methane budget 2000–2012

“…Interestingly, the situation is different for Russia where top-down and bottomup approaches show similar mean emissions from natural wetlands (mostly boreal, ∼ 13-14 Tg CH 4 yr −1 ), consistently with Kaplan (2002) but not with Matthews and Fung (1987), who infer almost 50 Tg CH 4 yr −1 for Russia. Wetland emissions from Russia appear very uncertain, as also found by Bohn et al (2015) for western Siberia. Wetland emissions from tropical South America are found more consistent in this work than in Kirschke et al (2013), where topdown inversions showed 2 times less emission than bottomup models.…”

“…In these models, methane emissions from wetlands to the atmosphere are computed as the product of an emission density (which can be negative; mass per unit area and unit time) multiplied by a wetland extent (see the model intercomparison studies by Melton et al, 2013, andBohn et al, 2015). The CH 4 emission density is represented in land surface models with varying levels of complexity.…”

“…This would allow further refinement of the model parameterizations (Turetsky et al, 2014;Glagolev et al, 2011). A comparison of the model ensemble estimates against bottom-up inventory for western Siberia by Glagolev et al (2011) made by Bohn et al (2015) showed that there still is a sizable disagreement between their results. A more complete analysis of the literature for freshwater emissions has led to a 50 % increase of the reported range compared to Kirschke et al (2013).…”

“…One simplification for CH 4 compared to CO 2 is that the oceanic contribution to the global methane budget is very small (∼ 1-3 %), making source estimation mostly a continental problem (USEPA, 2010a). Finally, we lack observations to constrain (1) process models that produce estimates of wetland extent (Stocker et al, 2014;Kleinen et al, 2012) and emissions Wania et al, 2013), (2) other inland water sources (Bastviken et al, 2011), (3) inventories of anthropogenic emissions (USEPA, 2012;EDGARv4.2FT2010, 2013, and (4) atmospheric inversions, which aim at representing or estimating the different methane emissions from global to regional scales Kirschke et al, 2013;Bohn et al, 2015;Spahni et al, 2011;Tian et al, 2016). Finally, information contained in the ice core methane records has only been used in a few studies to evaluate process models Singarayer et al, 2011).…”

Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling

“…Wetland maps generated using these different approaches show substantial differences. Remote sensing data sets estimate relatively high levels of inundation in regions of Canada that are not forested or have many small lakes (see further discussion in Melton et al, 2013;Bohn et al, 2015). By contrast, modeling approaches that dynamically generate wetland area or use land cover maps assign more wetlands over regions with high water tables but little surface water as seen by remote sensing based inundation data sets.…”