Sensitivity analysis of a wetland methane emission model based on temperate and arctic wetland sites

Modelling of wetland CH<sub>4</sub> fluxes using wetland soil emission models is used to determine the size of this natural source of CH<sub>4</sub> emission on local to global scale. Most process models of CH<sub>4</sub> formation and soil-atmosphere CH<sub>4</sub> transport processes operate on a plot scale. For large scale emission modelling (regional to global scale) upscaling of this type of model requires thorough analysis of the sensitivity of these models to parameter uncertainty. We applied the GLUE (Generalized Likelihood Uncertainty Analysis) methodology to a well-known CH<sub>4</sub> emission model, the Walter-Heimann model, as implemented in the PEATLAND-VU model. The model is tested using data from two temperate wetland sites and one arctic site. The tests include experiments with different objective functions, which quantify the fit of the model results to the data. <br><br> The results indicate that the model 1) in most cases is capable of estimating CH<sub>4</sub> fluxes better than an estimate based on the data avarage, but does not clearly outcompete a regression model based on local data; 2) is capable of reproducing larger scale (seasonal) temporal variability in the data, but not the small-scale (daily) temporal variability; 3) is not strongly sensitive to soil parameters, 4) is sensitive to parameters determining CH<sub>4</sub> transport and oxidation in vegetation, and the temperature sensitivity of the microbial population. The GLUE method also allowed testing of several smaller modifications of the original model. <br><br> We conclude that upscaling of this plot-based wetland CH<sub>4</sub> emission model is feasible, but considerable improvements of wetland CH<sub>4</sub> modelling will result from improvement of wetland vegetation data

The growing season greenhouse gas balance of a continental tundra site in the Indigirka lowlands, NE Siberia.

Carbon dioxide and methane fluxes were measured at a tundra site near Chokurdakh, in the lowlands of the Indigirka river in north-east Siberia. This site is one of the few stations on Russian tundra and it is different from most other tundra flux stations in its continentality. A suite of methods was applied to determine the fluxes of NEE, GPP, <i>R</i><sub>eco</sub> and methane, including eddy covariance, chambers and leaf cuvettes. Net carbon dioxide fluxes were high compared with other tundra sites, with NEE=−92 g C m<sup>−2</sup> yr<sup>−1</sup>, which is composed of an <i>R</i><sub>eco</sub>=+141 g C m<sup>−2</sup> yr<sup>−1</sup> and GPP=−232 g C m<sup>−2</sup> yr<sup>−1</sup>. This large carbon dioxide sink may be explained by the continental climate, that is reflected in low winter soil temperatures (−14°C), reducing the respiration rates, and short, relatively warm summers, stimulating high photosynthesis rates. Interannual variability in GPP was dominated by the frequency of light limitation (<i>R<sub>g</sub></i><200 W m<sup>−2</sup>), whereas <i>R</i><sub>eco</sub> depends most directly on soil temperature and time in the growing season, which serves as a proxy of the combined effects of active layer depth, leaf area index, soil moisture and substrate availability. The methane flux, in units of global warming potential, was +28 g C-CO<sub>2</sub>e m<sup>−2</sup> yr<sup>−1</sup>, so that the greenhouse gas balance was −64 g C-CO<sub>2</sub>e m<sup>−2</sup> yr<sup>−1</sup>. Methane fluxes depended only slightly on soil temperature and were highly sensitive to hydrological conditions and vegetation composition

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and surface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at. (Petrescu et al., 2020b)

Comparative Analysis of Major Global Forestry Datasets for Country Level Estimates of C Stock Change in Living Biomass

Carbon (C) stock change and related Carbon Dioxide (CO2) emissions/removals from living biomass for Forest Land remaining Forest Land (IPCC sector 5-FL-1) are one of the most difficult to estimate because of the large natural fluxes of CO2 and the high accuracy requirements for deriving net changes of the large C stock. Following the Good Practice Guide (GPG) methodology of IPCC (2003) with IPCC default values and data from FAO (FRA and ForesSTAT 2010) for harvested woodfuel and industrial roundwood and with fires emissions from the Global Fires Emission Database GFED v.3, a Tier-1 level estimate of country-specific C stock for sector 5-FL-1 was carried out for the years 1990, 2000 and 2005, which mounted to about 6 Pg C or about half of the mean total CO2 emissions in that period. The resulting bottom up globally harmonized CO2 inventory for 5-FL-1 from 1990 to 2010 completes further the Land Use, Land-Use Change and Forestry sector in the Emission Database for Global Atmospheric Research (EDGAR). Even though the GPG methodology of IPCC (2003) is assumed adequate and detailed enough to account well for C stock changes, the estimates of sector 5-FL-1 in the national GHG inventories remain highly uncertain. In all three years ~35% of the national inventories for 5-FL-1 show similarities but the values differ in most cases by an average factor of -5 to +5. In all other cases, Tier 1 values are the opposite of what countries report regarding sink and source. Vis-à-vis forested area the average difference for Annex I countries between Tier 1 calculations and FRA2010 and UNFCCC mount to 9% and 16% respectively. It is concluded that differences are mainly due to the activity data explicitly forested area, the definition of forests used by each country and losses which are not always accounted by countries or perhaps double counted. The feasibility for establishing national inventories in a consistent way for all world countries was demonstrated but only with a limited accuracy of Tier 1. Better forestry data, such as annual activity data from satellite images are needed to derive estimates at higher Tier, but are too scarce at the moment for obtaining more accurate estimates of global level C stock budgets