Evaluation of wetland CH₄in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations

Abstract

Abstract. Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model (UKESM) and is capable of generating estimates of wetland methane emissions. In this study, we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 and 0.88 for most regions; however, the seasonal cycle amplitude is typically underestimated (by between 1.8 and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data are found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration outperforms the other, but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1, 19.5 and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the Catchment-based Macro-scale Floodplain (CaMa-Flood) model to explicitly represent river and floodplain water dynamics and find that these JULES-CaMa-Flood simulations are capable of providing a wetland extent that is more consistent with observations in this regions, highlighting this as an important area for future model development.

Recent methane surges reveal heightened emissions from tropical inundated areas

“…11;. The weaker seasonal changes in wetland extent and associated CH4 emissions were also identified previously for process-based or data-driven biogeochemical models over tropical regions (Pandey et al, 2021;Parker et al, 2018Parker et al, , 2022, resulting in smaller estimates of year-to-year emission anomalies. On the other hand, large uncertainties remain in the model representation of CH4 production, oxidation and vegetation-mediated transport processes for tropical wetlands (Pangala et al, 2017;Shaw et al, 2022), where direct flux measurements are sparse (Delwiche et al, 2021;Helfter et al, 2022).…”

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models‐Advances, Applications, and Challenges

“…There are other model improvements with regard to hydrology, that will help modeling wetland dynamics in the future, e.g., specifying surface water transport (Getirana et al, 2021;X. Lu et al, 2016;Parker et al, 2022;Ricciuto et al, 2021), or parametrizing floodplain dynamics (Schrapffer et al, 2020), and coastal processes O'Meara et al, 2021O'Meara et al, , 2024Ward et al, 2020). With more experiments and measurements (see below), improvements have been made regarding the representation of microbial processes and transport mechanisms.…”

“…However, first steps are being taken to establish a representation of microtopography and varying water level (Shi et al, 2015), realizing that particularly in northern wetlands methane emissions vary on area basis following hummock‐hollow patterns. There are other model improvements with regard to hydrology, that will help modeling wetland dynamics in the future, e.g., specifying surface water transport (Getirana et al, 2021; X. Lu et al, 2016; Parker et al, 2022; Ricciuto et al, 2021), or parametrizing floodplain dynamics (Schrapffer et al, 2020), and coastal processes (LaFond‐Hudson & Sulman, 2023; O'Meara et al, 2021, 2024; Ward et al, 2020). With more experiments and measurements (see below), improvements have been made regarding the representation of microbial processes and transport mechanisms.…”

“…A fully coupled ESM should be able to predict fluctuations in atmospheric methane concentration in the past (Kleinen et al, 2023). In combination with more global data sets for validation (see below), it becomes possible to identify global hotspots as well as regions where large discrepancies occur between model and observations (Parker et al, 2022). Currently, no land model distinguishes between wetland types such as fen, bog, marsh, or swamp, neither with regard to hydrology nor with regard to nutrient status and vegetation community.…”

Comment on essd-2024-115

“…CH4 emissions from wetlands are computed as the product of an emission flux density and a CH4 producing area or surface extent (see Supplementary Material; Bohn et al, 2015;Melton et al, 2013). The areal extent of different wetland types (having large differences in areal CH4 emission rates) appears to be a primary contributor to uncertainties in the absolute flux of CH4 emissions from wetlands, with meteorological response being the main source of uncertainty for seasonal and interannual variability (Poulter et al, 2017;Kuhn et al, 2021;Parker et al, 2022;McNicol et al, 2023;Karlson and Bastviken 2023).…”