Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations

<p>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...

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Main Authors: Parker, RJ, Wilson, C, Comyn-Platt, E, Hayman, G, Marthews, TR, Bloom, AA, Lunt, MF, Gedney, N, Dadson, SJ, McNorton, J, Humpage, N, Boesch, H, Chipperfield, MP, Palmer, PI, Yamazaki, D
Format: Journal article
Language:English
Published: European Geosciences Union 2022
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author Parker, RJ
Wilson, C
Comyn-Platt, E
Hayman, G
Marthews, TR
Bloom, AA
Lunt, MF
Gedney, N
Dadson, SJ
McNorton, J
Humpage, N
Boesch, H
Chipperfield, MP
Palmer, PI
Yamazaki, D
author_facet Parker, RJ
Wilson, C
Comyn-Platt, E
Hayman, G
Marthews, TR
Bloom, AA
Lunt, MF
Gedney, N
Dadson, SJ
McNorton, J
Humpage, N
Boesch, H
Chipperfield, MP
Palmer, PI
Yamazaki, D
author_sort Parker, RJ
collection OXFORD
description <p>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.</p> <br> <p>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 CH<sub>4</sub> 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.</p> <br> <p>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.</p>
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spelling oxford-uuid:ec6c0aae-7622-4fb8-bb6c-9d4eca7e1ca02023-04-25T15:19:19ZEvaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observationsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ec6c0aae-7622-4fb8-bb6c-9d4eca7e1ca0EnglishSymplectic ElementsEuropean Geosciences Union2022Parker, RJWilson, CComyn-Platt, EHayman, GMarthews, TRBloom, AALunt, MFGedney, NDadson, SJMcNorton, JHumpage, NBoesch, HChipperfield, MPPalmer, PIYamazaki, D<p>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.</p> <br> <p>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 CH<sub>4</sub> 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.</p> <br> <p>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.</p>
spellingShingle Parker, RJ
Wilson, C
Comyn-Platt, E
Hayman, G
Marthews, TR
Bloom, AA
Lunt, MF
Gedney, N
Dadson, SJ
McNorton, J
Humpage, N
Boesch, H
Chipperfield, MP
Palmer, PI
Yamazaki, D
Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations
title Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations
title_full Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations
title_fullStr Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations
title_full_unstemmed Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations
title_short Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations
title_sort evaluation of wetland ch4 in the joint uk land environment simulator jules land surface model using satellite observations
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