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Modelled C and N2O fluxes and emission factors

Jönsson, Anna Maria LU orcid ; Fiore, Angela ; Lagergren, Fredrik LU ; Lippmann, Tanya ; Lundblad, Mattias ; Naudts, Kim ; Petersson, Hans ; Yan, Yanzi ; Mariani, Lorella and Di Cristofaro, Eleonora (2025)
Abstract
The aim of Task 4.1 is to derive emission factors based on bottom-up land surface modelling, using two process-based dynamic global vegetation models (DGVMs), LPJ-GUESS and ORCHIDEE. In recent years, emission factors have been proposed as a useful methodology for estimating greenhouse gas emissions under different land-management strategies. Whilst emission factors are straightforward to implement and offer several practical advantages, conventional emission factors do not account for climatic variability (e.g., increased runoff following heavy precipitation in a wet year may reduce N₂O emissions) or spatial heterogeneity (differences in soil properties also impact CO2 and N2O emissions). To investigate the extent to... (More)
The aim of Task 4.1 is to derive emission factors based on bottom-up land surface modelling, using two process-based dynamic global vegetation models (DGVMs), LPJ-GUESS and ORCHIDEE. In recent years, emission factors have been proposed as a useful methodology for estimating greenhouse gas emissions under different land-management strategies. Whilst emission factors are straightforward to implement and offer several practical advantages, conventional emission factors do not account for climatic variability (e.g., increased runoff following heavy precipitation in a wet year may reduce N₂O emissions) or spatial heterogeneity (differences in soil properties also impact CO2 and N2O emissions). To investigate the extent to which incorporating spatially and temporally explicit processes improves the robustness of GHG emission estimates, we assess the added value of representing spatial and temporal variability associated with land management and extreme weather events using two case studies: carbon emissions from forestry in Sweden and nitrogen emissions from agriculture in Italy. Model simulations were evaluated against available observational data. The simulations demonstrated the usefulness of DGVMs for estimating spatial and temporal changes in carbon stock (forest in Sweden) and N2O emissions (agriculture in Italy), as influenced by weather and climate extremes and different management methods. The model comparison revealed that uncertainties in model parameterization can significantly affect simulation outcome, highlighting the need for further model development, calibration, and evaluation.
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author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Book/Report
publication status
published
subject
pages
59 pages
publisher
The AVENGERS project
project
Attributing and verifying european and national greenhouse gas and aerosol emissions and reconciliation with statistical bottom up estimates
language
English
LU publication?
yes
id
5b08f9a2-03e9-40c0-8303-2699ed2992e6
alternative location
https://avengers-project.eu/wp-content/uploads/2025/12/AVENGERS_deliverable_D41.pdf
date added to LUP
2026-01-14 10:24:17
date last changed
2026-01-16 15:04:42
@techreport{5b08f9a2-03e9-40c0-8303-2699ed2992e6,
  abstract     = {{The aim of Task 4.1 is to derive emission factors based on bottom-up land surface modelling, using two process-based dynamic global vegetation models (DGVMs), LPJ-GUESS and ORCHIDEE. In recent years, emission factors have been proposed as a useful methodology for estimating greenhouse gas emissions under different land-management strategies. Whilst emission factors are straightforward to implement and offer several practical advantages, conventional emission factors do not account for climatic variability (e.g., increased runoff following heavy precipitation in a wet year may reduce N₂O emissions) or spatial heterogeneity (differences in soil properties also impact CO<sub>2</sub> and N<sub>2</sub>O emissions). To investigate the extent to which incorporating spatially and temporally explicit processes improves the robustness of GHG emission estimates, we assess the added value of representing spatial and temporal variability associated with land management and extreme weather events using two case studies: carbon emissions from forestry in Sweden and nitrogen emissions from agriculture in Italy. Model simulations were evaluated against available observational data. The simulations demonstrated the usefulness of DGVMs for estimating spatial and temporal changes in carbon stock (forest in Sweden) and N<sub>2</sub>O emissions (agriculture in Italy), as influenced by weather and climate extremes and different management methods. The model comparison revealed that uncertainties in model parameterization can significantly affect simulation outcome, highlighting the need for further model development, calibration, and evaluation.<br/>}},
  author       = {{Jönsson, Anna Maria and Fiore, Angela and Lagergren, Fredrik and Lippmann, Tanya and Lundblad, Mattias and Naudts, Kim and Petersson, Hans and Yan, Yanzi and Mariani, Lorella and Di Cristofaro, Eleonora}},
  institution  = {{The AVENGERS project}},
  language     = {{eng}},
  month        = {{12}},
  title        = {{Modelled C and N<sub>2</sub>O fluxes and emission factors}},
  url          = {{https://avengers-project.eu/wp-content/uploads/2025/12/AVENGERS_deliverable_D41.pdf}},
  year         = {{2025}},
}