Global Lagrangian Transport Modeling (Lead: Empa)


  1. To globally and quantitatively analyze methane variability in the 20-year period 1990-2010
  2. To validate a priori process-based natural wetland, rice, and fire methane emissions of the period 1990-2010 using inverse modeling (WP1) and to gain new best estimates of the emission strengths of the different main source categories and emission regions on a monthly basis for this period

Approach and Methods.
We will use the Lagrangian particle dispersion model FLEXPART to simulate global atmospheric CH4 over 1990-2010. FLEXPART will be set up in a domain-filling mode at a high horizontal resolution of 1°x1° with about 3 million particles permanently transported forward based on wind fields from meteorological analyses. The Lagrangian framework has the advantage that the history of individual particles can be followed allowing for comprehensive analysis of transport pathways and timescales and to establish a link between sources (emissions) and receptors (measurement stations) in a straightforward manner, a prerequisite for source inversion problems. Within the frame of the LORAT and URMEL projects (Henne et al., 2012; Schnadt Poberaj et al., 2012), FLEXPART was extended to also transport chemically active species that experience loss by reaction with OH, the primary loss reaction of CH4. CH4 tracer mass is lost further by soil uptake and stratospheric loss reactions with prescribed Cl and O(1D) radicals.

Task 1:
Preparation and realization of model simulations. Two separate simulations of the period 1990-2010 are planned, a reference simulation intended to reproduce a realistic atmospheric state, and a sensitivity simulation studying the effects of transport, in which emissions allowed to vary seasonally, but not interannually. For these simulations, state-of-the-art CH4 surface emissions will be collected and processed to be prescribed in the model on a monthly basis. In the second part of the project, the reference simulation will be repeated using the newly developed wetland emissions inventories from WP1. In all simulations every particle will carry with it different CH4 tracers representing concentrations from different source categories additionally separated by region where required. For each source category, four different tracers representing different age classes (emissions 0-1, 1-2, 3-4, and >4 months ago) will be simulated. These tracers will provide the necessary information to assess the sensitivity of CH4 concentrations to temporally varying emissions in inverse modeling.

Task 2:
Model validation. This task will be carried out in tight collaboration with WP3. The results from the reference simulations of WP2 and WP3 will be extensively validated against surface and satellite measurements allowing to identify and correct potential deficiencies of both models.

Task 3:
Analysis of reference and sensitivity simulations. Individual source category tracers from the reference and sensitivity simulations will be compared providing information on the relative importance of interannual variations in individual emissions and of transport on simulated CH4 variability. Particular emphasis will be placed to investigating the effects of ENSO-induced variations of wetland and biomass burning methane emissions and of transport on atmospheric CH4 variability.

Task 4:
Inverse modeling. In this task, the process-based wetland and biomass burning emissions inventories available from and generated in WP1 will be evaluated using inverse modeling. Improved estimates of these emissions will be determined from the WP2 reference simulations in conjunction with atmospheric CH4 data from global observations networks, and an inversion algorithm. The inversion algorithm adjusts the a priori emissions, determined by the process-based models (WP1) and used in the FLEXPART simulations, to minimize the differences between observed and simulated concentrations taking into account the respective uncertainties. We will adopt a Kalman filter similar to the method applied by Bruhwiler et al. (2005), and was developed at Empa in the project URMEL.

The reference simulations of this work package will be complemented by SOCOL simulations covering the same time period (WP3). The FLEXPART and SOCOL simulations will be set up using the same emissions inventories guaranteeing best comparability of the different simulations and allowing to take advantage of the complementary information provided by both models, i.e. a high resolution Lagrangian transport model with prescribed OH fields and a global low resolution climate model with interactively coupled chemistry and a Eulerian transport algorithm.


  1. FLEXPART simulations of the period 1990-2010 prescribing different wetland and biomass burning inventories
  2. Improved understanding of the relative importance of individual CH4 sources on simulated CH4 variability
  3. Improved understanding of the impact of ENSO on CH4 variability
  4. Improved estimates of emissions inventories of the period 1990-2010 including wetland and biomass burning emissions through inverse modeling
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