Two inverse models are used for estimating methane fluxes for high northern latitudes.
NILU has developed FLEXINVERT, a regional inverse model based on the Lagrangian particle dispersion model, FLEXPART, and a Bayesian optimisation method. FLEXINVERT is designed to to be flexible in terms of the species to be optimised and to the scale of the problem, from hemispheric to regional scale. FLEXINVERT can be coupled to the output of a Eulerian model to provide initial conditions for the high-resolution Lagrangian model. FLEXINVERT can be used to optimise the fluxes of species that undergo atmospheric chemistry so long as this can be approximated as a linear process, and must be incorporated into the FLEXPART runs. FLEXINVERT is continuously undergoing development and a version for CO2 is planned this year. In addition, developments for a version to handle very large problems, such as using satellite observations, are planned and will involve using a Variational (or gradient) method for the optimisation.
About the code
FLEXINVERT is programmed in Fortran90 and requires the LAPACK and NetCDF4 libraries. FLEXINVERT requires pre-computed FLEXPART output. Details about FLEXPART can be found on the FLEXPART homepage.
Carbon Tracker Europe-CH4
Carbon Tracker Europe – Methane (CTE-CH4) is a state-of-the-art atmospheric inverse model developed for estimating global methane emissions. The model emission estimates are constrained by a global network of atmospheric concentration observations, which are assimilated in the model system. The model system is based on an Ensemble Kalman Filter (EnKF), and uses a the atmospheric chemistry-transport model, TM5, for modelling the transport and chemical loss of methane in the atmosphere. The atmospheric loss of CH4 is due to reaction with the OH radical and is the largest sink of methane. Prior emissions of CH4 are obtained from existing databases and models, and categorised in the model into anthropogenic, biogenic, fire, termite and oceanic sources. Only anthropogenic and biogenic sources are optimised in CTE-CH4.
Variants of the Carbon Tracker model system are being used in many countries for many applications. The development work, focusing on carbon dioxide, originated at NOAA-ESRL with Carbon Tracker North America and was continued at Wageningen University, Netherlands, with Carbon Tracker Europe. Since then the model has been developed for other greenhouse gases and their isotopes and new regions of interest (China and Australasia). The Finnish Meteorological Institute is working on developing Carbon Tracker Europe with a focus on Europe and the high northern latitudes.
About the code
The CTE-CH4 optimization scheme is coded in Python (CarbonTracker Data Assimilation Shell CTDAS; van der Laan-Luijkx et al., 2017). To run CTDAS, Python2.x version, and modules numpy and netCDF4 are required. CTDAS is coupled with TM5 atmospheric transport model, which is coded in Fortran90 and run by Python. To run TM5, the MPI, HDF4, HDF5, NetCDF4 and LAPACK libraries must be installed, and parallel computing capabilities are highly recommended. For more details on TM5 code, see the TM5 webpage and the SourceForge page. For CTE-CH4 specific TM5 code please contact the Finnish Meteorological Institute.
Model specific inputs
- FLEXPART output files from a backwards mode run.
- Global coarse resolution fields of atmospheric concentration to be used for the initial conditions.
- Meteorological fields for TM5 atmospheric transport model, e.g. European Centre for Medium-Range Weather Forecasts ERA Interim meteorological fields (ECMWF).
- Global fields of atmospheric concentration to be used for the inital conditions.
- Monthly tropospheric OH concentrations* to account for atmospheric chemical loss calculated in off-line chemistry in TM5. Furthermore, stratospheric sink due to reaction with OH, Cl and O(1D) is included by applying reaction rates based on a 2D photochemical Max-Planck-Institute (MPI) model.
* For OH concentration fields and the simulated methane atmospheric loss rates, please contact TM5 development team (TM5 webpage) and/or the Finnish Meteorological Institute.
In this project, observations of atmospheric concentrations of mainly methane and carbon dioxide were used. However, some other tracers are also possible to be integrated in the models. The atmospheric observations can be obtained from e.g. following sites.
- World Data Centre for Greenhouse Gases (WDCGG)
- NOAA ESRL/GMD database (NOAA ESRL/GMD)
- ICOS Carbon Potal (ICOS-CP)
A global or regional methane emissions, calculated from various inventories and process models can be used as prior emission estimates for different sources such as those from anthropogenic activities, biosphere, fire, ocean and termites. Anthropogenic methane emissions contribute to more than half of the global total methane sources and natural (e.g. wetland) sources are the second largest source. The emissions from termites, fire and ocean account for a minor part (< 20%) of total methane emissions. Note that the combinations of these inputs are carefully chosen to avoid double counting.
The example of prior emission estimates used in this project are:
- Wetland (incl. peatlands and minral soils: from process-based ecosystem models, such as LPX-Bern (versions 1.0 and DYPTOP) and LPJG-WHyME.
- Anthropogenic emissions (incl. fossil fuel, enteric fermentation and waste decomposition): from the Emission Database for Global Atmospheric Research inventory (EDGAR v4.2 FT2010)
- Rice cultivation emissions: from process-based ecosystem models LPX-Bern, EDGAR
- Wild fire and biomass burning emissions: from the Global Fire Emissions Database (GFED; GFED v3.1 GFED v3.2, GFED v4)
- Geological (natural thermogenic) emissions: based on the estimate of Etiope et al. 2009
- Wild animals: from Houweling et al. 1999, Ito and Inatomi (2012)
- Termites: from Sanderson et al. 1996
- Oceans: from Lambert et al. 1993, Tsuruta et al. 2017
** For the flux data based on publications, please contact the authors