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.
To run FLEXINVERT the following input data are required:
- FLEXPART output files from a backwards mode run.
- Observations of atmospheric concentrations of, e.g., methane.
- Global coarse resolution fields of atmospheric concentration to be used for the initial conditions.
A global prior methane emission estimate is calculated from various inventory and process model estimates of different sources. These are as follows:
- Wetland and rice cultivation emissions: from the process-based ecosystem model LPX-Bern
- Anthropogenic emissions (excluding rice cultivation and agricultural burning): from the inventory EDGAR-v4.2FT2010
- Wild fire and biomass burning emissions: from the inventory GFED-3.2
- Geological (natural thermogenic) emissions: based on the estimate of Etiope et al. 2009
- Wild animals: from Houweling et al. 1999
- Termites: from Sanderson et al. 1996
- Oceans: from Lambert et al. 1993
Carbon Tracker Europe
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). 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 Finnish Meteorological Institute.
In order to run the Carbon Tracker Europe – CH4, the following data are needed:
- Global atmospheric methane concentration data (assimilated in the CTE-CH4 system). It can be obtained from the World Data Centre for Greenhouse Gases (WDCGG)
- TM5 atmospheric transport model is driven by the European Centre for Medium-Range Weather Forecasts ERA Interim meteorological fields (ECMWF).
- Atmospheric chemical loss is calculated using off-line chemistry with monthly tropospheric OH concentrations. 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 data fields please contact TM5 development team here and/or the Finnish Meteorological Institute for the simulated methane atmospheric loss rates.
The prior methane emissions are given to CTE-CH4 model system as global maps of anthropogenic, biosphere, fire, ocean and termite emissions. 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.
- Ocean emission estimates are calculated as the product of gas transfer velocity, gas solubility and the difference between air and seawater partial pressures of methane. ECWMF ERA-Interim meteorological data and NOAA/ESRL marine boundary layer reference for methane (available here) are used in the calculations. For the ocean flux data please contact the Finnish Meteorological Institute.
- For methane emissions from termites, estimates given by A. Ito and M. Inatomi (Biogeosciences 9:759—773, 2012) are used. Please contact the authors for the data.
- Methane emissions due to fires are obtained from the Global Fire Emissions Database version 3.1 (GFED).
- For prior anthropogenic emissions, the Emission Database for Global Atmospheric Research version 4.2 (EDGARv4.2) is used. These emissions include enteric fermentation, fossil fuels and waste decomposition but agricultural waste burning and large-scale biomass burning are already included in fire emissions and thus not added to CTE-CH4 prior anthropogenic emissions.
- For prior biosphere emissions, the estimates from the biogeochemical process model LPX-Bern are used. The emissions include wet peat and mineral soil contributions but not rice cultivation as this is already included in EDGAR. For LPX-Bern flux data please contact the LPX-Bern development team.