Starting in the early 2000's, atmospheric composition has gradually become an increasingly important (optional) component of the Integrated Forecasting System (IFS). We provide pre-operational daily global forecasts of aerosols and species such as ozone, carbon monoxide, and methane.
As part of the European Copernicus programme on environmental monitoring, greenhouse gases, aerosols, and chemical species have been introduced in the ECMWF model allowing assimilation and forecasting of atmospheric composition. At the same time, the added atmospheric composition variables are being used to improve the Numerical Weather Prediction (NWP) system itself, most notably through the interaction with the radiation scheme and the use in observation operators for satellite radiance assimilation.
For the CO2 modelling in the IFS, the land vegetation fluxes are modelled on-line by the CTESSEL carbon module (Boussetta et al., 2013). The anthropogenic fluxes are based on the annual mean EDGARv4.2 inventory (Janssens-Maenhout et al., 2012) using the most recent year available (i.e. 2008) with estimated and climatological trends to extrapolate to the current year. The ocean fluxes are from the Takahashi et al (2009) climatology and the fire fluxes are from GFAS (Kaiser et al., 2012). Because the budget is currently not constrained by observations, the CO2 global bias can grow from one year to the next. In order to avoid this, the initial CO2 forecast fields are updated every year with the most recent atmospheric 3D fields from the MACC-II CO2 flux inversion system (Chevallier et al., 2010), whenever these become available, typically in October.
Methane fluxes are prescribed in the IFS using inventory and climatological data sets, consistent with those used as prior information in the CH4 flux inversions from Bergamaschi et al. (2009). The anthropogenic fluxes are from the EDGAR 4.2 database (Janssens-Maenhout et al, 2012) for the year 2008, i.e. the last available year. All the anthropogenic categories are based on annual mean values, except for rice, which has been modulated with a seasonal cycle of the Matthews monthly inventory for rice (Matthews et al., 1991). The wetland fluxes are from the Kaplan climatology described in Bergamaschi et al. (2007). The biomass burning emissions are from the MACC-II GFAS dataset (Kaiser et al., 2012). The other sources/sinks include wild animals (Houweling et al., 1999), termites (Sanderson et al., 1996), oceans (Houweling et al., 1999 and Lambert and S. Schmidt, 1993) and a soil sink (Ridgwell et al., 1999). For the chemical sink in the troposphere and the stratosphere, the climatological chemical loss rates from Bergamaschi et al. (2009) are used. These are based on OH fields optimised with methyl chloroform using the TM5 model (Krol et al., 2005) and prescribed concentrations of the stratospheric radicals using the 2-D photochemical Max-Planck-Institute model.
- Bergamaschi, P., C. Frankenberg, J. F. Meirink, M. Krol, F. Dentener, T. Wagner, U. Platt, J. O. Kaplan, S. Koerner, M. Heimann, E.J. Dlugokencky and A. Goede (2007), Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on inverse model simulations, J. Geophys. Res., 112 , D02304, 10.1029/2006JD007268.
- Bergamaschi, P., C. Frankenberg, J. F. Meirink, M. Krol, M. G. Villani, S. Houweling, F. Dentener, E. J. Dlugokencky, J. B. Miller, L. V. Gatti, A. Engel, and I. Levin (2009), Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals, J. Geophys. Res., 114, 10.1029/2009JD012287.
- Boussetta, S., G. Balsamo, A. Beljaars, A. Agusti-Panareda, J.-C. Calvet, C. Jacobs, B. van den Hurk, P. Viterbo, S. Lafont and E. Dutra, L. Jarlan, M. Balzarolo, D. Papale and G. van der Werf (2013), Natural carbon dioxide exchanges in the ECMWF Integrated Forecasting System: Implementation and offline validation, J. Geophys. Res., 118, 1-24, doi:10.1002/jgrd.50488.
- Houweling, S., T. Kaminski, F. Dentener, J. Lelieveld, M. Heimann (1999), Inverse modeling of methane sources, sinks using the adjoint of a global transport model, J. Geophys. Res., 104, D21, 26137-26160, DOI:10.1029/1999JD900428.
- Janssens-Maenhout, G., F. Dentener, J. Van Aardenne, S. Monni , V.Pagliari, L. Orlandini, Z. Klimont, J. Kurokawa, H. Akimoto , T. Ohara, R. Wankmueller, B. Battye , D. Grano, A. Zuber and T. Keating (2012), EDGAR-HTAP: a Harmonized Gridded Air Pollution Emission Dataset Based on National Inventories, Ispra (Italy) , European Commission Publications Office, JRC68434, EUR report No EUR 25 299 - 2012, ISBN 978-92-79-23122-0, ISSN 1831-9424.
- Kaiser, J.W., A. Heil, M. O. Andreae, A. Benedetti, N. Chubarova, L. Jones, J.-J. Morcrette, M. Razinger, M. G. Schultz, M. Suttie, and G. R. van der Werf (2012), Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527-554, doi:10.5194/bg-9-527-2012.
- Krol, M., S. Houweling, B. Bregman, M. van den Broek, A. Segers, P. van Velthoven, W. Peters, F. Dentener and P. Bergamaschi (2005), The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, ACP, 5, 417-432.
- Lambert G. and S. Schmidt (1993), Reevaluation of the oceanic flux of methane: Uncertainties and long term variations, Chemosphere Global Change Sci., 26, 579-589.
- Matthews, E., I. Fung and J. Lerner (1991), Methane emission from rice cultivation: Geographic and seasonal distribution of cultivated areas and emissions, Global Biogeochem. Cycles, 5, 3-24, doi:10.1029/90GB02311.
- Ridgwell, A.J., S. J. Marshall and K. Gregson (1999), Consumption of atmospheric methane by soils: A process-based model, Global Biogeochem. Cycles, 13, 59-70, doi: 10.1029/1998GB900004.
- Sanderson, M.G. (1996), Biomass of termites and their emissions of methane and carbon dioxide: A global database, Global Biogeochem. Cycles, 10, 543-557, doi: 10.1029/96GB01893.
- Takahashi, T., S.C. Sutherland, R. Wanninkhof, C. Sweeney, R.A. Feely, D.W. Chipman, B. Hales, G. Friederich, F. Chavez, C. Sabine, A. Watson, D.C.E. Bakker, U. Schuster, N. Metzl, H. Yoshikawa-Inoue, M. Ishii, T. Midorikawa, Y. Nojiri, A. Körtzinger, T. Steinhoff, M. Hoppema, J. Olafsson, T.S. Arnarson, B. Tilbrook, T. Johannessen, A. Olsen, R. Bellerby, C.S. Wong, B. Delille, N. R. Bates and H.J.W. de Baar (2009), Climatological mean and decadal changes in surface ocean pCO2 and net sea-air CO2 flux over the global oceans, Deep-Sea Res. II, 56, 554-577.
The physical parameterizations dedicated to aerosol processes mainly follow the aerosol treatment in the LOA/LMD-Z model (Boucher et al. 2002; Reddy et al. 2005). Five types of tropospheric aerosols are considered: sea salt, dust, organic and black carbon, and sulphate aerosols. Prognostic aerosols of natural origin, such as mineral dust and sea salt are described using three size bins. For dust, bin limits are at 0.03, 0.55, 0.9, and 20 microns; for sea salt, bin limits are at 0.03, 0.5, 5 and 20 microns.
Emissions of dust depend on the 10-m wind, soil moisture, the UV-visible component of the surface albedo and the fraction of land covered by vegetation when the surface is snow-free. A correction to the 10-m wind to account for gustiness is also included (Morcrette et al. 2008).
Sea-salt emissions are diagnosed using a source function based on work by Guelle et al. (2001) and Schulz et al. (2004). In this formulation, wet sea-salt mass fluxes at 80% relative humidity are integrated for the three size bins between 2 and 4 µm merging work by Monahan et al. (1986) and Smith and Harrison (1998).
Sources for the other aerosol types which are linked to emissions from domestic, industrial, power generation, transport and shipping activities, are taken from the SPEW (Speciated Particulate Emission Wizard), and EDGAR (Emission Database for Global Atmospheric Research) annual- or monthly-mean climatologies. More details on the sources of these aerosols are given in Dentener et al. (2006). Emissions of OM, BC and SO2 linked to fire emissions are obtained using the GFAS system based on MODIS satellite observations of fire radiative power, as described in Kaiser et al. (2011).
Several types of removal processes are considered: dry deposition including the turbulent transfer to the surface, gravitational settling, and wet deposition including rainout by large-scale and convective precipitation and washout of aerosol particles in and below the clouds. The wet and dry deposition schemes are standard, whereas the sedimentation of aerosols follows closely what was introduced by Tompkins (2005) for the sedimentation of ice particles. Hygroscopic effects are also considered for organic matter and black carbon aerosols. A detailed description of the ECMWF forecast and analysis model including aerosol processes is given in Morcrette et al. (2009) and Benedetti et al. (2009).
- Benedetti, A., J.-J. Morcrette, O. Boucher, A. Dethof, R. J. Engelen, M. Fischer, H. Flentjes, N. Huneeus, L. Jones, J. W. Kaiser, S. Kinne, A. Mangold, M. Razinger, A. J. Simmons, M. Suttie, and the GEMS-AER team, Aerosol analysis and forecast in the ECMWF Integrated Forecast System. Part II : Data assimilation, Journal of Geophysical Research, 114, D13205, doi:10.1029/2008JD011115, 2009.
- Boucher, O., M. Pham, and C. Venkataraman, 2002: Simulation of the atmospheric sulfur cycle in the LMD GCM: Model description, model evaluation, and global and European budgets, Note 23, 26 pp., Inst. Pierre-Simon Laplace, Paris, France. (Available at http://icmc.ipsl.fr/images/publications/scientific_notes/note23.pdf)
- Dentener, F., et al., 2006: Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom, Atmos. Chem. Phys., 6, 4321 – 4344.
- Guelle, W., M. Schulz, Y. Balkanski, and F. Dentener, 2001: Influence of the source formulation on modeling the atmospheric global distribution of the sea salt aerosol, J. Geophys. Res., 106, 27,509– 27,524.
- Kaiser, J.W., A. Heil, M.O. Andreae, A. Benedetti, N. Chubarova, L. Jones, J.-J.Morcrette, M. Razinger, M.G. Schultz, M. Suttie, and G.R. van der Werf, 2011: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences Discuss., 8(4), 7339-7398.
- Monahan, E. C., D. E. Spiel, and K. L. Davidson, 1986: A model of marine aerosol generation via whitecaps and wave disruption, in Oceanic Whitecaps, edited by E. C. Monahan and G. MacNiocaill, pp. 167–174, D. Reidel, Norwell, Mass.
- Morcrette, J.-J., A. Beljaars, A. Benedetti, L. Jones, and O. Boucher, 2008: Sea-salt and dust aerosols in the ECMWF IFS model , Geophys. Res. Lett., 35, L24813, doi:10.1029/2008GL036041
- Morcrette, J.-J., O. Boucher, L. Jones, D. Salmond, P. Bechtold, A. Beljaars, A. Benedetti, A. Bonet, J. W. Kaiser, M. Razinger, M. Schulz, S. Serrar, A. J. Simmons, M. Sofiev, M. Suttie, A. M. Tompkins, and A. Untch, 2009: Aerosol analysis and forecast in the ECMWF Integrated Forecast System. Part I: Forward modelling, Journal of Geophysical Research, 114D, D06206, doi:10.1029/2008JD011235, 2009.
- Reddy, M. S., O. Boucher, N. Bellouin, M. Schulz, Y. Balkanski, J.-L. Dufresne and M. Pham, 2005: Estimates of global multi-component aerosol optical depth and direct radiative perturbation in the Laboratoire de Météorologie Dynamique general circulation model, Journal of Geophysical Research, 110, D10S16, doi:10.1029/2004JD004757.
- Schulz, M., G. de Leeuw, and Y. Balkanski, 2004: Sea-salt aerosol source functions and emissions, in Emission of Atmospheric Trace Compounds, edited by C. Granier, P. Artaxo, and C. E. Reeves, pp. 333–354, Kluwer Acad., Norwell, Mass.
- Smith, M. H., and N. M. Harrison, 1998: The sea spray generation function, J. Aerosol Sci., 29, Suppl. 1, S189–S190.
- Tompkins, A. M., 2005: A revised cloud scheme to reduce the sensitivity to vertical resolution, Tech. Memo. 0599, 25 pp., Res. Dep., Eur. Cent. for Medium-Range Weather Forecasts, Reading, U. K
The C-IFS is a version of the Integrated Forecasting System (IFS) with atmospheric chemistry fully included, complementing the aerosol modules and greenhouse gas variables for atmospheric composition. C-IFS for atmospheric chemistry supersedes a coupled system IFS-CTM, in which a global Chemistry Transport Model was two-way coupled to the IFS. C-IFS is computationally much more efficient than the IFS-CTM, and it avoids inconsistencies of the coupled approach.
- The currently implemented chemical mechanism is an extended version of the CB05 chemical mechanism (Yarwood et al., 2005) as implemented in the Transport Model 5 (TM5), which describes tropospheric chemistry with 53 species and 107 reactions (Huijnen et al., 2010).
- Stratospheric ozone concentrations are currently parameterized with the Cariolle scheme (Cariolle and Teyssèdre, 2007).
- The chemical solver used in C-IFS is the Euler Backward Iterative (EBI) solver (Hertel et al., 1996).
- The chemical time step employed is typically 15 minutes.
- Dry deposition is simulated using pre-calculated dry deposition velocities.
- Wet deposition in C-IFS is based on the Harvard wet deposition scheme (Jacob et al., 2000 and Lui et al., 2001). It accounts for sub-grid wet removal by considering cloud and precipitation area fraction. The input fields to the wet deposition routine are provided by the cloud scheme (Forbes et al. 2011).
- No emissions from lightning are a considerable contribution to the global atmospheric NOx budget. The estimate of the flash-rate density (flashes per time unit and area unit) is based on parameters of the convection scheme. C-IFS currently uses the formulation of Meijer et al., 2001, which is based on convective precipitation.
- A global mass fixer is applied to ensure the mass conservation of the semi-lagrangian advection scheme.
- Cariolle, D. and Teyssèdre, H.: A revised linear ozone photochemistry parameterization for use in transport and general circulation models: multi-annual simulations, Atmos. Chem. Phys., 7, 2183-2196, doi:10.5194/acp-7-2183-2007, 2008
- Forbes, R., A.M. Tompkins and A. Untch, A new prognostic bulk microphysics scheme for the IFS, ECMWF tech. memo. 649, 2011.
- Hertel, O., R. Berkowicz, and J. Christensen, Test of two numerical schemes for use in atmospheric transport-chemistry models Atmos. Environ., 27A,16, 2591-2611, 1993
- Huijnen, V., Williams, J., van Weele, M., van Noije, T., Krol, M., Dentener, F., et al.: The global chemistry transport model TM5: description and evaluation of the tropospheric chemistry version 3.0, Geosci. Model Dev., 3, 445-473, doi:10.5194/gmd-3-445-2010, 2010
- Jacob, D.J. H. Liu, C.Mari, and R.M. Yantosca, Harvard wet deposition scheme for GMI, Harvard University Atmospheric Chemistry Modeling Group, 2000. from http://acmg.seas.harvard.edu/geos/wiki_docs/deposition/wetdep.jacob_etal_2000.pdf
- Liu, H., Jacob, D.J., Bey, I., Yantosca, R.M., 2001. Constraints from 210 Pb and 7 Be on wet deposition and transport in a global three-dimensional chemical tracer model driven by assimilated
meteorological ﬁelds. Journal of Geophysical Research 106, 12109e12128
- Meijer, E.W., P. F. J. van Velthoven, D. W. Brunner, H. Huntrieser and H. Kelder: Improvement and evaluation of the parameterisation of nitrogen oxide production by lightning, Physics and Chemistry of the Earth, Part C, Volume 26, Issue 8, Pages 577-583, 2001.
- Yarwood, G., S. Rao, M. Yocke, and G.Z.Whitten, Updates to the carbon bond chemical mechanism: CB05, Report to the U.S. Environmental Protection Agency, RT-04-00675, Yocke and Company, Novato, California, United States, 2005