CAMS Reanalysis

The CAMS reanalysis dataset covers the period January 2003 to June 2021. The CAMS reanalysis is the latest global reanalysis data set of atmospheric composition (AC) produced by the Copernicus Atmosphere Monitoring Service (CAMS), consisting of 3-dimensional time-consistent AC fields, including aerosols, chemical species and greenhouse gases (GHGs) through the separate CAMS global greenhouse gas reanalysis (EGG4). The data set builds on the experience gained during the production of the earlier MACC reanalysis and CAMS interim reanalysis.

The CAMS reanalysis was produced using 4DVar data assimilation in Cycle 42r1 of ECMWF’s Integrated Forecasting System (IFS), with 60 hybrid sigma/pressure (model) levels in the vertical, with the top level at 0.1 hPa. Atmospheric data are available on these levels and they are also interpolated to 25 pressure levels, 10 potential temperature levels and 1 potential vorticity level. "Surface or single level" data are also available.

Generally, the data are available at a sub-daily and monthly frequency and consist of analyses and 48h forecasts, initialised daily from analyses at 00 UTC.

For sub-daily data for the CAMS reanalysis, the analyses are available 3-hourly. The daily forecast, run from 00 UTC, has 3-hourly steps from 0 to 48 hours for the 3D model level and pressure level fields, and hourly steps from 0 to 48 hours for the surface fields.

Several parameters are also available as synoptic monthly means, for each particular time and forecast step and as monthly means of daily means, for the month as a whole.

Monthly means for analyses and instantaneous forecasts are created from data with a valid time in the month, between 00 and 23 UTC, which excludes the time 00 UTC on the first day of the following month. Monthly means for accumulations and mean rates are created from data with a forecast period falling within the month. For example, monthly means of daily means for accumulations and mean rates are created from contiguous data with forecast periods spanning from 00 UTC on the first day of the month to 00 UTC on the first day of the following month.

Model level fields are in GRIB2 format. All other fields are in GRIB1, unless otherwise indicated.

Product description

Period from 2003 to June 2021

Spatial resolution  ~80 km. The data are available either as spectral coefficients with a triangular truncation of T255 or on a reduced Gaussian grid with a resolution of N128. These grids are "linear grids", sometimes referred to as TL255.

On the Atmosphere Data Store (ADS), the fields were interpolated from their native representation to a regular 0.75°x0.75° lat/lon grid.

Level listings

Pressure levels: 1000/950/925/900/850/800/700/600/500/400/300/250/200/150/100/70/50/30/20/10/7/5/3/2/1

Potential temperature levels: 300/315/320/330/350/370/395/475/600/850

Potential vorticity level: 2000

Model levels: 1 to 60, (full description of model levels)


  • Agustí-Panareda, A., and Coauthors, 2014: Forecasting global atmospheric CO2Atmos. Chem. Phys., 14, 11959-11983,
  • Agusti-Panareda, A, S. Massart, F. Chevallier, G. Balsamo, S. Boussetta, E. Dutra, and A. Beljaars, 2016: A biogenic CO2 flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO2 analyses and forecasts, Atmos. Chem. Phys., 16, 10399–10418,
  • Agusti-Panareda, A., M. Diamantakis, V. Bayona, F. Klappenbach, and A. Butz, 2017: Improving the inter-hemispheric gradient of total column atmospheric CO2 and CH4 in simulations with the ECMWF semi-Lagrangian atmospheric global model, Geosci. Model Dev., 10, 1-18,
  • Bergamaschi, P., and Coauthors, 2009: Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals, J. Geophys. Res., 114, D22301,
  • Bozzo, A., S. Remy, A. Benedetti, J.Flemming, P. Bechtold, M.J. Rodwell, and J.-J. Morcrette, 2017: Implementation of a CAMS-based aerosol climatology in the IFSA. ECMWF Technical Memorandum 801, 33 pp,
  • Flemming, J., and Coauthors, 2015: Tropospheric chemistry in the Integrated Forecasting System of ECMWF. Geosci. Model Dev., 8, 975–1003,
  • Flemming, J., and Coauthors, 2017: The CAMS interim Reanalysis of Carbon Monoxide,Ozone and Aerosol for 2003–2015. Atmos. Chem. Phys., 17, 1945–1983,
  • Massart, S., A. Agusti-Panareda, I. Aben, A. Butz, F. Chevallier, C. Crevoisier, R. Engelen, C. Frankenberg, and O. Hasekamp, 2014:  Assimilation of atmospheric methane products into the MACC-II system: from SCIAMACHY to TANSO and IASI, Atmos. Chem. Phys., 14, 6139-6158,
  • Massart, S., and Coauthors, 2016: Ability of the 4-D-Var analysis of the GOSAT BESD XCO2 retrievals to characterize atmospheric CO2 at large and synoptic scales, Atmos. Chem. Phys., 16, 1653-1671,
  • Spahni, R., and Coauthors, 2011: Constraining global methane emissions and uptake by ecosystems. Biogeosciences, 8, 1643–1665,
  • Stein, O., M. G. Schultz, I. Bouarar2, H. Clark, V. Huijnen, A. Gaudel, M. George, and C. Clerbaux, 2014: On the wintertime low bias of Northern Hemisphere carbon monoxide found in global model simulations. Atmos. Chem. Phys., 14, 9295–9316,
  • Takahashi, T., and Coauthors, 2009: Climatological mean and decadal change in surface ocean pCO2, and netsea–air CO2flux over the global oceans. Deep-Sea Research II, 56, 554–577,