CAMS: Global atmospheric composition forecast data documentation

Introduction

The global production system is used to produce the daily forecasts of pollutants, aerosols and greenhouse gases across the globe. Satellite observations of atmospheric composition are merged with a detailed computer simulation of the atmosphere using a method called data assimilation. The resulting analyses, i.e. maps of atmospheric composition, are used as initial conditions for the daily forecasts of atmospheric composition. Analyses and forecasts for aerosols and chemical species are produced twice a day.

A global production system is also used to assimilate re-processed observations from many satellites for the last few decades, using the same data assimilation process. The result is called reanalysis and it provides a consistent data set that can be used for scientific studies and trend analyses. The current atmospheric composition reanalysis produced by the Copernicus Atmosphere Monitoring Service is the CAMS global reanalysis (EAC4).

The IFS model and data assimilation system configurations (47r1)

The model used in the CAMS Global atmospheric composition forecast is the Integrated Forecasting System (IFS) that also produces ECMWF weather forecasts, but with additional modules enabled for aerosols, reactive gases and greenhouse gases that have been developed within CAMS and precursor projects GEMS and MACC. The following processes of atmospheric composition are considered:

• transport of trace gases and aerosols
• injection of emissions
• uptake and release by vegetation and the land and sea surface
• removal by dry deposition at the surface
• removal by scavenging in precipitation
• chemical conversion
• aerosol microphysics

Emissions and surface fluxes of the trace gases and aerosols used in the global CAMS productions are:

• anthropogenic emissions from the CAMS-GLOB-ANT inventory (available from the ADS)
• natural and biogenic emissions from the CAMS-GLOB-NAT and CAMS-GLOB-BIO data sets (available from the ADS)
• biomass burning emissions inferred from satellite observations of fire activity (GFAS v1.4)
• wind-blown desert dust and sea salt emissions modelled by the IFS depending on the meteorological forecasts
• CO2 fluxes from vegetation modelled by the C-TESSEL surface scheme (specifically for the CAMS GHG system) 
• natural and biogenic fluxes provided from additional climatological data sets 

For details about simulations and assimilation of chemistry, carbon dioxide, methane and aerosols please see References.

The IFS uses a four-dimensional variational data assimilation method (4D-VAR) for the assimilation of a wide range of meteorological observations as well as satellite retrievals of atmospheric composition.

The IFS model documentation for various model cycles can be found on https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/ifs-documentation. Please note that the IFS cycle changes during the years, and this page documents the current operational cycle (47r1); please have a look at the table below for details of changes from previous cycles.

Configuration for CAMS Global atmospheric composition global forecasts (daily)

Twice a day (at 00:00 and 12:00 UTC), CAMS issues a five-day forecast of global atmospheric composition. The forecast consists of 56 reactive traces gases in the troposphere, stratospheric ozone and seven different types of aerosol: desert dust, sea salt, organic matter, black carbon, sulphate, and (since 9 July 2019) nitrate and ammonium.

The initial conditions of the forecast are obtained by combining a previous forecast with satellite observations of aerosol optical depth, ozone (O₃), carbon monoxide (CO), nitrogen dioxide (NO₂) , and sulfur dioxide (SO₂) through the 4D-VAR data assimilation process.

Satellite observations of fire radiative power (GFAS) from the day before the forecast start are used to derive timely biomass burning emissions for the forecast. 

The forecasts are evaluated at quarterly intervals by an external team lead by KNMI and evaluation reports can be found here: https://atmosphere.copernicus.eu/eqa-reports-global-services.

Evolution of the CAMS global forecasting system

The IFS undergoes changes regularly in order to improve the forecast. Changes are listed below with their identifier (Cycle 46r1 for instance) or a specific title when the cycle number did not change.

Data access

The data is now available from the Atmosphere Data Store (ADS), either interactively through its download web form or programmatically using the CDS API service:

CAMS global atmospheric composition forecasts

For details about the migration to the CDS API please see here. Currently only string should be used in an API request.

Analysis data are not available on the ADS webform but they can be downloaded using the CDS API service. The keyword 'type' and 'leadtime_hour' have to be set on 'analysis' and '0' respectively. Please have a look at the following example:

import cdsapi

c = cdsapi.Client()

c.retrieve(
    'cams-global-atmospheric-composition-forecasts',
    {
        'date': '2021-03-24',
        'time': [
            '06:00', '12:00', '18:00',
        ],
        'leadtime_hour': '0',
        'area': [
            70, -18, 69,
            -17,
        ],
        'type':'analysis',
        'variable': 'total_aerosol_optical_depth_670nm',
        'format': 'netcdf_zip',
    },
    'download.netcdf_zip')

A subset of the CAMS Global atmospheric composition forecast data for the latest three days can be accessible also through the FTP dissemination service. For list of variables available on the FTP please see here.

The data remains also listed for now in ECMWF's public data catalogue. To access the data use the ECMWF Web API with ‘dataset’:'cams_nrealtime'. Please have a a look at the following link for Python CAMS real-time examples. 

Users with access to MARS can also browse the data on the MARS catalogue under class=mc and expver= 0001.

Data availability (HH:MM)

CAMS Global analyses and forecasts:

00 UTC forecast data availability guaranteed by 10:00 UTC

12 UTC forecast data availability guaranteed by 22:00 UTC

It is possible that the data will be available earlier but without guarantee.

Spatial grid

CAMS Global atmospheric composition forecasts data currently has a resolution of approximately 40 km (approximately 0.35 degrees). The data are archived either as spectral coefficients with a triangular truncation of T511 or on a reduced Gaussian grid with a resolution of N256. These grids are so called "linear grids", sometimes referred to as T_L511. 

Temporal frequency

The CAMS Global 5-day forecasts run twice daily from 00 and 12 UTC and the data are available every hour (for surface fields) and every 3 hours (for model- and pressure-level fields). The analyses are available every 6 hours at 00 UTC, 06 UTC, 12 UTC and 18 UTC.

Data format

Model level fields are in GRIB2 format. All other fields are in GRIB1, unless otherwise indicated. NetCDF format is available on ADS but it is experimental.

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

Model levels: 1/to/137, which are described at https://www.ecmwf.int/en/forecasts/documentation-and-support/137-model-levels. Before 9 July 2019, the vertical levels were 60, which are described at https://www.ecmwf.int/en/forecasts/documentation-and-support/60-model-levels.

Parameter listings

Physical characteristics of the different types of aerosols (i.e mass density, the radius mode and geometric standard deviation of the lognormal size distribution and the bin limits and hygroscopic factor), are available in Rémy et al. (2019). The suggested reference for the IFS aerosol optical properties and hygroscopic growth tables is Bozzo at al (2020).

Parameters labelled as "Fast access" are stored on disk while parameters labelled as "slow access" are stored on tape. Retrieval of this data will be MUCH SLOWER than disk-resident data.

Table 1: single level parameters (last reviewed on 23 Apr 2021 )

Table 2: Multi-level parameters (last reviewed on 23 Apr 2021 )

Satellite observations

Satellite observations are used by CAMS to constrain the global forecast model, ensuring the forecasts are as accurate as possible. The process of merging the numerical forecast model with the observations is called data assimilation.

The CAMS Global production system uses satellite data in its 4-dimensional variational (4D-Var) data assimilation system to constrain the initial atmospheric state that is used for the 5-day forecast. Apart from all the meteorological observations that form part of the ECMWF numerical weather prediction system, CAMS uses additional observations on atmospheric composition. Three categories of observations are listed below. The assimilated observations are the observations that are currently used to constrain the model; the monitored observations are the observations that are being tested for future implementation; and the planned observations are observations that are being considered for future implementation in the CAMS system.

Satellite data monitoring statistics are available here.

Evaluation and Quality Assurance reports

The global forecasting system is continually being evaluated to ensure the output meets the expected requirements. Comprehensive Evaluation and Quality Assurance (EQA) reports are provided on a quarterly basis. Before each upgrade of the global forecasting system, the new system is tested and evaluated, and a so-called "e-suite EQA report" is produced. All reports are available here.

Quality monitoring graphics

CAMS uses a multitude of independent data sets to routinely monitor its global forecasts. It works with various data providers, acquiring the observations with appropriate timeliness and generating graphics that show the differences between the forecasts and the independent observations. See at https://atmosphere.copernicus.eu/global-services

Guidelines

• Convert mass mixing ratio (MMR) to mass concentration or to volume mixing ratio (VMR)

• Representations of SO2 and SO4 in CAMS reanalysis

• For details on how convert from mixing ratio (kg per kg dry air) to concentration (kg/m3): CAMS Surface concentration of a given species
• Web charts of biomass-burning AOD

  The actual aerosol species represented in the model are defined more according to their chemical composition (e.g. sulphate, black carbon, organic matter) rather than their source type (e.g. biomass burning or fossil fuels).

  What is presented on the web charts as "biomass-burning AOD" is the sum of organic matter AOD (omaod550) and black carbon AOD (bcaod550). Organic matter is almost always the dominant component out of these.

  Historically, biomass-burning was the main source of such aerosols in the model, apart from a much lower background of primary OM+BC from fossil fuels and of OM from biogenic secondary organic aerosol (SOA).

  However, since a representation of anthropogenic SOA as a source of OM was added (in cycle 43r1, implemented in January 2017), the model also captures the relatively high levels of this OM around polluted cities.

  To sum up, if you want the data used for those web charts, take omaod550+bcaod550 (which will usually be fairly close to omaod550 alone). However, note that this will also show significant aerosol plumes from anthropogenic pollution as well as biomass burning.

Known issues

References

• Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D., 2019: Modelling CO₂ weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019
  
• 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, https://doi.org/10.5194/gmd-10-1-2017
• Agustí-Panareda, A., Massart, S., Chevallier, F., Balsamo, G., Boussetta, S., Dutra, E., and Beljaars, A., 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, https://doi.org/10.5194/acp-16-10399-2016
• Agustí-Panareda, A., Massart, S., Chevallier, F., Boussetta, S., Balsamo, G., Beljaars, A., Ciais, P., Deutscher, N. M., Engelen, R., Jones, L., Kivi, R., Paris, J.-D., Peuch, V.-H., Sherlock, V., Vermeulen, A. T., Wennberg, P. O., and Wunch, D., 2014: Forecasting global atmospheric CO₂, Atmos. Chem. Phys., 14, 11959–11983, https://doi.org/10.5194/acp-14-11959-2014
• Barré, J., Aben, I., Agustí-Panareda, A., Balsamo, G., Bousserez, N., Dueben, P., Engelen, R., Inness, A., Lorente, A., McNorton, J., Peuch, V.-H., Radnoti, G., and Ribas, R.: Systematic detection of local CH₄emissions anomalies combining satellite measurements and high-resolution forecasts, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-550, in review, 2020.
• Bozzo, A. and Benedetti, A. and Flemming, J. and Kipling, Z. and Rémy, S., 2020: An aerosol climatology for global models based on the tropospheric aerosol scheme in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 3, 1007–1034, https://doi.org/10.5194/gmd-13-1007-2020
• Diamantakis, M. and Agustí-Panareda, A., 2017: A positive definite tracer mass fixer for high resolution weather and atmospheric composition forecasts, ECMWF Technical Memoranda, No. 819, 2017.
• Flemming, J., Huijnen, V., Arteta, J., Bechtold, P., Beljaars, A., Blechschmidt, A.-M., Diamantakis, M., Engelen, R. J., Gaudel, A., Inness, A., Jones, L., Josse, B., Katragkou, E., Marecal, V., Peuch, V.-H., Richter, A., Schultz, M. G., Stein, O., and Tsikerdekis, A., 2015: Tropospheric chemistry in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 8, 975–1003, https://doi.org/10.5194/gmd-8-975-2015
• Flemming, J. and Benedetti, A. and Inness, A. and Engelen, R. J. and Jones, L. and Huijnen, V. and Remy, S. and Parrington, M. and Suttie, M. and Bozzo, A. and Peuch, V.-H. and Akritidis, D. and Katragkou, E., 2017: The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015,  Atmos. Chem. Phys.,17, 1945-1983, https://doi.org/10.5194/acp-17-1945-2017
• Huijnen, V. and Flemming, J. and Chabrillat, S. and Errera, Q. and Christophe, Y. and Blechschmidt, A.-M. and Richter, A. and Eskes, H., 2016: C-IFS-CB05-BASCOE: stratospheric chemistry in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 9, 3071–3091, https://doi.org/10.5194/gmd-9-3071-2016
• Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M., 2019: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019
• Inness, A. and Flemming, J. and Heue, K.-P. and Lerot, C. and Loyola, D. and Ribas, R. and Valks, P. and van Roozendael, M. and Xu, J. and Zimmer, W., 2019: Monitoring and assimilation tests with TROPOMI data in the CAMS system:
  near-real-time total column ozone, Atmos. Chem. Phys., 19, 3939–3962, https://acp.copernicus.org/articles/19/3939/2019/
• Inness, A. and Coauthors, 2015: Data assimilation of satellite-retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF's Composition-IFS, Atmos. Chem. Phys., 15, 5275-5303, https://doi.org/10.5194/acp-15-5275-2015
• Massart, S., Agusti-Panareda, A., Aben, I., Butz, A., Chevallier, F., Crevoisier, C., Engelen, R., Frankenberg, C., and Hasekamp, O., 2014: Assimilation of atmospheric methane products into the MACC-II system: from SCIAMACHY to TANSO and IASI, Atmos. Chem. Phys., 14, 6139–6158, https://doi.org/10.5194/acp-14-6139-2014
• Massart, S., Agustí-Panareda, A., Heymann, J., Buchwitz, M., Chevallier, F., Reuter, M., Hilker, M., Burrows, J. P., Deutscher, N. M., Feist, D. G., Hase, F., Sussmann, R., Desmet, F., Dubey, M. K., Griffith, D. W. T., Kivi, R., Petri, C., Schneider, M., and Velazco, V. A., 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, https://doi.org/10.5194/acp-16-1653-2016
• Rémy, S. and Kipling, Z. and Flemming, J. and Boucher, O. and Nabat, P. and Michou, M. and Bozzo, A. and Ades, M. and Huijnen, V. and Benedetti, A. and Engelen, R. and Peuch, V.-H. and Morcrette, J.-J., 2019: Description and evaluation of the tropospheric aerosol scheme in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS-AER, cycle 45R1), Geosci. Model Dev., 12, 4627-4659, https://doi.org/10.5194/gmd-12-4627-2019

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