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Introduction

Here we document the CAMS reanalysis datasets, the CAMS global reanalysis (EAC4) which currently covers the period 2003-December 2021, and CAMS global greenhouse gas reanalysis (EGG4) which currently covers the period 2003-2020.

The CAMS reanalysis is the latest global reanalysis data set of atmospheric composition (AC) produced by the Copernicus Atmosphere Monitoring Service, consisting of 3-dimensional time-consistent AC fields, including aerosols, chemical species and greenhouse gases, 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 CY42R1 of ECMWF’s Integrated Forecast 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, 10 potential temperature and 1 potential vorticity level(s). "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.

The data are archived in the ECMWF data archive (MARS) and can be obtained from the Atmosphere Data Store.

The IFS model and data assimilation system

The 4DVar data assimilation uses 12 hour assimilation windows from 09 UTC to 21 UTC and 21 UTC to 09 UTC.

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. The model used in the CAMS reanalyses includes several updates to the aerosol and chemistry modules on top of the standard CY42R1 release, which are listed below. Please note that 42r1 documentation is not available on the page, but the code for the earlier and later cycles is available for reference.

Aerosol model

  • Updated aerosol optical properties, especially for organic matter (Bozzo et al., 2017).
  • Bug fixes to sedimentation, which was unreasonably weak for some dust and sea-salt bins, with corresponding re-tuning of sea-salt scavenging.
  • SO2 dry deposition velocities updated to match those used in the chemistry scheme (from SUMO).
  • New parametrisation of anthropogenic Secondary Organic Aerosol (SOA) production, proportional to non-biomass-burning CO emissions.
  • More detailed SO2 to sulfate aerosol conversion with dependence on temperature and relative humidity, and overall decrease in the conversion timescale especially at high latitudes.
  • Increased sulfate dry deposition velocity over ocean.
  • Mass fixer extended to aerosol species.
  • Scaling of biomass-burning Black Carbon (BC) emissions using the ratio of BC AOD (CAMS interim reanalysis) / BC AOD (CAMS interim control run).
  • 80% of SO2 emissions are released in the two lowest model levels (as an update of tendencies) rather than at surface (fluxes)

Chemistry mechanism

The chemical mechanism of the IFS is an extended version of the Carbon Bond 2005 (CB05) chemical mechanism as implemented in CTM Transport Model 5 (TM5). In the CAMS reanalysis the model as documented in Flemming et al. (2015) and Flemming et al. (2017) is used with the following updates:

  • Update of heterogeneous rate coefficients for N2O5 and HO2 based on clouds and aerosol
  • Modification of photolysis rates by aerosol
  • Dynamic tropopause definition based on T profile for coupling to stratosphere and tropospheric mass diagnostics
  • Monthly mean VOC emissions calculated by the MEGAN model using MERRA reanalysed meteorology (Sindelarova et al., 2014) for the period 2003-2017. From 2018 onwards emissions adopt a climatology of the biogenic MEGAN-MACC emissions based on monthly data for 2011-2017.
  • Bugfixes, in particular for diurnal cycle of dry deposition whose correction has decreased ozone dry deposition (about 15-20%)     
  • The version number for the chemistry scheme is CHEM_VER=15

Greenhouse Gases

The model configuration for greenhouse gases is based on the specification of the following components documented in the listed papers below:

Emission datasets

The emissions datasets used to produce the CAMS reanalyses are listed in Table 1. They include the MACCity anthropogenic emission, GFAS fire emissions, MEGAN biogenic emissions and several GHG emission datasets.

Anthropogenic emissions used were not adjusted for any COVID-19 lockdowns in 2020.


Table 1: Emission datasets used in the CAMS reanalysis

Data setVersion/Period
MACCity anthropogenic emissionsMACCity (trend: ACCMIP + RCP8.5) & CO emission upgrade Stein et al. (2014)
GFAS

v1.2: 20030101-

Dry depositionSumo dry deposition
VOC emissionsMonthly mean VOC emissions calculated by the MEGAN model using MERRA reanalysed meteorology (Sindelarova et al., 2014)
CO2 ocean fluxesTakahashi et al. (2009) climatology
CO2 emissions from aviationBased on ACCMIP NO emissions from aviation scaled to annual total CO2 from EDGAR aviation emissions.

CO2 ecosystem fluxes

bias corrected with BFAS

Based on CHTESSEL (modelled online in C-IFS)
CO2 anthropogenic emissionsEDGARv4.2FT2010 (2003-2010)
CH4 wetland emissionsLPJ-HYMN climatology (Spanhi et al., 2011)
CH4 total emissionsbased on EDGARv4.2FT2010 , LPJ-HYMN wetland climatology and other natural sources/sinks (2003-2010)
CH4 chemical sinkbased on Bergamaschi et al. (2009) dataset
CH4 anthropogenic emissionsEDGARv4.2FT2010 (2003-2010)

Data organisation and access

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

Please have a look at How to migrate to CDS API on the Atmosphere Data Store (ADS) for more details.

Users with access to MARS can browse the data on the MARS catalogue under class=mc and expver=eac4 for the CAMS global reanalysis and under class=mc and expver=egg4 for the CAMS global greenhouse gas reanalysis.

Data organisation in MARS 


CAMS global reanalysis (EAC4)

CAMS global greenhouse gas reanalysis (EGG4)

Stream
  • oper: sub-daily
  • mnth: synoptic monthly means
  • moda: monthly means of daily means
  • oper: sub-daily
  • mnth: synoptic monthly means
  • moda: monthly means of daily means
Type
  • an: analyses
  • fc: forecasts
  • an: analyses
  • fc: forecasts
Levtype
  • sfc: surface or single level
  • pl: pressure levels
  • pt: potential temperature levels
  • pv: potential vorticity level
  • ml: model levels
  • sfc: surface or single level
  • pl: pressure levels
  • ml: model levels

Spatial grid

The CAMS reanalysis data have a resolution of approximately 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 so called "linear grids", sometimes referred to as TL255.

On the ADS the fields were interpolated from their native representation to a regular 0.75°x0.75° lat/lon grid.

Vorticity and divergence were also used to pre-calculate u and v on the same grid.

Temporal frequency

For sub-daily data for the CAMS reanalysis (stream=oper, for MARS users) the analyses (type=an) 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. 

Note that the surface fields from the CAMS global greenhouse reanalysis (egg4) are only available 3-hourly from 2013 onwards

Monthly means

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

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.

Note that monthly means are available only on model level 60.

Data format

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

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, which are described at L60 model level definitions.

CAMS global reanalysis (EAC4) Parameter listings

PLEASE NOTE: any data labelled as "slow-access" is stored on MARS tapes instead of disk. Retrieval of this data will be MUCH SLOWER than disk-resident data. You should not select any tape-resident data unless absolutely required for your purposes.

Table1: Fast-access main variables (single-level)

name

units

Variable name in ADS

shortName

paramId

10m u-component of windm s-1

10m_u_component_of_wind

10u165.128
10m v-component of windm s-1

10m_v_component_of_wind

10v

166.128

2m dewpoint temperatureK

2m_dewpoint_temperature

2d

167.128

2m temperatureK

2m_temperature

2t

168.128

Black carbon aerosol optical depth at 550 nm~

black_carbon_aerosol_optical_depth_550nm

bcaod550211.210
Dust aerosol optical depth at 550 nm~

dust_aerosol_optical_depth_550nm

duaod550209.210
Land-sea mask(0 - 1)

land_sea_mask

lsm172.128
Mean sea level pressurePa

mean_sea_level_pressure

msl151.128
Organic matter aerosol optical depth at 550 nm~organic_matter_aerosol_optical_depth_550nmomaod550210.210
Particulate matter d < 1 µmkg m-3particulate_matter_1umpm172.210
Particulate matter d < 2.5 µmkg m-3particulate_matter_2.5umpm2p573.210
Particulate matter d < 10 µmkg m-3particulate_matter_10umpm1074.210
Sea salt aerosol optical depth at 550 nm~sea_salt_aerosol_optical_depth_550nmssaod550208.210
Sulphate aerosol optical depth at 550 nm~sulphate_aerosol_optical_depth_550nm"suaod550212.210
Surface Geopotentialm2 s-2surface_geopotential~51.162
Total aerosol optical depth at 469 nm~total_aerosol_optical_depth_469nmaod469213.210
Total aerosol optical depth at 550 nm~total_aerosol_optical_depth_550nmaod550207.210
Total aerosol optical depth at 670 nm~total_aerosol_optical_depth_670nmaod670214.210
Total aerosol optical depth at 865 nm~total_aerosol_optical_depth_865nmaod865215.210
Total aerosol optical depth at 1240 nm~total_aerosol_optical_depth_1240nmaod1240216.210
Total column carbon monoxide*kg m-2total_column_carbon_monoxidetcco127.210
Total column ethane*kg m-2total_column_ethanetc_c2h6

45.218

Total column formaldehyde*kg m-2total_column_formaldehydetchcho128.210
Total column hydrogen peroxide*kg m-2total_column_hydrogen_peroxidetc_h2o23.218
Total column hydroxyl radical*kg m-2total_column_hydroxyl_radicaltc_oh30.218
Total column isoprene*kg m-2total_column_isoprenetc_c5h816.218
Total column nitric acid*kg m-2total_column_nitric_acidtc_hno36.218
Total column nitrogen dioxide*kg m-2total_column_nitrogen_dioxidetcno2125.210
Total column nitrogen monoxide*kg m-2total_column_nitrogen_monoxidetc_no27.218
Total column ozone*kg m-2total_column_ozonetco3206.128
Total column peroxyacetyl nitrate*kg m-2total_column_peroxyacetyl_nitratetc_pan13.218
Total column propane*kg m-2total_column_propanetc_c3h847.218
Total column sulphur dioxide*kg m-2total_column_sulphur_dioxidetcso2126.210
Total column water vapour*kg m-2total_column_water_vapourtcwv137.128

PLEASE NOTE: *Total column (in kg m-2) is available at the surface (model level 60 for MARS users). Total column refers to the total amount of the selected variable in a column of air extending from the surface of the Earth to the top of the atmosphere (model level 1 for MARS users). Total column can also be referred to as total <selected variable>, or vertically integrated <selected variable>.

Table 2: Fast-access main variables (multi-level)

name

units

Variable name in ADS

shortName

paramId

Carbon monoxide*kg kg-1carbon_monoxideco123.210
Dust aerosol (0.03 - 0.55 µm) mixing ratio*kg kg-1dust_aerosol_0.03-0.55um_mixing_ratioaermr044.210
Dust aerosol (0.55 - 0.9 µm) mixing ratio*kg kg-1dust_aerosol_0.55-0.9um_mixing_ratioaermr055.210
Dust aerosol (0.9 - 20 µm) mixing ratio*kg kg-1dust_aerosol_0.9-20um_mixing_ratioaermr066.210
Ethane*kg kg-1ethanec2h645.217
Formaldehyde*kg kg-1 formaldehydehcho124.210
Hydrogen peroxide*kg kg-1hydrogen_peroxideh2o23.217
Hydrophilic black carbon aerosol mixing ratio*kg kg-1hydrophilic_black_carbon_aerosol_mixing_ratioaermr099.210
Hydrophilic organic matter aerosol mixing ratio*kg kg-1hydrophilic_organic_matter_aerosol_mixing_ratioaermr077.210
Hydrophobic black carbon aerosol mixing ratio*kg kg-1hydrophobic_black_carbon_aerosol_mixing_ratioaermr1010.210
Hydrophobic organic matter aerosol mixing ratio*kg kg-1hydrophobic_organic_matter_aerosol_mixing_ratioaermr088.210
Hydroxyl radical*kg kg-1hydroxyl_radicaloh30.217
Isoprene*kg kg-1isoprenec5h816.217
Nitric acid*kg kg-1nitric_acidhno36.217
Nitrogen dioxide*kg kg-1nitrogen_dioxideno2121.210
Nitrogen monoxide*kg kg-1nitrogen_monoxideno27.217
Ozone*kg kg-1ozoneo3203
Peroxyacetyl nitrate*kg kg-1peroxyacetyl_nitratepan13.217
Propane*kg kg-1propanec3h847.217
Sea salt aerosol (0.03 - 0.5 µm) mixing ratio*kg kg-1sea_salt_aerosol_0.03-0.5um_mixing_ratioaermr011.210
Sea salt aerosol (0.5 - 5 µm) mixing ratio*kg kg-1sea_salt_aerosol_0.5-5um_mixing_ratioaermr022.210
Sea salt aerosol (5 - 20 µm) mixing ratio*kg kg-1sea_salt_aerosol_5-20um_mixing_ratioaermr033.210
Specific humidity*kg kg-1specific_humidityq133
Sulphate aerosol mixing ratio*kg kg-1sulphate_aerosol_mixing_ratioaermr1111.210
Sulphur dioxide*kg kg-1sulphur_dioxideso2122.210
TemperatureKtemperaturet

130

U-component of windm s-1u_component_of_windu131
V-component of windm s-1v_component_of_windv132

PLEASE NOTE: *In the CAMS Global Reanalysis, this variable is the mass mixing ratio at different pressure or model levels in kg kg-1

Table 3: Slow-access additional variables (single-level radiation)

name

units

Variable name in ADS

shortName

paramId

Near IR albedo for diffuse radiation(0 - 1)near_ir_albedo_for_diffuse_radiationalnid18.128
Near IR albedo for direct radiation(0 - 1)near_ir_albedo_for_direct_radiationalnip17.128
Snow albedo(0 - 1)snow_albedoasn32.128
UV visible albedo for diffuse radiation(0 - 1)uv_visible_albedo_for_diffuse_radiationaluvd16.128
UV visible albedo for direct radiation(0 - 1)uv_visible_albedo_for_direct_radiationaluvp15.128

Table 4: Slow-access additional variables (single-level chemical vertical integrals)

name

units

Variable name in ADS

shortName

paramId

Total column acetone*kg m-2total_column_acetonetc_ch3coch352.128
Total column aldehydes*kg m-2total_column_aldehydestc_ald212.128
Total column ethanol*kg m-2total_column_ethanoltc_c2h5oh46.218
Total column ethene*kg m-2total_column_ethenetc_c2h410.128
Total column formic acid*kg m-2total_column_formic_acidtc_hcooh43.218
Total column methane*kg m-2total_column_methanetc_ch44.218
Total column methanol*kg m-2total_column_methanoltc_ch3oh42.128
Total column methyl peroxide*kg m-2total_column_methyl_peroxidetc_ch3ooh7.218
Total column olefins*kg m-2total_column_olefinstc_ole11.218
Total column organic nitrates*kg m-2total_column_organic_nitratestc_onit15.218
Total column paraffins*kg m-2total_column_paraffinstc_par9.218
Vertically integrated mass of dust aerosol (0.03 - 0.55 µm)kg m-2vertically_integrated_mass_of_dust_aerosol_0.03-0.55umaermssdus43.215
Vertically integrated mass of dust aerosol (0.55 - 9 µm)kg m-2vertically_integrated_mass_of_dust_aerosol_0.55-9umaermssdum44.215
Vertically integrated mass of dust aerosol (9 - 20 µm)kg m-2vertically_integrated_mass_of_dust_aerosol_9-20umaermssdul45.215
Vertically integrated mass of hydrophilic black carbon aerosolkg m-2vertically_integrated_mass_of_hydrophilic_black_carbon_aerosolaermssbchphil78.215
Vertically integrated mass of hydrophilic organic matter aerosolkg m-2vertically_integrated_mass_of_hydrophilic_organic_matter_aerosolaermssomhphil62.215
Vertically integrated mass of hydrophobic black carbon aerosolkg m-2vertically_integrated_mass_of_hydrophobic_black_carbon_aerosolaermssbchphob77.215
Vertically integrated mass of hydrophobic organic matter aerosolkg m-2vertically_integrated_mass_of_hydrophobic_organic_matter_aerosolaermssomhphob61.215
Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm)kg m-2vertically_integrated_mass_of_sea_salt_aerosol_0.03-0.5umaermsssss19.215
Vertically integrated mass of sea salt aerosol (0.5 - 5 µm)kg m-2vertically_integrated_mass_of_sea_salt_aerosol_0.5-5umaermssssm20.215
Vertically integrated mass of sea salt aerosol (5 - 20 µm)kg m-2vertically_integrated_mass_of_sea_salt_aerosol_5-20umaermssssl21.215
Vertically integrated mass of sulphate aerosolkg m-2vertically_integrated_mass_of_sulphate_aerosolaermsssu87.215

PLEASE NOTE: *Total column (in kg m-2) is available at the surface (model level 60 for MARS users). Total column refers to the total amount of the selected variable in a column of air extending from the surface of the Earth to the top of the atmosphere (model level 1 for MARS users). Total column can also be referred to as total <selected variable>, or vertically integrated <selected variable>.

Table5: Slow-access additional variables (single-level meteorological)

name

units

Variable name in ADS

shortName

paramId

High cloud cover(0 - 1)high_cloud_coverhcc188.128
High vegetation cover(0 - 1)high_vegetation_covercvh28.128
Lake cover(0 - 1)lake_covercl26.128
Leaf area index, high vegetationm2 m-2leaf_area_index_high_vegetationlai_hv67.128
Leaf area index, low vegetationm2 m-2leaf_area_index_low_vegetationlai_lv66.128
Lifting threshold speedm s-1lifting_threshold_speedaerlts53.210
Low cloud cover(0 - 1)low_cloud_coverlcc186.128
Low vegetation cover(0 - 1)low_vegetation_covercvl27.128
Mean altitude of maximum injectionmmean_altitude_of_maximum_injectionmami119.210
Medium cloud cover(0 - 1)medium_cloud_covermcc

187.218

Sea-ice cover(0 - 1)sea_ice_coversicgrd31.129
Sea surface temperatureK

Sea surface temperature

sst34.128
Skin reservoir contentm of water equivalentskin_reservoir_contentsrc198.128
Skin temperatureKskin_temperatureskt235.128
Snow depthm of water equivalentsnow_depthsd141.128
Soil clay content%soil_clay_contentaerscc54.210
Soil type~soil_typeslt43.128
Surface pressurePasurface_pressuresp134.128
Surface roughnessmsurface_roughnesssr173.128
Total cloud cover(0 - 1)total_cloud_covertcc164.128
Total column waterkg m-2total_column_watertcw136.128
Type of high vegetation~ type_of_high_vegetationtvh30.128
Type of low vegetation~type_of_low_vegetationtvl29.128

Table 6: Slow-access additional variables (multi-level chemical)

name

units

Variable name in ADS

shortName

paramId

Note
Acetone*kg kg-1acetonech3coch352.217
Acetone product*kg kg-1acetone_productaco253.217Model-level only
Aldehydes*kg kg-1aldehydesald212.217
Amine*kg kg-1aminenh240.217
Ammonia*kg kg-1ammonianh319.217Model-level only
Ammonium*kg kg-1ammoniumnh421.217Model-level only
Dimethyl sulfide*kg kg-1dimethyl_sulfidedms18.217Model-level only
Dinitrogen pentoxide*kg kg-1dinitrogen_pentoxiden2o533.217Model-level only
Ethanol*kg kg-1ethanolc2h5oh46.217
Ethene*kg kg-1ethenec2h410.217
Formic acid*kg kg-1formic_acidhcooh43.217
Hydroperoxy radical*kg kg-1hydroperoxy_radicalho228.217Model-level only
Lead*kg kg-1leadpb26.217Model-level only
Methacrolein MVK*kg kg-1methacrolein_mvkispd50.217Model-level only
Methacrylic acid*kg kg-1methacrylic_acidmcooh44.217Model-level only
Methane (chemistry)*kg kg-1methane_chemistrych4_c4.217
Methane sulfonic acid*kg kg-1methane_sulfonic_acidmsa22.217Model-level only
Methanol*kg kg-1methanolch3oh42.217
Methyl glyoxal*kg kg-1methyl_glyoxalch3cocho23.217Model-level only
Methyl peroxide*kg kg-1methyl_peroxidech3ooh7.217
Methylperoxy radical*kg kg-1methylperoxy_radicalch3o229.217Model-level only
Nitrate*kg kg-1nitrateno3_a51.217Model-level only
Nitrate radical*kg kg-1nitrate_radicalno332.217Model-level only
Olefins*kg kg-1olefinsole11.217
Organic ethers*kg kg-1organic_ethersror36.217Model-level only
Organic nitrates*kg kg-1organic_nitratesonit15.217
Paraffins*kg kg-1paraffinspar9.217
Pernitric acid*kg kg-1pernitric_acidho2no234.217Model-level only
Peroxides*kg kg-1peroxidesrooh14.217Model-level only
Peroxy acetyl radical*kg kg-1peroxy_acetyl_radicalc2o335.217Model-level only
Propene*kg kg-1propenec3h648.217Model-level only
Radon*kg kg-1radonra181.210Model-level only
Stratospheric ozone tracer*kg kg-1stratospheric_ozone_tracero3s24.217Model-level only
Terpenes*kg kg-1terpenesc10h1649.217Model-level only

PLEASE NOTE: *In the CAMS Global Reanalysis, this variable is the mass mixing ratio at different pressure or model levels in kg kg-1

Table 7: Slow-access additional variables (multi-level meteorological)

name

units

Variable name in ADS

shortName

paramId

Note
Fraction of cloud cover(0 - 1)fraction_of_cloud_covercc248Model-level only
Geopotentialm2 s-2geopotentialz129Model-level 1 only
Potential vorticityK m2 kg-1 s-1potential_vorticitypv60.128Pressure-level only
Relative humidity%relative_humidityr157.128Pressure-level only
Specific cloud ice water contentkg kg-1specific_cloud_ice_water_contentciwc247Model-level only
Specific cloud liquid water contentkg kg-1specific_cloud_liquid_water_contentclwc246Model-level only
Specific rain water contentkg kg-1specific_rain_water_contentcrwc75Model-level only
Specific snow water contentkg kg-1specific_snow_water_contentcswc76Model-level only
Vertical velocityPa s-1vertical_velocityw135

CAMS global greenhouse gases reanalysis (EGG4) Parameter listings

Table 1: Single-level radiation variables 

name

units

Variable name in ADSNote
Downward UV radiation at the surfaceJ m-2downward_uv_radiation_at_the_surface
Forecast albedo(0 - 1)forecast_albedo
Photosynthetically active radiation at the surfaceJ m-2photosynthetically_active_radiation_at_the_surface
Snow albedo(0 - 1)snow_albedo
Sunshine durationssunshine_duration
Surface net solar radiationJ m-2surface_net_solar_radiation
Surface net solar radiation, clear skyJ m-2surface_net_solar_radiation_clear_sky
Surface net thermal radiationJ m-2surface_net_thermal_radiation
Surface net thermal radiation, clear skyJ m-2surface_net_thermal_radiation_clear_sky
Surface solar radiation downward, clear skyJ m-2surface_solar_radiation_downward_clear_sky
Surface solar radiation downwardsJ m-2surface_solar_radiation_downwards
Surface thermal radiation downward, clear skyJ m-2surface_thermal_radiation_downward_clear_sky
Surface thermal radiation downwardsJ m-2surface_thermal_radiation_downwards
TOA incident solar radiationJ m-2toa_incident_solar_radiation
Top net solar radiationJ m-2top_net_solar_radiation
Top net solar radiation, clear skyJ m-2top_net_solar_radiation_clear_sky
Top net thermal radiationJ m-2top_net_thermal_radiation
Top net thermal radiation, clear skyJ m-2top_net_thermal_radiation_clear_sky

Table 2: Single-level chemical vertical integrals

name

units

Variable name in ADSNote
CH4 column-mean molar fractionppbch4_column_mean_molar_fraction
CO2 column-mean molar fractionppmco2_column_mean_molar_fraction

Table 3: Single-level emissions

name

units

Variable name in ADSNote
Accumulated carbon dioxide ecosystem respirationkg m-2accumulated_carbon_dioxide_ecosystem_respiration
Accumulated carbon dioxide gross primary productionkg m-2accumulated_carbon_dioxide_gross_primary_production
Accumulated carbon dioxide net ecosystem exchangekg m-2accumulated_carbon_dioxide_net_ecosystem_exchange
Anthropogenic emissions of carbon dioxidekg m-2 s-1anthropogenic_emissions_of_carbon_dioxide
Flux of carbon dioxide ecosystem respirationkg m-2 s-1flux_of_carbon_dioxide_ecosystem_respiration
Flux of carbon dioxide gross primary productionkg m-2 s-1flux_of_carbon_dioxide_gross_primary_production
Flux of carbon dioxide net ecosystem exchangekg m-2 s-1flux_of_carbon_dioxide_net_ecosystem_exchange
GPP coefficient from biogenic flux adjustment systemdimensionlessgpp_coefficient_from_biogenic_flux_adjustment_system
Methane loss rate due to radical hydroxyl (OH)s-1methane_loss_rate_due_to_radical_hydroxyl_oh
Methane surface fluxeskg m-2 s-1methane_surface_fluxes
Ocean flux of carbon dioxidekg m-2 s-1ocean_flux_of_carbon_dioxide
Rec coefficient from biogenic flux adjustment systemdimensionlessrec_coefficient_from_biogenic_flux_adjustment_system
Wildfire flux of carbon dioxidekg m-2 s-1wildfire_flux_of_carbon_dioxide
Wildfire flux of methanekg m-2 s-1 wildfire_flux_of_methane

Table 4: Single-level meteorological

name

units

Variable name in ADSNote
10m u-component of windm s-110m_u_component_of_wind
10m v-component of windm s-110m_v_component_of_wind
2m dewpoint temperatureK2m_dewpoint_temperature
2m temperatureK2m_temperature
Boundary layer heightmboundary_layer_height
Convective available potential energyJ kg-1convective_available_potential_energy
Convective inhibitionJ kg-1convective_inhibition
Convective precipitationmconvective_precipitation
Evaporationm of water equivalentevaporation
High cloud cover(0 - 1)high_cloud_cover
Land-sea mask(0 - 1)land_sea_mask
Large-scale precipitationmlarge_scale_precipitation
Low cloud cover(0 - 1)low_cloud_cover
Mean sea level pressurePamean_sea_level_pressure
Medium cloud cover(0 - 1)medium_cloud_cover
Potential evaporationmpotential_evaporation
Precipitation typedimensionlessprecipitation_type
Sea surface temperatureKsea_surface_temperature
Sea-ice cover(0 - 1)sea_ice_cover
Skin reservoir contentm of water equivalentskin_reservoir_content
Skin temperatureKskin_temperature
Snow depthm of water equivalentsnow_depth
Surface Geopotentialm2 s-2surface_geopotential
Surface latent heat fluxJ m-2surface_latent_heat_flux
Surface sensible heat fluxJ m-2surface_sensible_heat_flux
Total cloud cover(0 - 1)total_cloud_cover
Total column cloud ice waterkg m-2total_column_cloud_ice_water
Total column cloud liquid waterkg m-2total_column_cloud_liquid_water
Total column waterkg m-2total_column_water
Total column water vapourkg m-2total_column_water_vapour
Total precipitationmtotal_precipitation
Visibilitymvisibility

Table 5: Multi-level chemical

name

units

Variable name in ADSNote
Carbon dioxidekg kg-1carbon_dioxide
Methanekg kg-1methane

Table 6: Multi-level meteorological

name

units

Variable name in ADSNote
Fraction of cloud cover(0 - 1)fraction_of_cloud_cover
Geopotentialm2 s-2geopotential
Logarithm of surface pressure~logarithm_of_surface_pressure
Potential vorticityK m^2 kg^-1 s^-1potential_vorticity
Relative humidity%relative_humidity
Specific cloud ice water contentkg kg-1specific_cloud_ice_water_content
Specific cloud liquid water contentkg kg-1specific_cloud_liquid_water_content
Specific humiditykg kg-1specific_humidity
Specific rain water contentkg kg-1specific_rain_water_content
Specific snow water contentkg kg-1specific_snow_water_content
TemperatureKtemperature
U-component of windm s-1u_component_of_wind
V-component of windm s-1v_component_of_wind
Vertical velocityPa s-1vertical_velocity

CAMS global reanalysis (EAC4) Satellite Data 

The atmospheric composition satellite retrievals used as input into the CAMS reanalysis EAC4 are listed below. The following abbreviations are used in Table 1. TC: Total column, TRC: Tropospheric column, PROF: profiles, PC: Partial columns, ColAv: Column average mixing ratio, QR= quality flag given by data providers, SOE: Solar elevation, MODORO: Model orography, PRESS_RL= pressure at bottom of layer, LAT: Latitude.

ParameterInstrumentSatelliteProductPeriodData provider/ Version

Blacklist Criteria

(i.e. these data are not used)

Averaging kernels used
O3SCIAMACHYEnvisatTC20020803-20120408ESA, CCI (BIRA)


QR>0

SOE<6

no
O3MIPASEnvisatPROF

20030127- 20040326

20050127-20120331

ESA, NRT

ESA, CCI (KIT)


QR>0 for CCI data

no
O3MLSAuraPROF

20040803-20180312

NRT: 20180313-

NASA, V4QR>0no
O3OMIAuraTC

KNMI reproc: 20041001-20150531

NRT:20150601-

KNMI/NASA, V003

QR>0

SOE<10

no
O3GOME-2Metop-ATC

20070123-20121231

201301-201612

NRT:20170101-20181231

ESA, CCI (BIRA), fv0100

ESA, CCI (BIRA), fv0300

QR>0

SOE<10

no
O3GOME-2Metop-BTC

201301-201612

NRT: 20170101-

ESA, CCI (BIRA), fv0300

QR>0

SOE<10

no
O3SBUV/2NOAA-14PC 13L200407-200609NASA, v8.6

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

 no
O3SBUV/2NOAA-16

PC 13L

PC 13L

PC 21L

200301-200706

20111201-20130708

NRT: 20130709-201406


NASA, v8.6

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

no
O3SBUV/2NOAA-17PC 13L

200301-201108


NASA, v8.6

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

no
O3SBUV/2NOAA-18PC 13L

200507-201211


NASA, v8.6

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

no
O3SBUV/2NOAA-19

PC 13L

PC 21L

200903-20130708

NRT: 20130709-

NASA, v8.6

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

no
COMOPITTTerraTC

20020101-20161231

NRT: 2017010-

NCAR, V6 (TIR)

LAT>65.

LAT< -65

QR>0

Night time data over Greenland

yes
NO2SCIAMACHYEnvisatTRC

20030101-20101231

20110101-20120409

KNMI V1p

KNMI V2

QR>0

SOE<6

LAT>60

LAT< -60

yes
NO2OMIAuraTRC

20041001-20101231

20110101-20121231

NRT: 20130101 -

KNMI, COl3

KNMI, Domino

KNMI NRT

QR>0

SOE<6

LAT>60

LAT< -60

yes
NO2GOME-2Metop-ATRC

20070418-20171106

NRT:20171112-

AC SAF, GDP4.8

QR>0

yes
NO2GOME-2Metop-BTRC

201301-20171106-

NRT: 20171112-

AC SAF, GDP4.8

QR>0

yes
AODAATSREnvisatTC20021201-20120331

ESA, CCI (Swansea)

abs(LAT)> 70no
AODMODISTerraTC

20021001-20161231

NRT: 20170101-

NASA, COl6abs(LAT)> 70no
AODMODISAquaTC

20021001-20161231

NRT: 20170101-

NASA, Col6abs(LAT)> 70no

CAMS global greenhouse gases reanalysis (EGG4) Satellite Data 

The atmospheric composition satellite retrievals used as input into the CAMS reanalysis EGG4 are listed below. The following abbreviations are used in Table 1. TC: Total column, TRC: Tropospheric column, PROF: profiles, PC: Partial columns, ColAv: Column average mixing ratio, QR= quality flag given by data providers, SOE: Solar elevation, MODORO: Model orography, PRESS_RL= pressure at bottom of layer, LAT: Latitude.

ParameterInstrumentSatelliteProductPeriodData provider/ Version

Blacklist Criteria

(i.e. these data are not used)

Averaging kernels used
CO2SCIAMACHYEnvisatColAv20030101-20120324

ESA CCI (Bremen)

QR>0yes
CO2IASIMetop-AColAv

20070701-20150531

LMD v8.0

MODORO > 6000yes
CO2IASIMetop-BColAv20130201-LMD v4.0MODORO > 6000yes
CO2TansoGOSATColAv

20090601-

ESA CCI (SRON)

QR>0yes
CH4SCIAMACHYEnvisatColAv20030108-20120408ESA CCI (SRON) v7.0

MODORO > 6000

QR > 0

yes
CH4IASIMetoP-AColAv

20070701-20150630

LMD V8.3

MODORO > 6000

LAT<-60. and LSMASK = land

yes
CH4IASIMetop-BColAv20130201-LMD V8.1

MODORO > 6000

LAT<-60. and LSMASK = land

yes
CH4TansoGOSATColAv20090601-ESA CCI (SRON)QR > 0yes

Validation reports

Validation Reports for the CAMS  Global reanalysis and CAMS global greenhouse gas reanalysis can be found on the CAMS Quality Assurance website.

Guidelines

The following advice is intended to help users understand particular features of the CAMS reanalysis data:

  • Users who want to use meteorological data only are advised to use the ERA5 meteorological reanalysis.
  • MARS users please use the 'GEMS Ozone' (param 210203) and 'Total Column GEMS Ozone' (param 210206) fields. These are produced specifically for CAMS using the full tropospheric chemistry scheme, see also CAMS Global data: What is "GEMS ozone".
  • O3 and O3S are quite different in the re-analysis. The reason for that is that O3S was not subject to data assimilation. Hence it represents stratospheric ozone as simulated with the Cariolle scheme. The difference between O3 and O3S in the troposphere is that O3S is only subject to chemical loss and deposition, i.e. tropospheric chemical ozone production does not occur. The only source of O3S in the troposphere is the influx from the stratosphere.

Known issues CAMS global reanalysis (EAC4)

  • Anthropogenic emissions used were not adjusted for any COVID-19 lockdowns in 2020.

  • Validation of AOD with Aeronet data has show there are some hot spots around outgassing volcanoes (in particular Mauna Loa and Mexico City) with high analysis AOD values that degrade the global average RMSE. If calculating global mean statistics it is advisable to exclude those two stations as unrepresentative. This is a side effect of model-resolution orography not resolving the height of the volcanoes that has been unmasked by recent enhancements to the SO2 oxidation scheme which improve aerosol on the global scale.
  • During 2003 the ozone analysis has a degraded quality (bigger biases with respect to observations) in Arctic and Antarctic free troposphere because MIPAS and SCIAMACHY data of lower quality were assimilated.
  • Between March-August 2004 no ozone profile data were available for assimilation. This affects the vertical structure of the ozone analysis and we see larger biases wrt ozone sondes, especially in the Antarctic.
  • From 2013 onwards there is a larger seasonally varying bias in ozone in the free troposphere, particularly in the Arctic and Antarctic that is not seen in the control run. The reason for this bias is a change in the observing system, namely the change from 13-layer SBUV/2 data to 21-layer SBUV/2 data in July 2013 (see Table 2) that unfortunately has an impact on tropospheric ozone. A similar bias is seen in the NRT CAMS ozone analysis which also uses the 21-layer SBUV/2 data after 2013.
  • During 2003 the seasonal cycle of the tropospheric column NO2 is not well represented because of the assimilation of SCIAMACHY NO2 data of degraded quality.
  • The use of the NOx variable from the CAMS reanalysis (as well as from the CAMS interim re-analysis and the CAMS operational system) is not recommended. The user is advised to download NO and NO2 separately and to add them up. Please note that a conversion of the mass mixing ratios [kg/kg] to volume mixing rations / molar fractions [mol/mol] is needed to do this in a meaningful way.

Because of its relatively short lifetime, NO2 in the CAMS reanalysis is largely affected by the prescribed emissions (e.g. anthropogenic MACCity, GFAS biomass burning) and only to a smaller part by the assimilated observations (see also Inness et al., 2013). Consequently, trends or anomalies calculated from the NO2 reanalysis fields will mainly reflect the trends in the underlying emissions. For example over China, the MACCity emissions have been kept constant since 2012 while more recent emission inventories show a decrease after 2012. This has to be kept in mind when trying to interpret NO2 trends or anomalies calculated from the CAMS reanalysis.

This list will be updated as we become aware of further issues in the CAMS reanalysis.

Known issues CAMS global greenhouse gas reanalysis (EGG4)

  • Anthropogenic emissions used were not adjusted for any COVID-19 lockdowns in 2020.
  • T surface fields from the CAMS global greenhouse reanalysis are only available 3-hourly from 2013 onwards while they are available hourly from 2003-2012.
  • Stratospheric biases in CH4 (under investigation).

This list will be updated as we become aware of further issues in the CAMS reanalysis.

How to cite the CAMS Global Reanalysis

Please acknowledge the use of the CAMS global reanalysis as indicated below:

(1) Acknowledge according to the dataset licence (in this case, please check the licence to use Copernicus products (Clause 5 in particular)) - this should appear in the acknowledgement section of your publication.

(2) Provide the download reference by indicating where data is downloaded from (in the acknowledgement section of your publication)  e.g:

(3) Cite the relevant dataset (as part of the bibliography in your publication) e.g:

Inness, A, Ades, M, Agustí-Panareda, A, Barré, J, Benedictow, A, Blechschmidt, A, Dominguez, 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): CAMS global reanalysis (EAC4). Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS).  (Accessed on <DD-MMM-YYYY>), https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview

Inness, A, Ades, M, Agustí-Panareda, A, Barré, J, Benedictow, A, Blechschmidt, A, Dominguez, 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): CAMS global reanalysis (EAC4) monthly averaged fields. Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS). (Accessed on <DD-MMM-YYYY>),https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=overview

References

  •  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.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
  • Agustí-Panareda, A., and Coauthors, 2014: Forecasting global atmospheric CO2Atmos. Chem. Phys., 14, 11959-11983, https://doi.org/10.5194/acp-14-11959-2014.
  • 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, https://doi.org/10.5194/acp-16-10399-2016.
  • 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.
  • Bergamaschi, P., and Coauthors, 2009: Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals, J. Geophys. Res., 114, D22301, https://doi.org/10.1029/2009JD012287.
  • 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, https://www.ecmwf.int/sites/default/files/elibrary/2017/17219-implementation-cams-based-aerosol-climatology-ifs.pdf
  • Diamantakis, M., A. Agusti-Panareda: A positive definite tracer mass fixer for high resolution weather and atmospheric composition forecasts, ECMWF Technical Memoranda, No. 819, 2017, https://www.ecmwf.int/en/elibrary/17914-positive-definite-tracer-mass-fixer-high-resolution-weather-and-atmospheric
  • Flemming, J., and Coauthors, 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 Coauthors, 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.
  • 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, https://doi.org/10.5194/acp-14-6139-2014
  • 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,https://doi.org/10.5194/acp-16-1653-2016.
  • Spahni, R., and Coauthors, 2011: Constraining global methane emissions and uptake by ecosystems. Biogeosciences, 8, 1643–1665, https://doi.org/10.5194/bg-8-1643-2011.
  • 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, https://doi.org/10.5194/acp-14-9295-2014.
  • 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, https://doi.org/10.1016/j.dsr2.2008.12.009.
  • A. Inness A., Chabrillat S,. Flemming J., Huijnen V., Langenrock B., Nicolas J., Polichtchouk I., Razinger M., 2020: Exceptionally Low Arctic Stratospheric Ozone in Spring 2020 as Seen in the CAMS Reanalysis. J. Geophys. Res., 125, D033563, https://doi.org/10.1029/2020JD033563.


Further CAMS reanalysis references will be available from the ECMWF e-Library in the future. 

An ECMWF newsletter article 'The new CAMS global reanalysis of atmospheric composition' is available from: https://www.ecmwf.int/node/18821

Mailing list

To be kept informed of the latest news associated to the CAMS Reanalysis products, you may subscribe to the CAMS Global Reanalysis mailing list.

This document has been produced in the context of the Copernicus Atmosphere Monitoring Service (CAMS).

The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of CAMS on behalf of the European Union (Delegation Agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.

The users thereof use the information at their sole risk and liability. For the avoidance of all doubt, the European Commission and the European Centre for Medium-Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.