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Introduction

Here we document the CAMS reanalysis datasets, the CAMS global reanalysis (EAC4) which currently covers the period 2003 - December 2023, 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

Introduction

Here we document the CAMS reanalysis dataset, which, eventually, will cover the period January 2003 to near real time (NRT), though the first tranche of data, released in October 2017, only covers the year 2003. 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. 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 0 UTC.

The data are archived in the ECMWF data archive (MARS) and can be retrieved using the ECMWF Public Dataset service via the WebAPI (Member State users can access the data using MARS directly, in the usual manner). In the future, the data will be available from the CAMS data server.

The IFS model and data assimilation system

The 4DVar data assimilation uses 12 hour windows from 09 UTC to 21 UTC and 21 UTC to 09 UTC (the following day).

The IFS model documentation for various model cycles can be found on https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/publications/ifs-documentation. The model used in the CAMS reanalysis 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

• The aerosol model contains new Updated aerosol optical properties, especially for organic matter (see Bozzo et al. 2017: "Implementation of a CAMS-based aerosol climatology in IFS" ECMWF Technical memo).
• For Organic Matter (OM), there is significantly less extinction per mass.
• Aerosol optical properties at 10 micron wavelength
• , 2017).
• Bug fixes to sedimentation, which was unreasonably weak for some Bugfix of dust and sea-salt sedimentationDecrease of the fraction of bins, with corresponding re-tuning of sea-salt aerosol subjected to in-cloud scavenging from 0.7 to 0.2 (to compensate the low bias brought by the activation of sedimentation)
• SO2 dry deposition velocities from SUMO (same as SO2 in chemistry)
• .
• SO₂ dry deposition velocities updated to match those used in the chemistry scheme (from SUMO).
• New parametrisation of anthropogenic Secondary Organic Aerosol (SOA) production scaled on , proportional to non-biomass-burning CO emissions.
• SO2 to SO4 conversion complexified: a temperature dependency was added; conversion increased by 50% where relative humidity > 98% and T> 273.15 K
• SO2 to SO4 conversion e-folding time was decreased from 8 to 4 days at the equator and from 3 to 0.5 days at the pole
• SO4 dry deposition velocity was ncreased over the oceans
• Use of mass fixer for aerosol species
• 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). Not done for OM because of the change in optical properties.
• 80% of SO2 SO₂ emissions are released in the two lowest model levels (as an update of tendencies) rather than at surface (fluxes)
• Use of an external file to define the altitude of ~1500 volcanoes. Where there is a volcano, SO2 emissions are released 3 model levels higher than the altitude of the volcano.

Chemistry mechanism

Chemistry mechanism

The chemical mechanism of the IFS is an extended version of the Carbon Bond 2005 (CB05) 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 all VOCs and for whole the period 2003-2015 period2017. 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:

• Emissions for CO2 are documented in Agusti-Panareda et al. (2014), Massart et al. (2016).
• Bias correction for CO2 ecosystem fluxes based on the Biogenic Flux Adjustement Adjustment Scheme is documented by Agusti-Panareda et al. (2016)
• Emissions and loss rate for CH4 is documented in Massart et al. (2014)
• Mass fixer configuration for CO2 and CH4 is documented by Agusti-Panareda et al. (2017) and Diamantakis and Agusti-Panareda (2017).

Emission datasets

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

Table 1: Emission datasets used in the CAMS reanalysis

Data organisation

The data can be accessed using the MARS keywords class=mc and expver=eac4 (or ‘dataset’ : “eac4” for the ECMWF Public Dataset service via the WebAPI). Subdivisions of the data are labelled using stream, type and levtype.

Stream:

• oper: sub-daily
• mnth: synoptic monthly means
• moda:monthly means of daily means

Type:

• an: analyses
• fc: forecasts

Levtype:

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:

• CAMS global reanalysis (EAC4)

• CAMS global reanalysis (EAC4) monthly averaged fields

• CAMS global greenhouse gas reanalysis (EGG4)

• CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields

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 

Spatial grid

Spatial grid

The CAMS reanalysis The CAMS reanalysis data have a resolution of 80kmapproximately 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.

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 0 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. 

Monthly means

Monthly means

Several parameters are also available 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.

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 https://www.ecmwf.int/en/forecasts/documentation-and-support/60-model-levels.

Parameter listings (This still needs to be worked on)

Tables 2-? below describe the surface and single level parameters (levtype=sfc), ....

Table 2: Monthly means of total column fields (Stream=moda/mnth).

Table 3: Monthly means of pressure level fields (Stream=moda/mnth) and fields on lowest model level (i.e. fields on model level 60).

Satellite Data

L60 model level definitions.

CAMS global reanalysis (EAC4) Parameter listings

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Table 1 Table 1

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

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

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Table 3 Table 3

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

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Table 4 Table 4

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

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Table 5 Table 5

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

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Table 6 Table 6

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

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Table 7 Table 7

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

CAMS global greenhouse gases reanalysis (EGG4) Parameter listings

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EGG4 Table 1 EGG4 Table 1

Table 1: Single-level radiation variables 

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EGG4 Table 2 EGG4 Table 2

Table 2: Single-level chemical vertical integrals

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EGG4 Table 3 EGG4 Table 3

Table 3: Single-level emissions

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EGG4 Table 4 EGG4 Table 4

Table 4: Single-level meteorological

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EGG4 Table 5 EGG4 Table 5

Table 5: Multi-level chemical

Table 6: Multi-level meteorological

CAMS global reanalysis (EAC4) Satellite Data 

The atmospheric composition satellite retrievalsused 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.

CAMS global greenhouse gases reanalysis (EGG4) Satellite Data 

The atmospheric composition satellite retrievalsused 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.

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.

The following advice is intended to help users understand particular features of the CAMS global greenhouse gas reanalysis (EGG4):

• In the IFS all the tracers are in principle represented as specific ratios, i.e. with respect to the total air mass. However, in the continuity equation the tracers are treated as if they were mixing ratios (with respect to dry air), which means that changes in the humidity do not have an impact on the evolution of the tracer mixing ratio. Because of this, we recommend treating the tracer mixing ratios with respect to dry air but when integrating with pressure in the computation of the total column mass of the tracer, the total pressure should be used (instead of the dry pressure). This is a compromise to keep consistency with the NWP approach which assumes specific ratios and the tracer continuity equation which assumes mixing ratios. For more details on this conundrum, see ECWMF Tech memo https://www.ecmwf.int/sites/default/files/elibrary/2019/19114-dry-mass-versus-total-mass-conservation-ifs.pdf. 

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:

Downloaded from the Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS) (<URL to dataset overview page>)

(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, 2023: Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020, Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023
• Agustí-Panareda, A., and Coauthors, 2014: Forecasting global atmospheric CO₂, Atmos. 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/80422-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 website 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.

Table 1: Satellite retrievals of atmospheric composition that were assimilated in the CAMS reanalysis

Blacklist Criteria

(i.e. these data are not used)

QR>0

SOE<6

20030127- 20040326

20050127-20120331

ESA, NRT

ESA, CCI (KIT)

QR>0 for CCI data

20040803-20151231

NRT:

KNMI reproc: 20041001-20150531

NRT:

QR>0

SOE<10

20070123-

NRT:

QR>0

SOE<10

201301-

NRT:

QR>0

SOE<10

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

200301-200706

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

200301-201108

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

200507-201211

NASA, v8.6

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

200903-

NRT:

QR>0

SOE<6

MODORO > 1000. and PRESS_RL > 450.

20020101-20151231

NRT:

LAT>65.

LAT< -65

QR>0

Night time data over Greenland

20030101-20101231

20110101-20120409

KNMI V1p

KNMI V2

QR>0

SOE<6

LAT>60

LAT< -60

20041001-20101231

20110101-20121231

NRT: 20130101 -

KNMI, COl3

KNMI, Domino

KNMI NRT

QR>0

SOE<6

LAT>60

LAT< -60

20070418-20161231

NRT:

QR>0

201301-20161231

NRT:

QR>0

ESA, CCI (Swansea)

20021001-20151231

NRT:

20021001-20151231

NRT:

ESA CCI (Bremen)

20070701-20150531

LMD v8.0

20090601-20131231

ESA CCI (SRON)

MODORO > 6000

QR > 0

20070701-20150630

MODORO > 6000

LAT<-60. and LSMASK = land

MODORO > 6000

LAT<-60. and LSMASK = land

Control run (perhaps remove this for now)

In parallel to the CAMS reanalysis a control run without data assimilation was run that covers the same period as the CAMS reanalysis. This control run uses the same model configuration as the CAMS reanalysis and is made up of 24h long cycling forecasts from 0 UTC. The meteorological initial fields at 0UTC were always taken from the CAMS reanalysis. Comparing the CAMS reanalysis with the control run allows us to identify the impact of the data assimilation.

The control run is available from MARS or WebAPI using expver=gqk3, stream=oper, type=fc.

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.

Known issues

At the time of writing (2017-10) we are aware of these issues with the CAMS reanalysis:

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

How to cite the CAMS Reanalysis

Please acknowledge the use of the CAMS reanalysis as stated in the Copernicus C3S/CAMS License agreement:

Any such publication or distribution shall state that "neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains."

References

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