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Model Name

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Modelling Centre

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ACCESS-CM2

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CSIRO-ARCCSS (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia, Australian Research Council Centre of Excellence for Climate System Science)

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Australian Community Climate and Earth System Simulator Climate Model Version 2

Pressure levels

For pressure level data the model output is available on the pressure levels according to the table below. Note that not all models provide the same pressure levels. 

Grids

CMIP6 data is reported either on the model’s native grid or re-gridded to one or more target grids with data variables provided near the center of each grid cell (rather than at the boundaries).  For CMIP6 there is a requirement to record both the native grid of the model and the grid of its output (archived in the CMIP6 repository) a “nominal_resolution”.  The "nominal_resolution” enables users to identify which models are relatively high resolution and have data that might be challenging to download and store locally.

Pressure levels

For pressure level data the model output is available on the pressure levels according to the table below. Note that since the model output is standardised all models produce the data on the same pressure levels.

Ensembles

Each modelling centre typically run the same experiment using the same model several times to confirm the robustness of results and inform sensitivity studies through the generation of statistical information. A model and its collection of runs is referred to as an ensemble. Within these ensembles, three four different categories of sensitivity studies are done, and the resulting individual model runs are labelled by three four integers indexing the experiments in each category. 

e.g. r<W>i<X>p<Y>f<Z>, where W, X, Y and Z are positive integers as defined below:

• The first category, labelled “realization”realization_index (referred to with letter r), performs experiments which differ only in random perturbations of the initial conditions of the experiment. Comparing different realizations allow estimation of the internal variability of the model climate. 
• The second category refers to , labelled initialization_index (referred to with letter i), refers to variation in initialisation parameters. Comparing differently initialised output provides an estimate of how sensitive the model is to initial conditions. 
• The third category, labelled “physics”physics_index (referred to with letter p), refers to variations in the way in which sub-grid scale processes are represented. Comparing different simulations in this category provides an estimate of the structural uncertainty associated with choices in the model design. 

Each member of an ensemble is identified by a triad of integers associated with the letters r, i and p which index the “realization”, “initialization” and “physics” variations respectively. For instance, the member "r1i1p1" and the member "r1i1p2" for the same model and experiment indicate that the corresponding simulations differ since the physical parameters of the model for the second member were changed relative to the first member. 

It is very important to distinguish between variations in experiment specifications, which are globally coordinated across all the models contributing to CMIP5, and the variations which are adopted by each modelling team to assess the robustness of their own results. The “p” index refers to the latter, with the result that values have different meanings for different models, but in all cases these variations must be within the constraints imposed by the specifications of the experiment. 

For the scenario experiments, the ensemble member identifier is preserved from the historical experiment providing the initial conditions, so RCP 4.5 ensemble member “r1i1p2” is a continuation of historical ensemble member “r1i1p2”.

Parameter listings

Table 1: CMIP5 data on pressure levels

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count

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name

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units

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Daily data

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1

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temperature

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2

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geopotential_height

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relative_humidity

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specific_humidity

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5

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u_component_of_wind

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v_component_of_wind

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Table 2: CMIP5 data on single levels

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count

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name

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units

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10m_wind_speed

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10m_v_component_of_wind

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10m_wind_speed

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2m_temperature

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daily_near_surface_relative_humidity

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eastward_turbulent_surface_stress

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evaporation

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maximum_2m_temperature_in_the_last_24_hours

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mean_precipitation_flux

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mean_sea_level_pressure

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minimum_2m_temperature_in_the_last_24_hours

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near_surface_relative_humidity

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near_surface_specific_humidity

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northward_turbulent_surface_stress

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runoff

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sea_ice_fraction

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sea_ice_plus_snow_amount

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sea_ice_surface_temperature

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sea_ice_thickness

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sea_surface_height_above_geoid

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sea_surface_salinity

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sea_surface_temperature

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skin_temperature

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snow_depth_over_sea_ice

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snowfall

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soil_moisture_content

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surface_latent_heat_flux

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surface_pressure

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surface_sensible_heat_flux

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surface_snow_amount

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surface_solar_radiation_downwards

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• The fourth category labelled forcing_index (referred to with letter f) is used to distinguish runs of a single CMIP6 experiment, but with different forcings applied.

Parameter listings

Data Format

The CDS subset of CMIP6 data are provided as NetCDF files. NetCDF (Network Common Data Form) is a file format that is freely available and commonly used in the climate modelling community. See more details:  What are NetCDF files and how can I read them

 A CMIP6 NetCDF file in the CDS contains:

• Global metadata: these fields can describe many different aspects of the file such as
  • when the file was created
  • the name of the institution and model used to generate the file
  • Information on the horizontal grid and regridding procedure
  • links to peer-reviewed papers and technical documentation describing the climate model,
  • links to supporting documentation on the climate model used to generate the file,
  • software used in post-processing.
• variable dimensions: such as time, latitude, longitude and height
• variable data: the gridded data
• variable metadata: e.g. the variable units, averaging period (if relevant) and additional descriptive data

File naming conventions

When you download a CMIP6 file from the CDS it will have a naming convention that is as follows:

<variable_id>_<table_id>_<source_id>_<experiment_id>_<variant_label>_<grid_label>_<time_range>.nc

 Where:

• variable_id: variable is a short variable name, e.g. “tas” for “temperature at the surface”.
• table_id: this refers to the MIP table being used. The MIP tables are used to organise the variables. For example, Amon refers to monthly atmospheric variables and Oday contains daily ocean data.
• source_id: this refers to the model used that produced the data.
• experiment_id: refers to the set experiments being run for CMIP6. For example, PiControl, historical and 1pctCO2 (1 percent per year increase in CO2).
• variant_label: is a label constructed from 4 indices (ensemble identifiers) r<k>i<l>p<m>f<n>, where k, l, m and n are integers
• grid_label: this describes the model grid used. For example, global mean data (gm), data reported on a model's native grid (gn) or regridded data reported on a grid other than the native grid and other than the preferred target grid (gr1).
• time_range: the temporal range is in the form YYYYMM[DDHH]-YYYY[MMDDHH], where Y is year, M is the month, D is day and H is hour. Note that day and hour are optional (indicated by the square brackets) and are only used if needed by the frequency of the data. For example, daily data from the 1st of January 1980 to the 31st of December 2010 would be written 19800101-20101231.

Quality control of the CDS-CMIP6 subset

The CDS subset of the CMIP6 data have been through a set of quality control checks before being made available through the CDS. The objective of the quality control process is to ensure that all files in the CDS meet a minimum standard. Data files were required to pass all stages of the quality control process before being made available through the CDS. Data files that fail the quality control process are excluded from the CDS-CMIP6 subset, data providers are contacted and if they are able to release a new version of the data with the error corrected then providing this data passes all remaining QC steps may be available for inclusion in the next CMIP6 data release.

 The main aim of the quality control procedure is to check for metadata and gross data errors in the CMIP6 files and datasets. A brief description of each of the QC checks is provided here:

1. CF-Checks: The CF-checker[1] tool checks that each NetCDF4 file in a given dataset is compliant with the Climate and Forecast (CF) conventions[2], compliance ensures that the files are interoperable across a range of software tools.
2. PrePARE: The PrePARE[3] software tool is provided by PCMDI (Program for Climate Model Diagnosis and Intercomparison) to verify that CMIP6 files conform to the CMIP6 data protocol. All CMIP6 data should meet this required standard however this check is included to ensure that all data supplied to the CDS have passed this QC test.
3. nctime: The nctime[4] checker checks the temporal axis of the NetCDF files. For each NetCDF file the temporal element of the file is compared with the time axis data within the file to ensure consistency. For a time-series of data comprised of several NetCDF files nctime ensures that the entire timeseries is complete, that there are no temporal gaps or overlaps in either the filename or in the time axes within the files.
4. Errata: The dataset is checked to ensure that no outstanding Errata record exists.
5. Data Ranges: A set of tests on the extreme values of the variables are performed, this is used to ensure that the values of the variables fall into physically realistic ranges.
6. Handle record consistency checks: This check ensures that the version of the dataset used is the most recently published dataset by the modelling centre, it also checks for any inconsistency in the ESGF publication and excludes any datasets that may have an inconsistent ESGF publication metadata.
7. Exists at all partner sites: It is asserted that each dataset exists at all three partner sites CEDA, DKRZ and IPSL.

It is important to note that passing these quality control tests should not be confused with validity: for example, it will be possible for a file to pass all QC steps but contain errors in the data that have not been identified by either data providers or data users.

 In cases where the quality control picks up errors that are related to minor technical details of the conventions, or behavior that is in line with expectations for climate model output despite being unexpected in a physical system, the data will be published with details of the errors referenced in the documentation. An example of the 2^nd type of error is given by negative salinity values which occur in one model as a result of rapid release of fresh water from melting sea-ice. These negative values are part of the noise associated with the numerical simulation and reflect what is happening in the numerical model.

[1] https://github.com/cedadev/cf-checker

[2] http://cfconventions.org/

[3] https://cmor.llnl.gov/mydoc_cmip6_validator/

[4] https://github.com/Prodiguer/nctime

Citation and license information

The CMIP6 data Citation Service provides information for data users on how to cite CMIP6 data and on the data license. The long-term availability and long-term accessibility are granted by the use of DOIs for the landing page e.g http://doi.org/10.22033/ESGF/CMIP6.1317.

Available CMIP6 data citations are discoverable in the ESGF or in the Citation Search at: http://bit.ly/CMIP6_Citation_Search.

Known issues

CDS users will be directed to the CMIP6 ES-DOC Errata Service for known issues with the wider CMIP6 data pool. Data that is provided to the CDS should not contain any errors or be listed in the Errata service, however this will still be a useful resource for CDS users as data they may be looking for but cannot access may have been withheld from the CDS for justifiable reasons.

Subsetting and downloading data

CDS users will now be able to apply subsetting operations to CMIP6 datasets. This mechanism [1] that runs at each of the partner sites: CEDA, DKRZ and IPSL. The WPS can receive requests for processing based on dataset identifiers, a temporal range, a bounding box and a range of vertical levels. Each request is converted to a job that is run asynchronously on the processing servers at the partner sites. NetCDF files are generated and the response contains download links to each of the files. Users of the CDS will be able to make subsetting selections using the web forms provided by the CDS catalogue web-interface. More advanced users will be able to define their own API requests that will call the WPS. Output files will be automatically retrieved so that users can access them directly within the CDS.

How to use the subsetting tool

 Walkthrough and screenshots need to be provided by CDS team

[1] The "roocs" WPS framework: https://roocs.github.io/

References

Durack, P J. (2020) CMIP6_CVs. v6.2.53.5. Available at: https://github.com/WCRP-CMIP/CMIP6_CVs (Accessed: 26 October 2020).

Eyring, V. et al. (2016) ‘Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization’, Geoscientific Model Development, 9(5), pp. 1937–1958. doi: 10.5194/gmd-9-1937-2016.

IPCC (2020) Sixth Assessment Report. Available at: https://www.ipcc.ch/assessment-report/ar6/ (Accessed: 26 October 2020).

Moss, R. et al. (2008) ‘Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies’, Intergovernmental Panel on Climate Change, Geneva, pp. 132.

O’Neill, B.C. et al. (2014) ‘A new scenario framework for climate change research: the concept of shared socioeconomic pathways.’, Climatic Change, 122, pp. 387–400. doi: https://doi.org/10.1007/s10584-013-0905-2

World Climate Research Programme (2020) CMIP Phase 6 (CMIP6): Overview CMIP6 Experimental Design and Organization. Available at: https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 (Accessed: 2 November 2020).

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surface_thermal_radiation_downwards

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surface_upwelling_longwave_radiation

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surface_upwelling_shortwave_radiation

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toa_outgoing_clear_sky_longwave_radiation

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toa_outgoing_clear_sky_short_wave_radiation

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toa_outgoing_longwave_radiation

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toa_outgoing_shortwave_radiation

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total_cloud_cove

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Data availability matrix

A data availability matrix for the C3S CMIP5 exists at: https://cp-availability.ceda.ac.uk.

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Data Format

The CDS subset of CMIP5 data are provided as NetCDF files. NetCDF (Network Common Data Form) is a file format that is freely available and commonly used in the climate modelling community. See more details:  What are NetCDF files and how can I read them

A CMIP5 NetCDF file in the CDS contains: 

• Global metadata: these fields can describe many different aspects of the file such as
• when the file was created
• the name of the institution and model used to generate the file
• links to peer-reviewed papers and technical documentation describing the climate model,
•  links to supporting documentation on the climate model used to generate the file, 
• software used in post-processing. 
• variable dimensions: such as time, latitude, longitude and height
• variable data: the gridded data
• variable metadata: e.g. the variable units, averaging period (if relevant) and additional descriptive data

The metadata provided in NetCDF files adhere to the Climate and Forecast (CF) conventions (v1.4 for CMIP5 data). The rules within the CF-conventions ensure consistency across data files, for example ensuring that the naming of variables is consistent and that the use of variable units is consistent.

File naming conventions

When you download a CMIP5 file from the CDS it will have a naming convention that is as follows:

<variable>_<cmor_table>_<model>_<experiment>_<ensemble_member>_<temporal_range>.nc

Where

• variable is a short variable name, e.g. “tas” for ”temperature at the surface”
• cmor_table is a reference to the realm (an earth system component such as atmosphere or ocean) and frequency of the variable, e.g. “Amon” indicates that a variable is present in the atmosphere realm at a monthly frequency (link to list of these)
• model is the name of the model that produced the data
• ensemble member is the ensemble identifier in the form “r<X>i<Y>p<Z>”, X, Y and Z are integers
• the temporal range is in the form YYYYMM[DDHH]-YYYY[MMDDHH], where Y is year, M is the month, D is day and H is hour. Note that day and hour are optional (indicated by the square brackets) and are only used if needed by the frequency of the data. For example daily data from the 1st of January 1980 to the 31st of December 2010 would be written 19800101-20101231.

Quality control of the CDS-CMIP5 subset

The CDS subset of the CMIP5 data have been through a set of quality control checks before being made available through the CDS. The objective of the quality control process is to ensure that all files in the CDS meet a minimum standard. Data files were required to pass all stages of the quality control process before being made available through the CDS. Data files that fail the quality control process are excluded from the CDS-CMIP5 subset or if possible the error is corrected and a note made in the history attribute of the file. The quality control of the CDS CMIP5 subset checks for metadata errors or inconsistencies against the Climate and Forecast (CF) Conventions and a set of CMIP5 specific file naming and file global metadata conventions. 

Various software tools have been used to check the metadata of the CDS CMIP5 data:

• The Centre for Environmental Data Analysis (CEDA) compliance checking tool CEDA-CC is used to check that: 
• the file name adheres to the CMIP5 file naming convention, 
• the global attributes of the NetCDF file are consistent with filename,
• there are no omissions of required CMIP5 metadata.

• The CF-Checker Climate and Forecast (CF) conventions checker ensures that any metadata that is provided is consistent with the CF conventions. 

• A time-axis-checker is used to check the temporal dimension of the data:
• for individual files the time dimension of the data is checked to ensure it is valid and is consistent with the temporal information in the filename, 
• where more than one file is required to generate a time-series of data, the files have been checked to ensure there are no temporal gaps or overlaps between the files.

The data within the files were not individually checked however where it was known that a variable from a given model had a gross error, e.g in the sign convention of a flux, then these data were also omitted from the CDS-CMIP5 subset. 

It is important to note that passing of these quality control tests should not be confused with validity: for example, it will be possible for a file to be fully CF compliant and have fully compliant CMIP5 metadata but contain gross errors in the data that have not been noted. 

For a detailed description of all the quality control of the data please see the accompanying documentation  

Known issues

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