CMIP6: Global climate projections

Introduction

What are global climate projections?

Global climate projections are simulations of the climate system performed with general circulation models which represent physical processes in the atmosphere, ocean, cryosphere and land surface. These models may cover the entire globe or a specific region and use information about the external influences on the system. Such simulations have been generated by multiple independent climate research centres in an effort coordinated by the World Climate Research Program (WCRP) and assessed by the Intergovernmental Panel on Climate Change (IPCC). These climate projections underpin the conclusions of the IPCC Assessment Reports that “Continued emission of greenhouse gases will cause further warming and long-lasting changes in all components of the climate system, increasing the likelihood of severe, pervasive and irreversible impacts for people and ecosystems”.

The Climate Model Intercomparison Project (CMIP)

The Climate Model Intercomparison Project (CMIP) was established in 1995 by the World Climate Research Program (WCRP) to provide climate scientists with a database of coupled Global Circulation Model (GCM) simulations.

 The CMIP process involves institutions (such as national meteorological centres or research institutes) from around the world running their climate models with an agreed set of input parameters (forcings). The modelling centres produce a set of standardised output. When combined, these produce a multi-model dataset that is shared internationally between modelling centres and the results compared.

 Analysis of the CMIP data allows for improving understanding of

• the climate, including its variability and change,
• the societal and environmental implications of climate change in terms of impacts, adaptation and vulnerability,
• informing the Intergovernmental Panel on Climate Change (IPCC) reports.

Comparison of different climate models allows for

• determining why similarly forced models to produce a range of responses,
• evaluating how realistic the different models are in simulating the recent past,
• examining climate predictability.

CMIP6

CMIP6 will aim to address 3 main questions:

• How does the Earth system respond to forcing?
• What are the origins and consequences of systematic model biases?
• How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? (Eyring et al, 2016)

There are some differences between the experimental design and organisation of CMIP6 and its predecessor CMIP5. It was decided that for CMIP6, a new and more federated structure would be used, consisting of the following three major elements:

1. A handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850 – near-present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP
2. Common standards, coordination, infrastructure and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble
3. An ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases (World Climate Research Programme, 2020).

The CMIP6 data archive is distributed through the Earth System Grid Federation (ESGF) though many national centres have either a full or partial copy of the data. A quality-controlled subset of CMIP6 data are made available through the Climate Data Store (CDS) for the users of the Copernicus Climate Change Service (C3S).

Global climate projections in the CDS

The global climate projections in the Climate Data Store (CDS) are a quality-controlled subset of the wider CMIP6 data. These data represent only a small subset of CMIP6 archive. A set of 51 core variables from the CMIP6 archive were identified for the CDS. These variables are provided from 9 of the most popular CMIP6 experiments.

 The CDS subset of CMIP6 data have been through a quality control procedure which ensures a high standard of dependability of the data. It may be for example that similar data can be found in the main CMIP6 archive however these data come with very limited quality assurance and may have metadata errors or omissions.

Experiments

Shared Socioeconomic Pathway (SSP) Experiments

The SSP scenario experiments can be understood in terms of two pathways, a Shared Socioeconomic Pathway (SSP) and a Representative Concentration Pathway (RCP). The two pathways are represented by the three digits that make up the experiment’s name. The first digit represents the SSP storyline for the socio-economic mitigation and adaptation challenges that the experiment represents (Figure 1). The second and third digits represent the RCP climate forcing that the experiment follows. For example, experiment ssp245 follows SSP2, a storyline with intermediate mitigation and adaptation challenges, and RCP4.5 which leads to a radiative forcing of 4.5 Wm^-2 by the year 2100.

Figure 1 - The socioeconomic “Challenge Space” to be spanned by the CMIP6 SSP experiments (O’Neil et al. 2014).

 Experiments in the CDS

The CDS-CMIP6 subset consists of the following CMIP6 experiments:

[1] The Paris Agreement https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement

Models, grids and pressure levels

Models 

The models included in the CDS-CMIP6 subset are detailed in the table below including a brief description of the model where this information is readily available, further details can be found on the Earth System Documentation site.

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, four different categories of sensitivity studies are done, and the resulting individual model runs are labelled by 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_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, 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_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.
• 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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