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.

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. 

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 different categories of sensitivity studies are done, and the resulting individual model runs are labelled by three integers indexing the experiments in each category. 

• The first category, labelled “realization”, 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 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”, 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

temperature

geopotential_height

relative_humidity

specific_humidity

u_component_of_wind

v_component_of_wind

Table 2: CMIP5 data on single levels

10m_wind_speed

10m_v_component_of_wind

10m_wind_speed

2m_temperature

daily_near_surface_relative_humidity

eastward_turbulent_surface_stress

evaporation

maximum_2m_temperature_in_the_last_24_hours

mean_precipitation_flux

mean_sea_level_pressure

minimum_2m_temperature_in_the_last_24_hours

near_surface_relative_humidity

near_surface_specific_humidity

northward_turbulent_surface_stress

runoff

sea_ice_fraction

sea_ice_plus_snow_amount

sea_ice_surface_temperature

sea_ice_thickness

sea_surface_height_above_geoid

sea_surface_salinity

sea_surface_temperature

skin_temperature

snow_depth_over_sea_ice

snowfall

soil_moisture_content

surface_latent_heat_flux

surface_pressure

surface_sensible_heat_flux

surface_snow_amount

surface_solar_radiation_downwards

surface_thermal_radiation_downwards

surface_upwelling_longwave_radiation

surface_upwelling_shortwave_radiation

toa_outgoing_clear_sky_longwave_radiation

toa_outgoing_clear_sky_short_wave_radiation

toa_outgoing_longwave_radiation

toa_outgoing_shortwave_radiation

total_cloud_cove

Data availability matrix

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

The data availability matrix filter allows users to search the CDS CMIP5 data subset for the data availability for one or more variables within one or more experiments simultaneously. By specifying a minimum ensemble size, each model returned as a result of the search criteria must have at least the number of ensemble members specified by the user. This functionality allows users to determine if a given combination of variables and experiments is available in enough ensemble members for their scientific analysis. The data availability matrix returns a list displaying which models, experiments and ensembles have all of the selected criteria.  The results can be exported either in JSON or CSV format. The first 11 rows of the CSV is metadata, row 12 contains the table headers for the results. The JSON export has 3 main keys: provenance, query and results. Provenance has metadata, query contains information about the selected parameters which gave the results.

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

• Please note that not all the combinations of models and variables exist. This feature is inherited from the ESGF system, where the main target is to publish as much as possible data and even publish incomplete datasets, which might be of use. This allows to have more data available with the price that not everything is fully complete. 

Related articles

• Page:
  CMIP6: Global climate projections
• Page:
  CORDEX: Regional climate projections
• Page:
  CMIP6: Decadal climate predictions
• Page:
  CMIP: Global climate projections
• Page:
  C3S Climate projections