The World's New Climate Projections

CMIP6 Visualisation Tool


CMIP6 Visualisation Tool

The new climate projections

Climate change is one of the defining issues of our century. A new set of climate projections has been developed by the wide scientific community as part of Phase 6 of the Climate Model Intercomparison Project. This new dataset will also underpin the forthcoming Sixth IPCC Assessment Report (AR6), scheduled to be published in 2021 and 2022.

What these climate projections tell us is how warm the globe is going to get, if we continue to emit a lot of greenhouse gas emissions or how much we can limit warming, if we restrain future emissions. However, that is not the only thing. The data is much richer, with 100s of variables for each model, including variables like soil carbon content, deep ocean warming, cloud cover and many others.

For anyone interested in this new enormous dataset, there are various ways to access it. The primary portal for researchers are the Earth System Grid Federation servers. That is where all the latest datasets are stored and where a huge effort of the scientific community makes sure that the data is also maintained. For example, if an error is found, new versions of the datasets will be published there with documentation provided by es-doc.

For anyone interested in what these experiments actually try to model, and what the story is behind the various scenarios, like SSP5-8.5 or abrupt4x-CO2, it is best to look into the original scientific literature. For example, an overview of the scenario design is provided here with further details in O'Neill et al. (2016). We played our small part by contributing the greenhouse gas concentrations that underly all those different scenarios (see Greenhouse Gas Factsheets).

This website provides large scale averages, not gridded data. Sometimes terabytes of data are a bit unwieldy and a lot of researchers and the public are just interested in the large scale averaged timeseries. That is what this visualisation tool is about. We downloaded a lot of the monthly CMIP6 data and aggregated it to global, hemispheric and land and ocean averages. We also crunched averages for all of the AR6 regions defined in Iturbide et al., ESSD 2020. Thus, if that is what you are interested in, you are lucky, as all the data is now at your fingertips.

Data format

As described in Nicholls et al. (2021) (see citation below), we use the custom .MAG format for all of our data outputs. This format is a text-based format, designed to make life easy for all data processing programs (including those that can't handle binary data). A description of the data format can be found at pymagicc's documentation

We are developing examples of how this data can be used in a public GitLab repository, https://gitlab.com/netcdf-scm/calibration-data. In this repository, we currently have examples of how to download, read and plot the data in Python. Please feel free to use these as a starting point for your own analysis. If you do build new things (particularly in languages other than Python), please make a merge request so that we continue to share the knowledge.

Disclaimer

The data provided here is derived from the CMIP6 archive on the ESGF servers. If you are using this data, you must also abide by the CMIP5 and CMIP6 terms of use (found at https://pcmdi.llnl.gov/mips/cmip5/terms-of-use.html and https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html respectively). The tools we use to help us comply with these terms of use are demonstrated at https://netcdf-scm.readthedocs.io/en/latest/usage/using-cmip-data.html.

CMIP6 and CMIP5 model data that is aggregated here by the University of Melbourne's Climate Energy College is licensed under the same license as the raw CMIP data (typically, Creative Commons Attribution-ShareAlike 4.0 International License but the license of each file should be checked before use). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

About Us and Citation

This data portal has been developed by Zebedee Nicholls, Jared Lewis, Malte Meinshausen from the Climate & Energy College as well as Melissa Makin from Science IT and Usha Nattala, Geordie Zhang, Simon Mutch and Edoardo Tescari from the Melbourne Data Analytics Platform (MDAP) at the University of Melbourne. If you use this data, please cite (further citation formats available at https://doi.org/10.1002/gdj3.113)

Nicholls Z, Lewis J, Makin M, Nattala U, Zhang GZ, Mutch SJ, Tescari E and Meinshausen, M. Regionally aggregated, stitched and de-drifted CMIP-climate data, processed with netCDF-SCM v2.0.0. Geosci Data J. 2020; 00:000-000. https://doi.org/10.1002/gdj3.113

Other data formats, missing or erroneous data

If you would like to discuss other possible data formats, think that any data is missing or believe we have made an error in our processing, please check our issue tracker and if your issue is not already there, please raise one. We will aim to respond as quickly as possible.

Website failures

If you have any issues with the website (e.g. it fails in some way), please contact znicholls@unimelb.edu.au, malte.meinshausen@unimelb.edu.au, jared.lewis@unimelb.edu.au and melissa.makin@unimelb.edu.au.

Search

Search our data

Search

API

A read-only API is available to search the archive and download zipped .MAG files.

The search and download endpoints all take the following search parameters

Values for these can be determined from the search form with the exception of mip_era, normalised and timeseriestype which use the mappings below. Note that variable_id always uses the short form, e.g. "tas" instead of "Near-Surface Air Temperature".

Search Form API Request
normalised
Raw ""
Normalised "21-yr-running-mean"
Dedrifted "21-yr-running-mean-dedrift"
mip_era
All "all"
CMIP5 "cmip5"
CMIP6 "CMIP6"
timeseriestype
Monthly monthly
Annual-mean (average over the year, centred on the middle of the year i.e. July 2) "average-year-mid-year"

Search 
/api/v1/search

The search endpoint provides a way to programmatically search the archive in the same way that the Search page works. 

curl -i "https://cmip6.science.unimelb.edu.au/api/v1/search?experiment_id=ssp585&variable_id=tas"
HTTP/1.0 200 OK
Content-Type: application/json
...
{
    "count": 25,
    "results": [
    {
        "activity_id": "ScenarioMIP",
        "experiment_id": "ssp585",
        "grid_label": "gn",
        "institution_id": "NUIST",
        "member_id": "r1i1p1f1",
        "mip_era": "CMIP6",
        "normalisation_method": "",
        "source_id": "NESM3",
        "table_id": "Amon",
        "time_range": "185001-210012",
        "timeseriestype": "monthly",
        "mip_era": "CMIP6",
        "url": "https://cmip6.science.unimelb.edu.au/magdownload?path=CMIP6%2Fmag%2Fmonthly%2FCMIP6%2FScenarioMIP%2FNUIST%2FNESM3%2Fssp585%2Fr1i1p1f1%2FAmon%2Ftas%2Fgn%2Fv20190728%2Fnetcdf-scm_tas_Amon_NESM3_ssp585_r1i1p1f1_gn_185001-210012.MAG",
        "variable_id": "tas",
        "version": "v20190728"
    },
...

Bulk downloading files 
/api/v1/download_zip

All the files for a set of filters can be downloaded as a zipped archive.

wget -q -O ssp585_tas.zip "https://cmip6.science.unimelb.edu.au/api/v1/download_zip?experiment_id=ssp585&variable_id=tas"
HTTP/1.0 200 OK
Content-Type: application/zip
...