Date (Creation)

2022-04-26

Date (Revision)

2022-04-26

Date (Publication)

2022-04-26

Date (released)

2022-04-26

Edition

1.0

Unique resource identifier

https://doi.org/10.5285/e67242f2-e9aa-4402-85a3-be42d13354af

Codespace

doi

Unique resource identifier

GB/NERC/BAS/PDC/01640

Codespace

https://data.bas.ac.uk/

Unique resource identifier

NE/N01829X/1

Codespace

award

Other citation details

Please cite this item as: Holmes, C. (2022). Sea Ice Area climatologies and 21st century change in the CMIP5 and CMIP6 multi-model ensembles (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/e67242f2-e9aa-4402-85a3-be42d13354af

Credit

No credit.

Status

Completed

Point of contact

Organisation name	Individual name	Electronic mail address	Role
British Antarctic Survey	Holmes, Caroline		Author
NERC EDS UK Polar Data Centre		PDCServiceDesk@bas.ac.uk	Point of contact

Maintenance and update frequency

As needed

Maintenance note

Completed

Global Change Master Directory (GCMD) Science Keywords

• EARTH SCIENCE > Cryosphere > Sea Ice

Theme

• CMIP5

• CMIP6

• Climate Projections

• Sea Ice

Place

• Global

• Southern Hemisphere

GEMET - INSPIRE themes, version 1.0

• Oceanographic geographical features

Access constraints

Other restrictions

Other constraints

no limitations to public access

Access constraints

Other restrictions

Other constraints

no limitations

Use constraints

License

Other constraints

Open Government Licence v3.0

Use constraints

Other restrictions

Other constraints

This data is governed by the NERC Data Policy: https://www.ukri.org/who-we-are/nerc/our-policies-and-standards/nerc-data-policy/

Use constraints

Other restrictions

Other constraints

This data is governed by the NERC data policy and supplied under Open Government Licence v.3

Use constraints

Other restrictions

Other constraints

None

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Unique resource identifier

url

Codespace

url

Association Type

Cross reference

Spatial representation type

Text, table

Language

English

Character set

UTF8

Topic category

• Climatology, meteorology, atmosphere

Begin date

1950-01-01

End date

2100-12-31

Supplemental Information

It is recommended that careful attention be paid to the contents of any data, and that the author be contacted with any questions regarding appropriate use. If you find any errors or omissions, please report them to polardatacentre@bas.ac.uk.

Title

European Petroleum Survey Group (EPSG) Geodetic Parameter Registry

Date (Publication)

2008-11-12

Cited responsible party

Organisation name	Individual name	Electronic mail address	Role
European Petroleum Survey Group		EPSGadministrator@iogp.org	Publisher

Unique resource identifier

urn:ogc:def:crs:EPSG::3031

Version

6.18.3

Hierarchy level

Dataset

Statement

Methodology:

Model data

The tables contain the model names for all models included in the analysis, and the headers reference the CMIP experiment or forcing scenario from which data was taken. The table also lists additional details of the runs used. The details of the experiment protocol for CMIP5 and CMIP6 are given in Taylor et al. (2012) and O'Neill et al (2016) respectively:

Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485-498. https://doi.org/10.1175/BAMS-D-11-00094.1

O'Neill, B. C., Tebaldi, C., Vuuren, D. P. V., Eyring, V., Friedlingstein, P., Hurtt, G., et al. (2016). The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), 3461-3482. https://doi.org/10.5194/gmd-9-3461-2016

Sea ice Concentration data from satellites

'NASA-Team v1.1': Cavalieri, D. J., Parkinson, C. L., Gloersen, P., & Zwally, H. J. (1996), updated yearly. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1.1 [Dataset]. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/8GQ8LZQVL0VL. [Accessed Apr 1 2021].

"Bootstrap v3.1": Comiso, J. C. (2017). Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS, Version 3.1 [Dataset]. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/7Q8HCCWS4I0R. [Accessed Apr 1 2021].

Processing

CMIP data was accessed from the CEDA archives of CMIP5 and CMIP6 data on the UK computing resource JASMIN. CMIP5 data was accessed in July 2020; CMIP6 data was accessed in November 2020.

SIA was calculated from the CMIP5 variable 'sic' or CMIP6 variable 'siconc', or, where available, is the CMIP6 variable 'siareas'. Global mean surface air temperature was calculated from CMIP variable 'tas'.

We define an 'exceedance year' as the year in which a model's 5-year trailing average February SIA drops below 0.1Mkm2 . Exceedance year is calculated separately for historical and scenario runs, and the exceedance year taken as the minimum of these values. Due to the use of the trailing average, there are no exceedances in the first four years of the historical (1950-1953) or future (2015-2018) analysis.

Historical climatologies (SIAhist) use the period 1979-2014; CMIP5 historical runs end in 2005, so climatologies also use data from rcp45 or, where this is unavailable, rcp85. End-of-the-century climatologies, to calculate the change in SIA or TAS (dSIA and dTAS in data table), use the period 2081-2100.

Ensemble linear regressions were performed (using python's statsmodels ordinary least squares method). Constrained projections were then produced using these linear regression models and one of the observation-derived SIA datasets as the predictor.

Other data

Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) data from Meehl et al. (2020):

Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J. F., Stouffer, R. J., et al. (2020). Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6(26). https://doi.org/10.1126/sciadv.aba1981

Data description: regression statistics

For each combination of forcing scenario, dataset (i.e. CMIP5 or CMIP6 or special subsets therein) and season (February or September) and for each pair of variables named ''''''xname' and 'yname', the datafile regression_statistics.csv contains full regression statistics:

* ensemble minimum, mean and max (as in Holmes et al. (2022) Table 1) and standard deviation of each variable

* r2 and its p-value, regression slope and intercept

* Where relevant, the constrained pr...(8)

Data collection:

The data is aggregated over the Southern Ocean, derived from CMIP models at various spatial resolution.

Data quality:

Missing values in one of the future scenario run columns indicate that the given model was not run for that scenario, or, that data was not available to the authors.

Metadata

File identifier

e67242f2-e9aa-4402-85a3-be42d13354af XML

Metadata language

English

Character set

UTF8

Hierarchy level

Dataset

Hierarchy level name

dataset

Date stamp

2022-04-26

Metadata standard name

ISO 19115 Geographic Information - Metadata

Metadata standard version

ISO 19115:2003(E)

Metadata author

Organisation name	Individual name	Electronic mail address	Role
NERC EDS UK Polar Data Centre		polardatacentre@bas.ac.uk	Point of contact