CMIP6 ScenarioMIP UA MCM-UA-1-0 ssp370 r1i1p1f2 Amon tas gn v20190731

Stouffer, Ronald

Dataset
Summary
[ Derived from parent entry - See data hierarchy tab ]

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.UA.MCM-UA-1-0.ssp370' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Manabe Climate Model v1.0 - University of Arizona climate model, released in 1991, includes the following components: aerosol: Modifies surface albedoes (Haywood et al. 1997, doi: 10.1175/1520-0442(1997)010<1562:GCMCOT>2.0.CO;2), atmos: R30L14 (3.75 X 2.5 degree (long-lat) configuration; 96 x 80 longitude/latitude; 14 levels; top level 0.015 sigma, 15 mb), land: Standard Manabe bucket hydrology scheme (Manabe 1969, doi: 10.1175/1520-0493(1969)097<0739:CATOC>2.3.CO;2), landIce: Specified location - invariant in time, has high albedo and latent heat capacity, ocean: MOM1.0 (MOM1, 1.875 X 2.5 deg; 192 x 80 longitude/latitude; 18 levels; top grid cell 0-40 m), seaIce: Thermodynamic ice model (free drift dynamics). The model was run by the Department of Geosciences, University of Arizona, Tucson, AZ 85721, USA (UA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: 250 km, ocean: 250 km, seaIce: 250 km.
Project
IPCC-AR6_CMIP6 (Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets)
Location(s)
global
Spatial Coverage
Longitude 0 to 360 Latitude -90 to 90
Temporal Coverage
2015-01-17 to 2100-12-17 (gregorian)
Use constraints
Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)
Data Catalog
World Data Center for Climate
Access constraints
registered users
Size
30.33 MiB (31799088 Byte)
Format
NetCDF
Status
completely archived
Creation Date
Future Review Date
2032-07-31
Download Permission
Please login to check permission and download options
Cite as
[ Derived from parent entry - See data hierarchy tab ]
Stouffer, Ronald (2023). IPCC DDC: UA MCM-UA-1-0 model output prepared for CMIP6 ScenarioMIP ssp370. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/AR6.C6SPUAMUs370

BibTeX RIS
VariableCodeAggregationUnit
air_temperatureCF
tas (IPCC_DDC_AR6: 1095)
monK

Is source of

[1] IPCC. (2023). Cross-Chapter Box 11.1, Figure 3 | Illustration of the AR6 global warming level (GWL) sampling approach to derive the timing and the response at a given GWL for the case of Coupled Model Intercomparison Project Phase 6 (CMIP6) data. In IPCC, 2023: Chapter 11. https://www.ipcc.ch/report/ar6/wg1/figures/chapter-11/ccbox-11-1-figure-3
[2] IPCC. (2023). Figure 4.2 | Selected indicators of global climate change from CMIP6 historical and scenario simulations. In IPCC, 2023: Chapter 4. https://www.ipcc.ch/report/ar6/wg1/figures/chapter-4/figure-4-2
[3] IPCC. (2023). Figure 4.12 | Near-term change of seasonal mean surface temperature. In IPCC, 2023: Chapter 4. https://www.ipcc.ch/report/ar6/wg1/figures/chapter-4/figure-4-12
[4] IPCC. (2023). Figure 4.20 | Difference of surface temperature change between June–July–August (JJA) and December –January–February (DJF). In IPCC, 2023: Chapter 4. https://www.ipcc.ch/report/ar6/wg1/figures/chapter-4/figure-4-20
[5] IPCC. (2023). Figure 4.31 | Projected spatial patterns of change in annual average near-surface temperature (°C) at different levels of global warming. In IPCC, 2023: Chapter 4. https://www.ipcc.ch/report/ar6/wg1/figures/chapter-4/figure-4-31
[6] IPCC. (2023). Figure 4.33 | Area fraction of significant precipitation change at 1.5°C, 2°C, 3°C, and 4°C of global warming. In IPCC, 2023: Chapter 4. https://www.ipcc.ch/report/ar6/wg1/figures/chapter-4/figure-4-33
[7] IPCC. (2023). Figure 4.19 | Mid- and long-term change of annual mean surface temperature. In IPCC, 2023: Chapter 4. https://www.ipcc.ch/report/ar6/wg1/figures/chapter-4/figure-4-19
[8] IPCC. (2023). Figure 10.21 | Projected Mediterranean summer warming. In IPCC, 2023: Chapter 10. https://www.ipcc.ch/report/ar6/wg1/figures/chapter-10/figure-10-21
[9] DOI Fischer, E. (2023). Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.31 v20230531. doi:10.5285/8fa708d0474d4a3caa5c9f645a89d282
[10] DOI Fischer, E. (2023). Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.19 (v20230203). doi:10.5285/dce10ff4596241e190aaea9291cc4249
[11] DOI Fischer, E. (2023). Chapter 4 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 4.12 (v20230203). doi:10.5285/0078d944259049a4b1bc5947623f6e97
[12] DOI Jury, M.; Haarsma, R.; Dosio, A.; Doblas-Reyes, F.; Terray, L. (2023). Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.21 (v20220622). doi:10.5285/9f83afcc47ca49feb1d5702de9fa8869
[13] IPCC. (2023). Code for Figure 10.21 of the Working Group I Contribution to the IPCC Sixth Assessment Report. https://github.com/IPCC-WG1/Chapter-10_Fig21
[14] DOI Jury, M.W.; Haarsma, R.; Dosio, A.; Doblas-Reyes, F.J.; Terray, L. (2022). IPCC AR6 WGI - Figure 10.21. doi:10.5281/zenodo.6787550

Parent

CMIP6 ScenarioMIP UA MCM-UA-1-0 ssp370
Details
[Entry acronym: C6SPUAMUs370r1112Amtasgn90731] [Entry id: 3925315]