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Climate mode datasets and generating codes from CMIP6 pre-Industrial control simulations and observations

University of Tasmania, Australia
Mohapatra, Sandeep ; Sen Gupta, Alex ; Bindoff, Nathaniel ; Lyu, Yuxuan
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/617aa17c-9480-4fa8-9bfc-afbcfdcfd38e&rft.title=Climate mode datasets and generating codes from CMIP6 pre-Industrial control simulations and observations&rft.identifier=CMIP6pi-2025a&rft.description=Internal climate variability encompasses processes ranging from daily weather fluctuations to multidecadal interactions within the climate system. Understanding these processes is crucial for distinguishing natural variability from human-induced climate change. A large component of internal variability on sub-seasonal to multi-decadal time scales are associated with recurring patterns or ‘climate modes’. Using pre-industrial control (piControl) simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigate eight critical climate modes: Eastern Pacific El Niño (EP-El Niño), Central Pacific El Niño (CP-El Niño), Interdecadal Pacific Oscillation (IPO), Indian Ocean Dipole (IOD), Subsurface Dipole Mode (SDM), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and Southern Annular Mode (SAM). These modes were derived from 23 CMIP6 models, each with over 500 years of simulation data, ensuring robust statistical insights into their spatial and temporal structures. The datasets were validated against observational data, revealing broad-scale consistency and highlighting biases in regional features and amplitudes. For example, the models effectively capture spatial patterns such as the tripolar SST anomaly of the IPO and the equatorial Pacific warming of EP-El Niño. However, regional discrepancies, like exaggerated warming or cooling in specific areas, were observed. Despite these biases, the datasets provide critical tools for understanding climate variability, conducting detection and attribution studies, and improving climate projections. Details regarding the generated NetCDF files are provided in the accompanying README file.All datasets are publicly accessible (https://doi.org/10.5281/zenodo.17274477, and additionally linked to this record), supporting future research and policy development to address climate variability and its implications for climate change adaptation and mitigation.Maintenance and Update Frequency: notPlannedStatement: Using pre-industrial control (piControl) simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigated nine critical climate modes: Eastern Pacific El Niño (EP-El Niño), Central Pacific El Niño (CP-El Niño), Interdecadal Pacific Oscillation (IPO), Indian Ocean Basin Mode (IOBM), Indian Ocean Dipole (IOD), Subsurface Dipole Mode (SDM), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and Southern Annular Mode (SAM). These modes were derived from 23 CMIP6 models, each with over 500 years of simulation data, mostly defined using EOF (only AMO is defined based regression) analysis based on existing literature. The datasets were validated against observational data, revealing broad-scale consistency. Python, NCL, Ferret and CDO were used for data processing and plotting of figures. 23 models were employed: CanESM5, HadGEM3-GC31-LL, EC-Earth3-CC, CMCC-CM2-SR5, CNRM-CM6-1, GISS-E2-1-G, CMCC-ESM2, EC-Earth3, E3SM1-0, MIROC6, MRI-ESM2-0, HadGEM3-GC31-MM, BCC-CSM2-MR, IPSL-CM6A-LR, MPI-ESM1-2-HR, ACCESS-ESM1-5, ACCESS-CM2, CESM2, GFDL-CM4, CIESM, FGOALS-g3, SAM0-UNICON, CNRM-ESM2-1. Observation/reanalysis products used: ORAS5 (for SDM), ERA5 (for SAM and NAO) and ERSSTv5 (for EP El Nino, CP El Nino, IPO, IOBM, IOD, AMO)&rft.creator=Mohapatra, Sandeep &rft.creator=Sen Gupta, Alex &rft.creator=Bindoff, Nathaniel &rft.creator=Lyu, Yuxuan &rft.date=2025&rft.coverage=westlimit=-180.00; southlimit=-90.00; eastlimit=180.00; northlimit=90.00&rft.coverage=westlimit=-180.00; southlimit=-90.00; eastlimit=180.00; northlimit=90.00&rft_rights=Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/&rft_rights=Cite data as: Mohapatra S, Sen Gupta A, Bindoff NL, Lyu Y. (2025) Climate mode datasets and generating codes from CMIP6 pre-Industrial control simulations and observations. [Data Set] Institute for Marine and Antarctic Studies. https://doi.org/10.25959/CMIP6pi-2025a&rft.type=dataset&rft.language=English Access the data
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Cite data as: Mohapatra S, Sen Gupta A, Bindoff NL, Lyu Y. (2025) Climate mode datasets and generating codes from CMIP6 pre-Industrial control simulations and observations. [Data Set] Institute for Marine and Antarctic Studies. https://doi.org/10.25959/CMIP6pi-2025a

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Internal climate variability encompasses processes ranging from daily weather fluctuations to multidecadal interactions within the climate system. Understanding these processes is crucial for distinguishing natural variability from human-induced climate change. A large component of internal variability on sub-seasonal to multi-decadal time scales are associated with recurring patterns or ‘climate modes’.

Using pre-industrial control (piControl) simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigate eight critical climate modes: Eastern Pacific El Niño (EP-El Niño), Central Pacific El Niño (CP-El Niño), Interdecadal Pacific Oscillation (IPO), Indian Ocean Dipole (IOD), Subsurface Dipole Mode (SDM), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and Southern Annular Mode (SAM). These modes were derived from 23 CMIP6 models, each with over 500 years of simulation data, ensuring robust statistical insights into their spatial and temporal structures. The datasets were validated against observational data, revealing broad-scale consistency and highlighting biases in regional features and amplitudes. For example, the models effectively capture spatial patterns such as the tripolar SST anomaly of the IPO and the equatorial Pacific warming of EP-El Niño. However, regional discrepancies, like exaggerated warming or cooling in specific areas, were observed. Despite these biases, the datasets provide critical tools for understanding climate variability, conducting detection and attribution studies, and improving climate projections. Details regarding the generated NetCDF files are provided in the accompanying README file.

All datasets are publicly accessible (https://doi.org/10.5281/zenodo.17274477, and additionally linked to this record), supporting future research and policy development to address climate variability and its implications for climate change adaptation and mitigation.

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Maintenance and Update Frequency: notPlanned
Statement: Using pre-industrial control (piControl) simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigated nine critical climate modes: Eastern Pacific El Niño (EP-El Niño), Central Pacific El Niño (CP-El Niño), Interdecadal Pacific Oscillation (IPO), Indian Ocean Basin Mode (IOBM), Indian Ocean Dipole (IOD), Subsurface Dipole Mode (SDM), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and Southern Annular Mode (SAM). These modes were derived from 23 CMIP6 models, each with over 500 years of simulation data, mostly defined using EOF (only AMO is defined based regression) analysis based on existing literature. The datasets were validated against observational data, revealing broad-scale consistency. Python, NCL, Ferret and CDO were used for data processing and plotting of figures. 23 models were employed: CanESM5, HadGEM3-GC31-LL, EC-Earth3-CC, CMCC-CM2-SR5, CNRM-CM6-1, GISS-E2-1-G, CMCC-ESM2, EC-Earth3, E3SM1-0, MIROC6, MRI-ESM2-0, HadGEM3-GC31-MM, BCC-CSM2-MR, IPSL-CM6A-LR, MPI-ESM1-2-HR, ACCESS-ESM1-5, ACCESS-CM2, CESM2, GFDL-CM4, CIESM, FGOALS-g3, SAM0-UNICON, CNRM-ESM2-1. Observation/reanalysis products used: ORAS5 (for SDM), ERA5 (for SAM and NAO) and ERSSTv5 (for EP El Nino, CP El Nino, IPO, IOBM, IOD, AMO)

Notes

Credit
This research was supported by the Australian Research Council Special Research Initiative, Australian Centre for Excellence in Antarctic Science (ACEAS) (Project Number SR200100008).

Issued: 09 10 2025

Data time period: 2022-07-14

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(Data access via Zenodo)

doi : https://doi.org/10.5281/zenodo.17274476

(Data access via IMAS storage)

url : https://data.imas.utas.edu.au//attachments/617aa17c-9480-4fa8-9bfc-afbcfdcfd38e

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