Data supplement for manuscript Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90,  https://doi.org/10.5194/esd-16-75-2025. Provided are the following monthly angular momentum time series, 1995-2015:Atmospheric angular momentum: AAM_ERA_Int_monthly_1995-2015.aamOceanic angular momentum:OAM_OCCIPUT_EnsMean_monthly_1995-2015.oamOAM_OCCIPUT_ens*_monthly_1995-2015.oamHydrologic angular momentum: HAM_SLR_DORIS_monthly_1995-2015.ascCryospheric angular momentum:Cryo_AM_Greenland_SLR_DORIS_monthly_1995-2015.ascCryo_AM_Antarctica_SLR_DORIS_monthly_1995-2015.ascGravitational attraction and loading angular momentum: GAL_SLR_DORIS_monthly_1995-2015.asc For content see ReadMe.txt. Terms of usage:If you use the OAM time series, please cite: Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90,  https://doi.org/10.5194/esd-16-75-2025.Bessières, L., Leroux, S., Brankart, J.M., Molines, J.M., Moine, M.P., Bouttier, P.A., Penduff, T., Terray, L., Barnier, B., Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: Application at eddying resolution. Geosci. Model Dev. 10, 1091–1106. doi:10.5194/gmd-10-1091-2017.Hogg, A.M., Penduff, T., Close, S.E., Dewar, W.K., Constantinou, N.C., Mart ́ınez-Moreno, J., 2022. Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics. J. Geophys. Res. Oceans 127, e2022JC018440. doi:10.1029/2022JC018440.Penduff, T., Bernier, B., Terray, L., Bessières, L., Sérazin, G., Gregorio, S., Brankart, J.M., Moine, M.P., Brankart, J.M., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate. CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling 19, 26–29.  If you use the angular momentum estimates of the other geophysical fluids, please cite: Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90,  https://doi.org/10.5194/esd-16-75-2025. Contact: L. Börger ([email protected])

Data supplement for manuscript Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90,  https://doi.org/10.5194/esd-16-75-2025. Provided are the following monthly angular momentum time series, 1995-2015:Atmospheric angular momentum: AAM_ERA_Int_monthly_1995-2015.aamOceanic angular momentum:OAM_OCCIPUT_EnsMean_monthly_1995-2015.oamOAM_OCCIPUT_ens*_monthly_1995-2015.oamHydrologic angular momentum: HAM_SLR_DORIS_monthly_1995-2015.ascCryospheric angular momentum:Cryo_AM_Greenland_SLR_DORIS_monthly_1995-2015.ascCryo_AM_Antarctica_SLR_DORIS_monthly_1995-2015.ascGravitational attraction and loading angular momentum: GAL_SLR_DORIS_monthly_1995-2015.asc For content see ReadMe.txt. Terms of usage:If you use the OAM time series, please cite: Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90,  https://doi.org/10.5194/esd-16-75-2025.Bessières, L., Leroux, S., Brankart, J.M., Molines, J.M., Moine, M.P., Bouttier, P.A., Penduff, T., Terray, L., Barnier, B., Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: Application at eddying resolution. Geosci. Model Dev. 10, 1091–1106. doi:10.5194/gmd-10-1091-2017.Hogg, A.M., Penduff, T., Close, S.E., Dewar, W.K., Constantinou, N.C., Mart ́ınez-Moreno, J., 2022. Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics. J. Geophys. Res. Oceans 127, e2022JC018440. doi:10.1029/2022JC018440.Penduff, T., Bernier, B., Terray, L., Bessières, L., Sérazin, G., Gregorio, S., Brankart, J.M., Moine, M.P., Brankart, J.M., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate. CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling 19, 26–29.  If you use the angular momentum estimates of the other geophysical fluids, please cite: Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90,  https://doi.org/10.5194/esd-16-75-2025. Contact: L. Börger ([email protected])

"Synthetic observations" of along-track SSH have been extracted online during the production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and locations where a true SLA observation exists in the AVISO database for the along-track altimetry from the TOPEX, Jason-1, Jason-2 and Jason-3 satellite continuous series over the period 1993-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (monthly to multi-decadal) based on the ISBA hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica.
The synthetic along-track SSH dataset from the model is available over the altimetry period (1993-2018) on Zenodo. It is provided there along with a time-mean model SSH (gridded model field) over the same period that can be used as a proxy for mean dynamic topography ("MDT"). See the README file for more information.

"Synthetic observations" of along-track SSH have been extracted online during the  production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and locations where a true SLA observation exists in the AVISO database for the along-track altimetry from the TOPEX, Jason-1, Jason-2 and Jason-3 satellite continuous series over the period 1993-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal)  based on the ISBA hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica.This synthetic along-track SSH dataset from the model is available over the altimetry period (1993-2018). It is provided there along with a time-mean model SSH (gridded model field) over the same period that can be used as a proxy for mean dynamic topography ("MDT").See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html

"Synthetic observations" of along-track SSH have been extracted online during the  production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and locations where a true SLA observation exists in the AVISO database for the along-track altimetry from the TOPEX, Jason-1, Jason-2 and Jason-3 satellite continuous series over the period 1993-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal)  based on the ISBA hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica.This synthetic along-track SSH dataset from the model is available over the altimetry period (1993-2018). It is provided there along with a time-mean model SSH (gridded model field) over the same period that can be used as a proxy for mean dynamic topography ("MDT").See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html

This dataset contains surface eddy kinetic energy over the Southern Ocean region, sourced from a 50-member ensemble of 0.25° ocean model simulations. It is used in the paper "Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics" published in Journal of Geophysical Research - Oceans. This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (Bessières et al., 2017, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (Madec, 2008). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in Brankart, 2013) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. Data is for the period 1979-2015. The sea level anomaly is found according to Close et al (2020) and converted into surface geostrophic velocity anomaly using the geostrophic relation. This velocity field is then used to calculate the eddy kinetic energy (EKE). Data is averaged over calendar month, and restricted to the latitude range 40°-60°S. A full description of this process is included in the companion paper. The dataset includes EKE files (eke_0??.nc), with monthy EKE saved for the period 1979-2015 for each ensemble member, and a single file (tau.nc) for the monthly-averaged wind stress over the same period.

This dataset contains surface eddy kinetic energy over the Southern Ocean region, sourced from a 50-member ensemble of 0.25° ocean model simulations. It is used in the paper "Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics" published in Journal of Geophysical Research - Oceans. This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (Bessières et al., 2017, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (Madec, 2008). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in Brankart, 2013) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. Data is for the period 1979-2015. The sea level anomaly is found according to Close et al (2020) and converted into surface geostrophic velocity anomaly using the geostrophic relation. This velocity field is then used to calculate the eddy kinetic energy (EKE). Data is averaged over calendar month, and restricted to the latitude range 40°-60°S. A full description of this process is included in the companion paper. The dataset includes EKE files (eke_0??.nc), with monthy EKE saved for the period 1979-2015 for each ensemble member, and a single file (tau.nc) for the monthly-averaged wind stress over the same period.

This data set contains the interannual variability fields for the sea level (ssh_var_inter_1993_2015_annuel.tar.gz) and its steric (hsterica_var_inter_1993_2015_annuel.tar.gz) and manometric (obp_var_inter_1993_2015_annuel.tar.gz) components over 1993-2015 from the OCCIPUT ensemble simulations. It is used in the paper « Atmospherically forced and chaotic interannual variability of regional sea level and its components over 1993-2015 » published in Journal of Geophysical Research - Oceans. This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (Bessières et al., 2017, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (Madec, 2008). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in Brankart, 2013) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. For each member, the simulated sea surface height (SSH) over 1993-2015 is considered. As NEMO is a Boussinesq model, it conserves volume instead of mass. Therefore, the steric effect is missing into the global mean sea level change (Greatbatch 1994). To overcome this issue, we remove the global mean estimate for the sea level time series at each grid point. Then the sea level anomalies obtained are averaged per year and a linear trend is removed from each member. The same processes are applied to the steric and manometric sea level time series. Here is an example of the file header dimensions: member = UNLIMITED ; // (50 currently) time = 23 ; y = 1021 ; x = 1442 ; variables: float ssh(member, time, y, x) ; ssh:long_name = "sea level interannual variability" ; ssh:standard_name = "sea_level interannual variability" ; ssh:units = "m" ; ssh:FillValue = "nan" ; float nav_lat(member, y, x) ; nav_lat:axis = "Y" ; nav_lat:long_name = "Latitude" ; nav_lat:standard_name = "latitude" ; nav_lat:units = "degrees_north" ; float nav_lon(member, y, x) ; nav_lon:axis = "Y" ; nav_lon:long_name = "Longitude" ; nav_lon:standard_name = "longitude" ; nav_lon:units = "degrees_east" ; float time(time) ; time:long_name = "time" ; time:standard_name = "time" ; time:units = "years since 1992" ; nav_lat and nav_lon represent the latitude and longitude of the NEMO model whereas var represents the interannual variability time series.

This data set contains the interannual variability fields for the sea level (ssh_var_inter_1993_2015_annuel.tar.gz) and its steric (hsterica_var_inter_1993_2015_annuel.tar.gz) and manometric (obp_var_inter_1993_2015_annuel.tar.gz) components over 1993-2015 from the OCCIPUT ensemble simulations. It is used in the paper « Atmospherically forced and chaotic interannual variability of regional sea level and its components over 1993-2015 » published in Journal of Geophysical Research - Oceans. This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (Bessières et al., 2017, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (Madec, 2008). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in Brankart, 2013) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. For each member, the simulated sea surface height (SSH) over 1993-2015 is considered. As NEMO is a Boussinesq model, it conserves volume instead of mass. Therefore, the steric effect is missing into the global mean sea level change (Greatbatch 1994). To overcome this issue, we remove the global mean estimate for the sea level time series at each grid point. Then the sea level anomalies obtained are averaged per year and a linear trend is removed from each member. The same processes are applied to the steric and manometric sea level time series. Here is an example of the file header dimensions: member = UNLIMITED ; // (50 currently) time = 23 ; y = 1021 ; x = 1442 ; variables: float ssh(member, time, y, x) ; ssh:long_name = "sea level interannual variability" ; ssh:standard_name = "sea_level interannual variability" ; ssh:units = "m" ; ssh:FillValue = "nan" ; float nav_lat(member, y, x) ; nav_lat:axis = "Y" ; nav_lat:long_name = "Latitude" ; nav_lat:standard_name = "latitude" ; nav_lat:units = "degrees_north" ; float nav_lon(member, y, x) ; nav_lon:axis = "Y" ; nav_lon:long_name = "Longitude" ; nav_lon:standard_name = "longitude" ; nav_lon:units = "degrees_east" ; float time(time) ; time:long_name = "time" ; time:standard_name = "time" ; time:units = "years since 1992" ; nav_lat and nav_lon represent the latitude and longitude of the NEMO model whereas var represents the interannual variability time series.

This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in Close et al, 2020, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (http://www.marine.copernicus.eu). The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate. The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (Bessières et al., 2017). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in Close et al, 2020. ---- References: Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, doi: 10.5194/gmd-10-1091-2017. Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. doi: 10.1016/j.pocean.2020.102314 Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.