DOI: 10.5281/zenodo.5835167
title: Ensemble statistics for modelled Eddy Kinetic Energy in the Southern Ocean

description: 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.
description: Funded by the Ocean Surface Topography Science Team: PIRATE - Probabilistic InteRpretation of Altimeter and in-siTu obsErvations
description: {"references": ["Bessi\u00e8res, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and S\u00e9razin, 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", "Brankart, J.-M. (2013). Impact of uncertainties in the horizontal density gradient upon low resolution global ocean modelling. Ocean Modelling, 66, 64\u201376. doi: j.ocemod.2013.02.004", "Close, S., Penduff, T., Speich, S., Molines, J.-M., (2020). A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations, Progress in Oceanography, 184, doi: 10.1016/j.pocean.2020.102314.", "Dussin, R., Barnier, B., Brodeau, L., Molines, J. M. (2016). The making of Drakkar forcing set DFS5. DRAKKAR/MyOcean report 01-04-16, IGE, Grenoble, France", "Madec, G. (2008). NEMO Ocean Engine. Note du Pole de mod\u00e9lisation. Institut Pierre-. Simon Laplace (IPSL)Madec, G. (2016). NEMO ocean engine. Institut Pierre-Simon Laplace (IPSL). Retrieved from https://www.nemo-ocean.eu/doc", "Penduff, T., Barnier, B., Terray, L., Bessi\u00e8res, L., S\u00e9razin, G., Gregorio, S., Brankart, J., Moine, M., Molines, J., and Brasseur, P.: Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19, 2014"]}