DOI: 10.5281/zenodo.5564399
title: Atmospherically-forced and chaotic interannual variability of the sea level and its components over 1993-2015 from the OCCIPUT ensemble simulations

description: 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 <em><strong>dimensions:</strong></em> <em><strong>member = UNLIMITED ; // (50 currently)</strong></em> <em><strong>time = 23 ;</strong></em> <em><strong>y = 1021 ;</strong></em> <em><strong>x = 1442 ;</strong></em> <em><strong>variables:</strong></em> <em><strong> float ssh(member, time, y, x) ;</strong></em> <em><strong> ssh:long_name = "sea level interannual variability" ;</strong></em> <em><strong> ssh:standard_name = "sea_level interannual variability" ;</strong></em> <em><strong> ssh:units = "m" ;</strong></em> <em><strong> ssh:FillValue = "nan" ;</strong></em> <em><strong> float nav_lat(member, y, x) ;</strong></em> <em><strong> nav_lat:axis = "Y" ;</strong></em> <em><strong> nav_lat:long_name = "Latitude" ;</strong></em> <em><strong> nav_lat:standard_name = "latitude" ;</strong></em> <em><strong> nav_lat:units = "degrees_north" ;</strong></em> <em><strong> float nav_lon(member, y, x) ;</strong></em> <em><strong> nav_lon:axis = "Y" ;</strong></em> <em><strong> nav_lon:long_name = "Longitude" ;</strong></em> <em><strong> nav_lon:standard_name = "longitude" ;</strong></em> <em><strong> nav_lon:units = "degrees_east" ;</strong></em> <em><strong> float time(time) ;</strong></em> <em><strong> time:long_name = "time" ;</strong></em> <em><strong> time:standard_name = "time" ;</strong></em> <em><strong> time:units = "years since 1992" ;</strong></em> nav_lat and nav_lon represent the latitude and longitude of the NEMO model whereas var represents the interannual variability time series.
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. https://doi.org/10.1016/j.ocemod.2013.02.004", "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", "Greatbatch, R. J. , 1994: A note on the representation of steric sea level in models that conserve volume rather than mass, J. Geophys. Res., 99(C6), 12767\u201312771.", "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,\u00a0 Special\u00a0 Issue\u00a0 on\u00a0 High\u00a0 Resolution\u00a0 Ocean\u00a0 Climate Modelling, 19, 2014"]}