Sea Level Trends Over 1993-2015 And 2005-2015 From The Occiput Ensemble Simulation

• Main Author: William, LLOVEL
• Format: Dataset
• Language: English
• Published: Zenodo 2018
• Subjects: Ensemble simulation; sea level trend; chaotic versus atmospherically-froced sea level trends; Dewar; Gomez; Leroux; Sea ice
• Online Access: https://dx.doi.org/10.5281/zenodo.1487982; https://zenodo.org/record/1487982

Contributions of atmospheric forcing and chaotic ocean variability to regional sea level trends over 1993-2015 William Llovel, Thierry Penduff, Benoit Meyssignac, Jean-Marc Molines, Laurent Terray, Laurent Bessières and Bernard Barnier This data set contains 50 sea level trend fields computed globally over 1993-2015 and 2005-2015 from the OCCIPUT ensemble hindcast. These fields are studied in the paper “Contribution of atmospheric forcing and chaotic ocean variability to regional sea level trends over 1993-2015” in revision in Geophysical Research Letters . These sea level trends come from the OceaniC Chaos – ImPacts, structure, predictability (OCCIPUT) ensemble of 1/4° ocean/sea-ice simulations (Penduff et al, 2014; Bessières et al., 2017). This ensemble consists of 50 global hindcasts at ¼° horizontal resolution performed over 1960-2015. The configuration is based on the NEMO 3.5 model and implemented on an eddy-permitting quasi-isotropic horizontal mesh whose grid spacing is about 27 km at the equator and decreases poleward. The 50 members are initialized on January 1 st 1960 from the final state of a 21-year one-member spinup. A small stochastic perturbation is applied within each ensemble member during the first year (1960) and switched off at the end of 1960, yielding 50 different oceanic states on January 1 st 1961. Each member is then integrated until the end of 2015 with the same atmospheric forcing (DSF5.2) based on the ERA-Interim atmospheric reanalysis. We therefore obtain an ensemble of 50 simulations with the same numerical model and forcing, but different initial conditions. A one-member 327-year climatological simulation based the exact same code and setup is used to estimate the impact of spurious model drift on sea level trends. This simulation was forced each year with the same annual atmospheric cycle derived from DFS5.2. The spurious trends of simulated sea level was estimated at every grid point by computing sea level trends in the climatological simulation over the corresponding years of the 1993-2015 OCCIPUT simulations. This spurious trend map was then removed from the 50 trend maps derived from the ensemble simulation (as also done in Penduff et al., 2018). As NEMO conserves volume rather than mass, the global mean steric effect is missing and the global mean sea level is not properly computed (Greatbatch, 1994). The global mean sea level trends were thus subtracted from the regional sea level trends within each member: the trend provided in the present dataset are anomalies respective to their global mean sea level trend (and corrected for the model drift). Here is an example of the file header. dimensions: y = 1021 x = 1442 variables: float nav_lat(y, x) nav_lat:axis = "Y" nav_lat:standard_name = "latitude" nav_lat:long_name = "Latitude" nav_lat:units = "degrees_north" nav_lat:nav_model = "grid_T" float nav_lon(y, x) nav_lon:axis = "X" nav_lon:standard_name = "longitude" nav_lon:long_name = "Longitude" nav_lon:units = "degrees_east" nav_lon:nav_model = "grid_T" double trend(y, x) trend:units = "m/yr" trend:_FillValue = 0. // global attributes: :_NCProperties = "version=1|netcdflibversion=4.4.1|hdf5libversion=1.8.14" :Conventions = "CF" :title = "NCL Efficient Approach by W. Llovel" nav_lat and nav_lon represent the latitude and longitude of the NEMO model whereas trend represents the trend anomalies. The trend anomaly estimates are in m.yr -1 . 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 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), 12767–12771. Penduff T, Juza M, Barnier B, Zika J, Dewar WK, Treguier A-M, Molines JM, Audiffren N (2011) Sea-level expression of intrinsic and forced ocean variabilities at interannual time scales. J Clim 24:5652–5670. Penduff, T., Barnier, B., Terray, L., Bessières, L., Sérazin, 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 Penduff, T., W. Llovel, S. Close, B.-I. Garcia-Gomez, G. Sérazin, L. Bessières, S. Leroux, Trends of coastal sea level between 1993 and 2015: roles of atmospheric forcing and oceanic chaos, in prep.