Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution

Abstract. This paper presents the technical implementation of a new, probabilistic version of the NEMO ocean–sea-ice modelling system. Ensemble simulations with N members running simultaneously within a single executable, and interacting mutually if needed, are made possible through an enhanced message-passing interface (MPI) strategy including a double parallelization in the spatial and ensemble dimensions. An example application is then given to illustrate the implementation, performances, and potential use of this novel probabilistic modelling tool. A large ensemble of 50 global ocean–sea-ice hindcasts has been performed over the period 1960–2015 at eddy-permitting resolution (1∕4°) for the OCCIPUT (oceanic chaos – impacts, structure, predictability) project. This application aims to simultaneously simulate the intrinsic/chaotic and the atmospherically forced contributions to the ocean variability, from mesoscale turbulence to interannual-to-multidecadal timescales. Such an ensemble indeed provides a unique way to disentangle and study both contributions, as the forced variability may be estimated through the ensemble mean, and the intrinsic chaotic variability may be estimated through the ensemble spread.

[1]  Stefano Pierini.  Ensemble Simulations and Pullback Attractors of a Periodically Forced Double-Gyre System , 2014 .

[2]  Gurvan Madec,et al.  Chaotic variability of the meridional overturning circulation on subannual to interannual timescales , 2012 .

[3]  O. Talagrand,et al.  Evaluation of probabilistic prediction systems for a scalar variable , 2005 .

[4]  G. Madec.  NEMO ocean engine , 2008 .

[5]  Cma Training,et al.  Predictability of Weather and Climate , 2011 .

[6]  Guillem Candille,et al.  A generic approach to explicit simulation of uncertainty in the NEMO ocean model , 2015 .

[7]  K.,et al.  The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability , 2015 .

[8]  John Marshall,et al.  Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review , 2016 .

[9]  Pierre F. J. Lermusiaux,et al.  Uncertainty estimation and prediction for interdisciplinary ocean dynamics , 2006, J. Comput. Phys..

[10]  Jian Lu,et al.  High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6 , 2016 .

[11]  C. Deser,et al.  Forced and Internal Components of Winter Air Temperature Trends over North America during the past 50 Years: Mechanisms and Implications* , 2016 .

[12]  Thierry Penduff,et al.  Sea Level Expression of Intrinsic and Forced Ocean Variabilities at Interannual Time Scales , 2011 .

[13]  Jean-Michel Brankart,et al.  Impact of data from upcoming altimetric missions on the prediction of the three-dimensional circulation in the tropical Atlantic Ocean , 2009 .

[14]  Peter Jan,et al.  Particle Filtering in Geophysical Systems , 2009 .

[15]  Emanuele Di Lorenzo,et al.  Intrinsic and Forced Interannual Variability of the Gulf of Alaska Mesoscale Circulation , 2007 .

[16]  Thierry Penduff,et al.  Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution , 2006 .

[17]  Thierry Penduff,et al.  Intrinsic Variability of the Atlantic Meridional Overturning Circulation at Interannual-to-Multidecadal Time Scales , 2015 .

[18]  Jean-Michel Brankart,et al.  Impact of uncertainties in the horizontal density gradient upon low resolution global ocean modelling , 2013 .

[19]  W. Cleveland,et al.  Smoothing by Local Regression: Principles and Methods , 1996 .

[20]  G. Terrell.  Statistical theory and computational aspects of smoothing , 1997 .

[21]  Rong‐Hua Zhang,et al.  Latitudinal dependence of Atlantic meridional overturning circulation (AMOC) variations , 2010 .

[22]  Jean-Michel Brankart,et al.  Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations , 2011 .

[23]  William E. Johns,et al.  Variability of shallow and deep western boundary currents off the Bahamas during 2004–05: results from the 26°N RAPID–MOC Array , 2008 .

[24]  Bernard Barnier,et al.  The Making of the DRAKKAR FORCING SET DFS5 , 2014 .

[25]  Patrick Marchesiello,et al.  Thermal forcing for a global ocean circulation model using a three-year climatology of ECMWF analyses , 1995 .

[26]  Guillem Candille,et al.  Assessment of an ensemble system that assimilates Jason-1/Envisat altimeter data in a probabilistic model of the North Atlantic ocean circulation , 2014 .

[27]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[28]  James R. Montgomery,et al.  A Generic Approach , 1989 .

[29]  G. Evensen,et al.  Sequential Data Assimilation Techniques in Oceanography , 2003 .

[30]  Thierry Penduff,et al.  Intrinsic Variability of Sea Level from Global Ocean Simulations: Spatiotemporal Scales , 2015 .