Abstract
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.
Highlights
Probabilistic approaches, based on large ensemble simulations, have been helpful in many branches of Earth-system modelling sciences to tackle the difficulties inherent to the complex and chaotic nature of the dynamical systems at play
We focus on the model set-up, the integration strategy, and the numerical performances of the system, followed by a few illustrative preliminary results in Sect. 5. 4.1 Regional and global configurations E-ORCA025 is the main ensemble simulation aimed for OCCIPUT
6 Conclusions We have presented in this paper the technical implementation of a new, probabilistic version of the NEMO ocean modelling system
Summary
Probabilistic approaches, based on large ensemble simulations, have been helpful in many branches of Earth-system modelling sciences to tackle the difficulties inherent to the complex and chaotic nature of the dynamical systems at play. A consequence is that, in the turbulent regime (i.e. for 1/4◦ or finer resolution), ocean models spontaneously generate a chaotic intrinsic variability under purely climatological atmospheric forcing, i.e. forced with the same repeated annual cycle from year to year This purely intrinsic variability has a significant imprint on many ocean variables, especially in eddyactive regions, and develops on spatio-temporal scales ranging from mesoscale eddies up to the size of entire basins, and from weeks to multiple decades Building on the results obtained from climatological simulations, the ongoing OCCIPUT project (Penduff et al, 2014) aims to better characterize the chaotic low-frequency intrinsic variability (LFIV) of the ocean under a fully varying atmospheric forcing, from a large (50-member) ensemble of global ocean–sea-ice hindcasts at 1/4◦ resolution over the last 56 years (1960–2015).
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