Sea-Ice–Ocean Modelling

Author(s):  
Rüdiger Gerdes ◽  
Peter Lemke
Keyword(s):  
Sea Ice ◽  
2021 ◽  
Author(s):  
Maciej Muzyka ◽  
Jaromir Jakacki ◽  
Anna Przyborska

<p>The Regional Ocean Modelling System has been begun to implement for region of Baltic Sea.  A preliminary curvilinear grid with horizontal resolution ca. 2.3 km has been prepared based on the grid, which was used in previous application in our research group (in Parallel Ocean Program and in standalone version of Los Alamos Sea Ice Model - CICE).  Currently the grid has 30 sigma layers, but the final number of levels will be adjusted accordingly.</p><p>So far we’ve successfully compiled the model on our machine, run test cases and created Baltic Sea case, which is working with mentioned Baltic grid. The following parameters: air pressure, humidity, surface temperature, long and shortwave radiation, precipitation and wind components are used as an atmospheric forcing. The data arrive from our operational atmospheric model - Weather Research and Forecasting Model (WRF).</p><p>Our main goal is to create efficient system for hindcast and forecast simulations of Baltic Sea together with sea ice component by coupling ROMS with CICE. The reason for choosing these two models is an active community that takes care about model’s developments and updates. Authors also intend to work more closely with the CICE model to improve its agreement with satellite measurements in the Baltic region.<br><br>Calculations were carried out at the Academic Computer Centre in Gdańsk.</p>


2017 ◽  
Vol 10 (3) ◽  
pp. 1091-1106 ◽  
Author(s):  
Laurent Bessières ◽  
Stéphanie Leroux ◽  
Jean-Michel Brankart ◽  
Jean-Marc Molines ◽  
Marie-Pierre Moine ◽  
...  

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.


2016 ◽  
Author(s):  
Laurent Bessières ◽  
Stéphanie Leroux ◽  
Jean-Michel Brankart ◽  
Jean-Marc Molines ◽  
Marie-Pierre Moine ◽  
...  

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 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 project. This application is aimed to simultaneously simulate the intrinsic/chaotic and the atmospherically-forced contributions to the ocean variability, from meso-scale turbulence to interannual-to-multidecadal time scales. Such an ensemble indeed provides a unique way to disantangle 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.


2008 ◽  
Author(s):  
A Martin ◽  
JA Hall ◽  
R O’Toole ◽  
SK Davy ◽  
KG Ryan

2016 ◽  
Author(s):  
Marta Vázquez ◽  
Raquel Nieto ◽  
Anita Drumond ◽  
Luis Gimeno

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