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user/poddo/atm_press_flt – NEMO
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Version 10 (modified by poddo, 12 years ago) (diff)

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Atmospheric Pressure for Filtered free surface! =

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Introduction

Starting from NEMO_3.3 release the OPA component of the system includes also the possibility to force the model using the Atmospheric pressure filed provided externally. The code has been developed by MERCATOR and tested using the time-splitting option. The main goal of this work is to test the usage of Atmospheric pressure Forcing using the filtered free surface formulation. Originally it has been planned also to study the influence of different lateral open boundary condition parameterizations, however due to the decision to froze the OBC and BDY modules developments until a merge of the two modules will be completed the sensitivity study of the LOBC has been postponed.

Experiment set-up

Basic NEMO model configuration is described in Oddo et al. 2009. Model domain covers the whole Mediterranean Sea plus a “large” Atlantic box, where the model is nested with the Global ¼ Degree MERCATOR operational model. External Lateral Open Boundary Condition data are monthly climatology obtained from 10 year global experiment. The main difference with standard NEMO code concerns the volume conservation. In Standard OPA code, in case of filtered free surface option, the volume conservation is imposed. In the present version of the model the Mass/Volume? transport across the model domain lateral open boundaries is driven by a generalized Flather’s algoritm (Oddo et al. 2008) see eq. 3 in Odo et al 2010. Four different experiment have been performed in order to understand the differences in using the Atmospherci pressure forcing in case of time-splitting and filtered free surface formulation.
EXP1 reproduces the Oddo et al 2010, experiment with NEMO_3.3.1, so it has the filtered free surface formulation and no atmospheric pressure (AP)
EXP2 is the same model step-up of EXP1 but with time-splitting option.
EXP3 is the same of EXP1 plus the introduction of the AP.
EXP4 is EXP2 plus the introduction of the AP forcing.
All the experiments start from SeaDataNet? climatology on January 2004 and run until December 2007.

Results and Discussion

The differences of the mean SSH (averaged over the entire simulation period) between EXP1 and EXP3 is shown in the upper panel of the figure below, the analogous for the difference between EXP2 and EXP4 is shown in the bottom panel (units are m). Introducing the Atmospheric pressure forcing, a large scale zonal gradient is observed in both the cases (filtered and time splitting algorithms). This large scale pattern is the oceanic counterpart of the climatological (over the 4 simulated years) atmospheric pressure field used to force the model.

The differences of the SSH Standard Deviation between the experiments is shown in the figure below. In both the cases (filtered or time-splitting) the introduction of the AP forcing increases the standard deviation of the SSH during the simulated period (differences are, in general, negative). As a consequence of the additional forcing introduced, the basin has a large scale oscillation increasing the SSH variability. However, the larger variability (temporal/spatial) is smoothed (“filtered”) in the filtered free-surface case as evident from the smaller values in the upper panel of the figure below. This different behavior is particularly evident in the shallow areas.

Conclusions

Under this model set-up (Domain, surface forcing and lateral open boundary conditions) the introduction of the AP forcing seems to produce reasonable results also when the filtered option is used to handle the barotropic component of the equations of motion. Further investigation, on the LOBC sensitivity are needed.

Bibliography

  1. Oddo and N. Pinardi. Lateral open boundary conditions for nested limited area models: A scale selective approach. Ocean Modelling Volume 20, Issue 2, 2008, Pages 134-156
  1. Oddo, M. Adani, N. Pinardi, C. Fratianni, M. Tonani, and D. Pettenuzzo. A nested Atlantic-Mediterranean Sea general circulation model for operational forecasting. Ocean Sci., 5, 461-473, 2009

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