Deep convection simulation from the MITgcm (MIT General Circulation Model) (IVOMLS project)

Dataset: Deep convection simulation using MITgcm All experiments are preformed using the MIT General Circulation Model (MITgcm). The model is configured to allow non-hydrostatic dynamics to explicitly resolve deep convection. The model domain is a box with periodic boundary conditions in the x and y...

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Bibliographic Details
Main Authors: Ito, Takamitsu, Bracco, Annalisa, Sun, Daoxun
Format: Dataset
Language:English
Published: Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu 2017
Subjects:
Online Access:https://hdl.handle.net/1912/9154
Description
Summary:Dataset: Deep convection simulation using MITgcm All experiments are preformed using the MIT General Circulation Model (MITgcm). The model is configured to allow non-hydrostatic dynamics to explicitly resolve deep convection. The model domain is a box with periodic boundary conditions in the x and y directions of 32 x 32 km with horizontal resolution of 250 m. The box has a uniform depth of 2 km with 41 z-levels whose thicknesses increases from 10 m at surface to 100 m near the bottom. The linear equation of state is used throughout this study. 16 sensitivity experiments are designed to explore the behavior of oxygen uptake during the deep convection events under different cooling conditions. Two validation runs are also applied by forcing the model using observational data from Argo. In this data set, horizontally averaged profiles and vertical transport of dissolved oxygen and temperature from all experiments are included. A few transect of dissolved oxygen and temperature are also included to demonstrate the evolution of the convection event. For a complete list of measurements, refer to the supplemental document 'Field_names.pdf', and a full dataset description is included in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: http://www.bco-dmo.org/dataset/706167 NSF Division of Ocean Sciences (NSF OCE) OCE-1357373