Welcome to the ECCO Version 4 Tutorial

This website contains a set of tutorials about how to use the ECCO Central Production Version 4 (ECCO v4) global ocean and sea-ice state estimate. The tutorials were written in Python and make use of the ecco_v4_py Python library, a library written specifically for loading, plotting, and analyzing ECCO v4 state estimate fields.

Additional Resources

The ECCO v4 state estimate is the output of a free-running simulation of a global ca. 1-degree configuration of the MITgcm. Prior to public release, the model output files model are assembled into NetCDF files. If you would like to work directly with the flat binary “MDS” files provided by the model then take a look at the xmitgcm Python package. The xgcm Python package provides tools for operating on model output fields loaded with xmitgcm. If you wish to analyze the MITgcm model output using Matlab then we recommend the gcmfaces toolbox.

The ecco_v4_py package used in this tutorial was inspired by the xmitgcm package and gcmfaces toolboxes.

Getting Started

• The ECCO Ocean and Sea-Ice State Estimate
• ECCO v4 state estimate ocean, sea-ice, and atmosphere fields
• Python and Python Packages
• Using Python to Download ECCO Datasets
• Downloading Subsets of ECCO Datasets
• Using wget to Download ECCO Datasets from PO.DAAC
• AWS Cloud: getting started and retrieving ECCO datasets
• Tutorial Overview

ECCO Data Structures

• The Dataset and DataArray objects used in the ECCOv4 Python package.
  • Objectives
  • Introduction
  • Import external packages and modules
  • Downloading and opening state estimate NetCDF files (datasets)
  • The Dataset object
  • The DataArray Object
  • Summary
• Coordinates and Dimensions of ECCOv4 NetCDF files
  • Objectives
  • Introduction
  • The Dimensions and Coordinates of THETA
  • The Dimension Coordinates of the Arakawa C-Grid
  • All ECCOv4 coordinates
  • Dimensions and Coordinates of Velocities
  • Summary

Input/Output, Data Structure Manipulation

• Loading the ECCOv4 native model grid parameters
  • Objectives
  • Introduction
  • Load the ECCOv4 model grid parameter NetCDF file
• Loading the ECCOv4 state estimate fields on the native model grid
  • Objectives
  • Introduction
  • NetCDF File Format
  • Open/view one ECCOv4 NetCDF file
  • Opening a subset of a single ECCOv4 variable NetCDF file
  • Opening multiple files of a single ECCOv4 variable using open_mfdataset
  • Combining datasets using xarray.merge
  • Opening and handling very large ECCOv4 datasets using Dask
  • Summary
• ECCOv4 Loading llc binary files in the ‘compact’ format
  • Objective
  • Introduciton
  • Objectives
  • The read_llc_to_tiles subroutine
  • Related routines
  • Example 1: Load a 2D llc ‘compact’ binary file
  • Load ecco-grid information to make a fancier lat-lon plot
  • Convert the ndarray into a DataArray
  • Example 2: Load a 3D ‘compact’ llc binary file with 3rd dimension = Time
  • Convert the ndarray into a DataArray
  • Example 3: Load a 3D ‘compact’ llc binary file with 3rd dimension = Depth
  • Convert the ndarray into a DataArray
  • Parting thoughts
• Combining multiple Datasets
  • Objectives:
  • Opening multiple Datasets centered on different coordinates
  • Merging multiple Datasets from state estimate variables
  • Adding the model grid Dataset
  • Merging and memory
  • Summary
• Saving Datasets and DataArrays to NetCDF
  • Objectives
  • Introduction
  • Saving an existing Dataset to NetCDF
  • Saving a new custom Dataset to NetCDF
  • Verifying our new NetCDF files
  • Saving the results of calculations
  • Summary

Operating on Data Variables

• Accessing and Subsetting Variables
  • Objectives
  • Introduction
  • Accessing fields inside Dataset and DataArray objects
  • Subsetting variables using the[], sel, isel, and where syntaxes
  • Conclusion
• Operating on Numpy arrays
  • Objectives
  • Introduction
  • Variable assignments
  • Dealing with pass-by-reference: right hand side operations
  • Dealing with pass-by-reference: copy and deepcopy
  • Conclusion
• Memory management in Python
  • Objectives
  • Introduction
  • Datasets to download
  • View vs. copy
  • Memory footprint
  • Deleting variables to free up memory
  • Xarray and memory management

Plotting & Interpolation

• Plotting Tiles
  • Objectives
  • Introduction
  • The default orientation of the lat-lon-cap tile fields
  • Plotting single tiles using imshow, pcolormesh, and contourf
  • Plotting fields from one tile using Cartopy
  • Plotting all 13 tiles simultaneously: No Projection
  • Plotting all 13 tiles with plot_proj_to_latlon_grid
  • Conclusion
• Interpolating fields from the model llc grid to a regular lat lon grid
  • Objectives
  • Introduction
  • How to interpolate scalar ECCO fields to a lat-lon grid
  • resample_to_latlon
  • Demonstrations of resample_to_latlon
  • Interpolating ECCO vectors fields to lat-lon grids
  • Saving interpolated fields to netCDF

Scalar and Vector Calculations

• Example calculations with scalar quantities
  • Objectives
  • Introduction
  • Global calculations with SSH
  • Sea surface height
  • Constructing Monthly means from Daily means
  • Regional calculations with THETA
  • Conclusion
• Calculating gradients and curl on the ECCO native grid
  • Objectives
  • Introduction
  • Preliminaries
  • Calculating zonal and meridional gradients of scalar ECCO fields located at tracer cell points
  • Calculating gradients of vector fields located on the ‘u’ and ‘v’ points (vorticity/divergence)
  • Calculating gradients of vector fields located on the grid cell centers (wind/surface stress curl)

Intro to PO Tutorials

• Intro to PO Tutorials: Getting Started
  • What are the Intro to PO Tutorials?
  • Who are these tutorials for?
  • What do I need to get started?
  • Which concepts are covered?
• Part 1: Geostrophic balance
  • Objectives
  • Introduction
  • Download the ECCO output
  • Open/view ECCO files
  • Compute and map geostrophic balance
  • Assessment of geostrophic balance
  • Exercises
  • ecco_po_tutorials module
  • References
• Part 2: Thermal Wind
  • Objectives
  • Introduction
  • Viewing and Plotting Density
  • Computing thermal wind balance
  • Assessment
  • Exercises
  • References
• Part 3: Steric height
  • Objectives
  • Introduction
  • Open files
  • Compute steric height anomaly
  • Compare steric height with sea surface height
  • Thermosteric and halosteric contributions
  • Exercises
  • References

More Advanced Calculations

• Compute meridional heat transport
  • Datasets to download
  • Load Model Variables
  • Grab latitude band: 26N array as an example
  • Select the Atlantic ocean basin for RAPID-MOCHA MHT
  • MHT at the approximate RAPID array latitude
  • Now compare global and Atlantic MHT at many latitudes
  • MHT as a function of depth
  • Exercise: reproduce figure from (Ganachaud and Wunsch, 2000)
  • References
• Compute MOC along the approximate OSNAP array from ECCO
  • Datasets to download
  • Define the OSNAP lines
  • Compute the overturning streamfunction in depth space
  • Compare to Observations
• ECCOv4 Global Volume Budget Closure
  • Objectives
  • Introduction
  • ETAN in a Boussinesq Model
  • Evaluating the model sea level anomaly ETAN volume budget
  • Calculate LHS: time tendency:
  • Plot the time-mean , total , and one example field
  • Calculate RHS: tendency due to surface fluxes,
  • Plot the time-mean, total, and one month average of
  • Calculate RHS: tendency due to volumetric flux divergence,
  • Comparison of LHS and RHS
  • ETAN budget closure through time
  • Predicted vs. actual
  • Time-mean due to net freshwater fluxes
• Global Heat Budget Closure
  • Objectives
  • Introduction
  • Evaluating the heat budget
  • Datasets to download
  • Prepare environment and load ECCOv4 diagnostic output
  • Calculate total tendency of ()
  • Calculate tendency due to advective convergence ()
  • Calculate tendency due to diffusive convergence ()
  • Calculate tendency due to forcing ()
  • Save to dataset
  • Load budget variables from file
  • Comparison between LHS and RHS of the budget equation
  • Heat budget closure through time
  • Time-mean vertical profiles
• Salt, Salinity and Freshwater Budgets
  • Objectives
  • Introduction
  • Datasets to download
  • Prepare environment and load ECCOv4 diagnostic output
  • Evaluating the salt budget
  • Evaluating the salinity budget
  • Evaluating the freshwater budget
  • Save budget terms
  • Load budget variables from file
  • Comparison between LHS and RHS of the budget equation
• Calculate ocean thermal forcing from ECCOv4r4 data, direct from PO.DAAC S3 storage
  • Objective
  • Introduction
  • Setup
  • Setup
  • Initial check – initiate S3 access and load one ECCO dataset
  • Calculate ocean thermal forcing time series from monthly ECCOv4r4 data
  • Plot time series of ocean thermal forcing

Support

• Getting Help
  • The ECCO Support Mailing List
  • Problems installing Python libraries?

Indices and tables

• Index

• Module Index

• Search Page