# Plotting ocean variables in density space (using xhistogram)

## Scientific Motivation

Most of the ocean variables in the CMIP6 dataset are in z-coordinates (like the overturning in the top panel of the figure below). But the natural coordinate system for the interior ocean is density (see bottom panel of figure). I propose to use xhistogram (https://github.com/xgcm/xhistogram) to bin CMIP6 variables into density coordinates, and to plot the residual overturning circulation for various CMIP6 models, alongside other variables of interest.

The goal of this project is not to do budgets in the water mass transformation framework, but simply to do model intercomparison in density space.

## Proposed Hacking

*Updated, 09/17/19:* I propose that we initially use GSW-Python to find density from temperature and salinity in CMIP6 models, and write a script to pass the required info to xhistogram and return that variable in density space. We could then compare the variables in various models, e.g. heat transport, O2, etc. in density space, and explore any subtleties between the models.

(an older version of this project description focussed more on the equation of state)

## Anticipated Data Needs

It is clear that we need (at least) monthly (full depth) thetao, so, vmo, basin. I would also like to look at the meridional heat transport, hfy

msftmrho may be useful for comparison

Having other tracers (O2, carbon, nutrients?) may also be used to contrast the volume transport with tracer concentrations and transports

It is not clear to me whether or not the following variables are available, but they might be useful if they can be found: sea_water_equation_of_state, rhozero, cpocean

## Anticipated Software Tools

xhistogram, xarray

## Desired Collaborators

Anyone interested in the ROC, the water mass transformation framework, or with knowledge of the Equation of State in one or several of the CMIP6 models. Some experience with xarray would be helpful.

**Please help me define what this project will look like and what data variables we need. I have worked with transformation to buoyancy coordinates in the MITgcm, but I have little experience working with CMIP6 data.**