polaris.tasks.ocean.horiz_press_grad.eos_expansion.edge_specvol_layer_mean

polaris.tasks.ocean.horiz_press_grad.eos_expansion.edge_specvol_layer_mean(expansion: Dataset, temperature: DataArray, salinity: DataArray, pressure: DataArray) → DataArray

The layer mean of the edge-shared profile, [alpha-edge-layer-mean].

This is edge_specvol() evaluated at the layer-mean state, and it is the same arithmetic for a reason worth stating rather than leaving to be rediscovered: [alpha-taylor] is linear in \(\Theta\), \(S\) and \(p\) jointly, so the layer mean of \(\hat\alpha^{(e)}\) is \(\hat\alpha^{(e)}\) evaluated at the layer means of its arguments. The reconstruction of §3.4 is mean-preserving, so the layer means of \(\Theta(p)\) and \(S(p)\) are the prognostic layer means; and PressureMid is the exact arithmetic midpoint of the two interface pressures, so the layer mean of \(p\) is PressureMid. The shape of the reconstruction inside the layer therefore cannot affect this quantity.

It is source 4 of the remainder that needs it: the mismatch against VertCoord’s own increment, [eos-remainder], is the second-order Taylor remainder of the equation of state across the edge.

Parameters:

• expansion (xarray.Dataset) – The edge-shared expansion from edge_expansion().

• temperature (xarray.DataArray) – Layer-mean conservative temperature in degC.

• salinity (xarray.DataArray) – Layer-mean absolute salinity in g kg-1.

• pressure (xarray.DataArray) – Layer-mean sea gauge pressure in Pa (PressureMid).

Returns:

specvol (xarray.DataArray) – The layer mean of \(\hat\alpha^{(e)}\) in m3 kg-1.