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@spencerkclark spencerkclark commented Dec 17, 2021

Predicting or computing the net surface shortwave radiative flux based on the downward surface shortwave radiative flux outside the physics driver is difficult, because the effective albedo changes throughout the day. In the middle of the day, sunlight is typically more direct and more readily absorbed (notebook).

This PR adds an option to compute the override for the net surface shortwave radiative flux within the physics driver based on the ratio of the net to downward shortwave radiative flux at the surface predicted by RRTMG (this ratio is one minus the albedo). When the RRTMG-predicted downward shortwave radiative flux at the surface is zero, we assume that the override for the net shortwave radiative flux at the surface will also be zero to avoid divide by zero issues.

I tested this in a new ML-corrected run. If we look over the month of August, it has the desired effect. The Sahara-mean bias in net shortwave radiative flux at the surface goes from about -9.1 W/m^2 using the basic derived model to about 1.6 W/m^2 using the derived model enabled via this PR (notebook).

2021-12-20-august-net-shortwave-bias

When using ML to predict the surface radiative fluxes, it is important to use
an albedo that is at least consistent with the coarse model when computing the
net shortwave radiative flux at the surface.  This adds an option to do this by
computing the absorption (i.e. 1 - albedo) using the RRTMG fluxes, and makes
the assumption that the absorption is zero if the RRTMG downward shortwave flux
at the surface is zero.
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