Assessing Uncertainties and Approximations in Solar Heating of the
Climate System
Abstract
In calculating solar radiation, climate models make many
simplifications, in part to reduce computational cost and enable climate
modeling, and in part from lack of understanding of critical atmospheric
information. Whether known errors or unknown errors, the community’s
concern is how these could impact the modeled climate. The
simplifications are well known and most have published studies
evaluating them, but with individual studies it is difficult to compare.
Here we collect a wide range of such simplifications in either radiative
transfer modeling or atmospheric conditions and assess potential errors
within a consistent framework on climate-relevant scales. We build
benchmarking capability around a solar heating code (Solar-J) that
doubles as a photolysis code for chemistry and can be readily adapted to
consider other errors and uncertainties. The broad classes here include:
use of broad wavelength bands to integrate over spectral features;
scattering approximations that alter phase function and optical depths
for clouds and gases; uncertainty in ice-cloud optics; treatment of
fractional cloud cover including overlap; and variability of ocean
surface albedo. We geographically map the errors in W m-2 using a full
climate re-creation for January 2015 from a weather forecasting model.
For many approximations assessed here, mean errors are
~2 W m-2 with greater latitudinal biases and are likely
to affect a model’s ability to match the current climate state.
Combining this work with previous studies, we make priority
recommendations for fixing these simplifications based on both the
magnitude of error and the ease or computational cost of the fix.