Cluster‐based evaluation of model compensating errors: A case study of cloud radiative effect in the Southern Ocean

Geophysical Research Letters (2019)

This paper defines new metrics, using clusters generated from a machine learning algorithm, to estimate mean and compensating errors in different model runs.

As a test case, we investigate the Southern Ocean shortwave radiative bias using clusters derived by applying self‐organizing maps to satellite data. In particular, the effects of changing cloud phase parameterizations in the MetOffice Unified Model are examined.

Differences in cluster properties show that the regional radiative biases are substantially different than the global bias, with two distinct regions identified within the Southern Ocean, each with a different signed bias.