Abstract
All Earth System Models (ESMs) have climatological biases relative to
the observed historical climate. The quality of a model and, more
importantly, the accuracy of its predictions are often associated with
the magnitude and properties of its biases. For more than a decade, new
strategies have been developed to empirically reduce such biases in the
model components of ESMs during their execution. The present study
considers a cyclostationary class of empirical runtime bias corrections
to a climate model, referred to here as ERBCs. Such ERBCs are state
independent and designed to reduce biases in the climatological annual
cycle of the model. We present a new procedure for deriving such ERBCs
called Climatological Adaptive Bias Correction (CABCOR). CABCOR is
argued to be superior to the standard relaxation approach to defining
ERBCs because it requires only a climatological, rather than a
multi-year time evolving, observational reference dataset. Additionally,
it is demonstrated that the CABCOR approach can produce bias-corrected
models with smaller climatological biases than the relaxation approach.
This is determined by performing a systematic analysis of the biases
produced by ERBCs derived with each approach.