Testing and improving ENSO models by process using transfer functions

Abstract

Some key elements of ENSO are not consistently well captured in GCMs. However, modifying the wrong parameters may lead to the right result for the wrong reason. We introduce "transfer functions" to quantify the input/ output relationship of individual processes from model output, to compare them to the corresponding observed processes. Two key transfer functions are calculated: first, the relationship between western Pacific Rossby waves and the reflecting Kelvin waves; second, the frequency-dependent relation between Kelvin waves traveling toward the eastern boundary and sea surface temperature response. These are estimated for TAO array data, the Cane-Zebiak model, and the GFDL CM2.1 coupled GCM. Some feedbacks are found to be biased in both models. Re-tuning parameters to fit observed transfer functions leads to a deteriorated solution, implying that compensating errors lead to the seemingly accurate simulation. This approach should be broadly useful in making climate model improvement more systematic and observation-driven.

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