A study of impacts of coupled model initial shocks and state-parameter optimization on climate predictions using a simple pycnocline prediction model

Abstract

A skillful decadal prediction that foretells varying regional climate conditions over seasonal-interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climateobserving system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a "twin" experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as "truth" and sampled to produce "observations" that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal-interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state-parameter optimization greatly enhances the model predictability. While valid "atmospheric" forecasts are extended 5 times, the decadal predictability of the "deep ocean" is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions. © 2011 American Meteorological Society.