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Abstract:
This study explores the viability of parameter estimation in the comprehensive
general circulation model ECHAM6 using ensemble Kalman filter data assimilation
techniques. Four closure parameters of the cumulus-convection scheme are estimated
using increasingly less idealized scenarios ranging from perfect-model experiments to
the assimilation of conventional observations. Updated parameter values from
experiments with real observations are used to assess the error of the model state on
short 6 h forecasts and on climatological timescales. All parameters converge to their
default values in single parameter perfect-model experiments. Estimating parameters
simultaneously has a neutral effect on the success of the parameter estimation, but
applying an imperfect model deteriorates the assimilation performance. With real
observations, single parameter estimation generates the default parameter value in one
case, converges to different parameter values in two cases, and diverges in the fourth
case. The implementation of the two converging parameters influences the model
state: Although the estimated parameter values lead to an overall error reduction on
short timescales, the error of the model state increases on climatological timescales.
