The quasi-biennial oscillation in a warmer climate: sensitivity to different gravity wave parameterizations

In order to simulate the quasi-biennial oscillation (QBO) with a realistic period and amplitude, general circulation models commonly include parameterizations of small scale gravity waves (GW). In this work, we explore how different GW parameterization setups determine the response of QBO properties to a warmer climate. Atmosphere-only experiments in both present day and warmer climate serve as testbed to analyze the effect of four different GW parameterization setups, active in the tropics. Having tuned the GW parameterizations to produce a realistic QBO in present day climate, we analyze changes of QBO properties in the warmer climate. The QBO period decreases in two parameterization setups by similar to 30 %, while the QBO period remains unchanged in the remaining two parameterization setups. In all parameterization setups, the QBO amplitude in the warmer climate weakens below 10 hPa but shows different behaviour above 10 hPa. We show that changes in QBO amplitude and changes in QBO period are inconsistent among experiments. In the chosen experimental design, the inconsistent future change in QBO properties among the suite of experiments depends solely on the choice of the GW parameterization setup

Parameter estimation using data assimilation in an atmospheric general circulation model: Parameter estimation using data assimilation in an atmosphericgeneral circulation model: from a perfect toward the real world

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

Parameter estimation using data assimilation in an atmospheric general circulation model: Parameter estimation using data assimilation in an atmosphericgeneral circulation model: from a perfect toward the real world

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

Parameter estimation using data assimilation in an atmospheric general circulation model: Parameter estimation using data assimilation in an atmosphericgeneral circulation model: from a perfect toward the real world

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