Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation

Abstract

Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction.

Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

“…Most of these studies, however, have been performed in the perfect model scenario (Aksoy et al 2006a;Tong and Xue 2008a,b;Wu et al 2012;Zhang et al 2012). These studies show that model parameters can indeed converge toward the truth values, even in coupled general circulation models (CGCM; Liu et al 2014;Li et al 2018). With improved parameters, model bias can be reduced (Tong and Xue 2008a,b) and the prediction skill can be improved (Wu et al 2012;Zhang et al 2012).…”

Improving MJO simulation by enhancing the interaction between boundary layer convergence and lower tropospheric heating

“…However, the Tokioka constraint may cause large changes in the mean states of precipitation and associated circulations (Kim et al 2011), particularly in coupled climate models. The Tokioka constraint also alters the global energy balance as well as the global climate variability including the ENSO (Li et al 2018). Moreover, the Tokioka constraint may over-suppress deep or middle clouds in the convective scheme with a single updraft since a fixed criterion for all cloud types was used.…”

Empirical values and assumptions in the convection of numerical models

“…Hence, empirical values and assumptions selected this way might yield good results when compared to observations from certain locations and less good results for others. Commonly, manual tuning of convective parameters is used, although various automatic methods have recently been used to estimate parameters, including the variational method (Emanuel and Živković-Rothman, 1999), Bayesian calibration (Hararuk et al, 2014;Wu et al, 2018), simulated annealing method (Jackson et al, 2004(Jackson et al, , 2008Liang et al, 2014), genetic algorithm (Lee et al, 2006), or ensemble data assimilation (Ruiz et al, 2013;Li et al, 2018), among others.…”