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

Li, S.; Zhang, S.; Liu, Z.; Lu, L.; Zhu, J.; Zhang, X.; Wu, X.; Zhao, M.; Vecchi, G. A.; Zhang, R. H.; Lin, X.

doi:10.1002/2017ms001222

Back to Previous Page

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

2018

By Li, S. ; Zhang, S. ; Liu, Z. ; ...

Source: Journal of Advances in Modeling Systems 10(4), 989-1010, 2018

[PDF-3.57 MB]

Details Related Documents You May Also Like

Personal Author:

Li, S. ; Zhang, S. ; Liu, Z. ; Lu, L. ; Zhu, J. ; ... More +

NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; GFDL (Geophysical Fluid Dynamics Laboratory) ;

Description:

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.

Keywords:

[+]

Source:

Journal of Advances in Modeling Systems 10(4), 989-1010, 2018

DOI:

https://doi.org/10.1002/2017ms001222

Document Type:

Journal Article ;

Rights Information:

CC BY-NC-ND

Main Document Checksum:

[+]

File Type:

No Additional Files

More +

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

Details:

Supporting Files

Related Documents

You May Also Like