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Article
Article Type:
Research Article

Nonlinear Ensemble Parameter Perturbation for Climate Models

Xudong Yin State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China

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Juanjuan Liu State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Bin Wang State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, and Center for Earth System Science, Tsinghua University, Beijing, China

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Print Publication:
01 Feb 2015
DOI:
https://doi.org/10.1175/JCLI-D-14-00244.1
Page(s):
1112–1125
Restricted access

Abstract

Model parameters can introduce significant uncertainties in climate simulations. Sensitivity analysis provides a way to quantify such uncertainties. Existing sensitivity analysis methods, however, cannot estimate the maximum sensitivity of the simulated climate to perturbations in multiple parameters. This study proposes the concept of nonlinear ensemble parameter perturbation (NEPP), which is independent of model initial conditions, to estimate the maximum effect of parameter perturbations on simulating Earth’s climate. The NEPP is obtained by solving a maximization problem, whose cost function is defined by the maximum deviation of a unique ensemble of short-term predictions with large enough members caused by parameter perturbations and whose optimal solution is obtained by an ensemble-based gradient approach. This method is used to investigate the effects of NEPP on the climate of the Lorenz-63 model and a complex climate model, the Grid-Point Atmospheric Model of IAP LASG, version 2 (GAMIL2). It is found that the NEPP is capable of estimating the maximum change in climate simulation caused by perturbations in multiple parameters when the Lorenz-63 model is used. With a low computational cost, the NEPP can cause remarkable changes in the climatology of GAMIL2. The results also illustrate that the effects of parameter perturbations on short-term weather predictions and those on long-term climate simulations are correlated.

Corresponding author address: Juanjuan Liu, Institute of Atmospheric Physics, Chinese Academy of Sciences, P.O. Box 9804, Beijing 100029, China. E-mail: ljjxgg@mail.iap.ac.cn

Abstract

Model parameters can introduce significant uncertainties in climate simulations. Sensitivity analysis provides a way to quantify such uncertainties. Existing sensitivity analysis methods, however, cannot estimate the maximum sensitivity of the simulated climate to perturbations in multiple parameters. This study proposes the concept of nonlinear ensemble parameter perturbation (NEPP), which is independent of model initial conditions, to estimate the maximum effect of parameter perturbations on simulating Earth’s climate. The NEPP is obtained by solving a maximization problem, whose cost function is defined by the maximum deviation of a unique ensemble of short-term predictions with large enough members caused by parameter perturbations and whose optimal solution is obtained by an ensemble-based gradient approach. This method is used to investigate the effects of NEPP on the climate of the Lorenz-63 model and a complex climate model, the Grid-Point Atmospheric Model of IAP LASG, version 2 (GAMIL2). It is found that the NEPP is capable of estimating the maximum change in climate simulation caused by perturbations in multiple parameters when the Lorenz-63 model is used. With a low computational cost, the NEPP can cause remarkable changes in the climatology of GAMIL2. The results also illustrate that the effects of parameter perturbations on short-term weather predictions and those on long-term climate simulations are correlated.

Corresponding author address: Juanjuan Liu, Institute of Atmospheric Physics, Chinese Academy of Sciences, P.O. Box 9804, Beijing 100029, China. E-mail: ljjxgg@mail.iap.ac.cn
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