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Abstract
An important source of uncertainty, which causes further uncertainty in numerical simulations, is that residing in the parameters describing physical processes in numerical models. Therefore, finding a subset among numerous physical parameters in numerical models in the atmospheric and oceanic sciences, which are relatively more sensitive and important parameters, and reducing the errors in the physical parameters in this subset would be a far more efficient way to reduce the uncertainties involved in simulations. In this context, we present a new approach based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) method. The approach provides a framework to ascertain the subset of those relatively more sensitive and important parameters among the physical parameters. The Lund–Potsdam–Jena (LPJ) dynamical global vegetation model was utilized to test the validity of the new approach in China. The results imply that nonlinear interactions among parameters play a key role in the identification of sensitive parameters in arid and semi-arid regions of China compared to those in northern, northeastern, and southern China. The uncertainties in the numerical simulations were reduced considerably by reducing the errors of the subset of relatively more sensitive and important parameters. The results demonstrate that our approach not only offers a new route to identify relatively more sensitive and important physical parameters but also that it is viable to then apply “target observations” to reduce the uncertainties in model parameters.
Highlights
The predictability and uncertainty of weather and climate are hot topics in atmospheric and oceanic sciences
The results suggest that the uncertainties in the simulations of the carbon cycle due to parameter errors can be reduced through conditional nonlinear optimal perturbation related to parameter (CNOP-P)-type identification of the most sensitive and important parameters combination
The uncertainties in physical parameters of numerical models are the main source of numerical simulation ability and forecast skill, such as numerical simulation for land process (Kuczera and Parent 1998; Vrugt et al 2003)
Summary
The predictability and uncertainty of weather and climate are hot topics in atmospheric and oceanic sciences. Reducing the uncertainties in physical parameter errors in numerical models through observations, optimization methods, or data assimilation is a crucial area of research Efforts in this area will help to improve the simulation ability and forecasting skill of such models in atmospheric and oceanic science studies (Lu and Hsieh 1997; Janiskova and Morcrette 2005; Pulido et al 2012; Smith et al 2013). Zaehle et al (2005) applied a Monte Carlo-type stratified sampling approach to identify the sensitive and important physical parameters in a model and provide reasonable estimations of a model output variable. They used Latin hypercube sampling method to create random sample of parameter value.
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