Probabilistic climate change predictions applying Bayesian model averaging

By S. Min, Daniel Simonis, Andreas N. Hense. 2007

91 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Xiaohong Liu
2. Y. Qian
3. G. Lin
4. Ben Yang
5. R. Leung
6. Yuehan Zhang
7. Minghuai Wang
8. Sally McFarlane

In Global and Planetary Change

1. Xiaohong Liu
2. Y. Qian
3. G. Lin
4. Ben Yang
5. R. Leung
6. Yuehan Zhang
7. Minghuai Wang
8. Sally McFarlane

Citations in the corpus

• Error Reduction and Convergence in Climate Prediction (2008, background): “Models may have compensating errors, where errors in one parameterization compensate for errors in other parameterizations to produce a realistic climate simulation (Wang 2007; Golaz et al. 2007; Min et al. 2007; Murphy et al. 2007).”
• Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model (2011, background): “Larger number or ranges of input parameters usually result in higher uncertainties in climate simulations because of nonlinear interactions and compensating errors of parameters (Gilmore et al., 2004; Molders, 2005; Min et al., 2007; Murphy et al., 2007).”
• Uncertainty quantification and parameter tuning in the CAM5 Zhang‐McFarlane convection scheme and impact of improved convection on the global circulation and climate (2013, background): “…current situations may break down as the climate evolves or because compensating errors that exist among the parameterization schemes for different physical processes no longer cancel out in the future [Gilmore et al., 2004;Molders, 2005;Golaz et al., 2007; Min et al., 2007; Murphy et al., 2007].”