Bayesian estimation of local signal and noise in multimodel simulations of climate change

By Qingyun Duan, T. Phillips. 2010

141 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/1bc6b60409e670a47d1232d11c03cb0862f086f3

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. Xiaotong Zhang
2. Zhiqiang Xiao
3. N. Burgess
4. R. A. Garcia
5. Ming Pan
6. M. Cabeza
7. Shunlin Liang
8. Qingyun Duan

In Global and Planetary Change

1. C. Rahbek
2. M. Cabeza
3. R. A. Garcia
4. N. Burgess
5. Miguel B. Araújo
6. Xiaotong Zhang
7. Qiaohong Sun
8. Joshua B. Fisher

Citations in the corpus

• Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. (2017, methodology): “Further details of the EM algorithm and the BMA method can be found in Duan and Phillips (2010).”
• Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, background): “These metrics were spatially aggregated point-wise measures of regional deviations (Duan & Phillips, 2010).”
• Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau (2017, methodology): “For example, simple model averaging (SMA) approach outperformed all individual models for estimating climatic and hydrologic variables (Duan & Phillips, 2010; Raftery et al., 2005; Wu et al., 2012).”
• Shifts in plant distributions in response to climate warming in a biodiversity hotspot, the Hengduan Mountains (2018, methodology): “…models to represent the LGM and future climate conditions because the average from multiple models correlated better with the observed data than did those from any single one, especially in the case of the QTPsl (Duan & Phillips, 2010; Hu, Jiang, & Fan, 2014; Su, Duan, Chen, Hao, & Cuo, 2013).”
• A nonstationary bias‐correction technique to remove bias in GCM simulations (2016, background): “…reacts [Kiktev et al., 2003], simplified assumptions in model construction [Phillips and Gleckler, 2006], uncertainties in model parameterization [Duan and Phillips, 2010], and unrealistic estimations of natural forcing [Li et al., 2007], GCM outputs are subject to bias in their representation…”