Selecting global climate models for regional climate change studies

By David W. Pierce, Tim Barnett, Benjamin D. Santer, P. Gleckler. Proceedings of the National Academy of Sciences of the United States of America, 2009

635 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/1fc86837a087298dbdf2abdaa4a4a2adea0413be

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. Reto Knutti
2. Vipin Kumar
3. Gerald A. Meehl
4. C. Tebaldi
5. Reinhard Furrer
6. Miguel B. Araújo
7. N. Burgess
8. C. Rahbek

In Ecology, Evolution, Behavior and Systematics

1. Miguel B. Araújo
2. Richard G. Pearson
3. C. Rahbek
4. Jane Elith
5. M. Cabeza
6. R. A. Garcia
7. N. Burgess
8. T. Auld

In Global and Planetary Change

1. Reto Knutti
2. C. Rahbek
3. Miguel B. Araújo
4. Jane Elith
5. Vipin Kumar
6. C. Tebaldi
7. M. Cabeza
8. Gerald A. Meehl

Citations in the corpus

• Is Cassava the Answer to African Climate Change Adaptation? (2012, background): “5°C, indicating relatively strong disagreement in GCM signals even for temperature, an issue already reported in the literature (Majda and Gershgorin 2010; Matsueda and Palmer 2011; Neelin et al. 2006; Pierce et al. 2009).”
• Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? (2012, background): “Although different skill ranking approaches might result in a different candidate set of skilful models, if the ensemble average is based on more than five GCMs, model choice has a reduced effect on the average forecast (Pierce et al., 2009).”
• Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, background): “It is important to evaluate the ability of models to simulate historical climate patterns (Pierce et al., 2009), but that alone may not be sufficient for climate models in view of non-stationarity and long lead time projections.”
• Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, background): “In climatology, multi-model ensemble averages have often been shown to improve the outcome of climate simulations (Phillips & Gleckler, 2006; Tebaldi & Knutti, 2007; Gleckler et al. , 2008; Reichler & Kim, 2008; Pierce et al. , 2009; Knutti et al. , 2010; but see Fordham et al. , in press ).”
• Challenges in Combining Projections from Multiple Climate Models (2010, background): “…with observations of presentday climate than any single model, and that the average also consistently scores high in almost all diagnostics (Lambert and Boer 2001; Phillips and Gleckler 2006; Randall et al. 2007; Gleckler et al. 2008; Pincus et al. 2008; Reichler and Kim 2008; Pierce et al. 2009).”
• Skill of CMIP5 climate models in reproducing 20th century basic climate features in Central America (2015, methodology): “…composed of three squared terms, and therefore SS can also be expressed as: SS = RHO − CBIAS − UBIAS (2) RHO is the square of the spatial correlation between the observed and modeled patterns, and CBIAS and UBIAS are the Conditional and Unconditional Biases respectively (see Pierce et al., 2009).”