Uncertainty in predictions of the climate response to rising levels of greenhouse gases

By D. Stainforth, T. Aina, C. Christensen, M. Collins, N. Faull, D. Frame, J. Kettleborough, Simon Knight, Andrew J. Martin, J. Murphy and 6 more. Nature, 2005

1,232 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/7f68f20776306551223425ed48c96c36a78ae0fe

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. M. Webb
2. Reto Knutti
3. Sandrine Bony
4. M. Zelinka
5. Stephen A. Klein
6. J. Dufresne
7. D. Hartmann
8. Brian J. Soden

In Global and Planetary Change

1. Reto Knutti
2. M. Webb
3. Sandrine Bony
4. M. Zelinka
5. Stephen A. Klein
6. J. Dufresne
7. D. Hartmann
8. C. Tebaldi

Citations in the corpus

• Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model (2011, background): “…include perturbing the initial conditions, perturbing the input parameters of the model, ensemble simulations with multiple parameterization schemes, or ensemble simulations with multiple models, and so on (Allen et al., 2000; Giorgi and Mearns, 2002; Stainforth et al., 2005; Lopez et al., 2006).”
• Feedbacks, Timescales, and Seeing Red (2009, neutral): “This shape for the envelope of uncertainty of climate sensitivity has been found in many studies (e.g., Gregory et al. 2002, Forest et al. 2002, Stainforth et al. 2005, Roe & Baker 2007) and can be understood as a direct consequence of Equation 11.”
• Assessing uncertainty and complexity in regional-scale crop model simulations (2017, methodology): “Parameter uncertainty, similar to other types of environmental modelling approaches (Beven, 2006; Stainforth et al., 2005), was found to be an important aspect of groundnut growth prediction.”
• Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, methodology): “Generating model simulations with multiple sets of parameters generates (Stainforth et al., 2005) a large number of simulations from a single GCM but requires enormous computational resources (Stainforth et al., 2002, 2005).”
• Spatial Analysis to Quantify Numerical Model Bias and Dependence (2008, background): “Another reason is that, as reported by Shao, Stein, and Ching (2007), bilinear interpolation seems to work better than naive interpolation in aggregating numerical model outputs in general.”
• The use of the multi-model ensemble in probabilistic climate projections (2007, methodology): “Keywords: regional climate change; probabilistic projections; multi-model ensembles; global climate models; structural uncertainty; performance-based weighting On of c *A”
• Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. (2009, methodology): “Uncertainty in model structure can be assessed by using more than one model (Randall et al., 2007) or by varying model parameters (Murphy et al., 2004; Stainforth et al., 2005).”
• Climate model genealogy (2011, background): “For some aspects, a large PPE can span a “model space” similar or larger than CMIP3, e.g., for the range of feedbacks and climate sensitivity [Sanderson et al., 2010; Collins et al., 2010; Stainforth et al., 2005].”