Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles

By C. Tebaldi, Richard L. Smith, D. Nychka, L. Mearns. 2005

690 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ab2c828554a3ed004d3ed75f08352ebbc3b2061b

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Citations in the corpus

How reliable are climate models? (2007, background): “For many other variables such as precipitation (e.g. Groisman and Easterling, 1994) and temperature in the free atmosphere (Thorne et al., 2005), difficulties associated with changes in instrumentation and in the interpretation of the measurements make uncertainties in observed changes even larger.”
Calculation of probability density functions for temperature and precipitation change under global warming (2008, methodology): “Recently, other researchers have used Bayesian methods based on the assumption that it is the model results that are a sample from a PDF and, in the case of Tebaldi et al. [2005] and Furrer et al. [2007] at least, it is assumed that this is unbiased: The mean of the PDF is the unknown real-world c”
When Climate Models Agree: The Significance of Robust Model Predictions* (2011, neutral): “These studies involve running each of several climate models (or model versions) with the same (or similar) initial conditions and under the same (or similar) emission scenarios (see, e.g., Stainforth et al. 2005; Tebaldi et al. 2005; Murphy et al. 2007).”
Climate warming, water storage, and Chinook salmon in California’s Sacramento Valley (2008, methodology): “Warming experiments like DT2 and DT4 are consistent with current projections of future warming, noting that projections of precipitation change are much more uncertain (Maurer 2007; Tebaldi et al. 2005).”
Climate model genealogy (2011, background): “characterized by some measure of spread across an ensemble of simulations [Furrer et al., 2007; Tebaldi et al., 2005; Tebaldi and Knutti, 2007].”
The end of model democracy? (2010, background): “Some studies have recently proposed to down-weight or eliminate some ‘bad’ climate models, recalibrate projections or estimate uncertainties based on metrics of skill (e.g. Boe et al. 2009; Eyring et al. 2005; Giorgi and Mearns 2003; Perkins and Pitman 2009; Perkins et al. 2009; Schmittner et al. 20”
The use of the multi-model ensemble in probabilistic climate projections (2007, methodology): “The regional nature of the analyses by Giorgi & Mearns (2002, 2003), Tebaldi et al. (2004, 2005) and Smith et al. (submitted) was adopted by Greene et al. (2006).”
Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west (2010, methodology): “This approach combines predictions from individual AOGCMs into a single probabilistic climate projection for a region of interest using the criteria of bias and convergence [34].”
Bayesian estimation of local signal and noise in multimodel simulations of climate change (2010, methodology): “In cases where significant intervariable correlation exists, different approaches would be called for [Tebaldi et al., 2005].”
The role of uncertainty in climate change adaptation strategies—A Danish water management example (2013, background): “…(GCM) and regional climate models (RCM) are typically assessed by ensemble modelling, where multiple climate models with different process equations are used for making probabilistic projections (e.g. Tebaldi et al. 2005; Smith et al. 2009; Christensen et al. 2007a, 2010; Déqué and Somot 2010).”
Confidence, uncertainty and decision-support relevance in climate predictions (2007, methodology): “Can we rule-out or weight the models in such ensembles? Much effort is currently focused on assigning models’ weights based, to some extent, on their ability to reproduce recent climate (Murphy et al. 2004; Tebaldi et al. 2005; UKCIP08 2006).”
Holism, entrenchment, and the future of climate model pluralism (2010, background): “Out of this motivation have grown various sophisticated attempts to apply Bayesian probability approaches to the plurality of model (‘expert’) voices (Tebaldi et al., 2005).”
Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model (2011, background): “UQ of predicted future climate is usually based on the ability of models to produce the current climate (Allen et al., 2000; Tebaldi et al., 2005).”
Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment (2007, methodology): “Bayesian methods have been applied to multimodel ensembles to characterize uncertainty and probability distribution functions (PDFs) for future climate changes at regional scales [Tebaldi et al., 2004, 2005].”
Weighting of model results for improving best estimates of climate change (2010, methodology): “Examples include the reliability ensemble averaging (REA) method of Giorgi and Mearns (2002), the climate prediction index of Murphy et al. (2004), the M statistics weighting used by Watterson (2008) and several approaches based on Bayesian statistics (e.g., Tebaldi et al. 2005; Greene et al. 2006”
Multivariate Bayesian analysis of atmosphere–ocean general circulation models (2007, methodology): “Tebaldi et al. (2005) evaluates probabilistic climate change on a regional level.”
Bayesian Modeling of Uncertainty in Ensembles of Climate Models (2009, methodology): “Based on that comparison, we believe that there are robustness advantages to either of the approaches of the present article, compared with that of Tebaldi et al. (2005).”