A Probability and Decision-Model Analysis of a Multimodel Ensemble of Climate Change Simulations

By Jouni Räisänen, Tim N. Palmer. 2001

206 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/313785fe9f16ecbfc12a1fcc5803d9a111d32f15

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. C. Tebaldi
3. J. Annan
4. J. Hargreaves
5. P. Mujumdar
6. Jouni Raäisaänen
7. D. Nychka
8. Subimal Ghosh

In Global and Planetary Change

1. Reto Knutti
2. C. Tebaldi
3. P. Mujumdar
4. J. Annan
5. J. Hargreaves
6. Subimal Ghosh
7. Jouni Raäisaänen
8. D. Nychka

Citations in the corpus

• Bayesian estimation of local signal and noise in multimodel simulations of climate change (2010, unknown): “one‐vote’ scheme [Räisänen and Palmer, 2001; Santer et al., 2007].”
• Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles (2005, background): “Recent work (Raisanen and Palmer, 2001; Giorgi and Mearns, 2003) has addressed the need for probability forecasts, but offered only an assessment of the probability of exceeding thresholds, identified as the (weighted) fraction of the ensemble members for which the thresholds in question were exceed”
• An improvement of analog model strategy for more reliable local climate change scenarios (2005, background): “It is important to note that different RCMs=GCMs tend to give different results on a local and regional scale (Benestad, 2002; R€ais€anen, 2001a; R€ais€anen and Palmer, 2001; R€ais€anen 2001b), and it is therefore important not to focus on results from just one RCM=GCM GCM for making scenarios for…”
• Decision scaling: Linking bottom‐up vulnerability analysis with climate projections in the water sector (2012, background): “Probability is assigned according to the number of runs which fall into each climate state sector [Raisanen and Palmer, 2001].”
• The economic value of ensemble forecasts as a tool for risk assessment: From days to decades (2002, methodology): “Angström’s model can also be applied to the climate-change problem (Räisänen and Palmer 2001; Palmer and Räisänen 2002).”
• Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe (2013, methodology): “However, it must also be noted that the DEL & CF results tend to be overconfident in their projections of the number of hot days and the impact of high temperature stress on harvest index, and thus not ‘reliable’, which is not a desirable feature (Räisänen and Palmer, 2001).”
• Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment (2007, methodology): “For example, there have been systematic evaluations of uncertainties due to climate model projections using multimodel ensembles [ Raisanen and Palmer, 2001; Giorgi and Mearns, 2003]; multiensemble experiments with one GCM [Murphy et al., 2004].”
• Bayesian Modeling of Uncertainty in Ensembles of Climate Models (2009, methodology): “statistical methods, such as confidence intervals and hypothesis tests, should be applied to combine the results of different climate models, and their ideas have been applied in subsequent reports on climate change such as Wigley (1999), and a publicly available computer package (Hulme et al.”
• Does climate policy need probabilities (2003, methodology): “Both New and Hulme (2000) and Raisanen and Palmer (2001) used 'raw' GCM data (sometimes called 'unintelligent' or 'simple' downscaling since it only involves interpolation of the output) in their probabilistic studies whereas Benestad (2003) and Osborn et al. (2003) applied some downscaling…”
• The use of the multi-model ensemble in probabilistic climate projections (2007, methodology): “Note that the empirical distribution is what determines the results of the method by Räisänen & Palmer (2001) and Palmer & Räisänen (2002).”
• Reliability of the CMIP3 ensemble (2010, methodology): “[5] The paradigm of a statistical indistinguishable ensemble has been widely adopted in numerical weather prediction [e.g., Toth et al., 2003] but less commonly in climate change research [Räisänen and Palmer, 2001].”
• Climate Models: An Assessment of Strengths and Limitations (2008, background): “2001; de Elía, Laprise, and Denis 2002), although use of multiple sources of future climate may lessen this vulnerability and offer opportunity for probabilistic estimates of regional climate change (Raisanen and Palmer 2001; Giorgi and Mearns 2003; Tebaldi et al. 2005).”
• How reliable are climate models? (2007, methodology): “By applying a simple decision-making model, Räisänen and Palmer (2001) and Palmer and Räisänen (2002) showed that probabilistic climate change projections may have considerable value even for variables such as precipitation for which the agreement between different models is relatively low.”
• Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulations (2004, background): “Previous work [Raisanen and Palmer, 2001] treated the ensemble members as equiprobable realizations, and determined probabilities of climate change by computing the fraction of ensemble members in which change exceeded specific thresholds.”