Representing uncertainty in climate change scenarios: a Monte-Carlo approach
214 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/c4528c0c82e425b4eb219e2571bb3ef9e6f8af54
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In Economics and Econometrics
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Citations in the corpus
- A Probability and Decision-Model Analysis of a Multimodel Ensemble of Climate Change Simulations (2001, background): “Ensembles describing future climate change should ideally take into account all these sources of uncertainty (New and Hulme 2000).”
- Model and parameter uncertainty in IDF relationships under climate change (2015, background): “New and Hulme [34] presented uncertainty in climate change scenarios by developing a hierarchical model addressing uncertainty in future greenhouse gas emissions, climate sensitivity and unpredictability in GCMs for seasonal mean temperature and precipitation over the United Kingdom.”
- Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment (2007, methodology): “[4] Climate change impact assessment models developed based on GCM output are subjected to a range of uncertainties due to both ‘‘incomplete knowledge’’ and ‘‘unknowable future scenario’’ [Hulme and Carter, 1999; New and Hulme, 2000].”
- Estimating the social cost of non-CO2 GHG emissions: Methane and nitrous oxide (2012, methodology): “The MAGICC climate model has been extensively used in the Intergovernmental Panel on Climate Change (IPCC) reports (e.g. Houghton et al., 1992, Houghton et al., 2001) and other published research (e.g. Hulme and Viner, 1998, New and Hulme, 2000, Wigley and Raper, 2002, Wigley et al., 2009).”
- Does climate adaptation policy need probabilities? (2004, methodology): “Perhaps the most ambitious approach is provided by Jones (2000a), who used a similar approach to New and Hulme (2000) , but extended this to numerous impact models using critical impact thresholds.”
- 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…”