Quantification of modelling uncertainties in a large ensemble of climate change simulations
By J. Murphy, D. Sexton, David N. Barnett, G. Jones, M. Webb, M. Collins, D. Stainforth. Nature, 2004
1,747 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/c17ad0f3224b43afadd3aaac7eb3be82efb503f3
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Citations in the corpus
- Using multiple observationally‐based constraints to estimate climate sensitivity (2006, background): “…change in response to a doubling of atmospheric CO2) is likely to lie in the range of 1.5–4.5 C was originally proposed in 1979 [National Academy of Sciences, 1979], and this estimate has essentially remained unchallenged ever since [e.g., Intergovernmental Panel on Climate Change, 2001].”
- Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, methodology): “…(1) the quality of the distributional data, as above; but also (2) maps of the bias and error in the predictions obtained with these models (Murphy et al., 2004; Palmer et al., 2005); (3) maps of the variation in the predictive power of each predictor across space obtained using, for…”
- Intellectual property rights and low carbon technology transfer: Conflicting discourses of diffusion and development (2010, background): “They recognise the role that new technologies and technological change within existing industries can play in improving competitive advantage through increased factor productivity and/or the development of new, competitive products ( Murphy et al. 2004 ) and broadening the industrial base of a count”
- On Constraining Estimates of Climate Sensitivity with Present-Day Observations through Model Weighting (2011, methodology): “1 2011 American Meteorological Society The distribution might also be narrowed by invoking Bayes’ theorem and weighting each prediction of climate sensitivity by the likelihood of the corresponding model (Murphy et al. 2004; Stainforth et al. 2005; Knutti et al. 2010).”
- Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate (2009, methodology): “Two models were considered, based on the probability distributed moisture (PDM) model ( Moore 1985 ) and both developed for spatial generalisation and use on ungauged catchments (Young 2002).”
- Stochastic climate theory and modeling (2015, neutral): “The models can differ in the dynamical core and the physical parameterizations 55,63,75 or use the same dynamical core but utilize either different static parameters in their physical parameterizations 151 or altogether different physics packages 6,31,106,152 .”
- 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).”