Quantifying the uncertainty in forecasts of anthropogenic climate change

By Myles Allen, P. Stott, J. Mitchell, R. Schnur, T. Delworth. Nature, 2000

652 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8cd409025610a602e1e56b10e66c9f054f1c8917

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In Global and Planetary Change

Citations in the corpus

Quantification of modelling uncertainties in a large ensemble of climate change simulations (2004, methodology): “Our ensemble produces a range of regional changes much wider than indicated by traditional methods based on scaling the response patterns of an individual simulatio”
Uncertainty Analysis of Climate Change and Policy Response (2003, neutral): “Other studies have used one or two representative scenarios to calculate future uncertainty ( Allen et al .”
Bayesian Modeling of Uncertainty in Ensembles of Climate Models (2009, methodology): “On the other hand, Stott and Kettleborough (2002) applied the same method as Allen et al. (2000) to four SRES scenarios up to 2050 and, after taking uncertainties of the individual projections into account, found little discrepancy among the projections associated with different scenarios.”
Does climate policy need probabilities (2003, methodology): “…uncertainty is the method of constraining certain climate parameters by using recent observed changes in the climate system (Tol and de Vos, 1998; Allen et al., 2000; Forest et al., 2000; Andronova and Schlesinger, 2001; Forest et al., 2001; Forest et al., 2002; Gregory et al., 2002; Knutti et…”
Challenges in Combining Projections from Multiple Climate Models (2010, methodology): “In contrast, the probabilistic method by Watterson (2008) and studies based on detection and attribution fingerprint scaling (Allen et al. 2000; Stott and Kettleborough 2002; Stott et al. 2006) assume no improvement with additional models, which is equivalent to the assumption that each model is a…”
The Geostationary Earth Radiation Budget project (2005, background): “Ascribing these known changes to specific mechanisms is, however, a very challenging problem, for example, see Allen et al. (2000).”
Model and parameter uncertainty in IDF relationships under climate change (2015, methodology): “[2] applied Bayesian models to multimodel ensembles to characterize uncertainty, and obtained pdf of temperature at regional scales for future climate.”
Model‐based uncertainty in species range prediction (2006, background): “…from potential inaccuracies in the available species distribution data sets (Griffiths et al., 1999; Soberó n & Peterson, 2004) and, perhaps most fundamentally in the case of climate change applications, uncertainties are inherent in predicted scenarios of future climate (Allen et al., 2000).”
Machine learning in geosciences and remote sensing (2016, background): “Estimating the abundance of airborne particulate matter is a critical yet challenging task for both retrospective study and forecasting of air quality (Grell et al., 2005; Chuang et al., 2012), visibility and climate change (Hansen et al., 1988; Allen et al., 2000).”
Climate Change and Biodiversity: Some Considerations in Forecasting Shifts in Species' Potential Distributions (2005, background): “In recent years, research efforts have focused on quantifying uncertainties among models, with the aim of producing more reliable estimations (Allen et al. 2000; Murphy et al. 2004).”
Multivariate Bayesian analysis of atmosphere–ocean general circulation models (2007, background): “Recent work in determining probabilities of global temperature change includes Allen et al. (2000), Schneider (2001), Wigley and Raper (2001), Forest et al. (2002), Gregory et al. (2002) Knutti et al. (2002, 2003), Frame et al. (2005). Tebaldi et al.”