The use of the multi-model ensemble in probabilistic climate projections
Top Cited Papers
- 14 June 2007
- journal article
- Published by The Royal Society in Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Vol. 365 (1857), 2053-2075
- https://doi.org/10.1098/rsta.2007.2076
Abstract
Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an ‘ensemble of opportunity’, are discussed in detail.Keywords
This publication has 60 references indexed in Scilit:
- Estimated PDFs of climate system properties including natural and anthropogenic forcingsGeophysical Research Letters, 2006
- Constraints on climate change from a multi‐thousand member ensemble of simulationsGeophysical Research Letters, 2005
- Probabilistic prediction of climate using multi-model ensembles: from basics to applicationsPhilosophical Transactions Of The Royal Society B-Biological Sciences, 2005
- Probabilistic climate change projections for CO2 stabilization profilesGeophysical Research Letters, 2005
- Constraining climate forecasts: The role of prior assumptionsGeophysical Research Letters, 2005
- Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulationsGeophysical Research Letters, 2004
- Probability of regional climate change based on the Reliability Ensemble Averaging (REA) methodGeophysical Research Letters, 2003
- Detecting anthropogenic influence with a multi‐model ensembleGeophysical Research Letters, 2002
- Objective estimation of the probability density function for climate sensitivityJournal of Geophysical Research: Atmospheres, 2001
- Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statisticsJournal of Geophysical Research: Oceans, 1994