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SUMMARY:Interpreting multimodel ensembles - Prof Richard Chandler\, UCL
DTSTART:20250513T100000Z
DTEND:20250513T110000Z
UID:TALK231847@talks.cam.ac.uk
CONTACT:Yao Ge
DESCRIPTION:Abstract: \nEnsembles of simulations from multiple climate mod
 els (‘simulators’) underpin much of our understanding of the climate s
 ystem\, and in particular the potential evolution of future climate in res
 ponse to different scenarios of socioeconomic development and the associat
 ed greenhouse gas emissions. No simulator is perfect\, however\; and ensem
 ble outputs contain structured variation reflecting simulator inter-relati
 onships\, as well as shared discrepancies between the simulators and the r
 eal climate system. This structure must be accounted for when using ensemb
 les to learn about aspects of the real climate\, especially when defensibl
 e assessments of uncertainty are needed to support decision-making. This t
 alk will discuss the issues involved\, and describe a statistical framewor
 k for addressing the problem. A theoretical analysis leads to a mathematic
 al result with major implications for the design and analysis of multimode
 l ensembles\; whilst the practical application of the framework will be de
 monstrated using future climate projections for the United Kingdom from tw
 o contrasting ensembles (UKCP18 and EuroCORDEX). These ensembles have diff
 erent structures and properties: the approach is shown to reconcile the su
 bstantial differences between the original ensemble outputs\, in terms of 
 both the real-world climate of the future and the associated uncertainties
 .\n\nBiography: \nRichard is a Professor in the Department of Statistical 
 Science at University College London\, where he has worked since completin
 g his PhD at UMIST in 1994. He has extensive experience of developing and 
 applying statistical methods for the environmental sciences. Particular in
 terests include the analysis of time series and space-time data\, with app
 lication areas including hydrology and the impacts of climate change. Othe
 r areas of interest include the assessment of uncertainty when interpretin
 g model outputs\; the use of mis-specified models\; and the use of nonprob
 ability samples to draw population inferences in ecology.
LOCATION:Chemistry Dept\, Unilever Lecture Theatre and Teams
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