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David A. Stainforth

Researcher at London School of Economics and Political Science

Publications -  82
Citations -  7183

David A. Stainforth is an academic researcher from London School of Economics and Political Science. The author has contributed to research in topics: Climate change & Climate model. The author has an hindex of 28, co-authored 74 publications receiving 6446 citations. Previous affiliations of David A. Stainforth include University of Oxford & University of Twente.

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Quantification of modelling uncertainties in a large ensemble of climate change simulations

TL;DR: A systematic attempt to determine the range of climate changes consistent with these uncertainties, based on a 53-member ensemble of model versions constructed by varying model parameters, which produces a range of regional changes much wider than indicated by traditional methods based on scaling the response patterns of an individual simulation.
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Uncertainty in predictions of the climate response to rising levels of greenhouse gases.

TL;DR: Results from the ‘climateprediction.net’ experiment are presented, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details, finding model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11’K.
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Confidence, uncertainty and decision-support relevance in climate predictions.

TL;DR: A reassessment of the role of complex climate models as predictive tools on decadal and longer time scales is argued for and a reconsideration of strategies for model development and experimental design is considered.
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The Development of a Free-Surface Bryan–Cox–Semtner Ocean Model

TL;DR: In this paper, a version of the Bryan-Cox-Semtner numerical ocean general circulation model, adapted to include a free surface, is described, which is designed for the following uses: tidal studies (a tidal option is explicitly included), assimilation of altimetric data (since the surface elevation is now a prognostic variable); and in situations where accurate relaxation to obtain the streamfunction in the original model is too time consuming.