English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Over the last few decades, climate scientists have devoted much effort to thedevelopment of large numerical models of the atmosphere and the ocean. Whilethere is no question that such models provide important and useful informationon complicated aspects of atmosphere and ocean dynamics, skillful predictionalso requires a phenomenological approach, particularly for very slowprocesses, such as glacial-interglacial cycles. Phenomenological models areoften represented as low-order dynamical systems. These are tractable, and arich source of insights about climate dynamics, but they also ignore largebodies of information on the climate system, and their parameters are generallynot operationally defined. Consequently, if they are to be used to predictactual climate system behaviour, then we must take very careful account of theuncertainty introduced by their limitations. In this paper we consider theproblem of the timing of the next glacial inception, about which there ison-going debate. Our model is the three-dimensional stochastic system ofSaltzman and Maasch (1991), and our inference takes place within a Bayesianframework that allows both for the limitations of the model as a description ofthe propagation of the climate state vector, and for parametric uncertainty.Our inference takes the form of a data assimilation with unknown staticparameters, which we perform with a variant on a Sequential Monte Carlotechnique (`particle filter'). Provisional results indicate peak glacialconditions in 60,000 years.

On the use of simple dynamical systems for climate predictions: A Bayesian prediction of the next glacial inception