Uncertainty in weather and climate prediction
By J. Slingo, Tim N. Palmer. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2011
375 citations recorded by the source corpus.
Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/6b7e7a5328fe82110e9f40e15e0ce6aff749c660
How researchers see this paper
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In Atmospheric Science
- Michael Ghil
- Valerio Lucarini
- A. Thorpe
- Gilbert Brunet
- É. Mémin
- Peter Bauer
- Alan J. Geer
- John F. Symons
In Global and Planetary Change
- Valerio Lucarini
- Michael Ghil
- A. Thorpe
- Gilbert Brunet
- É. Mémin
- Peter Bauer
- Alan J. Geer
- John F. Symons
Citations in the corpus
- The physics of climate variability and climate change (2019, background): “e and negative Lyapunov exponents, until nonlinear eects become important and the set of initial states propagated by the system’s vector eld populates its attractor. Reproduced with permission from Slingo and Palmer (2011). (b) Evolution in time of such an initial ball, in a generic chaotic system.”
- Fluid flow dynamics under location uncertainty (2013, background): “The modeling and the handling along time of such uncertainties are crucial, for instance, for ensemble forecasting issues in meteorology and oceanography, where ensembles of runs are generated through randomization of the dynamics’parameters, accompanied eventually by stochastic forcing that mimics ”
- The quiet revolution of numerical weather prediction (2015, background): “The recognition of imperfect forecasts 24 and determining how to calculate analysis and forecast uncertainty using an ensemble approach 25 represent major and unique accomplishments in physical sciences.”