Publications
Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model.
Philos Transact A Math Phys Eng Sci 366 (2008) 2561-2579
The impact of a nonlinear dynamic cellular automaton (CA) model, as a representation of the partially stochastic aspects of unresolved scales in global climate models, is studied in the European Centre for Medium Range Weather Forecasts coupled ocean-atmosphere model. Two separate aspects are discussed: impact on the systematic error of the model, and impact on the skill of seasonal forecasts. Significant reductions of systematic error are found both in the tropics and in the extratropics. Such reductions can be understood in terms of the inherently nonlinear nature of climate, in particular how energy injected by the CA at the near-grid scale can backscatter nonlinearly to larger scales. In addition, significant improvements in the probabilistic skill of seasonal forecasts are found in terms of a number of different variables such as temperature, precipitation and sea-level pressure. Such increases in skill can be understood both in terms of the reduction of systematic error as mentioned above, and in terms of the impact on ensemble spread of the CA's representation of inherent model uncertainty.
Introduction. Stochastic physics and climate modelling.
Philos Transact A Math Phys Eng Sci 366 (2008) 2421-2427
Finite computing resources limit the spatial resolution of state-of-the-art global climate simulations to hundreds of kilometres. In neither the atmosphere nor the ocean are small-scale processes such as convection, clouds and ocean eddies properly represented. Climate simulations are known to depend, sometimes quite strongly, on the resulting bulk-formula representation of unresolved processes. Stochastic physics schemes within weather and climate models have the potential to represent the dynamical effects of unresolved scales in ways which conventional bulk-formula representations are incapable of so doing. The application of stochastic physics to climate modelling is a rapidly advancing, important and innovative topic. The latest research findings are gathered together in the Theme Issue for which this paper serves as the introduction.
Laboratory and modeling studies of cloud-clear air interfacial mixing: Anisotropy of small-scale turbulence due to evaporative cooling
New Journal of Physics 10 (2008)
Monte Carlo simulations of the randomly forced Burgers equation
Europhysics Letters: a letters journal exploring the frontiers of physics 84 (2008)
Historical reconstruction of the Atlantic Meridional Overturning Circulation from the ECMWF operational ocean reanalysis
Geophysical Research Letters 34 (2007)
Another look at stochastic condensation for subgrid cloud modeling: Adiabatic evolution and effects
Journal of the Atmospheric Sciences 64 (2007) 3949-3969
Dynamically-based seasonal forecasts of Atlantic tropical storm activity issued in June by EUROSIP
Geophysical Research Letters 34 (2007)
How good is an ensemble an capturing truth? Using bounding boxes for forecast evaluation
Quarterly Journal of the Royal Meteorological Society 133 (2007) 1309-1325
Convective forcing fluctuations in a cloud-resolving model: Relevance to the stochastic parameterization problem
JOURNAL OF CLIMATE 20 (2007) 187-202
Historical Overview of Climate Change Science
in Intergovernmental Panel on Climate Change (IPCC), 4th Assessment Report, Working Group 1: The Physical Basis of Climate Change, (2007) 1
Initialisation strategies for decadal hindcasts for the 1960-2005 period within the ENSEMBLES project. ECMWF Tech Memo.
(2007) 521
Seasonal Forecast Datasets - A resource for Calibrating Regional Climate Change Projections?
CLIVAR Exchanges 43 (2007) 6-7
Using numerical weather prediction to assess climate models
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 133 (2007) 129-146
Ensemble decadal predictions from analysed initial conditions.
Philos Transact A Math Phys Eng Sci 365 (2007) 2179-2191
Sensitivity experiments using a coupled model initialized from analysed atmospheric and oceanic observations are used to investigate the potential for interannual-to-decadal predictability. The potential for extending seasonal predictions to longer time scales is explored using the same coupled model configuration and initialization procedure as used for seasonal prediction. It is found that, despite model drift, climatic signals on interannual-to-decadal time scales appear to be detectable. Two climatic states have been chosen: one starting in 1965, i.e. ahead of a period of global cooling, and the other in 1994, ahead of a period of global warming. The impact of initial conditions and of the different levels of greenhouse gases are isolated in order to gain insights into the source of predictability.
Erratum: "Changing frequency of occurrence of extreme seasonal temperatures under global warming" (Geophysical Research Letters (2005) vol. 32 10.1029/2005GL023365)
Geophysical Research Letters 33 (2006)
