QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 135 (2009) 1117-1132
The Invariant Set Postulate: a new geometric framework for the foundations of quantum theory and the role played by gravity
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 465 (2009) 3165-3185
Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts Reply
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 90 (2009) 1551-1554
Proceedings of the XXVII International Symposium on Lattice Field Theory ‘Lattice 2009' (2009)
Quarterly Journal of the Royal Meteorological Society 135 (2009) 1538-1559
The relative merits of three forecast systems addressing the impact of model uncertainty on seasonal/annual forecasts are described. One system consists of a multi-model, whereas two other systems sample uncertainties by perturbing the parametrization of reference models through perturbed parameter and stochastic physics techniques. Ensemble reforecasts over 1991 to 2001 were performed with coupled climate models started from realistic initial conditions. Forecast quality varies due to the different strategies for sampling uncertainties, but also to differences in initialisation methods and in the reference forecast system. Both the stochastic-physics and perturbed-parameter ensembles improve the reliability with respect to their reference forecast systems, but not the discrimination ability. Although the multi-model experiment has an ensemble size larger than the other two experiments, most of the assessment was done using equally-sized ensembles. The three ensembles show similar levels of skill: significant differences in performance typically range between 5 and 20%. However, a nine-member multi-model shows better results for seasonal predictions with lead times shorter than five months, followed by the stochastic-physics and perturbed-parameter ensembles. Conversely, for seasonal predictions with lead times longer than four months, the perturbed-parameter ensemble gives more often better results. All systems suggest that spread cannot be considered a useful predictor of skill. Annual-mean predictions showed lower forecast quality than seasonal predictions. Only small differences between the systems were found. The full multi-model ensemble has improved quality with respect to all other systems, mainly from the larger ensemble size for lead times longer than four months and annual predictions. © 2009 Royal Meteorological Society and Crown Copyright.
ENSEMBLES: A new multi-model ensemble for seasonal-to-annual predictions - Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs
Geophysical Research Letters 36 (2009)
A new 46-year hindcast dataset for seasonal-to-annual ensemble predictions has been created using a multi-model ensemble of 5 state-of-the-art coupled atmosphere-ocean circulation models. The multi-model outperforms any of the single-models in forecasting tropical Pacific SSTs because of reduced RMS errors and enhanced ensemble dispersion at all lead-times. Systematic errors are considerably reduced over the previous generation (DEMETER). Probabilistic skill scores show higher skill for the new multi-model ensemble than for DEMETER in the 4-6 month forecast range. However, substantially improved models would be required to achieve strongly statistical significant skill increases. The combination of ENSEMBLES and DEMETER into a grand multi-model ensemble does not improve the forecast skill further. Annual-range hindcasts show anomaly correlation skill of ∼0.5 up to 14 months ahead. A wide range of output from the multi-model simulations is becoming publicly available and the international community is invited to explore the full scientific potential of these data. Copyright 2009 by the American Geophysical Union.
Philos Trans 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.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 134 (2008) 1789-1799
Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model.
Philos Trans 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.
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)
Small-scale mixing between cloudy air and unsaturated clear air is investigated in numerical simulations and in a laboratory cloud chamber. Despite substantial differences in physical conditions and some differences in resolved scales of motion, results of both studies indicate that small-scale turbulence generated through cloud-clear air interfacial mixing is highly anisotropic. For velocity fluctuations, numerical simulations and cloud chamber observations demonstrate that the vertical velocity variance is up to a factor of two larger than the horizontal velocity variance. The Taylor microscales calculated separately for the horizontal and vertical directions also indicate anisotropy of turbulent eddies. This anisotropy is attributed to production of turbulent kinetic energy (TKE) by buoyancy forces due to evaporative cooling of cloud droplets at the cloud-clear air interface. Numerical simulations quantify the effects of buoyancy oscillations relative to the values expected from adiabatic and isobaric mixing, standardly assumed in cloud physics. The buoyancy oscillations result from microscale transport of liquid water due to the gravitational sedimentation of cloud droplets. In the particular modeling setup considered here, these oscillations contribute to about a fifth of the total TKE production. © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.
Europhysics Letters: a letters journal exploring the frontiers of physics 84 (2008)
The potential impacts of pollution on a nondrizzling stratus deck: Does aerosol number matter more than type?
Journal of Geophysical Research D: Atmospheres 113 (2008)
In this paper results from a cloud-resolving model that can efficiently examine the impact of aerosol on nondrizzling stratus clouds will be shown. Because the model tracks aerosol and cloud droplets in a Lagrangian framework, it does not suffer from numerical errors associated with advection, and unlike most Eulerian approaches, the method can track cloud boundaries as they move across a grid cell. After illustrating the capability of the model to reproduce various observed cloud statistics such as the cloud water mixing ratio and the mean cloud droplet radius from the DYCOMS-II field program, the ability of the model to assess the impact of changes in aerosol number and composition on a stratus deck will be highlighted. Specifically, by using activation curves appropriate for. soluble, insoluble, or a mixture of both types of aerosol and for certain extreme aerosol regimes, i.e., a majority of the aerosol are hydrophobic carbon aerosol, limiting situations were examined to bound their impact on clouds. However, though these situations may be somewhat extreme, they could occasionally occur in the atmosphere, e.g., an oceanic stratus field downwind of a large ship or an urban area. Not unexpectedly, results from these simulations support previous ship track observations that for increasing aerosol numbers, cloud droplet number concentrations increase, whereas cloud droplet radii decrease. However, these simulations also suggest that the correlation between cloud droplet number concentration and aerosol number concentration may be not only a function of aerosol number concentration but also aerosol types and/or cloud dynamics.
Bulletin of the American Meteorological Society 89 (2008) 459-470
Trustworthy probabilistic projections of regional climate are essential for society to plan for future climate change, and yet, by the nonlinear nature of climate, finite computational models of climate are inherently deficient in their ability to simulate regional climatic variability with complete accuracy. How can we determine whether specific regional climate projections may be untrustworthy in the light of such generic deficiencies? A calibration method is proposed whose basis lies in the emerging notion of seamless prediction. Specifically, calibrations of ensemble-based climate change probabilities are derived from analyses of the statistical reliability of ensemble-based forecast probabilities on seasonal time scales. The method is demonstrated by calibrating probabilistic projections from the multimodel ensembles used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), based on reliability analyses from the seasonal forecast Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) dataset. The focus in this paper is on climate change projections of regional precipitation, though the method is more general. © 2008 American Meteorological Society.
CLIVAR Exchanges 43 (2007) 6-7
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 133 (2007) 129-146
Convective forcing fluctuations in a cloud-resolving model: Relevance to the stochastic parameterization problem
JOURNAL OF CLIMATE 20 (2007) 187-202
Philos Trans 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.
Quarterly Journal of the Royal Meteorological Society 133 (2007) 1309-1325
Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. Ensemble forecasting is sometimes viewed as a method of obtaining (objective) probabilistic forecasts. How is one to judge the quality of an ensemble at forecasting a system? The probability that the bounding box of an ensemble captures some target (such as 'truth' in a perfect model scenario) provides new statistics for quantifying the quality of an ensemble prediction system: information that can provide insight all the way from ensemble system design to user decision support. These simple measures clarify basic questions, such as the minimum size of an ensemble. To illustrate their utility, bounding boxes are used in the imperfect model context to quantify the differences between ensemble forecasting with a stochastic model ensemble prediction system and a deterministic model prediction system. Examining forecasts via their bounding box statistics provides an illustration of how adding stochastic terms to an imperfect model may improve forecasts even when the underlying system is deterministic. Copyright © 2007 Royal Meteorological Society.