Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model
in Stochastic Physics and Climate Modelling, Cambridge University Press (2009) 15
The Microscope McCrone Research Institute 4 (2009) 157-163
We describe laboratory-grown snow crystals that exhibit a triangular, plate-like morphology, and we show that the occurrence of these crystals is much more frequent than one would expect from random growth perturbations of the more-typical hexagonal forms. We then describe an aerodynamic model that explains the formation of these crystals. A single growth perturbation on one facet of a hexagonal plate leads to air flow around the crystal that promotes the growth of alternating facets. Aerodynamic effects thus produce a weak growth instability that can cause hexagonal plates to develop into triangular plates. This mechanism solves a very old puzzle, as observers have been documenting the unexplained appearance of triangular snow crystals in nature for nearly two centuries.
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
Quarterly Journal of the Royal Meteorological Society 135 (2009) 1095-1103
There is a growing interest in using stochastic parametrizations in numerical weather and climate prediction models. Previously, Palmer (2001) outlined the issues that give rise to the need for a stochastic parametrization and the forms such a parametrization could take. In this article a method is presented that uses a comparison between a standard-resolution version and a high-resolution version of the same model to gain information relevant for a stochastic parametrization in that model. A correction term that could be used in a stochastic parametrization is derived from the thermodynamic equations of both models. The origin of the components of this term is discussed. It is found that the component related to unresolved wave-wave interactions is important and can act to compensate for large parametrized tendencies. The correction term is not proportional to the parametrized tendency. Finally, it is explained how the correction term could be used to give information about the shape of the random distribution to be used in a stochastic parametrization. © 2009 Royal Meteorological Society.
Revolution in climate prediction is both necessary and possible: A declaration at the world modelling summit for climate prediction
Bulletin of the American Meteorological Society 90 (2009) 175-178
Addressing the global climate change, the World climate Research Program (WCRP) held a World Modeling summit for Climate Prediction on 6-9 May 2008 in Reading, England, to develop a strategy in revolutionizing prediction of the climate. The summit was cosponsored by the World Weather Research Program (WWRP) and the International Geosphere-Biosphere Program (IGBP). The event has given emphasis on the simulation and prediction of the physical climate system. The summit tried to identify challenges which are grouped into following areas such as process-based model evaluation; data assimilation, analysis, and initialization; detection and attribution of climate events; and ensembles.
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.
Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts Reply
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 90 (2009) 1551-1554
Future change in wintertime atmospheric blocking simulated using a 20-km-mesh atmospheric global circulation model
Journal of Geophysical Research Atmospheres 114 (2009)
Future change in the frequency of atmospheric blocking is investigated through present-day (1979-2003) and future (2075-2099) simulations using 20-, 60-, 120-, and 180-km-mesh atmospheric general circulation models (AGCMs) under the Intergovernmental Panel on Climate Change Special Reports on Emission Scenarios AlB emission scenario, focusing on the Northern Hemisphere winter (December-February). The results of present-day climate simulations reveal that the AGCM with the highest horizontal resolution is required to accurately simulate Euro-Atlantic blocking, whereas the AGCM with the lowest horizontal resolution is in good agreement with reanalysis data regarding the frequency of Pacific blocking. While the lower-resolution models accurately reproduce long-lived Pacific blocking, they are unable to accurately simulate long-lived Euro-Atlantic blocking. This result suggests that the maintenance mechanism of Euro-Atlantic blocking is different from that of Pacific blocking. In the future climate simulations, both frequencies of Euro-Atlantic and Pacific blockings are predicted to show a significant decrease, mainly in the western part of each peak in present-day blocking frequency, where the westerly jet is predicted to increase in strength; no significant change is predicted in the eastern part of each peak. The number of Euro-Atlantic blocking events is predicted to decrease for almost all blocking durations, whereas the decrease in the number of Pacific blockings is remarkable for long-duration events. It is possible that long-lived (>25 days) Euro-Atlantic and Pacific blockings will disappear altogether in the future. Copyright 2009 by the American Geophysical Union.
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.
A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System
JOURNAL OF THE ATMOSPHERIC SCIENCES 66 (2009) 603-626
Proceedings of the XXVII International Symposium on Lattice Field Theory ‘Lattice 2009' (2009)
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.
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.
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.