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.
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.
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.
Europhysics Letters: a letters journal exploring the frontiers of physics 84 (2008)
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.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 133 (2007) 129-146
CLIVAR Exchanges 43 (2007) 6-7
Initialisation strategies for decadal hindcasts for the 1960-2005 period within the ENSEMBLES project. ECMWF Tech Memo.
in Intergovernmental Panel on Climate Change (IPCC), 4th Assessment Report, Working Group 1: The Physical Basis of Climate Change, (2007) 1
Historical reconstruction of the Atlantic Meridional Overturning Circulation from the ECMWF operational ocean reanalysis
Geophysical Research Letters 34 (2007)
A reconstruction of the Atlantic Meridional Overturning Circulation (MOC) for the period 1959-2006 has been derived from the ECMWF operational ocean reanalysis. The reconstruction shows a wide range of time-variability, including a downward trend. At 26N, both the MOC intensity and changes in its vertical structure are in good agreement with previous estimates based on trans-Atlantic surveys. At 50N, the MOC and strength of the subpolar gyre are correlated at interannual time scales, but show opposite secular trends. Heat transport variability is highly correlated with the MOC but shows a smaller trend due to the warming of the upper ocean, which partially compensates for the weakening of the circulation. Results from sensitivity experiments show that although the time-varying upper boundary forcing provides useful MOC information, the sequential assimilation of ocean data further improves the MOC estimation by increasing both the mean and the time variability. Copyright 2007 by the American Geophysical Union.
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.
Convective forcing fluctuations in a cloud-resolving model: Relevance to the stochastic parameterization problem
JOURNAL OF CLIMATE 20 (2007) 187-202
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.
Journal of the Atmospheric Sciences 64 (2007) 3949-3969
The theory of stochastic condensation, which models the impact of an ensemble of unresolved supersaturation fluctuations S′ on the volume-averaged droplet-size distribution f (r), is revisited in the modern context of subgrid cloud parameterization. The exact transition probability density for droplet radius driven by independent, Gaussian S′ fluctuations that are periodically renewed is derived and shown to be continuous but not smooth. The Fokker-Planck model follows naturally as the smooth-in-time approximation to this discrete-in-time process. Evolution equations for the moments of f(r) that include a contribution from subgrid S′ fluctuations are presented; these new terms are easily implemented in moment-based cloud schemes that resolve supersaturation. New, self-consistent expressions for the evolution of f(r) and mean supersaturation S̄ in a closed, adiabatic volume are derived without approximation; quite appropriately, these coupled equations exactly conserve total water mass. The behavior of this adiabatic system, which serves as a surrogate for a closed model grid column, is analyzed in detail. In particular, a new nondimensional number is derived that determines the relative impact of S′ fluctuations on droplet spectral evolution, and the contribution of fluctuations to S̄ is shown to be negative definite and maximal near the accommodation length and has a direct correspondence to the analysis of Cooper. Observational support for the theory of stochastic condensation is found in cloud droplet spectra from cumulus cloud fields measured during the Rain in the Cumulus over the Ocean (RICO) and Small Cumulus Microphysics Study (SCMS) campaigns. Increasing spectral broadening with increasing spatial scale is discovered and compares well with theoretical predictions. However, the observed spectra show evidence of non-Gaussian S′ fluctuations and inhomogeneous mixing, processes neglected in the current theory.
Geophysical Research Letters 34 (2007)
Most seasonal forecasts of Atlantic tropical storm numbers are produced using statistical-empirical models. However, forecasts can also be made using numerical models which encode the laws of physics, here referred to as "dynamical models". Based on 12 years of re-forecasts and 2 years of real-time forecasts, we show that the so-called EUROSIP (EUROpean Seasonal to Inter-annual Prediction) multi-model ensemble of coupled ocean atmosphere models has substantial skill in probabilistic prediction of the number of Atlantic tropical storms. The EUROSIP real-time forecasts correctly distinguished between the exceptional year of 2005 and the average hurricane year of 2006. These results have implications for the reliability of climate change predictions of tropical cyclone activity using similar dynamically-based coupled ocean-atmosphere models.
CLIMATE RESEARCH 33 (2006) 19-26