# Publications

## The potential impacts of pollution on a nondrizzling stratus deck: Does aerosol number matter more than type?

Journal of Geophysical Research: 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.

## Introduction. Stochastic physics and climate modelling.

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.

## Monte Carlo simulations of the randomly forced Burgers equation

Europhysics Letters: a letters journal exploring the frontiers of physics **84** (2008)

## Toward seamless prediction: Calibration of climate change projections using seasonal forecasts

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.

## Seasonal Forecast Datasets - A resource for Calibrating Regional Climate Change Projections?

CLIVAR Exchanges **43** (2007) 6-7

## Initialisation strategies for decadal hindcasts for the 1960-2005 period within the ENSEMBLES project. ECMWF Tech Memo.

(2007) 521

## 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

## 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.

## Convective forcing fluctuations in a cloud-resolving model: Relevance to the stochastic parameterization problem

JOURNAL OF CLIMATE **20** (2007) 187-202

## 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

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.

## Another look at stochastic condensation for subgrid cloud modeling: Adiabatic evolution and effects

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.

## Using numerical weather prediction to assess climate models

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY **133** (2007) 129-146

## Dynamically-based seasonal forecasts of Atlantic tropical storm activity issued in June by EUROSIP

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.

## Ensemble decadal predictions from analysed initial conditions.

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.

## Numerical simulation of cloud-clear air interfacial mixing: Effects on cloud microphysics

Journal of the Atmospheric Sciences **63** (2006) 3204-3225

This paper extends the previously published numerical study of Andrejczuk et al. on microscale cloud-clear air mixing. Herein, the primary interest is on microphysical transformations. First, a convergence study is performed - with well-resolved direct numerical simulation of the interfacial mixing in the limit - to optimize the design of a large series of simulations with varying physical parameters. The principal result is that all conclusions drawn from earlier low-resolution (Δx = 10-2 m) simulations are corroborated by the high-resolution (Δx = 0.25 × 10-2 m) calculations, including the development of turbulent kinetic energy (TKE) and the evolution of microphysical properties. This justifies the use of low resolution in a large set of sensitivity simulations, where microphysical transformations are investigated in response to variations of the initial volume fraction of cloudy air, TKE input, liquid water mixing ratio in cloudy filaments, relative humidity (RH) of clear air, and size of cloud droplets. The simulations demonstrate that regardless of the initial conditions the evolutions of the number of cloud droplets and the mean volume radius follow a universal path dictated by the TKE input, RH of clear air filaments, and the mean size of cloud droplets. The resulting evolution path only weakly depends on the progress of the homogenization. This is an important conclusion because it implies that a relatively simple rule can be developed for representing the droplet-spectrum evolution in cloud models that apply parameterized microphysics. For the low-TKE input, when most of the TKE is generated by droplet evaporation during mixing and homogenization, an inhomogencous scenario is observed with approximately equal changes in the dimensionless droplet number and mean volume radius cubed. Consistent with elementary scale analysis, higher-TKE inputs, higher RH of cloud-free filaments, and larger cloud droplets enhance the homogeneity of mixing. These results are discussed in the context of observations of entrainment and mixing in natural clouds. © 2006 American Meteorological Society.

## Malaria early warnings based on seasonal climate forecasts from multi-model ensembles

NATURE **439** (2006) 576-579

## Impact of increasing greenhouse gas concentrations in seasonal ensemble forecasts

GEOPHYSICAL RESEARCH LETTERS **33** (2006) ARTN L07708

## Another look at stochastic condensation in clouds: Exact solutions, fokker-planck approximations and adiabatic evolution

12th Conference on Cloud Physics, and 12th Conference on Atmospheric Radiation (2006)

In this manuscript - which closely follows Jeffery et al. (2006) - we have taken "another look" at stochastic condensation in the hope of clarifying the earlier derivations and fully exploring the implications of this theory. In contrast to the derivations of Levin and Sedunov (1966a, b) and Manton (1979), we begin with a simple model of stochastic condensation - independent, Gaussian supersaturation fluctuations (S′) renewed after a time τ - that is exactly solvable. This model is trivial to simulate on a computer and can be used to compare and contrast Lagrangian and Eulerian approaches for modeling droplet spectra (Andrejczuk et al. 2006). The Fokker-Planck approximation to this exact solution follows by replacing the discrete sampling of S′ with its continuous surrogate. The Fokker-Planck diffusivity and operator are thus seen to be the natural smooth-in-time approximation to a discrete-in-time process. We have also taken another - look at the equation for the mean supersaturation, S̄FP, in the presence of S′ fluctuations modeled using the Levin-Sedunov-Mazin Fokker-Planck operator. While this problem is treated in an approximate fashion (and with little transparency) in Voloshchukand Sedunov (1977), we derive the expression for (S′|r)FP without approximation and show how this expression "closes" the S̄FP-equation self-consistently, thereby ensuring that total water mass is exactly conserved. Using the quasi-stationary (QS) evaluation of S̄FP, we derive the exact correction term to SFP, QS (i.e. the S′ contribution corresponding to the Levin-Sedunov-Mazin model). The correction term is negative definite, peaks in magnitude when (r)r is near the accommodation length (≈ 2 μm), and decays as (r-1)r as the droplet spectrum grows to large sizes. This exact result has a direct correspondence to the analysis of Cooper (1989). Using our self-consistent equation for S̄FP, we evaluate spectral broadening in an adiabatic parcel and find some broadening to larger sizes (consistent with earlier estimates, e.g. Manton (1979)), but a more significant decrease in 〈r2〉 r at fixed liquid water content which may have implications for modeled cloud reflectivity. While the proceeding discussion is largely a clarification and elucidation of previous work, most notably Voloshchuk and Sedunov (1977), we have also extended the theory of stochastic condensation by deriving the non-dimensional number, ND, that determines the relative impact of S′-fluctuations on droplet spectral evolution in an adiabatic volume and in the QS limit. For constant updraft velocity and Fokker-Planck diffusivity, ND is also a constant, ranging from 10-2 to 102 for typical atmospheric conditions and model grid sizes when the assumed S′-standard deviation is 1%. We find significant spectral broadening, and in particular decreasing 〈r2〉r, for ND > 1, and discover that S̄FP, QS can be negative in a rising adiabatic parcel when ND > 6.5 for droplets of zero initial size. Using in-situ droplet spectra from cumulus cloud fields observed during the RICO and SCMS field campaigns, we have verified a seminal prediction of the theory of stochastic condensation - increasing broadening with increasing spatial scale - by averaging the observed spectra over segments containing one or more clouds. In addition, scale-dependent values of ND retrieved from the segment-averaged spectra using our adiabatic model show good consistency with the previously discussed theoretical estimates. We believe this encouraging result to be the first observational confirmation of the stochastic condensation mechanism and the decades-old, pioneering work of Levin-Sedunov-Mazin. Moreover, these results suggest that the parameterization of unresolved S′-fluctuations using Fokker-Planck theory or other means will become increasingly important as explicit (bin) microphysics schemes are applied at larger scales (Lynn et al. 2005), where an increasing fraction of individual clouds are, themselves, unresolved. However, important differences between the observed and modeled droplet spectra are also observed. In particular, the observed spectra suggest non-Gaussian S′ fluctuations and the inhomogeneous mixing process of Baker et al. (1980). Further work is needed to assess the impact of non-Gaussian S′-fluctuations and large renewal times on droplet spectral broadening and to derive differential operators that can model their ensemble effect in the equations of cloud physics.