ECMWF Training Course Adrian Tompkins May 2001 Numerical Weather Prediction Parametrization of diabatic processes Cloud Parametrization Adrian Tompkins Clouds 1

Outline LECTURE 1: Introduction to Cloud Issues ECMWF Training Course May 2001 Outline LECTURE 1: Introduction to Cloud Issues Physical processes to represent What are the potential problems in GCMs? History of cloud schemes LECTURE 2: Cloud Cover in GCMs LECTURE 3: The ECMWF cloud scheme LECTURE 4: Validation of cloud schemes Clouds 1

GCMs: Issues and approaches

Clouds in GCMs - What are the problems ? ECMWF Training Course May 2001 Clouds in GCMs - What are the problems ? Many of the observed clouds and especially the processes within them are of subgrid-scale size (both horizontally and vertically) GCM Grid cell 40-400km Clouds 1

Macroscale Issues of Parameterization ECMWF Training Course May 2001 Macroscale Issues of Parameterization VERTICAL COVERAGE Most models assume that this is 1 ~500m ~100km This can be a poor assumption with coarse vertical grids. Many climate models still use fewer than 30 vertical levels currently, some recent examples still use only 9 levels Clouds 1

Macroscale Issues of Parameterization ECMWF Training Course May 2001 Macroscale Issues of Parameterization HORIZONTAL COVERAGE, a ~500m ~100km Clouds 1

Macroscale Issues of Parameterization ECMWF Training Course May 2001 Macroscale Issues of Parameterization Vertical Overlap of cloud Important for Radiation and Microphysics Interaction ~500m ~100km Clouds 1

Macroscale Issues of Parameterization ECMWF Training Course May 2001 Macroscale Issues of Parameterization In cloud inhomogeneity in terms of cloud particle size and number ~500m ~100km Clouds 1

Macroscale Issues of Parameterization ECMWF Training Course May 2001 Macroscale Issues of Parameterization Just these issues can become very complex!!! ~500m ~100km Clouds 1

Clouds in GCMs - What are the problems ? ECMWF Training Course May 2001 Clouds in GCMs - What are the problems ? radiation convection microphysics turbulence dynamics Clouds are the result of complex interactions between a large number of processes Clouds 1

Clouds in GCMs - What are the problems ? ECMWF Training Course May 2001 Clouds in GCMs - What are the problems ? Many of these processes are only poorly understood - For example, the interaction with radiation Cloud top and base height Cloud fraction and overlap Amount of condensate Cloud-radiation interaction In-cloud conden-sate distribution Cloud environment Phase of condensate Cloud particle shape Cloud particle size Cloud macrophysics Cloud microphysics “External” influence Clouds 1

What do we want to represent? Complexity small ice Medium ice Large ice Ice Mass Liquid Mass Ice mass Ice number Cloud Mass “Single Moment” Schemes “Double Moment” Schemes “Spectral/Bin” Microphysics Most GCMs only have simple single-moment schemes

NOT DISTINCT - CAN HAVE MIXTURE OF APPROACHES ECMWF Training Course May 2001 Clouds in GCMs - How ? Main variables: Cloud fraction, a - refers to horizontal cover since cloud fills vertical Cloud condensate mass (cloud water and/or ice), ql. Diagnostic approach Prognostic approach NOT DISTINCT - CAN HAVE MIXTURE OF APPROACHES Clouds 1

Cloud microphysical processes ECMWF Training Course May 2001 Cloud microphysical processes We would like to include into our models: Formation of clouds Release of precipitation Evaporation of both clouds and precipitation Therefore we need to describe the change of phase from water vapour to water droplets and ice crystals the transformation of small cloud droplets/ice crystals to larger rain drops/ice particles the evaporation/sublimation of cloud and precipitation size particles Clouds 1

Microphysical processes and water substances ECMWF Training Course May 2001 Microphysical processes and water substances Nucleation of particles Diffusion growth Collision-Coalescence Collection Breakup of drops Sedimentation Ice enhancement Melting Evaporation/sublimation Involving: Water vapour Cloud liquid water Precipitation liquid water Cloud ice Precipitation ice Processes Uncertain - Parameterization often highly simplified Reference: “Microphysics of Clouds and Precipitation” by H.R.Pruppacher and J. D. Kett and “A short course in Cloud Physics” by Rogers and Yau (all subsequent diagrams from latter text unless otherwise stated) Clouds 1

Microphysics: Complex System! Overview of Warm Phase Microphysics T>273K Mixed Phase Microphysics 235K<T<273K Pure ice Microphysics T<235K

Droplet Classification

Nucleation of Water: Homogeneous Nucleation ECMWF Training Course May 2001 Nucleation of Water: Homogeneous Nucleation Drop of pure water forms from vapour Kelvin’s formula for critical radius for initial droplet to be “survive” strongly dependent on supersaturation requires several hundred percent supersaturation (not observed in the atmosphere), Clouds 1

Nucleation of Water: Heterogeneous Nucleation Collection of water molecules on a foreign substance, RH > ~80% (Haze particles) (Note, not same when drying) Particles are called Cloud Condensation Nuclei (CCN) CCN always present in sufficient numbers in lower and middle troposphere Activation at supersaturations of <1% Therefore we assume that condensation occurs if RH>100% (n.b. deep convection)

Heterogeneous Nucleation “Curvature term” Small drop – high radius of curvature easier for molecule to escape equilibrium e/es “Solution term” Reduction in vapour pressure due to dissolved substance

Diffusion growth (water) ECMWF Training Course May 2001 Diffusion growth (water) Nucleation small droplets once droplet is activated, water vapour diffuses towards it = condensation reverse process = evaporation droplets that are formed by diffusion growth attain a typical size of 0.1 to 10 mm rain drops are much larger than that drizzle: 50 to 100 mm rain: >100 mm other processes must also act in precipitating clouds For r > 1 mm and neglecting diffusion of heat D=Diffusion coefficient, S=Supersaturation Note inverse radius dependency Clouds 1

Parameterizing Nucleation and droplet growth Nucleation: Since “Activation” occurs at supersaturations less than 1% most schemes assumes all supersaturation is immediately removed as liquid water Note that this assumption means that models can just use one “prognostic” equation for the total water mass, the sum of vapour and liquid Usually, the growth equation is not explicitly solved, and in single-moment schemes simple (diagnostic) assumptions are made concerning the droplet number concentration when needed (e.g. radiation)

Collision-Coalescence ECMWF Training Course May 2001 Collision-Coalescence Drops of different size move with different fall speeds - collision and fusion large drops grow at the expense of small droplets Collection efficiency low for small drops process depends on width of droplet spectrum and is more efficient for broader spectra - Paradox large drops can only be produced in clouds of large vertical extent – Aided by turbulence and entrainment important process for low latitudes where deep clouds of high water content are present Clouds 1

Parameterizating “Autoconversion” of cloud drops to raindrops ECMWF Training Course May 2001 Parameterizating “Autoconversion” of cloud drops to raindrops Autoconversion (Kessler, AMS monogram 1969) ql qlcrit Gp Sundqvist, QJRMS, 1978 what are the issues for data assimilation? ql qlcrit Gp “Non-local” collection P=Precipitation Flux Clouds 1

Schematic of Warm Rain Processes Coalescence ~10 microns RH>100.6% “Activation” Diffusional Growth Heterogeneous Nucleation RH>78% (Haze) CCN Different fall speeds

Ice Nucleation Ice processes complex and poorly understood Droplets do not freeze at 0oC! Can also be split into Homogeneous and Heterogeneous processes Processes depend on temperature and history of cloud Homogeneous freezing of water droplet occurs between –35 and –40oC (often used assumption in microphysical schemes). Frequent observation of ice above these temperatures indicates role for heterogeneous processes

Ice Nucleation Fletcher 1962 Spontaneous freezing of liquid droplets smaller than 5 mm requires temperature less than -40oC. Observations of liquid in cloud are common at -20oC. Ice crystals start to appear in appreciable numbers below around -15oC. Heterogenous Nucleation responsible: Process less clear Ice nuclei: Become active at various temperatures less than 0oC, many fewer Observations: < -20oC Ice free clouds are rare > 5oC ice is unlikely ice supersaturation ( > 10% ) observations are common Fletcher 1962

Heterogeneous Nucleation

Ice habits can be complex, depends on temperature: influences fall speeds and radiative properties http://www.its.caltech.edu/~atomic/snowcrystals/

Mixed Phase clouds: Bergeron Process (I) ECMWF Training Course May 2001 Mixed Phase clouds: Bergeron Process (I) The saturation water vapour pressure with respect to ice is smaller than with respect to water A cloud, which is saturated with respect to water is supersaturated with respect to ice ! Clouds 1

Ice particles grow at the expense of water droplets ECMWF Training Course May 2001 Bergeron process (II) Ice particle enters water cloud Ice particles grow at the expense of water droplets Cloud is supersturated with respect to ice Diffusion of water vapour onto ice particle Cloud will become sub-saturated with respect to water Water droplets evaporate to increase water vapour Clouds 1

Modification of Sundqvist to take Bergeron Process into account ECMWF Training Course May 2001 Modification of Sundqvist to take Bergeron Process into account Sundqvist, QJRMS, 1978 ql qlcrit Gp Collection Bergeron Process Otherwise, most schemes have neglected ice processes, removing ice super-saturation “al la Warm rain” – See Lohman and Karcher JGR 2002(a,b) for first attempts to include ice microphysics in GCM Clouds 1

Aggregation Ice crystals can aggregate together to form snow Temperature dependent, process increases in efficiency as temperature exceeds –5C, when ice surface becomes sticky Also a secondary peak between –10 and –16C when dendrite arms get entangled

Riming If vapour exceeds the water saturation mixing ratio, water can condense on ice crystal, and then subsequently freeze to form “graupel”: Round ice crystals with higher densities and fall speeds than snow dendrites Graupel and Hail are also formed by aggregating liquid water drops in mixed phased clouds (“riming”) If the Latent heat of condensation and fusion keeps temperature close to 273K, then high density hail particle forms, since the liquid water “spreads out” before freezing. Generally referred to as “Hail” – The higher fall speed (up to 40 m/s) imply hail only forms in convection with strong updraughts able to support the particle long enough for growth http://www.its.caltech.edu/~atomic/snowcrystals/

Aggregation and Riming: Simple stratified picture

From Fleishauer et al 2002, JAS Ice Habits Ice habits can be complex: influences fall speeds and radiative properties Note shape/diameter distribution not monotonic with height, Turbulence! From Fleishauer et al 2002, JAS

Falling Precipitation Need to know size distribution For ice also affected by ice habit Poses problem for numerics Courtesy: R Hogan, U. Reading From R Hogan www.met.rdg.ac.uk/radar

Pure ice Phase: Homogeneous Ice Nucleation At cold temperature (e.g. upper troposphere) difference between liquid and ice saturation vapour pressures is large. If air mass is lifted, and does not contain significant liquid particles or ice nuclei, high supersaturations with respect to ice can occur, reaching 160 to 170%. Long lasting contrails are a signature of supersaturation Institute of Geography, University of Copenhagen

Homogeneous/heterogeneous nucleation However even in “polluted” NH air, homogeneous nucleation can dominate in strong updraughts However wind fluctuations can occur on mesoscale length-scales comparible or smaller to grid length. Upscale cascade from turbulence Gravity waves Subgrid instabilities (cloud top instabilities)…… It is clear that models are deficient in representing these E.g: Lohmann shows inadequacy of ECMWF model, and how enhancement of turbulence activity can produce improved spectra From haag and Kaerchner aircraft ECMWF resolved motions only

3000 km supersaturated segment observed ahead of front Limb Sounder and Mozaic Data (Pictures courtesy of Klaus Gierens and Peter Spichtinger, DLR) Recent observations (e.g. Mozaic aircraft and Microwave sounders) have revealed such values are common. 3000 km supersaturated segment observed ahead of front

Heteorogeneous Nucleation On the other hand, in air polluted by organic and mineral dust, supersaturation achieve perhaps 130%. Research is ongoing to determine the nature of ice nuclei at these colder temperatures This process probably more prevalent in NH where air is less ‘clean’ 22nd April 2003: Modis Image from http://modis.gsfc.nasa.gov

Summary: Warm Cloud E.g: Stratocumulus Evaporation Condensation (Rain formation - Fall Speeds - Evaporation of rain)

Summary: Deep Convective Cloud Heteorogeneous Nucleation of ice Splintering/Bergeron Process Melting of Snow and Graupel Precipitation Falls Speeds Evaporation in Sub-Cloud Layer

Summary: Cirrus cloud Homogeneous Nucleation (representation of supersaturation) Heterogeneous Nucleation (representation of nuclei type and concentration) Sedimentation of Ice crystals? Size distribution and formation of snow?

Cloud Schemes - A Brief History ECMWF Training Course May 2001 Cloud Schemes - A Brief History Clouds 1

Simple Bulk Microphysics ECMWF Training Course May 2001 Simple Bulk Microphysics VAPOUR (prognostic) Evaporation Condensation CLOUD (prognostic) Evaporation Autoconversion RAIN (diagnostic) WHY? Clouds 1

Microphysics - a “complex” GCM scheme ECMWF Training Course May 2001 Microphysics - a “complex” GCM scheme Fowler et al., JCL, 1996 Similar complexity to many schemes in use in CRMs Mostly treated as instant “No supersaturation assumption” “Threshold” linear or exponential terms with efficiency adjustments Clouds 1

Cloud Cover: Why Important? ECMWF Training Course May 2001 Cloud Cover: Why Important? In addition to the influence on radiation, the cloud cover is important for the representation of microphysics Imagine a cloud with a liquid condensate mass ql The incloud mass mixing ratio is ql/a a small GCM grid box a large precipitation not equal in each case since autoconversion is nonlinear Reminder: Autoconversion (Kessler, 1969) Complex microphysics perhaps a wasted effort if assessment of a is poor Clouds 1