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Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins.

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Presentation on theme: "Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins."— Presentation transcript:

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2 Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins Adrian Tompkins

3 2 Outline 1.Why were cloud resolving models (CRMs) conceived? 2.What do they consist of? 3.How have they developed? 4.To which purposes have they been applied? 5.What is their future?

4 3 In the early 1960s there were three sources of information concerning cumulus clouds –Direct observations E.G: Warner (1952) Limited coverage of a few variables Why were cloud resolving models conceived?

5 4 In the early 1960s there were three sources of information concerning cumulus clouds –Direct observations –Laboratory Studies Realism of laboratory studies? Difficulty of incorporating latent heating effects Turner (1963) Why were cloud resolving models conceived?

6 5 In the early 1960s there were three sources of information concerning cumulus clouds –Direct observations –Laboratory Studies –Theoretical Studies Linear perturbation theories Quickly becomes difficult to obtain analytical solutions when attempting to increase realism of the model Why were cloud resolving models conceived?

7 6 In the early 1960s there were three sources of information concerning cumulus clouds –Laboratory Studies –Theoretical Studies –Analytical Studies Obvious complementary role for Numerical simulation of convective clouds –Numerical integration of complete equation set –Allowing more complete view of simulated convection Why were cloud resolving models conceived?

8 7 Outline 1.Why were cloud resolving models conceived? 2.What do they consist of ?

9 8 What is a CRM? The concept GCM grid too coarse to resolve convection - Convective motions must be parametrized GCM Grid cell ~100km In a cloud resolving model, the momentum equations are solved on a finer mesh, so that the dynamic motions of convection are explicitly represented. But, with current computers this can only be accomplished on limited area domains, not globally!

10 9 What is a CRM? The physics dynamics radiation turbulence microphysics SW IR 1. Momentum equations surface fluxes 2. Turbulence Scheme 5. Surface Fluxes 3. Microphysics 4. Radiation?

11 10 What is a CRM? The Issues 1. RESOLUTION: Dependence on turbulence formulation DOMAIN SIZE: Purpose of simulation. 3. LARGE-SCALE FLOW? Reproduction of observations? Lateral BCs. 4. DIMENSIONALITY: 2 or 3 dimensional dynamics? TIME: Length of integration. 5

12 11 Lateral Boundary Conditions W Early models used impenetrable Lateral Boundary Conditions L Cloud development near boundaries affected by their presence No longer in use Periodic Boundary Conditions J Easy to implement J Model boundaries are invisible L No mean ascent is allowable (W=0) Open Boundary Conditions J Mean vertical motion is unconstrained L Very difficult to avoid all wave reflection at boundaries L Difficult to implement, also need to specific BCs

13 12 Spatial and Temporal Scales? 1. O(1km) 1. Deep convective updraughts 2. O(100m) 2. Turbulent Eddies 3. O(10km ) 3. Anvil cloud associated with one event 4. O(1000km ) 4. Mesoscale convective systems, Squall lines, organised convection ~30 minutes days-weeks

14 13 DYNAMICAL CORE MICROPHYSICS (ice and liquid phases) SUBGRID-SCALE TURBULENCE BOUNDARY CONDITIONS RADIATION (sometimes - Expensive!) Open or periodic Lateral BCs Lower boundary surface fluxes Upper boundary Newtonian damping (to prevent wave reflection) What do they consist of ?

15 14 DYNAMICAL CORE Prognostic equations for u,v,w,,r v,(p) affected by, advection, turbulence, microphysics, radiation, surface fluxes... MICROPHYSICS (ice and liquid phases) Prognostic equations for bulk water categories: rain, liquid cloud, ice, snow, graupel… sometimes also their number concentration. HIGHLY UNCERTAIN!!! SUBGRID-SCALE TURBULENCE Attempt to parameterize flux of prognostic quantities due to unresolved eddies Most models use 1 or 1.5 order schemes ALSO UNCERTAIN!!! What do they consist of ?

16 15 Basic Equations Continuity: This is known as the anelastic approximation, where horizontal and temporal density variations are neglected in the equation of continuity. Horizontal pressure adjustments are considered to be instantaneous. This equation thus becomes a diagnostic relationship. This excludes sound waves from the equation solution, which are not relevant for atmospheric motions, and would require small timesteps for numerical stability. Based on Batchelor QJRMS (1953) and Ogura and Phillips JAS (1962) Note: Although the analastic approximation is common, some CRMs use a fully elastic equation set, with a full or simplified prognostic continuity equation. See for example, Klemp and Wilhelmson JAS (1978), Held et al. JAS (1993). Reference: Emanuel (1994), Atmospheric Convection

17 16 Basic Equations Momentum: Where: Diabatic terms (e.g. turbulence) Coriolis Pressure Gradient Overbar = mean state Buoyancy DYNAMICAL CORE Since cloud models are usually applied to domains that are small compared to the radius of the earth it is usual to work in a Cartesian co-ordinate system The Coriolis parameter if applied, is held constant, since its variation across the domain is limited Mixing ratio of vapour and condensate variables

18 17 Moisture: Basic Equations Thermodynamic: –Diabatic processes: Radiation Diffusion Microphysics (Latent heating) Equation of State: Condensation Evaporation Microphysics terms

19 18 SUBGRID-SCALE TURBULENCE All scales of motion present in turbulent flow Smallest scales can not be represented by model grid - must be parameterised. Assume that smallest eddies obey statistical laws such that their effects can be described in terms of the large- scale resolved variables Progress is made by considering flow, u, to consist of a resolved component, plus a local unresolved perturbation: Doing this, eddy correlation terms are obtained: e.g.

20 19 SUBGRID-SCALE TURBULENCE Many models used First order closure (Smagorinsky, MWR 1963) Make analogy between molecular diffusion: and likewise for other variables: v,r, etc… K are the coefficients of eddy diffusivity K set to a constant in early models Improvements can be made by relating K to an eddy length- scale l and the wind shear. Dimensionless Constant = Reference Cotton and Anthes, 1989 Storm and Cloud Dynamics

21 20 Length scale of turbulence related to grid-length Further refinement is to multiply by a stability function based on the Richardson number: Ri. In this way, turbulence is enhanced if the air is locally unstable to lifting, and suppressed by stable temperature stratification First order schemes still in use (e.g. U.K. Met Office LEM) although many current CRMs use a One and a half Order Closure - In these, a prognostic equation is introduced for the turbulence kinetic energy (TKE), which can then be used to diagnose the turbulent fluxes of other quantities. Note: Krueger,JAS 1988, uses a more complex third order scheme SUBGRID-SCALE TURBULENCE Reference: Stull(1988), An Introduction to Boundary Layer Meteorology See Boundary Layer Course for more details!

22 21 The condensation of water vapour into small cloud droplets and their re-evaporation can be accurately related to the thermodynamical state of the air. However, the processes of precipitation formation, its fall and re-evaporation, and also all processes involving the ice phase (e.g. ice cloud, snow, hail) are: Not completely understood Operate on scales smaller than the model grid Therefore parameterisation is difficult but important Microphysics

23 22 Microphysics Most schemes use a bulk approach to microphysical parameterization Just one equation is used to model each category q total q rain Warm - Bulk q vap q rain q liq q snow q graup q ice Ice - Bulk Ice - Bin resolving Different drop size bins From Dare 2004, microphysical scheme at BMRC

24 23 Microphysics For example: Sources and sinks Fall speed of graupel For Example, (Lin et al. 1983) snow to graupel conversion Not many papers mention numerics. Often processes are considered to be resolved by the O(10s) timesteps used in CRMs, and therefore a simple explicit solution is used; beginning of timestep value of q graup used to calculate the RHS of the equation. If sinks result in a negative mass, some models reset to zero (i.e. not conserving). q snow-crit = kg kg -1 S =0 below this threshold T 0 =0 o C

25 24 Outline 1.Why were cloud resolving models conceived? 2.What do they consist of? 3.How have they developed?

26 25 HISTORY:1960s One of the first attempts to numerically model moist convection made by Ogura JAS (1963) Same basic equation set, neglecting: –Diffusion - Radiation - Coriolis Force Reversible ascent (no rain production) Axisymmetric model domain –3km by 3km –100m resolution –6 second timestep 3km Warm air bubble 3km 100m

27 26 Possible 2D domain configurations Motions function of r and z + Pseudo-3D motions (subsidence) - No wind shear possible - Difficult to represent cloud ensembles Use continued mainly in hurricane modelling Motions functions of x and z + can represent ensembles - Lack of third dimension in motions - Artificially changes separation scale Still much used to date Axi-symmetric z r Slab Symmetric z x For reference see Soong and Ogura JAS (1973)

28 27 Ogura 1963 Liquid Cloud 7 Minutes14 Minutes Cloud reaches domain top by 14 Minutes Cloud occupies significant proportion of model domain

29 28 History:

30 29 Outline 1.Why were cloud resolving models conceived? 2.What do they consist of? 3.How have they developed? 4.To which purposes have they been applied?

31 30 Use of CRMs 1990s really saw an expansion in the way in which CRMs have been used Long term statistical equilibrium runs - Investigating specific process interactions Testing assumptions of cumulus parametrization schemes Developing aspects of parametrizations Long term simulation of observed systems All of the above play a role in the use of CRMs to develop parametrization schemes

32 31 Uses: Radiative-Convective equilibrium experiments Sample convective statistics of equilibrium, and their sensitivity to external boundary conditions –e.g Sea surface Temperature Also allows one to examine process interactions in simplified framework Computationally expensive since equilibrium requires many weeks of simulation to achieve equilibrium –2D: Asai J. Met. Soc. Japan (1988), Held et al. JAS (1993), Sui et al. JAS (1994), Grabowski et al. QJRMS (1996), 3D: Tompkins QJRMS (1998), J. Clim. (1999) Long term integrations until fields reach equilibrium Radn cooling = surface rain = moisture fluxes = convective heating

33 32 Uses: Investigating specific process interactions Large scale organisation: –Gravity Waves: Oouchi, J. Met. Soc. Jap (1999) –Water Vapour: Tompkins, JAS, (2001) Cloud-radiative interactions: –Tao et al. JAS (1996) Convective triggering in Squall lines: –Fovell and Tan MWR (1998) USE CRM TO INVESTIGATE A CERTAIN PROCESS THAT IS PERHAPS DIFFICULT TO EXAMINE IN OBSERVATIONS UNDERSTANDING THIS PROCESS ALLOWS AN ATTEMPT TO INCLUDE OR REPRESENT IT IN PARAMETRIZATION SCHEMES

34 33 Example: 350m resolution 3D CRM simulation used in a variety of parametrization ways 90 km Used to understand coldpool triggering Used as a cloud-field proxy to develop parametrization to correct radiative geometrical biases Used to set closure parameters for a simplified cloud model Tompkins JAS 2001 Di Giuseppe & Tompkins JGR 2003, JAS2005 Tompkins & di Giuseppe 2006 Di Giuseppe & Tompkins JAS 2003 Used to justify PDF decision in cloud scheme of ECHAM5 Tompkins JAS 2002

35 34 Uses: Testing Cumulus Parametrization schemes Parametrizations contain representations of many terms difficult to measure in observations –e.g. Vertical distribution of convective mass fluxes for mass-flux schemes Assume that despite uncertain parametrizations (e.g. microphysics, turbulence), CRMs can give a reasonable estimate of these terms. Gregory and Miller QJRMS (1989) is a classic example of this, where a 2D CRM is used to derive all the individual components of the heat and moisture budgets, and to assess approximations made in convective parametrization schemes.

36 35 Gregory and Miller QJRMS 1989 Updraught, Downdraught, non-convective and net cloud mass fluxes They compared these profiles to the profiles assumed in mass flux parameterization schemes - concluded that the downdraught entraining plume model was a good one for example – but note resolution issues.

37 36 Uses: Developing aspects of parametrization schemes The information can be used to derive statistics for use in parametrization schemes E.g. Xu and Randall, JAS (1996) used CRM to derive a diagnostic cloud cover parameterisation where CC cloud cover relative humiditycloud mixing ratio

38 37 Uses: Developing Parametrization Schemes PARAMETRIZATION GCMs - SCMs CRMsOBSERVATIONS Validation (and development) Validation (and development) Validation (and development) Provide extra quantities not available from data

39 38 SimulationObservations Simulation All types of convection developed in response to applied forcing - Could be considered a successful validation exercise? For example, Grabowski (1998) JAS performed week-long simulations of convection during GATE, in 3D with a 400 by 400 km 3D domain. CRMs OBSERVATIONS Validation

40 39 Simulation of Observed Systems Still controversy about the way to apply Large- scale forcing Relies on argument of scale separation (as do most convective parametrization schemes) CRM domain W With periodic BCs must have zero mean vertical velocity. Normal to apply terms:

41 40 Simulation of Observed Systems Radiosonde stations measure An observational array measures the mean mass flux. If an observational array contains a convective event, but is not large enough to contain the subsidence associated with this event, then the measured large scale mean ascent will also contain a component due to the net cumulus mass flux M c

42 41 GCSS - GEWEX Cloud System Study (Moncrieff et al. Bull. AMS 97) Use observations to evaluate parameterizations of subgrid-scale processes in a CRM Step 1 Evaluate CRM results against observational datasets Step 2 Use CRM to simulate precipitating cloud systems forced by large-scale observations Step 3 Evaluate and improve SCMs by comparing to observations and CRM diagnostics Step 4 PARAMETERISATION GCMS - SCMS CRMsOBSERVATIONS

43 42 GCSS: Validation of CRMs Redelsperger et al QJRMS 2000 SQUALL LINE SIMULATIONS Observations - Radar Open BCs Periodic BCs Simulations from different models ( total hydrometeor content ) Conclude that only 3D models with ice and open BCs reproduce structure well

44 43 GCSS: Comparison of many SCMs with a CRM Bechtold et al QJRMS 2000 SQUALL LINE SIMULATIONS CRM

45 44 Issues of this approach Confidence is gained in the ability of the SCMs and CRMs to simulate the observed systems Sensitivity tests can show which physics is central for a reasonable simulation of the system… But… Is the observational dataset representative? What constitutes a good or bad simulation? Which variables are important and what is an acceptable error? Given the model differences, how can we turn this knowledge into improvements in the parameterization of convection? Is an agreement between the models a sign of a good simulation, or simply that they use similar assumptions? (Good Example: Microphysics)

46 45 Outline 1.Why were cloud resolving models conceived? 2.What do they consist of? 3.How have they developed? 4.To which purposes have they been applied? 5.What is their future?

47 46 Future - Issues Fundamental issues remain unresolved: –Resolution? At 1 or 2 km horizontal resolution much of the turbulent mixing is not resolved, but represented by the turbulence scheme. Indications are that CRM solutions have not converged with increasing horizontal resolution at 100m. –Dimensionality 2D slab symmetric models are still widely used, despite contentions to their numerical cheapness –Representation of microphysics? –Representing interaction with large scale dynamics? Re-emergence of open BCs?

48 47 –Grabowski and Smolarkiewicz, Physica D Places a small 2D CRM (roughly 200km, simple microphysics, no turbulence) in every grid-point of the global model –Still based on scale separation and non-communication between grid- points –Advantages and Disadvantages? From Khairoutdinov, illustrating multimodelling framework developed at CSU Cloud Resolving Convective Parametrization 2D CRMs in a global model

49 48 CAM CRCP OBS Improves diurnal cycle and tropical variability? Cloud Resolving Convective Parametrization 2D CRMs in a global model

50 49 Convective-scale Limited Area NWP Example of 1km UK Met Office Unified Model (MetUM) Simulation of Thunderstorms on 25 th Aug 2005

51 50 Convective-scale Limited Area NWP Example of 1km UK Met Office Unified Model (MetUM) Simulation of Thunderstorms on 25 th Aug 2005, 13 UTC Model simulated OLR and surface rain rate Meteosat low resolution infra-red and radar-derived surface rain rate

52 51 MODIS 13:10 UTC Convective-scale Limited Area NWP Example of 1km UK Met Office Unified Model (MetUM) Simulation of Thunderstorms on 25 th Aug 2005, 13 UTC Model simulated OLR and surface rain rate MODIS high resolution visible image

53 52 Global CRMs Global cloud resolving model simulations? Or at least cloud-permitting model simulations –3.5 km resolution 7 day forecast of the NICAM global model on the Earth Simulator (FRCGC, JAMSTEC) –Miura et al., (2007), Geophys. Res. Lett., vol. 34. –Courtesy of M. Satoh

54 53 Summary CRMs have been proven as very useful tools for simulating individual systems and in particular for investigating certain process interactions. They can also be used to test and develop parametrization schemes since they can provide supplementary information such as mass fluxes not available from observational data. However, if they are to be used to develop parametrization schemes, it is necessary to keep their limitations in mind (turbulence, microphysics) –not a substitute for observations, but complementary Care should be taken in the experimental design! –Large scale forcing The distinctions between traditional CRMs, limited area NWP and even GCMs is beginning to blur!

55 54 Summary - Feedback Welcome! LECTURE 1: Discussed microphysical processes. Examined the basic issues that must be considered when considering cloud parameterisation. LECTURE 2: We focussed on cloud cover, and in particular on statistical schemes which diagnose cloud cover from knowledge of the subgrid- scale variability of T and q t. LECTURE 3: Overview of the ECMWF cloud scheme. LECTURE 4: We considered some different methods of cloud validation with their respective strengths and weaknesses. LECTURE 5: Discussed what Cloud Resolving Models are and how they have been used for parametrization development.


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