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**Richard Forbes, forbes@ecmwf.int**

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

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**Outline Why were cloud resolving models (CRMs) conceived?**

What do they consist of? How have they developed? To which purposes have they been applied? What is their future? Cloud Resolving Models

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**Why were cloud resolving models conceived?**

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

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**Why were cloud resolving models conceived?**

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)

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**Why were cloud resolving models conceived?**

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

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**Why were cloud resolving models conceived?**

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

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**Outline Why were cloud resolving models conceived?**

What do they consist of ? Cloud Resolving Models

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**What is a CRM? The concept**

GCM Grid cell ~100km GCM grid too coarse to resolve convection - Convective motions must be parametrized 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!

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**What is a CRM? The physics**

radiation SW IR 1. Momentum equations 2. Turbulence Scheme dynamics 3. Microphysics 4. Radiation? microphysics turbulence 5. Surface Fluxes surface fluxes Cloud Resolving Models

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

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**Lateral Boundary Conditions**

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 W

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**Spatial and Temporal Scales?**

1. O(1km) 1. Deep convective updraughts ~30 minutes 3. O(10km) 3. Anvil cloud associated with one event 2. O(100m) 2. Turbulent Eddies 4. O(1000km) 4. Mesoscale convective systems, Squall lines, organised convection days-weeks Cloud Resolving Models

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**What do they consist of ? MICROPHYSICS SUBGRID-SCALE TURBULENCE**

(ice and liquid phases) SUBGRID-SCALE TURBULENCE RADIATION (sometimes - Expensive!) DYNAMICAL CORE Open or periodic Lateral BCs Lower boundary surface fluxes Upper boundary Newtonian damping (to prevent wave reflection) BOUNDARY CONDITIONS

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**(ice and liquid phases)**

What do they consist of ? DYNAMICAL CORE Prognostic equations for u,v,w,q,rv,(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!!!

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**Reference: Emanuel (1994), Atmospheric Convection**

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 Cloud Resolving Models

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**Basic Equations Momentum: DYNAMICAL CORE Pressure Gradient Coriolis**

Diabatic terms (e.g. turbulence) Mixing ratio of vapour and condensate variables Buoyancy Where: Overbar = mean state 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 Cloud Resolving Models

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**Basic Equations Thermodynamic: Equation of State: Moisture:**

Diabatic processes: Radiation Diffusion Microphysics (Latent heating) Equation of State: Moisture: Microphysics terms Condensation Evaporation Cloud Resolving Models

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**All scales of motion present in turbulent flow **

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. Cloud Resolving Models

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**Dimensionless Constant = 0.02 -0.1**

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. Reference Cotton and Anthes, 1989 Storm and Cloud Dynamics Dimensionless Constant = Cloud Resolving Models

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**Reference: Stull(1988), An Introduction to Boundary Layer Meteorology**

SUBGRID-SCALE TURBULENCE 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 Reference: Stull(1988), An Introduction to Boundary Layer Meteorology See Boundary Layer Course for more details! Cloud Resolving Models

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Microphysics 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 Cloud Resolving Models

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**From Dare 2004, microphysical scheme at BMRC**

Microphysics Most schemes use a bulk approach to microphysical parameterization Just one equation is used to model each category qtotal qrain Warm - Bulk qvap qrain qliq qsnow qgraup qice Ice - Bulk Ice - Bin resolving Different drop size bins

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**Microphysics For example: Fall speed of graupel Sources and sinks**

For Example, (Lin et al. 1983) snow to graupel conversion qsnow-crit = 10-3 kg kg-1 S =0 below this threshold T0 =0oC 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 qgraup used to calculate the RHS of the equation. If sinks result in a negative mass, some models reset to zero (i.e. not conserving).

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**Outline Why were cloud resolving models conceived?**

What do they consist of? How have they developed? Cloud Resolving Models

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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 100m

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**Possible 2D domain configurations**

Axi-symmetric z r Slab Symmetric z x 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 For reference see Soong and Ogura JAS (1973)

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**significant proportion**

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

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History:

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**Outline Why were cloud resolving models conceived?**

What do they consist of? How have they developed? To which purposes have they been applied? Cloud Resolving Models

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**1990s really saw an expansion in the way in which CRMs have been used**

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 Cloud Resolving Models

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**Uses: Radiative-Convective equilibrium experiments**

Long term integrations until fields reach equilibrium Radn cooling = = convective heating surface rain = moisture fluxes 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)

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**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

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**Example: 350m resolution 3D CRM simulation used in a variety of parametrization ways**

Used to understand coldpool triggering Used to set closure parameters for a simplified cloud model Tompkins JAS 2001 Di Giuseppe & Tompkins JAS 2003 Used as a cloud-field proxy to develop parametrization to correct radiative geometrical biases Used to justify PDF decision in cloud scheme of ECHAM5 Tompkins JAS 2002 90 km Di Giuseppe & Tompkins JGR 2003, JAS2005 Tompkins & di Giuseppe 2006

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**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.

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**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.

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**Uses: Developing aspects of parametrization schemes**

CC cloud cover relative humidity cloud mixing ratio 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

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**Uses: Developing Parametrization Schemes**

GCMs - SCMs CRMs OBSERVATIONS Validation (and development) Validation (and development) Validation (and development) Provide extra quantities not available from data

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

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**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:

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**Simulation of Observed Systems**

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 Mc Radiosonde stations measure

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**GCSS - GEWEX Cloud System Study (Moncrieff et al. Bull. AMS 97)**

PARAMETERISATION GCMS - SCMS GCSS - GEWEX Cloud System Study (Moncrieff et al. Bull. AMS 97) CRMs OBSERVATIONS 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

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**Simulations from different models (total hydrometeor content)**

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

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

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**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)

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**Outline Why were cloud resolving models conceived?**

What do they consist of? How have they developed? To which purposes have they been applied? What is their future? Cloud Resolving Models

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**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?

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**Cloud Resolving Convective Parametrization 2D CRMs in a global model**

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

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**Cloud Resolving Convective Parametrization 2D CRMs in a global model**

CAM CRCP OBS Improves diurnal cycle and tropical variability?

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

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

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

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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

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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!

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**Summary - Feedback Welcome! forbes@ecmwf.int**

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 qt . 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. Cloud Resolving Models

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