A new Convection Parameterization:
Some Achievements and Some Challenges
Hans-F Graf, Cambridge
Till M Wagner, Oxford (was Cambridge )
IPAM, UCLA, May 2010
Some motivation:
- Clouds are beautiful
- Clouds drive general
circulation
- Clouds are un-resolved
Convective clouds
Layered clouds
Radiation
Turbulence
State of the art:
Separation into stratiform and convective clouds, detraining water
from convective clouds is source for stratiform clouds => radiative
effects
If convective clouds change so do stratiform.
Latent heat
water
To simulate better stratiform clouds a spectrum of cumulus clouds is
necessary.
Model tuning via coupled
convective and stratiform
clouds
Disparity of Scales
• Deep convective clouds
– larger vertical than horizontal extent
– reaching depth of a few to several kilometres
• Boundary layer clouds
– on top of turbulent boundary layer
– 100m to one kilometre thick
• Explicit representation in numerical model
requires horizontal to vertical aspect ratio < 1:
– dx < 5km for deep convective clouds
– dx < 1km for boundary layer clouds
• Current horizontal spatial resolution too coarse
– in climate models: dx = 100-200 km
– convection unresolved
– paramaterization needed to include effect
Vertical scales:
Since in-cloud vertical temperature gradient is ~6K/1km a
vertical model resolution of 1 km in the middle troposphere is
inadequate to find the freezing level.
However, freezing is critical for cloud microphysics,
efficiency of rain formation and, ultimately, latent heat
release.
Also aerosol- cloud microphysics interaction effects may
critically depend on whether mixed phase is reached and this
may affect the maximum cloud height, especially if weak free
tropospheric inversion layers are present.
Hence, deep convection parameterization has to be done on
higher vertical resolution of the order of 100 m. (Graf 2004)
Convection Parameterizations
• Adjustment schemes (Manabe, 1965):
– Only temperature, moisture adjustment
– No transport
– No microphysics
• Moisture budget schemes (Kuo, 1974):
– Redistribution of temperature, moisture
– Coupled to moisture convergence
• Spectral mass flux scheme (Arakawa & Schubert, 1974)
– Too difficult
• Bulk mass flux schemes (Tiedtke, 1989; Gregory & Rowntree,
1990):
– Temperature, moisture, momentum, tracer transport
– Basic microphysics
Arakawa-Schubert parameterisation
quasi-equilibrium closure
• Solve
Quasi-equilibrium closure!
for
to determine the cloud mass flux
Kij = effect of cloud j on cloud i, Fi = environmental forcing for cloud i
MBj = mass flux at base of cloud j
• Characteristics
– Mass flux must be positive semidefinite (
)
– Positive semidefiniteness of solution not guaranteed

usually no exact solution possible

approximate/optimal solution (Lord, 1982; Hack et al, 1984)
Convective Cloud Field Model
Nober and Graf 2005
• Goals
– Better representation of dynamics and microphysics
• mixed phase microphysics
• droplet nucleation & cloud ice initiation
– Additional in-cloud variables besides massflux
• vertical velocity spectrum
• super-saturation & nucleation rate
• Concept:
 single cloud model + cloud spectrum calculation
Model Concept –
single cloud model
• Entraining parcel model: 1D, Lagrangian:
(Weinstein & MacCready, 1969, extended Graf 1979, Wagner 2009)
– high vertical resolution (~100m)
– vertical velocity and parcel diameter
– more detailed microphysics:
• water vapour, liquid water, rain, ice, snow
• aerosol (SO4 as proxy)  CDNC
• Calculates cloud types that can potentially exist within grid cell
– different initial conditions: radius, vertical velocity
Model Concept cloud spectrum calculation
• Multivariate Lotka-Volterra (LV) system of ODEs
– describes competitive system of N species
• Dynamical closure of Arakawa-Schubert type
– clouds forced by environment (CAPE)
– clouds mutually interact
via influence
of the environment
– dynamical evolution
of cloud spectrum
within large scale time step
(Arakawa and Schubert, 1974)
Convective Cloud Field Model
- dynamical cloud balance
K = cloud-cloud interaction
ni = number of species i
F = environment impact on cloud
A = cloud work function (conversion of potential to
kinetic energy)
•
Multivariate Lotka-Volterra (LV) system of ODEs
•
Describes competitive system of N species
•
Carrying capacity of species i :
– Equilibrium amount of species i in absence of other species
– Used as solution of the relaxed Arakawa-Schubert scheme (just one
cloud at a time, Moorthi & Suarez, 1992)
Comparing AS against CCFM:
Convective cloud field closure formulation can be regarded as a
generalisation of the quasi-equilibrium equation of Arakawa and
Schubert (1974) dropping two assumptions:
1) kinetic energy equilibrium dK/dt = 0 is not assumed
2) stationarity, i.e. dx/dt = 0 or equivalently dM/dt = 0 is not assumed
Further, an explicit cloud model is used and this allows to also
include explicit vertical velocities and a much better possibility to
include more complex cloud microphysics (e.g. activation of CCN!).
For those wishing
the details:
The original model was
completely rewritten,
tested and a solid
mathematical derivation
and description is
provided here:
CCFM is now included into Global Climate Model
(ECHAM5-HAM, also in single column mode!) and Limited
Area Model (REMO and REMOTE)
In single column mode both reanalysis and radio sonde data can
be used to facilitate comparison against observations.
For initializing the cloud spectrum a maximum cloud base radius
is determined from the height of PBL. Then a number (usually 1520) of smaller initial cloud radii is set down to a minimum of 100m.
First guess of cloud spectrum results from carrying capacity.
Vertical velocity at cloud base is dependent on TKE.
There are only few useful data
sets to compare a model with
reality. One of those is from
ARM stations.
CCFM in single column mode
clearly much improves the
sequence of precipitation
events and their variable
intensity both, over land and
over tropical ocean.
One of the really nice features of CCFM is that it allows to identify rainfall
intensity spectra for each time step and for each grid cell.
The daily cycle of precipitation very closely follows observations.
Zonal mean precipitation is well covered. Note that both re-analysis data
sets give higher tropical rainfall!
Annual mean
precipitation
Only sparse observations!
ECHAM5-CCFM is in ballpark
of re-analysis and the highly
tuned ECHAM5-Tiedtke.
CCFM reduces bias in tropical
West Pacific but introduces
problems in tropical Indian
Ocean.
Boreal summer (JJA) precipitation
All model based data sets
have problem with too
much rainfall over West
Pacific; Indian and SE
Asian monsoons also are
problematic. NH storm
tracks are too dry. CCFM
is generally in the ballpark,
but does a good job over
West Pacific.
CCFM creates strongest negative bias in
East Pacific ITCZ. Northern Indian Ocean
much too much precipitation. ECHAM-T and
ECHAM-CCFM too much precipitation over
Himalayas and southern Tibetan Plateau
Anomalously strong
low and convergence
over Tibetan Plateau
forced by radiative
heating?
Wind vectors and
geopotential
difference from
zonal mean during
the Indian summer
monsoon season.
Anomalous cyclonic
circulation, forced by
convective heating?
The Tibet problem
Solar constant
What is the effect of the
daily cycle in cloud cover on
the radiative budget?
Diffuse radiation
is minimal
PBL clouds like Cu hum are not present in the models, hence solar radiation
directly heats the ground, creating a strong heat low and this affects large
scale circulation and moisture transport.
REMO over the Tibetan Plateau
• Integration for 01 April – 31, Oct. 1998
•A large domain with 0.5º (~55km) resolution applied in Asian region in
forecast mode (30 hours) driven with ECMWF 6-hr reanalysis data
• REMO1/2 represents reasonably well large-scale circulation and
seasonal surface climate (Temp. and Precip.)
REMO1/6
REMO1/2
ECMWF
•‘Self-nesting’
• REMO1/6 applied
with 1/6º (~18km)
driven with REMO1/2
6-hourly at lateral
boundary in climate
mode
• 3-hourly outputs are
analyzed
• No land surface
changes included
Direct comparison of rainfall simulated by REMO at different
resolution, driven 6-hourly by ECMWF re-analysis for two well
maintained stations (GAME-Tibet). Cui et al. 2007
REMO 1/2o
REMO 1/6o
While REMO 1/2 fails to simulate correct precipitation amount, REMO 1/6
does well. The coarse model produces less latent heat and surface cooling.
The Tibet problem …
Is very much a problem of
resolution.
The Indian summer monsoon problem
What is the contribution of aerosol-microphysics interaction
to the solution?
The Indian summer monsoon bias of ECHAM is enhanced by CCFM
Adding aerosols over land slightly improves the situation…
SEA summer
monsoon:
Too much rain over
Arabian Sea and
Bay of Bengal, too
much right over
Himalayas and
ECHAM-T too much
over warm pool.
Echam5-Tiedtke
Echam5-CCFM
Increasing the number of aerosol particles simply by
factor of 10 over land and sea has strong impact on BoB
precipitation and South China Sea. There precipitation
ECHAM5-CCFM 10xCCN
bias decreases. Arabian Sea bias slightly enhances.
Using CCFM in the ECHAM-HAM (including prognostic aerosol) environment will
have strong impact on model performance.
Strong winds over Arabian
Sea lead to enhanced water
vapour flux?
Enhanced trough over Bay of
Bengal enhancing convection?
CCFM already now improves geopotential height distribution, problems over
Tibet remain as do the strong winds over the warm waters of the Indian Ocean.
The Indian summer monsoon problem
Is more complex since it involves a number of processes
not covered completely by convection parameterization.
Conclusions
CCFM is a new parameterization of convective clouds that has a
number of benefits. It is, without any tuning, able to reproduce and to
also at some places improve simulated convective activity.
If run in SCM (without large scale the biases of the host model) CCFM
strongly improves precipitation and related parameters.
This gives space for further improvements, especially since
processes like microphysics in convective clouds, impact of aerosols
of different nature can relatively easily be included.
CCFM provides more information (e.g. on convective transport,
spectrum of precipitation intensity etc.) than other parameterizations.
Challenges remain with respect to organized convection, entrainment
and detrainment profiles, close link to stratiform clouds, and others.