r. forbes, 17 nov 09 ecmwf clouds and radiation university of reading ecmwf cloud and radiation...
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R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
ECMWF Cloud and Radiation Parametrization: Recent Activities
Richard Forbes,
Maike Ahlgrimm,
Jean-Jacques Morcrette,
Martin Köhler
“Evaluation of models” University of Reading, 17-18 Nov 2009
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Some ECMWF Cloud/Radiation Recent Parametrization Activities
1. Development of cloud and precipitation parametrization (prognostic variables and microphysical
processes…..)
2. Evaluation of cloud/precip with CloudSat/CALIPSO(Radar reflectivity)
3. Evaluation of cloud regimes (TCu - new dual-mass flux shallow convection scheme)
4. Representation of aerosol and radiative impacts(GEMS/MACC)
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
1. Cloud Scheme Developments
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of ReadingECMWF Cloud Scheme Developments
WATER VAPOUR
CLOUDLiquid/Ice
PRECIP Rain/Snow
Evaporation
Autoconversion
Evaporation
Condensation
CLOUD FRACTION
Current Cloud Scheme
• 2 prognostic cloud variables (condensate & cloud fraction) + water vapour.
• Diagnostic liquid/ice split as a function of temperature between 0°C and -23°C.
• Diagnostic representation of precipitation.
CLOUD FRACTION
New Cloud Scheme
• 5 prognostic “cloud” variables (liquid, ice, snow, rain, cloud fraction).
• Additional sources/sinks for new processes.
• New explicit/implicit solver
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
New 5-prognostic cloud microphysicsLiquid vs Ice Fraction
New prognostic schemeCurrent diagnostic scheme
Temperature
Liqu
id W
ater
Fra
ctio
n
1.0
0.0-23ºC
Temperature
0ºC
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Model Ice Water Path (IWP) (1 year climate)
New 5 prognostic cloud microphysics Ice vs. Snow
CloudSat 1 year climatologyFrom Waliser et al. (2008)
Current scheme (IWC)
New scheme (IWC+SWC)
Observed Ice Water Path (IWP)
g m-2
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
From Waliser et al. (2009),
JGR
Widely varying estimates of IWP from different satellite datasets!
VerificationAnnual average Ice Water Path from Satellite
CloudSat
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
2. Evaluation with CloudSat
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Radar ReflectivityAlong-track model vs. CloudSat comparison
Spatial distribution of cloud/precipitation reflectivities
generally very good!
However, there are some discrepancies that are highlighted by
the radar reflectivity comparison
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Radar Reflectivity vs. Height Frequency of Occurrence
Tropics over ocean 30S to 30N for February 2007
Radar Reflectivity Statistics
Significantly higher occurrence of cloud in model – but is this due to overestimating the precipitation fraction?
Lack of low reflectivity mid-level and low-level cloud ?
Relatively too frequent low-level
high reflectivity convective rainfall
Peak reflectivities too high altitude (from convective
snow)
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
3. Regime Evaluation(Maike Ahlgrimm)
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Regime evaluation
Defining a regime:•Use criteria like cloud top height, cloud thickness, cloud fraction.•Geographical region•Use model (dynamical) quantities.•Different issues for ground based, satellite (vertical profile vs, 2D view).
Compositing:•To avoid focussing on potentially unrepresentative individual cases.•To get large enough sample size without losing characteristics of cloud type.
Zonal cross-section of frequency of cloud/precipitation occurrence
Maike Ahlgrimm
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Example: Trade cumulus using CALIPSO
DualM
65.1%
46.5%
Control
CALIPSOControl Criteria:•Cloud top height <4km•Over ocean•30S to 30N•Cloud fraction <50%
DualM
CALIPSO
CALIPSO
Maike Ahlgrimm
Compensating errors:Model cloud occurs too often, but has too little cloud fraction when it occurs.
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Example: Mid-latitude “cold air outbreak”
Criteria from model:• Surface pressure ≤ 1015 hPa• Potential temperature difference 700 hPa to lowest model
level ≤ 9K• Over ocean
Add criteria from satellite, such as cloud top height….
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
4. Radiation
and aerosol
J-J Morcrette
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Recent developments in aerosol representation in the ECMWF IFS (GEMS)
• ECMWF IFS model including prognostic aerosols has been run in two configurations:– In aerosol free-wheeling mode: aerosol
advection and “full” (but simplified) aerosol physics using temperature, humidity, winds etc. from the analyses/forecasts every 12 hours
– In analysis mode with subsequent forecasts
• In both configurations, what is included is– Sea salt aerosols (3 bins, 0.03–0.5–5–20 m)– Dust aerosols (3 bins, 0.03–0.55–0.9–20 m)– Organic matter (hydrophilic, hydrophobic)– Black carbon (hydrophilic, hydrophobic)
– Sulphate aerosols (SO4 from SO2 sources)
Morcrette et al. (2008)Benedetti et al. (2009)
Model AOD analysis Jul 2003
MISR AOD Jul 2003
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
AATSR MERIS SEVERI
MISR MODIS GEMS
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Comparisons AERONET, ECMWF climatology, GEMS-AER, GlobAEROSOL-SEVIRI (Azores)
Azores/Cabo Verde 500nm
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
To improve model parametrizations…
The challenge is to determine real differences between the model and observations, identify the most important physical processes, understand their interactions and improve their representation in the model.
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Some Questions to Highlight
• How do we compare incompatible model and obs ?(different quantities, spatial and temporal scales, obs limitations/errors)
– Forward models/simulators/emulators– Sub-columns or appropriate averaging– Understand the observation limitations/errors
• How do we evaluate physical processes ?– Regime-dependent evaluation (where particular processes dominate)– Model sensitivity studies….– Combining different observations to evaluate physical relationships?
• How do we disentangle model compensating errors ?– Exploit synergy of different observations (to provide information on clouds, radiation,
aerosol, water vapour all at the same time!)
• How important is variability on different spatial and temporal scales ?– Need temporal and spatial heterogeneity from observations– Cloud cover, cloud condensate, humidity, aerosols…..
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Questions ?
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
A mixed ‘uniform-delta’ total water distribution is assumed
qt
G(q
t)
qs
Cloud cover is integral under supersaturated
part of PDF
1-C
qtG
(qt)
C
qs
ECMWF cloud parametrizationIn the real world
ECMWF Cloud Parametrization Representing sub-grid variability
R. Forbes, 17 Nov 09“ECMWF Clouds and Radiation”
University of Reading
Radar ReflectivityCross-section through tropical convection
CloudSat Radar Reflectivity
Model Radar Reflectivity (Ice, Liq, Snow, Rain)
Model Radar Reflectivity (Ice, Liq only)