Cloud Simulation in VOCALS - Royal Meteorological Society · PDF fileCloud Simulation in VOCALS Ian Boutle with thanks to Cyril Morcrette and Steven Abel ... • Can implement a prognostic

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Cloud Simulation in VOCALSIan Boutle with thanks to Cyril Morcrette and Steven Abel


Contents

• Representation of stratocumulus during VOCALS in the Met Office Unified Model (MetUM)

• Case study of simulations at various resolutions

• Analysis of prognostic cloud scheme increments for stratocumulus

• Improvements to the cloud forecast

• Implementation of a prognostic cloud scheme in a “cloud resolving” model


Representation of stratocumulus during VOCALS in the MetUM


Stratocumulus schematic

A test for many of the parametrization schemes in GCMs


Wyant et al. (2010)Cloud Fraction, October average


• Strong diurnal cycle in liquid water path and low cloud fraction

• Typically under-estimated in all models

• A mixture of good cloud cover, wrong water content and bad cloud cover, right water content

• Use the MetUM to investigate this…

Liquid water path

Cloud fraction

Wyant et al. (2010)


VOCALS case-study

• Field experiment conducted during October and November 2008 to study the south-east Pacific stratocumulus.

• Additional observations available from Ronald H. Brown research vessel, FAAM BAE-146 and NSF C-130 flying out from Chile.

• Case study on 12th and 13th November 2008.

• Nested simulations driven from global NWP model (44km), with 12km, 4km and 1km horizontal resolution nested domains.


Cloud fraction from 12km simulation


Title

Cloud fraction from 1km simulation


Snapshot of cloud fields at 15Z

GOES-10 Visible Image MetUM SW flux at TOA


Nested Model Results

• All resolutions are surprisingly similar in their cloud evolution!

• Causes of LWP under-estimation are not resolution based.

• In-cloud water contents are always under-estimated

• Look at PC2 increments in global model to understand importance of different parametrization schemes on the cloud cover.

LWP

Cloud Cover

Obs from AMSR-E, SSMI and TMI

Obs from GOES-10


PC2 Cloud Scheme in Global Model• Calculates sources and sinks of cloud liquid

condensate/fraction from:• Radiation (LW cooling, SW heating)• Microphysics (rain-out of condensate)• Advection (moves cloud around)• Convection (cloud detrained from convective plume)• Erosion (evaporation at cloud edges)• Boundary-layer (vertical mixing)• Pressure change (large-scale vertical motion)• Initiation (scheme closure at clear/overcast sky)


Convection

LW Rad

Erosion

Precipitation

SW Rad

Boundary layer

Liquid Water Path Increments


Important cloud liquid processes• Convection main source of cloud water –

detrainment from plume as it rises and terminal detrainment at capping inversion

• Long-wave cooling also important – mainly at stratocumulus cloud top

• Boundary-layer also creates some cloud by moistening the cloud layer

• Precipitation main sink of cloud water• Erosion also important – mixing of saturated

and sub-saturated air at cloud boundaries

• Short-wave heating removes some cloud during the day-time


Convection

LW Rad

Erosion

SW Rad

Liquid Cloud Fraction Increments


Important cloud fraction processes• Convection is main source again, for the same

reason• Long-wave cooling is negligible – cloud fraction

already near 1 at cloud top!• No precipitation effect on cloud fraction (by

scheme design)• Erosion is therefore the main sink• Again, short-wave heating removes cloud

during the day, almost in balance with the long-wave cooling


What does all this tell us about LWP and cloud cover?

• Drizzle production is biggest sink of cloud liquid water• Autoconversion process converts cloud liquid (qcl) into rain water

(qrain) based on a process rate:

• A, B and C are parametrized values (Nd is cloud droplet number)

• Hypothesis to test: reducing the autoconversion rate should reduce drizzle and increase LWP

Bd

A NqclCt

qrain)()(

)( =∂

∂


Two possible autoconversion schemes

• Control MetUM uses Tripoli and Cotton (1980) parametrization

• Khairoutdinov and Kogan (2000) has much lower autoconversion rate, based on large-eddy simulations of stratocumulus

• Wood (2005) suggests that KK autoconversion rate is in better agreement to observations of stratocumulus


• General increase in LWP and cloud cover from autoconversion scheme change

• Increase in LWP is higher at night than during the day – not uniform

• In-cloud amounts are now good all the time, there is just too much cloud cover during the day

LWP

Cloud Cover

Obs from AMSR-E, SSMI and TMI

Obs from GOES-10


Implementation of a prognostic cloud scheme in a “cloud resolving” modelaka PC2 at 1km


PC2 Cloud Scheme in 1km Model• Calculates sources and sinks of cloud liquid

condensate/fraction from:• Radiation (LW cooling, SW heating)• Microphysics (rain-out of condensate)• Advection (moves cloud around)• Convection (cloud detrained from convective plume)• Erosion (evaporation at cloud edges)• Boundary-layer (vertical mixing)• Pressure change (large-scale vertical motion)• Initiation (scheme closure at clear/overcast sky)

Convection resolved at the grid-scale

What happens if we lose the main source of cloud?!


Snapshot at 12ZLiquid Water Path Cloud Fraction


Pressure Change


Precipitation / Erosion


Long Wave Radiation


Advection

Prevailing Wind


LW Rad

Pressure Change

Precipitation

SW Rad

Advection

Liquid water increments


LW Rad

Pressure Change

Erosion

SW Rad

Advection

Liquid cloud fraction increments


Cloud sources and sinks at 1km• Pressure change is now a large source of cloud

(fraction and liquid), due to resolved scale ascent in convective clouds

• Vertical advection transports cloud upwards• Precipitation is still main sink of cloud liquid• Erosion, which was always a sink in the global

model, is now a large source of fraction near the cloud tops

• Can now see the advection moving individual clouds downstream with the flow


Conclusions

• Stratocumulus is a fine balance between different parametrization schemes, and model resolution doesn’t really help too much with improving the simulation

• Most GCMs struggle to represent the diurnal cycle of cloud LWP

• Analysing the PC2 increments suggests the parametrization to focus on (microphysics) and changes to it lead to an improved simulation

• Can implement a prognostic cloud scheme in a high-resolution model, and it produces a reasonable simulation

• Large differences between global and 1km resolution PC2 process rates

• Need some careful thought about whether the assumptions behind the PC2 process rates are valid at this scale

Documents

Cloud Simulation in VOCALS - Royal Meteorological Society · PDF fileCloud Simulation in VOCALS Ian Boutle with thanks to Cyril Morcrette and Steven Abel ... • Can implement a prognostic