A Numerical Study of a TOGA COARE Super Cloud Cluster – Preliminary results

A Numerical Study of a TOGA COARE Super Cloud Cluster –

Preliminary results

Peter M.K. Yau and Badrinath Nagarajan

McGill University

Outline

•Motivation & Objectives•Case Overview•Modeling Strategy•Results & Conclusions•Future work

Motivation• MJO associated with supercloud clusters.

Processes organizing warm-pool convection a “zeroth-order problem” (Webster & Lucas 1992)

• Organizing mechanisms (OM) particularely at meso-and synoptic scale not well understood (Yanai et al 2000, Gabrowksi 2003).

• Improved understanding of OM on various scales should lead to:– better representation of convection in models– reduced forecast errors at the medium range – better representation and understanding of the

role of convection on water vapor distribution in the vertical

Objective Use a real data multi-grid (15-5-1 km)

numerical modeling approach to• simulate supercloud clusters (SCCs)

over TOGA COARE• diagnose the processes that:

– organize MCSs,– cause clustering of MCSs, and

• study the impact of convection on water vapor distribution in the vertical

Case Overview – IOP of TOGA COARE

Yanai et al (2000)

OLR (W m-2)•Once a day•Averaged 5S - 5N

•OLR < 215 W m-2

Shaded

•Focus of this study on SCC A

1Nov 92

28 Feb 93

1Dec 92

1Jan 93

1Feb 93

IFA

Longitude

Tim

e

Time cluster

MCSs

Time cluster:•Lifetime > 24 h

MCS:•Lifetime < 24 h

The 6 DEC. 92 – 6 JAN. 93 SUPER CLOUDCLUSTER

Data Used:•Hourly GMS Infrared data•0-10S average

•Areas < 235 K precipitating (GATE/COARE convection)

EVOLUTION of IFA time cluster (11-13 DEC 92)

Data Used:•Precipitation retrieved from SSM/I, VIS/IR satellite data Sheu et al (1996), Curry et al (1999)•3 hourly/ 30 km resolution

mm h-1

mm/h

Data Used:•Precipitation retrieved from SSM/I, VIS/IR satellite data Sheu et al (1996), Curry et al (1999)•3 hourly/ 30 km resolution

EVOLUTION of IFA time cluster (11-13 DEC 92)

Madden & Julian (1994)

Schematics of Nakazawa (1988)

IFA

Longitude

Tim

e

1

2

3 4

5

6

7

89

1011

12 13

14

15

16

Time cluster:•Lifetime > 24 h

Westwardpropagating

Eastwardpropagating

Propagation of Time Clusters

IFA

Longitude

Tim

e

1

2

3 4

5

6

7

89 1

011

12

1314

15

16

Westward propagating Eastward propagating

IFA

200 hPaPROPAGATION OF TIME CLUSTERS

Time Evolution of Domain Average Brightness Temperature

Early morning minimum

Afternoon minimum(land)

Afternoon minimum(ocean)

• Brightness temperature minimum occurs: –Early morning for 8 time clusters,

–Afternoon for 4 time clusters • Suggests that most of the time

clusters are indeed MCSs

Organizing Mechanisms

• Large scale flow features (e.g., 2-day waves)

• Vertical wind shear (Le Mone et al 1999)

• Mid-level mesovortices (Nagarajan et al 2004) – Dec. 15, 1992

• Mapes gravity-wave mechanism

Longitude

Tim

e

1

2

3 4

5

6

7

89

1011

12 13

14

15

16

Westward propagating Eastward propagating

IFA

TIME CLUSTERS & 2-DAY PERIODICITY

K

1-4, 7-9, 11-13associated with2-day wave (Chen et. al 1996,Takayabu et. al 1996)

TIME CLUSTERS & VERTICAL SHEAR* (wind speed)

DATE 1000-850 hPa

800-400 hPa

6 – 19 Dec. 9228 - 31 Dec. 921, 4-6 Jan. 93

< 3.0 m s-1 < 5.0 m s-1

20 - 28 Dec. 92

> 3.0 m s-1 > 5.0 m s-1

27 Dec. 922-3 Jan. 93

< 3.0 m s-1 > 5.0 m s-1

*Areal & Temporal AveragesTemporal average: Duration of the time clusterAreal average: 0-10S, longitudinal extent of time cluster

SummaryDuring the lifetime of the SCC (6Dec-

6Jan):• Identified 16 time clusters consisting

of eastward & westward propagating cloud clusters.

• Convection generally associated with 2-day wave activity

• Convection occurred in a weak vertical wind shear environment except between 20-28 Dec 1992.

The Model

• Canadian mc2 model (Benoit et al. 1997)

• Fully compressible equations• Semi-Lagrangian, semi-implicit

numerics• One-way nesting of lateral boundary

conditions• RPN1 physics package

1 Recherche en Prevision du Numerique

1-month long time series

00 UTC/6 Dec. 92

03UTC/7 Dec. 92

00 UTC/7 Dec. 92 03 UTC/8 Dec. 92

00 UTC/6 Jan. 93

Time series based on last 24 h of each 27h long simulation.

•00 UTC chosen because of high availability of rainfall data for assimilation •Time integration strategy follows guichard et al. (2003)

3900 km

3900 km

130E 160E 190E

10N

EQ

10S

MC2 MODEL DOMAIN

Grid Size: 549 x 279 x 40, Horizontal grid length: 15 kmModel Top: 26 km

IFA

Modeling Strategy• Model Parameters:

– KF CPS (deep convection), BM CPS (shallow convection), Kong and Yau (1997) explicit bulk 2-ice microphysics, time step(90 s)

• Initial Conditions:– ECMWF operational analysis (0.5 o)

enhanced:• radiosonde data (Cieleski et al 2003), • temperature & moisture profiles modified

by 1D-VAR rainfall rate assimilation scheme (Nagarajan et al. 2006) and

• ABL moistening due to diurnal SST warming (Nagarajan et al 2001, 2004).

• 6-hourly lateral boundary conditions

IFA averaged surface precipitation rate

Missing data

IFA averaged surface sensible and latent heat flux

Horizontal size distribution of clouds (Model Domain)

Wielicki & Welch (1986)

Missing data

Domain-averaged surface precipitation rate (140-180E, 0-10S)

Missing data

RH

(h)

Heig

ht

(km

)IFA Av. RH

• The IFA-mean and temporal variability of:– surface fluxes of latent and sensible heat,

surface precipitation reasonable

• Large scale :– Simulated surface precipitation overpredicted– Horizontal size of cloud clusters are reasonably

simulated.

• Month long mesoscale simulation captures reasonably the life cycle of the super cloud cluster.

Conclusions

Future Work• Nesting to higher resolutions (5 km

and 1 km) with new three-moment 4 - ice microphysics (Milbrandt and Yau 2005a,b)

• Diagnose mechanisms that organized the super cloud cluster

• Diagnose processes for water vapor and temperature distributions