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1 

ARM/GCSS/SPARC 

TWP-ICE CRM Intercomparison Study 

Ann Fridlind1, Andrew Ackerman1, Jon Petch2, Paul Field2, Adrian Hill2, 

Greg McFarquhar3, Shaocheng Xie4, and Minghua Zhang5

7 January 2009: Specification finalized 

6 July 2010: Figure 4 corrected 

16 August 2010: File access locations updated 

1. Introduction 

The Tropical Warm Pool—International Cloud Experiment (TWP-ICE) took 

place over and around Darwin, Australia, from January 20 through February 13, 2006. 

As described by May et al. [2008], TWP-ICE became “the first field program in the 

tropics that attempted to describe the evolution of tropical convection, including the 

large-scale heat, moisture, and momentum budgets at 3-hourly time resolution, while at 

the same time obtaining detailed observations of cloud properties and the impact of the 

clouds on the environment.” The experiment specifically focused on the properties of 

outflow cirrus, aiming to document their relationship to environmental conditions. The 

experimental domain (Figure 1) was centered on a highly-instrumented site operated by 

the US Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate 

Research Facility and a polarimetric weather radar operated by the Australian Bureau of 

Meteorology (BOM), surrounded by a 3-hourly sounding array and surface energy budget 

sites. TWP-ICE was also coordinated with the Aerosol and Chemical Transport in 

tropIcal conVEction (ACTIVE) Program funded by the UK Natural Environment 

Research Council, which gathered extensive in situ measurements of environmental 

aerosol properties [Vaughan et al., 2008]. The data gathered during TWP-ICE and 

ACTIVE are now fully archived at the ARM Climate Research Facility (ACRF)6

 and the 

British Atmospheric Data Centre7

, respectively. 

A principal motivation for TWP-ICE and the organizations that funded it is the 

improvement of climate forecasting skill by general circulation models (GCMs), which 

are hindered by inadequate representation of cloud properties and their relationship to 

environmental conditions [Randall et al., 2007]. As such, TWP-ICE aimed to provide 

the observations required to better understand and model tropical convection generally 

and cirrus outflow in particular. Because cloud properties vary on short time and space 

 

1

 NASA Goddard Institute for Space Studies, New York, NY 

2

 Met Office, Exeter, UK 

3

 Department of Atmospheric Sciences, University of Illinois, Urbana, IL 

4

 Lawrence Livermore National Laboratory, Livermore, CA 5

 Institute for Planetary and Terrestrial Atmospheres, Stony Brook University, Stony Brook, NY 6 http://www.arm.gov/campaigns/twp2006twp-ice

7

 http://badc.nerc.ac.uk/data/active
2 

scales and many fundamental aspects of their behavior remain poorly understood, an 

important building block for improvement of GCM cloud parameterizations has been 

detailed simulation of observed cloud systems with cloud-resolving models (CRMs). The 

GEWEX8

 Cloud Systems Study (GCSS) program has coordinated such CRM studies 

internationally, often based on major field experiments. Here we present the 

specifications for a GCSS CRM intercomparison case study based on the analysis of data 

gathered during the TWP-ICE and ACTIVE programs, to be performed as a joint exercise 

of the ARM Cloud Modeling Working Group (CMWG) and the GCSS Precipitating 

Cloud Systems (PCS) working group, as well as the Stratospheric Processes And their 

Role in Climate (SPARC) program. While ARM and GCSS have previously 

coordinated, this will be the first joint ARM/GCSS/SPARC intercomparison, motivated 

by SPARC’s goal of understanding the influence of tropical deep convection on water 

 

8

 The Global Energy and Water Cycle Experiment (GEWEX) is a core project of the World Climate 

Research Programme, which is jointly sponsored by the World Meteorological Organization and the 

International Council for Science. 

Figure 1. The TWP-ICE experimental domain as mapped for derivation of large-scale 

atmospheric forcing budgets. Figure courtesy of Shaocheng Xie, Lawrence Livermore 

National Laboratory. 
3 

vapor concentrations and convective transport through the tropical tropopause and the 

unique opportunity to contribute to that understanding through high-resolution cloud 

modeling analysis of the TWP-ICE and ACTIVE measurements. 

2. Objectives 

The two principal goals of this intercomparison are to answer the following 

science questions: (i) what physical processes control the amount of moisture transport to 

the tropical upper troposphere and (ii) what physical processes control anvil cirrus cloud 

longevity? To answer these questions, this intercomparison will focus on the physical 

processes that control upper tropospheric moisture and cirrus in nature and in cloudresolving models. Making comprehensive comparisons between observations and 

models is therefore a third, integral goal of this work, to be addressed first. 

2.1. Objective #1: Evaluate Model Performance and Methodology 

Because no previous deep convection model intercomparisons to our knowledge 

have focused closely on upper tropospheric moisture, we will first evaluate the 

performance of the models and the suitability of observations in constraining the model 

results. Considering that many aspects of cloud behavior remain poorly understood, 

especially in the upper troposphere, it is not surprising that CRMs starting from the same 

initial and boundary conditions may produce quite different results when it comes to 

anvil mass flux [e.g., Barth et al., 2008] or upper tropospheric moisture. Representation 

of anvil outflow—a final stage of complex storm system evolution—presents a challenge 

where models might be expected to exhibit the widest disagreement with available 

measurements, and measurements themselves are expected to be hardest to analyze. The 

first goal of this model intercomparison is to answer the following related questions: 

• where do simulations and data disagree most widely? 

• are data sources sufficient to evaluate model performance? 

• what additional data gathering efforts should be pursued? 

• is the methodology used here sufficient to answer these questions? 

The approach to answering these questions will be to gather as many data sources 

as feasible and compare them with CRM results (Table 1). To accomplish this, it is 

necessary to request many diagnostics not previously included in GCSS PCS case 

studies. For example, ice water path retrievals are available at horizontal resolutions of 

order 20 km, 4 km, and less than 1 km (instantaneous vertical profile), and the last of 

these is currently reliable only where surface precipitation rate does not exceed circa 

0.02 mm/h. Therefore, in addition to requesting that models report mean ice water path 

(a standard diagnostic), maximum values are requested at 20-km and 4-km resolution, 

and mean and maximum values are also requested at grid-scale resolution over columns 

where surface precipitation rate is less than 0.02 mm/h. In this manner, the data sources 

lead to most diagnostics requested. Measurements of each physical quantity will be 

evaluated against model performance as a group, such as precipitation (Figure 2, 

corresponding to the first measurement category in Table 1). 
4

Table 1. Data streams and model diagnostics for model evaluation, categorized by model 

physical variable 

Physical 

Variable 

Data Set (Principal Investigator) Model Diagnostics (Table)* 

C-pol radar (May) PR2p5, PR2p5_2p5max, 

PR2p5_2p5F.02, .2, 2, 20 (5) 

large-scale forcing (Xie/Zhang)** PRs (5) 

disdrometer (Williams) PRs, PRs_max (5) 

bucket gauges (Williams) PRs, PRs_max (5) 

Precipitation rate 

ECMWF model*** PRs, PRs_55max (5) 

Figure 2. Comparison of requested diagnostics (described in Section 4) from example 

baseline and sensitivity test simulations (described in Section 3). Data courtesy of Shaocheng 

Xie (large-scale forcing data), Peter May (C-Pol radar data), Christopher Williams 

(disdrometer and bucket gauge data), and ECMWF (model). 
5 

Physical 

Variable 

Data Set (Principal Investigator) Model Diagnostics (Table)* 

disdrometer (Williams) qr, qr_max (4) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

qc_PR, qc_PRmax (4) 

Liquid water 

mixing ratio

ECMWF model*** qc, qc_55max (4) 

MWR ground retrieval (Turner) LWP_PR, LWP_PRmax (5) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

LWP_PR, LWP_PRmax (5) 

large-scale forcing (Xie/Zhang)** LWP_PR, LWP_PRmax (5) 

VISST satellite retrieval (Minnis) LWP, LWP_4max (5) 

TRMM satellite retrieval (Wentz) LWP, LWP_25max (5) 

Liquid water 

path 

ECMWF model*** LWP, LWP_55max (5) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

qi_PR, qi_PRmax (4) 

hybrid ground-satellite retrieval (Liu) qi, qi_20max (4) 

Ice water mixing 

ratio

ECMWF model*** qi, qi_55max (4) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

IWP_PR, IWP_PRmax (5) 

VISST satellite retrieval (Minnis) IWP, IWP_4max (5) 

hybrid ground-satellite retrieval (Liu) IWP, IWP_20max (5) 

Ice water path 

ECMWF model*** IWP, IWP_55max (5) 

Total condensate 

mixing ratio

CSI aircraft data (McFarquhar) qc+qi fields (3) 

corrected radiosondes (Jakob/Hume) qv, min, max (4) 

RH_RHi, min, max (4)

aircraft in situ data (Whiteway, Hacker) qv, RH_RHi fields (3) 

large-scale forcing (Xie/Zhang)** qv, RH_RHi (4) 

Water vapor

ECMWF model*** qv, 55min, 55max (5) 

RH_RHi, 55min, 55max (5) 

ARSCL ground retrieval (Clothiaux) CBH, min, max (5)

VISST satellite retrieval (Minnis) CBH_4 (5) 

Cloud base 

height

ECMWF model*** CBH_55 (5) 

ARSCL ground retrieval (Clothiaux) CTH, min, max (5) 

large-scale forcing (Xie/Zhang)** CTH (5) 

VISST satellite retrieval (Minnis) CTH_4 (5) 

Cloud top height 

ECMWF model*** CTH_55 (5) 

TSI ground instrument (Morris) CF (5) 

SFA ground retrieval (Long) CF (5) 

Cloud cover 

MODIS satellite retrieval CF (5) 
6 

Physical 

Variable 

Data Set (Principal Investigator) Model Diagnostics (Table)* 

C-pol radar (May) dBZ fields (3), dBZ2p5_2p5max, 

2p5F10, 30, 50 (4) 

S-band radar (Williams) dBZ, max (4) 

Radar 

reflectivity 

disdrometer (Williams) dBZ, max (4) 

Doppler velocity S-band radar (Williams) DopV_min, max (4) 

disdrometer (Williams) DopV_min, max (4) 

Particle number 

concentration 

CIP aircraft data (McFarquhar) N_100 fields (3) 

Particle 

projected area 

CIP aircraft data (McFarquhar) A_100 fields (3) 

disdrometer (Williams) MMDs_0, 0min, 0max (5) 

C-pol radar (May) MMD2p5_2p5 (5) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

qlRe_PR, PRmin, PRmax (4) 

qiRe_PR, PRmin, PRmax (4) 

Particle size****

VISST satellite retrieval (Minnis) Nc and Ac fields (3) 

Ni and Ai fields (3) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

qlOD_PR, qlOD_PRmax (5) 

qiOD_PR, qiOD_PRmax (5) 

Cloud optical 

thickness 

VISST satellite retrieval (Minnis) OD (5) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

SWdn_PR, PRmin, PRmax (4) 

SWup_PR, PRmin, PRmax (4) 

LWdn,_PR PRmin, PRmax (4) 

LWup_PR, PRmin, PRmax (4) 

QCRAD ground data (Long) SWdn, min, max (4) 

SWup, min, max (4) 

LWdn, min, max (4) 

LWup, min, max (4) 

large-scale forcing (Xie/Zhang)** SWdn, SWup, LWdn, LWup (4) 

Surface 

broadband 

radiative fluxes 

ECMWF model*** SWdn, 55min, 55max (4) 

SWup, 55min, 55max (4) 

LWdn, 55min, 55max (4) 

LWup, 55min, 55max (4) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

same as previous 

VISST satellite retrieval (Minnis) SWup_TOA4, min, max (5) 

LWup_TOA4, min, max (5) 

large-scale forcing (Xie/Zhang)** same as previous 

Top-ofatmosphere 

broadband 

radiative fluxes 

ECMWF model*** same as previous 

large-scale forcing (Xie/Zhang)** same as previous 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

same as previous 

Column 

radiative 

absorption 

ECMWF model*** same as previous 
7 

Physical 

Variable 

Data Set (Principal Investigator) Model Diagnostics (Table)* 

radiometric aircraft data 

(Tooman/McCoy)

SWdn, min, max (4) 

SWup, min, max (4) 

LWdn, min, max (4) 

LWup, min, max (4) 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

same as previous 

Broadband 

radiative flux 

profiles 

ECMWF model*** same as previous 

Broadband 

radiative heating 

rate profiles 

TCPRHP ground retrieval 

(McFarlane/Mather/Comstock) 

SWhr_PR, PRmin, PRmax (4) 

LWhr_PR, PRmin, PRmax (4) 

Latent heating 

rate profiles 

radar retrieval (Schumacher) LHhr (4) 

Apparent heat 

source and 

moisture sink 

profiles 

large-scale forcing (Xie/Zhang)** QTadv, micro, rad (4) 

QVadv, micro (4) 

surface eddy-covariance data 

(Beringer) 

LHflx, min, max (5) 

SHflx, min, max (5) 

large-scale forcing (Xie/Zhang)** LHflx, SHflx (5) 

Surface sensible 

and latent heat 

fluxes 

ECMWF model*** LHflx, SHflx (5) 

*Field, profile, and scalar names are defined in Tables 3–5. 

** Three-hour domain-mean values used in the variational analysis (see Section 3.2.1) are not generally 

independent from other data streams listed (e.g., precipitation from C-Pol, liquid water path from MWR, 

cloud top height from VISST, and surface broadband radiative fluxes from QCRAD). 

***ECMWF model outputs are data streams available through the ACRF that give an example of GCMscale output. 

****Particle size definitions discussed in Section 4. 

2.2. Objective #2: Quantify Convective Transport to the Tropopause 

After systematically evaluating model performance against available data, the 

second primary goal of this study is to analyze predicted convective transport to (and 

from) the tropopause region, including answering the following related questions: 

• what are the temporal and spatial characteristics of the vertical mass transport, 

and how does it influence predicted water vapor mass mixing ratios in the vicinity 

of the tropopause? 

• is ice sublimation a significant source of water vapor to any elevations in the 

upper troposphere (e.g., Horváth and Soden, 2008)? 

• to what extent does dehydration occur in overshooting convection, and is it a 

significant sink of water vapor near the tropopause (e.g., Sherwood and Dessler, 

2001)? 

• what are the primary uncertainties in simulation results and field data? 

To aid this effort, four idealized tracers are included in the simulations (see 

Section 3.2.2) in order to efficiently evaluate transport from specific layers in the 

troposphere: the boundary layer, lower troposphere, mid-troposphere, and upper 
8 

troposphere. Here we have used boundaries for the upper troposphere layer that are 

consistent with those generally adopted as boundaries of the so-called tropical tropopause 

layer (TTL) [e.g., Gettelman and Forster, 2004], namely starting where the radiative 

heating rate becomes positive and ending at the cold point tropopause, which during 

TWP-ICE persisted at circa 14–17 km. These will facilitate intercomparison of features 

such as predicted detrainment profile. 

We also request budget profiles to allow evaluation of the sources and sinks of 

water vapor and hydrometeors as cloud systems evolve and dissipate (bottom of Table 4). 

Preliminary evaluation of these quantities led to the adoption of the single sensitivity test 

(see Section 3.2.1), namely the nudging of lower tropospheric thermodynamic fields, 

which is found to significantly influence the strength and depth of predicted convective 

overshooting into the TTL. 

Evaluation of the predicted influence of deep convective penetrations on TTL 

moisture during consecutive monsoonal events observed during TWP-ICE will go handin-hand with model evaluation. This will be an opportunity to build on the experience of 

the cloud modeling community in constructing forcing data sets from field measurement 

arrays, driving CRM simulations in a manner consistent with those field measurements, 

and evaluating internal consistency of CRM predictions with all available remote-sensing 

and in situ measurements. During TWP-ICE, in situ data include water vapor 

measurements by an open-path tunable diode laser instrument up to elevations at the base 

of the TTL. While there are many questions that this intercomparison will be not be able 

to address by analyzing a single simulation and sensitivity test (e.g., regarding the 

relationship of microphysics treatments to CRM predictions), it is expected that general 

patterns will emerge from this analysis and individual CRM studies can build upon this 

foundation. 

2.3. Objective #3: Study Anvil Cirrus Evolution 

The third goal of this study is to examine anvil cirrus evolution, from formation to 

dissipation. Considering the several monsoon events that are observed and simulated: 

• does the lag time between peak convective activity and peak anvil ice water path 

increase with convective event intensity (e.g., Horváth and Soden, 2008)? 

• does precipitation efficiency increase with convective event intensity (e.g., Li et 

al., 2002)? 

• in the long-lived anvil cirrus trailing the largest convective event during TWPICE, are observed and simulated anvil properties (e.g., ice water path, crystal 

sizes, fall speeds and habits) indicative of secondary ice nucleation and/or local 

generation of supersaturation (e.g., via radiatively induced ascent or cloud 

mixing)? 

Tropical anvil cloud cover and precipitation efficiency have been hypothesized to 

play strong roles in the expected response of tropical deep convection to increasing sea 

surface temperatures from greenhouse warming. GCM predictive skill may therefore 

depend upon the ability of models to represent processes such as anvil generation and 

evolution. Based on sequential observed and modeled monsoon events of increasing 
9 

intensity, we will test basic relationships that have been reported among convective 

intensity, anvil coverage, and precipitation efficiency. We will then focus more closely 

on the anvil cirrus observed after the largest event. While initial anvil outflow was 

dominated by spherical and irregular particle habits, the aging process was accompanied 

by a shift to increasing numbers of bullet rosettes, indicative of subsequent vapor growth. 

We will investigate the sources of moisture and whether initially detrained crystals are 

supplemented by secondary ice nucleation. 

3. Case Description 

3.1. Time Period 

The Darwin region experienced active monsoon conditions only during the first 

days of the campaign (Figure 3), culminating in the passage of a major mesoscale 

convective complex (MCC) directly through the center of the TWP-ICE domain on 

January 23 and 24, followed by suppressed conditions through February 3, and monsoon 

break conditions thereafter. 

This first TWP-ICE model intercomparison study will consist of continuous CRM 

simulation of active and suppressed monsoon conditions from 0Z 18 January – 0Z 3 

February (18.0-34.0 in ordinal days of 2006). Analysis will focus on model performance 

over several time periods: 

• active monsoon conditions (19.5–25.5), comprising the first 6 days following a 

minimal 36 hours for model spin-up 

• suppressed monsoon conditions (28–34), comprising the last 6 days of the 

simulated time period (avoiding the few days following 25 January when large 

quantities of aging anvil cirrus were advected into the domain from a mesoscale 

convective system located approximately 24 hours upwind) 

• three 24-hour periods bounding the primary build-up and decay of consecutive 

monsoon events of varying strength (19.5–20.5, 22.125–23.125, and 23.5–24.5), 

which we refer to hereafter as events A, B, and C 

• the 24-hour period of outflow cirrus evolution after event C (24.5-25.5) 

Related model intercomparison studies are being separately developed for singlecolumn models (SCMs) and limited-area models (LAMs). It is expected that the 16-day 

time period chosen here for CRMs will be a subset of that simulated by SCMs (which 

will likely include the full TWP-ICE duration through February 13) and a superset of that 

simulated by LAMs (which will likely comprise more detailed simulations of a few 

shorter time periods). 
10 

3.2. Model Set-Up 

3.2.1. Baseline and sensitivity test 

Although the TWP-ICE experimental domain contains both land and ocean 

regions, the low-lying land areas become saturated during the monsoon season, behaving 

in a manner that has been characterized as maritime in nature. To further facilitate CRM 

representation of relatively slowly developing and advecting monsoon features such as 

cold pools over the TWP-ICE region in a framework that remains as simple as possible, 

we adopt an idealized marine case study baseline set-up here: 

• model domain footprint representative of the TWP-ICE domain size of 

approximately 31,000 km2

 (e.g., about 176 km east-west by 176 km north-south, 

or a 2-dimensional model domain representing a similar area) 

• model domain height of at least 24 km 

• fully-periodic horizontal boundary conditions 

• uniform initial conditions at 0Z 18 January derived from mean observed profiles 

(see Section 3.2.4) 

• random initial perturbations of –0.25 to 0.25 K in grid cells located below 500 m 

• run time of 16 days, ending at 0Z 3 February 

• sea surface temperature fixed at 29°C and interactive surface fluxes 

• surface albedo fixed at 0.07 in all shortwave bands 

Figure 3. Cumulative rainfall at three locations in the Northern Territory during the 2005-

2006 monsoon season. Figure courtesy of Lori Chappel, Australian BOM. 
11 

• ozone profile fixed based on sonde and OMI measurements (see Section 3.2.4) 

• interactive diurnal radiation with domain centered on the Darwin ACRF site 

(12.425°S, 130.891°E) 

• horizontally uniform nudging of horizontal winds above 500 m to the mean 

observed profiles with a 2-hour time scale (see Section 3.2.4) 

• application of large-scale forcings derived from observations, adopted at full 

strength below 15 km, linearly decreasing to zero strength at 16 km (see 

Section 3.2.4) 

• horizontally uniform nudging of mean water vapor and mean potential 

temperature to mean observed profiles with a 6-hour time scale, adopted at full 

strength above 16 km, linearly decreasing to zero strength at 15 km 

• sponge layer nudging of horizontal winds and potential temperature toward their 

horizontal means using a nudging coefficient that increases with a sin2

 vertical 

dependence from zero at 20 km to (100 s)-1 at 24 km and above 

Large-scale forcings, provided as 3-hour centered-in-time domain-mean profiles 

with 10-mb resolution (see Section 3.2.4), are to be interpolated linearly in space (to 

model vertical grid spacing) and time (to model time step). Whereas horizontal flux 

divergence of potential temperature and water vapor are straightforward, large-scale 

vertical flux divergence of potential temperature and water vapor can be applied based on 

either observed or predicted profiles of potential temperature and water vapor. Here we 

specify that the observed mean profiles be used (rather than predicted profiles) in order to 

increase consistency of large-scale forcings applied to each model (because their 

predictions will deviate from one another). In the case of condensate and tracers, 

observed profiles are not available, and large-scale vertical wind (wLS) will therefore need 

to be applied to predicted fields (f) through first-order upwinding that operates on the 

local vertical gradient of a field to compute the tendency as follows: 

for wLS(z+z/2) < 0, f(z)/t = -wLS(z+z/2)[f(z+z)-f(z)]/z 

for wLS(z-z/2) > 0, f(z)/t = -wLS(z-z/2)[f(z)-f(z-z)]/z 

in which wLS is staggered by half a grid cell from f. For prognostic model variables (such 

as total water mixing ratio) that include a component subject to a large-scale forcing that 

uses the observed mean profiles (water vapor in this example) and another component 

subject to a large-scale forcing that uses the simulated local field (condensate in this 

example), a hybrid approach is to be used, combining each component’s appropriate 

forcing contribution. 

The specified nudging of water vapor and potential temperature was found to be 

necessary in order to keep simulated environmental conditions aloft realistic, consistent 

with an understanding that the large-scale forcing data are insufficiently constrained by 

measurements above about 15 km. The use of nudging aloft allows analysis of the 

interaction of deep convection outflow with ambient water vapor in the tropopause 

region, thus serving SPARC study goals in particular. Horizontally uniform nudging of 

the mean predicted profiles to the mean observed profiles preserves horizontal variations. 
12 

Because it has been noted that the derived large-scale forcing profiles may tend to 

moisten the lower troposphere significantly more than observed, influencing the strength 

of convection and the depth of convective penetration, a single sensitivity test is specified 

in addition to the baseline simulation described above, namely: 

• horizontally uniform nudging of mean water vapor and potential temperature to 

mean observed profiles with a 6-hour time scale, adopted at full strength above 1 

km, linearly decreasing to zero strength at 500 m 

In both of the simulations specified (the baseline and the sensitivity test), budget 

diagnostics (see Section 4) will allow analysis of the influence of nudging on the 

simulated fields. 

3.2.2. Idealized tracers 

Although extensive trace gas measurements were made by ACTIVE and TWPICE, it was found that concentrations in the Darwin region did not exhibit sufficient 

coherent, consistent vertical structure to be of use in constraining numerical simulations 

of vertical transport. In order to permit a first-order analysis of vertical transport among 

layers of the troposphere, four idealized tracers are used here, one each to represent 

source regions of air from the surface boundary layer (0–250 m), lower troposphere (2–4 

km), mid-troposphere (4–6 km), and upper troposphere (14–17 km, which matches the 

canonical definition of the TTL well during the simulation dates). Because the 

simulation time (weeks) is very long relative to typical timescales of vertical convective 

transport (hours), we adopt the following treatment (identical for both baseline and 

sensitivity test simulations): 

• initialize each tracer mixing ratio to 1.0 in its source layer (0–250 m, 2–4 km, 4–6 

km, and 14–17 km) and 0.0 elsewhere 

• subject tracers to advection, mixing, and large-scale vertical motion (as typically 

done) 

• decay each tracer everywhere with a 6-hour e-folding time (i.e., apply a constant 

loss rate dC/dt = -C/, where C is the local tracer mixing ratio and  = 6 hours) 

• reset each tracer to 1.0 in its source layer at the end of each time step 

This procedure permits efficient analysis of convective transport over long 

simulation times. For instance, mean boundary layer tracer concentration profiles can be 

compared after events A, B, and C during each simulation to evaluate relative strength of 

transport to and from the TTL. 

3.2.3. Aerosols 

While thermodynamic factors play a dominant role in the determination of 

condensate mass concentration in many convective clouds, cloud particle number 

concentration and size distribution fields are also influenced by ambient aerosol 

properties in a manner that remains poorly understood in the case of deep convection. 

Furthermore, the properties of natural aerosols over the depth of the troposphere are 

difficult to represent concisely because composition, morphology, and number size 

distribution all vary. The approach we take here is similar to that adopted by the deep 
13 

convection CRM intercomparison conducted by Barth et al. [2007]. Namely, we provide 

a relatively simplified representation of aerosol properties based on available 

measurements (Figure 4, see Section 3.2.4), but do not specify how models should make 

use of it since they may employ varying degrees of complexity to represent aerosol 

properties. 

To start, median profiles of aerosol number concentration are derived for three 

size cuts (dry diameter greater than 0.01, 0.04, and 0.5 μm) based on data gathered from 

condensation particle counter (CPC), cloud and aerosol spectrometer (CAS), and 

forward-scattering spectrometer probe (FSSP, dry sizes estimated where relative 

humidity between 75% and 95% by assuming growth factors for ammonium sulfate) on 

the Egret and Dornier aircraft from the surface to circa 15 km (data courtesy of Martin 

Gallagher and Paul Williams, University of Manchester). Size distribution parameters 

for three lognormal modes (modal diameters of 0.03, 0.18, and 4.4 μm, and standard 

deviations of 1.12, 1.45, and 1.8) are then adopted as typical of active monsoon 

conditions during the 2005–2006 Darwin monsoon based on Allen et al. [2008]. Finally, 

Figure 4. Aerosol number size distributions between the surface and 14 km, based on 

ACTIVE in situ measurements as described in Section 2.2.3. 

20060120 - 20060127

0.01 0.10 1.00 10.00

Diameter [um]

10-6

10-4

10-2

100

102

104

106

dN/dlogDp [cm-3]

 0.5 km

 1.5 km

 2.5 km

 3.5 km

 5.5 km

 6.5 km

 7.5 km

 8.5 km

 9.5 km

10.5 km

11.5 km

12.5 km

13.5 km

14.5 km
14 

a number concentration in each mode is calculated from the median profiles for each of 

the three size cuts. 

Aerosol composition in the lower troposphere was dominated by sulfate, 

ammonium and organic species [Allen et al., 2008]. During the active monsoon, this 

composition can be approximated as a mixture of 80% by mass ammonium sulfate and 

20% organics. During the inactive monsoon, it can be approximated as 70% ammonium 

bisulfate and 30% organics. If a uniform composition is assumed, 75% ammonium 

sulfate and 25% organics by mass is recommended. 

3.2.4. Input files and model output templates 

All files associated with this study will be accessible through the ACRF TWPICE home page9

. Input files comprise 3-hourly domain-mean observed thermodynamic 

and large-scale forcing profiles and time- and domain-mean ozone and aerosol number 

size distribution profiles. Templates are also provided for each requested output file type 

(see Section 4). 

4. Results to Submit 

Model results are requested from 19.5–34 Julian (skipping the first 36 hours for 

model spin-up). Requested files fall into the three general categories (see Table 2): 

• instantaneous 3-D fields at 3-hour intervals (Table 3) 

• instantaneous 1-D profiles at 10-minute intervals (Table 4) 

• instantaneous scalar quantities at 10-minute intervals (Table 5) 

All files must be submitted in netCDF format with specified variable names and 

dimensions (Tables 2–5) in order to be analyzed. The output files must conform with 

version 1.3 of the NetCDF Climate and Forecast Metadata Conventions10, which will 

facilitate the development of automated software to plot model results through a web 

interface. A brief summary of CF 1.3 requirements and recommendations is available 

online11, as is a web interface to a compliance checker12. Submitted output should use the 

dimension and variable names and provide all the metadata that appears in the supplied 

output templates (see Section 3.2.4), which conform to the CF 1.3 requirements (thus, if 

the templates are faithfully mimicked, there is no need for participants to learn anything 

about CF 1.3). Variables that are not submitted should be omitted from submitted files in 

order to conserve disk space. For variables that are submitted, missing or blank fields 

(e.g., minimum cloud base in a clear column) should be filled with the value defined by 

each netCDF variable’s_FillValue attribute. Submitted results will be made available 

through the ACRF TWP-ICE home page (see Section 5.3). 

A few conventions are adopted throughout Tables 3–5. First, all condensate 

species are grouped into three categories: cloud water, rain water, and ice. All ice and 

 

9

 http://www.arm.gov/campaigns/twp2006twp-ice

10 http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.3

11 http://cf-pcmdi.llnl.gov/conformance/requirements-and-recommendations/1.3/

12 http://puma.nerc.ac.uk/cgi-bin/cf-checker.pl
15 

mixed-phase particles are placed in the ice category. Models that carry a single size 

distribution for all liquid (e.g., size-resolved microphysics) should divide the distribution 

into cloud water and rain water based on particle size: radius  40 μm in the cloud water 

category and radius > 40 μm in the rain category. 

Second, all diagnostics are requested at the model's own vertical and horizontal 

grid resolution unless otherwise specified. When horizontal resolutions larger than a 

given model's horizontal grid scale are specified (e.g., 2.5 km, 4 km, 20 km, or 55 km), 

they should be treated by dividing the model domain into as many sub-domains as 

possible at the requested resolution (e.g., three 55-km sub-domains fit into a 192-km 

domain), and the remaining model domain should be neglected (placement of the 

subdomains is not important, as long as they do not overlap). The requested diagnostic 

should then be reported over the available sub-domains. No vertical resolution changes 

should ever be made (only horizontal and only when requested). 

Third, cloud boundaries are defined as locations where total cloud liquid and ice 

mass (excluding rain) exceed 10-6 kg kg-1 at the specified resolution. This applies to 

diagnostics that include cloud base, cloud height, cloud fraction, and mass flux. 

Fourth, condensate optical depths are requested at a wavelength of 0.65 μm, or, 

alternatively, at any wavelength or wavelength band in the range of 0.3–0.7 μm. Aside, 

as noted in Table 5, when the total domain mean optical depth is called for, column 

values over 100 should be set to 100 prior to averaging over the domain (in order to allow 

comparison with satellite retrievals). 

Finally, two types of size information are requested: effective radius and mass 

mean diameter. All effective radius values should be expressed as 3Q/(4A), where Q is 

the relevant condensate mass mixing ratio, A is the projected area, and  is the 

condensate mass density (1.0 g cm-3 should be used for both liquid and ice groups, 

regardless of actual density). To avoid numerical problems, effective radius values 

should be reported only where the relevant condensate mass exceeds 10-6 kg kg-1. (Please 

note that unlike the condensate mass that defines cloud boundaries, which excludes rain, 

effective radius diagnostics may include rain water.) Grid cell averages over multiple 

particle groups should be made by first totaling Q and A, and then, if the total Q > 10-6 kg 

kg-1, calculating the quotient. Similarly, mean values over multiple grid cells should be 

made by first totaling all relevant Q and A values, and then, if the total Q > 10-6 kg kg-1, 

calculating the quotient. The mass mean diameter is the fourth moment of the 

distribution divided by the third moment. For a Marshall-Palmer rain distribution, for 

instance, the mass mean diameter requested in Table 5 would be a factor of 8/3 times the 

2.5-km value of effective radius requested in Table 4. 

Aside, potential temperature is defined here with a reference pressure of 1000 mb. 

Water vapor and hydrometeor budget profiles requested in K d-1 should be converted 

from mass mixing ratio per unit time by multiplying by the latent heat of evaporation and 

dividing by the specific heat of air at constant pressure. 
16 

Table 2. Summary of outputs, files, and naming conventions 

Type Interval Naming Convention* Files/Run 

3-D fields 3-hour MODEL.VARIABLE.nc 19 

Vertical profiles 10-minute MODEL.profiles.nc 1 

Scalars 10-minute MODEL.scalars.nc 1 

*'MODEL' is a unique character label to be chosen by the participant. 'VARIABLE' is one of the 19 

variable names listed in the first column of Table 3 (e.g., T, u, v, etc.). 

Table 3. 3-D fields at 3-hour intervals (one file per field) 

Name Units Description 

Dimensions 

time — number of output times = 117 

xc — number of east-west columns 

xb — number of east-west column boundaries = xc + 1 

yc — number of north-south columns 

yb — number of north-south column boundaries = yc + 1 

zc — number of vertical layers 

zb — number of vertical layer boundaries (including surface 

and top of model) = zc + 1 

Axes (dimension of same name as axis) 

time h hours since 2006-01-18 00:00:00.0 UTC 

xc m location of east-west column centers 

xb m location of east-west column boundaries 

yc m location of north-south column centers 

yb m location of north-south column boundaries 

zc m height above sea surface of layer centers 

zb m height above sea surface of layer boundaries 

Variables (dimensions are time; xc or xb; yc or yb; zc or zb) 

T K air temperature 

u m s-1 eastward wind 

v m s-1 northward wind 

w m s-1 vertical wind (positive upward) 

qv kg kg-1 water vapor mass mixing ratio 

qc kg kg-1 cloud water mass mixing ratio 

qr kg kg-1 rain water mass mixing ratio 

qi kg kg-1 ice mass mixing ratio 

Nc L-1 number concentration of cloud drops where cloud water 

mass mixing ratio > 10-6 kg kg-1

Nr L-1 number concentration of rain drops where rain water 

mass mixing ratio > 10-6 kg kg-1

Ni L-1 number concentration of ice particles where ice mass 

mixing ratio > 10-6 kg kg-1

N_100 L-1 total number concentration of cloud, rain and ice particles 

with maximum dimension exceeding 100 μm where 

total condensate mass mixing ratio > 10-6 kg kg-1

Ac μm2 L-1 projected area of cloud drops, per unit volume of air, 

where cloud water mass mixing ratio > 10-6 kg kg-1

Ar μm2 L-1 projected area of rain drops, per unit volume of air, 

where rain water mass mixing ratio > 10-6 kg kg-1
17 

Name Units Description 

Ai μm2 L-1 projected area of ice particles, per unit volume of air, 

where ice mass mixing ratio > 10-6 kg kg-1

A_100 μm2 L-1 total projected area of cloud, rain and ice particles with 

maximum dimension exceeding 100 μm, per unit 

volume of air, where total condensate mass mixing 

ratio > 10-6 kg kg-1

RH_RHi — water vapor mixing ratio divided by saturation mixing 

ratio (with respect to water where T  273.15, ice 

where T < 273.15 K) 

dBZ dBZ Rayleigh-regime radar reflectivity 

DopV m s-1 Rayleigh-regime reflectivity-weighted Doppler velocity 

(positive downward) where reflectivity > –10 dBZ 

Table 4. Profiles at 10-minute intervals (one file) 

Name Units Description 

Dimensions 

time — number of output times= 2089 

zc — number of vertical layers 

zb — number of vertical layer boundaries (including surface 

and top of model) = zc + 1 

Axes (dimension of same name as axis) 

time h hours since 2006-01-18 00:00:00.0 UTC 

zc m height above sea surface of layer centers 

zb m height above sea surface of layer boundaries 

Variables (dimensions are time; zc or zb) 

P Pa mean air pressure 

T K mean air temperature 

rhobar_air kg m-3 reference density of air 

u m s-1 mean eastward wind 

v m s-1 mean northward wind 

w_min m s-1 minimum vertical wind (maximum downdraft) 

w_max m s-1 + maximum (maximum updraft) 

RH_RHi — mean water vapor mass mixing ratio divided by 

saturation mass mixing ratio (with respect to water in 

grid cells where T  273.15, ice where T < 273.15 K) 

RH_RHi_min — + minimum 

RH_RHi_max — + maximum 

RH_RHi_55min — minimum water vapor mass mixing ratio divided by 

saturation mass mixing ratio (with respect to water in 

grid cells where T  273.15, ice where T < 273.15 K) 

at 55-km resolution 

RH_RHi_55max — + maximum 

qv kg kg-1 mean water vapor mass mixing ratio 

qv_min kg kg-1 + minimum 

qv_max kg kg-1 + maximum 

qv_55min kg kg-1 minimum water vapor mass mixing ratio at 55-km 

resolution 

qv_55max kg kg-1 + maximum 
18 

Name Units Description 

qc kg kg-1 mean cloud water mass mixing ratio 

qc_max kg kg-1 + maximum 

qc_PR kg kg-1 mean cloud water mass mixing ratio of columns where 

surface precipitation rate < 0.02 mm h-1

qc_PRmax kg kg-1 + maximum 

qc_55max kg kg-1 maximum cloud water mass mixing ratio at 55-km 

resolution 

qr kg kg-1 mean rain water mass mixing ratio 

qr_max kg kg-1 + maximum 

qr_55max kg kg-1 maximum rain water mass mixing ratio at 55-km 

resolution 

qi kg kg-1 mean ice mass mixing ratio 

qi_max kg kg-1 + maximum 

qi_PR kg kg-1 mean ice mass mixing ratio of columns where surface 

precipitation rate < 0.02 mm h-1

qi_PRmax kg kg-1 + maximum 

qi_20max kg kg-1 maximum ice mass mixing ratio at 20-km resolution 

qi_55max kg kg-1 maximum ice mass mixing ratio at 55-km resolution 

CF — fractional area covered by cloudy grid cells 

CF_up0 — fractional area covered by cloudy grid cells in which 

vertical wind > 0 m s-1

CF_up5 — fractional area covered by cloudy grid cells in which 

vertical wind > 5 m s-1

BCF_up — fractional area covered by buoyant cloudy grid cells in 

which vertical wind > 0 m s-1

MF_up0 kg m-2 s-1 mass flux averaged over cloudy grid cells in which 

vertical wind > 0 m s-1

MF_up5 kg m-2 s-1 mass flux averaged over cloudy grid cells in which 

vertical wind > 5 m s-1

BMF_up kg m-2 s-1 mass flux averaged over buoyant cloudy grid cells in 

which vertical wind > 0 m s-1

dBZ_2p5max dBZ maximum Rayleigh-regime radar reflectivity 

at 2.5-km resolution 

dBZ_2p5F10 — fractional area of Rayleigh-regime radar reflectivity at 

2.5-km resolution > 10 dBZ 

dBZ_2p5F30 — and > 30 dBZ 

dBZ_2p5F50 — and > 50 dBZ 

DopV m s-1 mean Rayleigh-regime reflectivity-weighted Doppler 

velocity (positive downward) over grid cells where 

reflectivity > –10 dBZ 

DopV_min m s-1 + minimum 

DopV_max m s-1 + maximum 

trBL — mean boundary-layer tracer mixing ratio 

trBL_min — + minimum 

trBL_max — + maximum 

trLT — mean lower-troposphere tracer mixing ratio 

trLT_min — + minimum 

trLT_max — + maximum 
19 

Name Units Description 

trMT — mean mid-troposphere tracer mixing ratio 

trMT_min — + minimum 

trMT_max — + maximum 

trUT — mean upper-troposphere tracer mixing ratio 

trUT_min — + minimum 

trUT_max — + maximum 

SWdn W m-2 mean shortwave downwelling radiative flux 

SWdn_min W m-2 + minimum 

SWdn_max W m-2 + maximum 

SWdn_55min W m-2 minimum shortwave downwelling radiative flux at 55-km 

resolution 

SWdn_55max W m-2 + maximum 

SWdn_PR W m-2 mean shortwave down radiative flux of columns where 

surface precipitation rate < 0.02 mm h-1

SWdn_PRmin W m-2 + minimum 

SWdn_PRmax W m-2 + maximum 

SWup W m-2 mean shortwave upwelling radiative flux 

SWup_min W m-2 + minimum 

SWup_max W m-2 + maximum 

SWup_55min W m-2 minimum shortwave upwelling radiative flux at 55-km 

resolution 

SWup_55max W m-2 + maximum 

SWup_PR W m-2 mean shortwave upwelling radiative flux of columns 

where surface precipitation rate < 0.02 mm h-1

SWup_PRmin W m-2 + minimum 

SWup_PRmax W m-2 + maximum 

LWdn W m-2 mean longwave downwelling radiative flux 

LWdn_min W m-2 + minimum 

LWdn_max W m-2 + maximum 

LWdn_55min W m-2 minimum longwave downwelling radiative flux at 55-km 

resolution 

LWdn_55max W m-2 + maximum 

LWdn_PR W m-2 mean longwave downwelling radiative flux of columns 

where surface precipitation rate < 0.02 mm h-1

LWdn_PRmin W m-2 + minimum 

LWdn_PRmax W m-2 + maximum 

LWup W m-2 mean longwave upwelling radiative flux 

LWup_min W m-2 + minimum 

LWup_max W m-2 + maximum 

LWup_55min W m-2 minimum longwave upwelling radiative flux at 55-km 

resolution 

LWup_55max W m-2 + maximum 

LWup_PR W m-2 mean longwave upwelling radiative flux of columns 

where surface precipitation rate < 0.02 mm h-1

LWup_PRmin W m-2 + minimum 

LWup_PRmax W m-2 + maximum 

SWhr K d-1 mean shortwave broadband heating rate 

SWhr_min K d-1 + minimum 
20 

Name Units Description 

SWhr_max K d-1 + maximum 

SWhr_PR K d-1 mean shortwave broadband heating rate of columns 

where surface precipitation rate < 0.02 mm h-1

SWhr_PRmin K d-1 + minimum 

SWhr_PRmax K d-1 + maximum 

LWhr K d-1 mean longwave broadband heating rate 

LWhr_min K d-1 + minimum 

LWhr_max K d-1 + maximum 

LWhr_PR K d-1 mean longwave broadband heating rate of columns where 

surface precipitation rate < 0.02 mm h-1

LWhr_PRmin K d-1 + minimum 

LWhr_PRmax K d-1 + maximum 

LHhr K d-1 mean latent heating rate 

QVact K d-1 mean water vapor tendency 

QVhorLS K d-1 mean large-scale water vapor horizontal flux convergence 

QVverLS K d-1 mean large-scale water vapor vertical flux convergence 

QVadv K d-1 mean water vapor resolved and subgrid-scale vertical flux 

convergence (excluding large-scale) 

QVmicro K d-1 mean water vapor tendency from exchange with 

hydrometeors 

QVnudge K d-1 mean water vapor tendency from nudging 

QHact K d-1 mean hydrometeor tendency 

QHverLS K d-1 mean large-scale hydrometeor vertical flux convergence 

QHadv K d-1 mean hydrometeor resolved and subgrid-scale vertical 

flux convergence (excluding large-scale) 

QHsed K d-1 mean sedimentation flux convergence of hydrometeors 

QTact K d-1 mean potential temperature tendency 

QThorLS K d-1 mean large-scale potential temperature horizontal flux 

convergence 

QTverLS K d-1 mean large-scale potential temperature vertical flux 

convergence 

QTadv K d-1 mean potential temperature resolved and subgrid-scale 

vertical flux convergence (excluding large-scale) 

QTmicro K d-1 mean potential temperature tendency from microphysics 

QTrad K d-1 mean potential temperature tendency from radiative 

heating 

QTnudge K d-1 mean potential temperature tendency from nudging 

Table 5. Scalars at 10-minute intervals (one file) 

Name Units Description 

Dimension 

time — number of output times = 2089 

Axis (dimension of same name as axis) 

time h hours since 2006-01-18 00:00:00.0 UTC 

Variables (all have single dimension, time) 

LWP g m-2 mean liquid water path 

LWP_max g m-2 + maximum 
21 

Name Units Description 

LWP_PR g m-2 mean liquid water path where surface precipitation rate < 

0.02 mm h-1

LWP_PRmax g m-2 + maximum 

LWP_4max g m-2 maximum liquid water path at 4-km resolution 

LWP_25max g m-2 maximum liquid water path at 25-km resolution 

LWP_55max g m-2 maximum liquid water path at 55-km resolution 

IWP g m-2 mean ice water path 

IWP_max g m-2 + maximum 

IWP_PR g m-2 mean ice water path where surface precipitation rate < 

0.02 mm h-1

IWP_PRmax g m-2 + maximum 

IWP_4max g m-2 maximum ice water path at 4-km resolution 

IWP_20max g m-2 maximum ice water path at 20-km resolution 

IWP_55max g m-2 maximum ice water path at 55-km resolution 

PRs kg m-2 s-1 mean surface precipitation rate 

PRs_max kg m-2 s-1 maximum surface precipitation rate 

PRs_55max kg m-2 s-1 maximum surface precipitation rate at 55-km resolution 

PR2p5 kg m-2 s-1 mean precipitation rate at 2.5-km 

PR2p5_2p5max kg m-2 s-1 maximum precipitation rate at 2.5-km elevation and 

resolution 

PR2p5_2p5F0p02 — fractional area where precipitation rate at 2.5-km 

elevation and resolution > 0.02 mm h-1

PR2p5_2p5F0p2 — and > 0.2 mm h-1

PR2p5_2p5F2 — and > 2 mm h-1

PR2p5_2p5F20 — and > 20 mm h-1

CBH km mean cloud base height in cloudy columns 

CBH_min km + minimum 

CBH_max km + maximum 

CBH_4 km mean cloud base height in cloudy columns at 4-km 

resolution 

CBH_55 km mean cloud base height in cloudy columns at 55-km 

resolution 

CTH km mean cloud top height in cloudy columns 

CTH_min km + minimum 

CTH_max km + maximum 

CTH_4 km mean cloud top height in cloudy columns at 4-km 

resolution 

CTH_55 km mean cloud top height in cloudy columns at 55-km 

resolution 

CF — fractional area covered by cloudy columns 

MMDs_0 mm mean mass mean diameter of raindrops at the surface 

where reflectivity > 0 dBZ 

MMDs_0min mm + minimum 

MMDs_0max mm + maximum 

MMD2p5_25 mm mean mass mean diameter of raindrops at 2.5 km where 

reflectivity > 25 dBZ 

qlOD — mean optical thickness of cloud water and rain

qlOD_max — + maximum
22 

Name Units Description 

qlOD_PR — mean optical thickness of cloud water and rain in 

columns where surface precipitation rate < 0.02 mm 

h-1

qlOD_PRmax — + maximum 

qiOD — mean optical thickness of ice 

qiOD_max — + maximum 

qiOD_PR — mean optical thickness of ice in columns where surface 

precipitation rate < 0.02 mm h-1

qiOD_PRmax — + maximum 

OD — mean optical thickness of cloud water, rain and ice, with 

column values of optical thickness > 129 set to 129

OD_min — + minimum

OD_max — + maximum (129 if greater than 129)

SWdnTOA W m-2 mean shortwave downwelling flux at top-of-atmosphere 

SWupTOA W m-2 mean shortwave upwelling flux at top-of-atmosphere 

SWupTOA_4min W m-2 minimum shortwave upwelling flux at top-of-atmosphere 

at 4-km resolution 

SWupTOA_4max W m-2 + maximum 

LWupTOA W m-2 mean longwave upwelling flux at top-of-atmosphere 

LWupTOA_4min W m-2 +minimum longwave upwelling flux at top-ofatmosphere at 4-km resolution 

LWupTOA_4max W m-2 + maximum 

LHflx W m-2 mean latent heat flux at surface (positive upward and 

excluding precipitation) 

LHflx_min W m-2 + minimum 

LHflx_max W m-2 + maximum 

SHflx W m-2 mean sensible heat flux at surface (positive upward) 

SHflx_min W m-2 + minimum 

SHflx_max W m-2 + maximum 

5. Schedule and Expected Outcomes 

5.1. Deadlines 

Preliminary results are requested by 1 March 2009. Final results are required by 1 

July 2009. 

5.2. Publications 

It is expected that at least three modeling groups will submit results that will be 

contribute to papers addressing the objectives outlined in Sections 2.1–2.3. Submitted 

results will be included in these papers and participants included as co-authors. 

5.3. Archive of Model Results and Analyzed Measurements 

There are two ways it is planned to allow efficient future use of the results 

generated by this intercomparison study after it is completed. First, as described above, 

3-D fields from each of the CRMs that submit results will be archived. It is intended that 
23 

these fields can be downloaded and used (e.g., for work on improving GCM 

parameterizations) whether or not researchers choose to run their own CRM simulations 

of this case study. Second, the data analyses performed for this study (e.g., mean profiles 

during the six days of active monsoon, six days of suppressed monsoon, and 24-hour subperiods) will also be archived. It is intended that the TWP-ICE case study may then be 

used more readily as a benchmark for future model development and evaluation. Model 

results and analyzed data will be archived at the ACRF TWP-ICE home page13. 

Acknowledgments 

This work was supported by the DOE Office of Science, Office of Biological and 

Environmental Research, through Interagency Agreement DE-AI02-06ER64173, the 

NASA Radiation Sciences Program, the DOE National Energy Research Scientific 

Computing Center, and the NASA Advanced Supercomputing Division. 

References 

Allen, G., G. Vaughan, K. N. Bower, P. I. Williams, J. Crosier, M. Flynn, P. Connolly, J. 

F. Hamilton, J. D. Lee, J. E. Saxton, N. M. Watson, M. Gallagher, H. Coe, J. 

Allan, T. W. Choularton, and A. C. Lewis, 2008: Aerosol and trace gas 

measurements in the Darwin area during the wet season, JGR, 113, D06306, 

doi:10.1029/2007JD008706. 

Barth, M.C., S.-W. Kim, C. Wang, K.E. Pickering, L.E. Ott, G. Stenchikov, M. Leriche, 

S. Cautenet, J.-P. Pinty, C. Barthe, C. Mari, J. Helsdon, R. Farley, A.M. Fridlind, 

A.S. Ackerman, V. Spiridonov, and B. Telenta, 2007: Cloud-scale model 

intercomparison of chemical constituent transport in deep convection, Atmos. 

Chem. Phys., 7, 4709-4731. 

Gettelman, A. and P. M. de F. Forster, 2002: A climatology of the tropical tropopause 

layer, J. Meteorol. Soc. Japan, 80, 911–924. 

Horváth, A., and B. J. Soden, 2008: Lagrangian diagnostics of tropical deep convection 

and its effect upon upper-tropospheric humidity, J. Clim., 21, 1013–1028. 

Li, X., C.-H. Sui, and K.-M. Lau, 2002: Precipitation efficiency in the tropical 

convective regime: A 2-D cloud resolving modeling study, J. Meteorol. Soc. 

Japan, 80, 205–212. 

May, P. T., J. H. Mather, G. Vaughan, and C. Jakob, 2008: Characterizing oceanic 

convective cloud systems—The Tropical Warm Pool International Cloud 

Experiment, Bull. Am. Meteorol. Soc., 154, 153-155, doi:10.1175/BAMS-89-2-

153. 

Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. 

Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, 2007: 

Climate Models and Their Evaluation, In: Climate Change 2007: The Physical 

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Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)], 

 

13 http://www.arm.gov/campaigns/twp2006twp-ice
24 

Cambridge University Press, Cambridge, United Kingdom and New York, NY, 

USA. 

Sherwood, S. C., and A. E. Dessler, 2001: A model for transport across the tropical 

tropopause, J. Atmos. Sci., 58, 765–779. 

Vaughan, G., C. Schiller, A. R. MacKenzie, K. N. Bower, T. Peter, H. Schlager, N. R. P. 

Harris, and P. T. May, 2008: Studies in a natural laboratory: High-altitude aircraft 

measurements around deep tropical convection, Bull. Am. Meteorol. Soc., in 

press. 
	


 	





	

 

 

		




 







	
 




 












	



		 






 










		







	









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