QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
Published online in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/qj.317
Modelling suppressed and active convection:
Comparisons between three global atmospheric models
M. R. Willett,a *† P. Bechtold,b D. L. Williamson,c J. C. Petch,a† S. F. Miltona† and
S. J. Woolnoughd
a
Meteorology R&D, Met Office, Exeter, UK
b ECMWF, Reading, UK
c
National Center for Atmospheric Research, Boulder, CO, USA
d Department of Meteorology, University of Reading, UK
ABSTRACT: The Met Office Unified Model, the European Centre for Medium-Range Weather Forecasts (ECMWF)
Integrated Forecasting System and the National Center for Atmospheric Research (NCAR) Community Atmosphere Model
3 were used to compare simulations of suppressed and active convection by global atmospheric models (GAMs) in the
Tropical West Pacific. The case used a three-week period selected from the Tropical Ocean and Global Atmosphere–
Coupled Ocean–Atmosphere Response Experiment (TOGA–COARE). The global models were initialized from ERA-40
on each day of the case and the 12–36 h forecasts were concatenated together to create a continuous sequence of forecasts.
One active and two suppressed subperiods were identified and used to examine the behaviour of the models within
different convective regimes. All the GAMs were able to clearly distinguish between the suppressed and active regimes
in terms of precipitation rate, precipitable water and radiative fluxes, and they all produced strong deep convection
during the most active period. However, there were significant differences in the behaviour of the GAMs convection
parametrizations during the most suppressed period. There was good agreement in the long-wave radiative fluxes where
the GAMs were in good agreement in terms of convective activity, but there was less agreement in the short-wave
fluxes, with the biggest differences being in the most active period. Throughout, there were large differences in the
relative proportion of convective and large-scale precipitation produced by the GAMs. By making comparisons with
reference data in the form of observations, reanalyses and cloud-resolving models, several issues that were specific
c Royal Meteorological Society and Crown Copyright,
to individual GAMs are identified and discussed. Copyright 2008
KEY WORDS
GEWEX Cloud System Study; TOGA–COARE; convection; numerical weather prediction; GCM; forecast
Received 4 January 2008; Revised 21 May 2008; Accepted 8 August 2008
1. Introduction
In this paper, we describe results from the global
atmospheric model (GAM) component of a wider casestudy that uses GAMs, cloud-resolving models (CRMs)
and single-column models (SCMs). The case-study was
conducted by the Precipitating Cloud Systems Working
Group (PCSWG) of the Global Energy and Water Cycle
Experiment (GEWEX) Cloud System Study (GCSS).
This is the first time that a GCSS case-study has included
a GAM component in combination with CRMs and
SCMs. The objective of the GCSS is to support the
development of new parametrizations of all cloud-related
processes for large-scale models (Randall et al., 2000).
The PCSWG specifically aims to support the development
*Correspondence to: M. R. Willett, Met Office, FitzRoy Road, Exeter
EX1 3PB, UK.
E-mail: [email protected]
† The contributions of M.R. Willett, J.C. Petch and S.F. Milton were
written in the course of their employment at the Met Office, UK and
are published with the permission of the Controller of HMSO and the
Queen’s Printer for Scotland.
c Royal Meteorological Society and Crown Copyright, 2008
Copyright of the parametrization of precipitating convective cloud
systems in global climate models and global numerical
weather-prediction (NWP) models.
A good simulation of both suppressed and active convection is essential for the representation of modes of
tropical variability such as the Madden–Julian Oscillation
(MJO) in GAMs. However, it has often been suggested
that GAMs fail to fully capture the observed variability
in the Tropics, especially in terms of precipitation (e.g.
Yang and Slingo, 2001; Scinocca and McFarlane, 2004),
although it should be noted that most of the studies have
used climate GAMs rather than much higher resolution
NWP GAMs. The ability of convection parametrizations
to simulate tropical convection has often been examined
by the GEWEX PCSWG by intercomparing CRMs and
SCMs (e.g. Bechtold et al., 2000; Redelsperger et al.,
2000). However, the studies have all focused on active
convection rather than suppressed convection. In addition,
they are not able to directly assess the performance of a
convection parametrization within a GAM but only indirectly by extrapolating from the appropriate SCM results.
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M. R. WILLETT ET AL.
While this methodology is of value, there will be situations where the systematic errors in a GAM and the
SCM version of the GAM will differ due to differences
in dynamical feedbacks (Petch et al., 2007).
This study removes the limitations of the traditional
CRM/SCM intercomparison by expanding the framework
to include also three GAMs. The study uses a three-week
period of the Tropical Ocean and Global Atmosphere–
Coupled Ocean–Atmosphere Response Experiment
(TOGA–COARE; Webster and Lukas, 1992), which was
selected because it contained both convectively active
and suppressed regimes. An overview of this case-study,
the rationale for the inclusion of GAMs, the basic experimental design and a discussion of interpretation of errors
in this framework are given in Petch et al. (2007). In this
paper we aim to compare and contrast three different
GAMs in different tropical convective regimes, to explain
some of the differences between the GAMs and to identify whether weaknesses are common to all models or are
model-specific problems. To this end, the results from the
CRMs as well as reanalyses and observations are used
as reference data; the use of the CRMs as a reference
by which to assess the GAMs is important because it
allows comparisons of parameters (e.g. convective mass
flux) that are not available from other sources.
The three different GAMs used in this study were the
Met Office Unified Model (MetUM), the European Centre
for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) and the National Center
for Atmospheric Research (NCAR) Community Atmosphere Model 3 (CAM3). The MetUM and the CAM3
are used for climate modelling, and the MetUM and the
IFS for operational NWP out to seasonal time-scales, and
therefore the accurate representation of tropical variability is a desirable quality in all the GAMs. The CAM3 is
not used for operational NWP but in this case is operated
in a forecast mode (i.e. it is frequently reinitialized from
an analysis).
Whilst the examination of process errors in GAM forecasts has been a natural approach in NWP, it has only
recently been applied to climate models that are not associated with a NWP centre, primarily because such climate
models do not include an analysis component with which
to generate initial conditions. Phillips et al. (2004) provide an overview of a forecast approach for climate models; they have demonstrated that a model-specific analysis
system is not needed, and that climate models can be
successfully initialized from NWP analyses or reanalyses if careful interpolation is applied. This approach
has recently been used by Boyle et al. (2005) and
Williamson et al. (2005) to examine the NCAR CAM2
at Atmospheric Radiation Measurements (ARM) sites, by
Williamson and Olson (2007) to compare forecast errors
in CAM2 and CAM3 at the ARM Southern Great Plains
site, and by Boyle et al. (2008) to examine errors in several climate models during TOGA–COARE. The work
presented here differs from that of Boyle et al. (2008) by
the inclusion of both NWP and climate models, and by
the use of CRM data in the assessment of the GAMs.
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Copyright Copyright, 2008
In section 2 we describe the experimental design
including a description of the global models and the
periods used. Section 3 discusses the evolution of the
forecasts from the ERA-40 analysis. Section 4 compares
the global model forecasts and evaluates these against
reference data from observations, CRMs and ERA-40. In
section 5 we expand this analysis by evaluating the GAM
in three different convective regimes. Section 6 contains
a summary and the main conclusions of the study.
2. Experimental design
2.1. Models used in this study
The configuration of the MetUM version 6.1 used in this
case-study is that used operationally for global forecasts
between January and December 2005. The MetUM is also
used at a lower resolution as the atmospheric component
of the Met Office Hadley Centre Global Environment
Model (HadGEM). The dynamics is a two-time level,
semi-implicit, semi-Lagrangian formulation and is nonhydrostatic (Davies et al., 2005). The physics schemes are
described in Martin et al. (2005) and references therein.
The horizontal resolution is 0.83 degrees longitude by
0.56 degrees latitude (90 km × 60 km on the Equator)
and there are 38 levels in the vertical with the model top
at around 3 hPa. The dynamics and physics schemes all
use a time step of 20 min except for the radiation scheme,
which uses a 3 h time step, and the convection scheme,
which uses a 10 min time step.
The configuration of the IFS corresponds to that
used operationally in spring 2005 (ECMWF, 2007).
The vertical resolution comprises 60 levels between the
surface and the model top at 1 hPa. However, the
horizontal resolution has been reduced to T159 (125 km)
with respect to the T511 (40 km) operational resolution.
The physics and dynamics use a 30 min time step except
for the radiation scheme, which uses a 3 h time step.
The CAM3 is the atmospheric component of the
Community Climate System Model (CCSM3), which is
intended for coupled ocean–atmosphere–sea-ice applications, including climate change studies such as those
carried out for the Intergovermental Panel on Climate
Change. The CCSM3 is documented in Collins et al.
(2006) and in a series of papers in a special issue of
Journal of Climate (Gent, 2006). For the forecast application here, CAM3 is run in a stand-alone mode with specified sea surface temperatures (SSTs) and sea-ice extent,
while coupled with the Community Land Model (CLM;
Bonan et al., 2002; Oleson et al., 2004). A complete technical description of CAM3 is provided by Collins et al.
(2004). The CAM3 is configured as it normally is for climate simulation: T42 spectral truncation with 26 vertical
levels, with the top around 2 hPa. The model uses centred time differencing with a time step of 20 min. The
parametrizations are calculated for the 40 min centred
time step. The radiation is calculated every hour.
Unlike climate models, operational NWP models are
typically updated several times per year. It is therefore
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
MODELLING SUPPRESSED AND ACTIVE CONVECTION
unavoidable that when the results from NWP models
are presented in the literature, the NWP models will no
longer be identical to those currently used operationally.
However, the development of operational models tends to
be evolutionary rather than revolutionary, and therefore
it is believed that the results presented in this paper are
still relevant.
2.2. Periods chosen for the study and the synoptic
scale flow
The focus of this experiment is the modelling of the
atmosphere over the tropical ocean for periods of suppressed and active convection. We have chosen to use
a period selected from TOGA–COARE (Webster and
Lukas, 1992). TOGA–COARE was a field experiment,
and it provides suitable observations from a region of
the Tropical West Pacific for a four-month period beginning on 1 November 1992. Observations taken during TOGA–COARE have been processed by Cielsielski
et al. (2003) to provide appropriate forcing for SCMs or
CRMs. The observations were focused on the TOGA–
COARE intensive flux array (IFA), a region of about
400 km × 250 km centred on 2◦ S 155◦ E.
This case uses a three-week period from the
TOGA–COARE campaign starting at 0000 utc 9 January 1993 and ending at 0000 utc 30 January 1993. The
first forecast of the GAMs was started at 0000 utc 8 January, so that a 24–48 h forecast would be available for
the first day of the case. The period was selected because
it includes two cycles of suppressed convection followed
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by more active convection; the selection of the period is
described fully in Petch et al. (2007). They identified two
convectively suppressed subperiods and a convectively
active subperiod, which are defined in Table I; the distinct
humidity structures within each subperiod can clearly be
seen in the time series of relative humidity from ERA-40
in Figure 1. These subperiods are used in section 5 to
evaluate the GAMs within different convective regimes.
Unlike the SCMs and CRMs, the behaviour of the
global models at the location of interest will be dependent
on the large-scale circulation, and it is therefore useful to
put the forecasts for the IFA into the context of the largescale synoptic conditions. The period of study used in this
work (and the whole TOGA–COARE) occurred during a
warm event of the El Niño Southern Oscillation (ENSO;
Gutzler et al., 1994; McBride et al., 1995). The SST in
the IFA from the ERA-40 was between 29.0 and 29.5 ◦ C.
Over the period of the study there is a significant amount
of variation in the zonal winds (Figure 2). The shift from
strong upper level westerlies to weak easterlies between
18 and 20 January is associated with the passing of an
active phase of the MJO, which can be clearly seen in the
precipitable water from ERA-40 (Figure 3). There were
no tropical cyclones or typhoons in the region during the
period of this case-study.
2.3. Procedure for running the global models
and the processing of results
The global models were initialized from the ERA-40
(Uppala et al., 2005) temperature, humidity, geopotential
height and wind fields. For technical reasons, each
modeller was free to decide on which additional ERA-40
Table I. Definitions of the three different convective regimes. fields to use in their initialization. Forecasts were run
for 48 h from 0000 utc on every day for the period
Name
Start
End
of the case. The 0–24 h forecasts were concatenated
B-Suppressed
0000 utc 12 Jan
0000 utc 15 Jan together to form a continuous sequence of forecasts for
B-Active
0000 utc 15 Jan
0000 utc 20 Jan each period; this is referred to as the 0–24 h forecast.
C-Suppressed
0000 utc 23 Jan
0000 utc 26 Jan Similarly, the 12–36 and 24–48 h forecasts were used
to form continuous sequences of 12–36 and 24–48 h
16.0
B_Act
0.40
0.4
0
0.20
10.0
0
0.4
0.80
0.80
0
0.6
0.6
2.0
0
0
0.6
3.0
0
5.0
0.6
7.0
Height (km)
C_Sup
0.
40
B_Sup
13.0
60
0.8
1.0
0
0.
0.8
0
0.3
0.0
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Date in January 1993
Figure 1. Time series of daily mean relative humidity in fraction from ERA-40. The contour interval is 0.1. Below 0.5, the contours are dashed
and at 0.5 and above the contours are solid; the contours are shaded when the relative humidity is greater than 0.8.
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Copyright Copyright, 2008
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
1884
M. R. WILLETT ET AL.
(a)
200 hPa
15
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
ERA 40
Obs
10
U (m/s)
5
0
−5
B_Sup
−10
−15
(b)
8
B_Act
C_Sup
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Date in January 1993
850 hPa
15
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
ERA 40
Obs
10
U (m/s)
5
0
−5
B_Sup
−10
−15
8
B_Act
C_Sup
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Date in January 1993
Figure 2. Time series of daily mean zonal wind in m s−1 at (a) 200 hPa and (b) 850 hPa from the 12–36 h forecasts from the MetUM, IFS and
CAM3, and from ERA-40 and the observations.
forecasts for each period. The diagnostics were averaged
over the grid points within the TOGA–COARE IFA and
meaned in time to form three-hourly and daily averages.
The MetUM contained 20 grid points within the IFA, the
IFS six grid points and the CAM3 a single grid point;
any possible implications of this are discussed during the
analysis of results.
3. Time evolution of the global model forecasts
When a global model is run from analyses then some
degree of adjustment would be expected. Phillips et al.
(2004) argue that if the analyses are perfect, then this
adjustment reflects errors in the model, and as long as
the dynamical state of the forecast remains close to that
of the analyses, the forecast errors are predominantly due
to deficiencies in the model parametrizations. It is this
signal that we wish to study. However, analyses are not
perfect (because of errors in the background field and
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Copyright Copyright, 2008
observations, observation coverage, etc.) and some of the
adjustment will be due to analysis errors. These sources
of forecast error are difficult to separate out, but for this
case-study we discuss this issue further below.
Figure 4 shows the surface precipitation rate as a
function of forecast time; this was created by meaning
together all the 0–48 h forecasts for the case. The
surface precipitation rate is used here as a measure of the
amount of adjustment. All three models produce more
precipitation on the first day of forecast than on the
second day, with the largest differences being during the
more active periods (not shown).
ERA-40, from which the global models are initiated, is
known to have a positive moist bias in the lower levels
over the tropical oceans (Uppala et al., 2005). In order
to assess the impact of the stability and moisture in the
analysis on model precipitation spin-down, the IFS was
run from a new interim analysis that is based on the
most recent IFS forecast model and assimilation system
(notably a 4D-Var system with automated bias correction,
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
MODELLING SUPPRESSED AND ACTIVE CONVECTION
12Z 19th Jan
30
20
70
30
−30
120 130 140 150 160 170 180 190
longitude
longitude
20
0
50 4 0 7
6 0
70
40
4400 60
−20
660
0
60 40
50
40
50
40
30
−30
120 130 140 150 160 170 180 190
20
40
30
40
0
−10
40
40
20
20
−30
120 130 140 150 160 170 180 190
50
50
−20
40
50
50
50
60
60
50
−10
10
60
6070
50
latitude
60
0
20
40
50
60
40
40
30
30
20
30
60
latitude
60
20
50
50
50
30
10
50
40
50
−20
50
40
50
−10
50
50
60
30
40
50
0
30
30
10
40
20
12Z 25th Jan
30
40
20
(c)
30
60
20
latitude
30
20
(b)
50
12Z 13th Jan
30
30
(a)
1885
longitude
Precipitation rate (mm/day)
Figure 3. The total column (precipitable) water from ERA-40 from (a) 13 January during B-Suppressed, (b) 19 January during B-Active and (c)
25 January during C-Suppressed. The total column water is in kg m−2 and the contour interval is 10 kg m−2 ; the contours are dashed below 40
kg m−2 and shaded above 60 kg m−2 . The solid rectangle near the centre of the plots indicates the region of interest in this study.
20
MetUM
IFS
CAM3
IFS from revised ERA40
15
10
5
0
0
6
12
18
24
30
36
42
48
Forecast range (hrs)
Figure 4. Surface precipitation rate in mm day−1 as a function of
forecast time-averaged over each forecast in periods B and C.
revised model physics and higher horizontal resolution).
(At the time of this work, the new interim analysis was
not available to the other models in this study.) From
Figure 4, it can be seen that the new reanalysis results
in much less variation in total surface precipitation with
forecast time. Therefore, it is probable that a significant
proportion of the spin-down seen in the GAMs is a result
of the ERA-40 analysis rather than deficiencies in the
models. It was decided to use the 12–36 h forecasts in
this work because they provide the best balance between
the need to minimize the spin-down effects and the need
to minimize differences in the large-scale circulations.
4. Evaluation of the global model forecasts
This experiment is part of a wider study that allows
the global models to be evaluated not just against
observations and the ERA-40 analysis, but also against
CRMs. In this paper, we use the mean of three of
the CRMs used in this case-study (Woolnough, personal
communication). The CRMs are able to provide the
estimates of quantities that are otherwise not directly
available (e.g. convective mass flux). It should be noted
that whereas the global models are initialized from
ERA-40 and provide their own forcing through the
large-scale circulation, the CRMs (and the SCMs) are
initialized and forced from data derived directly from
the observations (Cielsielski et al., 2003). Although
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Copyright Copyright, 2008
there is no certainty that the same convective regimes
will be observed in the both GAMs and in the CRMs
and SCMs, Petch et al. (2007) have shown that there
is good agreement between the MetUM and the Met
Office’s CRM in this case. Indeed, all the GAMs have
a good correlation with the observations and CRMs in
terms of precipitation and precipitable water (Figure 5),
zonal winds (Figure 2) and to a lesser degree radiative
fluxes (Figure 6): the convective regimes B-Suppressed,
B-Active and C-Suppressed are all readily identifiable in
the GAM forecasts. The active regime that starts on 26
January in the observations and the CRMs is not clearly
seen in the GAM forecasts or in ERA-40 from which the
global models are initialized.
The characteristics of any model are likely to be
different during different convective regimes and this
is discussed later; however, making comparisons over
longer time-scales (e.g. monthly means) is common
practice when assessing NWP GAMs and is often used
to identify systematic errors. By comparing three-week
time means of the 12–36 h forecast over the entire period
from the GAMs, ERA-40 and also the observations and
CRMs, we can identify errors or characteristics of the
models. In addition, by comparing the error structures
seen in this case-study with the known systematic errors
of the GAMs, it is possible to test the robustness of this
case-study in the diagnosis of model errors.
Figure 7 shows the differences in temperature, specific
and relative humidity between ERA-40 and the 12–36
h forecasts of the GAMs, time meaned over the entire
period. Because the GAMs are initialized from ERA-40,
these differences represent the adjustment of the temperature and humidity profiles by the GAMs, and are a
measure of model error; the differences between ERA-40
and the observations are also shown in Figure 7. The
GAMs are initialized from ERA-40 and therefore the differences in Figure 7 represent the error growth within the
GAMs. The temperature biases in the global models are
in general small (up to 1.5 K, but typically much less than
this). Both the IFS and CAM3, and to a lesser extent the
MetUM, are slightly cooler than ERA-40 in the lowest
1 km but the differences are less than 1.5 K. The MetUM
has a cold bias of up to 0.7 K between 10 and 14 km and
a warm bias of up to 0.7 K between 4 and 8 km; this
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
1886
M. R. WILLETT ET AL.
Surface Precipitation rate
Precipitation rate (mm/day)
60
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
CRMs
Obs
40
B_Sup
B_Act
C_Sup
20
0
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Date in January 1993
Precipitable water
90
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
CRMs
Obs
ERA 40
B_Sup
Precipitable water (kg/m2)
80
70
B_Act
C_Sup
60
50
40
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Date in January 1993
Figure 5. Time series of daily mean surface precipitation rate in mm day−1 and precipitable water in kg m−2 from the observations, the mean
of the CRMs, the 12–36 h forecasts from the MetUM, IFS and CAM3, and for precipitable water only from ERA-40.
is typical for this configuration of the MetUM, as can be
seen in the operational MetUM’s bias in tropical oceans
with respect to its own analyses (Figure 8). In this case,
the MetUM also has a warm bias relative to ERA-40
at about 500 m, which is not present in the operational
MetUM’s bias; this discrepancy may be a result of systematic differences between ERA-40 and the operational
MetUM’s analyses that are mainly caused by differences
in the underlying models. The CAM3 has a warm bias
of 0.5–1.5 K between 5 and 10 km, which is consistent
with the zonally averaged annual-mean temperature differences between the CAM3 and ERA-40 in Hack et al.
(2006). The specific humidity of the MetUM and IFS
are generally in good agreement with ERA-40; however,
the relative humidity profile of the MetUM does show a
slight dry bias between 5 and 10 km and a small moist
bias between 12 and 13 km, which again is typical for
this configuration of the MetUM (not shown). The specific humidity of the CAM3 is up to 2 g kg−1 drier than
c Royal Meteorological Society and Crown
Copyright Copyright, 2008
ERA-40 between 300 m and 3 km, which is reflected
in its relatively low precipitable water (Table II), and
it is more moist than ERA-40 near the surface. Above
5 km, the CAM3 is more moist than ERA-40, and there
is a distinct peak in the moist bias at about 6 km, which
is again consistent with the error structures discussed in
Hack et al. (2006). In general, the mean errors seen in
this case replicate those seen elsewhere over much larger
physical scales and over different time-scales. This is very
important because it validates the use of this case-study
to diagnose and investigate model errors.
The MetUM and the IFS have a maximum in relative
humidity at the lifting condensation level (LCL) that
is also seen in CRMs, ERA-40 and the observations
(Figure 9). However, the CAM3 does not have this
maximum in relative humidity (this is discussed further
in section 5.2). The MetUM has a more pronounced
maximum than the IFS or the reference data. This feature
is probably related to the deficiencies in the representation
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
MODELLING SUPPRESSED AND ACTIVE CONVECTION
Surface downward shortwave flux
450
Surface downward SW flux (W/m2)
1887
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
CRMs
Obs
B_Sup
400
350
B_Act
C_Sup
300
250
200
150
100
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Date in January 1993
OLR
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
CRMs
Obs
B_Sup
OLR (W/m2)
350
300
B_Act
C_Sup
250
200
150
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Date in January 1993
Figure 6. Time series of daily mean surface downward short-wave radiation in W m−2 and outgoing long-wave radiation in W m−2 from the
observations, the mean of the CRMs and the 12–36 h forecasts from the MetUM, IFS and CAM3.
(a) 16.0
13.0
10.0
7.0
Height (km)
Height (km)
10.0
(b) 16.0
MetUM
IFS
CAM3
Obs
5.0
3.0
2.0
(c) 16.0
MetUM
IFS
CAM3
Obs
13.0
10.0
7.0
Height (km)
13.0
5.0
3.0
2.0
7.0
5.0
3.0
2.0
1.0
1.0
1.0
0.3
0.3
0.3
0.0
−2
0
2
0.0
Temp bias (K)
−2
0
q bias (g/kg)
2
MetUM
IFS
CAM3
Obs
0.0
0.0
0.2
−0.2
RH bias (fraction)
Figure 7. Time-mean profiles over periods B and C of the difference in (a) temperature in K, (b) specific humidity in g kg−1 and (c) relative
humidity in fraction between ERA-40 and the 12–36 h forecasts from the MetUM (dotted), the IFS (dashed) and the CAM3 (dot-dashed), and
between the ERA-40 and the observations (solid).
c Royal Meteorological Society and Crown
Copyright Copyright, 2008
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
1888
M. R. WILLETT ET AL.
increase the fraction and the water content of the largescale clouds.
The MetUM produces significantly less precipitation
than the other two global models or the observations.
This may be related to the spin-down from ERA-40
(Figure 4) or to systematic large-scale circulation errors
in the MetUM.
16.0
13.0
10.0
Height (km)
7.0
Case Study
Oper (T+24)
Oper (T+120)
5.0
5. Analysis in three different convective regimes
3.0
2.0
5.1. B-Suppressed
1.0
During B-Suppressed there is a period of weak convection
forecast in all three global models. The forecast precipitation rates from the 12–36 h forecasts are all less than 5
mm day−1 and precipitable water amounts are typically
about 50 kg m−2 (Table II). The upper level zonal wind
reverses direction, going from a 5 m s−1 easterly to a
5 m s−1 westerly during this period (Figure 2).
Figure 10 shows the total convective mass fluxes of the
global models and the mean of cloudy updraught mass
flux from the CRMs during B-Suppressed. (The CRMs’
mass flux profile should give a reasonable reference in
terms of general shape, convective cloud base and convective cloud top; however, the magnitude of the mass
flux is likely to be overestimated because, as a result
of data availability, the cloudy, updraught mass flux is
shown, which includes both buoyant and non-buoyant
plumes, rather than the more directly comparable buoyant, cloudy, updraught mass flux. The IFS’s total mass
flux profile includes not only the cloudy mass flux, as in
the other GAMs, but also the sub-cloud layer mass flux,
which is determined by a linear interpolation between
cloud base and the surface. The IFS’s total mass flux is
therefore non-zero below cloud base, but above cloud
base it is directly comparable to the total mass flux of
the other GAMs.) The global models have very different
mass flux profiles, which suggests that they have very
different mixtures of shallow convection, deep convection and congestus. However, all three GAMs do forecast
deep convection that weakens at about 5–6 km as a result
of suppression by the large-scale dynamics (Figures 11
and 12) and the dry environment (Figure 1), which is
in good agreement with the CRMs. In the CAM3, this
dynamical suppression and the dry environment result in
a very sharp reduction in mass flux at about 6 km. The
detrainment of moist convective air at this level results
in a relatively large convective humidity increment of
+1 g kg−1 day−1 (Figure 12), which contributes towards
the CAM3’s moist bias at 6 km that is visible in the
humidity profile for this subperiod (Figure 13).
There are large differences between the mass flux
profiles of the global models and CRMs in the lower
troposphere during B-Suppressed. The MetUM, the IFS
and the CRMs all have a maximum in the mass flux
profile near cloud base that would suggest the presence
of shallow convection or congestus. However, the CAM3
does not have this maximum and the mass flux is
constant between cloud base and 6 km, implying that
0.3
0.0
−1.0
−0.5
0.0
0.5
1.0
Temp bias (K)
Figure 8. Time-mean profiles of temperature biases in K for the MetUM
in this case-study, and for the operational MetUM global T+24 and
T+120 forecasts for tropical oceans in January 2005. The case-study
biases are relative to ERA-40 and the operational forecast biases are
relative to the MetUM’s own analyses.
of cold pools during convectively active periods identified
in Petch et al. (2007).
Averaged over the three weeks of the case, the IFS produces a third of its total precipitation as large-scale (L-S)
precipitation whereas the MetUM and CAM3 produce
very little (Table II). Factors that will contribute to the
relative proportions of convective and L-S precipitation
produced by a GAM include resolution, the efficiency of
the convection scheme at removing water vapour from
the atmosphere and the inclusion of a prognostic cloud
scheme; all three are relevant in this case. Rasch et al.
(2006) report that the CAM3 at T85 resolution produced
10% of its total tropical precipitation as L-S precipitation
but the T42 version, which is used in this study, produced much less. The MetUM and the IFS both use a
mass flux convection scheme with a convective available
potential energy (CAPE) closure for deep convection, but
the IFS uses a CAPE time-scale of 60 min (although
higher-resolution versions of the IFS do use shorter CAPE
time-scales), whereas the MetUM uses a time-scale that is
dependent on relative humidity (Willett and Milton, 2006)
and would typically have a 15 min time-scale during the
active period. Previous work has shown that increasing
the CAPE time-scale can increase the proportion of tropical L-S precipitation in the MetUM, but a short CAPE
time-scale is used operationally because it improves the
distribution of tropical precipitation (Milton et al., 2005).
Dai and Trenberth (2004) and Rasch et al. (2006) have
suggested that the CAM3’s convection scheme is too
efficient at removing water vapour from the atmosphere.
The IFS uses a prognostic cloud scheme but the MetUM
and CAM3 do not. In the MetUM and the CAM3, any
liquid water that is detrained during convection is instantaneously evaporated, whereas in the IFS it acts to directly
c Royal Meteorological Society and Crown
Copyright Copyright, 2008
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
MODELLING SUPPRESSED AND ACTIVE CONVECTION
1889
Table II. Summary of the entire period, B-Suppressed, B-Active and C-Suppressed.
Period
Entire Period
B-Suppressed
B-Active
C-Suppressed
Tot precipa
(mm day−1 )
L-S precipb
(mm day−1 )
PWc
(kg m−2 )
OLRd
(W m−2 )
Surf down SWe
(W m−2 )
ERA-40
Obs
CRMs
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
–
8.2
8.7
4.9
8.7
7.7
–
–
–
0.1
3.1
0.2
51.4
54.2
53.6
50.9
51.5
49.0
–
232.4
250.9
244.6
247.0
228.1
–
257.7
255.6
269.7
231.4
238.0
ERA-40
Obs
CRMs
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
–
−1.4
2.2
2.3
2.3
4.3
–
–
–
0.0
0.6
0.0
49.0
52.8
52.1
50.4
48.6
46.7
–
269.8
283.3
276.9
278.0
278.2
–
302.9
296.5
293.5
265.7
285.5
ERA-40
Obs
CRMs
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
–
17.4
17.3
10.7
19.6
15.1
–
–
–
0.2
10.9
0.4
59.7
60.3
56.0
57.6
56.6
56.7
–
200.7
215.7
211.0
217.6
208.3
–
213.6
210.0
242.4
199.3
196.1
ERA-40
Obs
CRMs
MetUM (12–36)
IFS (12–36)
CAM3 (12–36)
–
2.9
2.3
0.1
4.1
3.4
–
–
–
0.0
1.0
0.0
43.8
47.5
47.9
42.1
47.1
43.6
–
250.1
295.2
269.9
266.7
225.8
–
294.5
291.9
297.5
255.4
279.8
Model
a
Total precipitation.
precipitation.
c Precipitable water.
d Outgoing long-wave radiation.
e Surface downward short-wave radiation.
b Large-scale
deep convection is the dominant process. The mass flux
profile of the IFS has a very large amount of shallow
convection below 2 km that is not seen in either the
CRMs or the other GAMs. The mass flux profile of the
MetUM is in good agreement with the CRMs during
B-Suppressed in terms of cloud-base height, profile shape
and vertical extent. Between the cloud base at about
500 m, and the freezing level, at about 5 km, the mass
flux falls off steadily, which would imply that MetUM is
producing congestus-type convection, even though this is
not explicitly parametrized.
The total precipitation rates of the MetUM and the IFS
are in good agreement with the CRMs for B-Suppressed
(Table II). The CAM3, however, produces significantly
more precipitation, which is probably associated with the
larger amount of deep convection in its forecast. The outgoing long-wave radiation (OLR) of the global models
is in good agreement with the CRMs and observations.
The MetUM’s surface downward short-wave radiation
(SDSW) also agrees well with the CRMs and observations, but the CAM3 and the IFS have less SDSW.
c Royal Meteorological Society and Crown
Copyright Copyright, 2008
5.2. B-Active
B-Active is characterized in all three GAMs by high
precipitation rates (Figure 5), high humidity throughout
the depth of the troposphere (Figure 1) and strong deep
convection (Figure 10).
The mass flux profiles of the global models all have
strong deep convection during B-Active, which is consistent with the CRMs (Figure 10). There is good agreement
between the global models and the CRMs on maximum
height reached by the convection, but there are some
small differences in the height of cloud base (in the
MetUM, CAM3 and CRMs, this corresponds to the lowest height at which the mass flux is non-zero, and in the
IFS it corresponds to the height of the low-level maximum in the mass flux). The height of cloud base in
the MetUM is consistent with that in the CRMs. The
cloud base in the CAM3 and IFS is slightly lower than
in the MetUM because the near-surface relative humidity
is lower (Figure 13), but the differences are small. The
IFS is alone in forecasting a large amount of shallow
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
1890
M. R. WILLETT ET AL.
1.5
MetUM
IFS
CAM3
CRMs
Obs
1.0
Height (km)
ERA40
0.5
0.00
0.60
0.70
0.80
RH (fraction)
0.90
1.00
Figure 9. Time-mean profiles over periods B and C of relative humidity
in fraction for the lower troposphere from the MetUM, IFS, CAM3,
CRMs, observations and ERA-40.
convection, but its convective tendencies at these levels
are similar to those of the other GAMs in terms of structure and magnitude (Figures 11 and 12).
The MetUM’s mass flux profile is similar to the
CAM3’s and much larger than the IFS’s in the upper
troposphere, but the MetUM’s convective tendencies are
actually smaller than those in the other two models
(Figure 11). The MetUM’s long-wave cooling in the
upper troposphere is not balanced by the convective
heating (Figure 11) during B-Active, and consequently
a cold bias develops at these levels (Figure 13). By
examining the MetUM’s behaviour on a time step by
time step basis, it is shown that the model’s deep
convection frequently terminates too low down and
subsequently mid-level convection initiates from where
13.0
Height (km)
10.0
(b)
B–Suppressed
MetUM
IFS
CAM3
CRMs
7.0
5.0
3.0
2.0
(c)
B–Active
16.0
MetUM
IFS
CAM3
CRMs
13.0
10.0
7.0
5.0
3.0
2.0
16.0
13.0
10.0
Height (km)
16.0
Height (km)
(a)
deep convection terminated (not shown). In this context,
mid-level convection is convection that is not initiated
from the surface but rather responds to a local instability
in the thermodynamic profile. In the upper troposphere,
the time-mean tendencies of mid-level convection tend
to be relatively low because of cancellation effects and
the lack of moisture. Consequently, the MetUM has
mass flux and convective cloud at these upper levels
but the convective tendencies are relatively small. In
contrast, during B-Active, the convective tendencies
from the IFS do reach up to the tropopause and even
penetrate into the lower stratosphere, possibly modelling
the effects of convective plumes ‘overshooting’ their level
of neutral buoyancy. The mass flux of the CRMs does
not reach this high, but this is probably because the
forcing for the CRMs was set to zero above 15 km.
The height reached by CAM3’s convective tendencies lies
somewhere between those of the IFS and the MetUM.
During B-Active, the IFS produces half of its precipitation as large-scale rain (Table II), and its large-scale cloud
and precipitation schemes produce a significant warming
in the upper troposphere and a cooling in the lower troposphere (Figure 11). This signal is consistent with that
expected for the production of stratiform precipitation,
which observations suggest constitutes half of tropical
rainfall (Schumacher and Houze, 2003). It is important
for GAMs to capture the correct proportions of convective and stratiform precipitation because this has a
direct impact on the tendencies from the moist processes
(Hartmann, Hendon and Houze, 1984). There is no obvious sign of stratiform precipitation in the other GAMs.
However, it should be noted that the identification of
stratiform precipitation in the GAMs is not easy because
it is not explicitly parametrized or diagnosed and may be
produced by the convection scheme or L-S precipitation
scheme or by both, depending upon the model.
The sub-cloud humidity convection and boundarylayer tendencies in the MetUM and the IFS are well
mixed, but in the CAM3 they are larger at the surface
than they are at the LCL (Figure 12). This lack of
5.0
3.0
2.0
1.0
1.0
0.3
0.3
0.3
0.0
0.000 0.005 0.010 0.015 0.020 0.025
0.0
0.00
Mass flux (kg/m2/s)
0.02
0.03
0.04
Mass flux (kg/m2/s)
0.05
MetUM
IFS
CAM3
CRMs
7.0
1.0
0.01
C–Suppressed
0.0
0.000 0.005 0.010 0.015 0.020 0.025
Mass flux (kg/m2/s)
Figure 10. Profiles of time-mean total convective mass flux from 12–36 h forecasts of the MetUM (dotted), the IFS (dashed) and the CAM3
(dot-dashed), and the mean of cloudy updraught mass flux from the CRMs (solid) from (a) B-Suppressed, (b) B-Active and (c) C-Suppressed in
kg m−2 s−1 . The shaded region shows the maximum and minimum time-mean cloudy updraught mass flux of the CRMs. Please note that the
suppressed subperiods have a different x-scale than the active subperiod.
c Royal Meteorological Society and Crown
Copyright Copyright, 2008
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
MODELLING SUPPRESSED AND ACTIVE CONVECTION
B–Sup: IFS
B–Sup: MetUM
7.0
5.0
3.0
2.0
Height (km)
13.0
13.0
10.0
10.0
7.0
7.0
5.0
3.0
2.0
3.0
2.0
1.0
1.0
0.3
0.3
0.3
−5
0
0.0
5
Temperature increments (K day−1)
13.0
10.0
7.0
5.0
3.0
2.0
−5
B–Act: MetUM
0.0
−5
0
5
Temperature increments (K day−1)
5
B–Act: CAM3
B–Act: IFS
16.0
Total
SW
LW
Dyn
Conv
BL+LSP
+LSC
0
Temperature increments (K day−1)
Height (km)
16.0
13.0
16.0
13.0
10.0
10.0
7.0
7.0
5.0
3.0
2.0
5.0
3.0
2.0
1.0
1.0
1.0
0.3
0.3
0.3
0.0
−15.0
−7.5
0.0
0.0
−15.0
15.0
Temperature increments (K day−1)
7.5
−7.5
C–Sup: MetUM
7.0
5.0
3.0
2.0
Total
SW
LW
Dyn
Conv
BL+LSP
+LSC
C–Sup: CAM3
16.0
16.0
13.0
13.0
10.0
10.0
7.0
7.0
Height (km)
10.0
7.5
C–Sup: IFS
Height (km)
13.0
0.0
0.0
15.0
7.5
0.0
7.5
15.0
Temperature increments (K day−1)
15.0
Temperature increments (K day−1)
16.0
Height (km)
5.0
1.0
0.0
Height (km)
16.0
Height (km)
Height (km)
10.0
Total
SW
LW
Dyn
Conv
BL+LSP
+LSC
B–Sup: CAM3
16.0
Height (km)
16.0
13.0
5.0
3.0
2.0
5.0
3.0
2.0
1.0
1.0
1.0
0.3
0.3
0.3
0.0
−5
0
1891
5
Temperature increments (K day−1)
0.0
−5
0
5
Temperature increments (K day−1)
0.0
−5
0
5
Temperature increments (K day−1)
Figure 11. Mean temperature budgets in K day−1 during B-Suppressed (top row), B-Active (middle row) and C-Suppressed (bottom row) from
the MetUM (left column), IFS (middle column) and CAM3 (right column) in K day−1 . The figures show the tendencies from the short-wave
radiation (blue), the long-wave radiation (green), the dynamics (purple), the convection (red), the stratiform precipitation, stratiform cloud,
boundary layer and vertical diffusion (orange), and the total tendency (black).
mixing in the CAM3’s humidity tendencies may explain
its sub-cloud humidity structure, which is too moist near
the surface and too dry at the LCL, and which is very
different to the other GAMs (Figure 13).
In B-Active, there is a distinct minimum in the convective temperature tendencies in the IFS just below
the freezing level, which is not seen in the CAM3 or
the MetUM (Figure 11). The source of this minimum
is the melting of falling snow. This process is represented in all the models but its magnitude will depend
on precipitation production, in which part of the system the precipitation is considered to be falling (i.e.
updraught, downdraught or environment), and possibly
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Copyright Copyright, 2008
other factors. In the IFS, the local minimum in the
convective warming is partially offset by a local maximum in the large-scale moist processes, and hence there
is only a small corresponding minimum in the dynamical
cooling.
The OLR of all three global models during B-Active
is very similar and there is good agreement with the
CRMs and the observations. However, there is again
much greater variability among the GAMs in terms
of the SDSW (Figure 6). Averaged over B-Active, the
SDSW in the MetUM is 30 W m−2 larger than in the
observations and CRMs, and more than 40 W m−2 larger
than the other GAMs (Table II).
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
1892
M. R. WILLETT ET AL.
B–Sup: IFS
B–Sup: MetUM
5.0
3.0
2.0
10.0
10.0
7.0
7.0
5.0
3.0
2.0
3.0
2.0
1.0
1.0
0.3
0.3
0.3
−5
0
10.0
7.0
−1
−1
−5
B–Act: MetUM
5.0
3.0
2.0
0.0
5
kg−1
day−1)
10.0
10.0
7.0
7.0
5.0
3.0
2.0
5.0
3.0
2.0
1.0
0.3
0.3
7.5
0.0
−15.0
15.0
−7.5
C–Sup: MetUM
7.0
5.0
0.0
7.5
0.0
7.5
15.0
15.0
Humidity increments (g kg−1 day−1)
15.0
Total
Dyn
Conv
BL+LSP
+LSC
3.0
2.0
C–Sup: CAM3
16.0
13.0
16.0
13.0
10.0
10.0
7.0
7.0
Height (km)
10.0
7.5
C–Sup: IFS
Height (km)
16.0
13.0
0.0
Humidity increments (g kg−1 day−1)
Humidity increments (g kg−1 day−1)
5.0
3.0
2.0
5.0
3.0
2.0
1.0
1.0
1.0
0.3
0.3
0.3
0.0
−5
0
5
Humidity increments (g kg−1 day−1)
0.0
day−1)
B–Act: CAM3
0.3
0.0
5
kg−1
16.0
13.0
1.0
−7.5
0
Humidity increments (g
1.0
0.0
−15.0
−5
B–Act: IFS
16.0
13.0
Total
Dyn
Conv
BL+LSP
+LSC
0
Humidity increments (g
day )
Height (km)
16.0
13.0
0.0
5
Humidity increments (g kg
Height (km)
5.0
1.0
0.0
Height (km)
16.0
13.0
Height (km)
7.0
B–Sup: CAM3
16.0
13.0
Height (km)
Height (km)
10.0
Total
Dyn
Conv
BL+LSP
+LSC
Height (km)
16.0
13.0
−5
0
5
Humidity increments (g kg−1 day−1)
0.0
−5
0
5
Humidity increments (g kg−1 day−1)
Figure 12. Mean humidity budgets during B-Suppressed (top row), B-Active (middle row) and C-Suppressed (bottom row) from the MetUM
(left column), IFS (middle column) and CAM3 (right column) in K day−1 . The figures show the tendencies from the dynamics (purple), the
convection (red), the stratiform precipitation, stratiform cloud, boundary layer and vertical diffusion (orange), and the total tendency (black).
5.3. C-Suppressed
During C-Suppressed, the precipitable water falls to
below 45 kg m−2 in ERA-40 and precipitation rates are
typically less than 4 mm day−1 (Table II). The region
between 2 and 5 km is much drier than in B-Suppressed
(Figure 1) and this is reflected in the lower precipitable
water values.
The three global models have very different mass flux
profiles during C-Suppressed (Figure 10); the CAM3
produces just deep convection, the MetUM produces
just shallow convection and the IFS produces deep and
shallow convection. In contrast, the mean mass flux
c Royal Meteorological Society and Crown
Copyright Copyright, 2008
profile of the CRMs implies that there is a significant
amount of congestus and shallow convection as well as
weak deep convection, which terminates below 8 km.
Despite large differences in the temperature and humidity profiles, the mass flux profile of the CAM3 is very
similar to that in the first suppressed period; there is deep
convection with a sharp reduction in the mass flux just
above the freezing level at about 6 km, and again there is
no shallow convection. Lack of shallow convection is a
consistent feature of the CAM3 in this case. The CAM3
does include a combined shallow and mid-level convection scheme, but it is known to produce too little shallow
convection. The precipitable water of the CAM3 is only a
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
MODELLING SUPPRESSED AND ACTIVE CONVECTION
B–Suppressed
16.0
13.0
10.0
7.0
5.0
3.0
2.0
16.0
MetUM
IFS
CAM3
7.0
5.0
3.0
2.0
5.0
3.0
2.0
1.0
0.3
0.3
0.3
−2
0
0.0
2
−2
0
B–Suppressed
B–Active
MetUM
IFS
CAM3
13.0
10.0
Height (km)
5.0
3.0
2.0
16.0
MetUM
IFS
CAM3
13.0
10.0
7.0
5.0
3.0
2.0
5.0
3.0
2.0
1.0
1.0
0.3
0.3
0.3
0
0.0
2
−2
0
q bias (g/kg)
B–Suppressed
B–Active
MetUM
IFS
CAM3
13.0
10.0
Height (km)
5.0
3.0
2.0
16.0
MetUM
IFS
CAM3
13.0
10.0
7.0
5.0
3.0
2.0
5.0
3.0
2.0
1.0
1.0
0.3
0.3
0.3
0.0
0.2
0.0
RH bias (fraction)
−0.2
0.0
RH bias (fraction)
0.2
MetUM
IFS
CAM3
7.0
1.0
−0.2
2
C–Suppressed
16.0
7.0
0.0
0
q bias (g/kg)
Height (km)
10.0
−2
q bias (g/kg)
16.0
13.0
0.0
2
MetUM
IFS
CAM3
7.0
1.0
−2
2
C–Suppressed
16.0
7.0
0.0
0
Temp bias (K)
Height (km)
10.0
−2
Temp bias (K)
16.0
13.0
0.0
2
MetUM
IFS
CAM3
7.0
1.0
Temp bias (K)
Height (km)
10.0
1.0
0.0
Height (km)
13.0
Height (km)
MetUM
IFS
CAM3
Height (km)
Height (km)
10.0
C–Suppressed
B–Active
16.0
13.0
1893
0.0
−0.2
0.0
0.2
RH bias (fraction)
Figure 13. Time-mean profiles for subperiods B-Suppressed (left column), B-Active (middle column) and C-Suppressed (right column) of the
difference in temperature in K (top row), specific humidity in g kg−1 (middle row) and relative humidity in fraction (bottom row) between
ERA-40 and the 12–36 h forecast from the MetUM (dotted), the IFS (dashed) and the CAM3 (dot-dashed).
little higher than in ERA-40, and the precipitation rate is a
little higher than seen in the observations and the CRMs,
which again is probably because of the predominance of
deep convection.
During C-Suppressed, the IFS, like the CAM3, forecasts deep convection that reaches well into the upper troposphere (Figure 10), but deep convection in the CRMs is
weaker and terminates much lower down at about 8 km.
A direct consequence of this additional deep convection is
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Copyright Copyright, 2008
that the IFS produces more precipitation than the CRMs
(Table II). The IFS forecasts higher humidity between the
surface and 3 km than in either ERA-40, from which it
is initialized, or the other two GAMs (Figure 13). This
additional moistening probably reflects differences in the
large-scale circulation relative to ERA-40. In ERA-40, the
precipitable water reduces to the east of the IFA (Figure 3)
and there are low-level easterly winds (Figure 2) that will
act to advect dry air into the IFA. However, in the IFS
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
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M. R. WILLETT ET AL.
the easterlies are significant weaker than in ERA-40 and
hence less dry air will be advected into the IFA. Again,
the IFS has a large peak in the mass flux at around 500 m;
this feature is present and has a similar magnitude in all
three subperiods.
In C-Suppressed, the MetUM behaves very differently
to the other global models, which both produce deep convection and precipitation. The MetUM forecasts shallow
convection that terminates below 2.5 km and it does not
produce any precipitation (there is also a small amount of
convection that initiates and terminates in the upper troposphere, which is probably mid-level convection responding to a local instability, but it has a negligible impact
on the time-mean convective increments). The large-scale
descent warms and dries the environment throughout
most of the free troposphere (Figures 11 and 12) that
will suppress deep convection.
Unlike during the other subperiods, there is a large
disparity in the OLR from the GAMs, CRMs and
observations (Table II). The IFS has much more active
convection than the MetUM in this subperiod, but its
OLR is similar to that of the MetUM. The CAM3 has
some deep convection during C-Suppressed and this in
part explains why its OLR is low. However, it is not
clear why its OLR is 40 W m−2 lower than in the IFS,
which has a similar level of convective activity in terms
of mass flux and precipitation, or why the CAM3’s OLR
is so much lower in C-Suppressed than in B-Suppressed.
As in B-Suppressed, the SDSW in the IFS is significantly
less than in the other GAMs, CRMs or observations. This
is probably a reflection of the relatively large amount of
shallow convection that is present in the IFS (Figure 10).
Out of the three subperiods, C-Suppressed has the
least agreement amongst the GAMs and least agreement
between the GAMs and the CRMs in terms of the simulation of convection and the radiative fluxes. Convection
in the IFS and CAM3 was arguably too deep whilst in
the MetUM it was too shallow. A possible explanation for
this is that whilst all of the GAMs explicitly include deep
and shallow convection schemes, none of them explicitly includes anything in-between. The GAMs, therefore,
either produce shallow convection that is too weak and
shallow, or deep convection, which because of its relatively low entrainment rates and hence lack of sensitivity
to environmental moisture (as was seen in Derbyshire
et al., 2004) is too strong and deep. Therefore, in this
subperiod, the GAMs struggle to reproduce the congestus or weak deep convection seen in the CRMs. This may
be mitigated by making deep convection, and in particular
the entrainment rate, more adaptive to the environmental moisture or perhaps by the inclusion of an explicit
congestus scheme.
6. Summary
This paper is one of the first to evaluate global NWP
and climate models, both operating in forecast mode
against CRMs, as well as observations and ERA-40.
In general, the NWP models do capture many of the
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Copyright Copyright, 2008
observed features of this case. There are, however, many
differences in behaviour among the models and they
all exhibit some characteristics that could be considered
erroneous when comparisons are made to the analysis,
CRMs or observations.
Previous studies have suggested that global models
often lack variability in the level of convective activity
in the Tropics. This study only used short-term forecasts
and hence much, but by no means all, of the variability
in the forecasts would be driven by variability in the
analysis. Notwithstanding this, the GAMs’ forecasts all
differentiated between the active and suppressed phases
in terms of precipitation and radiative fluxes, and they all
produced strong deep convection during the active period.
However, during C-Suppressed there was significantly
more variability in the behaviour of convection in the
GAMs and less agreement with the CRMs; the MetUM
forecast shallow convection, the IFS and the CAM3
both forecast weak deep convection, whilst the CRMs
produced very weak deep convection or congestus. It is
suggested that making deep convection more adaptive to
the environmental moisture or the inclusion of an explicit
congestus scheme may improve the simulation of weak
deep convection or congestus.
One of the most notable differences among the GAMs
was the relative amount of convective and L-S precipitation that they produce. The IFS generates, on average,
one-third of its total precipitation as L-S precipitation,
but the MetUM and the CAM3 produce almost none.
Three possible reasons are suggested for this, which are
all shown to be relevant in this case: the inclusion or not
of a prognostic cloud scheme; the relative efficiencies of
the convection schemes at removing moisture from the
environment; the horizontal resolution.
Where the GAMs and the CRMs are in good agreement in terms of convective activity, there is good
agreement amongst the GAMs’ OLR, and a reasonable
agreement between the GAMs and the reference data.
However, there is much more disparity in the surface
downward short-wave flux, especially during the most
active period, which implies that the representation of
convective cloud is a significant source of uncertainty in
the models.
By making intercomparisons of the GAMs and by
using reference data (CRMs, observations and ERA40), we have been able to highlight a number of undesirable characteristics in the GAMs. For example, the
MetUM had a lack of convective heating in the upper
troposphere during the active period that is responsible for a cold bias, the IFS persistently generates large
amounts of shallow convection, and consequently has
less SDSW than the other GAMs, and the CAM3 is
unable to produce significant amounts of shallow convection, and consequently produces too much deep convection and precipitation during the most suppressed subperiod.
The methodology used in the paper is clearly a valuable
tool in the assessment of GAMs and it offers a valuable
addition to the SCM/CRM framework of GCSS. The
budgets are an essential component to this methodology
Q. J. R. Meteorol. Soc. 134: 1881–1896 (2008)
DOI: 10.1002/qj
MODELLING SUPPRESSED AND ACTIVE CONVECTION
and are fundamental to understanding GAM behaviour.
Here, we have used just temperature and humidity, but
further insight into model behaviour may be gained
by also including the momentum budget. Budget data
do have the important benefit that self-consistency may
be checked, which gives a degree of assurance when
obtaining data from different sources. CRMs do provide
a valuable reference from which to assess GAMs, but
there are a few important considerations to be made.
The large-scale forcing experienced by the CRMs will be
different to that produced by GAMs but the differences
can be quantified (e.g. Petch et al., 2007). The other
factor is the difficulty of obtaining directly comparable
diagnostics from CRMs and GAMs, but this may be
mitigated by careful choice of diagnostic requirements
at the experiment design stage.
In this case, the GAMs used a wide range of resolutions, and although we have not explicitly examined the
impact of this, it may be an important factor in determining model behaviour. Therefore, it is planned to use
the MetUM, which is routinely used for climate modelling, and global and mesoscale NWP, to examine the
sensitivity to resolution in this case.
Acknowledgements
The authors would like to thank all those who contributed
to this GCSS case-study, and thank both of the reviewers
for their valuable comments.
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