Changes in the large-scale environment during convective precipitation events in the
tropical western Pacific simulated by version 4.3 of the Canadian Atmospheric
Model (CanAM4.3) are compared against those simulated by version 5.0 of the
super-parameterized Community Atmosphere Model (spCAM5). This is done by
compositing sub-hourly output of convective rainfall, convective available
potential energy (CAPE), CAPE generation due to large-scale forcing in the
free troposphere (dCAPELSFT) and near-surface vertical velocity
(ω) over the time period May–July 1997. Compared to spCAM5, CanAM4.3
tends to produce more frequent light convective precipitation (<0.2 mm h-1) and underestimates the frequency of extreme convective
precipitation (>2 mm h-1). In spCAM5, 5 % of convective
precipitation events lasted less than 1.5 h and 75 % lasted between 1.5
and 3.0 h, while in CanAM4.3 80 % of the events lasted less than 1.5 h.
Convective precipitation in spCAM5 is found to be a function of
dCAPELSFT and the large-scale near-surface ω with
variations in ω slightly leading variations in convective
precipitation. Convective precipitation in CanAM4.3 does not have the same
dependency and instead is found to be a function of CAPE.
Global climate models (GCMs) typically have a horizontal grid scale that is
much larger than individual deep convective clouds, which requires
parameterizations of convection and its effect on the large-scale
atmosphere. Convective and other GCM parameterization often have adjustable
parameters “tuned” within their range of uncertainty so that the model
simulates reasonable climatological distributions of temperature, clouds
and wind fields (Mauritsen et al., 2012). However, these long-term
climatological averages are the result of many shorter timescale sub-grid
convective events which may have their biases masked by averaging. It is
known that climate models tend to exhibit less rainfall variance than
observations (Scinocca and McFarlane, 2004; DeMott et al., 2007), they tend to
produce light precipitation (<10 mm d-1) more often than
observed (Zhang and Mu, 2005b; Sun et al., 2006; Dai, 2006) and they underestimate the occurrence of extreme precipitation events (Wilcox and
Donner, 2007; Boyle and Klein, 2010; Wang et al., 2017).
Studies have attributed biases in simulated precipitation variability to
convective parameterizations employed in models (Zhang and Mu, 2005b; DeMott
et al., 2007; Wang and Zhang, 2013; Wang et al., 2016). In general,
convective parameterizations require a closure, which may or may not be
activated (triggered) based on whether certain conditions are satisfied. If
activated, the closure computes the cloud base mass flux which in turn is
used to compute fluxes of mass, moisture and precipitation above cloud
base. Although convective precipitation is generated in response to the
closure, it has been shown that the design of the trigger function may also
influence the simulation of precipitation (Truong et al., 2009). Commonly
used convective schemes employ triggers and closures based on net column
moisture convergence (Tiedtke, 1989) or convective available potential
energy (Zhang and McFarlane, 1995) while some convective schemes use
grid scale upward motion in the lower troposphere as a trigger function
(Donner, 1993; Bechtold et al., 2001).
A super-parameterization framework (Grabowski, 2001; Khairoutdinov and
Randall, 2001) has been used to replace conventional convective and boundary
layer parameterizations with cloud system resolving models (CSRMs) in GCMs.
When evaluated against observations and compared to GCMs,
super-parameterized GCMs show improved tropical rainfall variability
associated with the El Niño–Southern Oscillation (Stan et al., 2010),
the Madden–Julian Oscillation (Thayer-Calder and Randall, 2009; Kooperman et
al., 2016), improved light and extreme precipitation over the US (Li et al.,
2012), and more realistic diurnal cycle of summertime convection over
mid-latitude continents (Guichard et al., 2004; Khairoutdinov et al., 2005).
Therefore, super-parameterized GCMs may be useful to provide guidance for
improving sub-grid convective parameterizations. However, the additional
computing cost for the super-parameterization implementation is prohibitive
for most modelling groups, which is a major reason why it is still not widely
used.
In the analysis that follows, we examine how models simulate deep tropical
convective events, so it is worthwhile to summarize the behaviour one might
expect. Tropical convective clouds are often organized into a specific
pattern known as the “building block model” (Mapes et al., 2006). Within
this pattern, shallow convective clouds precede deep convective clouds which
are then followed by stratiform anvil clouds. Shallow convective clouds
pre-moisten the lower-troposphere and thus support the growth of deep
convective clouds (Johnson et al., 1999; Sherwood, 1999; Sobel et al.,
2004), while deep convective clouds detrain large amounts of condensate in
the upper-troposphere and therefore contribute to the development of
stratiform anvil clouds. The stratiform clouds, with cloud base near the
melting level (Zipser, 1977), generate about 40 % of the tropical
precipitation (Schumacher and Houze, 2003). Falling through the unsaturated
air under the cloud base, some fraction of the stratiform precipitation
evaporates, generating negatively buoyant downdrafts which may penetrate to
the surface (Zipser, 1977). By mass continuity the stratiform downdrafts
induce upward motion in the background atmosphere, thus contributing to
moistening and cooling of the lower-troposphere. The forced lift and the
low-level moistening and cooling contribute to increasing low-level
instability, and thus may promote further initiation of new convection
(Mapes, 1993; Mapes and Houze, 1995; Fovell et al., 2006). Some features of
the building block models, the shallow convective pre-moistening and the
strength of the stratiform circulation, have not been realistically
simulated in global climate models (Mitovski et al., 2010).
For this study we use sub-hourly output from version 4.3 of the Canadian
Atmospheric Model (CanAM4.3) and version 5 of the super-parameterized
Community Atmospheric Model (spCAM5) to isolate strong convective
precipitation events in each model for a 3-month period in the tropical
western Pacific (TWP). To evaluate the ability of CanAM4.3 to simulate
convective precipitation relative to spCAM5 and the relationship between
precipitation and the environment, composites of convective available
potential energy (CAPE), CAPE generation in the free troposphere,
large-scale near-surface -ω and convective precipitation are analysed
for all convective events in this region.
Model description
Version 4.3 of the Canadian Atmospheric Global Climate Model (CanAM4.3) has
several improvements relative to its predecessor, CanAM4 (von Salzen et al.,
2013), including improvements to parameterizations of radiation and land
surface processes. CanAM4.3 uses a hybrid vertical coordinate system with
49 levels between the surface and 1 hPa, with a resolution of about 100 m
near the surface. The triangular spectral truncation of the model dynamical
core is T63, with an approximate horizontal resolution of 2.8∘
latitude and longitude.
The mass flux scheme of Zhang and McFarlane (ZM) is used in CanAM4.3 to
parameterize the effect of deep convection on the large-scale environment
(Zhang and McFarlane, 1995). The diagnostic closure of Zhang and McFarlane (1995) in CanAM4 has been replaced with a prognostic closure (Scinocca and
McFarlane, 2004). The diagnostic closure assumes that convection consumes
CAPE at a rate that is proportional to the (positive) difference between the
ambient value and some specified threshold value. The triggering condition
is that CAPE is greater than zero. A quasi-equilibrium state could emerge if
the large-scale CAPE production balances the convective consumption, but it
is not imposed a priori. The prognostic closure also does not assume
quasi-equilibrium a priori, but a quasi-equilibrium state could in principle
emerge. The trigger condition in the prognostic closure is also CAPE greater
than zero. When activated, the prognostic closure computes the cloud base
mass flux which increases proportionally with CAPE and is then dissipated
within a specified timescale.
To account for the effect of cumulus clouds with cloud tops below the
ambient freezing level on the large-scale environment, CanAM4.3 employs a
shallow convection scheme (von Salzen and McFarlane, 2002). The shallow
convection scheme includes a parameterization of autoconversion processes to
account for the effect of drizzle formation in shallow cumulus clouds
following the approach in Lohmann and Roeckner (1996). The shallow
convection scheme employs a diagnostic cloud base closure (Grant, 2001)
based on a simplified turbulent kinetic energy budget for the convective
boundary layer. The shallow scheme is not allowed to be active if the deep
scheme is triggered at the same grid point and is vertically limited so that
it operates mainly within the lower troposphere.
Version 5 of the Community Atmosphere Model (CAM5) used for the
super-parameterized run has a horizontal resolution of 1.9∘×2.5∘ (latitude × longitude), 30 vertical levels from the
surface to 3.6 hPa and a time step of 1800s for the physical
parameterizations (Neale et al., 2012). Version 5.0 of the
super-parameterized Community Atmosphere Model (spCAM5) employs a
2-D CSRM within each CAM5 grid cell to replace the convective
parameterization of moist convection and other atmospheric parameterizations.
The CSRM uses 32 columns each with 4 km horizontal grid spacing and 28
vertical layers, between 992 and 14.3 hPa, coinciding with the lowest 28
levels in CAM5. Details of the CSRM and information on CSRM implementation
within CAM can be found in Khairoutdinov and Randall (2001, 2003) and Wang et
al. (2011).
For both models the period of analysis is limited to the period between 1 May
and 24 July of 1997 after each model simulation had been spun up (1 January
1996 to 30 April 1997 for CanAM4.3 and 1 January 1997 to 30 April 1997 for
spCAM5). For CanAM4.3, a six-member ensemble was generated by uniquely
adjusting the seed for the random number generator on 1 January 1997. This
was done to improve the statistical representation of the results from this
model as data from all ensemble members were used in the analysis. The spCAM5
spin-up is done using CAM5. Output over the domain 150–170∘ E and
0–10∘ N is extracted and used for our analysis. Over this domain
the 4 km 10 min CSRM output from spCAM5 is used to compute the quantities
needed for the analysis while output from CanAM4.3 is available every 15 min
(the model dynamical time step). Both models used monthly varying prescribed sea surface
temperatures (SSTs) and sea ice fractions
based on observations (Hurrell et al., 2008) as well as transient
concentrations of trace gases and aerosols that are representative of
conditions during the time period of the simulations.
MethodologyConvective precipitation definition
Within spCAM5, the CAM5 atmospheric parameterizations have been replaced by
CSRMs, so it was necessary for our analysis to devise a method to separate
the convective from the total precipitation. Convective precipitation in
spCAM5 was defined to be the total precipitation from all convective CSRM
columns divided by the total number of columns (i.e. divided by 32). A CSRM
column is categorized as convective if at any level the vertical velocity is
greater than 1 m s-1 or less than -1 m s-1 and the sum of the
cloud liquid and cloud ice water is greater than 0.1 g kg-1. This
definition is used in the studies of Suhas and Zhang (2015) and Song and
Zhang (2018) which follow the study of Xu and Randall (2001) which set the
thresholds based on examination of convective updraft and downdraft
statistics in cloud-resolving model simulations of tropical and mid-latitude
convection. Convective precipitation in CanAM4.3 is generated within the deep
and shallow convection schemes with the majority coming from the deep scheme.
The sensitivity of the results to the definition of convective precipitation
from spCAM5 was evaluated by repeating the analyses using total instead of
convective precipitation (Figs. S1, S2 and S3 in the Supplement). Results in Figs. 1, 3a,
c and 4 were found to be similar using either the total or convective
precipitation from spCAM5, implying insensitivity, for this study, to the
exact definition of thresholds in the method of Suhas and Zhang (2015). This
is mainly because in the regions being studied the precipitation was found
to be mainly convective (>70 %).
Convective event definition
Previous studies used observations (Mapes et al., 2006; Mitovski et al.,
2010) and cloud-resolving model simulations (Suhas and Zhang, 2015) to
isolate strong precipitation events and diagnose convection–environment
interactions relative to the peak of these events. Although this is a useful
diagnostic approach for the development of closure schemes, it lacks
information regarding initiation of precipitation. For our analysis, we use
a slightly different approach. An initiation time (t0) of a convective
event is defined as the time at which convective precipitation within a GCM
grid box exceeds 0.01 mm h-1 after following a 3 h period with no
convective precipitation. An end time (tf) of a convective event is
defined as the time when convective precipitation, after exceeding 1 mm h-1, drops down to less than 0.01 mm h-1 within a time
period of up to 12 h after initiation. Using this approach, we isolated 831
convective events in spCAM5 and 1452 in CanAM4.3 in the tropical western
Pacific. Since the methodology isolates precipitating events that can last
between 0.5 and 12 h, a scaled time (ST) is computed so that all events,
regardless of lifetime, start and end at the same scaled time. The ST is
calculated following Eq. (1):
STt=t-t0tf-t0×100%.
This approach improves comparison of composited events since features that
precede or lag a rainfall peak, e.g. high CAPE and low convective inhibition
(CIN) prior to peak rainfall and low CAPE and high CIN after peak rainfall,
will occur at the same scaled time for all events regardless of lifetime.
Definition of convective available potential energy (CAPE) and CAPE
generation
As defined in von Salzen and McFarlane (2002), CAPE in joule per kilogram (J kg-1) for an
undiluted parcel of air rising from near the surface (SFC) to the level of
neutral buoyancy (LNB) with the effect of condensate loading and without the
effect of latent heat of fusion is calculated following Eq. (2):
CAPE=-g∫zSFCzLNBTvp-TveTvedz,
where g is the gravity, Tvp is the virtual temperature of a rising air
parcel, and Tve is the virtual temperature of the large-scale
environment.
CAPE as defined in Eq.2 includes two terms. The first term results from
integration of the negative buoyancy between the surface and level of the
free convection and represents the convective inhibition that the parcel of
air has to overcome while it is lifted from the boundary layer into the
convective layer. The second term results from integration over the region
of positive buoyancy between the level of free convection and level of
neutral buoyancy.
Following Zhang (2003, Eq. 5), CAPE is generated by radiative and advective
large-scale processes (∂CAPE/∂t)LS and consumed by
convective processes (∂CAPE/∂t)CONV. The prognostic
equation of CAPE is calculated following Eq. (3):
∂CAPE∂t=∂CAPE∂tLS+∂CAPE∂tCONV.
The large-scale (LS) generation term on the right-hand side can be further
separated into generation of CAPE by large-scale processes near the surface
(dCAPELSS) and generation of CAPE by large-scale processes in the free
troposphere (dCAPELSFT).
Definition of large-scale vertical velocity
By integrating the continuity equation in (x,y,p) coordinates, starting from
the top of the atmosphere, ω (Pa s-1) is computed from the mean
divergence in a layer p using Eq. (4):
ωp2-ωp1=p1-p2∂u∂x+∂v∂yp,
where ω at the top of the atmosphere is assumed to be zero, p1 is the
pressure level above the layer p and p2 is the pressure level under the
layer p.
ResultsTime-domain mean fields
Table 1 shows the domain (150–170∘ E and 0–10∘N) and time
(May–July) mean values for dCAPELSFT, ω, CAPE and
convective precipitation. Spatial standard deviations (in brackets) for each
variable were computed using the time mean distributions over the domain.
Both models show similar mean values for dCAPELSFT, ω and
convective precipitation. The CAPE values, however, are roughly 3-fold larger
in spCAM5 (664 J kg-1) than in CanAM4.3 (220 J kg-1). For
comparison, we computed CAPE using soundings from three tropical western
Pacific sites: Chuuk, Caroline Islands (7.45∘ N and 151.8∘ E); Pohnpei, Caroline
Islands (6.95∘ N and 158.2∘ E); and Majuro, Marshall
Islands. (7.08∘ N and 171.39∘ E). The observed May–July
1997 mean CAPE is 1080 J kg-1.
Domain (150–170∘ E and 0–10∘ N) and time
(May–July 1997) means for spCAM5 and CanAM4.3 along with spatial standard
deviations (in brackets) that were computed using the time mean distributions
over the domain.
The CAPE budget equation (Eq. 3) states that any change in CAPE between two
time intervals is due to CAPE generation by the large-scale processes and
due to CAPE consumption by convection during the two time intervals. It is
known that in GCMs convection is activated too frequently (Zhang and Mu,
2005b) thus resulting in the removal of CAPE which is too frequent and in the inability of CAPE to
accumulate to higher values. Since CanAM4.3 employs CAPE in its closure to
compute mass flux and precipitation, activation too often will likely affect
the precipitation rates, resulting in precipitation which is too frequent and light .
It has been shown that GCMs tend to generate too frequent light
precipitation (Sun et al., 2006; Dai, 2006; Wang et al., 2016) and
underestimate the frequency of extreme precipitation (Wilcox and Donner,
2007; Boyle and Klein, 2010).
Frequency density of convective precipitation
Relative to spCAM5, CanAM4.3 overestimates the frequency of light convective
precipitation (<0.2 mm h-1) and underestimates the frequency of
extreme convective precipitation (>2 mm h-1) (Fig. 1b).
Frequency density was defined as the ratio of the number of time steps with
convective precipitation per 0.1 mm h-1 convective precipitation bin to
the total number of time steps. When compared to observations, models also
exhibit less rainfall variance (Sun et al., 2006; Dai, 2006; DeMott et al.,
2007; Mitovski et al., 2010).
(a) Mean values of CAPE generation in the free troposphere
(solid) and near-surface large-scale -ω (dashed) per
0.1 mm h-1 convective precipitation bin in spCAM5 (black) and CanAM4.3
(blue). (b) Frequency density of convective precipitation per
0.1 mm h-1 bin. Frequency density of 1 % in panel (b), or
1 mm h-1 convective precipitation in panels (a) and
(b), correspond to 82 CanAM4.3 and 122 spCAM5 samples per
grid cell. (c) Frequency density of convective event length, and
(d) mean peak convective precipitation as function of convective
event length. Frequency density of 10 % in panel (c) corresponds
to 33 CanAM4.3 and 83 spCAM5 convective events.
We show that dCAPELSFT (Fig. 1a) increases with convective
precipitation intensity in spCAM5 and in CanAM4.3. In addition, omega
(ω) systematically increases with convective precipitation intensity
in spCAM5 but not in CanAM4.3. For convective precipitation rates between
0.5 and 2.5 mm h-1, CanAM4.3 shows a linear increase in ω.
Convective events, as defined in Sect. 3.2, can last between 0.5 and 12 h.
Figure 1c shows the fraction of convective events, from the total number of
convective events, as a function of the event length. Figure 1d shows the
average peak convective precipitation as a function of the event length.
About 5 % of the 831 spCAM5 events last less than 1.5 h, 75 % of
the events last between 1.5 and 3.0 h and only 1 % of the events last
longer than 5 h (Fig. 1c), with the most intense convective precipitation
being associated with longer-lasting events (Fig. 1d). In comparison, 80 % of the 1452 events in CanAM4.3 are shorter than 1.5 h and only 0.1 %
of the events last longer than 5 h with the most intense convective
precipitation being associated with shorter-lasting events.
To examine sensitivity to the definition of convective precipitation (Sect. 3.1) we repeated our analysis using total instead of convective
precipitation (Fig. S1). The results are similar except for the length of
convective events and peak precipitation (Figs. 1d and S1d). This is due
mainly to CanAM4.3 frequently producing light non-convective precipitation.
This affected the event length while peak rainfall was more influenced by
convective precipitation. This suggests that non-convective precipitation is
more important for long-lasting events.
While observations of sub-hourly precipitation are not available in the
region used in this study, 3 h observations of total precipitation are
available from the Tropical Rainfall Measuring Mission (TRMM) (Kummerow et
al, 1998). To compare with the 3 h TRMM data, 3B42v7 (TRMM, 2011), the spCAM5 and CanAM4.3 precipitation
rates were averaged to 3 h means. For the time and region used in this
study, 3 h mean total and convective precipitation are available from
simulations using the Community Atmospheric Model version 5.1, CAM5.1 (Neale
et al., 2012). Data from CAM5.1 are included since it uses resolved physics
that are similar to spCAM5 but like CanAM4.3 uses parameterizations to
represent sub-grid scale processes. The frequency distribution for total
precipitation (Fig. 2b) shows that spCAM5 is more similar to TRMM than
CanAM4.3 and CAM5.1, both of which are in turn more similar to each other
than spCAM5 or TRMM. The similarity between CanAM4.3 and CAM5.1 and their
difference relative to spCAM5 also holds for convective precipitation
(Fig. 2a). These results suggest, at least for 3 h means, the exact
definition of convective rainfall is less important than differences between
CanAM4.3 and spCAM5.
(a) Frequency density of 3-hourly convective precipitation
per 0.1 mm h-1 bin, (b) frequency density of 3-hourly total
precipitation per 0.1 mm h-1 bin.
Relation between convective precipitation, large-scale ω and
dCAPELSFT
In comparison to Fig. 1a, which shows one quantity (dCAPELSFT or
ω) as a function of convective rainfall, Fig. 3 shows convective
rainfall histograms as a function of two quantities. We find that convective
precipitation in spCAM5 correlates best with both near-surface -ω and
dCAPELSFT (Fig. 3a) with no dependence on CAPE (Fig. 3c).
Reversible and undiluted CAPE computed from semi-daily radiosonde profiles of
temperature and humidity also shows that tropical precipitation intensity is
not correlated with CAPE intensity (Mitovski and Folkins, 2014). As in
Fig. 1a, the strongest convective precipitation is associated with strong
large-scale near-surface ascent and strong dCAPELSFT. In
addition, for a constant ω the rainfall rates increase with
increasing dCAPELSFT, while for a constant value of
dCAPELSFT rainfall rates increase with decreasing ω
(increasing ascent) with rain rates becoming more dependent on ω for
larger dCAPELSFT. In the case when one of the quantities is in
its lowest 25th percentile, for instance
dCAPELSFT<50 J kg-1 h-1 or
ω>80 Pa s-1, precipitation rates do not exceed
1 mm h-1.
(a) spCAM5 and (b) CanAM4.3 mean convective
precipitation as function of near-surface ω and
dCAPELSFT. (c) spCAM5 and (d) CanAM4.3 mean
convective precipitation as function of CAPE and dCAPELSFT. Each
of the plots consists of 1600 bins, 40 on x axis and 40 on y axis. The
convective precipitation within each bin is an average of at least 20
values.
Precipitation simulated by CanAM4.3 (Fig. 3b) does not correlate with
ω but does correlate with both CAPE and dCAPELSFT (Fig. 3d)
with greater rainfall rates being associated with larger values of CAPE and
dCAPELSFT. This is expected since the precipitation generated within
the ZM convection scheme is proportional to the updraft mass flux and the
cloud water content. The updraft mass flux is closely related to the cloud
base mass flux, which is computed within the prognostic CAPE-based closure
(Scinocca and McFarlane, 2004).
Repeating Fig. 3, using dCAPELSFT and ω at levels ranging from
992 to 232 hPa, we found that greater rainfall rates in spCAM5 are
associated with more negative (stronger ascent) ω at 992 hPa and less
negative ω at 232 hPa (not shown). Since ω was computed using Eq. (4),
a negative ω at pressure 992 hPa is approximately equal to the net
column mass divergence above that pressure level. Therefore, greater
rainfall rates in spCAM5 are associated with strong low-level ascent or
strong net column mass divergence and larger dCAPELSFT.
Composites over convective events
Prior the start of the convective event (time = 0) in spCAM5 the
near-surface environment is characterized by a weak large-scale subsidence
(Fig. 4a) and increasing relative humidity in the lower troposphere
(Fig. 4g). An observed low-level moistening prior to deep convection has been
previously attributed to moistening by shallow convective clouds (Sherwood,
1999; Sobel et al., 2004, DeMott et al., 2007). The moistening impacts the
growth of convective clouds by modifying the dilution effect of entrainment
on the buoyancy of rising air parcels (Sherwood, 1999; Raymond, 2000). The
strength and depth of the pre-moistening are thus crucial in the transition
from shallow to deep convection. The large-scale subsidence gradually weakens
and diminishes about 20 min prior to time = 0, roughly when CAPE reaches
maximum and CIN reaches minimum (Fig. 4c). Therefore, a transition from a
large-scale subsidence to large-scale ascent may be important in triggering
convection. A near-surface ω tendency has been previously used as a
trigger in the Donner convection scheme (Donner, 1993; Wilcox and Donner,
2007) in a version of the Geophysical Fluid Dynamic Laboratory (GFDL)
Atmospheric model, version 3 (AM3) GCM. In their model, convection is
triggered when near-surface ω becomes positive and exceeds a
specified value and convective inhibition is less than 100 J kg-1.
Although dCAPELSFT is positive prior to time = 0 (Fig. 4e),
precipitation is not initiated until ω becomes negative (large-scale
ascent). The strongest ascent occurs around scaled time (ST) = 45 %, shortly before the time of the strongest convective
rainfall at ST = 55 % and strongest dCAPELSFT at
ST = 65 %. Although dCAPELSFT shows great similarity with
the convective precipitation, it lags the precipitation by
ST = 5 %–10 %, which may imply that large-scale generation of
dCAPELSFT during the event lifetime may be a consequence of the
model dynamics, i.e. response of the model to convective heating. During the
decaying phase, after ST = 75 %, dCAPELSFT is still
relatively strong but ω becomes positive (subsidence), CAPE reaches
minimum and CIN reaches maximum, which likely prevent any further convection.
Reversible and undiluted CAPE computed from 12-hourly radiosonde profiles of
temperature and humidity shows similar behaviour, with CAPE reaching a
maximum prior to peak rainfall and minimum after peak rainfall (Mitovski and
Folkins, 2014). The minimum CAPE and maximum CIN after peak rainfall are
likely due to a combination of two effects, the export of boundary layer air
with high moist static energy (MSE) into the middle troposphere by convective
plumes and the injection of middle troposphere air with low MSE into the
boundary layer by mesoscale downdrafts (Zipser, 1977; Sherwood and Wahrlich,
1999). The effect of these two processes will also contribute to low-level
drying, which is seen in spCAM5, the low- to mid-level dip in the relative
humidity patterns that occurs after peak rainfall, but not in CanAM4.3.
Prior to time = 0, the time on the x axis is in hours. After
time = 0, the time is in % of the event duration time. The rain events
start at time = 0 and end at t=100. The y axis on the left shows
values for the black curves and the y axis on the right shows values for
the blue curves. The panels on the left (a, c, e, g) are for spCAM5
and the panels on the right (b, d, f, h) are for CanAM4.3. The first
row (a, b) shows convective precipitation and large-scale near-surface ω; the second row (c, d) shows CAPE and CIN; the
third row (e, f) shows dCAPELSFT and the fourth
row (g, h) shows relative humidity patterns.
In addition to examining the time evolution of dCAPELSFT over
convective events we compared its tendency (time change) and convective
precipitation in spCAM5 (Fig. S4). We found that the tendency in
dCAPELSFT (Fig. S4) becomes positive about 20 min prior to the
start of a convective event (t=0) reaching a maximum slightly prior the
precipitation maximum. While a thorough examination of this is beyond the scope
of this paper, we hypothesize that the trend in dCAPELSFT could be
associated with the trend in near-surface ω; i.e. an increasing trend
in the large-scale ascent contributes to increasing CAPE and a positive trend
in large-scale CAPE generation.
The large-scale environment prior the start of convective events in CanAM4.3
is quite different from spCAM5 with strong ascent and relatively weak CAPE.
CanAM4.3 shows some moistening prior to time = 0, but this moistening occurs
in a very shallow layer near the surface leaving the troposphere between 900
and 600 hPa relatively dry. The shallow mass flux patterns (not included)
indicate that shallow convection is only active in the lowest 100 hPa.
Relative to observations, GCMs also tend to have drier lower troposphere,
which has been linked to convection schemes employed in the model (Wang and
Zhang, 2013). Convective rainfall is found to occur once CAPE exceeds 300 J kg-1 (Fig. 4d). In contrast to spCAM5, peak convective rainfall in CanAM4.3
(Fig. 4b) occurs closer to the end of the convective events corresponding
with a peak in CAPE and CAPE generation (Fig. 4f). The strong correlation
between convective precipitation and CAPE in CanAM4.3 is expected since
convective precipitation in CanAM4.3 is proportional to cloud base mass flux
which is in turn computed within the ZM prognostic closure as a function of
CAPE (Scinocca and McFarlane, 2004). Convective precipitation in CanAM4.3
does not seem to correlate well with CIN (Fig. 4b), and this is likely
because CIN is not independently included in the ZM closure in CanAM4.3.
Therefore, any discussion of CIN and linkage to CanAM4.3 precipitation is
out of the scope of this paper. We should point out that CIN is
tightly coupled with precipitation over mid-latitude summertime continent
but not with precipitation over oceans (Myoung and Nielsen-Gammon, 2010).
The performance of various trigger functions and closures have been
previously evaluated, and it was found that in the tropics the
best-performing trigger functions are based on dCAPELSFT and grid
scale vertical velocity in the lower troposphere (Suhas and Zhang, 2014; Song
and Zhang, 2017). Replacing CAPE with dCAPELSFT in the ZM closure
resulted in the National Center for Atmospheric Research Community Climate
Model version 3 (NCAR CCM3) simulating a more realistic Madden–Julian
Oscillation (Zhang and Mu, 2005a), an improved summer and winter mean
tropical precipitation, and less frequent light
precipitation (Zhang and Mu, 2005b). Including a relative humidity at the parcel origin in the trigger
function also improves the simulation of convection (Zhang and Mu, 2005b;
Suhas and Zhang, 2014).
In general, most commonly used deep convection schemes in climate models
employ closures based on CAPE or based on net column moisture convergence.
We show that convective precipitation generated within a CAPE-based closure
is correlated to CAPE and CAPE generation in the free troposphere, while in
spCAM5 precipitation is correlated to dCAPELSFT and ω. Since we
computed ω from the horizontal winds starting from the top of the
atmosphere (Eq. 4), near-surface ω is closely linked with the net
column mass convergence. Thus, it would be beneficial to compare correlation
of convective precipitation generated within a net column moisture
convergence-based closure.
Summary
In the absence of high spatial resolution and sub-hourly observations,
sub-hourly output from a super-parameterized Atmospheric Global Climate Model (AGCM)(spCAM5) was used to study
interactions between convective precipitation and the large-scale environment
in the tropical western Pacific and to evaluate these interactions in a
traditional AGCM (CanAM4.3). This is done by compositing model output of
CAPE, CAPE generation in the free troposphere (dCAPELSFT) and
large-scale near-surface vertical velocity (ω) over convective events
during 1 May and 24 July 1997.
Although the domain mean convective precipitation, dCAPELSFT and
ω are found to be similar in the simulation period of May–July 1997
(Table 1), notable differences between CanAM4.3 and spCAM5 are found when
compositing over convective events. The lengths of the convective events are
shorter in CanAM4.3 with 80 % of the events lasting less than 1.5 h
compared to 5 % in spCAM5. The strongest convective precipitation in
CanAM4.3 is generated within shorter events while the strongest convective
precipitation in spCAM5 is associated with longer-lasting events. Compared to
spCAM5, CanAM4.3 overestimates the frequency of light convective
precipitation (<0.2 mm h-1) and underestimates the frequency of
extreme convective precipitation (>2 mm h-1). When evaluated
against observations, GCMs also tend to produce too frequent too light
precipitation (Sun et al., 2006; Dai, 2006; Wang et al., 2016) which has been
related to too frequent activation of CAPE-based convective scheme (Zhang and
Mu, 2005b).
Convective precipitation generated within spCAM5 is found to depend on both
CAPE generation rate and near-surface vertical velocity, two fields commonly
used in trigger and closure functions of convective parameterization
schemes. In CanAM4.3, which is representative of a CAPE-based closure scheme,
convective precipitation is found to be a function of CAPE only (or CAPE
generation). Interaction with the large-scale environment is found to differ
between the models. In spCAM5, the maximum relative humidity is in the
boundary layer roughly 1 h prior to t=0 (Fig. 4g). Increasing boundary
layer moistening prior to peak rainfall seen in observations has been
attributed to pre-moistening by shallow convective clouds prior to deep
convection (Johnson et al., 1999; Sherwood, 1999; Sobel et al., 2004). The
large-scale subsidence changes to large-scale ascent prior to t=0 and is
coincident with the maximum CAPE value. After the initiation time in spCAM5,
there is similarity between variations in ω, dCAPELSFT and
convective precipitation, with variations in ω slightly preceding and
variations in dCAPELSFT slightly lagging variations in convective
precipitation. In CanAM4.3 no dependence on ω was found, instead the
model shows a dependence on CAPE and dCAPELSFT. The spCAM5 relative
humidity patterns show a “dip” after peak rainfall, which has been
previously linked to the injection of low moist static energy air from the
middle into lower troposphere by mesoscale downdrafts (Zipser, 1977;
Sherwood and Wahrlich, 1999). Although the relative humidity in CanAM4.3 has
maximum in the boundary layer, this maximum is more persistent about peak
rainfall and it occurs in a thin layer close to the surface. The height and
time of the maximum humidity is coincident with the height and time of the
shallow convective mass flux, suggesting that shallow convection, although
important in moistening the boundary layer, does not penetrate to higher
levels leaving the troposphere above 900 hPa relatively dry.
Although the sub-grid moist convection is a very complicated topic, in this
study we see evidence that precipitation variability can be influenced by the
design and the nature of the trigger and closure functions. The diagnostics
described in this paper provide information regarding initiation and
evolution of rainfall and can be used to study trigger conditions necessary
for initiation of deep convection and the deep convection closure in regional
and global models. We thus suggest that it is worthwhile to investigate the
sensitivity of the precipitation generated within the ZM scheme in CanAM4.3
to various trigger and closure assumptions. For example, our results suggest
that near-surface ω might provide a better trigger in combination
with CAPE generation in the closure scheme used within
CanAM4.3.
Code and data availability
Codes to perform the analysis described in the paper
are available at
https://github.com/jc-cccma/sub-hourly-convection-analysis with
version 4.0.0 (Mitovski et al., 2019b). Model output from spCAM5 and CanAM4.3
that are to be used as input for the codes can be found at
https://zenodo.org/record/2658842 (Mitovski et al., 2019a). Data from
TRMM can be found at 10.5067/TRMM/TMPA/3H/7 (TRMM, 2011). Model output
from CAM5.1 was accessed from
https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.cam5.1.amip.2d.001.atm.hist.3hourly_ave.html
(last access: 20 March 2019; Neale et al., 2012).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-12-2107-2019-supplement.
Author contributions
TM conceived the method, performed the analysis and
wrote the paper, JNSC designed and performed the CanAM4.3 simulations and
contributed to writing the paper. KvS and NAM provided expert advice to
improve the analysis and the paper. GJZ supplied spCAM5 model output
and provided advice to improve the analysis.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors are grateful to Paul Vaillancourt and
Rashed Mahmood for reviewing the paper. The authors also thank Chengzhu (Jill) Zhang of Lawrence Livermore National Laboratory for providing the
spCAM5 output used in this study. Guang J. Zhang is supported by the U.S. Department of Energy, Office of Science,
Biological and Environmental Research Program (BER), under award number DE-SC0016504.
Review statement
This paper was edited by Paul Ullrich and reviewed by two anonymous referees.
References
Bechtold, P., Bazile, E., Guichard, F., Mascart, P., and Richard, E.: A
mass-flux convection scheme for regional and global models, Q. J. Roy.
Meteor. Soc., 127, 869–886, 2001.Boyle, J. and Klein, S. A.: Impact of horizontal resolution on climate model
forecasts of tropical precipitation and diabatic heating for the TWP-ICE
period, J. Geophys. Res., 115, D23113, 10.1029/2010JD014262, 2010.Dai, A.: Precipitation characteristics in eighteen coupled climate models, J.
Climate, 19, 4605–4630, 10.1175/JCLI3884.1, 2006DeMott, C., Randall, D. A., and Khairoutdinov, M.: Convective precipitation
variability as a tool for general circulation model analysis, J. Climate, 20,
91–112, 10.1175/JCLI3991.1, 2007.Donner, L. J.: A cumulus parameterization including mass fluxes, vertical
momentum dynamics, and mesoscale effects, J. Atmos. Sci., 50, 889–906,
10.1175/1520-0469(1993)050<0889:ACPIMF>2.0.CO;2, 1993.
Fovell, R. G., Mullendore, G., and Kim, S. H.: Discrete propagation in
numerically simulated nocturnal squall lines, Mon. Weather Rev., 134,
3735–3752, 2006.Grabowski, W. W.: Coupling cloud processes with the large-scale dynamics
using the cloud-resolving convection parameterization (CRCP). J. Atmos. Sci.,
58, 978–997, 10.1175/1520-0469(2001)058<0978:CCPWTL>2.0.CO;2, 2001.
Grant, A. L. M.: Cloud-base fluxes in the cumulus-capped boundary layer, Q.
J. Roy. Meteor. Soc., 127, 407–421, 2001.Guichard, F., Petch, J. C., Redelsperger, J.-L., Bechtold, P., Chaboureau,
J.-P., Cheinet, S., Grabowski, W., Grenier, H., Jones, C. G., Köhler, M.,
Piriou, J.-M., Tailleux, R., and Tomasini, M.: Modelling the diurnal cycle of
deep precipitating convection over land with cloud-resolving models and
single-column models, Q. J. Roy. Meteor. Soc., 130, 3139–3172,
10.1256/qj.03.145, 2004.Hurrell, J. W., Hack, J. J., Shea, D., Caron, J. M., and Rosinski, J.: A new
sea surface temperature and sea ice boundary dataset for the community
atmosphere model, J. Climate, 21, 5145–5153, 10.1175/2008JCLI2292.1,
2008.
Johnson, R. H., Rickenbach, T. M., Rutledge, S. A., Ciesielski, P. E., and
Schubert, W. H.: Trimodal characteristics of tropical convection, J. Climate,
12, 2397–2418, 1999.
Khairoutdinov, M. F. and Randall, D. A.: A cloud resolving model as a cloud
parameterization in the NCAR community climate system model: preliminary
results, Geophys, Res. Lett., 28, 3617–3620, 2001.
Khairoutdinov, M. F. and Randall, D. A.: Cloud resolving modeling of the ARM
summer 1997 IOP: Model formulation, results, uncertainties, and
sensitivities, J. Atmos. Sci., 60, 607–625, 2003.Khairoutdinov, M. F., Randall, D. A., and DeMott, C.: Simulations of the
atmospheric general circulation using a cloud resolving model as a
super-parameterization of physical processes,J. Atmos. Sci., 62, 2136–2154,
10.1175/JAS3453.1, 2005.Kooperman, G. J., Pritchard, M. S., Burt, M. A., Branson, M. D., and Randall,
D. A.: Robust effects of cloud superparameterization on simulated daily
rainfall intensity statistics across multiple versions of the Community Earth
System Model, J. Adv. Model. Earth Syst., 8, 140–165,
10.1002/2015MS000574, 2016.Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson, J.: The Tropical
Rainfall Measuring Mission (TRMM) sensor package, J. Atmos. Oceanic Technol.,
15, 809–817, 10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2, 1998.Li, F., Rosa, D., Collins, W. D., and Wehner, M. F.:
“Super-parameterization”: A better way to simulate regional extreme
precipitation?, J. Adv. Model. Earth Syst., 4, M04002,
10.1029/2011MS000106, 2012.
Lohmann, U. and Roeckner, E.: Design and performance of a new cloud
microphysics scheme developed for the ECHAM general circulation model, Clim.
Dynam., 12, 557–572, 1996.
Mapes, B. E.: Gregarious tropical convection, J. Atmos. Sci., 50, 2026–2037,
1993.
Mapes, B. E. and Houze Jr., R. A.: Diabatic divergence profiles in tropical
mesoscale convective systems, J. Atmos. Sci., 52, 1807–1828, 1995.Mapes, B., Tulich, S., Lin, J., and Zuidema, P.: The mesoscale convection
life cycle: Building block or prototype for large-scale tropical waves?,
Dynam. Atmos. Oceans, 42, 3–29, 10.1016/j.dynatmoce.2006.03.003, 2006.Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta,
M., Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz,
D., Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a
global model, J. Adv. Model. Earth Syst., 4, M00A01,
10.1029/2012MS000154, 2012.Mitovski, T. and Folkins, I.: Anomaly patterns about strong convective events
in the tropics and midlatitudes: Observations from radiosondes and surface
weather stations, J. Geophys. Res.-Atmos., 119, 385–406,
10.1002/2013JD020447, 2014.Mitovski, T., Folkins, I., von Salzen, K., and Sigmond, M.: Temperature,
relative humidity, and divergence response to high rainfall events in the
tropics: Observations and models, J. Climate, 23, 3613–3625,
10.1175/2010JCLI3436.1, 2010.Mitovski, T., Cole, J. N. S., and Zhang, G. J.: Sub-hourly output over
Tropical Western Pacific for June, July, August 1997 (Version v4.0), Zenodo,
10.5281/zenodo.2658842, 2019a.Mitovski, T., Cole, J. N. S., McFarlane, N. A., von Salzen, K., and Zhang, G.
J.: Sub-Hourly Convection Analysis Code (Version v4.0.0), Zenodo,
10.5281/zenodo.2619590, 2019b.Myoung, B. and Nielsen-Gammon J. W.: Sensitivity of Monthly Convective
Precipitation to Environmental Conditions, J. Climate, 23, 166–188,
10.1175/2009JCLI2792.1, 2010.
Neale, R. B., Gettelman, A., Park, S., Conley, A. J., Kinnison, D., Marsh,
D., Smith, A. K., Vitt, F., Morrison, H., Cameron-Smith, P., Collins, W. D.,
Iacono, M. J., Easter, R. C., Liu, X., Taylor, M. A., Chen, C.-C, Lauritzen,
P. H., Williamson, D. L., Garcia, R., Lamarque, J.-F., Mills, M., Tilmes, S.,
Ghan, S. J., and Rasch, P. J.: Description of the NCAR Community Atmosphere
Model (CAM 5.0), NCAR Tech. Note TN-486, Boulder, Colorado, USA, 274 pp.,
2012.
Raymond, D.: Thermodynamic control of tropical rainfall, Q. J. Roy. Meteor.
Soc., 126, 889–898, 2000.
Schumacher, C. and Houze Jr., R. A.: Stratiform rain in the tropics as seen
by the TRMM Precipitation Radar, J. Climate, 16, 1739–1756, 2003.
Scinocca, J. F. and McFarlane, N. A.: The variability of modeled tropical
precipitation, J. Atmos. Sci., 61, 1993–2015, 2004.
Sherwood, S. C.: Convective precursors and predictability in the tropical
western Pacific, Mon. Weather Rev., 127, 2977–2991, 1999.
Sherwood, S. C. and Wahrlich, R.: Observed evolution of tropical deep
convective events and their environment, Mon. Weather Rev., 127, 1777–1795,
1999.
Sobel, A. H., Yuter, S. E., Bretherton, C. S., and Kiladis, G. N.:
Large-scale meteorology and deep convection during TRMM KWAJEX, Mon. Weather
Rev., 132, 422–444, 2004.Song, F. and Zhang, G. J.: Improving trigger functions for convective
parameterization schemes using GOAmazon observations, J. Climate, 30,
8711–8726, 10.1175/JCLI-D-17-0042.1, 2017.Song, F. and Zhang, G. J.: Understanding and Improving the Scale Dependence
of Trigger Functions for Convective Parameterization Using Cloud-Resolving
Model Data, J. Climate, 31, 7385–7399, 10.1175/JCLI-D-17-0660.1, 2018.Stan, C., Khairoutdinov, M., DeMott, C. A., Krishnamurthy, V., Straus, D. M.,
Randall, D. A., Kinter III, J. L., and Shukla, J.: An ocean-atmosphere
climate simulation with an embedded cloud resolving model, Geophys. Res.
Lett., 37, L01702, 10.1029/2009GL040822, 2010.Suhas, E. and Zhang, G. J.: Evaluation of trigger functions for convective
parameterization chemes using observations, J. Climate, 27, 7647–7666,
10.1175/JCLI-D-13-00718.1, 2014.Suhas, E. and Zhang, G. J.: Evaluating convective parameterization closures
using cloud resolving model simulation of tropical deep convection, J.
Geophys. Res.-Atmos., 120, 1260–1277, 10.1002/2014JD022246, 2015.
Sun, Y., Solomon, S., Dai, A., and Portmann, R.: How often does it rain? J.
Climate, 19, 916–934, 2006.Thayer-Calder, K. and Randall, D. A.: The role of convective moistening in
the Madden-Julian Oscillation, J. Atmos. Sci., 66, 3297–3312,
10.1175/2009JAS3081.1, 2009.
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in
large-scale models, Mon. Weather Rev., 117, 1779–1800, 1989.Tropical Rainfall Measuring Mission (TRMM): TRMM (TMPA) Rainfall Estimate L3
3 hour 0.25 degree x 0.25 degree V7, Greenbelt, MD, Goddard Earth Sciences
Data and Information Services Center (GES DISC),
10.5067/TRMM/TMPA/3H/7, 2011.Truong, N. M., Tien, T. T., Pielke Sr., R. A., Castro, C. L., and Leoncini,
G.: A modified Kain–Fritsch scheme and its application for simulation of an
extreme precipitation event in Vietnam, Mon. Weather Rev., 137, 766–789,
10.1175/2008MWR2434.1, 2009.von Salzen, K. and McFarlane, N. A.: Parameterization of the bulk effects of
lateral and cloud-top entrainment in transient shallow cumulus clouds, J.
Atmos. Sci., 59, 1405–1430,
10.1175/1520-0469(2002)059<1405:POTBEO>2.0.CO;2, 2002.von Salzen, K., Scinocca, J. F., McFarlane, N. A., Li, J., Cole, J. N. S.,
Plummer, D., Verseghy, D., Reader, M. C., Ma, X., Lazare, M., and Solheim,
L.: The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4).
Part I: representation of physical processes, Atmos.-Ocean, 51, 104–125,
10.1080/07055900.2012.755610, 2013.Wang, M., Ghan, S., Easter, R., Ovchinnikov, M., Liu, X., Kassianov, E.,
Qian, Y., Gustafson Jr., W. I., Larson, V. E., Schanen, D. P., Khairoutdinov,
M., and Morrison, H.: The multi-scale aerosol-climate model PNNL-MMF: model
description and evaluation, Geosci. Model Dev., 4, 137–168,
10.5194/gmd-4-137-2011, 2011.Wang, X. and Zhang, M.: An analysis of parameterization interactions and
sensitivity of single-column model simulations to convection schemes in CAM4
and CAM5, J. Geophys. Res.-Atmos., 118, 8869–8880, 10.1002/jgrd.50690,
2013.Wang, Y., Zhang, G. J., and Craig, G.: Stochastic convective parameterization
improving the simulation of tropical precipitation variability in the NCAR
CAM5, Geophys. Res. Lett., 43, 6612–6619, 10.1002/2016GL069818, 2016.Wang, Y., Zhang, G. J., and He, Y. J.: Simulation of precipitation extremes
using a stochastic convective parameterization in the NCAR CAM5 under
different resolutions, J. Geophys. Res.-Atmos., 122, 12875–12891,
10.1002/2017JD026901, 2017.Wilcox, E. M. and Donner, L. J.: The Frequency of Extreme Rain Events in
Satellite Rain-Rate Estimates and an Atmospheric General Circulation Model,
J. Climate, 20, 53–69, 10.1175/JCLI3987.1, 2007.
Xu, K.-M. and Randall, D. A.: Updraft and downdraft statistics of simulated
tropical and midlatitude cumulus convection, J. Atmos. Sci., 58, 1630–1649,
10.1175/1520-0469(2001)058<1630:UADSOS>2.0.CO;2, 2001.Zhang, G. J.: Convective quasi-equilibrium in the tropical western Pacific:
Comparison with midlatitude continental environment, J. Geophys. Res., 108,
4592, 10.1029/2003JD003520, 2003.
Zhang, G. J., and McFarlane, N. A.: Sensitivity of climate simulations to the
parameterization of cumulus convection in the CCC-GCM, Atmos.-Ocean, 3,
407–446, 1995.Zhang, G. J. and Mu, M.: Simulation of the Madden-Julian Oscillation in the
NCAR CCM3 Using a Revised Zhang-McFarlane Convection Parameterization Scheme,
J. Climate, 18, 4046–4064, 10.1175/JCLI3508.1, 2005a.Zhang, G. J. and Mu, M.: Effects of modifications to the Zhang-McFarlane
convection parameterization on the simulation of the tropical precipitation
in the National Center for Atmospheric Research Community Climate Model,
version 3, J. Geophys. Res., 110, D09109, 10.1029/2004JD005617, 2005b.
Zipser, E. J.: Mesoscale and convective-scale downdrafts as distinct
components of a squall-line structure, Mon. Weather Rev., 105, 1568–1589,
1977.