ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-1997-2018Tropical continental downdraft characteristics: mesoscale systems versus
unorganized convectionSchiroKathleen A.kschiro@atmos.ucla.eduNeelinJ. Davidhttps://orcid.org/0000-0001-9414-9962Department of Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, CA, USAKathleen A. Schiro (kschiro@atmos.ucla.edu)12February20181831997201021July201728August201719December201720December2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/1997/2018/acp-18-1997-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/1997/2018/acp-18-1997-2018.pdf
Downdrafts and cold pool characteristics for strong mesoscale convective
systems (MCSs) and isolated, unorganized deep precipitating convection are
analyzed using multi-instrument data from the DOE Atmospheric Radiation
Measurement (ARM) GoAmazon2014/5 campaign. Increases in column water vapor
(CWV) are observed leading convection, with higher CWV preceding MCSs than
for isolated cells. For both MCSs and isolated cells, increases in wind
speed, decreases in surface moisture and temperature, and increases in
relative humidity occur coincidentally with system passages. Composites of
vertical velocity data and radar reflectivity from a radar wind profiler show
that the downdrafts associated with the sharpest decreases in surface
equivalent potential temperature (θe) have a probability of
occurrence that increases with decreasing height below the freezing level.
Both MCSs and unorganized convection show similar mean downdraft magnitudes
and probabilities with height. Mixing computations suggest that, on average,
air originating at heights greater than 3 km must undergo substantial mixing,
particularly in the case of isolated cells, to match the observed cold pool
θe, implying a low typical origin level. Precipitation
conditionally averaged on decreases in surface equivalent potential
temperature (Δθe) exhibits a strong relationship because
the most negative Δθe values are associated with a high
probability of precipitation. The more physically motivated conditional
average of Δθe on precipitation shows that decreases in
θe level off with increasing precipitation rate, bounded by the
maximum difference between surface θe and its minimum in the
profile aloft. Robustness of these statistics observed across scales and
regions suggests their potential use as model diagnostic tools for the
improvement of downdraft parameterizations in climate models.
Introduction
Convective downdrafts involve complex interactions between dynamics,
thermodynamics, and microphysics across scales. They form cold pools, which
are evaporatively cooled areas of downdraft air that spread horizontally and
can initiate convection at their leading edge (Byers and Braham, 1949; Purdom, 1976; Wilson and Schreiber, 1986; Rotunno et al., 1988; Fovell and Tan, 1998;
Tompkins, 2001; Khairoutdinov and Randall, 2006; Lima and Wilson, 2008;
Khairoutdinov et al., 2009; Böing et al., 2012; Rowe and Houze, 2015). The
boundary between the cold pool and the surrounding environmental air, known
as the outflow boundary or gust front, is key to sustaining multi-cellular
deep convection (e.g., Weisman and Klemp, 1986). It has also been shown to
trigger new convective cells in marine stratocumulus (Wang and
Feingold, 2009; Terai and Wood, 2013) and trade-wind cumulus clouds (Zuidema et al., 2012; Li et al., 2014). Downdrafts also have implications for new
particle formation in the outflow regions, which contribute to maintaining
boundary layer cloud condensation nuclei (CCN) concentrations in unpolluted environments (Wang et al.,
2016).
Precipitation-driven downdrafts are primarily a result of condensate loading
and the evaporation of hydrometeors in unsaturated air below cloud base
(e.g., Houze, 1993), with evaporation thought to be the main driver (Knupp and
Cotton, 1985; Srivastava, 1987). It was originally suggested by Zipser (1977)
that the downdrafts in the convective part of a system, referred to in the
literature as convective-scale downdrafts, are saturated, and that the downdrafts
in the trailing stratiform region (referred to as mesoscale downdrafts) are
unsaturated. Studies with large-eddy simulations (LES; Hohenegger and
Bretherton, 2011; Torri and Kuang, 2016) indicate, however, that most
convective downdrafts are unsaturated, consistent with evidence that the
evaporation of raindrops within the downdraft likely does not occur at a
sufficient rate to maintain saturation (Kamburova and Ludlam, 1966).
More recently, studies have shown the importance of downdraft parameters in
maintaining an accurate simulation of tropical climate in global climate
models (GCMs; Maloney and Hartmann, 2001; Sahany and Nanjundiah, 2008; Del
Genio et al., 2012; Langenbrunner and Neelin, 2018). Accurate simulation of
mesoscale convective systems (MCSs) in continental regions (Pritchard et al., 2011) was also shown to be
sensitive to downdraft–boundary layer interactions, with significantly
improved representation of MCS propagation in the central USA once such
interactions were resolved. Additionally, representing the effects of
downdrafts and cold pools in models has been shown to have positive effects
on the representation of the diurnal cycle of precipitation (Rio et al.,
2009; Schlemmer and Hohenegger, 2014).
This study aims to characterize downdrafts in a comprehensive way in the
Amazon for both isolated and mesoscale convective systems, and to provide
useful guidance for downdraft parameterization in GCMs. Data from the
DOE–Brazil Green Ocean Amazon (GoAmazon) campaign (2014–2015; Martin et al., 2016) provide an unprecedented opportunity to evaluate downdraft
characteristics in the Amazon with sufficiently large data sets for
quantifying robust statistical relationships describing leading order
processes for the first time. Relationships explored previously, primarily
in tropical oceanic (Barnes and Garstang, 1982; Feng et al., 2015; de Szoke et al., 2017)
or mid-latitude regions (Charba, 1974; Engerer et al., 2008), such
as time composites of wind and thermodynamic quantities relative to
downdraft precipitation, are also revisited and compared to our findings
over the Amazon. Downdrafts in MCSs and isolated cells are compared to
inform decisions concerning their unified or separate treatment in next
generation models. The effect of downdrafts on surface thermodynamics and
boundary layer recovery are examined, and the origin height of the
downdrafts is explored, combining inferences from radar wind profiler data for
vertical velocity and thermodynamic arguments from simple plume models.
Lastly, statistics describing cold pool characteristics at the surface are
presented and discussed for possible use as model diagnostics.
Data and methods
Surface meteorological values (humidity, temperature, wind speed,
precipitation) were obtained from the Aerosol Observing System Surface
Meteorology station (AOSMET; Atmospheric Radiation Measurement Climate Research Facility, 2013a) at the DOE ARM site in Manacapuru, Brazil,
established as part of the GoAmazon2014/5 campaign. The record used in this
study spans 10 January 2014–20 October 2015. Equivalent potential temperature is computed following
Bolton (1980).
Thermodynamic profiles are obtained from radiosonde measurements (Atmospheric Radiation Measurement Climate Research Facility, 2013b) within 6 h
of a convective event. Radiosondes are launched at approximately 01:30,
07:30, 13:30, and 19:30 local time (LT) each day, with occasional radiosondes
at 10:30 LT in the wet season. Profiles of vertical velocity and radar
reflectivity are obtained from a 1290 MHz radar wind profiler (RWP)
reconfigured for precipitation modes (Atmospheric Radiation Measurement Climate Research Facility, 2015). The RWP has a beam width of 6∘
(∼ 1 km at 10 km a.g.l.), a vertical resolution of 200 m, and a
temporal resolution of 6 s (see also Giangrande et al., 2016).
Precipitation data at 25 and 100 km, as well as convection
classifications, are derived from an S-band radar located approximately 67 km to the northeast of the primary GoAmazon2014/5 site (T3) at the Manaus
Airport (Atmospheric Radiation Measurement Climate Research Campaign Data, 2015). Composite constant altitude low-level gridded reflectivity maps
(constant altitude plan position indicators, CAPPIs) were generated, and the
radar data were gridded to a Cartesian coordinate grid with horizontal and
vertical resolution of 2 and 0.5 km, respectively. Rain rates were
obtained from the 2.5 km reflectivity using the reflectivity–rain rate (Z-R)
relation Z=174.8R1.56 derived from disdrometer data. The spatially
averaged rainfall rates over 25 and 100 km grid boxes surrounding the GoAmazon site were used in this
study. The center of the 100 km grid box is shifted slightly to the right of
center with respect to the T3 site due to reduced data quality beyond a 110 km radius.
Reflectivity (dBZ) from S-band radar on 1 April 2014 at 15:00 UTC
(11:00 LT) before the passage of an MCS, and at 17 July 2017 at 21:24 UTC
(17:24 LT) after the passage of an isolated cell. The red dot indicates the
location of the S-band radar, and the blue dot indicates the location of the
main GoAmazon site (T3).
Every downdraft associated with either MCSs or isolated cells that created a
subsequent drop in θe at the surface of more than 5 K in a
30 min period and have precipitation rates exceeding 10 mm h-1 within
that same period are composited. These criteria were chosen to examine the
most intense downdraft events with the most well-defined vertical velocity
signatures in the RWP data. Only data for events with complete vertical
velocity data coverage over the 1 h period spanning the passage of the
convective cells and centered around the maximum precipitation were
composited and evaluated.
Isolated convective cells were identified by S-band composite reflectivity,
as in Fig. 1, and are defined as being less than 50 km in any horizontal
dimension (contiguous pixels with reflectivity > 30 dBZ) with a
maximum composite reflectivity of greater than or equal to 45 dBZ. Following
the criteria defined above, this resulted in the selection of 11 events, all
of which were in the late morning or afternoon hours between 11:00 and 18:00 LT. Mesoscale convective systems follow the traditional definition of
regions of contiguous precipitation at scales of 100 km or greater
(contiguous pixels with reflectivity > 30 dBZ) in any horizontal
dimension (e.g., Houze, 1993, 2004). All of the events sampled are
characterized by a leading edge of convective cells with a trailing
stratiform region (Fig. 1), which is the most common MCS type (Houze et al.,
1990). The above criteria yielded 18 events: 12 in the late morning and
early afternoon hours (11:00–18:00 LT) and 6 in the late evening/early
morning hours (22:00–11:00 LT).
In Sect. 6, statistics are presented using nearly the entire 2-year
time series of meteorological variables at the GoAmazon2014/5 site, as well
as 15 years of data (1996–2010) from the DOE ARM site at Manus Island in
the tropical western Pacific. One-hour averages are computed in Δθe and precipitation.
Composites of meteorological variables from the AOSMET station at
site T3 3 h leading and 3 h lagging the minimum in equivalent potential
temperature (0 h; second panel) coincident with the passage of isolated
cells (green) and MCSs (blue). Shading denotes ±1 standard deviation of
anomalies with respect to 0 h; bars on precipitation are ±1 standard
deviation for each time interval. Standard errors would be smaller by a
factor of 0.3 for isolated cells and 0.2 for MCSs.
Surface thermodynamics
Composites of surface meteorological variables are displayed in Fig. 2 for
the 11 isolated cellular deep convective events coinciding with drops in
equivalent potential temperature of 5 K or greater and precipitation
rates greater than 10 mm h-1 (see Sect. 2). The composites are centered
3 h before and after the 5 min interval marking the sharpest decrease in
surface θe in the mean time series (time 0). All time series
averaged in the composites are shifted to the mean value at the θe minimum and shading on the composites shows ±1 standard deviation
for anomalies with respect to the θe minimum to provide a sense
of the variability. All differences quoted are the differences between the
maximum and minimum values within the 1 h time frame of convective cell
passage (±30 min of time 0), unless noted otherwise. Recovery
percentages are computed as the difference between the minimum and maximum
values between time 0 and some specified time afterwards, divided by the
difference between the minimum and maximum values within 30 min of time
0.
In the 2 h leading isolated convection, the column water vapor
(CWV) increases by 4.5 mm.
The mean value of θe 30 min before the minimum recorded
θe is 353.7 K. As the systems pass, the θe mean value
drops by an average 9.6 K to an average value of 344.2 K.
Since the isolated convective cells observed occur in the daytime hours, the
relative humidity is seen to drop steadily throughout the 3 h period leading
the convection following the rise in temperatures with the diurnal cycle.
The mean relative humidity (RH) rises to 82.3 % within 30 min of
system passage, which indicates that the downdrafts are sub-saturated when
they reach the surface. Within the hour, temperatures drop by 4.2
to 24.7 K, which is a smaller decrease than observed over
mid-latitude sites (see Table 2 in Engerer et al., 2008, for a review of
mid-latitude case studies) and specific humidity drops by 1.5 to 15.7 g kg-1. Mean winds reach 6.4 m s-1, consistent with
previous studies that document strong horizontal winds associated with the
leading edges of cold pools (e.g., Fujita, 1963; Wakimoto, 1982), but are lower
than the observed values for mid-latitude storms (Engerer et al., 2008).
Additionally, surface pressure often increases with the existence of a cold
pool and is referred to as the meso-high (Wakimoto, 1982). Here, it increases
marginally by 0.6 hPa, but this value is much less than the typical values
observed in mid-latitudes (e.g., Goff, 1976; Engerer et al., 2008). Lastly,
32.7 % (52.4 %) of the temperature and 88.8 % (88.9 %) moisture
depleted by the downdraft recovers within 1 h (2 h) of cell passage,
with moisture recovering more quickly and by a greater percentage than
temperature. It is likely that moisture recovers more quickly than
temperature because of increased evaporation, while cloud cover persistence
may continue to affect temperature. It is difficult to observe heat fluxes
using eddy covariance techniques during precipitation, however, so we are
unable to properly quantify this. Nevertheless, we include measurements of
heat fluxes (Atmospheric Radiation Measurement Climate Research Facility, 2014) in Supplement Figs. S1 and S2 to assess general trends.
Composites of surface meteorological variables are also shown in Fig. 2 for
the 18 MCSs with surface θe depressions of 5 K or greater and
coincident precipitation rates of 10 mm h-1 or greater. On average, the
environment is more humid for MCSs than for the isolated cases, as is seen
in the CWV composite. CWV between 1 and 3 h leading the MCSs is higher
on average than that observed leading the isolated cells, but increases to a
comparable magnitude of ∼ 59 mm within the hour. CWV increases
by an average of 1.5 mm in the 2 h leading the passage of MCSs, which is
slightly less than the increases reported in Taylor et al. (2017)
(∼ 4 mm) in the Sahel, though the Amazon is a more humid
environment. Values of θe leading the passage of MCSs (350.3 K)
are 3.4 K lower than the θe values leading the isolated cells
(353.7 K), mostly due to lower surface temperatures (27.0 K for MCSs
vs. 28.9 K for isolated cells). The precipitation occurs over a longer
period than in the cases of isolated cells, as there is often stratiform
rain trailing the leading convective cells. The stratiform rain and
associated downdrafts also sustain the cooling and drying of the near-surface layers for many hours lagging the precipitation maximum. The
relative humidity maximum in the cold pool is 90.2 % (Δ RH = 13.3 %), the specific humidity minimum is 15.4 g kg-1
(Δq= 1.6 g kg-1), and the temperature minimum is 22.8 K (ΔT= 4.2 K), with winds gusting to an average of 7.8 m s-1 with the passage of
the leading convective cells. The cold pools are thus cooler, drier, and
nearer to saturation for the MCSs than for the isolated cells. It is worth
noting that these statistics for MCSs are not greatly affected by the
inclusion of nighttime events; composites for afternoon only MCSs yield
similar results. Overall, on average, the environments in which MCSs live
are moister, they have colder, drier cold pools that are nearer to
saturation, the winds at their leading edges are gustier, and the boundary
layer recovers more slowly than for isolated cells.
Here, we composite events based on strict criteria identifying the strongest
convective events (see Table S1 in the Supplement for dates/times of events composited in
Figs. 2–6). In Figs. S1 and S2, we instead composite based on
either a minimum θe decrease or a minimum precipitation rate to
test the sensitivity of the results presented here and include additional
events. We also examine the sensitivity to averaging by compositing
time series of meteorological variables averaged at 30 min intervals and plot
results for 6 h leading and lagging the convection. The features discussed
above associated with the passage of isolated systems and MCSs are generally
robust to averaging and the choice of imposed criteria.
Downdraft origin and the effects of mixing
Many previous studies of moist convective processes use θe as a
tracer since it is conserved in the condensation and evaporation of water
and for dry and moist adiabatic processes (e.g., Emanuel, 1994). Tracing
surface θe to its corresponding value aloft has been used in many
studies of tropical convection to examine potential downdraft origin heights
(e.g., Zipser, 1969; Betts, 1973, 1976; Betts and Silva Dias, 1979; Betts et al.,
2002). This assumes that downdraft air conserves θe to a good
approximation and that downdraft air originates at one height above ground
level. Neither of these assumptions is likely to be true, as mixing is
likely occurring between the descending air and the environmental air and
thus originating from various levels. However, it can provide a useful
reference point for further considerations.
We examine the mean θe profiles to place bounds on mixing and
downdraft origin with simple thermodynamic arguments and plume computations.
The profiles composited in Fig. 3 were measured within the 6 h prior to
the same MCSs and isolated events composited in Fig. 2, less two MCS events
that did not have corresponding radiosonde measurements. Simply matching the
mean of the minimum θe value within the cold pools to the minimum
altitude at which those values are observed yields 2.1 km for MCSs (left
panel, Fig. 3) and 1.5 km for isolated cells (right panel, Fig. 3). Again,
this assumes that θe is conserved and that the air originates at
one altitude. If instead we assume that substantial mixing occurs with the
surrounding environment and that air originates at multiple levels in the
lower troposphere, it would be plausible for more of the air reaching the
surface to originate at altitudes greater than 1.5 and 2.1 km for isolated
cells and MCSs, respectively. This has been alluded to in previous studies
(e.g., Zipser, 1969; Gerken et al., 2016), which provide evidence that air
originates in the middle troposphere.
Mean profiles of θe within 6 h leading the passage of a
deep convective event for MCSs (16 profiles; a) and isolated cells (11
profiles; b). Dashed lines indicate the mean descent path for plumes
originating at various altitudes and mixing with the environment at various
rates; solid blue line shows mean descent without mixing. Error bars are
±1 standard error.
To examine this, we mix air from above the altitude where the θe
matched the surface value (shown in the composites in Fig. 2) downward
towards the surface, varying the entrainment rate (constant in pressure
coordinates). To start, we use a mixing of 0.001 hPa-1, as this is the
constant entrainment value used in Brown and Zhang (1997) and Holloway and
Neelin (2009), which can produce realistic updraft buoyancy profiles over
tropical oceans given simplified assumptions about freezing (no freezing)
and condensate loading (all condensate retained). For the MCS case, it is
plausible that a downdraft could originate at a height of 2.5 km given this
rate of mixing to reach the surface with characteristics given by Fig. 2. If
instead the air were to come from the level of minimum θe (≥3.2 km, on average), an assumption similar to that made by many downdraft
parameterizations (e.g., Zhang and McFarlane, 1995; Tiedtke, 1989; Kain and
Fritsch, 1990), mixing would need to be 2 times greater. For the isolated
cells, mixing rates appear to need to be greater in order to produce results
consistent with cold pool characteristics at the surface. If we start out at
0.002 hPa-1, the rate sufficient for a minimum θe origin for
the MCSs, this only yields an origin height of 1.7 km. If instead we assume
the air originates somewhere near the level of minimum θe, mixing
would need to be at least 0.004 hPa-1. For simplicity, the discussion
above is in terms of mean profiles – the standard error of the profiles is
shown at 50 mbar intervals – but computation based on individual profiles
yields a standard error in the inferred mixing of about 0.0005 hPa-1.
For reference, in the European Centre for Medium-Range Weather Forecasts
Integrated Forecasting System (ECMWF IFS) and Goddard Institute for Space
Studies (GISS) Model E2 GCM (Kim et al., 2011), downdrafts mix at a rate of 2 × 10-4 m-1
(roughly equivalent to 0.002 hPa-1 in pressure
coordinates in the lower troposphere).
To summarize, this analysis is suggestive of bounds on mixing coefficients
for downdraft parameterizations. If downdrafts of both convective types mix
at similar rates, these results suggest that downdrafts from isolated cells
originate at lower levels than MCSs, on average. If instead downdrafts
originate from the level of minimum θe, mixing rates of 0.002 for MCSs and 0.004 hPa-1 for isolated convection would
be consistent with mean thermodynamic conditions. In Sects. 5 and 6, we
provide a complementary probabilistic perspective on levels of origin.
The composite θe (K; a), mean reflectivity
(dBZ; b), mean vertical velocity (c; m s-1), and
probability of w < 0 m s-1(d) observed by the radar
wind profiler at T3 leading and lagging the passage of isolated cells. Plots
of w(e) and probability(f) zoomed in time and height (as outlined in red) are
shown to the right of the corresponding plots for visual clarity.
Vertical velocity and downdraft probability
Figure 4 composites reflectivity (Z), vertical velocity (w), and the
probability of observing downdrafts (w < 0 m s-1) for the 11
cases of isolated cellular convection meeting the minimum Δθe criteria of -5 K and minimum precipitation
criteria of 10 mm h-1. Time 0 is the time right before the sharpest decrease in θe and maximum precipitation
(slightly offset from the composites in Fig. 2). A 3 h window is composited for reference, but the interval of primary
interest is the 1 h window within which the minimum Δθe
and maximum precipitation are observed. To highlight the interval of
interest, the 1 h intervals leading and lagging this period are masked out.
The drop in θe is coincident with the passage of the isolated
cell and its main updraft and precipitation-driven downdraft. Mean
reflectivity exceeding 40 dBZ is observed during this period, as are strong
updrafts in the middle–upper troposphere. The cell then dissipates and/or
moves past the site within an hour. A downdraft is observed directly below
and slightly trailing the updraft core. This is the downdraft that is
associated with the largest drop in surface θe. As is suggested
in the literature, these are mainly driven by condensate loading and
evaporation of precipitation and are negatively buoyant. The probability of
observing negative vertical velocity (threshold < 0 m s-1)
within the 30 min of minimum Δθe and maximum
precipitation is highest in the lower troposphere (0–2 km), consistent with
precipitation-driven downdrafts observed in other studies (Sun et al., 1993;
Cifelli and Rutledge, 1994).
Same as Fig. 5, but leading and lagging the passage of MCSs.
There is also a high probability of downdrafts in air near the freezing
level (masked out in the vertical velocity retrievals, as there is large
error associated with retrievals near the freezing level; Giangrande et al.,
2016). It appears likely, however, that these downdrafts are discontinuous
in height more often than not, as high probabilities are not observed
coincidentally in the lowest levels beneath these downdrafts. These
middle- and upper-level downdrafts are documented in previous studies of MCSs, which
suggest that they form in response to the pressure field (e.g., Biggerstaff
and Houze, 1991), can occur quite close to the updraft (Lily, 1960; Fritsch,
1975), and are positively buoyant (Fovell and Ogura, 1988; Jorgensen and
LeMone, 1989; Sun et al., 1993). These motions produce gravity waves in the
stratosphere, as is discussed in Fovell et al. (1992).
Figure 5 shows the same composites for the 18 MCSs observed. They, too, have
high reflectivity (mean > 40 dBZ) in the 30 min coincident
with the minimum θe and a defined updraft extending up to the
upper troposphere. Downdrafts occurring coincident with the minimum θe are observed directly below the updraft signature in the mean vertical
velocity panel, and the probabilities are greatest below the freezing level.
There are likely also mesoscale downdrafts in the trailing stratiform region
of the MCSs, although difficult to discern here, which Miller and Betts (1977) suggest are more dynamically driven than the precipitation-driven
downdrafts associated with the leading-edge convection. These likely sustain
the low θe air in the boundary layer for hours after the initial
drop, observed in Fig. 2. Vertical motions in the stratiform region are
weaker than in the convective region, and on average, as in Cifelli and
Rutledge (1994), rarely exceed 1 m s-1.
Figure 6 is a concise summary of the results presented in Figs. 4 and 5,
showing the mean vertical velocity within the 30 min of sharpest Δθe for MCSs and isolated cells. Means are for
w > 0 m s-1 only (updrafts) or w < 0 m s-1 only (downdrafts) at
each height (as in Giangrande et al., 2016) and are thus characteristic of
magnitudes rather than bulk air motions. Updraft and downdraft strength
increases with height, consistent with results from previous studies
evaluating a broader range of conditions (May and Rajopadhyaya, 1999; Kumar
et al., 2015; Giangrande et al., 2016). The corresponding mean probability of
observing such motions at each height is shown in the right panel.
Probabilities, which can be interpreted loosely as convective area fractions
(Kumar et al., 2015; Giangrande et al., 2016), are largest below the freezing
level for downdrafts and in the 3–7 km region for updrafts. The probability
of downdrafts for both isolated cells and MCSs increases nearly linearly
towards the surface below the freezing level. Thus, this behavior in the
lowest 3 km summarizes our results from the previous two figures and
suggests that the mean properties of downdrafts are such that air
accumulates along descent – analogous to mixing. The probability and
vertical velocity for both MCSs and isolated cells correspond to mass flux
profiles that increase nearly linearly throughout the lower troposphere for
updrafts and that decrease nearly linearly throughout the lower troposphere
for downdrafts, as seen in Giangrande et al. (2016) over a broader range of
convective conditions. To give some sense of the error in these estimates,
Wilson score intervals (lower bound/upper bound) for the 18 MCS cases are
roughly 0.16/0.23 for a probability of 0.7, 0.21/0.21 for a probability of
0.5, and 0.23/0.16 for a probability of 0.3; for 11 events (as in the
isolated cases), the intervals are roughly 0.19/0.28 for a probability of
0.7, 0.25/0.25 for a probability of 0.5, and 0.28/0.19 for a probability of
0.3.
(a) Mean vertical velocity profiles for MCSs and isolated cells
for downdrafts (w < 0 m s-1; dashed) and updrafts (w > 0 m s-1; solid). (b) Mean probability of observing
updrafts or downdrafts as a function of altitude. Means are composited from
data within 30 min of largest drop in Δθe (0–0.5 h in
Figs. 4 and 5).
These results, and those presented in the previous section, suggest a range
of downdraft origin levels throughout the lowest few kilometers within both
organized and unorganized convective systems. Several observational studies
corroborate the evidence presented here that a majority of the air reaching
the surface in deep convective downdrafts originates at low levels (Betts,
1976; Barnes and Garstang, 1982; Betts et al., 2002; de Szoke et al., 2017).
Betts (1976) concluded that the downdraft air descends approximately only
to the depth of the subcloud layer (∼ 150 mbar). Betts et al. (2002) cited a range of 765–864 hPa for the first levels at which the
surface θe values matched those of the air aloft. Additionally,
there are many modeling studies that provide evidence of these low-level
origins (Moncrieff and Miller, 1976; Torri and Kuang, 2016). Recently, Torri
and Kuang (2016) used a Lagrangian particle dispersion model to show that
precipitation-driven downdrafts originate at very low levels, citing an
altitude of 1.5 km from the surface, with the mode of the distribution
nearer to 1 km. These conclusions are consistent with our results here,
suggesting that downdraft parameterizations substantially weight the
contribution of air from the lower troposphere (e.g., with substantial mixing
and/or modifying the height of downdraft origin).
(a) Precipitation (1 h averages) conditionally averaged by
coincident changes in equivalent potential temperature (Δθe) at the GoAmazon site. Precipitation values corresponds to the
θe values at the end of each differencing interval. Bins are a
width of 1∘ and error bars represent the standard error. (b) The
probability of precipitation (> 2 mm h-1) occurring for a
given Δθe. Error bars represent Wilson score intervals
from 5 to 95 %. (c) The frequency of occurrence of Δθe
and precipitation for a given Δθe (precipitation
> 2 mm h-1). Precipitation derived from S-band radar
reflectivity at spatial averages over 25 and 100 km grid boxes
surrounding the GoAmazon site is included for comparison to the in situ
precipitation.
Same as Fig. 7, except comparing results from in situ data only at
the GoAmazon2014/5 site (aqua) and the DOE ARM site at Manus Island (navy blue).
Relating cold pool thermodynamics to precipitation
As seen in previous sections, the passage of both organized and unorganized
convection can lead to substantial decreases in θe resulting
mainly from precipitation-driven downdrafts formed from the leading
convective cells. In this section, we search for robust statistical
relationships between key thermodynamic variables for potential use in
improving downdraft parameterizations in GCMs. These statistics differ from
those presented in Figs. 2–6, as these statistics are not conditioned on
convection type and they sample both precipitating and non-precipitating points
within the time series analyzed. All data available at the surface
meteorological station during the GoAmazon2014/5 campaign from 10 January
2014 to 20 October 2015 are included in these statistics.
Δθe conditionally averaged by coincident
precipitation (1 h averages) at the GoAmazon site (a, b) and at Manus Island (c, d). Precipitation values
corresponds to the θevalues at the
end of each differencing interval. Bins are a width of 1.5 mm hr-1.
Error bars represent the standard error (a, c), and the 10th and
90th percentile values for each bin are drawn for reference (b, d). Error bars on the probability represent Wilson score intervals from
5 to 95 %.
The first of these statistics conditionally averages precipitation rate by
Δθe (Fig. 7), variants of which have been
discussed in previous studies (Barnes and Garstang, 1982; Wang et al., 2016).
Our statistics mimic those shown in previous work relating column-integrated
moisture to deep convection over tropical land (Schiro et al., 2016) and
ocean (Neelin et al., 2009; Holloway and Neelin, 2009). The direction of
causality in the CWV–precipitation statistics, however, is the opposite of
what is presented here. CWV is thought to primarily be the cause of intense
precipitation and deep convection, while here the Δθe observed is a direct result of the precipitation processes and
associated downdraft. Nevertheless, examining the distribution of
Δθe observed at the surface and magnitudes of
the rain rates associated with the largest drops in Δθe across different regions in the tropics can place bounds on downdraft
behavior. We will also conditionally average Δθe
by precipitation rate, a more physically consistent direction of causality.
Figure 7 shows precipitation rates binned by Δθe
for in situ and radar-derived precipitation. Bins are 1 K in width
(bins with less than five observations are eliminated from the analysis) and
precipitating events are defined as having rain rates greater than 2 mm h-1. This threshold is chosen based on results
from Barnes and Garstang (1982), who suggested it as a minimum precipitation rate for observing
coincident decreases in θe at the surface. These statistics
mainly suggest that a majority of the substantial decreases in θe
at the surface occur coincidently with heavy precipitation, which is
particularly evident from the sharp increase in probability of precipitation
(middle panel).
S-band radar data are averaged in 25 and 100 km grid boxes surrounding
the GoAmazon2014/5 site to examine the precipitation–Δθe relation with model diagnostics in mind (Fig. 7). The
Δθe shown is in situ, since we do not have
spatial information in the moisture and temperature fields at a high enough
temporal frequency to match the radar data. Out to 25 km, the statistics are
very similar to those observed using in situ precipitation. Theoretical
(Romps and Jevanjee, 2016), modeling (Tompkins, 2001; Feng et al., 2015), and
observational (Feng et al., 2015) studies have all examined typical sizes of
cold pools, which can be on the order of 25 km in diameter for any one cell.
Cold pools can combine, however, to form a larger, coherent mesoscale-sized
cold pool (radius of 50 km or greater), as is commonly associated with
mesoscale convective systems (Fujita, 1959; Johnson and Hamilton, 1988).
Therefore, it is likely that our use of the in situ Δθe,
assuming cold pool properties are somewhat homogeneous in space, is
appropriate for scales up to 25 km. Beyond this scale, it is likely that the
Δθe would be smoothed by averaging, particularly for the
smaller isolated cells, as would precipitation. For the conditional average
precipitation (Fig. 7), this effect may be seen at the 100 km averaging
scale. The probabilities are, however, robust to averaging. This suggests
that when drops in θe occur locally, there tends to be good
correspondence to precipitation both locally and in the surrounding 25 and
100 km averaging areas.
The width of the distribution of precipitating points is of greatest
interest here. The distribution of precipitating points peaks just shy of a
Δθe of 0 K, indicating that most
precipitation events have low rain rates and do not occur coincidently with
an appreciable drop in θe. The frequency of precipitation drops
off roughly exponentially towards lower Δθe. An
interesting feature is the lower bound observed in Δθe near -15 K. The mean profiles in Fig. 3 show that, on average,
this value of -15 K would be consistent with air originating from the
level of minimum θe and descending undiluted to the surface. The
frequency of observing these values suggests that air very rarely reaches
the surface from these altitudes (3 km or higher) undiluted. The θe probability distribution is consistent with the results of Sect. 5,
indicating that the probability of air from a given level of origin reaching
the surface increases toward the surface through the lowest 3 km.
Figure 8 shows remarkable similarity in these statistics when comparing
across regions to a DOE ARM site at Manus Island in the tropical western
Pacific. As Δθe decreases, in situ precipitation rates
sharply increase. The probability density functions (PDFs), as well as the steepness and locations of the
pickups, are remarkably consistent. Again, the sharpness of these curves is
a result of the strongest precipitation events coinciding with the strongest
decreases in θe, shown in the middle panels in Fig. 8, where the
probability of observing coincident precipitation is greatest at low
Δθe.
It is then of interest to see if for a given precipitation rate we can
expect a particular Δθe, as this is the proper direction
of causality. Figure 9 conditionally averages Δθe by precipitation rate (1 h averages). The minimum
Δθe and maximum precipitation within a 3 h window are averaged to
minimize the effects of local precipitation maxima occurring slightly before
or after the maximum in Δθe. Comparing Fig. 8
and Fig. 9 shows that there can be strong precipitation events without
large, corresponding decreases in surface θe, but that large
decreases in surface θe are almost always associated with heavy
precipitation. Beyond about 10 mm h-1 there is a high probability of
observing large, negative Δθe and an apparent
limit in mean θe decreases with rain rate. This makes physical
sense, as discussed above (see also Barnes and Garstang, 1982), since cooling
is limited by the maximum difference between the surface θe and
the θe minimum aloft.
The average Δθe for rain rates exceeding 10 mm h-1 is about -5 K for the Amazon and -4 K for Manus Island
(Fig. 9). This statistic could be of use in constraining downdraft
parameters to be consistent with surface cooling and drying observed in
nature. The results for 100 km overlaid in Fig. 9 suggest that even though
precipitation rates at 100 km are not simply proportional to in situ rain
rates, the main feature of the statistic is robust to averaging
precipitation out to a typical GCM grid scale. There are still, however,
open questions about scale dependence and how much cooling or drying should
be observed for varying space and timescales, given that we are using in
situ Δθe for all of the statistics presented.
Overall, if convective precipitation is present in a GCM grid, a
corresponding Δθe should result within a range
consistent to those observed here, subject to scale dependence.
To summarize the results from Figs. 7–9 and provide additional diagnostics,
we can ask what fraction of precipitation occurs within a given time window
of an appreciable drop in θe, and how this fraction changes with
precipitation intensity. At the GoAmazon2014/5 site, for Δθe≤-2 K, the fraction of precipitation events within the
same hour exceeding 1, 5, and 10 mm h-1, respectively, is 43, 63,
and 74 %. Similar fractions (though smaller) are found at Manus Island:
37, 53, and 63 %, respectively. Increasing the required value of
Δθe yields smaller fractions; e.g., for
Δθe≤-4 K, corresponding fractions at the
GoAmazon2014/5 site are about 75 % of the above values (37, 53,
and 62 %, respectively). Based on arguments presented above about typical
cold pool sizes, these result are likely applicable to GCM grid scales of
0.25∘ or less, with evidence of consistency out to 1∘.
Conclusions
Convective events sampled during the GoAmazon2014/5 campaign compare
downdraft characteristics between MCSs and isolated cells and examine their
respective effects on surface thermodynamics. All events included in the
analysis passed directly over the GoAmazon2014/5 site with minimum
precipitation rates of 10 mm h-1 and Δθe less than
or equal to -5 K. The isolated events sampled occurred in the afternoon
hours only and were characterized by average decreases of 1.5 g kg-1 in
specific humidity, 4.2 K in temperature, and 9.6 K in θe, with an
increase of 4.2 m s-1 in wind speed at the surface. More than half
(59 %) of the deficit in θe observed with the passage of the
cells recovers within 1 h, on average, with the moisture recovering faster
than temperature and constituting a larger fraction of the total θe recovered. MCSs show similar decreases in temperature (4.2 K),
moisture (1.6 g kg-1), and thus θe (9.7 K) at the surface.
The θe recovers more slowly for MCSs due to the mesoscale
downdrafts and associated precipitation in their trailing stratiform
regions.
Vertical velocity profiles from a radar wind profiler show that the
probability of observing downdraft air during the 30 min of observed
minimum Δθe increases with decreasing height in the
lowest 3 km for both isolated cells and MCSs. This vertical structure of the
downdraft probability is consistent with negative vertical velocities
originating at various levels within this layer and continuing to the
surface. Considering complementary thermodynamic arguments, without mixing,
profiles of θe suggest that origin levels at average altitudes of
1.4 and 2.1 km for isolated cells and MCSs, respectively, would be
consistent with average cold pool θe for these cases. A minimum
in θe is observed between 3 and 7 km, on average, so for air to
originate above 3 km, simple plume calculations suggest that downdrafts in
MCSs would have to be mixing with environmental air at an approximate rate
of 0.002 hPa-1 along descent and at a rate roughly 2 times greater
(0.004 hPa-1) for isolated cells. This would imply mass entering the
downdraft throughout the lowest few kilometers. Overall, the vertical
velocity and thermodynamic constraints are consistent in suggesting a
spectrum of downdraft mass origin levels throughout the lowest few
kilometers.
Robust statistical relationships between Δθe and
precipitation are examined from nearly 2 years of data at the
GoAmazon2014/5 site and 15 years of data at the DOE ARM site at Manus Island
in the tropical western Pacific. We conditionally average precipitation by
Δθe, similar to the statistics of precipitation
conditioned on a thermodynamic quantity we consider for convective onset
statistics. Here, however, the most likely direction of causality differs in
that the θe drop is caused by the downdraft that delivers the
precipitation (as opposed to the thermodynamic profile providing convective
available potential energy for an updraft). For in situ precipitation, the
conditional average precipitation exhibits a sharp increase with decreasing
Δθe, which is similar in magnitude over land and ocean,
reaching roughly 10 mm h-1 at a Δθe of -10 K. For
area-averaged precipitation on scales typical of GCM grids, precipitation
magnitudes are smaller for strong, negative Δθe,
consistent with events with large Δθe occurring at
localized downdraft locations within a larger system with smaller
area-average precipitation. The probability distributions of Δθe (for precipitating and non-precipitating points) over land and ocean
are also remarkably similar. Distributions show exponentially decreasing
probability with decreasing Δθe, providing additional
evidence that downdraft plumes originating in the lowest levels are orders
of magnitude more likely than plumes descending with little mixing from the
height of minimum θe. Conditionally averaging Δθe by precipitation (the most likely direction of causality) suggests an
average limit in Δθe of -4 to -5 K given high
precipitation typical of downdraft conditions. The corresponding 90th
percentile yields Δθe of roughly -10 K, consistent with
results obtained from composting strong downdrafts. The robustness of these
statistics over land and ocean, and to averaging in space at scales
appropriate to a typical GCM resolution, suggests possible use of these
statistics as model diagnostic tools and observational constraints for
downdraft parameterizations.
All data used in this study can be accessed through the Department of Energy Atmospheric
Radiation Measurement Data Archive (www.arm.gov); please see references for DOIs, datasets, and date ranges used.
Analysis scripts and processed data are available upon request. Please contact the corresponding author at kschiro@atmos.ucla.edu.
Vertical velocity retrievals from radar wind profiler data are also available upon request; please contact Scott Giangrande at sgrande@bnl.gov.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-1997-2018-supplement.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5)
(ACP/AMT/GI/GMD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The U.S. Department of Energy Atmospheric Radiation Measurement (ARM)
Climate Research Facility GoAmazon2014/5 and Tropical West Pacific field
campaign data were essential to this work. This research was supported in
part by the Office of Biological and Environmental Research of the U.S.
Department of Energy grant DE-SC0011074, National Science Foundation grant
AGS-1505198, National Oceanic and Atmospheric Administration grant
NA14OAR4310274, and a Dissertation Year from the University of California,
Los Angeles Fellowship (KS). Parts of this material have been presented at
the Fall 2016 meeting of the American Geophysical Union and have formed part
of Kathleen Schiro's PhD thesis. We thank Scott Giangrande for providing RWP-derived
vertical velocity and for helpful discussions.
Edited by: Paul Zieger
Reviewed by: three anonymous referees
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