Radiosonde observations collected during the GoAmazon2014/5
campaign are analyzed to identify the primary thermodynamic regimes
accompanying different modes of convection over the Amazon. This analysis
identifies five thermodynamic regimes that are consistent with traditional
Amazon calendar definitions of seasonal shifts, which include one wet, one
transitional, and three dry season regimes based on a k-means cluster
analysis. A multisensor ground-based approach is used to project associated
bulk cloud and precipitation properties onto these regimes. This is done to
assess the propensity for each regime to be associated with different
characteristic cloud frequency, cloud types, and precipitation properties.
Additional emphasis is given to those regimes that promote deep convective
precipitation and organized convective systems. Overall, we find reduced
cloud cover and precipitation rates to be associated with the three dry
regimes and those with the highest convective inhibition. While
approximately 15 % of the dataset is designated as organized convection,
these events are predominantly contained within the transitional regime.
This paper has been authored by employees of
Brookhaven Science Associates, LLC, under contract DE-SC0012704 with the
U.S. DOE. The publisher by accepting the paper for publication acknowledges
that the United States Government retains a nonexclusive, paid-up,
irrevocable, worldwide license to publish or reproduce the published form of
this paper, or allow others to do so, for United States Government purposes.
Introduction
A primary source of uncertainty in global climate or Earth system model
(GCM; ESM) predictions of possible climate change is the representation of
cloud processes and associated cloud feedbacks that regulate Earth's energy
and water cycles (e.g., Klein and Del Genio, 2006; Del Genio, 2012). One
explanation for continuing deficiencies in climate model cloud-process
representations points to uncertainties in how deep convection is
parameterized. Unfortunately, the assumptions underpinning the
parameterizations are often poorly constrained by observations. Formulating
well-behaved convective parameterizations necessitates routine cloud
observations, married to their associated meso- and synoptic-scale controls
and collected over the variety of global convective regimes. Untangling
these cloud–climate controls in ways suitable for ongoing model development
demands long-term, multi-scale, multi-sensor observations that often require
challenging instrument deployments to capture cloud and precipitation
properties in remote and under-sampled global regimes (e.g., Louf et al.,
2019).
As home to the largest tropical rainforest on the planet, the Amazon Basin
experiences prolific and diverse cloud conditions that vary according to
pronounced changes in seasonal regimes. However, these clouds, regimes, and
their associated convective intensity are interconnected, with cloud
properties (coverage, depth, precipitation) strongly influenced by (and
influencing, via feedbacks) seasonal shifts in the thermodynamic forcing, as
well as larger-scale atmospheric Hadley and Walker circulation variability
(e.g., Fu et al., 1999; Machado et al., 2004; Misra, 2008). Recently, the
ongoing struggle of GCMs and weather prediction models to represent
aerosols, clouds, and their interactions over this expansive tropical area
motivated the 2-year U.S. Department of Energy's (DOE) Atmospheric Radiation
Measurement's (ARM) Observations and Modeling of the Green Ocean Amazon
(GoAmazon2014/5) campaign (e.g., Martin et al., 2016, 2017). As part of this
effort, ARM deployed its mobile facility (AMF; e.g., Miller et al., 2014)
downstream of Manaus, Brazil, in the central Amazon. The facility enabled the
capture of the thermodynamic state, aerosol, cloud, and precipitation
properties in this location through the deployment of multiple surface
state and atmospheric profiling facilities (e.g., Mather and Voyles, 2013).
We classify the primary thermodynamic regimes that are associated with the
cloud observations over Manaus using a k-means cluster analysis applied to
the morning radiosonde launches collected during the GoAmazon2014/5
campaign. This is done to isolate the potential controls of large-scale
conditions on convective regimes. Conceptually, this technique follows
previous tropical clustering efforts such as by Pope et al. (2009a, b), who
examined the variability found in Northern Australian monsoonal seasons.
Their motivations were to promote objective methods to identify key
monsoonal changes and establish cloud–precipitation regimes to evaluate the
representation of these processes in global models (e.g., May and Ballinger,
2007). A similar opportunity is expected for Amazon studies, hinted at by
several recent efforts (Marengo et al., 2017; Wright et al., 2017; Sena et
al., 2018) that illustrate the complex cloud processes and the possible
changing nature of yearly transitions from dry and rainy seasons in the
Amazon. The clustering approach may also yield an improved understanding of
the relationship between the intraseasonal variability and the different
Amazon convective regimes (Betts et al., 2002; Ghate and Kollias, 2016), as
well as new insights into shallow to deep cloud transitions and model
treatments therein (e.g., Khairoutdinov and Randall, 2006; Wu et al., 2009;
Hohenegger and Stevens, 2013; Zhuang et al., 2017, 2018;
Mechem and Giangrande, 2018; Chakraborty et al., 2018,
2020). Moreover, there is a continuing need to identify particular seasonal,
environmental, and aerosol controls on Amazon convection and its intensity
(Greco et al., 1990; Williams et al., 2002; Alcântara et al., 2011; Fan
et al., 2018; Wu and Lee, 2019; Rehbein et al., 2019).
The proposed regimes are projected onto the large-scale synoptic
patterns, forcing datasets, and remote-sensing cloud and precipitation
observations for the GoAmazon2014/5 campaign. Although there are limitations
when drawing conclusions from any 2-year campaign dataset, these efforts
are used to assess possible controls and convective cloud predictors as
related to (i) the interpretation and consistency of these radiosonde
clusters with previous wet/dry seasonal definitions for the Amazon, (ii) bulk
regime relationships to particular cloud presence/absence, (iii) the precipitation properties for these regimes to include diurnal cycles, and
(iv) the propensity for regimes to promote extremes in precipitation such as
null-event days or mesoscale convective systems (MCSs; Houze, 2004; Wang et
al., 2019, 2020). The GoAmazon2014/5 datasets are briefly
described in Sect. 2. The clustering algorithm, displays of the regimes
according to thermodynamic variability, and additional methodology
sensitivity testing are described in Sects. 2 and 3. Section 3 also
explores the relationships between these regimes and overarching synoptic
patterns, as well as area-averaged and observationally constrained vertical
profiles (e.g., horizontal moisture convergence) often used to force
single-column models (SCMs). Summaries of cloud properties associated with
these regimes are found in Sect. 4. This includes a discussion on the
propensity for the regimes to promote precipitation and the likelihood of
MCS events being initiated near the campaign facilities. Finally, key findings
for this study are summarized in Sect. 5.
GoAmazon2014/5 dataset and processing methods
Datasets for this study were collected by the U.S. DOE ARM facility during
its Observations and Modeling of the Green Ocean Amazon 2014–2015
campaign near Manaus, Brazil, from January 2014 through December 2015
(herein, GoAmazon2014/5 or MAO; Martin et al., 2016, 2017; Giangrande et
al., 2017). The primary datasets were from the routine ARM radiosonde
launches during the campaign at the main AMF field site downwind of
the city of Manaus, Brazil, and near Manacapuru, Brazil. These radiosondes
provide the thermodynamic quantities of interest and act as the basis for regime
clustering methods (Sect. 2.2).
ARM GoAmazon2014/5 products and datasets
Details of ARM radiosondes and their preprocessing and convective parameter
estimates follow previous ARM studies (e.g., Jensen et al., 2015). The
quantities of interest for this study include estimates of the convective
available potential energy (CAPE); the convective inhibition (CIN); the
relative humidity (RH) at low (surface to 3 km), middle (3 to 6 km), and
high levels (above 6 km) of the atmosphere; the 0–5 km wind shear; the
level of free convection (LFC); the lifting condensation level (LCL); and
the 0–3 km environmental lapse rate (ELR). Our CAPE calculations follow a
traditional parcel theory approach (condensation/evaporation of water vapor
only, assuming irreversible parcel ascent in a virtual potential temperature
framework; e.g., Bryan and Fritsch, 2002). The originating parcels for
CAPE and CIN estimates are defined by the level of the maximum virtual
temperature in the lowest kilometer (below 700 hPa). Thus, the standard
calculations for CAPE and CIN represent the most buoyant parcel in the
boundary layer such that the reported values are comparable to the most
unstable CAPE and CIN (herein, MUCAPE and MUCIN). Mixed-layer CAPE and CIN
estimates (mean parcel properties over the lowest 500 m, which we take to be
representative of the mixed layer) were also computed for comparison.
Cloud properties were collected by collocated instruments at the MAO site,
with additional information provided by observationally constrained
reanalysis datasets. For precipitation properties, surveillance S-band (3 GHz) radar observations were available to within 70 km of the MAO site as
collected by the System for the Protection of Amazonia (SIPAM) radar located
on the southern end of Manaus (e.g., Ponta Pelada airport; Martin et al.,
2016). These radar data were calibrated against satellite measurements and
subsequently gridded to a 2 km × 2 km horizontal grid at 2 km a.g.l. (e.g., Schumacher and Funk, 2018).
Cluster routines incorporate only the morning (12:00 UTC, 08:00 local time)
radiosondes that are launched in clear conditions. Clear conditions are
defined as those having no rainfall at the MAO site according to rain gauge
measurements to within an hour of launch time. Confirmation of
precipitation-free conditions was also performed using SIPAM observations
and manual checks for contaminated radiosondes. A more restrictive
precipitation constraint (i.e., no rainfall at the gauge site between 09:00
and 12:00 UTC) did not result in an appreciable change in the results
that follow. A motivation for using the morning radiosonde was to capture
pre-convective cloud conditions prior to the daily transition from clear
skies to shallow cumulus to deep convection, given previous studies on the
diurnal precipitation cycle for Manaus which peaks after local noon (e.g.,
Adams et al., 2013; Tanaka et al., 2014; Giangrande et al., 2017).
Additional concerns are that earlier (06:00 UTC) or later (18:00 UTC)
radiosonde launches are not representative of the pre-convective
environment and are more susceptible to existing clouds, overnight fog
(e.g., Anber et al., 2015), precipitation, and/or cold pool contamination. In
total, 607 daily radiosondes from the campaign (out of 696 radiosondes at 12:00 UTC in total) met these criteria, with 27 d removed due to
missing radiosondes. Of the days flagged as contaminated or missing at
12:00 UTC, approximately 30–40 d were associated with radar-designated
MCSs passing over MAO (Sect. 4).
Time–height (column) cloud properties are provided by a hybrid cloud radar–radar wind profiler (RWP) product developed during GoAmazon2014/5
(Giangrande et al., 2017; Feng and Giangrande, 2018). The product combines
the ARM multi-sensor (e.g., cloud radar, lidar, ceilometer, radiometer)
Active Remote Sensing of CLouds (ARSCL; Clothiaux et al., 2000) cloud-boundary designations with collocated 1290 MHz ultrahigh frequency (UHF)
RWP measurements (e.g., Giangrande, 2018; Wang et al., 2018), and gauge
observations. The RWP improves the ARSCL cloud-boundary estimates of cloud
echo top by sampling deeper precipitating clouds that otherwise
attenuate or extinguish the cloud radar beam. A simple cloud-type
classification is performed following McFarlane et al. (2013) and Burleyson
et al. (2015). Observed clouds are classified into seven categories
according to the height of the cloud and cloud thickness (Table S1 in the Supplement). These seven cloud categories are shallow, congestus, deep
convection, altocumulus, altostratus, cirrostratus/anvil, and
cirrus.
Large-scale synoptic perspectives on the regimes are obtained using
reanalysis fields from ERA5 (Hersbach and Dee, 2016) and the ARM variational
analysis product (VARANAL). VARANAL is derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis
fields and ARM observations during GoAmazon2014/15 using the constrained
variational analysis method of Zhang and Lin (1997). The product is
available at 3 h intervals on a regular vertical grid of 25 hPa over a
domain of ∼110 km radius around the MAO site (Xie et al.,
2014; Tang et al., 2001). The product is also constrained by the domain mean
precipitation as observed by the SIPAM radar. Additional details on these
products during GoAmazon2014/5 are found in Tang et al. (2016).
The k-means clustering methods
Regime classification is accomplished by applying an open-source, Scikit-learn, k-means algorithm to input radiosonde observations (toolkit from
Pedregosa et al., 2011). The choice of k-means solutions over other
configurations is done for simplicity and is consistent with previous
radiosonde applications. While the sensitivity of proposed regime
designations to different clustering approaches is not the subject of this
study, applying alternate configurations did not alter relative clusters or
composite interpretations.
One property of k-means clustering is that the number of clusters needs to be
prescribed. One expectation from the Amazon convective literature (e.g.,
Williams et al., 2002) is that three to four regimes account for the bulk
seasonal thermodynamic variability: (i) a wet season regime typically
defined as December through April, (ii) a dry season regime from June
through September, and (iii) one or two transitional regimes associated
with the months leading into the wet and dry regimes. However,
calendar definitions of the regimes vary in the literature (e.g., Zhuang et
al., 2017), which may cause additional confusion when interpreting the
findings across studies. From sensitivity testing (see Sect. 2.3), we
establish the number of clusters at five. Radiosonde temperature, dew point
temperature, and zonal/meridional wind information are the input at 20
equally spaced levels from 1000 to 200 hPa, similar to previous
applications over Northern Australia (Pope et al., 2009a, b). This input
resolution is coarser than the resolution of both the ARM radiosondes
(∼2 hPa) and that of the 25 hPa VARANAL resolution.
Additional tests (not shown) indicate that, for this particular case, the
k-means solutions are insensitive to improvements in the input radiosonde
resolution (to the 2 hPa level) or the input ordering of the data. Although the
results for this study present cluster solutions that do not use
standardized inputs (e.g., scaling all inputs to have a similar range and
standard deviation), it is common practice in recent studies to scale
inputs. Subsequent sections will comment on potential changes in cluster
results when scaled inputs are substituted.
Figure 1 shows the cluster classification according to calendar-based Amazon
definitions for the wet, dry, and transitional seasons. The dry season months
(Fig. 1c) are predominantly associated with regimes 1–3,
while the traditional Amazon wet season months (Fig. 1b)
are associated with regimes 4 and 5 with negligible contributions from the
remaining regimes. The ambiguous transitional season (here reflecting the
months of May, October, and November) indicates contributions from all
regimes though skewed towards regimes 4 and 5.
Breakdowns for the frequency to observe regime clusters (regimes 1
through 5 marked as R1 through R5) for the GoAmazon2014/5 radiosonde dataset
(12:00 UTC), as well as breakdowns for wet season (December, January, February, March,
April), dry season (June, July, August, September), and transitional season (May,
October, November) radiosondes.
In Fig. 2, we plot the time series of regime designations throughout the
campaign (Fig. 2a), with the associated monthly breakdowns for the
clusters (Fig. 2b). Qualitatively, the temporal coherence of the
individual clusters in the five-regime solution provides initial confidence
in the appropriateness of this regime breakdown. Instances of regimes 4 and
5 are aligned with classic transitional and wet season periods,
respectively, with regime 4 periods adjacent to regime 5 and not
sporadically distributed within other regimes. The remaining clusters are
interwoven within Amazon drier months. The observed cycling between these
dry clusters is of immediate interest as this variability may be indicative
of intraseasonal synoptic pattern phases in the dry season.
(a) Time series for Amazon regime cluster results (color coded as in Fig. 1) with corresponding 12 h (12:00–00:00 UTC) rainfall
accumulation (from the MAO rain gauge). The green shading indicates the wet
seasons, and the yellow shading indicates the dry seasons according to the
calendar definition. (b) Relative breakdown for the frequency of each regime
according to month.
The specifics of the GoAmazon2014/5 campaign and its particular
representativeness in the context of historical Amazon records should be
considered when assessing cluster appropriateness. As summarized by Marengo
et al. (2017), the climatological wet season onset for Manaus based on rainfall
records is typically mid-November (e.g., Liebmann and Marengo, 2001). Their
efforts indicate that traditional rainfall-based criteria and additional wet
season onset measures such as outgoing longwave radiation indicators (e.g.,
Kousky, 1988) imply that the 2014–2015 wet season onset date occurred much
later in the season (e.g., end of January 2015). One explanation for the
late onset, offered by Marengo et al. (2017), was that precipitation – the
obvious indicator for wet season onset – was heavily influenced by the
strengthening of the Madden–Julian Oscillation (MJO; Madden and Julian,
1994) and associated influences on Amazon rainfall. Based on cluster
outcomes in Fig. 2, we did not identify a prolonged cluster arguably
associated with a presumed wet season condition (e.g., regime 5) until
early December 2014. This coherent shift in the frequency of radiosonde
regime 5 designations coincides with an extended changeover in the
upper-level winds, as also shown in campaign thermodynamic summary plots
(e.g., Fig. 2 from Giangrande et al., 2017). Nevertheless, we record
multiple instances of regime 5 as early as November 2014, coinciding with a
pronounced dry to wet seasonal shift towards a deep-layer profile moisture
(RH; see also Fig. 2, Giangrande et al., 2017). As before, the motivation
for the k-means cluster method is not to pinpoint an exact rainy season
onset date (e.g., first appearance of a given cluster) but rather to identify
atmospheric regimes that may provide guidance towards subsets of attendant
environmental conditions conducive to different bulk cloud properties.
Establishing the number of clusters within k-means methods requires
sensitivity testing. Having too few clusters tends to overgeneralize and produce
overly large intra-cluster variability; having too many clusters leads to difficulties
in interpretation because there may be no physically meaningful distinction
between clusters. Similar to justifications proposed by Pope et al. (2009a,
b), we are interested in regimes associated with significant radiosonde
variability and therein potential relationships to cloud variability. One
criterion Pope et al. (2009a, b) recommended was that each cluster account
for no less than 10 % of the dataset. When adopting this approach, Amazon
solutions having more than five clusters generated additional clusters that
accounted for less than 10 % of the days.
When considering a six-cluster solution (Fig. S1 in the Supplement), the
solution further subdivided the three drier regime clusters into four.
However, the distinct separation between our wet (regime 5) and transitional
(regime 4) clusters showed little difference when the number of clusters was
increased from five to six. To be discussed in Sect. 3, the wet and
transitional regime separations predominantly differ from each other in
their zonal/meridional wind structures. This does not suggest that there are
not specific differences depending on whether the transition is wet to dry
or dry to wet, only that these differences are not as pronounced as the
drier intraseasonal shifts. In contrast, the four-cluster solution meets our
basic criterion for determining the number of clusters (Fig. S2). However, with only four clusters, the regime 4 and 5 clusters are
combined into a single, deep-moisture profile regime. We demonstrate in
later sections that the five-regime clustering is able to delineate useful
details in convective transitions and organization compared to the four-regime
solution. Because of this, the authors settle on the five-cluster solution
as it maintains a separate transitional regime that the authors believe is
consistent with the literature.
Thermodynamic and large-scale interpretation of Amazon regime clustersComposite regime thermodynamic profiles and parameter displays
In Fig. 3, we plot the composite radiosondes for all five regimes
classified in the previous section. Shaded regions provide reference for
composite radiosonde MUCAPE (red shading) and MUCIN (blue shading). Values
reported on these images are the median values of the MUCAPE and MUCIN
calculated for each individual sounding. The probability density plots in
Fig. 4 report the median values, distribution, quartiles, and
10th and 90th percentile extremes for the convective parameters of
interest calculated from the radiosondes. Differences in MUCAPE and MUCIN
across the regimes are largely driven by differences in the mid- to upper-level moisture and dew point temperature rather than temperature, a result
consistent with the understanding that horizontal temperature gradients over
the tropics are small and that variability in tropical convection is
predominantly associated with horizontal moisture gradients (weak
temperature gradient approximation; Sobel et al., 2001). For all regimes,
the standard deviations for MUCAPE and MUCIN parameters are similar (1100 and -15 J kg-1, respectively). For other fields, the standard deviations
vary with regime, with greater variability in the traditional dry season
time frames than in the wet season. For example, standard deviation for wind
shear is 4–6 m s-1 in the drier regimes and regime 4 versus 2–4 m s-1 in the
wetter regime 5 conditions. For mixed-layer CIN, median regime values become
less negative (from -85 J kg-1 for regime 1 to -33 J kg-1 for regime 5);
however, the relative distribution and regime rankings are similar. When
considering mixed-layer CAPE distributions, the values estimated for regime 1 (the highest MUCAPE regime) are noticeably smaller than the other regimes
(median values dropping to 550 J kg-1), whereas the remaining regimes all have
similar median mixed-layer CAPE values of approximately 1000 J kg-1 (similar
relative rankings otherwise). This discrepancy in mixed-layer CAPE and more
prohibitive mixed-layer CIN may explain the absence of deep convection under
regime 1 conditions (Sect. 4).
Composite 12:00 UTC radiosondes for each regime. MUCAPE, MUCIN, and
wind shear (surface to 5 km) parameters report regime median values.
Temporal patterns for regime 5 align with calendar wet season definitions
and deeper moisture conditions. As visible in Figs. 3 and 4, regime 5 is
associated with reduced values for MUCAPE but favorable (less negative) values for
MUCIN to promote frequent convection (e.g., Giangrande et al., 2016). Regime
5 also records the lowest LFC and LCL heights and reduced distribution
variability therein. Where regime breakdowns differ from traditional Amazon
ideas is with these methods which more frequently define wet to dry season
months such as April through June as transitional regime 4 (Fig. 3b)
periods. As suggested by Fig. 4f, the most significant difference we
observe between the regime 4 and 5 composites is associated with profile
winds, which includes increased lower-level wind shear in regime 4. A
separation for wet and transitional regimes according to wind shifts is
consistent with ideas of transpiration or shallow convection
preconditioning an eventual wet season onset (e.g., Wright et al., 2017),
e.g., favorable moisture conditions precede deeper cloud formation prior to
regional-scale wind shifts which lead to wet season onset. However, this
explanation would not apply to the reciprocal wet to dry transitional
periods. Nevertheless, this dry to wet transition may bear some resemblance
to the moistening and associated cumulus and congestus that occur as the MJO
over the tropical western Pacific transitions from suppressed to active
conditions (e.g., Johnson et al., 1999; Benedict and Randall, 2007; Mechem
and Oberthaler, 2013; Zermeño–Díaz et al., 2015). Finally, while
the differences in bulk wind shear are interesting between regimes 4 and 5,
the magnitude of these differences is modest (to within 5 m s-1). However,
differences in mean shear within regime 4 may be indicative of differences
in updraft structure (upright versus tilted), convective cold pool
circulations, and overall organization (e.g., Rotunno et al., 1988; Parker
and Johnson, 2000; Weisman and Rotunno, 2004).
Shaded probability density plots for select thermodynamic
quantities of interest estimated from the 12:00 UTC radiosondes in each Amazon
regime. The median values for each regime distribution are reported on each
violin (white text). The interior black box shows the interquartile range
and the thin black lines reflect the 95 % confidence interval.
Previous Amazon studies suggest that the dry to wet season transitional
periods (e.g., September through November) are more conducive to storm
electrification than wet to dry transitional periods (e.g., Williams et al.,
2002). Our clusters do not distinguish differences between these periods
(here dry season is defined traditionally from June through September).
Although the separations for regimes 1 (extreme dry) and 5 (extreme wet) are
robust in our input tests, when k-means methods use standardized inputs, this
change realigns five-cluster solutions towards pre- and post-dry-season
states (Fig. S3). While the authors did not pursue cluster
solutions using standardized inputs for our primary examples, one suggestion
is that standardized wind-field inputs (to yield the same variability as the
temperature and/or moisture fields) may help differentiate transitional
periods. In our Supplement images, we provide composite properties for
before (March through May) and after (September through November) the dry season
regime 4 instances (Fig. S4). Current regime 4 solutions
exhibit enhanced MUCAPE for soundings collected during dry to wet periods
that suggest those times as being more conducive for vigorous updrafts (median
MUCAPE values greater by ∼700 J kg-1).
The remaining clusters are associated with months traditionally classified
as the Amazon dry season. Shifts between the three drier clusters are
attributed to radiosonde mid- to upper-level moisture, with only minor
controls associated with shifts in winds. Regime 1 is the least frequently
observed for the Amazon campaign but the most significant outlier in terms
of thermodynamic parameters (e.g., Fig. 4). Regime 1 is also associated
with the driest overall profile conditions (at low and mid-levels), the
lowest mixed-layer CAPE, the highest LFC, and the most prohibitive MUCIN
conditions. Regime 3 favors humid conditions at the low to mid-levels when
compared to regimes 1 and 2, as well as larger values of mid- to upper-level
humidity. The enhanced humidity at low and mid-levels in Regime 3 may aid
in the initiation and maintenance of deep convection, while enhanced upper-level
humidity may promote saturated layers and ice-phase microphysical processes
associated with stratiform precipitation. As widespread stratiform
precipitation and MCSs have been reported also within the dry season (e.g.,
Wang et al., 2018, 2019), Sect. 4 explores which dry season regime or
regimes favor MCS.
Large-scale synoptic conditions projected onto these regimes
In Fig. 5, we plot the means of the 1000 hPa geopotential height and wind
field from the ERA5 (taken to represent the composite large-scale synoptic
patterns) projected onto each regime. Additional composites at the 200,
500, and 850 hPa levels are found in the Supplement
(Figs. S5–S7). For the wet regime (regime 5), the composites show
land–ocean contrasts, and composites carry strong impressions of the Chaco
low over the continent (and/or Bolivian high at the upper levels).
Signatures of the Bolivian high are present in the deep layer of the prevailing
southerly winds over the MAO site and are exclusive to regime 5 (Fig. 3e).
Unlike other composites, regime 5 also suggests 1000 hPa flows providing
moisture convergence into the Amazon Basin originating from the tropical
belt (northern tropical Atlantic; e.g., Drumond et al., 2014) and their
associated calm or weak westerly low-level wind components over the MAO
site. Although the 12:00 UTC regime thermodynamic profiles did not indicate a
pronounced difference between regime 4 and 5 moisture characteristics, ERA5
composites suggest that regime 4 conditions are associated with different
sources of moisture, with 1000 hPa winds over the Amazon Basin shifting
towards drier, easterly, zonal 1000 hPa flows. We speculate that the regime 4 to regime 5 shift visible in the large-scale composites may be associated with the
positioning and strength of the South Atlantic Convergence Zone (SACZ) and
its influences on the Amazon Basin during the wet season (e.g., Carvalho et
al., 2004). Drier season regimes have transitioned to southerly, low-level
flows, which is suggestive of drier, colder air reaching the central Amazon. These
patterns vary according to the positioning and strength of offshore features
that, in turn, funnel increasingly drier, colder air from the southeast
(e.g., tropical South Atlantic; Drumond et al., 2014).
Composite large-scale synoptic patterns (geopotential heights in
color – 0.01 m2 s-2 – and horizontal winds) projected onto each regime from ERA5 for the 1000 hPa level. The green star indicates the ARM MAO site.
GoAmazon2014/5 datasets recorded one complete transition from the dry season
to the wet season. In Fig. 6, we plot the composite 1000 hPa patterns
associated with regime 5, with each panel corresponding to a different
monthly composite between October and January. Noting that October contained
few instances of regime 5 conditions, composite ERA5 maps suggest
large-scale trends and flow patterns were reminiscent of regime 4 (e.g.,
transitional) composites (Fig. 5d) with weak indications of a
continental surface low pressure or of moisture inbound from southward
latitudes. December composite patterns, in contrast, better reflect
prevalent regime 5 composite behaviors (e.g., Fig. 5e) that by January
shift towards a westerly, low-level flow and are associated with low pressure
and the SACZ. Westerly shifts in the central Amazon rainy seasons have been
previously discussed as promoting a moist troposphere and frequent (albeit
not necessarily more intense) convection compared to easterly flow regimes
near the beginning of the rainy season (e.g., Betts et al., 2002; Cifelli et
al., 2002; Peterson et al., 2002).
Composite, monthly, large-scale synoptic patterns at 1000 hPa
(following Fig. 5) and radiosondes associated with regime 5. Plots
correspond left to right to (a) October, (b) November, (c) December, and (d) January.
To further explore attendant large-scale conditions and regime transitions,
in Fig. 7 we plot composite daily projections of horizontal moisture
advection and vertical velocity from the VARANAL product. Estimated
horizontal advection of moisture (e.g., -V⋅∇q, where V is horizontal wind
vector and q is the water vapor mixing ratio; Fig. 7a–e, green shading) is highest
(positive) at the lower levels for the regime 4 and 5 clusters and
maximized at the lowest levels below 700 hPa around the 12:00 UTC radiosonde
launch time (dashed line). Note that the large-scale vertical velocity w
(Fig. 7f–j) is constrained by the domain mean precipitation (assimilated
SIPAM observations), with the strength of vertical motion adjusted by the
diabatic heating derived from the SIPAM-estimated precipitation rates (e.g.,
Xie et al., 2014). Regimes with higher precipitation rates will indicate
stronger ascending motion associated with greater diabatic heating during
the afternoon precipitation periods. Interestingly, the large-scale w
patterns during the morning hours are similar across regimes 2 through 5.
Similarly, each regime indicates large-scale subsidence above 600 hPa that
peaks around radiosonde launch time. However, regime 1 is an outlier and
suggests substantial large-scale subsidence (above 600 hPa) and weak
lower-level ascent around the morning radiosonde.
Composite diurnal (UTC) large-scale SCM variational forcing
dataset (VARANAL) fields for (a–e) regime breakdowns of the horizontal
moisture advection (green signifies positive moisture advection) and (f–j) large-scale background vertical velocity (red signifies upward vertical motion).
12:00 UTC columns and 600 hPa (f–j) and 700 hPa (a–e) levels are highlighted as
dotted lines.
Finally, in Fig. 8 we isolate the variational analysis profiles
corresponding to the pre-convective radiosonde launches by plotting median
profiles and 10th/90th percentile values at 12:00 UTC. Regimes 4
and 5 share similar characteristics and enhanced moisture advection (lower
levels) and larger-scale w in the mean and extremes (90th percentile).
Regime 4 also displays stronger upward motions from near the surface to
650 hPa and stronger extremes in w from ∼750 hPa upward.
Since 12:00 UTC is prior to significant domain mean precipitation (Sect. 4.2), these enhancements in regime 4 motions are not influenced by
precipitation constraints. Similarly, moist regimes lack the extreme
negative (dry) moisture advection (10th percentile properties) found in
regimes 1–3.
Median profiles (thick solid lines) of (a) horizontal moisture
advection and (b) large-scale background vertical velocity (positive values signify upward motion) for each regime at 12:00 UTC. The 10th and 90th
percentile ranges for the variational analysis fields are represented by the
dashed–dotted lines.
Regime cloud and precipitation summaries and likelihood for precipitation extremesCloud frequency
Cumulative cloud frequency and diurnal summaries are plotted in Fig. 9.
Note, the “all” examples in Fig. 9f and g represent the summary dataset
behaviors that include all days including those having precipitation at 12:00 UTC. The characteristics are in line with monthly breakdowns previously
available for the GoAmazon2014/5 campaign as reported by Collow et al. (2016). In Fig. 10, we plot the frequency of specific cloud types for the
periods from 12:00 UTC (radiosonde launch) to 00:00 UTC to include the
relative frequency of null conditions over the site. For the frequency plots
in Fig. 10, multiple cloud layers can be identified in the same column;
therefore, individual cloud types and null conditions do not add up to
100 %.
The diurnal cycle of hourly mean cloud frequency (when cloud
coverage >2 %) as a function of height for each regime (a–f) according to multi-instrument cloud profiling retrieval. The mean 1 h
cloud frequency profiles are shown in (g).
(a) Relative frequency of occurrence for specific cloud types in
the column above the MAO site for regime periods between 12:00 and
00:00 UTC and (b) percentages when compared to cloud-free conditions.
Cloud properties in Figs. 9 and 10 indicate regime 1 is least favorable
for cloud coverage (total or daytime hours following the radiosondes). This
is consistent with the least favorable 12:00 UTC convective parameters,
moisture advection, and subsidence, as discussed in previous sections, as well
as GoAmazon2014/5 dry season studies on precipitation controls (e.g., Ghate
and Kollias, 2016). During GoAmazon2014/5, regime 1 was the only regime
for which a majority of the daytime hours over the site was not populated with
clouds (e.g., Fig. 10b). When clouds were present, the most frequent cloud
type was shallow cumulus. Upper-level cirrus clouds occupy a
substantial fraction of the cloud observations in all regimes and are
the second-most frequent clouds observed for regime 1 conditions.
Presumably, the prevalence of cirrus in regime 1 is attributable to cirrus being
generated remotely and then being advected over the site. Interestingly, Fig. 9 suggests there is an absence of cirrus and other cloud types in the
periods around the 12:00 UTC radiosonde launch. This provides confidence that
the 12:00 UTC radiosondes used as the basis of regime classifications are not
contaminated by clouds. All regimes suggest large-scale subsidence at upper
levels around 12:00 UTC (e.g., Fig. 7), which may explain the absence of
cirrus.
The drier cluster cloud summaries in Figs. 9 and 10 indicate increasing
cloudiness from regimes 1 to 3, with cloud frequency positively associated
with reduced MUCIN (lower MUCAPE) and higher column RH. Dry season cloud
frequency (regimes 1–3), including mid- (congestus) to upper-level (anvil,
including widespread/deep stratiform shields) cloud frequency, is
significantly lower than that observed for regimes 4 and 5 (Fig. 9g). Among the
drier regimes, regime 3 conditions are most conducive to clouds, although
the relative cloud frequency as plotted in Fig. 10 is similarly scaled to
the cloud types in regime 2. Moreover, diurnal cycles indicate that relative
contributions from congestus are mostly absent from regimes 1 to 3 in the mid-morning
to afternoon periods (e.g., bimodal), and the increase in frequency between
regimes 2 and 3 is attributed to enhanced shallow (echo tops <3 km)
and deeper (isolated) convection (echo tops >8 km). There is
weak evidence of overnight precipitating clouds during the dry season (e.g.,
Ghate and Kollias, 2016), which is observed during the relatively moist regime 3.
MAO clouds are most frequently observed during the moist regimes (regimes 4
and 5), with increases in frequency attributed to contributions from all
cloud types. Regime 5 indicates the highest frequency for shallow to
mid-level clouds (e.g., shallow, congestus, and alto) and the highest
frequency overall, as shown in Fig. 9g. Diurnal plots suggest a gradual
daytime shallow to deep cloud transition for regimes 4 and 5, consistent
with previous arguments for increased water vapor in the lower troposphere
as the primary factor responsible for triggering this transition (e.g.,
Ghate and Kollias, 2016). Interestingly, the bulk timing of this transition
is potentially contingent on the regime as this is apparently occurring
later in the day according to regime 5 composites. One explanation for the
delayed timing is that this transition may be slowed by the reduced incident
solar radiation associated with more frequent shallow clouds under regime 5
or wet season conditions (e.g., Zhuang et al., 2017). Variations in
shallow to deep timing are also consistent with differences in surface
energy balance partitioning, which is a strong function of soil moisture
(e.g., Findell and Eltahir, 2003a, b; Jones and Brunsell, 2009). Higher soil
moisture values in the wet regime favor a partitioning of the surface net
radiation toward more latent than sensible heat flux (i.e., smaller Bowen
ratio). This partitioning leads to a wetter boundary layer but weaker
generation of turbulent boundary-layer growth that should foster a slower
transition. Even in a tropical rainforest, the importance of moisture
availability has been shown to have a large impact on the Bowen ratio (Gerken et
al., 2018), suggesting this as a possible mechanism for modulating the onset
of deep convection.
Regime 5 indicates a trimodal distribution of convective clouds, as observed
in previous tropical studies (e.g., Johnson et al., 1999). Over the tropical
oceans, the congestus mode is associated with a mid-level stable layer near
the melting (0 ∘C) level (e.g., Johnson et al., 1999; Jensen and
Del Genio, 2006). This is thought to arise from radiative interactions
accompanying intrusions of dry air from poleward latitudes (e.g., Mapes and
Zuidema, 1996; Redelsperger et al., 2002; Pakula and Stephens, 2009) or
melting processes in organized stratiform precipitation (Mapes and Houze,
1995), although recent findings argue that the melting mechanism is not
essential for creating the stable layer (Nuijens and Emanuel, 2018). How these
two possible mechanisms explain the presence of the congestus mode across
the different Amazon regimes is not obvious. Regimes 1 and 2 are
characterized by intrusions of dry air from poleward latitudes, yet they exhibit the
lowest frequency of congestus. This indicates that other factors are
strongly suppressing the vertical development of congestus and cumulonimbus.
The higher frequency for congestus during regimes 4 and 5 is accompanied by
a greater incidence of organized convection (Sect. 4.3); this suggests the
possibility of the stratiform-cooling mechanism. To complicate matters, only
the composite soundings for regimes 2 and 5 (as shown in Fig. 3) exhibit
indications of a mid-level stable layer (∼ 700–550 hPa).
Finally, the bulk cloud characteristics as shown in Fig. 10 are similar
between regimes 4 and 5 during the morning to afternoon hours. However, an
important shift in cloud properties in regime 5 is observed during the
pre-radiosonde (overnight) periods, with regime 5 associated with more
frequent congestus. From such depictions, it is unclear whether this shift
in overnight cloudiness in regime 5 is associated with more frequent or
resilient congestus or possible contributions from MCSs. As discussed
below, MCSs and/or radar-based indicators for widespread precipitation are
more frequent for regime 4. This argues that the increase should be
attributed to additional and/or more resilient congestus, and this
explanation is consistent with the modest upper (anvil) peak for regime 4
and prominent congestus peak observed in regime 5.
Differences in precipitation behaviors across regimes
Model evaluation often benefits from precipitation constraints that include
comparisons to the diurnal cycle and other precipitation properties. In
Fig. 11, we plot the diurnal cycle of precipitation from the domain mean
precipitation rate used to constrain the 3-hourly VARANAL products, which is contingent on the regime events having measurable precipitation. As in
Fig. 9, a summary campaign behavior (all; Fig. 9f and g) that includes contributions
from days having precipitation at 12:00 UTC is also included. For these
breakdowns, precipitation rate (mm h-1) is based on SIPAM estimates for
the domain within the 110 km radius of the MAO site. The dotted lines in Fig. 11 correspond to the domain mean values, and the shading indicates a 1σ
standard deviation for regime events. These standard deviations indicate the
event-to-event variability; however, precipitation rates estimated by radar
may carry at minimum a 30 % uncertainty (e.g., bias or fractional
root mean square error) owing to miscalibration or other factors (e.g., Xie
et al., 2014; Giangrande et al., 2014). Note that the SIPAM rainfall estimates
used in VARANAL assume a single radar–rainfall relationship based on
disdrometer measurements collected under wet season conditions. This choice
implies that the dry season rainfall rates are likely overestimated
according to previous Amazon disdrometer studies performed for MAO during
wet and dry season precipitation (e.g., Wang et al., 2018).
Domain mean precipitation rate (for events with measurable
precipitation) from the SIPAM radar to within a 110 km radius of the MAO
site. The dotted lines report the dataset mean values, and the shading is
1σ standard deviation.
For radar-derived precipitation rates over the VARANAL domain, as in Fig. 11, the MAO location favors a pronounced daytime diurnal cycle, with peaks
occurring after local noon (e.g., 18:00 UTC). The well-behaved diurnal cycle
is consistent with climatologies over land from the Tropical Rainfall
Measurement Mission (TRMM; Nesbitt and Zipser, 2003; Yang and Smith, 2006;
Hirose et al., 2008), but this behavior may be fortuitous since complex
land surface cover, topography, and river and sea breeze controls influence
precipitation measurements in other parts of the Amazon Basin and
potentially mask a well-defined diurnal cycle (e.g., Adams et al., 2015;
Burleyson et al., 2016; Machado et al., 2018). The cloudiest times over the
MAO column do not perfectly align with domain mean precipitation properties,
but the times with the most frequent clouds we observe in Figs. 9 and 11
are typically near 18:00 UTC. Still, there are important shifts between
various regimes. For example, regime 5 domain mean precipitation from 21:00 UTC into the overnight hours skews higher than the other regimes and is
associated with increased MAO column cloudiness (e.g., Fig. 9e).
Overall, moist regimes favor more intense rainfall rates, with the highest
rainfall rates observed in regime 4, followed by regime 5. Although fewer
clouds, smaller total convective area, and lower domain rainfall rates are
observed during the drier season, the individual convective events
(updrafts, precipitation) can be quite strong (Giangrande et al., 2016;
Machado et al., 2018). This is evident from the relatively high domain mean
rainfall rates that are observed in regimes 2 and 3 for days when
precipitation is recorded.
In Fig. 12, we plot distributions of the maximum daily radar echo area
after 12:00 UTC (i.e., largest continuous area from any single radar scan,
one assigned per day), occupied by various thresholds for the reflectivity
factor, as proxies for deep convective core area coverage (Z>40 dBZ) and widespread rainfall area coverage (Z>20 dBZ). Thus,
this measurement is a daily reference for the largest individual cell (any
time) and not a measurement for the total convective area occupied by cells.
Previous studies, including Giangrande et al. (2016) and Machado et al. (2018), have indicated that rainy seasons favor larger total convective area
coverage. In terms of allowance for singular deeper convective cores (Fig. 12a), it is not surprising that regime 4 (e.g., transitional) is associated
with the largest convective cells based on higher expectations for MCS.
In terms of convective core properties associated with Z>40 dBZ
behaviors, multiple drier season distributions share comparable behaviors
with regime 5. This is consistent with suggestions that the dry season also
promotes isolated, intense convection.
As in Fig. 4, the maximum contiguous 2 km Constant Altitude Plan Position Indicator (CAPPI) radar echo
coverage (km2) for any radar scan within a regime day that is
occupied by radar echoes exceeding an intensity of (a)Z>40 dBZ
or (b)Z>20 dBZ for the hours between 12:00 and 00:00 UTC of that day.
Regimes 4 and 5, in contrast, favor a substantially wider distribution of
widespread precipitation coverage, as shown in Fig. 12b, when compared to
the drier regimes. An increase in widespread precipitation coverage (Z>20 dBZ) is consistent with the arguments for more ubiquitous
weak convection and/or MCS having trailing stratiform anvils (e.g.,
Romatschke and Houze, 2010). Interestingly, this may be interpreted as
weaker cells or precipitation winning out over less frequent but stronger
cells. This is suggested as being responsible for the reduced domain mean
precipitation rates compared to regime 2 (Fig. 11 reflects only
contributions from precipitation events). This view would also be consistent
with regime 3 being associated with additional congestus and/or periphery
stratiform precipitation, enabled through reduced MUCIN and greater humidity
above 600 hPa.
Radar-based null event or MCS event frequency
In addition to compositing clouds by regime, we explore a simple Bayesian
approach to query the likelihood a particular regime promotes different
precipitation modes, information that is highly useful for convective
parameterization and predictive efforts. If convection is initiated for a given
regime, what is the likelihood that the convection is nonprecipitating, is
isolated, or develops into a widespread precipitation event? In Fig. 13, we
break down the likelihood that precipitation events observed during
GoAmazon2014/5 fall under nonprecipitating (NULL), isolated precipitating
convection (ISO), or wide deeper convective (WDC) events. Among those WDC
events, we identify those events having mature-stage MCS characteristics
(i.e., MCS is a subset of the WDC events). For this study, NULL events are
defined by a minimum area of Z>20 dBZ that is less than 200 km2. For mature MCS definitions, we follow the guidelines established
in Houze et al. (2015) and Feng et al. (2018), where MCS is defined as
having a continuous 40 dBZ radar echo area exceeding 1000 km2 with a
continuous shield of 20 dBZ radar echo areas exceeding 10 000 km2. WDC
events are defined as the precipitation events having a continuous,
widespread shield of 20 dBZ echo exceeding 10 000 km2. For simplicity,
ISO events are defined as the remaining events that did not fall within NULL
or WDC categories (i.e., NULL + ISO + WDC = total events). For the
analysis in Fig. 13, 595 of the 607 rain-free radiosonde days were also
observed well by SIPAM.
The same as in the previous frequency plots but for the percentage of (a) NULL, (b) isolated, (c) wide deep convection (WDC),
and (d) MCS days associated with each regime cluster.
Variational forcing profiles at 12:00 UTC for non-MCS, local MCS,
and propagating MCS cases with rain rate less than 1.5 mm h-1. Profiles
correspond to the regime 4 conditions. Solid lines are median profile values
and dashed lines are the 95th percentile values.
Overall, NULL precipitation days are rare, accounting for less than 4 % of
our 2-year record (as shown in Fig. 13; Table S2). NULL events were
predominantly designated during the driest regimes, with regimes 1 and 2
accounting for 20 of the 23 (87 %) instances. WDC events account for
approximately 21 % of the dataset and are commonly observed for regimes 4 and
5 (approximately 81 %). Subsampling those WDC events, radar-based MCS
events are relatively uncommon, accounting for approximately 8 % of the
dataset. As we plot in Fig. 13, the majority of these MCS events was
observed during the moist regimes (regimes 4 and 5 accounting for
>70 % of the events), with approximately half of all MCSs
observed during regime 4. For completeness, the number of MCSs during
GoAmazon2014/5 was approximately double those reported, but we have chosen
to ignore radar-based MCSs that produced rainfall over the MAO site at the
time of the radiosonde launch. Additional manual inspection of the WDC events
also reveals that one-third of WDC events shared MCS-like characteristics
but fell short of study thresholds. Thus, potentially 20 % of the campaign
period was associated with MCS, although only half are considered for our
analysis. Similarly, MCS designations are arbitrary, and we anticipate
inconsistencies between this accounting and satellite tracking (e.g.,
Rehbein et al., 2019). One final consideration is that MCSs do not need to be
initiated locally (e.g., within the SIPAM radar domain of ∼500 km) to meet our radar-based definitions. We have inspected radar and
satellite observations for 44 of the 47 MCS events to manually identify MCSs from
our criteria that were initiated at distances >500 km upstream and then
propagated over the site. Table S2 identifies two MCS
categories, “propagating” and “local”, as reminiscent of previous Amazon
studies (e.g., Greco et al., 1990). By our breakdowns, MCSs during the drier
season are predominantly propagating events, while moist regimes include
contributions from both MCS categories.
As the regime most associated with mature MCS events, in Figs. S8 and S9
we plot composite radiosonde and parameter distributions (MUCAPE, MUCIN) for
regime 4: non-MCS, local (13 events), and propagating (7 events) events.
In Fig. 14, we plot a similar MCS breakdown for 12:00 UTC horizontal
moisture advection and w from VARANAL. Overall, we do not observe any obvious
difference between the composite properties among MCS and non-MCS events
within regime 4. Similarities between MCS and non-MCS events are also
reflected in the 12:00 UTC variational forcing composites shown in Fig. 14,
with local MCS and non-MCS events reflecting comparable mean conditions.
Propagating MCS events are less representative of composite behaviors and
suggest weaker thermodynamic conditions with the most favorable large-scale
controls. However, these large-scale moisture and velocity enhancements are
modest (e.g., vertical velocity increase of 2.5–5 hPa h-1).
Summary
To provide information on the potential controls for clouds experienced over the Amazon
Basin, a cluster analysis was performed on routine radiosondes launched
during GoAmazon2014/5. This effort follows similar applications of k-means
cluster methods that attempt to objectively disentangle larger-scale cloud
and precipitation controls from traditional calendar-driven wet/dry season
definitions. We identified five primary thermodynamic regimes and explored
these states in the context of traditional Amazon definitions, composite
large-scale synoptic patterns, and model forcing datasets. Column and
scanning radar observations were projected onto these states, highlighting
the propensities for each state to promote different cloud types,
frequencies, and changes to precipitation. Emphasis was placed on
intra-regime conditions associated with organized convection in the
transitional regime (regime 4) most favorable to MCS. Although caution is
recommended when considering the findings established over a limited,
2-year GoAmazon2014/5 deployment, a summary of our key findings are as
follows.
The k-means clustering of the 12:00 UTC radiosonde datasets yields five primary
clusters. The three drier regimes relate different states of mid- to upper-level moisture associated with the strength of similar large-scale features
that advect colder/drier air into the Amazon Basin. The wet to transitional
clusters exhibit similar deep moisture thermodynamic profiles, with regime 5
associated with evidence of moisture advection into the Amazon Basin from
the tropical belt.
GoAmazon2014/5 cloud frequencies, cloud types, and precipitation properties
for the five regimes correspond well to bulk changes in the large-scale
vertical air motion, moisture advection, local radiosonde thermodynamic
composite profile, and convective parameter shifts. Most regimes favor
frequent clouds and intense precipitation during the early afternoon hours
(after 16:00 UTC), with precipitation following a single-peak diurnal cycle.
These results are consistent with cumulative dataset results from the
GoAmazon2014/5 deployment (e.g., Collow et al., 2016; Ghate and Kollias,
2016; Zhuang et al., 2017).
The moist regimes were associated with modest MUCAPE, reduced MUCIN, and
higher humidity at all levels. The latter two controls are those suggested
as being the most favorable in the Amazon for more frequent clouds, deeper convection,
and widespread stratiform precipitation. These results are consistent with
previous studies on the propensity for stronger updrafts during dry or
dry to wet transitional seasons (e.g., Williams et al., 2002; Giangrande et
al., 2016; Wang et al., 2019).
Regimes 4 and 5 suggest prominent shallow to deep cloud transitioning (with
trimodal cloud profile behaviors observed in regime 5) with the timing of
these transitions potentially contingent on the regime. This later daytime
transitioning in regime 5 may suggest the transition has been slowed by
the reduced incident solar radiation (more frequent shallow clouds in
regime 5) or the higher soil moisture values (i.e., smaller Bowen ratio). This
transition timing aligns with previous Amazon findings from Zhuang et al. (2017) for wet and transitional season conditions.
The drier regimes reflect reduced column cloud frequency, bimodal instead of
trimodal distributions in vertical profiles of cloud frequency, an absence
of mid-level cloud contributions, shallow to deep transition signatures,
and rainfall properties attributed to weak or isolated (infrequent) deep
convection. Although convection is frequently observed during all regimes,
dry season regimes exhibit less frequent clouds and rare NULL
precipitation events.
When precipitation is observed, SIPAM radar designations indicate that most
convection is in isolated, deeper convective cells. Organized convection was
relatively frequent over MAO during this 2-year GoAmazon2014/5 deployment
(e.g., Rehbein et al., 2019), with approximately 10 %–20 % of the convection
observed over MAO associated with MCS. These MCSs were most frequently
observed under moist profile conditions (regimes 4 and 5) and over the 12:00 to 00:00 UTC period, with the best-defined MCSs observed during regime 4
GoAmazon2014/5 periods. Approximately half of the well-defined MCSs that
passed over MAO fell outside of the typical diurnal cycle and/or were not
associated with regime classifications.
When considering regime 4 favorability for deep convective events, it is
suggested that intra-regime (pre- and post-dry-season months) variability
may account for shifts in favorability for enhanced storm updrafts and/or
electrification. However, this study did not identify shifts in composite
thermodynamic profiles or convective parameter distributions between MCS and
non-MCS conditions. Additional checks of the large-scale synoptic patterns
and forcing datasets under MCS and non-MCS conditions indicate that
propagating MCSs may favor an enhancement in the large-scale upward
vertical motion (2.5–5 hPa h-1) and moisture tendencies during pre-convective
windows that offset weaker local thermodynamic environments. However, these
factors were arguably less important when compared to overall regime 4
proclivity for MCS.
Code and data availability
All ARM data (https://doi.org/10.5439/1025128, Coulter et al., 2009), including VARANAL (10.5439/1273323, Tang et al., 2001), ARSCL (10.5439/1027282, Giangrande and Johnson, 2003), SONDE (10.5439/1021460, Holdridge et al., 1994), and other PI datasets
used in this study, can be downloaded at http://www.arm.gov (last access: 25 June 2020) and are
associated with several value-added product (VAP) streams and
GoAmazon2014/5 PI datasets. Python machine-learning codes were provided by
Scikit-learn from Pedregosa et al. (2011). Radiosonde visuals were
supported by the Python package MetPy from May et al. (2020, 10.5065/D6WW7G29). ERA5
reanalysis products (production) are available at
https://www.ecmwf.int/en/newsletter/147/news/era5-reanalysis-production (last access: 29 April 2019) from
from Hersbach and Dee (2016).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-7489-2020-supplement.
Author contributions
SEG, DW, and DBM designed the research, SEG and DW
performed the research, and SEG and DBM wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
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
This study was supported by the U.S. Department of Energy (DOE) Atmospheric
System Research (ASR) program and the Climate Model Development and
Validation (CMDV) program.
Co-author David B. Mechem was funded by the U.S. Department of Energy's Atmospheric Systems
Research grant DE-SC0016522. The authors are grateful to Luiz Machado
(INPE), Ernani de Lima Nascimento (UFSM), Jiwen Fan (PNNL), and Andreas Prein
(NCAR) for their comments and discussion. The authors would also wish to
thank our three reviewers, David K. Adams (UNAM), Alan K. Betts (Atmospheric
Research), and Yizhou Zhuang (UCLA), who provided their time and thoughtful
suggestions towards significantly improving this paper.
Financial support
This research has been supported by the U.S. Department of Energy (grant nos. DE‐SC0012704 and DE-SC0016522).
Review statement
This paper was edited by Maria Assuncao Silva Dias and reviewed by David Adams, Yizhou Zhuang, and Alan K. Betts.
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