Cloud Characteristics, Thermodynamic Controls and Radiative Impacts During the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment

. Routine cloud, precipitation and thermodynamic observations collected by the ARM Mobile Facility (AMF) and 20 Aerial Facility (AAF) during the two-year DOE ARM Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign are summarized. These observations quantify the diurnal to large-scale thermodynamic regime controls on the clouds and precipitation over the undersampled, climatically important, Amazon basin region. The extended ground deployment of cloud-profiling instrumentation enabled a unique look at multiple cloud regime controls at high temporal and vertical resolution. This longer-term ground deployment coupled with two short-term aircraft intensive 25 observing periods allowed new opportunities to better characterize cloud and thermodynamic observational constraints as well as cloud radiative impacts for modeling efforts within typical Amazon ‘wet’ and ‘dry’ seasons. [2016]). directed complementary GoAmazon2014/5 studies on the large-scale environmental controls on clouds, cloud transitions and precipitation found et Collow the T3 site a wide range of shallow to deep cloud conditions, multi-sensor AMF methods forcing datasets domain precipitation of Amazonia S-band characterize and precipitation properties according to and diurnal cycles that the observed cloud characteristics between ‘wet’ (herein, through April) ‘dry’ September) behaviors. months of study, several intense (e.g., updraft and deep convective events in the GoAmazon2014/5 Thermodynamic profiling (radiosonde) forcing datasets T3 better anchor cloud properties these wet and dry regimes, flight operations gaps and discrepancies between point and spatial rainfall estimates. However, this total campaign precipitation may be below normal owing to a late onset of the 2014-2015 rainy season and other factors (e.g., Marengo et al. [2017]). Using collocated RWP 25 echo classification methodologies when available (as described by Giangrande et al. [2016]), it was possible to designate the fractional precipitation associated with convective and stratiform cloud regimes. For this dataset, ~76% of the accumulated precipitation was associated with convective precipitation. Additional details on diurnal and regime breakdowns follow in the subsequent sections. previous tropical ARM efforts over the TWP region. As one possible example for the appropriateness of the Amazon ‘green ocean’ moniker, CRE properties for the Amazon are found to be similar to the TWP ARM Manus location in the Western Pacific warm pool that favors frequent tropical convection with complex influences from adjacent large islands within the Maritime Continent [e.g., Mather 2005]. A natural contrast between Amazon SW CRE behaviors and those from ARM Nauru observations stems from the strong ENSO-driven variability over this site as a key driver for cloud coverage [e.g., 30 Jensen et al. 1998, Burleyson et al. 2015]. The cumulative Amazon CRE is also larger when compared to the Darwin wet season (given the ‘dry’ season for Darwin is void of substantial cloud/precipitation). This behavior is partially attributed to

troposphere (between 3-10 km, Fig. 2a), higher precipitation rates (over these daily integrations) and precipitable water (PW, Fig. 2b), as well as the buildup of relative humidity (RH) profiles through the mid-levels (Fig. 2d). CAPE, CIN and zonal/meridional winds (Figs. 2c, e and f) from radiosondes also illustrate large-scale thermodynamical changes and moisture transport associated with wet, dry and transitional periods [e.g., Li and Fu 2004;Fu and Li 2004]. Although radiosonde daily maximum values indicate only small seasonal changes in CAPE and CIN, we observe that the transitional 5 periods between the dry and wet seasons typically promote maximum relative CAPE trends coupled with relatively lower CIN and heightened moisture. These are the primary ingredients that promote more frequent and strong convection, provided convection can be triggered [e.g., Machado et al. 2004]. Although areal coverage of deeper convection is generally the largest during the wet season, recent profiler-based studies suggest the strongest storms (in terms of upward vertical air motion) were often observed towards the end of the dry season and into the transitional period (e.g., Giangrande et al. 10 [2016], Nunes et al. [2016]).
The diurnal variation of atmospheric state is illustrated by Fig. 3 and shows the evolutions for the mean and standard deviation of (a) CAPE, (b) CIN, (c) LCL and (d) MLH separated into dry (red bars) and wet (blue bars) components [e.g., Betts et al. 2002]. CAPE increases after sunrise, reaching a maximum near midday, whereas CIN is maximum (largest 15 negative value) overnight and decreases during the day. These behaviors are consistent with development of convection breaking the capping inversion and consuming CAPE. Both CAPE and CIN show a stronger diurnal cycle during the dry season compared to the wet season. The mean LCL increases by approximately 600-800 m from sunrise to the afternoon with larger magnitudes and range during the dry season. The mean MLH also increases by approximately 1 km from sunrise through the afternoon during the wet season, while during the dry season the increase is about 1.5 km. This increase in MLH 20 is consistent with daytime solar heating. Separating the diurnal cycle into dry (red bars) and wet (blue bars) season components indicates slightly stronger diurnal cycle signatures in CAPE, increased CIN and higher MLH for the dry season (similar to measurements obtained in SW Amazon by Fisch et al. [2004]), with a suppressed diurnal cycle in LCL height.
To better inform the observed cloud system variations over the ARM T3 site from the large-scale environmental condition 25 perspective, Fig. 4 plots the diurnal cycle of the large-scale vertical motion (omega), total advection (sum of horizontal and vertical) of moisture and relative humidity. These large-scale fields were derived from the ECMWF analysis outputs over the entire field campaign constrained using the surface rainfall rate from the SIPAM radar following the variational analysis method of Zhang and Lin [1997], updated with using Numerical Weather Prediction analysis in Xie et al. [2004] and again as in Tang et al. [2016] for the GoAmazon2014/5 deployment. This variational analysis was performed 3 hourly at 25 hPa 30 vertical resolution over a domain of about 110 km in radius, with the center located at the T1 site (Fig. 1).
The omega field shows strong upward air motion in the middle and upper troposphere during the mid to late afternoon (Fig.   4a). The evening and early morning hours exhibit upward air motion confined below 3-4 km, whereas above that level, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-452, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 24 May 2017 c Author(s) 2017. CC-BY 3.0 License. downward air motion is dominant. This downward motion is most pronounced between 0600 and 0900 LT. After sunrise, we observe low-level weak ascending motions and positive advection of moisture (Fig. 4d). Between 4-8 km, we observe dry middle tropospheric conditions in the RH field (Fig. 4g). Similar structures in all fields are found across wet and dry season breakdowns, however, middle and upper level descending motions during the evening and early morning hours are much stronger during the dry season, suppressing convection during those hours. In addition, the ascending motion between noon 5 and late afternoon is much weaker in the dry season compared to the wet season. The dry season also exhibits reduced lowlevel positive moisture advection and a much dryer lower and middle atmosphere.

The AAF Aircraft Dataset
The DOE AAF G-1 aircraft participated in two IOPs that coincided with the AMF deployment. Airborne measurements were conducted during 22 Feb. -23 Mar. 2014 and6 Sept. -4 Oct. 2014 representative of the wet and dry season, respectively. 10 The G-1 flight patterns were designed to sample shallow and growing cumulus convective clouds that formed downwind from Manaus to examine the evolution of urban pollution and its effect on cloud and precipitation properties [Martin et al. 2017]. Typical flights consisted of a series of level legs flown just below cloud base, just above cloud base, and higher in growing cumulus clouds, including legs over the T3 ground site. In total, sixteen and nineteen flights in warm cumulus clouds were included in the wet and dry season, respectively. 15 The G-1 payload was designed to measure the full spectrum of aerosol size from 0.015 µm to 3 µm and cloud particle sizes from 2 µm to 1.92 cm. For this study, three cloud particle distribution probes are combined to create the full drop-size distribution (DSD) depictions presented in Section 4. The Droplet Measurements Technologies Cloud Droplet Probe (CDP; 2-50 µm) is combined with the Spec Inc. 2-Dimensional Stereo probe (2-DS; 10 µm -3 mm) between 20 and 50 µm by 20 averaging the overlapping bins. The Spec Inc. High-Volume Precipitation Spectrometer (HVPS; 150 µm -1.92 cm) is used for droplets larger than 500 µm. Cloud droplet distributions are combined by averaging the DSD for each instrument separately over these flight periods. This was done for in-cloud conditions only. DSDs from the CDP are used for drops smaller than 20 µm. The DSDs from the CDP and 2-DS are averaged between 20 and 50 µm, 2-DS DSDs are used between 50 and 500 µm, and HVPS DSDs are used for drops larger than 500 µm. The 2-DS probe occasionally contained artifacts 25 known as 'stuck bits', i.e., when a photodiode becomes continuously occulted due to optical contamination or electronic noise [Lawson et al. 2006]. Each flight was visually inspected for artifacts, which were manually removed from the combined DSDs. Cloud condensation nuclei (CCN) were measured with a dual-column system manufactured by DMT (operated with a constant pressure inlet at 600 mbar), and Liquid Water Content (LWC) was measured using a multi-wire element probe (Science Engineering Associates (SEA) Water Content Meter WCM-2000) with wire sizes the same as King 30 and Johnson-Williams probes.

Radar Dataset and Multisensor Merging
The ARM 95-GHz W-band ARM Cloud Radar (WACR) [e.g., ARM 2005] is the primary profiling instrument to characterize the cloud conditions during GoAmazon2014/5. Cloud masking and designation products are performed using the multi-sensor WACR preprocessing approach following Active Remote Sensing of Clouds methodologies [ARSCL; Clothiaux et al. 2000;Kollias et al. 2005Kollias et al. , 2009, and additional quality control refinements following Kollias et al. [2014]. 5 These retrievals merge observations from the WACR and a collocated laser ceilometer, micropulse lidar (MPL), and microwave radiometer (MWR) to better identify cloud boundaries in the vertical at high temporal (~10s) and vertical (~24m) resolution.
There are several limitations when designating cloud boundaries and hourly CF observations from vertically pointing cloud 10 radars beyond the capabilities of single radar platforms or ARSCL methods [e.g., Lamer and Kollias 2015;Oue et al. 2016].
Primary among these is that the WACR experiences attenuation in rain that manifests as erroneously low or missing cloud top boundaries [e.g., Feng et al. 2009[e.g., Feng et al. , 2014. To lessen these impacts within this Amazonian deployment that favors frequent precipitating cumulus, a collocated and well-calibrated 1290-MHz UHF radar wind profiler (RWP; 8-degree beamwidth, 200-m gate spacing, 6-s temporal resolution) was co-gridded to improve cloud coverage through deeper precipitating clouds 15 [e.g., ARM 2009;Giangrande et al. 2013Giangrande et al. , 2016. For this study, a modification to the ARSCL cloud boundary designation is produced by merging RWP profiles (operating in 'precipitation' modes, as also described in Tridon et al. [2013]) during precipitation intervals following similar ARSCL-type cloud profile processing [Feng et al. 2014]. The substitution is accomplished using collocated surface rain gauge datasets to help define appropriate "precipitation periods". These are defined as continuous time periods when the surface rain rate from the gauge exceeds 1 mm h -1 . During these intervals, if 20 more than 10% of the derived WACR first echo-top heights associated with these precipitating clouds is found to be 500 m or more below the echo-top height as recorded by the RWP, a WACR attenuation flag is assigned and the RWP profiles and boundaries are inserted. CF estimates in these regions benefit from the additional ceilometer and MPL observations (not shown in Fig. 5) to detect clouds. Congestus clouds, including those having cloud tops ~6 km, are observed reasonably well by both radars. In these times, surface precipitation is limited (Fig. 5d). A deep convective cloud system passes over T3 between 1500-1900 UTC.
Heavy precipitation (surface measured rain rate > 60 mm h -1 ) is associated with extinction of the WACR signal, whereas the 30 RWP is able to reconstruct cloud boundaries up to 13 km. Additional precipitation periods are also identified by red bars on top of Fig. 5c, highlighting locations where the cloud boundary designation within precipitation is improved over traditional ARSCL methods.

Cloud Classification and Radiative Properties
A simple cloud-type classification is performed on the cloud boundary and masking dataset from Section 2.3. This approach follows McFarlane et al. [2013] and Burleyson et al. [2015]. These methods classify clouds into seven categories according to the height of the cloud boundaries and cloud thickness. The cloud categories include: shallow, congestus, deep 5 convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus (definitions summarized in Table 1). Figure 5c provides an example of the cloud classifications for 1 April 2014. Cloud classification is used to separate surface radiative properties among the different cloud types. To accomplish this, the nearest cloud profile is matched to the 1-min surface radiative flux data. As with Burleyson et al. [2015], the lowest cloud type present in the column during that time is used to designate the shortwave and longwave radiative flux measurements (Figs. 5e and f) for that cloud type. 10

Profiling Observations of Clouds and Precipitation During GoAmazon2014/5
As highlighted in Fig. 2, thermodynamic and cloud properties from this two-year Amazon dataset are diverse and sampled near continuously by the ARM instrumentation to provide unique constraints towards model improvement. First, T3 cloud observations will be summarized according to diurnal and seasonal breakdowns that follow from large-scale shifts between wet and dry Amazon precipitation regimes. Breakdowns of CF associated with each cloud category defined in the previous 15 section are located in Table 2. For composite CF summaries presented in this section, we capitalize on the high temporal and vertical resolution of the ARM instruments to partition CF according to hourly profile estimates.
Measureable precipitation (> 1 mm) was frequent over the T3 site during the campaign according to surface rain gauge observations (as highlighted in Fig. 2b). In this dataset, 216 days recorded measureable precipitation from multiple ARM 20 gauge and radar sensors, with eighty additional days recording light / trace precipitation (< 1 mm). The total campaign precipitation over T3 was approximately 3000 mm. This total T3 accumulation is representative of the regional SIPAM estimates reported in Zhuang et al. [2017], accounting for uncertainty in radar-based rainfall estimates, dataset gaps and discrepancies between point and spatial rainfall estimates. However, this total campaign precipitation may be below normal owing to a late onset of the 2014-2015 rainy season and other factors (e.g., Marengo et al. [2017]). Using collocated RWP 25 echo classification methodologies when available (as described by Giangrande et al. [2016]), it was possible to designate the fractional precipitation associated with convective and stratiform cloud regimes. For this dataset, ~76% of the accumulated precipitation was associated with convective precipitation. Additional details on diurnal and regime breakdowns follow in the subsequent sections. Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-452, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 24 May 2017 c Author(s) 2017. CC-BY 3.0 License. Figure 6 shows diurnal CF profile breakdowns for each cloud category. The 'cirrus' and 'shallow' cloud categories are combined into a single panel since these cloud definitions do not overlap in altitude. Seasonal variations in the diurnal CF by cloud category are described in the next section. Figure 6a indicates that cirrus clouds are the most commonly observed clouds during the afternoon and overnight hours, whereas shallow cloud observations dominate the early morning hours after 5 sunrise. Combining Fig. 6a with summary cloud occurrence values in Table 2, shallow cumulus in the Amazon are observed with relatively high frequency throughout most of the day (~ 22%). Shallow clouds in the early morning align with low-level weak ascending air motions (Fig. 4a) and the positive advection of moisture (Fig. 4d). The most common cirrus clouds locations correspond to relatively high RH regions in the upper atmosphere seen in Fig. 4g, where the air is close to saturation with respect to ice (not shown). Congestus and altocumulus exhibit weak secondary peaks in the pre-dawn hours (around 0500 LT). This is observed primarily as a wet season congestus behavior, possibly comparable to suggestions in previous Manaus diurnal rainfall efforts 25 [e.g., Machado et al. 2004]. However, this contribution would typically be dwarfed when combining the rainfall contributions from other cloud types. We note that the non-precipitating categories of altocumulus, altostratus and cirrostratus (Figs. 6b, e, and f) share similar diurnal phasing with cirrus clouds. Cirrus and cirrostratus are more commonly observed than alto-cloud designations. However, we have not differentiated the contributions to cirrus CF estimates that reflect deep convective or anvil cloud components from other cirrus clouds. Overall, inspection of large-scale forcing fields 30 supports that the diurnal cycle of high-level clouds is not well associated with the diurnal cycle of the large-scale dynamic and thermodynamics. This is indicative of clouds that originate from anvil remnants from deeper convective clouds that developed locally or advected from elsewhere.

Cloud and Precipitation Diurnal Cycles
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-452, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 24 May 2017 c Author(s) 2017. CC-BY 3.0 License. Figure 7 shows the diurnal cycle of precipitation properties at the T3 site as observed by the surface gauges collocated with RWP observations. These plots include wet, dry and transitional season contributions (although we do not isolate these transitional months). Average precipitation rates reflect the average across precipitating and nonprecipitating days, peaking around 1200 to 1600 LT (Fig. 7a), consistent with the deep convective CF in Fig. 6d. Note, the mean rainfall rates including 5 only times when precipitation is present (Fig. 7b) are more comparable between wet and dry season events, suggesting T3 results in Fig. 7a primarily reflect the additional frequency of convection during the wet season and not its relative intensity.
Rainfall rates have also been separated into convective and stratiform types as designated by the RWP [Giangrande et al. 2016]. For the composite campaign (dashed line in Fig. 7c), convective precipitation is dominant at ~76% of the fractional accumulation (~2300 mm) with a relatively flat contribution diurnally (esp. during the dry season). Since this fractional 10 accumulation is based on RWP estimates for convective fraction, it may tend to maximize convective precipitation fraction over traditional scanning radar-based retrievals (e.g., Steiner et al. [1995]). This is because unlike basing these designations on radar reflectivity factor properties and buffering, profiler methods also distinguish columns with convective vertical air motions (including those from elevated sloping updrafts that extend into transition or stratiform regions), as well as shallow congestus cloud precipitation as 'convective'. Stratiform precipitation (approx. 700 mm) is more frequent (in terms of 15 accumulation) during the overnight hours (30-60%) and associated with the trailing precipitation regions from the convective systems.

Seasonal Cloud Regime Cycles
Figure 7 also plots the diurnal breakdowns for average rainfall rate and fractional convective/stratiform accumulations during the two wet and dry seasons over T3. Our dataset contains 103 wet season days responsible for approximately 1600 20 mm of precipitation, and 52 dry season days responsible for approximately 600 mm of precipitation. Thus, wet season months are associated with a factor of 2 increase in average rainfall rates, but even larger increases occur during daytime hours with much smaller changes during the late evening and early morning (Fig. 7a). However, relative to those days having precipitation (Fig. 7b), the differences in the mean rainfall rate are less pronounced, implying dry season convection as stronger (instantaneously), since the overall convective cell coverage is also reduced during the dry season (e.g., 25 Giangrande et al. [2016]). Wet and dry season convective rain fractions have similar values (~80%) throughout most of the day (Fig. 7c) and only diverge during the early morning hours when wet season convective rain fractions drop to as low as 20%. These diurnal patterns suggest that organized systems pass over T3 primarily in the morning hours during the wet season, but are infrequent and only have a small impact on the multi-month mean statistics. season conditions. Quantitative interpretation for these behaviors is challenging owing to coupled cirrus-shallow cloud sampling factors during the overnight hours. For example, it is likely cirrus sampling is shielded (results stemming from an MPL detection) during the wet season owing to the added presence of lower-level clouds and higher relative humidity / attenuation limiting the usefulness of the cloud radar. In contrast, clear low-level conditions during the overnight hours of the dry season would likely promote improved designation of cirrus. In this regard, wet and dry season cirrus cloud contrasts 20 may be more pronounced than reported by this study.
As highlighted by Figs. 2 and 3, wet season thermodynamical conditions typically favor weaker CAPE, weaker CIN and higher RH in the lower to mid-atmospheric levels, while dry seasons feature stronger CAPE, stronger CIN and lower RH at the same levels. As inferred from the large-scale forcing fields in Fig. 4, wet season conditions favor higher column relative 25 humidity, as well as heightened moisture convergence throughout the profile. The wet season also features more favorable omega fields at mid-levels for shallow to deeper convective cloud transitions. This behavior is not surprising and also consistent with forcing datasets being constrained using mean domain precipitation estimates. However, as with first year  [2015] found that the largest source of shortwave surface CRE at these three TWP sites comes from low clouds owing to their high frequency of occurrence. Although deep convective clouds have a strong influence on surface shortwave radiation when present, their aggregate impact is limited by a lower frequency of occurrence compared to shallow cumulus. Compared to SW CRE, LW CRE is typically a factor of 5-6 smaller than SW 10 CRE [e.g., Culf et al. 1998;Malhi et al. 2002, Burleyson et al. 2015. This study will limit most interpretation to SW CRE.
The 2-year deployment during GoAmazon2014/5 allows us to examine the impact of various cloud types on the surface energy budget over the Amazon, providing new details for targeted model improvements of cloud radiative effects in this climatically important, but undersampled region. This deployment also provides a unique opportunity to contrast 'green ocean' cloud radiative effects during GoAmazon2014/5 with tropical ARM fixed-site measurements in the TWP. 15 The frequency of occurrence for the lowest cloud types and their associated radiative fluxes (Tables 2 and 3) are composited into hourly bins across the diurnal cycle (Fig. 10). The methodology to produce the radiative fluxes in these tables is similar to Burleyson et al. [2015] to facilitate comparison with previous results over the three TWP sites. We utilize 'as lowest cloud type' in the column designations in our analysis (e.g., column 2 in Table 2) because clouds closest to the surface typically 20 have the larger impact on the surface radiative fluxes [Burleyson et al. 2015]. However, we also note that it is not possible to separate the radiative impact of multi-layer clouds and sample sizes are potentially too small to only consider single-layer cloud periods. For higher-altitude cloud types, the frequency as lowest cloud in the column is lower than the total cloud frequencies discussed in Section 3 (as reported in column 1 of Table 2). The difference in frequencies is indicative of how often multi-layer clouds are present (e.g., cirrus clouds are often present above shallow cumulus). 25 One notable discrepancy with the previous study is that the instrumentation for classifying the clouds that produce significant precipitation (rain-rate > 1 mm h -1 ) during GoAmazon2014/5 is better than the approach used by Burleyson et al.
[2015] owing to the merging of the RWP dataset. Specifically, cloud profiles with rain-rate larger than 1 mm h -1 are discarded in Burleyson et al. [2015], but retained for our study. Therefore, we anticipate that cloud radiative effects from 30 precipitating convective clouds (including both congestus and deep convection) are better represented by this study.

Bulk Cloud Radiative Effects
The average aggregated shortwave (SW), longwave (LW) fluxes and CRE measured at the T3 site are given in Table 3, along with long term results from the three TWP sites (Darwin, Manus, Nauru) as reported in Burleyson et al. [2015]. SW CRE dominates (magnitude) as compared to LW CRE. The mean SW CRE (-94.4 W m -2 ) and LW CRE (14.5 W m -2 ) averaged across the diurnal cycle (nighttime included) over the entire GoAmazon2014/5 is most similar to those found at 5 Manus, which is the cloudiest of the three TWP sites and most influenced by convection in the Western Pacific warm pool.
The Darwin, Australia site has a strong monsoonal cycle (i.e., wet/dry season) and the Nauru site is strongly impacted by the  Note, the conditional CRE presented in Table 2 includes both single-layer clouds, as well as when additional cloud layers are above the lowest detected cloud layer. This is done deliberately to be consistent with the method used by Burleyson et al.
[2015] such that the GoAmazon2014/5 results can be directly compared with their long-term results from the ARM TWP sites. Examination of the averaged conditional SW CRE calculated using only single-layer clouds reveal a relative reduction 20 of ~26% for altocumulus and ~20% for shallow cumulus clouds, and negligible difference in other cloud types. The reduction in SW CRE when single-layer clouds are considered is likely caused by frequent multi-layer cloud occurrence of cirrus/cirrostratus clouds over shallow cumulus or altocumulus (i.e., artificially inflating the surface SW CRE of cumulus clouds due to additional SW flux reflection by the upper level clouds). The difference in conditional SW CRE between single-and multi-layer clouds do not change their contribution to the average CRE as discussed below. 25

Diurnal Cycle of Cloud Radiative Effects by Cloud Type
Comparisons between wet and dry season diurnal behaviors for the frequency of the lowest clouds in the column and the associated mean SW CRE are shown in Fig. 11. Shallow cumulus dominates the SW CRE in both seasons, although their frequency peak two hours earlier during wet season (1000-1100 LT) than during dry season (1200-1300 LT). While the dry season features reduced frequency and SW CRE of all cloud types, the contrast is most visible for the three convective cloud types. Shallow, congestus and deep convective cloud mean SW CRE in the wet season are 51%, 69% and 71% larger than that in the dry season, respectively.

Shallow Cumulus Cloud Properties
From the previous section, shallow cumulus (those most frequently observed during the campaign) are associated with large discrepancies in cloud radiative effects between the wet and dry season ( Table 2 and Fig. 11). Further investigation into 5 these clouds and their radiative differences is enabled using aircraft observations available during the GoAmazon2014/5 campaign IOP periods. As discussed in Section 2.2, three cloud particle size distribution probes are combined to create the full DSD (Fig. 12). Combining the cloud microphysical properties in shallow cumulus measured by aircraft observations and the cloud macrophysical properties measured by ground-based instrumentations allow us to explain the cloud radiative effect differences from wet and dry seasons reported in the previous section. 10 Cloud particle size distributions (Fig. 12c) in the wet season are characterized by a lesser occurrence of small droplets and a more frequent occurrence of large droplets when compared with cumulus clouds in the dry season. Total number concentration of cloud drops is more than a factor of 2 larger in the dry season versus the wet season (Fig. 12b). However, the corresponding LWC is roughly the same between the seasons (Fig. 12d). In-situ cloud condensation nuclei (CCN) 15 concentration is also larger in the dry season versus the wet season (Fig. 12a). Aircraft cloud and CCN measurements are consistent with studies that show clouds influenced by aerosol tend to have larger concentrations of smaller droplets and fewer precipitation sized drops for clouds with similar LWC [e.g., Twomey 1974;Cecchini et al. 2016]. Ground-based radar measurements of single layer shallow cumulus clouds at the T3 site show thicker clouds occurring more frequently in the wet season (Fig. 12e). Likewise, more frequent occurrence of large liquid water path (LWP) from the T3 ground-based 20 MWR in wet season is consistent with the presence of more robust (i.e., vertically developed) shallow cumulus clouds (Fig.   12f). Therefore, shallow cumulus in the wet season is characterized by fewer number but more frequent larger cloud droplets, while those in the dry season is characterized by more frequent smaller cloud droplets (Fig. 12b,c). Interestingly, these differences in DSDs result in comparable LWC between the wet/dry seasons (Fig. 12d). As a result, the stronger shallow cumulus conditional SW CRE in the wet season, which reflects the difference in microphysical properties, mainly 25 arises from higher values of vertically integrated properties such as LWP and cloud thickness (Fig. 12e,f). Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-452, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 24 May 2017 c Author(s) 2017. CC-BY 3.0 License.

Discussion, Summary and Future Opportunities
This study documents the continuous observations collected by the DOE AMF and AAF facilities to characterize cloud properties, collocated large-scale environments and cloud radiative effects over the two-year GoAmazon2014/5 campaign.

10
The propensity for cumulus to initiate, deepen and organize across the Amazon basin drives much of the observed wet and dry season CF profile diurnal contrasts. Amazon wet season environments promote enhanced shallow cumulus throughout the diurnal cycle, as well as additional deeper precipitating cloud development likely associated with reduced CIN, heightened moisture convergence and relative humidity through atmospheric mid-levels. Wet season and transitional periods exhibiting sharper CAPE and CIN contrasts enhance the likelihood for deep convection to have organized components, thus 15 promoting anvil and trailing stratiform regions that carry into the overnight hours and propagate across the Amazon basin.
Weaker secondary peaks in congestus CFs are also found during the wet season within pre-dawn hours, revealed with confidence from coupled ARM profiling observations. Nevertheless, relatively favorable thermodynamical conditions during both seasons supports local congestus to deeper cloud triggering for this ARM dataset, which includes over 200 days recording measurable rainfall. This regularly occurring daily precipitation is primarily attributed to isolated and locally-20 driven convective cells, supported by the 76% rainfall accumulation associated with convective modes, as well as the pronounced diurnal cycle for this rainfall centered near local noon. These ideas and T3 representativeness may be further explored using spatial properties as available from collocated SIPAM radar observations during GoAmazon2014/5.
Congestus and deeper convection is also shown to dominate the conditional surface SW CRE, similar to results from 25 previous tropical ARM efforts over the TWP region. As one possible example for the appropriateness of the Amazon 'green ocean' moniker, CRE properties for the Amazon are found to be similar to the TWP ARM Manus location in the Western Pacific warm pool that favors frequent tropical convection with complex influences from adjacent large islands within the Maritime Continent [e.g., Mather 2005]. A natural contrast between Amazon SW CRE behaviors and those from ARM Nauru observations stems from the strong ENSO-driven variability over this site as a key driver for cloud coverage [e.g., 30 Jensen et al. 1998, Burleyson et al. 2015. The cumulative Amazon CRE is also larger when compared to the Darwin wet season (given the 'dry' season for Darwin is void of substantial cloud/precipitation). This behavior is partially attributed to Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-452, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 24 May 2017 c Author(s) 2017. CC-BY 3.0 License.
the Darwin monsoonal environments that fluctuate between wider-spread tropical 'Active' cloud conditions and continental 'Break' monsoonal regimes that promote stronger convection [e.g., Holland 1986;May and Ballinger 2007;Giangrande et al. 2014]. Overall, cumulative results from CRE help emphasize the important role of shallow cumulus for the Amazon, including the dry season, and the favorable low-level conditions (e.g., weak ascending air motions, positive moisture advection and moist surface) throughout the year that promote elevated shallow cumulus frequency. Given this relative 5 importance, these clouds must be properly simulated in both global and regional climate models if the surface radiative budget (that affects land-atmosphere interactions and subsequent convective cloud and precipitation formations over the T3 site) is to be properly represented.
Ground-based multi-sensor measurements and aircraft observations further support thicker clouds occurring more frequently 10 in the wet season. These clouds are those having larger LWP that would also promote the heightened SW CRE and LW CRE contributions. Aircraft and ground-based cloud and CCN measurements and properties for shallow cumulus in this study also informs on the role of the Manaus pollution plume in cumulus cloud evolution. A key motivation behind GoAmazon2014/5 was the opportunity to test various cloud-aerosol interactions in this Amazon natural cloud laboratory setting. Shallow cumulus summaries provided in our study support prior efforts that suggest clouds influenced by aerosol tend to have larger 15 concentrations of smaller droplets and fewer precipitation sized drops for clouds with similar LWC. As the clean (wet) and polluted (dry) cloud conditions tend to align with large-scale regime thermodynamical controls, subsequent studies will need to differentiate the role of the Manaus plume that influences the observed differences in shallow cumulus microphysical properties, and examine the extent that the reduced frequency for organized precipitation events removes Manaus pollution.
In that regard, impacts on shallow cumulus clouds could have potentially a more profound impact as far as how shallow 20 clouds transition to deeper convection, hence affecting hydrological cycle and land-atmosphere feedbacks.

Acknowledgements
This manuscript has been authored by employees of Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy (DOE). The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Dr. Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-452, 2017 Manuscript under review for journal Atmos. Chem. Phys. Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-452, 2017 Manuscript under review for journal Atmos. Chem. Phys.   Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-452, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 24 May 2017 c Author(s) 2017. CC-BY 3.0 License. Figure 1: Location of the GoAmazon2014/5 key deployment sites and associated terrain elevation (shaded). The primary ARM AMF facilities were located at the T3 location. Range rings indicate distances from the SIPAM radar location near T1.