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Lightning is an important atmospheric process for igniting forest fires – often in remote locations where they are not easily suppressed – which results in potentially large emissions of many pollutants and short-lived climate forcers. Lightning also generates reactive nitrogen, resulting in the production of tropospheric ozone, the third most important greenhouse gas. Furthermore, the changing climate is expected to change the frequency and location of lightning. As such, lightning is an important component of climate models. The Canadian Atmospheric Model, CanAM, is one such climate model that did not contain an 'online' lightning parameterization. Fire ignition in CanAM was done via an unchanging climatological lightning input. In this study, we have added a new logistical regression lightning model (Etten-Bohm et al, 2021) into CanAM, creating the capacity for future lightning predictions with CanAM under different climate scenarios. The modelled lightning and fire area burned were evaluated against measurements in a historical period with good results. Then we simulate lightning and fire area burned in a future climate scenario in order to provide an estimate on how lightning and its impacts will change in the future. This study also presents the first time that CanAM’s land fire model was used online with its atmosphere to fully simulate fires in the global earth system.Reference:Etten-Bohm, M., J. Yang, C. Schumacher, and M. Jun : Evaluating the relationship between lightning and the large-scale environment and its use for lightning prediction in global climate models, JGR-atmospheres, 126, e2020JD033990, 2021.

Aerosol particles impact health, ecosystems, and climate, especially when in high concentrations in urban environments. Wet deposition is one of the most critical limiting mechanisms of particulate matter in the atmosphere. This mechanism captures particles inside clouds (rain-out) or below the cloud due to precipitation (washout). In the Amazon basin, the physical mechanisms and scavenging rates remain unknown in many regions. Several studies over the last decades have empirically ascertained the impact of wet deposition to develop local atmospheric models or to estimate the contribution of its effects. Here, we analyzed some of the physical and chemical properties of aerosols: nucleation mode (NU, 10-30 nm), Aitken (30-100 nm), accumulation mode (AC, 100-430 nm), total particle number (10-430 nm), Black Carbon equivalent (BCe), and chemical properties, such as organic aerosols (OA), sulfate (SO42−) and nitrate (NO3−). We obtained the data set from the Intensive Observation Periods (IOPs) of the GoAmazon2014 experiment, Iranduba-AM (T2 sampling site), ~ 9 km NE of Manaus city. We conducted three analyzes from these data: (I) daily cycles on dry and rainy days; (II) scavenging rates (TS), i.e., the difference in the concentration of aerosol one hour before and the first hour of rainfall events, generally, during local thunderstorms or Mesoscale Convective Systems - MCS; and (III) scavenging coefficients (λ). We verified significant statistics decreases at both NU and AIT modes, as well SO42− and NO3−. In TS analysis, we observed a similar decline (NU: -21%, AIT: -17%, AC: -22%), contributing to an overall removal of up to -13% (on average). The soluble fractions were removed easily (OA: -21%, SO42−: -16%, and NO3−: -16%) compared to insoluble fractions (BCe: -11%). In λ analysis, a substantial decrease in all size classes (NU: 2.0 × 10−4 s−1, AIT: 7.8 × 10−4 s−1, AC: 1.3 × 10−4 s−1, CN: 5.4 × 10−4 s−1) was also observed, with unexpected prominence to the AIT. We've attributed this result to introducing aerosol particles of 10-50 nm by deep convection, which may counteract the washout. Measurements of cloud properties might help confirm this hypothesis. Our preliminary findings are helpful for the local modeling of pollutant dynamics and provide evidence that wet deposition substantially removes sub-micron particles in the Amazon region. The wet deposition rates for other storms and clouds in the atmosphere, however, remain unknown.

A nocturnal Amazonian low-level jet (ALLJ) was recently diagnosed using reanalysis data. This work assesses the ability of CESM1.2.2 to reproduce the jet and explores the mechanisms by which the ALLJ influences convection in the Amazon. The coupled CESM simulates the nocturnal ALLJ realistically, while CAM5 does not. A low-level cold air temperature bias in the eastern Amazon exists in CAM5, and thus the ALLJ is weaker than observed. However, a cold SST bias over the equatorial North Atlantic in the coupled model offsets the cold air temperature bias, producing a realistic ALLJ. Climate models significantly underestimate March–May (MAM) precipitation over the eastern Amazon. We ran two sensitivity experiments using the coupled CESM by adding bottom-heavy diabatic heating at noon and midnight for 2.5 h along the coastal Amazon during MAM to mimic the occurrence of shallow precipitating convection. When heating is added during the early afternoon, coastal convection deepens and the ALLJ transports moisture inland from the ocean, preconditioning the environment for deep convective development during the ensuing hours. The increased convection over the eastern Amazon also moderately alleviates the equatorial Atlantic westerly wind bias, leading to deepening of the east Atlantic thermocline in the following months and partially improving the simulated June–August (JJA) Atlantic cold tongue in the coupled model. When heating is added at night, coastal convection does not strengthen as much and the ALLJ transports less moisture. Improvements in the simulated Atlantic winds and SST are negligible. Therefore, diurnal circulations matter to the organization of convection and rain across the Amazon, with impacts over the equatorial Atlantic.

Easterly waves (EWs) are off-equatorial tropical synoptic disturbances with a westward phase speed between 11 and 14 m s−1. Over the east Pacific in boreal summer, the combination of EWs and other synoptic disturbances, plus local mechanisms associated with sea surface temperature (SST) gradients, define the climatological structure of the intertropical convergence zone (ITCZ). The east Pacific ITCZ has both deep and shallow convection that is linked to deep and shallow meridional circulations, respectively. The deep convection is located around 9°N over warm SSTs. The shallow convection is located around 6°N and is driven by the meridional SST gradient south of the ITCZ. This study aims to document the interaction between east Pacific EWs and the deep and shallow meridional circulations during the Organization of Tropical East Pacific Convection (OTREC) field campaign in 2019 using field campaign observations, ERA5, and satellite precipitation. We identified three EWs during the OTREC period using precipitation and dynamical fields. Composite analysis shows that the convectively active part of the EW enhances ITCZ deep convection and is associated with an export of column-integrated moist static energy (MSE) by vertical advection. The subsequent convectively suppressed, anticyclonic part of the EW produces an increase of moisture and column-integrated MSE by horizontal advection that likely enhances shallow convection and the shallow overturning flow at 850 hPa over the southern part of the ITCZ. Therefore, EWs appear to strongly modulate shallow and deep circulations in the east Pacific ITCZ.

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption.

The CAPES Foundation grant 99999.000481/2016-05 and DOE grant DE-SC0016245 for financial support and FAPESP grant 2015/14497-0.

Afternoon deep convection over the Maritime Continent islands propagates offshore in the evening to early morning hours, leading to a nocturnal rainfall maximum over the nearby ocean. This work investigates the formation of the seaward precipitation migration off western Sumatra and its intraseasonal and seasonal characteristics using BMKG C-band radar observations from Padang and ERA5 reanalysis. A total of 117 nocturnal offshore rainfall events were identified in 2018, with an average propagation speed of 4.5 m s−1 within 180 km of Sumatra. Most offshore propagation events occur when the Madden–Julian oscillation (MJO) is either weak (real-time multivariate MJO index < 1) or active over the Indian Ocean (phases 1–3), whereas very few occur when the MJO is active over the Maritime Continent and western Pacific Ocean (phases 4–6). The occurrence of offshore rainfall events also varies on the basis of the seasonal evolution of the large-scale circulation associated with the Asian–Australian monsoons, with fewer events during the monsoon seasons of December–February and June–August and more during the transition seasons of March–May and September–November. Low-level convergence, resulting from the interaction of the land breeze and background low-level westerlies, is found to be the primary driver for producing offshore convective rain propagation from the west coast of Sumatra. Stratiform rain propagation speeds are further increased by upper-level easterlies, which explains the faster migration speed of high reflective clouds observed by satellite. However, temperature anomalies associated with daytime convective latent heating over Sumatra indicate that gravity waves may also modulate the offshore environment to be conducive to seaward convection migration.

We describe the existence of an Amazonian low-level jet (ALLJ) that can affect the propagation and life cycle of convective systems from the northeast coast of South America into central Amazonia. Horizontal winds from reanalysis were analyzed during March–April–May (MAM) of the two years (2014–15) of the GoAmazon2014/5 field campaign. Convective system tracking was performed using GOES-13 infrared imagery and classified into days with high and weak convective activity. The MAM average winds show a nocturnal enhancement of low-level winds starting near the coast in the early evening and reaching 1600 km inland by late morning. Mean 3-hourly wind speeds maximize at 9–10 m s−1 near 900 hPa, but individual days can have nighttime low-level winds exceeding 12 m s−1. Based on objective low-level wind criteria, the ALLJ is present 10%–40% of the time over the Amazon during MAM depending on the location and time of day. The evolution of the ALLJ across the Amazon impacts the frequency of occurrence of cloud clusters and the intensity of the moisture flux. In addition, the ALLJ is associated with the enhancement of northeasterly flow in the midtroposphere during active convective days, when vertical momentum transport may be occurring in the organized cloud clusters. During the weakly active convective period, the ALLJ is weaker near the coast but stronger across the central Amazon and appears to be linked more directly with the South American low-level jet.

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.

The ability of an observationally‐constrained cloud‐system resolving model (Weather Research and Forecasting; WRF, 4‐km grid spacing) and a global climate model (Energy Exascale Earth System Model; E3SM, 1‐degree grid spacing) to represent the precipitation diurnal cycle over the Amazon basin during the 2014 wet season is assessed. The WRF model coupled with a 3‐D variational data assimilation scheme reproduces the spatial variability of the precipitation diurnal cycle over the basin and the lifecycle of westward propagating MCSs initiated by the coastal sea‐breeze front. In contrast, a single morning peak in rainfall is produced by E3SM for simulations despite the nudging of large‐scale winds toward global reanalysis, indicating precipitation in E3SM is largely controlled by local convection associated with diurnal heating. The role of propagating MCS on the environment are discussed by using a multivariate perturbation analysis. We also find that the advection of moisture perturbations from ocean to inland regions have a higher correlation with the occurrence of MCSs in the Amazon than the intensity of colder air intrusion associated with sea breezes along the coast. Moreover, the presence of large cold pools over the central Amazon basin are responsible for the maintenance of propagating deep convection.

Stochastic parameterizations are increasingly becoming skillful in representing unresolved atmospheric processes for global climate models. The stochastic multicloud model, used to simulate the life cycle of the three most common cloud types (cumulus congestus, deep convective, and stratiform) in tropical convective systems, is one example. In this model, these clouds interact with each other and with their environment according to intuitive‐probabilistic rules determined by a set of predictors, depending on the large‐scale atmospheric state and a set of transition time scale parameters. Here we use a Bayesian statistical method to infer these parameters from radar data. The Bayesian approach is applied to precipitation data collected by the Shared Mobile Atmospheric Research and Teaching Radar truck‐mounted C‐band radar located in the Maldives archipelago, while the corresponding large‐scale predictors were derived from meteorological soundings taken during the Dynamics of the Madden‐Julian Oscillation field campaign. The transition time scales were inferred from three different phases of the Madden‐Julian Oscillation (suppressed, initiation, and active) and compared with previous studies. The performance of the stochastic multicloud model is also assessed, in a stand‐alone mode, where the cloud model is forced directly by the observed predictors without feedback into the environmental variables. The results showed a wide spread in the inferred parameter values due in part to the lack of the desired sensitivity of the model to the predictors and the shortness of the training periods that did not include both active and suppressed convection phases simultaneously. Nonetheless, the resemblance of the stand‐alone simulated cloud fraction time series to the radar data is encouraging.

The ability of an observationally-constrained cloud-system resolving model (Weather Research and Forecasting; WRF, 4-km grid spacing) and a global climate model (Energy Exascale Earth System Model;...

Thunderstorms in the Southern Great Plains of the United States are among the strongest on Earth and have been shown to be increasing in intensity and frequency during recent years. Assessing changes in storm characteristics under different climate scenarios, however, remains highly uncertain due to limitations in climate model physics. We analyse oxygen isotopes from Texas stalactites from 30–50 thousand years ago to assess past changes in thunderstorm size and duration using a modern radar-based calibration for the region. Storm regimes shift from weakly to strongly organized on millennial timescales and are coincident with well-known abrupt climate shifts during the last glacial period. Modern-day synoptic analysis suggests that thunderstorm organization in the Southern Great Plains is strongly coupled to changes in large-scale wind and moisture patterns. These changes in the large-scale circulation may be used to assess future predictions and palaeo-simulations of mid-latitude thunderstorm climatologies. Thunderstorm activity in the Southern Great Plains was closely coupled to abrupt climate shifts during the last glacial period, according to an analysis of oxygen isotopes in modern rainfall and ancient speleothems from Texas.

We explore the use of three advanced statistical and machine learning methods (a generalized linear model, random forest, and neural network) to predict the occurrence and rain rate distribution of...

Climate model simulations of rainfall in the tropics suffer from pervasive biases, and that can lead to degraded climate simulations in other regions as well. Over the past two decades, high-resolu...

Positive feedbacks between the cloud population and the environmental moisture field are central to theoretical expositions on the Madden–Julian oscillation (MJO). This study investigates the statistical incidence of positive moisture–convection feedbacks across multiple space and time scales over the tropical Indian Ocean. This work uses vertically integrated moisture budget terms from the ECMWF interim reanalysis [ERA-Interim (ERA-I)] in a framework proposed by Hannah et al. Positive moisture–convection feedbacks are primarily a low-frequency, low-wavenumber phenomenon with significant spectral signatures in the 32–48-day time scale. The efficacy of these feedbacks, however, is subject to horizontal moisture advection variations, whose relative importance varies with scale. Wave-filtered Tropical Rainfall Measuring Mission (TRMM) satellite precipitation is used to show that these moisture–convection feedbacks contribute more to moisture increases in the MJO than in other equatorial waves. A moving-window correlation analysis suggests that instances of moisture–convection feedbacks are more frequent in drier conditions, when column water vapor (CWV) is below its climatological mean value, with the implication that positive moisture–convection feedbacks shape the mean CWV field by moistening drier air columns, but that they are less effective in moistening already moist environments. Ground radar observations show that stratiform rain damps local CWV increases on short time scales (

This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. This study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weighted mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.

In the east Pacific (EP) intertropical convergence zone (ITCZ), Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) latent heating retrievals suggest a top-heavy structure; however, light precipitation and its associated low-level heating are underestimated by the PR. This study uses stratiform and deep convective precipitation from the TRMM PR and shallow precipitation from the more sensitive CloudSat radar to assess the seasonal latent heating structure in the EP ITCZ (130°–90°W) for 1998–2014. This study also uses reanalyses (MERRA-2, ERA-Interim, and NCEP–NCAR) to analyze the meridional circulation linked to variations in ITCZ heating. The TRMM/ CloudSat heating profiles suggest a distinct seasonality. During DJF, latent heating peaks at 800 hPa because of the predominance of shallow convection and rises to 700 hPa during MAM as the contribution from deep convective rain increases. During JJA and SON, stratiform precipitation increases and the latent heating has a double peak at 700 and 400 hPa. Additionally, the EP ITCZ heating has a meridional slope throughout most of the year as a result of the prevalence of shallow (deep) convection in the southern (northern) part of the ITCZ. While the reanalyses agree that the most bottom-heavy heating occurs in DJF and the most top-heavy heating occurs in JJA, they underestimate heating aloft compared to the satellite retrievals throughout the year and show varying ability in representing the shallow meridional circulation and deeper Hadley cell overturning.

In this study, we show that the well-documented exponential increase in the precipitation-water vapor (P-r) curve over tropical oceans also applies to tropical land, but that the land curve starts its exponential increase at smaller values of column moisture than over ocean. We demonstrate that daytime surface heating contributes to this characteristic shape of the land P-r curves. There is also significant geographical variation in the shape of the P-r curve within land and ocean regions, with the Amazon, the Maritime Continent and the eastern edges of oceans as distinct outliers. We further show that convective and stratiform rain intensities exhibit a pickup that is separate from the corresponding rain areas in the tropical P-r curve while shallow convective rain has a yet another pickup. These variations of the P-r curve characteristics likely represent geographical variations of environmental controls on storm life cycle.

We seek statistical methods to study the occurrence of multiple rain types observed by satellite on a global scale. The main scientific interests are to relate rainfall occurrence with various atmospheric state variables and to study the dependence between the occurrences of multiple types of rainfall (e.g. short-lived and intense vs. long-lived and weak; the heights of the rain clouds are also considered). Commonly in point process model literature, the spatial domain is assumed to be a small, and thus planar domain. We consider the log-Gaussian Cox Process (LGCP) models on the surface of a sphere and take advantage of cross-covariance models for spatial processes on a global scale to model the stochastic intensity function of the LGCP models. We present analysis results for rainfall observations from the TRMM satellite and atmospheric state variables from MERRA-2 reanalysis data over the tropical Eastern and Western Pacific Ocean, as well as over the entire tropical and subtropical ocean regions. Statistical inference is done through Monte Carlo likelihood approximation for LGCP models. We employ covariance approximation to deal with massive data.

Drop-size distributions (DSDs) provide important microphysical information about rainfall and are used in rainfall estimates from radar. This study utilizes a four-year DSD dataset of 163 rain events obtained using a Joss–Waldvogel impact disdrometer located in southeast Texas. A seasonal comparison of the DSD data shows that small (~1 mm diameter) drops occur more frequently in winter and fall, whereas summer and spring months see an increase in the relative frequency of medium and large (~&gt, 2 mm diameter) drops, with notable interannual variability in all seasons. Each rain event is classified by dynamic forcing and radar precipitation structure to more directly link environmental and storm organization properties to storm microphysics. Cold fronts and upper-level disturbances account for 80% of the rain events, whereas warm fronts, weakly forced situations, and tropical cyclones comprise the other 20%. Warm frontal storms and upper-level disturbances have smaller drops compared to the climatological DSD for southeast Texas, whereas the more dynamically vigorous cold fronts and weakly forced environments have larger drops. Tropical cyclones generally produce smaller drops than the climatology, but their DSD anomalies are sensitive to what part of the storm is sampled. Regardless of dynamic forcing, storms with precipitation structures that are mostly deep convective or stratiform rain formed from deep convection have larger drops, whereas stratiform rain formed from non-deep convection has smaller drops. Reflectivity-rain rate (Z-R) relationships that account for dynamic forcing and precipitation structures improve rainfall estimates compared to climatological Z-R relationships despite a wide spread in Z-R relationships by storm.

Gravitational waves (GWs) can offer a novel window into the structure and dynamics of neutron stars. Here we present the first search for long-duration quasi-monochromatic GW transients triggered by pulsar glitches. We focus on two glitches observed in radio timing of the Vela pulsar (PSR J0835-4510) on 12 December 2016 and the Crab pulsar (PSR J0534+2200) on 27 March 2017, during the Advanced LIGO second observing run (O2). We assume the GW frequency lies within a narrow band around twice the spin frequency as known from radio observatons. Using the fully-coherent transient-enabled F-statistic method to search for transients of up to four months in length. We find no credible GW candidates for either target, and through simulated signal injections we set 90% upper limits on (constant) GW strain as a function of transient duration. For the larger Vela glitch, we come close to beating an indirect upper limit for when the total energy liberated in the glitch would be emitted as GWs, thus demonstrating that similar post-glitch searches at improved detector sensitivity can soon yield physical constraints on glitch models., Comment: 10 pages, 3 figures (incl. appendices). Updated to match version accepted by PRD and fixed a few references

We describe the salient features of a field study whose goals are to quantify the vertical distribution of plant-emitted hydrocarbons and their contribution to aerosol and cloud condensation nuclei production above a central Amazonian rain forest. Using observing systems deployed on a 50-m meteorological tower, complemented with tethered balloon deployments, the vertical distribution of hydrocarbons and aerosols was determined under different boundary layer thermodynamic states. The rain forest emits sufficient reactive hydrocarbons, such as isoprene and monoterpenes, to provide precursors of secondary organic aerosols and cloud condensation nuclei. Mesoscale convective systems transport ozone from the middle troposphere, enriching the atmospheric boundary layer as well as the forest canopy and surface layer. Through multiple chemical transformations, the ozone-enriched atmospheric surface layer can oxidize rain forest–emitted hydrocarbons. One conclusion derived from the field studies is that the rain forest produces the necessary chemical species and in sufficient amounts to undergo oxidation and generate aerosols that subsequently activate into cloud condensation nuclei.

This study describes the characteristics of large-scale vertical velocity, apparent heating source (Q1) and apparent moisture sink (Q2) profiles associated with seasonal and diurnal variations of convective systems observed during the two intensive operational periods (IOPs) that were conducted from 15 February to 26 March 2014 (wet season) and from 1 September to 10 October 2014 (dry season) near Manaus, Brazil, during the Green Ocean Amazon (GoAmazon2014/5) experiment. The derived large-scale fields have large diurnal variations according to convective activity in the GoAmazon region and the morning profiles show distinct differences between the dry and wet seasons. In the wet season, propagating convective systems originating far from the GoAmazon region are often seen in the early morning, while in the dry season they are rarely observed. Afternoon convective systems due to solar heating are frequently seen in both seasons. Accordingly, in the morning, there is strong upward motion and associated heating and drying throughout the entire troposphere in the wet season, which is limited to lower levels in the dry season. In the afternoon, both seasons exhibit weak heating and strong moistening in the boundary layer related to the vertical convergence of eddy fluxes. A set of case studies of three typical types of convective systems occurring in Amazonia – i.e., locally occurring systems, coastal-occurring systems and basin-occurring systems – is also conducted to investigate the variability of the large-scale environment with different types of convective systems.

This study investigates why OLR plays a small role in the Real-time Multivariate (Madden–Julian oscillation) MJO (RMM) index and how to improve it. The RMM index consists of the first two leading principal components (PCs) of a covariance matrix, which is constructed by combined daily anomalies of OLR and zonal winds at 850 (U850) and 200 hPa (U200) in the tropics after being normalized with their globally averaged standard deviations of 15.3 W m−2, 1.8 m s−1, and 4.9 m s−1, respectively. This covariance matrix is reasoned mathematically close to a correlation matrix. Both matrices substantially suppress the overall contribution of OLR and make the index more dynamical and nearly transparent to the convective initiation of the MJO. A covariance matrix that does not use normalized anomalies leads to the other extreme where OLR plays a dominant role while U850 and U200 are minor. Numerous tests indicate that a simple scaling of the anomalies (i.e., 2 W m−2, 1 m s−1, and 1 m s−1) can better balance the roles of OLR and winds. The revised PCs substantially enhance OLR over the eastern Indian and western Pacific Oceans and change it less notably in other locations, while they reduce U850 and U200 only slightly. Comparisons with the original RMM in spatial structure, power spectra, and standard deviation demonstrate improvements of the revised RMM index.

From April 2014 to January 2015, ozone (O3) dynamics were investigated as part of GoAmazon 2014/5 project in the central Amazon rainforest of Brazil. Just above the forest canopy, maximum hourly O3 mixing ratios averaged 20 ppbv (parts per billion on a volume basis) during the June–September dry months and 15 ppbv during the wet months. Ozone levels occasionally exceeded 75 ppbv in response to influences from biomass burning and regional air pollution. Individual convective storms transported O3-rich air parcels from the mid-troposphere to the surface and abruptly enhanced the regional atmospheric boundary layer by as much as 25 ppbv. In contrast to the individual storms, days with multiple convective systems produced successive, cumulative ground-level O3 increases. The magnitude of O3 enhancements depended on the vertical distribution of O3 within storm downdrafts and origin of downdrafts in the troposphere. Ozone mixing ratios remained enhanced for > 2 h following the passage of storms, which enhanced chemical processing of rainforest-emitted isoprene and monoterpenes. Reactions of isoprene and monoterpenes with O3 are modeled to generate maximum hydroxyl radical formation rates of 6 × 106 radicals cm−3s−1. Therefore, one key conclusion of the present study is that downdrafts of convective storms are estimated to transport enough O3 to the surface to initiate a series of reactions that reduce the lifetimes of rainforest-emitted hydrocarbons.

© 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Global constraints on momentum and energy govern the variability of the rainfall belt in the intertropical convergence zone and the structure of the zonal mean tropical circulation. The continental-scale monsoon systems are also facets of a momentum- and energy-constrained global circulation, but their modern and palaeo variability deviates substantially from that of the intertropical convergence zone. The mechanisms underlying deviations from expectations based on the longitudinal mean budgets are neither fully understood nor simulated accurately. We argue that a framework grounded in global constraints on energy and momentum yet encompassing the complexities of monsoon dynamics is needed to identify the causes of the mismatch between theory, models and observations, and ultimately to improve regional climate projections. In a first step towards this goal, disparate regional processes must be distilled into gross measures of energy flow in and out of continents and between the surface and the tropopause, so that monsoon dynamics may be coherently diagnosed across modern and palaeo observations and across idealized and comprehensive simulations. Accounting for zonal asymmetries in the circulation, land/ocean differences in surface fluxes, and the character of convective systems, such a monsoon framework would integrate our understanding at all relevant scales: from the fine details of how moisture and energy are lifted in the updrafts of thunderclouds, up to the global circulations.

This study investigates the evolution, structure, and spatial variability of Madden–Julian oscillation (MJO) convection observed during the 2011/12 Dynamics of the MJO (DYNAMO) field campaign. Generally, the C-band radars located in the near-equatorial Indian Ocean—Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) on Addu Atoll (Gan) and NASA TOGA on the R/V Roger Revelle (Revelle)—observed similar trends in echo-top heights, stratiform rain fraction, and precipitation feature size across the MJO life cycle. These trends are closely related to changes in mid- to upper-tropospheric moisture, sea surface temperature (SST), zonal wind, and diagnosed vertical air motions. However, the evolution of convection, moisture, and vertical air motion at the R/V Mirai (Mirai), located in the intertropical convergence zone (ITCZ) at 8°S, exhibited a pattern nearly opposite to Gan and Revelle. When the MJO was active over the equator, convection was suppressed around Mirai owing to induced subsidence by the strong upward motion to the north. SST and zonal winds near Mirai were nearly invariant across the MJO life cycle, indicating little influence from the MJO in these fields. Compared to Gan and Revelle, Mirai had a significant amount of precipitation that fell from shallow and isolated convection. There were subtle differences in the evolution and properties of the convection observed between Gan and Revelle. Deep convection occurred slightly earlier at Gan compared to Revelle, consistent with the west-to-east progression of the MJO in the central Indian Ocean. Furthermore, convective deepening was more gradual over Revelle compared to Gan, especially during the October MJO event.

Three months of profiler observations from Darwin, Australia are separated into seven cloud types (shallow, mid-level, and deep convective; weak and robust stratiform rain; and transitional and ice-only anvil) to provide vertical velocity statistics on the full spectrum of tropical convective clouds over the course of a monsoon season. Consistent with past studies, convective updraughts increase in height and magnitude as the convective cloud height increases. Shallow/mid-level/deep convection has a mean maximum value of 0.3 m s−1 at 3 km/0.6 m s−1 at 5 km/2.5 m s−1 at 8 km. Deep convective extremes approach 18 m s−1 above 8 km while downdraughts in all convective cloud types are maximum below 4 km. Stratiform vertical velocities are weaker and less varied than in active convection, with maximum mean values

Abstract. This is study provides an overview of precipitation processes and their sensitivities to environmental conditions, in the Central Amazon Basin, during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. Taking advantage of the numerous measuring platforms and instruments systems operating during both campaigns sampling cloud structure and environmental conditions during 2014 and 2015, the rainfall variability among seasons, aerosol loading, land surface type, and topography have carefully been characterized. Differences between the wet and dry seasons were examined from a variety of different perspectives. The rain rate distribution, the total amount of rainfall, and the raindrop size distribution (the mean mass-weighted diameter) were quantified for the two seasons. The dry season has a higher average rain rate than the wet season and reflects more intense rain. While the cumulative wet season rainfall amount was four times larger than the total dry season rainfall, reflecting in large total rainfall accumulation. The typical size and life cycle of the Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as well their differences among the seasons. Moreover, we analyse the monthly mean thermodynamical and dynamical variables, measured by radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to the atmospheric aerosol loading is discussed with regard to the mean mass-weighted diameter and rain rate. This topic was evaluated during the wet season only due to the insignificant statistics of rainfall events for different ranges of aerosol loadings and the low frequency of precipitation events during the dry season. The aerosol impacts on the cloud droplet diameter is different for small and large drops. For the wet season, we observe no dependence on land surface type on the rain rate. However, during the dry season, urban areas exhibit the largest rain rate tail distribution, and deforested regions have the lowest mean rain rate. Airplane measurements were performed to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. The vertical motion turned out to be uncorrelated with cloud droplet sizes, but the cloud droplets number concentration revealed a linear relationship to the vertical motion. Clouds over forest exhibit larger droplets than clouds over pastures at all cloud levels. Finally, the connections between topography and rain rate were evaluated, showing a higher rain rate over higher elevations for the dry season.

Routine cloud, precipitation and thermodynamic observations collected by the ARM Mobile Facility (AMF) and 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 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.