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In this study, a pair of convection-permitting (2-km grid spacing), month-long, wet-season Weather Research and Forecasting (WRF) Model simulations with and without the eddy-diffusivity mass-flux (EDMF) scheme are performed for a portion of the Green Ocean Amazon (GoAmazon) 2014/15 field campaign period. EDMF produces an ensemble of subgrid-scale convective plumes that evolve in response to the boundary layer meteorological conditions and can develop into shallow clouds. The objective of this study is to determine how different treatments of shallow cumulus clouds (i.e., with and without EDMF) impact the total cloud population and precipitation across the Amazonian rain forest, with emphasis on impacts on the likelihood of shallow-to-deep convection transitions. Results indicate that the large-scale synoptic conditions in the EDMF and control simulations are nearly identical; however, on the local scale their rainfall patterns diverge drastically and the biases decrease in EDMF. The EDMF scheme significantly increases the frequency of shallow clouds, but the frequencies of deep clouds are similar between the simulations. Deep convective clouds are tracked using a cloud-tracking algorithm to examine the impact of shallow cumulus on the surrounding ambient environment where deep convective clouds initiate. Results suggest that a rapid increase of low-level cloudiness acts to cool and moisten the low to midtroposphere during the day, favoring the transition to deep convection.

Long-term temperature change and variability are expected to have significant impacts on future electric capacity and investments. This study improves upon past studies by accounting for hourly and monthly dynamics of electricity use, long-term socioeconomic drivers, and interactions of the electric sector with rest of the economy for a comprehensive analysis of temperature change impacts on cooling and heating services and their corresponding impact on electric capacity and investments. Using the United States as an example, here we show that under a scenario consistent with a socioeconomic pathway 2 (SSP2) and representative concentration pathway 8.5 (RCP 8.5), mean temperature changes drive increases in annual electricity demands by 0.5-8% across states in 2100. But more importantly, peak temperature changes drive increases in capital investments by 3-22%. Moreover, temperature-induced capital investments are highly sensitive to both long-term socioeconomic assumptions and spatial heterogeneity of fuel prices and capital stock characteristics, which underscores the importance of a comprehensive approach to inform long-term electric sector planning., Temperature changes as a result of climate change are expected to impact electric capacity and investment. Here, the authors show that in the United States under socioeconomic pathway 2 and RCP 8.5 mean temperature rises will drive increased electricity demand (0.5-8%) by 2100, along increases in capital investments by 3-22%.

Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class.

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. We propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL's BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

The characteristics of Madden–Julian oscillation (MJO) events that strengthen and weaken over the Maritime Continent (MC) are examined. The real-time multivariate MJO (RMM) index is used to assess changes in global MJO amplitude over the MC. The MJO weakens at least twice as often as it strengthens over the MC, with weakening MJOs being twice as likely during El Niño compared to La Niña years and the reverse for strengthening events. MJO weakening shows a pronounced seasonal cycle that has not been previously documented. During the Northern Hemisphere (NH) summer and fall the RMM index can strengthen over the MC. MJOs that approach the MC during the NH winter typically weaken according to the RMM index. This seasonal cycle corresponds to whether the MJO crosses the MC primarily north or south of the equator. Because of the seasonal cycle, weakening MJOs are characterized by positive sea surface temperature and moist-static energy anomalies in the Southern Hemisphere (SH) of the MC compared to strengthening events. Analysis of the outgoing longwave radiation (OLR) MJO index (OMI) shows that MJO precipitation weakens when it crosses the MC along the equator. A possible explanation of this based on previous results is that the MJO encounters more landmasses and taller mountains when crossing along the equator or in the SH. The new finding of a seasonal cycle in MJO weakening over the MC highlights the importance of sampling MJOs throughout the year in future field campaigns designed to study MJO–MC interactions.

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.

Long-term Atmospheric Radiation Measurement (ARM) datasets collected at the three tropical western Pacific (TWP) sites are used to evaluate the ability of the Community Atmosphere Model (CAM5) to simulate the various types of clouds, their seasonal and diurnal variations, and their impact on surface radiation. A number of CAM5 simulations are conducted at various horizontal grid spacing (around 2°, 1°, 0.5°, and 0.25°) with meteorological constraints from analysis or reanalysis. Model biases in the seasonal cycle of cloudiness are found to be weakly dependent on model resolution. Positive biases (up to 20%) in the annual mean total cloud fraction appear mostly in stratiform ice clouds. Higher-resolution simulations do reduce the positive bias in ice clouds, but they inadvertently increase the negative biases in convective clouds and low-level liquid clouds, leading to a positive bias in annual mean shortwave fluxes at the sites, as high as 65 W m−2 in the 0.25° simulation. Such resolution-dependent biases in clouds can adversely lead to biases in ambient thermodynamic properties and, in turn, produce feedback onto clouds. Both the model and observations show distinct diurnal cycles in total, stratiform, and convective cloud fractions; however, they are out of phase by 12 h and the biases vary by site. The results suggest that biases in deep convection affect the vertical distribution and diurnal cycle of stratiform clouds through the transport of vapor and/or the detrainment of liquid and ice. The approach used here can be easily adapted for the evaluation of new parameterizations being developed for CAM5 or other global or regional models.

Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in temperature and load biases from using an increasing number of weather stations over the western U.S. Our novel approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to evaluate the ability to capture weather patterns across different seasons. Using 8 stations across the western U.S., one from each IECC climate zone, results in an average absolute summertime temperature bias of ∼4.0 °C with respect to a high-resolution gridded dataset. The mean absolute bias drops to ∼1.5 °C using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.5%. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20–40% bias of peak building loads during both summer and winter, a significant error for capacity expansion planners who may use these types of simulations. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which was fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. The methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.

The changes in extreme rainfall associated with a warming climate have drawn significant attention in recent years. Mounting evidence shows that sub-daily convective rainfall extremes are increasing faster than the rate of change in the atmospheric precipitable water capacity with a warming climate. However, the response of extreme precipitation depends on the type of storm supported by the meteorological environment. Here using long-term satellite, surface radar and rain-gauge network data and atmospheric reanalyses, we show that the observed increases in springtime total and extreme rainfall in the central United States are dominated by mesoscale convective systems (MCSs), the largest type of convective storm, with increased frequency and intensity of long-lasting MCSs. A strengthening of the southerly low-level jet and its associated moisture transport in the Central/Northern Great Plains, in the overall climatology and particularly on days with long-lasting MCSs, accounts for the changes in the precipitation produced by these storms., The central United States has exhibited increased extreme precipitation. Here, using satellite, radar, and rain-gauge data, Feng et al. show that springtime total and extreme rainfall trends are linked to increased intensity and frequency of long-lived Mesoscale Convective Systems.

Influences of the diurnal cycle on the propagation of the Madden-Julian Oscillation (MJO) convection across the Maritime Continent (MC) are examined using cloud-permitting regional model simulations and observations. A pair of ensembles of control (CONTROL) and no-diurnal cycle (NODC) simulations of the November 2011 MJO episode are performed. In the CONTROL simulations, the MJO signal is weakened as it propagates across the MC, with much of the convection stalling over the large islands of Sumatra and Borneo. In the NODC simulations, where the incoming shortwave radiation at the top of the atmosphere is maintained at its daily mean value, the MJO convection signal propagating across the MC is enhanced. Examination of the surface energy fluxes in the simulations indicates that the surface downwelling shortwave radiation is larger in the presence of the diurnal cycle (CONTROL simulations) primarily because clouds preferentially form in the afternoon and are smaller during day time in comparison to nighttime. Furthermore, the diurnal covariability of surface wind speed and skin temperature results in a larger sensible heat flux and a cooler land surface in the CONTROL runs compared to NODC runs. An analysis of observations indicates that ahead of and behind the MJO active phase, the diurnal cycle of cloudiness enhances downwelling shortwave radiation and hence convection over the MC islands. This enhanced stationary convection competes with and disrupts the convective signal of MJO events that propagate over the waters surrounding the islands.

The isolation of the Amazon rain forest makes it challenging to observe precipitation forming there, but it also creates a natural laboratory to study anthropogenic impacts on clouds and precipitation in an otherwise pristine environment. Observations were collected upwind and downwind of Manaus, Brazil, during the “Observations and Modeling of the Green Ocean Amazon 2014–2015” experiment (GoAmazon2014/5). Besides aircraft, most of the observations were point measurements made in a spatially heterogeneous environment, making it hard to distinguish anthropogenic signals from naturally occurring spatial variability. In this study, 15 years of satellite data are used to examine the spatial and temporal variability of deep convection around the GoAmazon2014/5 sites using cold cloud tops (infrared brightness temperatures colder than 240 K) as a proxy for deep convection. During the rainy season, convection associated with the inland propagation of the previous day’s sea-breeze front is in phase with the diurnal cycle of deep convection near Manaus but is out of phase a few hundred kilometers to the east and west. Convergence between the river breezes and the easterly trade winds generates afternoon convection up to 10% more frequently (on average ~4 mm day−1 more intense rainfall) at the GoAmazon2014/5 sites east of the Negro River (T0e, T0t/k, and T1) relative to the T3 site, which was located west of the river. In general, the annual and diurnal cycles of precipitation during 2014 were similar to climatological values that are based on satellite data from 2000 to 2013.

Two Madden–Julian oscillation (MJO) episodes observed during the 2011 Atmospheric Radiation Measurement Program MJO Investigation Experiment (AMIE)/DYNAMO field campaign are simulated using a regional model with various cumulus parameterizations, a regional cloud-permitting model, and a global variable-resolution model with a high-resolution region centered over the tropical Indian Ocean. Model biases in relationships relevant to existing instability theories of MJO are examined and their relative contributions to the overall model errors are quantified using a linear statistical model. The model simulations capture the observed approximately log-linear relationship between moisture saturation fraction and precipitation, but precipitation associated with the given saturation fraction is overestimated especially at low saturation fraction values. This bias is a major contributor to the excessive precipitation during the suppressed phase of MJO. After accounting for this bias using a linear statistical model, the spatial and temporal structures of the model-simulated MJO episodes are much improved, and what remains of the biases is strongly correlated with biases in saturation fraction. The excess precipitation bias during the suppressed phase of the MJO episodes is accompanied by excessive column-integrated radiative forcing and surface evaporation. A large portion of the bias in evaporation is related to biases in wind speed, which are correlated with those of precipitation. These findings suggest that the precipitation bias sustains itself at least partly by cloud radiative feedbacks and convection–surface wind interactions.

Projections of building electricity consumption are used in multiple fields and for a variety of purposes, from energy utility investment decisions to global climate assessments. Existing approaches to modeling building electricity consumption span a range of structural methodologies, spatial resolutions, and temporal scales, potentially leading to divergent projections. This paper compares how two models with different structures and resolutions respond to a common set of population and climate drivers in the western U.S. The BEND model simulates hourly residential and commercial building electricity consumption at the county scale by weighting the results of simulations of thousands of representative buildings. In contrast, the projected electricity demand in GCAM-USA is determined for each state, year, sector (residential/commercial), and service (e.g., heating, cooling, and others), based on population, income, technology, energy prices, and average annual climate. This paper aggregates the two models’ results to a common resolution: annual residential and commercial building electricity consumption by state. Both models show similar responses to future projected population change and climate change, with population change having the larger impact between the two. Differences are primarily due to how the models capture changes in the aggregate energy efficiency of the building stock as it evolves over time.

Density currents (i.e., cold pools or outflows) beneath marine stratocumulus clouds are characterized using 30 days of ship-based observations obtained during the 2008 Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) in the southeast Pacific. An air density increase criterion applied to the Improved Meteorological (IMET) sensor data identified 71 density current front, core (peak density), and tail (dissipating) zones. The similarity in speeds of the mean density current propagation speed (1.8 m s−1) and the mean cloud-level advection relative to the surface layer wind (1.9 m s−1) allowed drizzle cells to deposit elongated density currents in their wakes. Scanning Doppler lidar captured prefrontal updrafts with a mean intensity of 0.91 m s−1 and an average vertical extent of 800 m. Updrafts were often surmounted by low-lying shelf clouds not connected to the overlying stratocumulus cloud. The observed density currents were 5–10 times thinner and weaker than typical continental thunderstorm cold pools. Nearly 90% of density currents were identified when C-band radar estimated areal average rain rates exceeded 1 mm day−1 over a 30-km diameter. Rather than peaking when rain rates were highest overnight, density current occurrence peaks between 0600 and 0800 local solar time when enhanced local drizzle co-occurred with shallow subcloud dry and stable layers. The dry layers may have contributed to density current formation by enhancing subcloud evaporation of drizzle. Density currents preferentially occurred in a large region of predominantly open cells but also occurred in regions of closed cells.

Cloud radiative effects are examined using long-term datasets collected at the U.S. Department of Energy’s three Atmospheric Radiation Measurement Program Climate Research Facilities in the tropical western Pacific Ocean. The surface radiation budget, cloud populations, and cloud radiative effects are quantified by partitioning the data by cloud type, time of day, and large-scale modes of variability such as El Niño–Southern Oscillation (ENSO) phase and wet/dry seasons at Darwin, Australia. The novel aspect of this analysis is the breakdown of aggregate cloud radiative effects by cloud type across the diurnal cycle. The Nauru Island (Republic of Nauru) cloud populations and subsequently the surface radiation budget are strongly impacted by ENSO variability, whereas the cloud populations over Manus Island (Papua New Guinea) shift only slightly in response to changes in ENSO phase. The Darwin site exhibits large seasonal monsoon-related variations. When present, deeper convective clouds have a strong influence on the amount of radiation that reaches the surface. Their limited frequency reduces their aggregate radiative impact, however. The largest source of shortwave cloud radiative effects at all three sites comes from low clouds. The observations are used to demonstrate that potential model biases in the amplitude of the diurnal cycle and mean cloud frequency would lead to larger errors in the surface energy budget when compared with biases in the timing of the diurnal cycle of cloud frequency. These results provide solid benchmarks to evaluate model simulations of cloud radiative effects in the tropics.

This paper presents an analysis of subtropical marine stratocumulus cloud fraction variability using a 30-min and 3° × 3° cloud fraction dataset from 2003 to 2010. Each of the three subtropical marine stratocumulus regions has distinct diurnal characteristics, but the southeast (SE) Pacific and SE Atlantic are more similar to each other than to the northeast (NE) Pacific. The amplitude and season-to-season diurnal cycle variations are larger in the Southern Hemisphere regions than in the NE Pacific. Net overnight changes in cloud fraction on 3° × 3° scales are either positive or neutral >77% of the time in the NE Pacific and >88% of the time in the SE Pacific and SE Atlantic. Cloud fraction often increases to 100% by dawn when cloud fraction at dusk is >30%. In the SE Pacific and SE Atlantic, a typical decrease in cloud area (median ≤ −5.7 × 105 km2) during the day is equivalent to 25% or more of the annual-mean cloud deck area. Time series for 3° × 3° areas where cloud fraction was ≥90% sometime overnight and

This paper investigates the mechanisms of convective cloud organization by precipitation-driven cold pools over the warm tropical Indian Ocean during the 2011 Atmospheric Radiation Measurement (ARM) Madden-Julian Oscillation (MJO) Investigation Experiment / Dynamics of the MJO (AMIE/DYNAMO) field campaign. A high-resolution regional model simulation is performed using the Weather Research and Forecasting model during the transition from suppressed to active phases of the November 2011 MJO. The simulated cold pool lifetimes, spatial extent and thermodynamic properties agree well with the radar and ship-borne observations from the field campaign. The thermodynamic and dynamic structures of the outflow boundaries of isolated and intersecting cold pools in the simulation and the associated secondary cloud populations are examined. Intersecting cold pools last more than twice as long, are twice as large, 41% more intense (measured by buoyancy), and 62% deeper than isolated cold pools. Consequently, intersecting cold pools trigger 73% more convective clouds than isolated ones. This is possibly due to stronger outflows that enhance secondary updraft velocities by up to 45%. However, cold pool-triggered convective clouds grow into deep convection not because of the stronger secondary updrafts at cloud base, but rather due to closer spacing (aggregation) between clouds and larger cloud clusters more » that formed along the cold pool boundaries when they intersect. The close spacing of large clouds moistens the local environment and reduces entrainment drying, allowing the clouds to further develop into deep convection. Implications to the design of future convective parameterization with cold pool-modulated entrainment rates are discussed. « less

The spatial patterns of subtropical marine stratocumulus cloud fraction variability on diurnal time scales are examined using high-temporal-resolution cloud masks that are based on 30-min, 4 km × 4 km geosynchronous infrared data for 2003–10. This dataset permits comparison of the characteristics of variability in low cloud fraction among the three subtropical marine stratocumulus regions in the northeastern (NE) Pacific, southeastern (SE) Pacific, and SE Atlantic Oceans. In all three regions, the largest diurnal cycles and earliest time of cloud breakup occur on the edges of the cloud field where cloud fractions are generally lower. The rate at which the cloud breaks up during the day is tied to the starting cloud fraction at dawn, which determines the amount of longwave cooling that is initially available to offset shortwave radiative fluxes during the day. The maximum rate of cloud breakup occurs near 1200 LT. Cloud fraction begins to increase by 1600 LT (before the sun sets) and reaches its maximum value just before dawn. The diurnal-cycle characteristics of the SE Pacific and SE Atlantic marine stratocumulus cloud decks are more similar to each other than to those in the NE Pacific. The NE Pacific cloud deck has a smaller-amplitude diurnal cycle, slower rates of cloud breakup during the day for a given cloud fraction at dawn, and a higher probability of cloud breakup overnight.

Routine cloud, precipitation and thermodynamic observations collected by the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) and Aerial Facility (AAF) during the 2-year US Department of Energy (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 regimes 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.

The diurnal cycle of marine stratocumulus in cloud-topped boundary layers is examined using ship-based meteorological data obtained during the 2008 Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx). The high temporal and spatial continuity of the ship data, as well as the 31-day sample size, allows the diurnal transition in degree of coupling of the stratocumulus-topped boundary layer to be resolved. The amplitude of diurnal variation was comparable to the magnitude of longitudinal differences between regions east and west of 80°W for most of the cloud, surface, and precipitation variables examined. The diurnal cycle of precipitation is examined in terms of areal coverage, number of drizzle cells, and estimated rain rate. East of 80°W, the drizzle cell frequency and drizzle area peaks just prior to sunrise. West of 80°W, total drizzle area peaks at 0300 local solar time (LST), 2–3 h before sunrise. Peak drizzle cell frequency is 3 times higher west of 80°W compared to east of 80°W. The waning of drizzle several hours prior to the ramp up of shortwave fluxes may be related to the higher peak drizzle frequencies in the west. The ensemble effect of localized subcloud evaporation of precipitation may make drizzle a self-limiting process where the areal density of drizzle cells is sufficiently high. The daytime reduction in vertical velocity variance in a less coupled boundary layer is accompanied by enhanced stratification of potential temperature and a buildup of moisture near the surface.

Widespread stratocumulus clouds were observed on nine transects from seven research cruises to the southeastern tropical Pacific Ocean along 20°S, 75°–85°W in October–November of 2001–08. The nine transects sample a unique combination of synoptic and interannual variability affecting the clouds; their ensemble diagnoses longitude–vertical sections of the atmosphere, diurnal cycles of cloud properties and drizzle statistics, and the effect of stratocumulus clouds on surface radiation. Mean cloud fraction was 0.88, and 67% of 10-min overhead cloud fraction observations were overcast. Clouds cleared in the afternoon [1500 local time (LT)] to a minimum of fraction of 0.7. Precipitation radar found strong drizzle with reflectivity above 40 dBZ. Cloud-base (CB) heights rise with longitude from 1.0 km at 75°W to 1.2 km at 85°W in the mean, but the slope varies from cruise to cruise. CB–lifting condensation level (LCL) displacement, a measure of decoupling, increases westward. At night CB–LCL is 0–200 m and increases 400 m from dawn to 1600 LT, before collapsing in the evening. Despite zonal gradients in boundary layer and cloud vertical structure, surface radiation and cloud radiative forcing are relatively uniform in longitude. When present, clouds reduce solar radiation by 160 W m−2 and radiate 70 W m−2 more downward longwave radiation than clear skies. Coupled Model Intercomparison Project phase 3 (CMIP3) simulations of the climate of the twentieth century show 40 ± 20 W m−2 too little net cloud radiative cooling at the surface. Simulated clouds have correct radiative forcing when present, but models have ~50% too few clouds.

We discuss the role of atmospheric gravity waves in modulating cloud radiative and dynamical properties over the southeast Pacific. Satellite imagery and satellite-retrieved cloud properties during October 2008 illustrate three distinct episodes of horizontal propagation of gravity wave trains across the large-scale stratocumulus (Sc) cloud deck capping the local marine boundary layer. In one period, 79 October 2008, the waves modulated cloud-top-height by up to 400 m peak-to-trough, propagating perpendicular to the synoptic boundary layer flow with phase speed 15.3 m s1, period approximate to 1 h and horizontal wavelength 55 km. The gravity waves were observed to be non-dispersive. These waves were first evident in the cloud deck near 30 degrees S, 85 degrees W during a 24 h period beginning at midday on 7 October 2008, and propagated northeastward toward the Peruvian coast for the following 48 h. During this time they induced both reversible and non-reversible changes in cloud-radiative and cloud-dynamic properties, such that areas of clear sky developed in the troughs of passing wave-fronts. These pockets of open cells persisted long after the passage of the gravity waves, advecting northwestward with the background wind. Using the analysis fields of the European Centre for Medium-Range Weather Forecasts in conjunction with infrared and microwave satellite imagery, we show that these gravity waves emerged from a disturbed subtropical jet stream. The radiant of the waves was coincident in all cases with centres of large negative residuals in nonlinear balance, suggesting that geostrophic readjustment of sharply divergent flows associated with the disturbed jet provided a source for the wave energy. Conversely, gravity waves were not observed in more quiescent jet conditions. This case study highlights the important and irreversible effects that gravity waves propagating in the troposphere can have on cloud radiative properties (and hence surface radiation budgets) over a very wide area. It also highlights the importance of synoptic influence on Sc-covered marine boundary layers. Copyright (c) 2012 Royal Meteorological Society

The TRMM Precipitation Radar is used to construct a high resolution (0.05° × 0.05°) climatology of rainfall over the latitude band extending to about 36° North and South. This study describes climatological patterns of rainfall frequency and intensity at high spatial resolution, with special focus on the seasonal and diurnal cycles in the frequency of rainfall events. We use this Tropics-wide dataset to highlight small-scale precipitation features that are too fine to be captured by the most widely used satellite-based rainfall datasets. The results shed light on the roles of changes in the wind direction, the land-sea thermal contrast, small-scale variations in sea surface temperature, and orography in shaping the seasonal and diurnal cycles of rainfall. In some regions of the tropics, diurnally locked local circulations are largely responsible for sharp gradients in the spatial distribution of seasonal mean precipitation. In other regions, we show that climatological rainfall frequency changes very sharply at coastlines, even though rainfall in these regions is expected to be controlled by relatively large scale weather systems.

Regional convection-permitting model simulations of cloud populations observed during the 2011 Atmospheric Radiation Measurement (ARM) Madden-Julian Oscillation Investigation Experiment/Dynamics of the Madden-Julian Oscillation Experiment (AMIE/DYNAMO) field campaign are evaluated against ground-based radar and ship-based observations. Sensitivity of model simulated reflectivity, surface rain rate, and cold pool statistics to variations of raindrop breakup/self-collection parameters in four state-of-the-art two-moment bulk microphysics schemes in the Weather Research and Forecasting (WRF) model is examined. The model simulations generally overestimate reflectivity from large and deep convective cells, and underestimate stratiform rain and the frequency of cold pools. In the sensitivity experiments, introduction of more aggressive raindrop breakup or decreasing the self-collection efficiency increases the cold pool occurrence frequency in all of the simulations, and slightly reduces the reflectivity and precipitation statistics bias in some schemes but has little effect on the overall mean surface precipitation. Both the radar observations and model simulations of cloud populations show an approximate power law relationship between convective echo-top height and equivalent convective cell radius.

[1] A high-resolution rainfall climatology based on observations from the Tropical Rainfall Measuring Mission's Precipitation Radar (PR) instrument is used to evaluate the influence of small tropical islands on climatological rainfall. Islands with areas between one hundred and several thousand km2 are considered in both the Indo-Pacific Maritime Continent and Caribbean regions. Annual mean climatological (1997–2007) rainfall over each island is compared with that over the surrounding ocean region, and the difference is expressed as a percentage. In addition to total rainfall, rain frequency and intensity are also analyzed. Results are stratified into two 12 h halves of the diurnal cycle as well as eight 3 h periods, and also by a measure of each island's topographic relief. In both regions, there is a clear difference between larger islands (areas of a few hundred km2 or greater) and smaller ones. Both rain frequency and total rainfall are significantly enhanced over larger islands compared to the surrounding ocean. For smaller islands the enhancement is either negligibly small, statistically insignificant, or, in the case of Caribbean rain frequency, negative. The enhancement in total rainfall over larger islands is partly attributable to greater frequency and partly to greater intensity. A diurnal cycle in island enhancement is evident in frequency but not intensity, except over small Caribbean islands where the converse is true. For the larger islands, higher orography is associated with greater rainfall enhancements. The orographic effect is larger (percentagewise) in the Caribbean than in the Maritime Continent. Orographic precipitation enhancement manifests more strongly as increased frequency of precipitation rather than increased intensity and is present at night as well as during the day. The lack of a clear diurnal cycle in orographic enhancement suggests that much of the orographic rainfall enhancement is attributable to mechanically forced upslope flow rather than elevated surface heating.

[1] In the paper “Rain on small tropical islands” by Sobel et al. (Journal of Geophysical Research, 116, D08102, doi:10.1029/2010JD014695, 2011), there were errors in the calculations, tables, and figures. M. Biasutti, who helped find and correct the errors, has been added as an author to this correction. Below find an explanation of the errors and the revised tables, which showed summary statistics. Revised figures are included as auxiliary material since the differences between the original and corrected figures are not discernible without careful inspection. [2] In Sobel et al. [2011], the authors used a climatology of observations from the Precipitation Radar (PR) aboard NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite to study precipitation over small tropical islands. However, the authors discovered an error in the initial processing of the PR data. All the calculations in the paper have been redone after removing the negative values, described below, and figures and tables containing errors have been adjusted. [3] When a pixel is within the swath of observation but good data are not obtained, the rain rate for that pixel (otherwise given in mmh!1) is set to a missing value of !99. A small fraction of the data—on average about 4% of the points with nonzero rain rates—take on this value, even though the “rain certain” flag on these data is set to 1, indicating observation of rain. These data were bad, and the nonphysical negative values were incorporated into the computations. Computing climatological rain rates at each grid point and excluding resulting negative values described below, eliminated some, but not all, of the problem. [4] The error can be described as a modest negative bias in the spatial mean climatological rain rate (of order 10% in rainy regions) plus a spatially variable, apparently largely random error. The error in intensity (conditional rain rate) was somewhat larger than that in total rain rate, while the climatological frequency was affected negligibly. [5] The erroneous values in the climatological grid point values appear to have occurred largely randomly in space, without any strong bias toward either islands or ocean. In the difference statistics, they appear to have been a source of approximately random error. Since the number of points involved in the computation of each statistic increases with island size, the influence of this error decreases with island size. [6] For rain frequency, the difference is essentially negligible for the entire range of island sizes. In the corrected scatter plots of intensity, the scatter is reduced near the y axis (islands whose area or orographic height are small, with the latter measured by the 75th percentile of altitude at 0.05" resolution). The differences in all statistics are for the most part small in the Pacific, while the intensity changes in particular are greater in the Caribbean. [7] To the extent that eliminating the errormakes any difference in the results, they become overall more statistically significant, without changing our overall interpretation or conclusions. [8] The enhancements of precipitation intensity and total precipitation over islands which are small in area and low relief were previously statistically insignificant but are now significant. [9] Our original study was intended to address how large (in either area or elevation) an island need be to have a detectable influence on tropical precipitation. After correction of the error, we cannot rule out that islands smaller than 315 km and relief lower than 50m as the 75th percentile of elevation— our arbitrary cutoffs for the definition of “small” and “flat”—are still large enough to have a detectable influence.