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Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.

While the Madden–Julian Oscillation (MJO) has been shown to affect tropical cyclones (TCs) worldwide through its modulation of large-scale circulation in the atmosphere, little or no role for the ocean has been identified to date in this influence of MJO on TCs. Using observations and numerical model simulations, we demonstrate that MJO events substantially impact TCs over the Maritime Continent (MC) region through an oceanic pathway. While propagating across the MC region, MJO events cause significant sea surface cooling with an area-averaged value of about 0.35 ± 0.12 °C. Hence, TCs over the MC region immediately following the passage of MJO events encounter considerably cooler sea surface temperatures. Consequently, the enthalpy fluxes under the storms are reduced and the intensification rates decrease by more than 50% on average. These results highlight an important role played by the ocean in facilitating MJO-induced sub-seasonal variability in TC activity over the MC region.

The frequency of North Pacific atmospheric rivers (ARs) affects water supply and flood risk over western North America. Thus, understanding factors that affect the variability of landfalling AR frequency is of scientific and societal importance. This study aims at identifying the sources of the moisture for North Pacific ARs and assessing how different modes of variability modulate these sources. To this end, the sources and variability of the background divergent component of the integrated moisture flux (DIVT) in ARs are identified using MERRA reanalysis. It is shown that in the boreal winter, this background DIVT in ARs is related to the outflow from the subsidence over the subtropics which transports moisture northward, while in summer it is related to the Asian monsoon and it transports moisture northwestward. This leads to a seasonal northwest/southeast movement of the AR frequency climatology. At the intra-seasonal scale, propagation of the Madden-Julian Oscillation introduces an anti-clockwise rotation of the background DIVT, with northward transport in phases 1 and 2, westward in 3 and 4, southward in 5 and 6 and eastward in 7 and 8, making landfall over the west coast of North America most likely during the last two phases. Similarly, El Niño Southern Oscillation variability also affects the frequency of ARs through modulation of the westerly background DIVT, favoring landfall over the US west coast during strong El Niño phases. It is shown that in general the likelihood of AR landfall over the western US is correlated with the zonal background DIVT over northeastern Pacific.

It is well documented that over the tropical oceans, column-integrated precipitable water (pw) and precipitation (P) have a nonlinear relationship. In this study moisture budget analysis is used to examine this P–pw relationship in a normalized precipitable water framework. It is shown that the parameters of the nonlinear relationship depend on the vertical structure of moisture convergence. Specifically, the precipitable water values at which precipitation is balanced independently by evaporation versus by moisture convergence define a critical normalized precipitable water, pwnc. This is a measure of convective inhibition that separates tropical precipitation into two regimes: a local evaporation-controlled regime with widespread drizzle and a precipitable water–controlled regime. Most of the 17 CMIP6 historical simulations examined here have higher pwnc compared to ERA5, and more frequently they operate in the drizzle regime. When compared to observations, they overestimate precipitation over the high-evaporation oceanic regions off the equator, thereby producing a “double ITCZ” feature, while underestimating precipitation over the large tropical landmasses and over the climatologically moist oceanic regions near the equator. The responses to warming under the SSP585 scenario are also examined using the normalized precipitable water framework. It is shown that the critical normalized precipitable water value at which evaporation versus moisture convergence balance precipitation decreases as a result of the competing dynamic and thermodynamic responses to warming, resulting in an increase in drizzle and total precipitation. Statistically significant historical trends corresponding to the thermodynamic and dynamic changes are detected in ERA5 and in low-intensity drizzle precipitation in the PERSIANN precipitation dataset.

A record-breaking extreme rainfall event, the highest amount recorded since 1866, hit Indonesia's capital, Jakarta, in early January 2020. This torrential rainfall was mainly caused by the convergence of moisture-rich air due to an unusual blocking of cross-equatorial northerly surge over Northwest Java by the southerly winds induced by a cyclonic flow over the Indian Ocean. This condition caused a local increase in the amount of water vapor over Jakarta that eventually led to the formation of clouds and heavy precipitation. In addition, the concurrent occurrences of convectively active phases of equatorial waves (Kelvin, TD-type, and eastward propagating inertia-gravity waves) and Madden-Julian Oscillation during the event also partly contributed to the enhanced local moisture over the region by increasing low-level moisture flux convergence. Together, these large-scale dynamical drivers provided a convective environment that fostered the development of a massive rain-producing mesoscale convective system and, consequently, extreme rainfall over the region.

Realistic simulation of the Earth’s mean state climate remains a major challenge and yet it is crucial for predicting the climate system in transition. Deficiencies in models’ process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections as well as incorrect attribution of the physical mechanisms governing the past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and longstanding biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when running as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback as well as the fidelity of the simulated base climate state are important for constraining the climate in the past and future.

Author(s): Balaguru, K; Patricola, CM; Hagos, SM; Leung, LR; Dong, L | Abstract: While El Nino–Southern Oscillation (ENSO) influences eastern North Pacific (ENP) tropical cyclones (TCs) through a variety of atmospheric processes when examined concurrently, ocean pathways dominate at longer lead times. The eastward displacement of the warm pool during an El Nino, which carries warm water into the ENP basin, is the primary oceanic mechanism. Despite this, the question of whether an accurate knowledge of preseason ENSO conditions enhances predictability of ENP TCs has not been addressed specifically. In this study, we show that relative to traditional indices of ENSO, the ENSO Longitude Index (ELI) captures changes in the location of deep convection and associated thermocline processes more accurately. Consequently, the ELI explains more variability in the upper-ocean heat content, and thus TC activity, at lead times of several months in the ENP basin. These results motivate the need to further explore the predictability of ENP TCs associated withnENSO.

In this study, we analyze the realism with which tropical cyclones (TCs) are simulated in the fully coupled low‐ and high‐resolution Energy Exascale Earth System Model (E3SM) version 1, with a focus on the latter. Compared to the low‐resolution (grid spacing of ∼1°), the representation of TCs improves considerably in the high‐resolution configuration (grid spacing of ∼0.25°). Significant improvements are found in the global TC frequency, TC lifetime maximum intensities, and the relative distribution of TCs among the different basins. However, at both resolutions, spurious TC activity is found in some basins, notably in the subtropical regions. Contrasting the simulated large‐scale TC environment with observations reveals that the model environment is unrealistically conducive for TC development in those regions. Further analysis indicates that these biases are likely related to those in thermodynamic potential intensity, caused by systematic SST biases, and vertical wind shear in the coupled model. TC‐ocean interaction is also examined in the high‐resolution configuration of the model. The salient features of the ocean's response to TC‐induced mixing and the ocean's impact on TC intensification are well‐reproduced. Finally, an evaluation of the influence of El Niño Southern Oscillation (ENSO) on TCs in the high‐resolution configuration of the model reveals that the ENSO‐TC relationship in the model has the right sign and is significant for the North Atlantic and Northwest Pacific, albeit weaker than in observations. In summary, the high‐resolution configuration of the E3SM model simulates TC activity reasonably and hence could be a useful tool for TC‐related research.

Traditional parameterizations of the interaction between convection and the environment have relied on an assumption that the slowly-varying large-scale environment is in statistical equilibrium with a large number of small and short-lived convective clouds. They fail to capture non-equilibrium transitions such as the diurnal cycle and theformation of meso-scale convective systems as well asobserved precipitation statisticsand extremes. Informed by analysis of radar observations, cloud-permitting model simulation, theory and machine learning, this work presents a new stochastic cloud population dynamics model for characterizing the interactions between convective and stratiform clouds,with the ultimate goal of informing the representation ofthese interactions in global climate models. 15 wet seasons of precipitating cloud observations by a C-band radar at Darwin, Australia are fed into a machine learning algorithm to obtain transition functions that close a set of coupled equation relating large-scale forcing, mass flux, the convective cell size distribution and the stratiform area. Under realistic large-scale forcing, the derived transition functions show that, on the one hand, interactions with stratiform clouds act to dampen the variability in the size and number of convective cells and therefore in the convective mass flux. On the other hand, for a given convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. The combination of these two factors gives rise to solutions with a number of convective cells embedded in a large stratiform area, reminiscent of mesoscale convective systems.

Regional climate simulations over the continental United States were conducted for the 2011 warm season using the Weather Research and Forecasting model at convection‐permitting resolution (4 km) with two commonly used microphysics parameterizations (Thompson and Morrison). Sensitivities of the simulated mesoscale convective system (MCS) properties and feedbacks to large‐scale environments are systematically examined against high‐resolution geostationary satellite and 3‐D mosaic radar observations. MCS precipitation including precipitation amount, diurnal cycle, and distribution of hourly precipitation intensity are reasonably captured by the two simulations despite significant differences in their simulated MCS properties. In general, the Thompson simulation produces better agreement with observations for MCS upper level cloud shield and precipitation area, convective feature horizontal and vertical extents, and partitioning between convective and stratiform precipitation. More importantly, Thompson simulates more stratiform rainfall, which agrees better with observations and results in top‐heavier heating profiles from robust MCSs compared to Morrison. A stronger dynamical feedback to the large‐scale environment is therefore seen in Thompson, wherein an enhanced mesoscale vortex behind the MCS strengthens the synoptic‐scale trough and promotes advection of cool and dry air into the rear of the MCS region. The latter prolongs the MCS lifetimes in the Thompson relative to the Morrison simulations. Hence, different treatment of cloud microphysics not only alters MCS convective‐scale dynamics but also has significant impacts on their macrophysical properties such as lifetime and precipitation. As long‐lived MCSs produced 2–3 times the amount of rainfall compared to short‐lived ones, cloud microphysics parameterizations have profound impact in simulating extreme precipitation and the hydrologic cycle.

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.

CMIP5 models exhibit a mean dry bias and a large inter-model spread in simulating South Asian monsoon precipitation but the origins of the bias and spread are not well understood. Using moisture and energy budget analysis that exploits the weak temperature gradients in the tropics, we derived a non-linear relationship between the normalized precipitation and normalized precipitable water that is similar to the non-linear relationship between precipitation and precipitable water found in previous observational studies. About half of the 21 models analyzed fall in the steep gradient of the non-linear relationship where small differences in the normalized precipitable water in the equatorial Indian Ocean (EIO) manifest in large differences in normalized precipitation in the region. Models with larger normalized precipitable water in the EIO during spring contribute disproportionately to the large inter-model spread and multi-model mean dry bias in monsoon precipitation through perturbations of the large-scale winds. Thus the intermodel spread in precipitable water over EIO leads to the dry bias in the multi-model mean South Asian monsoon precipitation. The models with high normalized precipitable water over EIO also project larger response to warming and dominate the inter-model spread in the multi-model projections of monsoon rainfall. Conversely, models on the flat side of the relationship between normalized precipitation and precipitable water are in better agreement with each other and with observations. On average these models project a smaller increase in the projected monsoon precipitation than that from multi-model mean. This study identified the normalized precipitable water over EIO, which is determined by the relationship between the profiles of convergence and moisture and therefore is an essential outcome of the treatment of convection, as a key metric for understanding model biases and differentiating model skill in simulating South Asian monsoon precipitation.

Sea surface temperature (SST) is one of the most important parameters for tropical cyclone (TC) intensification. Here, it is shown that the relationship between SST and TC intensification varies considerably from basin to basin, with SST explaining less than 4% of the variance in TC intensification rates in the Atlantic, 12% in the western North Pacific, and 23% in the eastern Pacific. Several factors are shown to be responsible for these interbasin differences. First, variability of SST along TCs’ tracks is lower in the Atlantic. This is due to smaller horizontal SST gradients in the Atlantic, compared to the Pacific, and stronger damping of prestorm SST’s contribution to TC intensification by the storm-induced cold SST wake in the Atlantic. The damping occurs because SST tends to vary in phase with TC-induced SST cooling: in the Gulf of Mexico and northwestern Atlantic, where SSTs are highest, TCs tend to be strongest and their translations slowest, resulting in the strongest storm-induced cooling. The tendency for TCs to be more intense over the warmest SST in the Atlantic also limits the usefulness of SST as a predictor since stronger storms are less likely to experience intensification. Finally, SST tends to vary out of phase with vertical wind shear and outflow temperature in the western Pacific. This strengthens the relationship between SST and TC intensification more in the western Pacific than in the eastern Pacific or Atlantic. Combined, these factors explain why prestorm SST is such a poor predictor of TC intensification in the Atlantic, compared to the eastern and western North Pacific.

Sea surface temperature (SST) and tropical cyclone heat potential (TCHP) are metrics used to incorporate the ocean’s influence on hurricane intensification into the National Hurricane Center’s Statistical Hurricane Intensity Prediction Scheme (SHIPS). While both SST and TCHP serve as useful measures of the upper-ocean heat content, they do not accurately represent ocean stratification effects. Here, it is shown that replacing SST within the SHIPS framework with a dynamic temperature Tdy, which accounts for the oceanic negative feedback to the hurricane’s intensity arising from storm-induced vertical mixing and sea surface cooling, improves the model performance. While the model with SST and TCHP explains about 41% of the variance in 36-h intensity changes, replacing SST with Tdy increases the variance explained to nearly 44%. These results suggest that representation of the oceanic feedback, even through relatively simple formulations such as Tdy, may improve the performance of statistical hurricane intensity prediction models such as SHIPS.

This study explores the impact of a nonhydrostatic dynamical core in high-resolution regional climate simulations using an aquaplanet framework. The Weather Research and Forecasting (WRF) model is used to conduct simulations with both hydrostatic (H) and nonhydrostatic (NH) solvers at horizontal grid spacings (Δx) of 36, 12, and 4 km. The differences between the H and NH simulated precipitation (ΔP) are notable even at Δx = 12 km in the intertropical convergence zone and the transition region to the drier subtropics. At gray zone grid spacing (12 km and 4 km) over the tropics, ΔP is sensitive to whether a cumulus parameterization scheme is used or not. With an idealized Witch of Agnesi land mountain, differences in the precipitation and circulation (vertical velocity) between the H and NH simulations are significant even at Δx = 36 km in the tropics due largely to the strong feedbacks related to moist processes. The differences increase as the model grid spacing and mountain half width (a) are reduced, accompanied by a shift toward a more nonhydrostatic flow regime at a = 24 km. Latent heat release drastically enhances the differences between the NH and H simulations and extends the effect of nonhydrostatic dynamics to a broader region over the mountain and downstream over the tropics. Overall, differences exist between H and NH simulations even at resolutions between 12 and 36 km, but the differences are sensitive to the representations of moist physics and other features such as horizontal diffusion used in the WRF model.

The nonhydrostatic Model for Prediction Across Scales (NH‐MPAS) provides a global framework to achieve high resolution using regional mesh refinement. Previous studies using the hydrostatic version of MPAS (H‐MPAS) with the physics parameterizations of Community Atmosphere Model version 4 (CAM4) found notable resolution‐dependent behaviors. This study revisits the resolution sensitivity using NH‐MPAS with both CAM4 and CAM5 physics. A series of aqua‐planet simulations at global quasiuniform resolutions and global variable resolution with a regional mesh refinement over the tropics are analyzed, with a primary focus on the distinct characteristics of NH‐MPAS in simulating precipitation, clouds, and large‐scale circulation features compared to H‐MPAS‐CAM4. The resolution sensitivity of total precipitation and column integrated moisture in NH‐MPAS is smaller than that in H‐MPAS‐CAM4. This contributes importantly to the reduced resolution sensitivity of large‐scale circulation features such as the intertropical convergence zone and Hadley circulation in NH‐MPAS compared to H‐MPAS. In addition, NH‐MPAS shows almost no resolution sensitivity in the simulated westerly jet, in contrast to the obvious poleward shift in H‐MPAS with increasing resolution, which is partly explained by differences in the hyperdiffusion coefficients used in the two models that influence wave activity. With the reduced resolution sensitivity, simulations in the refined region of the NH‐MPAS global variable resolution configuration exhibit zonally symmetric features that are more comparable to the quasiuniform high‐resolution simulations than those from H‐MPAS that displays zonal asymmetry in simulations inside the refined region. Overall, NH‐MPAS with CAM5 physics shows less resolution sensitivity compared to CAM4.

Impacts of regional grid refinement on large-scale circulations (“upscale effects”) were detected in a previous study that used the Model for Prediction Across Scales – Atmosphere coupled to the physics parameterizations of the Community Atmosphere Model version 4. The strongest upscale effect was identified in the Southern Hemisphere jet during austral winter. This study examines the detailed underlying processes by comparing two simulations at quasi-uniform resolutions (30 and 120 km) to three variable-resolution simulations in which the horizontal grids are regionally refined to 30 km in North America, South America, or Asia from 120 km elsewhere. In all the variable-resolution simulations, precipitation increases in convective areas inside the high-resolution domains, as in the reference quasi-uniform high-resolution simulation. With grid refinement encompassing the tropical Americas, the increased condensational heating expands the local divergent circulations (Hadley cell) meridionally such that their descending branch is shifted poleward, which also pushes the baroclinically unstable regions, momentum flux convergence, and the eddy-driven jet poleward. This teleconnection pathway is not found in the reference high-resolution simulation due to a strong resolution sensitivity of cloud radiative forcing that dominates the aforementioned teleconnection signals. The regional refinement over Asia enhances Rossby wave sources and strengthens the upper-level southerly flow, both facilitating the cross-equatorial propagation of stationary waves. Evidence indicates that this teleconnection pathway is also found in the reference high-resolution simulation. The result underlines the intricate diagnoses needed to understand the upscale effects in global variable-resolution simulations, with implications for science investigations using the computationally efficient modeling framework. This article is protected by copyright. All rights reserved.

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.

Accuracy of turbulence parameterization in representing planetary boundary layer (PBL) processes and surface–atmosphere interactions in climate models is critical for predicting the initiation and development of clouds. This study 1) evaluates WRF Model–simulated spatial patterns and vertical profiles of atmospheric variables at various spatial resolutions and with different PBL, surface layer, and shallow convection schemes against measurements; 2) identifies model biases by examining the moisture tendency terms contributed by PBL and convection processes through nudging experiments; and 3) investigates the main causes of these biases by analyzing the dependence of modeled surface fluxes on PBL and surface layer schemes over the tropical ocean. The results show that PBL and surface parameterizations have surprisingly large impacts on precipitation and surface moisture fluxes over tropical oceans. All of the parameterizations tested tend to overpredict moisture in the PBL and free atmosphere and consequently result in larger moist static energy and precipitation. Moisture nudging tends to suppress the initiation of convection and reduces the excess precipitation. The reduction in precipitation bias in turn reduces the surface wind and latent heat (LH) flux biases, which suggests the positive feedback between precipitation and surface fluxes is responsible, at least in part, for the model drifts. The updated Kain–Fritsch cumulus potential (KF-CuP) shallow convection scheme tends to suppress the deep convection, consequently decreasing precipitation. The Eta Model surface layer scheme predicts more reasonable LH fluxes and LH–wind speed relationship than those for the MM5 scheme. The results help us identify sources of biases of current parameterization schemes in reproducing PBL processes, the initiation of convection, and intraseasonal variability of precipitation.

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.