The Plant-Craig (PC) stochastic convective parameterization scheme is modified by linking the stochastic generation of convective clouds to the change of large-scale vertical pressure velocity at 500 hPa with time so as to better account for the relationship between convection and the large-scale environment. Three experiments using the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 5 (CAM5), are conducted: one with the default Zhang-McFarlane deterministic convective scheme, another with the original PC stochastic scheme, and a third with the modified PC stochastic scheme. Evaluation is focused on the simulation of the Indian summer monsoon (ISM), which is a long-standing challenge for all current global circulation models. Results show that the modified stochastic scheme better represents the annual cycle of the climatological mean rainfall over central India and the mean onset date of ISM compared to other simulations. Also, for the simulations of ISM intraseasonal variability for quasi-biweekly and 30-60-day modes, the modified stochastic parameterization produces more realistic propagation and magnitude, especially for the observed northeastward movement of the 30-60-day mode, for which the other two simulations show the propagation in the opposite direction. Causes are investigated through a moisture budget analysis. Compared to the other two simulations, the modified stochastic scheme with an appropriate representation of convection better represents the patterns and amplitudes of large-scale dynamical convergence and moisture advection and thus corrects the monsoon cycle associated with their covariation during the peaks and troughs of intraseasonal oscillation.

As the resolution of global climate model increases, whether trigger functions in current convective parameterization schemes still work remains unknown. In this study, the scale dependence of undilute and dilute dCAPE, Bechtold, and heated condensation framework (HCF) triggers is evaluated using the cloud-resolving model (CRM) data. It is found that all these trigger functions are scale dependent, especially for dCAPE-type triggers, with skill scores dropping from similar to 0.6 at the lower resolutions (128, 64, and 32 km) to only similar to 0.1 at 4 km. The average convection frequency decreases from 14.1% at 128 km to 2.3% at 4 km in the CRM data, but it increases rapidly in the dCAPE-type triggers and is almost unchanged in the Bechtold and HCF triggers across resolutions, all leading to large overpredictions at higher resolutions. In the dCAPE-type triggers, the increased frequency is due to the increased rate of dCAPE greater than the threshold (65 J kg(-1) h(-1)) at higher resolutions. The box-and-whisker plots show that the main body of dCAPE in the correct prediction and overprediction can be separated from each other in most resolutions. Moreover, the underprediction is found to be corresponding to the decaying phase of convection. Hence, two modifications are proposed to improve the scale dependence of the undilute dCAPE trigger: 1) increasing the dCAPE threshold and 2) considering convection history, which checks whether there is convection prior to the current time. With these modifications, the skill at 16 km, 8 km, and 4 km can be increased from 0.50, 0.27, and 0.15 to 0.70, 0.61, and 0.53, respectively.

We use 3D cloud-resolving model (CRM) simulations of two mesoscale convective systems at midlatitudes and a simple statistical ensemble method to diagnose the scale dependency of convective momentum transport (CMT) and CMT-related properties and evaluate a parameterization scheme for the convection-induced pressure gradient (CIPG) developed by Gregory et al. Gregory et al. relate CIPG to a constant coefficient multiplied by mass flux and vertical mean wind shear. CRM results show that mass fluxes and CMT exhibit strong scale dependency in temporal evolution and vertical structure. The upgradient-downgradient CMT characteristics for updrafts are generally similar between small and large grid spacings, which is consistent with previous understanding, but they can be different for downdrafts across wide-ranging grid spacings. For the small to medium grid spacings (4-64 km), Gregory et al. reproduce some aspects of CIPG scale dependency except for underestimating the variations of CIPG as grid spacing decreases. However, for large grid spacings (128-512 km), Gregory et al. might even less adequately parameterize CIPG because it omits the contribution from either the nonlinear-shear or the buoyancy forcings. Further diagnosis of CRM results suggests that inclusion of nonlinear-shear forcing in Gregory et al. is needed for the large grid spacings. For the small to median grid spacings, a modified Gregory et al. with the three-updraft approach help better capture the variations of CIPG as grid spacing decreases compared to the single updraft approach. Further, the optimal coefficients used in Gregory et al. seem insensitive to grid spacings, but they might be different for updrafts and downdrafts, for different MCS types, and for zonal and meridional components.

Using 4 years of CloudSat data, the simulation of tropical convective cloud-top heights (CCTH) above 6 km simulated by the convection scheme in the Community Atmosphere Model, version 5 (CAM5), is evaluated. Compared to CloudSat observations, CAM5 underestimates CCTH by more than 2 km on average. Further analysis of model results suggests that the dilute CAPE calculation, which has been incorporated into the convective parameterization since CAM4, is a main factor restricting CCTH to much lower levels. After removing this restriction, more convective clouds develop into higher altitudes, although convective clouds with tops above 12 km are still underestimated significantly. The environmental conditions under which convection develops in CAM5 are compared with CloudSat observations for convection with similar CCTHs. It is shown that the model atmosphere is much more unstable compared to CloudSat observations, and there is too much entrainment in CAM5. Since CCTHs are closely associated with cloud radiative forcing, the impacts of CCTH on model simulation are further investigated. Results show that the change of CCTH has important impacts on cloud radiative forcing and precipitation. With increased CCTHs, there is more cloud radiative forcing in tropical Africa and the eastern Pacific, but less cloud radiative forcing in the western Pacific. The contribution to total convective precipitation from convection with cloud tops above 9 km is also increased substantially.

As key variables in general circulation models, precipitation and moisture in four leading models from CMIP5 (phase 5 of the Coupled Model Intercomparison Project) are analyzed, with a focus on four tropical oceanic regions. It is found that precipitation in these models is overestimated in most areas. However, moisture bias has large intermodel differences. The model biases in precipitation and moisture are further examined in conjunction with large-scale circulation by regime-sorting analysis. Results show that all models consistently overestimate the frequency of occurrence of strong upward motion regimes and peak descending regimes of 500-hPa vertical velocity JCLI-D-17-0718.1 regime, models produce too much precipitation compared to observation and reanalysis. But for moisture, their biases differ from model to model and also from level to level. Furthermore, error causes are revealed through decomposing contribution biases into dynamic and thermodynamic components. For precipitation, the contribution errors in strong upward motion regimes are attributed to the overly frequent . In the weak upward motion regime, the biases in the dependence of precipitation on probability density function (PDF) make comparable contributions, but often of opposite signs. On the other hand, the biases in column-integrated water vapor contribution are mainly due to errors in the frequency of occurrence of , while thermodynamic components contribute little. These findings suggest that errors in the frequency of occurrence are a significant cause of biases in the precipitation and moisture simulation.

Precipitation and column water vapor data from 13 CMIP5 models and observational datasets are used to analyze atmospheric moisture recycling rate from 1988 to 2008. The comparisons between observations and model simulations suggest that most CMIP5 models capture two main characteristics of the recycling rate: 1) long-term decreasing trend of the global-average maritime recycling rate (atmospheric recycling rate over ocean within 608S-608N) and 2) dominant spatial patterns of the temporal variations of the recycling rate (i.e., increasing in the intertropical convergence zone and decreasing in subtropical regions). All models, except one, successfully simulate not only the long-term trend but also the interannual variability of column water vapor. The simulations of precipitation are relatively poor, especially over the relatively short time scales, which lead to the discrepancy of the recycling rate between observations and the CMIP5 models. Comparisons of spatial patterns also suggest that the CMIP5 models simulate column water vapor better than precipitation. The comparative studies indicate the scope of improvement in the simulations of precipitation, especially for the relatively short-time-scale variations, to better simulate the recycling rate of atmospheric moisture, an important indicator of climate change.

Local correlation between sea surface temperature (SST) and rainfall is weak or even negative in summer over the Indo-western Pacific warm pool, a fact often taken as indicative of weak ocean feedback on the atmosphere. An Atmospheric Model Intercomparison Project (AMIP) simulation forced by monthly varying SSTs derived from a parallel coupled general circulation model (CGCM) run is used to evaluate AMIP skills in simulating interannual variability of rainfall. Local correlation of rainfall variability between AMIP and CGCMsimulations is used as a direct metric of AMIP skill. This "perfect model'' approach sidesteps the issue of model biases that complicates the traditional skill metric based on the correlation between AMIP and observations. Despite weak local SST-rainfall correlation, the AMIP-CGCM rainfall correlation exceeds a 95% significance level over most of the Indo-western Pacific warm pool, indicating the importance of remote (e.g., El Nino in the equatorial Pacific) rather than local SST forcing. Indeed, the AMIP successfully reproduces large-scale modes of rainfall variability over the Indo-western Pacific warm pool. Compared to the northwest Pacific east of the Philippines, the AMIP-CGCMrainfall correlation is low from the Bay of Bengal through the South China Sea, limited by internal variability of the atmosphere that is damped in CGCM by negative feedback from the ocean. Implications for evaluating AMIP skill in simulating observations are discussed.

Several improvements are implemented in the Zhang-McFarlane (ZM) convection scheme to investigate the roles of convection parameterization in the formation of double intertropical convergence zone (ITCZ) bias in the NCAR CESM1.2.1. It is shown that the prominent double ITCZ biases of precipitation, sea surface temperature (SST), and wind stress in the standard CESM1.2.1 are largely eliminated in all seasons with the use of these improvements in convection scheme. This study for the first time demonstrates that the modifications of convection scheme can eliminate the double ITCZ biases in all seasons, including boreal winter and spring. Further analysis shows that the elimination of the double ITCZ bias is achieved not by improving other possible contributors, such as stratus cloud bias off the west coast of South America and cloud/radiation biases over the Southern Ocean, but by modifying the convection scheme itself. This study demonstrates that convection scheme is the primary contributor to the double ITCZ bias in the CESM1.2.1, and provides a possible solution to the long-standing double ITCZ problem. The atmospheric model simulations forced by observed SST show that the original ZM convection scheme tends to produce double ITCZ bias in high SST scenario, while the modified convection scheme does not. The impact of changes in each core component of convection scheme on the double ITCZ bias in atmospheric model is identified and further investigated.

This study investigates the influence of entrainment rate (lambda) on relative dispersion (epsilon) of cloud droplet size distributions (CDSD) in the 99 growing precipitating deep convective clouds during TOGA-COARE. The results show that entrainment suppresses epsilon, which is opposite to the traditional understanding that entrainment-mixing broadens CDSD. To examine how the relationship between epsilon and lambda is affected by droplets with different sizes, CDSDs are divided into three portions with droplet radius < 3.75 mu m (N-1), radius in the range of 3.75-12.75 mu m (N-2) and 12.75-23.25 mu m (N-3), respectively. The results indicate that although the droplet concentration at different sizes generally decrease simultaneously as lambda increases, the variation of standard deviation (sigma) depends mainly on N-3, while the mean radius (r(m)) decreases with decreasing N-3, but increases with decreasing N-1. So the influence of entrainment on CDSD causes a more dramatical decrease in a than that in r(m), and further leads to the decrease of a as entrainment enhances. In addition, a conceptual model of CDSD evolution during entrainment mixing processes is developed to illustrate the possible scenarios entailing different relationships between a and lambda. The number concentration of small droplets and the degree of evaporation of small droplets are found to be key factors that shift the sign (i.e., positive or negative) of the epsilon-lambda relationship.

With the incorporation of the Plant-Craig stochastic deep convection scheme into the Zhang-McFarlane deterministic parameterization in the Community Atmospheric Model version 5 (CAM5), its impact on extreme precipitation at different resolutions (2 degrees, 1 degrees, and 0.5 degrees) is investigated. CAM5 with the stochastic deep convection scheme (experiment (EXP)) simulates the precipitation extreme indices better than the standard version (control). At 2 degrees and 1 degrees resolutions, EXP increases high percentile (>99th) daily precipitation over the United States, Europe, and China, resulting in a better agreement with observations. However, at 0.5 degrees resolution, due to enhanced grid-scale precipitation with increasing resolution, EXP overestimates extreme precipitation over southeastern U.S. and eastern Europe. The reduced biases in EXP at each resolution benefit from a broader probability distribution function of convective precipitation intensity simulated. Among EXP simulations at different resolutions, if the spatial averaging area over which input quantities used in convective closure are spatially averaged in the stochastic convection scheme is comparable, the modeled convective precipitation intensity decreases with increasing resolution, when gridded to the same resolution, while the total precipitation is not sensitive to model resolution, exhibiting some degree of scale-awareness. Sensitivity tests show that for the same resolution, increasing the size of spatial averaging area decreases convective precipitation but increases the grid-scale precipitation.

With increasing computational capabilities, cumulus parameterizations that are adaptable to the smaller grid spacing and temporal interval for high-resolution climate model simulations are needed. In this study, we propose a method to improve the resolution adaptability of the Zhang-McFarlane (ZM) scheme, by implementing spatial and temporal averaging to the CAPE tendency. This method allows for a more consistent application of the quasi-equilibrium (QE) hypothesis at high spatial and temporal resolutions. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with spatiotemporal averaging at 4-32 km grid spacings are assessed using the Weather Research and Forecasting (WRF) model by comparing to cloud resolving model (CRM) simulation results coarse-grained to these same grid spacings. We show the original ZM scheme has poor resolution adaptability, with spatiotemporally averaged subgrid convective transport and convective precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves total transport and total precipitation. Temporal averaging further improves the resolution adaptability of the scheme. With better constrained (although smoothed) convective transport and precipitation, both the spatial distribution and time series of total precipitation at 4 and 8 km grid spacings are improved with the averaging methods. The results could help develop resolution adaptability for other cumulus parameterizations that are based on the QE assumption.

Using observations from the Green Ocean Amazon (GOAmazon) field campaign, this study aims to improve trigger functions of convection schemes. Results show that the CAPE generation rate (dCAPE)-type triggers are the first tier and that the Bechtold and heated condensation framework (HCF) triggers are a distant second tier. The composite analysis reveals that the undilute dCAPE trigger underpredicts convection when there is bottom-heavy upward motion but overpredicts convection with low-level downward and upperlevel upward motions. The empirical orthogonal function (EOF) analysis on vertical velocity shows that EOF1 (62.65%) exhibits upward motion throughout the troposphere and that EOF2 (28.05%) has lower-level upward motion and upper-level downward motion. Both of them have close relationships with precipitation, indicating the role of vertical velocity in triggering convection. The skill sensitivity analysis shows that the inclusion of 700-hPa upward motion significantly enhances the undilute dCAPE trigger. For the dilute dCAPE trigger, entrainment rate and dCAPE threshold are optimized to improve it. Opposite to dCAPEtype triggers, the Bechtold trigger overemphasizes the low-level vertical velocity and underpredicts the mature and decaying phases of long-lasting convection events. The HCF trigger overemphasizes the nearsurface moist static energy and overlooks the vertical velocity. The performance of dCAPE-type triggers on various convective systems over the Amazon region is examined. The eastward-propagating systems are best represented, with only a few underpredictions in their decaying stages. The weak locally occurring systems and marginal phases of westward-propagating systems are easy to underpredict. The revised dCAPE-type triggers perform better on different convection systems and the diurnal cycle of convection.

Turbulent drag caused by subgrid orographic form drag has significant effects on the atmosphere. It is represented through parameterization in large-scale numerical prediction models. An indirect parameterization scheme, the Turbulent Mountain Stress scheme (TMS), is currently used in the National Center for Atmospheric Research Community Earth System Model v1.0.4. In this study we test a direct scheme referred to as BBW04 (Beljaars et al. in Q J R Meteorol Soc 130:1327-1347, 2004., which has been used in several short-term weather forecast models and earth system models. Results indicate that both the indirect and direct schemes increase surface wind stress and improve the model's performance in simulating low-level wind speed over complex orography compared to the simulation without subgrid orographic effect. It is shown that the TMS scheme produces a more intense wind speed adjustment, leading to lower wind speed near the surface. The low-level wind speed by the BBW04 scheme agrees better with the ERA-Interim reanalysis and is more sensitive to complex orography as a direct method. Further, the TMS scheme increases the 2-m temperature and planetary boundary layer height over large areas of tropical and subtropical Northern Hemisphere land.

During boreal spring, observations show a double ITCZ over the eastern Pacific, with the northern ITCZ stronger than the southern ITCZ. However, it is opposite in most climate models. It is also evident that there exists a cold bias in tropical North Atlantic (TNA) sea surface temperature (SST) and a warm bias in southeastern Pacific (SEP) SST. In this study, the influences of TNA and SEP SSTs on the double-ITCZ bias are investigated by prescribing the observed SST in these regions in the NCAR CESM1. Results show that when TNA SST is prescribed, the northern ITCZ is substantially enhanced and the southern ITCZ is moderately reduced, although the SST response in these regions is small. When the SEP SST is prescribed, the southern ITCZ is reduced considerably. When bothTNAand SEP SSTs are prescribed, the double-ITCZ bias is reduced by similar to 68%. Moisture budget analysis suggests that dynamics, mainly the low-level convergence change, determines the above precipitation changes. Based on a mixed layer model, changes in low-level convergence are shown to be determined by surface pressure P-s changes. With prescribed TNA/SEP SSTs, SST gradients change the P-s in the region directly via the Lindzen-Nigam mechanism. The corresponding low-level circulation changes affect the 850-hPa thermodynamic state in a wider region, which in turn not only strengthens the SST-induced P-s change locally but also leads to P-s changes remotely, including the northern ITCZ region. Furthermore, the low-level convergence changes the vertical structure of moist static energy, altering the atmospheric stability and modulating precipitation distribution.

In this study, the stochastic deep convection parameterization of Plant and Craig (PC) is implemented in the Community Atmospheric Model version 5 (CAM5) to incorporate the stochastic processes of convection into the Zhang-McFarlane (ZM) deterministic deep convective scheme. Its impacts on deep convection, shallow convection, large-scale precipitation and associated dynamic and thermodynamic fields are investigated. Results show that with the introduction of the PC stochastic parameterization, deep convection is decreased while shallow convection is enhanced. The decrease in deep convection is mainly caused by the stochastic process and the spatial averaging of input quantities for the PC scheme. More detrained liquid water associated with more shallow convection leads to significant increase in liquid water and ice water paths, which increases large-scale precipitation in tropical regions. Specific humidity, relative humidity, zonal wind in the tropics, and precipitable water are all improved. The simulation of shortwave cloud forcing (SWCF) is also improved. The PC stochastic parameterization decreases the global mean SWCF from 252.25 W/m(2) in the standard CAM5 to 248.86 W/m(2), close to 247.16 W/m(2) in observations. The improvement in SWCF over the tropics is due to decreased low cloud fraction simulated by the stochastic scheme. Sensitivity tests of tuning parameters are also performed to investigate the sensitivity of simulated climatology to uncertain parameters in the stochastic deep convection scheme.

The double intertropical convergence zone (ITCZ) is a long-standing bias in the climatology of coupled general circulation models (CGCMs). The warm biases in southeastern Pacific (SEP) sea surface temperature (SST) are also evident in many CGCMs. In this study, the role of SEP SST in the double ITCZ is investigated by prescribing the observed SEP SST in the Community Earth System Model, version 1 (CESM1). Both the double ITCZ and dry equator problems are significantly improved with SEP SST prescribed. Both atmospheric and oceanic processes are involved in the improvements. The colder SST over the SEP decreases the precipitation, which enhances the southeasterly winds outside the prescribed SST region, cooling the ocean via increased evaporation. The enhanced descending motion over the SEP strengthens the Walker circulation. The easterly winds over the equatorial Pacific enhance upwelling and shoal the thermocline over the eastern Pacific. The changes of surface wind and wind curl lead to a weaker South Equatorial Countercurrent and stronger South Equatorial Current, preventing the warm water from expanding eastward, thereby improving both the double ITCZ and dry equator. The enhanced Walker circulation also increases the low-level wind convergence and reduces the wind speed in the tropical western Pacific, leading to warmer SST and stronger convection there. The stronger convection in turn leads to more cloud and reduces the incoming solar radiation, cooling the SST. These competing effects between radiative heat flux and latent heat flux make the atmospheric heat flux secondary to the ocean dynamics in the western Pacific warming.

The fractional entrainment rate in convective clouds is an important parameter in current convective parameterization schemes of climate models. In this paper, it is estimated using a 1-km-resolution cloud-resolving model (CRM) simulation of convective clouds from TWP-ICE (the Tropical Warm Pool-International Cloud Experiment). The clouds are divided into different types, characterized by cloud-top heights. The entrainment rates and moist static energy that is entrained or detrained are determined by analyzing the budget of moist static energy for each cloud type. Results show that the entrained air is a mixture of approximately equal amount of cloud air and environmental air, and the detrained air is a mixture of similar to 80 % of cloud air and 20 % of the air with saturation moist static energy at the environmental temperature. After taking into account the difference in moist static energy between the entrained air and the mean environment, the estimated fractional entrainment rate is much larger than those used in current convective parameterization schemes. High-resolution (100 m) large-eddy simulation of TWP-ICE convection was also analyzed to support the CRM results. It is shown that the characteristics of entrainment rates estimated using both the high-resolution data and CRM-resolution coarse-grained data are similar. For each cloud category, the entrainment rate is high near cloud base and top, but low in the middle of clouds. The entrainment rates are best fitted to the inverse of in-cloud vertical velocity by a second order polynomial.

We use a stochastic differential equation (SDE) model with a random precipitation trigger for mass balance to simulate the 20 s temporal resolution column precipitable water vapor (PWV) data during the tropical warm pool international cloud experiment (TWP-ICE) period of January 20 to February 15, 2006 at Darwin, Australia. The trigger is determined by an exponential cumulative distribution function, the time step size in the SDE simulation, and a random precipitation indicator uniformly distributed over [0, 1]. Compared with the observed data, the simulations have similar means, extremes, skewness, kurtosis, and overall shapes of probability distribution, and are temporally well synchronized for increasing and decreasing, but have about 20% lower standard deviation. Based on a 1000-day run, the correlations between the model data and the observations in TWP-ICE period were computed in a moving time window of 25 days and show quasi-periodic variations between (−0.675, 0.697). This shows that the results are robust for the stochastic model simulation of the observed PWV data, whose fractal dimension is 1.9, while the dimension of the simulated data is also about 1.9. This agreement and numerous sensitivity experiments form a test on the feasibility of using an SDE model to simulate precipitation processes in more complex climate models.

The Plant-Craig (PC) stochastic convective parameterization scheme is implemented into the National Center for Atmospheric Research Community Atmosphere Model version 5 (CAM5) to couple with the Zhang-McFarlane deterministic convection scheme. To evaluate its impact on tropical precipitation simulation, two experiments are conducted: one with the standard CAM5 and the other with the stochastic scheme incorporated. Results show that the PC stochastic parameterization decreases the frequency of weak precipitation and increases the frequency of strong precipitation, resulting in better agreement with observations. The most striking improvement is in the probability distribution function (PDF) of precipitation intensity, with the well-known too-much-drizzle problem in CAM5 largely eliminated. In the global tropical belt, the precipitation intensity PDF from the simulation agrees remarkably well with that of Tropical Rainfall Measuring Mission observations. The stochastic scheme also yields a similar magnitude of intraseasonal variability of precipitation to observations and improves the simulation of the eastward propagating intraseasonal signals of precipitation and zonal wind.

Principal component analysis (PCA) is utilized to explore the temporal and spatial variability of precipitation from GPCP and a CAM5 simulation from 1979 to 2010. In the tropical region, the interannual variability of tropical precipitation is characterized by two dominant modes (El Nino and El Nino Modoki). The first and second modes of tropical GPCP precipitation capture 31.9% and 15.6% of the total variance, respectively. The first mode has positive precipitation anomalies over the western Pacific and negative precipitation anomalies over the central and eastern Pacific. The second mode has positive precipitation anomalies over the central Pacific and negative precipitation anomalies over the western and eastern Pacific. Similar variations are seen in the first two modes of tropical precipitation from a CAM5 simulation, although the magnitudes are slightly weaker than in the observations. Over the Northern Hemisphere (NH) high latitudes, the first mode, capturing 8.3% of the total variance of NH GPCP precipitation, is related to the northern annular mode (NAM). During the positive phase of NAM, there are negative precipitation anomalies over the Arctic and positive precipitation anomalies over the midlatitudes. Over the Southern Hemisphere (SH) high latitudes, the first mode, capturing 13.2% of the total variance of SH GPCP precipitation, is related to the southern annular mode (SAM). During the positive phase of the SAM, there are negative precipitation anomalies over the Antarctic and positive precipitation anomalies over the midlatitudes. The CAM5 precipitation simulation demonstrates similar results to those of the observations. However, they do not capture both the high precipitation anomalies over the northern Pacific Ocean or the position of the positive precipitation anomalies in the SH.

This work examines the relationships of entrainment rate to vertical velocity, buoyancy, and turbulent dissipation rate by applying stepwise principal component regression to observational data from shallow cumulus clouds collected during the Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign over the ARM Southern Great Plains (SGP) site near Lamont, Oklahoma. The cumulus clouds during the RACORO campaign simulated using a large-eddy simulation (LES) model are also examined with the same approach. The analysis shows that a combination of multiple variables can better represent entrainment rate in both the observations and LES than any single-variable fitting. Three commonly used parameterizations are also tested on the individual cloud scale. A new parameterization is thus presented that relates entrainment rate to vertical velocity, buoyancy, and dissipation rate; the effects of treating clouds as ensembles and humid shells surrounding cumulus clouds on the new parameterization are discussed. Physical mechanisms underlying the relationships of entrainment rate to vertical velocity, buoyancy, and dissipation rate are also explored.

A two-moment microphysics scheme for deep convection was previously implemented in the NCAR Community Atmosphere Model version 5 (CAM5) by Song et al. The new scheme improved hydrometeor profiles in deep convective clouds and increased deep convective detrainment, reducing the negative biases in low and midlevel cloud fraction and liquid water path compared to observations. Here, the authors examine in more detail the impacts of this improved microphysical representation on regional-scale water and radiation budgets. As a primary source of cloud water for stratiform clouds is detrainment from deep and shallow convection, the enhanced detrainment leads to larger stratiform cloud fractions, higher cloud water content, and more stratiform precipitation over the ocean, particularly in the subtropics where convective frequency is also increased. This leads to increased net cloud radiative forcing. Over land regions, cloud amounts are reduced as a result of lower relative humidity, leading to weaker cloud forcing and increased OLR. Comparing the water budgets to cloud-resolving model simulations shows improvement in the partitioning between convective and stratiform precipitation, though the deep convection is still too active in the GCM. The addition of convective microphysics leads to an overall improvement in the regional cloud water budgets.

The weak negative shortwave (SW) radiative feedback (sw) during El Nino-Southern Oscillation (ENSO) over the equatorial Pacific is a common problem in the models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). In this study, the causes for the (sw) biases are analyzed using three-dimensional cloud fraction and liquid water path (LWP) provided by the 17 CMIP5 models and the relative roles of convective and stratiform rainfall feedbacks in (sw) are explored. Results show that the underestimate of SW feedback is primarily associated with too negative cloud fraction and LWP feedbacks in the boundary layers, together with insufficient middle and/or high cloud and dynamics feedbacks, in both the CMIP and Atmospheric Model Intercomparsion Project (AMIP) runs, the latter being somewhat better. The underestimations of SW feedbacks are due to both weak negative SW responses to El Nino, especially in the CMIP runs, and strong positive SW responses to La Nina, consistent with their biases in cloud fraction, LWP, and dynamics responses to El Nino and La Nina. The convective rainfall feedback, which is largely reduced owing to the excessive cold tongue in the CMIP runs compared with their AMIP counterparts, contributes more to the difference of SW feedback (mainly under El Nino conditions) between the CMIP and AMIP runs, while the stratiform rainfall plays a more important role in SW feedback during La Nina.

This study estimates entrainment rate and investigates its relationships with cloud properties in 156 deep convective clouds based on in-situ aircraft observations during the TOGA-COARE (Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment) field campaign over the western Pacific. To the authors' knowledge, this is the first study on the probability density function of entrainment rate, the relationships between entrainment rate and cloud microphysics, and the effects of dry air sources on the calculated entrainment rate in deep convection from an observational perspective. Results show that the probability density function of entrainment rate can be well fitted by lognormal, gamma or Weibull distribution, with coefficients of determination being 0.82, 0.85 and 0.80, respectively. Entrainment tends to reduce temperature, water vapor content and moist static energy in cloud due to evaporative cooling and dilution. Inspection of the relationships between entrainment rate and microphysical properties reveals a negative correlation between volume-mean radius and entrainment rate, suggesting the potential dominance of homogeneous mechanism in the clouds examined. In addition, entrainment rate and environmental water vapor content show similar tendencies of variation with the distance of the assumed environmental air to the cloud edges. Their variation tendencies are non-monotonic due to the relatively short distance between adjacent clouds.

Aimed at reducing deficiencies in representing the Madden-Julian oscillation (MJO) in general circulation models (GCMs), a global model evaluation project on vertical structure and physical processes of the MJO was coordinated. In this paper, results from the climate simulation component of this project are reported. It is shown that the MJO remains a great challenge in these latest generation GCMs. The systematic eastward propagation of the MJO is only well simulated in about one fourth of the total participating models. The observed vertical westward tilt with altitude of the MJO is well simulated in good MJO models but not in the poor ones. Damped Kelvin wave responses to the east of convection in the lower troposphere could be responsible for the missing MJO preconditioning process in these poor MJO models. Several process-oriented diagnostics were conducted to discriminate key processes for realistic MJO simulations. While large-scale rainfall partition and low-level mean zonal winds over the Indo-Pacific in a model are not found to be closely associated with its MJO skill, two metrics, including the low-level relative humidity difference between high- and low-rain events and seasonal mean gross moist stability, exhibit statistically significant correlations with the MJO performance. It is further indicated that increased cloud-radiative feedback tends to be associated with reduced amplitude of intraseasonal variability, which is incompatible with the radiative instability theory previously proposed for the MJO. Results in this study confirm that inclusion of air-sea interaction can lead to significant improvement in simulating the MJO.