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.

Many theories for the Madden-Julian oscillation (MJO) focus on diabatic processes, particularly the evolution of vertical heating and moistening. Poor MJO performance in weather and climate models is often blamed on biases in these processes and their interactions with the large-scale circulation. We introduce one of the three components of a model evaluation project, which aims to connect MJO fidelity in models to their representations of several physical processes, focusing on diabatic heating and moistening. This component consists of 20day hindcasts, initialized daily during two MJO events in winter 2009-2010. The 13 models exhibit a range of skill: several have accurate forecasts to 20days lead, while others perform similarly to statistical models (8-11days). Models that maintain the observed MJO amplitude accurately predict propagation, but not vice versa. We find no link between hindcast fidelity and the precipitation-moisture relationship, in contrast to other recent studies. There is also no relationship between models' performance and the evolution of their diabatic heating profiles with rain rate. A more robust association emerges between models' fidelity and net moistening: the highest-skill models show a clear transition from low-level moistening for light rainfall to midlevel moistening at moderate rainfall and upper level moistening for heavy rainfall. The midlevel moistening, arising from both dynamics and physics, may be most important. Accurately representing many processes may be necessary but not sufficient for capturing the MJO, which suggests that models fail to predict the MJO for a broad range of reasons and limits the possibility of finding a panacea.

The ultimate goal of this study is to improve the representation of convective transport by cumulus parameterization for mesoscale and climate models. As Part 1 of the study, we perform extensive evaluations of cloud-resolving simulations of a squall line and mesoscale convective complexes in midlatitude continent and tropical regions using the Weather Research and Forecasting model with spectral bin microphysics (SBM) and with two double-moment bulk microphysics schemes: a modified Morrison (MOR) and Milbrandt and Yau (MY2). Compared to observations, in general, SBM gives better simulations of precipitation and vertical velocity of convective cores than MOR and MY2 and therefore will be used for analysis of scale dependence of eddy transport in Part 2. The common features of the simulations for all convective systems are (1) the model tends to overestimate convection intensity in the middle and upper troposphere, but SBM can alleviate much of the overestimation and reproduce the observed convection intensity well; (2) the model greatly overestimates Z(e) in convective cores, especially for the weak updraft velocity; and (3) the model performs better for midlatitude convective systems than the tropical system. The modeled mass fluxes of the midlatitude systems are not sensitive to microphysics schemes but are very sensitive for the tropical case indicating strong microphysics modification to convection. Cloud microphysical measurements of rain, snow, and graupel in convective cores will be critically important to further elucidate issues within cloud microphysics schemes.

Following Part I, in which 3-D cloud-resolving model (CRM) simulations of a squall line and mesoscale convective complex in the midlatitude continental and the tropical regions are conducted and evaluated, we examine the scale dependence of eddy transport of water vapor, evaluate different eddy transport formulations, and improve the representation of convective transport across all scales by proposing a new formulation that more accurately represents the CRM-calculated eddy flux. CRM results show that there are strong grid-spacing dependencies of updraft and downdraft fractions regardless of altitudes, cloud life stage, and geographical location. As for the eddy transport of water vapor, updraft eddy flux is a major contributor to total eddy flux in the lower and middle troposphere. However, downdraft eddy transport can be as large as updraft eddy transport in the lower atmosphere especially at the mature stage of midlatitude continental convection. We show that the single-updraft approach significantly underestimates updraft eddy transport of water vapor because it fails to account for the large internal variability of updrafts, while a single downdraft represents the downdraft eddy transport of water vapor well. We find that using as few as three updrafts can account for the internal variability of updrafts well. Based on the evaluation with the CRM simulated data, we recommend a simplified eddy transport formulation that considers three updrafts and one downdraft. Such formulation is similar to the conventional one but much more accurately represents CRM-simulated eddy flux across all grid scales.

Closure is an important component of a mass flux-based convective parameterization scheme, and it determines the amount of convection with the aid of a large-scale variable (closure variable) that is sensitive to convection. In this study, we have evaluated and quantified the relationship between commonly used closure variables and convection for a range of global climate model (GCM) horizontal resolutions, taking convective precipitation and mass flux at 600 hPa as measures for deep convection. We have used cloud-resolving model simulation data to create domain averages representing GCM horizontal resolutions of 128km, 64 km, 32 km, 16 km, 8 km, and 4km. Lead-lag correlation analysis shows that except moisture convergence and turbulent kinetic energy, none of the other closure variables evaluated in this study show any relationship with convection for the six subdomain sizes. It is found that the correlation between moisture convergence and convective precipitation is largest when moisture convergence leads convection. This correlation weakens as the subdomain size decreases to 8km or smaller. Although convective precipitation and mass flux increase with moisture convergence at a given subdomain size, as the subdomain size increases, the rate at which they increase becomes smaller. This suggests that moisture convergence-based closure should scale down the predicted mass flux for a given moisture convergence as GCM resolution increases.

Realistic simulation of different modes of atmospheric variability ranging from diurnal cycle to interannual variation in global climate models (GCMs) depends crucially on the convection trigger criteria. In this study, using the data from constrained variational analysis by the Atmospheric System Research program for single-column models (SCM), the performance of the commonly used convective trigger functions in GCMs is evaluated based on the equitable threat score (ETS) value, a widely used forecast verification metric. From the ETS score, three consistently better-performing trigger functions were identified. They are based on the dilute and undilute convective available potential energy (CAPE) generation rate from large-scale forcing in the free troposphere (hereafter dCAPE) and parcel buoyancy at the lifting condensation level (Bechtold scheme). The key variables used to define these trigger functions are examined in detail. It is found that the dilute dCAPE trigger function performs the best consistently in both the tropical and midlatitude convective environment. Analysis of the composite fields of key variables of the trigger functions, based on the correct prediction, overprediction and underprediction of convection, and correct prediction of no-convection cases for convective onset, brings to light some critical factors responsible for the performance of the trigger functions. The lower-tropospheric advective forcing in dilute dCAPE trigger and vertical velocity in Bechtold trigger are identified to be the most importance ones. Suggestions are offered for further improvements.

Under global warming from the doubling of CO2, the equatorial Pacific experiences an El Nino-like warming, as simulated by most global climate models. A new climate feedback and response analysis method (CFRAM) is applied to 10 years of hourly output of the slab ocean model (SOM) version of the NCAR Community Climate System Model, version 3.0, (CCSM3-SOM) to determine the processes responsible for this warming. Unlike the traditional surface heat budget analysis, the CFRAM can explicitly quantify the contributions of each radiative climate feedback and of each physical and dynamical process of a GCM to temperature changes. The mean bias in the sum of partial SST changes due to each feedback derived with CFRAM in the tropical Pacific is negligible (0.5%) compared to the mean SST change from the CCSM3-SOM simulations, with a spatial pattern correlation of 0.97 between the two. The analysis shows that the factors contributing to the El Nino-like SST warming in the central Pacific are different from those in the eastern Pacific. In the central Pacific, the largest contributor to El Nino-like SST warming is dynamical advection, followed by PBL diffusion, water vapor feedback, and surface evaporation. In contrast, in the eastern Pacific the dominant contributor to El Nino-like SST warming is cloud feedback, with water vapor feedback further amplifying the warming.

The weak response of surface shortwave cloud radiative forcing (SWCF) to El Nino over the equatorial Pacific remains a common problem in many contemporary climate models. This study shows that two versions of the Grid-Point Atmospheric Model of the Institute of Atmospheric Physics (IAP)/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) (GAMIL) produce distinctly different surface SWCF response to El Nino. The earlier version, GAMIL1, underestimates this response, whereas the latest version, GAMIL2, simulates it well. To understand the causes for the different SWCF responses between the two simulations, the authors analyze the underlying physical mechanisms. Results indicate the enhanced stratiform condensation and evaporation in GAMIL2 play a key role in improving the simulations of multiyear annual mean water vapor (or relative humidity), cloud fraction, and incloud liquid water path (ICLWP) and hence in reducing the biases of SWCF and rainfall responses to El Nino due to all of the improved dynamical (vertical velocity at 500 hPa), cloud amount, and liquid water path (LWP) responses. The largest contribution to the SWCF response improvement in GAMIL2 is from LWP in the Nino-4 region and from low-cloud cover and LWP in the Nino-3 region. Furthermore, as a crucial factor in the low-cloud response, the atmospheric stability change in the lower layers is significantly influenced by the nonconvective heating variation during La Nina.

This study evaluates the Madden-Julian Oscillation (MJO) hindcast skill and investigates the hindcast biases in the dynamic and thermodynamic fields of the National Center for Atmospheric Research Community Atmosphere Model version 3. The analysis is based on the October 2011 MJO event observed during the Dynamics of the Madden-Julian Oscillation field campaign. The model captures the MJO initiation but, compared to the observations, the hindcast has a faster MJO phase speed, a dry relative humidity bias, a stronger zonal wind shear, and a weaker MJO peak amplitude. The MJO hindcast is then nudged toward the European Centre for Medium-Range Weather Forecast Reanalysis fields of temperature, specific humidity, horizontal winds, and surface pressure. The nudging tendencies highlight the model physics parameterization biases, such as not enough convective diabatic heating during the MJO initiation, not enough upper tropospheric stratiform condensation, and lower tropospheric reevaporation during the mature and decay phases and a strong zonal wind shear during the MJO evolution. To determine the role of temperature, specific humidity, and horizontal winds in the model physics parameterization errors, six additional nudging experiments are carried out, with either one or two of the fields allowed to evolve freely while the others are nudged. Results show that convection and precipitation increase when temperature or specific humidity are unconstrained and decrease when horizontal winds evolve freely or temperature alone is constrained to reanalysis. Budget analysis of moist static energy shows that the nudging tendency compensates for different process biases during different MJO phases. The diagnosis of such nudging tendencies provides a unique objective way to identify model physics biases, which usefully guides the model physics parameterization development.

The climate feedback-response analysis method (CFRAM) was applied to 10-yr hourly output of the NCAR Community Climate System Model, version 3, using the slab ocean model (CCSM3-SOM), to analyze the strength and spatial distribution of climate feedbacks and to characterize their contributions to the global and regional surface temperature T-s changes in response to a doubling of CO2. The global mean bias in the sum of partial T-s changes associated with the CO2 forcing, and each feedback derived with the CFRAM analysis is about 2% of T-s change obtained directly from the CCSM3-SOM simulations. The pattern correlation between the two is 0.94, indicating that the CFRAM analysis using hourly model output is accurate and thus is appropriate for quantifying the contributions of climate feedback to the formation of global and regional warming patterns. For global mean T-s, the largest contributor to the warming is water vapor feedback, followed by the direct CO2 forcing and albedo feedback. The albedo feedback exhibits the largest spatial variation, followed by shortwave cloud feedback. In terms of pattern correlation and RMS difference with the modeled global surface warming, longwave cloud feedback contributes the most. On zonal average, albedo feedback is the largest contributor to the stronger warming in high latitudes than in the tropics. The longwave cloud feedback further amplifies the latitudinal warming contrast. Both the land-ocean warming difference and contributions of climate feedbacks to it vary with latitude. Equatorward of 50 degrees, shortwave cloud feedback and dynamical advection are the two largest contributors. The land-ocean warming difference on the hemispheric scale is mainly attributable to longwave cloud feedback and convection.

In this study, a coupled atmosphere-surface "climate feedback-response analysis method" (CFRAM) was applied to the slab ocean model version of the NCAR CCSM3.0 to understand the tropospheric warming due to a doubling of CO2 concentration through quantifying the contributions of each climate feedback process. It is shown that the tropospheric warming displays distinct meridional and vertical patterns that are in a good agreement with the multi-model mean projection from the IPCC AR4. In the tropics, the warming in the upper troposphere is stronger than in the lower troposphere, leading to a decrease in temperature lapse rate, whereas in high latitudes the opposite it true. In terms of meridional contrast, the lower tropospheric warming in the tropics is weaker than that in high latitudes, resulting in a weakened meridional temperature gradient. In the upper troposphere the meridional temperature gradient is enhanced due to much stronger warming in the tropics than in high latitudes. Using the CFRAM method, we analyzed both radiative feedbacks, which have been emphasized in previous climate feedback analysis, and non-radiative feedbacks. It is shown that non-radiative (radiative) feedbacks are the major contributors to the temperature lapse rate decrease (increase) in the tropical (polar) region. Atmospheric convection is the leading contributor to temperature lapse rate decrease in the tropics. The cloud feedback also has non-negligible contributions. In the polar region, water vapor feedback is the main contributor to the temperature lapse rate increase, followed by albedo feedback and CO2 forcing. The decrease of meridional temperature gradient in the lower troposphere is mainly due to strong cooling from convection and cloud feedback in the tropics and the strong warming from albedo feedback in the polar region. The strengthening of meridional temperature gradient in the upper troposphere can be attributed to the warming associated with convection and cloud feedback in the tropics. Since convection is the leading contributor to the warming differences between tropical lower and upper troposphere, and between the tropical and polar regions, this study indicates that tropical convection plays a critical role in determining the climate sensitivity. In addition, the CFRAM analysis shows that convective process and water vapor feedback are the two major contributors to the tropical upper troposphere temperature change, indicating that the excessive upper tropospheric warming in the IPCC AR4 models may be due to overestimated warming from convective process or underestimated cooling due to water vapor feedback.

A new Zhang and McFarlane (ZM) cumulus scheme includes a two-moment cloud microphysics parameterization for convective clouds. This allows aerosol effects to be investigated more comprehensively by linking aerosols with microphysical processes in both stratiform clouds that are explicitly resolved and convective clouds that are parameterized in climate models. This new scheme is implemented in the Weather Research and Forecasting model, coupled with the physics and aerosol packages from the Community Atmospheric Model version 5. A case of July 2008 during the East Asian summer monsoon is selected to evaluate the performance of the new ZM and to investigate aerosol effects on monsoon precipitation. The precipitation and radiative fluxes simulated by the new ZM show a better agreement with observations compared to simulations with the original ZM that does not include convective cloud microphysics and aerosol-convective cloud interactions. Detailed analysis suggests that an increase in detrained cloud water and ice mass by the new ZM is responsible for this improvement. Aerosol impacts on cloud properties, precipitation, and radiation are examined by reducing the primary aerosols and anthropogenic emissions to 30% of those in the present (polluted) condition. The simulated surface precipitation is reduced by 9.8% from clean to polluted environment, and the reduction is less significant when microphysics processes are excluded from the cumulus clouds. Cloud fraction is reduced by the increased aerosols due to suppressed convection, except during some heavy precipitation periods when cloud fraction, cloud top height, and rain rate are increased due to enhanced convection.

Polar surface temperatures are expected to warm 2-3 times faster than the global-mean surface temperature: a phenomenon referred to as polar warming amplification. Therefore, understanding the individual process contributions to the polar warming is critical to understanding global climate sensitivity. The Coupled Feedback Response Analysis Method (CFRAM) is applied to decompose the annual- and zonal-mean vertical temperature response within a transient 1% yr(-1) CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4), into individual radiative and nonradiative climate feedback process contributions. The total transient annual-mean polar warming amplification (amplification factor) at the time of CO2 doubling is +2.12 (2.3) and +0.94 K (1.6) in the Northern and Southern Hemisphere, respectively. Surface albedo feedback is the largest contributor to the annual-mean polar warming amplification accounting for +1.82 and +1.04 K in the Northern and Southern Hemisphere, respectively. Net cloud feedback is found to be the second largest contributor to polar warming amplification (about +0.38 K in both hemispheres) and is driven by the enhanced downward longwave radiation to the surface resulting from increases in low polar water cloud. The external forcing and atmospheric dynamic transport also contribute positively to polar warming amplification: +0.29 and +0.32 K, respectively. Water vapor feedback contributes negatively to polar warming amplification because its induced surface warming is stronger in low latitudes. Ocean heat transport storage and surface turbulent flux feedbacks also contribute negatively to polar warming amplification. Ocean heat transport and storage terms play an important role in reducing the warming over the Southern Ocean and Northern Atlantic Ocean.