Superparameterized Community Atmosphere Model Research Articles

Effects of the Horizontal Scales of the Cloud‐Resolving Model on Tropical Cyclones in the Superparameterized Community Atmosphere Model

AbstractIn this study, the Superparameterized Community Atmosphere Model (SPCAM) is used to simulate tropical cyclones (TCs) using the hindcast approach. Three hindcast experiments are conducted with 32‐km (D32), 128‐km (D128), and 1,024‐km (D1024) horizontal scales in the sub‐grid cloud‐resolving models (CRMs). The results show that D1024 produces reasonable TCs compared with the reanalysis data. It is 3.42 TCs per 10 days for the reanalysis data, while there are 8.07, 4.88, and 3.73 for D32, D128, and D1024. The bias of overestimating TC numbers grows with decreasing CRM scale. The D32 experiment also produces stronger TCs with a higher precipitation rate and wind speed. The bias is highly related to the efficiency of adjusting convective instability in CRMs. The D32 exhibits higher column‐integrated water vapor under warm conditions compared with D1024, indicating its inefficiency in removing water vapor by the weaker convective mass fluxes in the small CRM scale. That is, the vertical transport of convection in a smaller horizontal scale will be restricted by stronger subsidence because CRM columns for compensating are limited. The distribution of accumulated convective instability is broader and more frequent in D32. As a result, large‐scale precipitating systems tend to develop in D32, leading to a higher probability of TC genesis. This study highlights the importance of sub‐grid configuration when estimating TC activities using SPCAM.

Excitation of the Madden‐Julian Oscillation in Response to Transient Ocean Warming in SPCAM

AbstractRecent studies have suggested that the Madden‐Julian oscillation (MJO) could be generated as part of atmospheric adjustment to localized equatorial heating in models properly resolving moist convection. Here we use the Super‐parameterized Community Atmosphere Model (SPCAM) to study atmospheric response to transient sea surface warming in the equatorial Indian Ocean, which leads to the excitation of an MJO event, robust across different experiments, in addition to the expected equatorial Rossby and Kelvin waves. A moist static energy (MSE) budget analysis suggests that longwave and surface turbulent latent heat flux anomalies are predominantly responsible for the MJO excitation; however, these terms oppose its eastward propagation. In contrast, advection of MSE generally weakens the MJO amplitude, but contributes to its eastward propagation. These results highlight the role of the MJO in the moist atmospheric adjustment to localized tropical heating, with implications for the MJO mechanisms and prediction.

Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions

AbstractWe explore the potential of feed‐forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the superparameterized community atmospheric model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ∼250 trials. Our DNN explains over 70% of the temporal variance at the 15‐min sampling scale throughout the mid‐to‐upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A closer look at the diurnal cycle reveals correct emulation of land‐sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints versus hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in real‐geography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight the advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models.

Boreal summer intraseasonal oscillation in a superparameterized general circulation model: effects of air–sea coupling and ocean mean state

Abstract. The effect of air–sea coupling on simulated boreal summer intraseasonal oscillation (BSISO) is examined using atmosphere–ocean-mixed-layer coupled (SPCAM3-KPP, referred to as SPK throughout) and uncoupled configurations of the superparameterized (SP) Community Atmospheric Model, version 3 (SPCAM3, referred to as SPA throughout). The coupled configuration is constrained to either observed ocean mean state or the mean state from the SP coupled configuration with a dynamic ocean (SPCCSM3), to understand the effect of mean-state biases on the BSISO. All configurations overestimate summer mean subtropical rainfall and its intraseasonal variance. All configurations simulate realistic BSISO northward propagation over the Indian Ocean and western Pacific, in common with other SP configurations. Prescribing the 31 d smoothed sea surface temperature (SST) from the SPK simulation in SPA worsens the overestimated BSISO variance. In both coupled models, the phase relationship between intraseasonal rainfall and SST is well captured. This suggests that air–sea coupling improves the amplitude of simulated BSISO and contributes to the propagation of convection. Constraining SPK to the SPCCSM3 mean state also reduces the overestimated BSISO variability but weakens BSISO propagation. Using the SPCCSM3 mean state also introduces a 1-month delay to the BSISO seasonal cycle compared to SPK with the observed ocean mean state, which matches well with observation. Based on a Taylor diagram, both air–sea coupling and SPCCSM3 mean-state SST biases generally lead to higher simulated BSISO fidelity, largely due to their abilities to suppress the overestimated subtropical BSISO variance.

Multiple‐Instance Superparameterization: 2. The Effects of Stochastic Convection on the Simulated Climate

AbstractThe cloud‐permitting model (CPM) of the superparameterized Community Atmosphere Model (SP‐CAM) is a stochastic parameterization. As reported in a companion paper, we have created a variant of SP‐CAM, called MP‐CAM, that uses the averaged feedback of 10 independent two‐dimensional CPMs in each global model column, in place of the single CPM of SP‐CAM. This ensemble‐averaged feedback is interpreted as an approximation to the feedback from a deterministic parameterization. We present evidence that the multiple‐instance superparameterization of MP‐CAM is indeed more deterministic than SP‐CAM. The climates of the SP and MP configurations are compared, giving particular attention to extreme precipitation events and convectively coupled large‐scale tropical weather systems, such as the Madden‐Julian Oscillation. A number of small but significant changes in the mean state climate are uncovered, and the deterministic parameterization slightly degrades the Madden‐Julian Oscillation simulation.

Multiple‐Instance Superparameterization: 1. Concept, and Predictability of Precipitation

AbstractWe have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model (SP‐CAM). The new configuration, called the multiple‐instance SP‐CAM, or MP‐CAM, uses the average heating and drying rates from 10 independent two‐dimensional cloud‐permitting models (CPMs) in each grid column of the global model, instead of a single CPM. The 10 CPMs start from slightly different initial conditions and simulate alternative realizations of the convective cloud systems. By analyzing the ensemble of possible realizations, we can study the predictability of the cloud systems and identify the weather regimes and physical mechanisms associated with chaotic convection. We explore alternative methods for quantifying the predictability of precipitation. Our results show that unpredictable precipitation occurs when the simulated atmospheric state is close to critical points as defined by Peters and Neelin (2006, https://doi.org/10.1038/nphys314). The predictability of precipitation is also influenced by the convective available potential energy and the degree of mesoscale organization. It is strongly controlled by the large‐scale circulation. A companion paper compares the global atmospheric circulations simulated by SP‐CAM and MP‐CAM.

Long‐Lived Mesoscale Convective Systems of Superparameterized CAM and the Response of CAM

AbstractMesoscale organized convection is generally misrepresented in the large‐scale convective parameterizations used in contemporary climate models. This impacts extreme weather events (e.g., Madden‐Jullian Oscillation) and the general circulation driven by the significant amount of latent heat released from mesoscale organized convection. Studies show that the missing processes could be partially recovered by embedding a 2‐D cloud‐resolving model in each general circulation model columns, that is, superparameterization. To enable analysis of mesoscale convective systems (MCSs) in the multiscale modeling framework, we apply a detection and hierarchical clustering algorithm on the 3‐hourly 2‐D cloud‐resolving model embedded in the superparameterized Community Atmosphere Model (SPCAM) 5.2. We then examine the fields of a long‐lived and large MCS cluster at the central Pacific. The MCS cluster shows a squall line‐like circulation throughout the life cycle in SPCAM. We simultaneously obtain the 3‐hourly CAM parameterized convection outputs based on the time step‐wise perfect initial conditions given by SPCAM. This allows pure model physics comparison without introducing initial condition errors. The results show that CAM has a systematically biased stratiform cooling and moistening response below 3 km to the given SPCAM deep convection favoring conditions. We show that this bias is mainly due to the CAM's stratiform microphysics scheme. The mesoscale organization in SPCAM thus provides a baseline for improvements of convective parameterization of CAM.

The importance of considering sub-grid cloud variability when using satellite observations to evaluate the cloud and precipitation simulations in climate models

Abstract. Satellite cloud observations have become an indispensable tool for evaluating general circulation models (GCMs). To facilitate the satellite and GCM comparisons, the CFMIP (Cloud Feedback Model Inter-comparison Project) Observation Simulator Package (COSP) has been developed and is now increasingly used in GCM evaluations. Real-world clouds and precipitation can have significant sub-grid variations, which, however, are often ignored or oversimplified in the COSP simulation. In this study, we use COSP cloud simulations from the Super-Parameterized Community Atmosphere Model (SPCAM5) and satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and CloudSat to demonstrate the importance of considering the sub-grid variability of cloud and precipitation when using the COSP to evaluate GCM simulations. We carry out two sensitivity tests: SPCAM5 COSP and SPCAM5-Homogeneous COSP. In the SPCAM5 COSP run, the sub-grid cloud and precipitation properties from the embedded cloud-resolving model (CRM) of SPCAM5 are used to drive the COSP simulation, while in the SPCAM5-Homogeneous COSP run only grid-mean cloud and precipitation properties (i.e., no sub-grid variations) are given to the COSP. We find that the warm rain signatures in the SPCAM5 COSP run agree with the MODIS and CloudSat observations quite well. In contrast, the SPCAM5-Homogeneous COSP run which ignores the sub-grid cloud variations substantially overestimates the radar reflectivity and probability of precipitation compared to the satellite observations, as well as the results from the SPCAM5 COSP run. The significant differences between the two COSP runs demonstrate that it is important to take into account the sub-grid variations of cloud and precipitation when using COSP to evaluate the GCM to avoid confusing and misleading results.

Microphysical variability of Amazonian deep convective cores observed by CloudSat and simulated by a multi-scale modeling framework.

Recently launched cloud observing satellites provide information about the vertical structure of deep convection and its microphysical characteristics. In this study, CloudSat reflectivity data is stratified by cloud type, and the contoured frequency by altitude diagrams reveal a double-arc structure in deep convective cores (DCCs) above 8 km. This suggests two distinct hydrometeor modes (snow versus hail/graupel) controlling variability in reflectivity profiles. The day–night contrast in the double arcs is about four times larger than the wet–dry season contrast. Using QuickBeam, the vertical reflectivity structure of DCCs is analyzed in two versions of the Superparameterized Community Atmospheric Model (SP-CAM) with single-moment (no graupel) and double-moment (with graupel) microphysics. Double-moment microphysics shows better agreement with observed reflectivity profiles; however, neither model variant captures the double-arc structure. Ultimately, the results show that simulating realistic DCC vertical structure and its variability requires accurate representation of ice microphysics, in particular the hail/graupel modes, though this alone is insufficient.

Changes in Temperature and Precipitation Extremes in Superparameterized CAM in Response to Warmer SSTs

Subdaily temperature and precipitation extremes in response to warmer SSTs are investigated on a global scale using the superparameterized (SP) Community Atmosphere Model (CAM), in which a cloud-resolving model is embedded in each CAM grid column to simulate convection explicitly. Two 10-yr simulations have been performed using present climatological sea surface temperature (SST) and perturbed SST climatology derived from the representative concentration pathway 8.5 (RCP8.5) scenario. Compared with the conventional CAM, SP-CAM simulates colder temperatures and more realistic intensity distribution of precipitation, especially for heavy precipitation. The temperature and precipitation extremes have been defined by the 99th percentile of the 3-hourly data. For temperature, the changes in the warm and cold extremes are generally consistent between CAM and SP-CAM, with larger changes in warm extremes at low latitudes and larger changes in cold extremes at mid-to-high latitudes. For precipitation, CAM predicts a uniform increase of frequency of precipitation extremes regardless of the rain rate, while SP-CAM predicts a monotonic increase of frequency with increasing rain rate and larger change of intensity for heavier precipitation. The changes in 3-hourly and daily temperature extremes are found to be similar; however, the 3-hourly precipitation extremes have a significantly larger change than daily extremes. The Clausius–Clapeyron scaling is found to be a relatively good predictor of zonally averaged changes in precipitation extremes over midlatitudes but not as good over the tropics and subtropics. The changes in precipitable water and large-scale vertical velocity are equally important to explain the changes in precipitation extremes.

Simultaneous characterization of mesoscale and convective‐scale tropical rainfall extremes and their dynamical and thermodynamic modes of change

AbstractThe Superparameterized Community Atmosphere Model (SPCAM) is used to identify the dynamical and organizational properties of tropical extreme rainfall events on two scales. We compare the mesoscales resolved by General Circulation Models (GCMs) and the convective scales resolved by Cloud‐Resolving Models (CRMs) to reassess and extend on previous results from GCMs and CRMs in radiative‐convective equilibrium. We first show that the improved representation of subgridscale dynamics in SPCAM allows for a close agreement with the 7%/K Clausius‐Clapeyron rate of increase in mesoscale extremes rainfall rates. Three contributions to changes in extremes are quantified and appear consistent in sign and relative magnitude with previous results. On mesoscales, the thermodynamic contribution (5.8%/K) and the contribution from mass flux increases (2%/K) enhance precipitation rates, while the upward displacement of the mass flux profile (‐1.1%/K) offsets this increase. Convective‐scale extremes behave similarly except that changes in mass flux are negligible due to a balance between greater numbers of strong updrafts and downdrafts and lesser numbers of weak updrafts. Extremes defined on these two scales behave as two independent sets of rainfall events, with different dynamics, geometries, and responses to climate change. In particular, dynamic changes in mesoscale extremes appear primarily sensitive to changes in the large‐scale mass flux, while the intensity of convective‐scale extremes is not. In particular, the increases in mesoscale mass flux directly contribute to the intensification of mesoscale extreme rain, but do not seem to affect the increase in convective‐scale rainfall intensities. These results motivate the need for better understanding the role of the large‐scale forcing on the formation and intensification of heavy convective rainfall.

Impact of Environmental Aerosols on a Developing Extratropical Cyclone in the Superparameterized Community Atmosphere Model

Abstract The impacts of environmental aerosols on the growth of an extratropical cyclone in a realistic winter flow setting are investigated using the superparameterized Community Atmosphere Model (SP-CAM) where cloud-scale dynamics and thermodynamics are explicitly resolved. An examination of the results from 13 ensemble pairs suggests that the growth rate of the cyclone is temporarily reduced as a result of increased aerosol concentrations. A convection–advection–moisture self-adjustment (CAMS) mechanism of aerosol–cyclone interaction is proposed to explain this finding. Specifically, the weakened growth is unambiguously attributed to the weakening of the cold advection underneath the midtropospheric trough of the cyclone. The weakened cold advection is in turn driven by a decrease of the zonal temperature gradient that is tied to the reduced latent heating in the stratiform cloud region of the cyclone. Invigoration of convection ahead of the cold front by aerosols is found to be directly responsible for a suppressed moisture supply into the stratiform cloud region and thus the reduced latent heating there. The regional climate implications of these results are discussed. Also highlighted is the importance of incorporating aerosol microphysical effects on deep convection in any modeling effort that aims to understand aerosol–circulation interaction at the extratropics.

A mechanism‐denial study on the Madden‐Julian Oscillation with reduced interference from mean state changes

AbstractMechanism‐denial experiments using Superparameterized Community Atmosphere Model are conducted to investigate the importance of extratropical and circumnavigating waves, wind‐evaporation feedback, and radiative‐convective feedback to the Madden‐Julian Oscillation (MJO). A common issue with mechanism‐denial studies is the interference from mean state changes when processes are turned off in the model. Here time‐invariant forcing and nudging on effective timescales longer than the intraseasonal timescale are implemented to maintain the mean state. The MJO activity remains largely unchanged with suppressed extratropical and circumnavigating waves when the mean state is maintained to be close to that of the control run, suggesting that excitation of MJO by extratropical and circumnavigating waves is not necessary for the existence of MJO in this model. It is also shown that the wind‐evaporation feedback slows down eastward propagation of the MJO, and the radiative‐convective feedback amplifies the MJO.

Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model

AbstractThis study evaluates several important statistics of daily rainfall based on frequency and amount distributions as simulated by a global climate model whose precipitation does not depend on convective parameterization—Super‐Parameterized Community Atmosphere Model (SPCAM). Three superparameterized and conventional versions of CAM, coupled within the Community Earth System Model (CESM1 and CCSM4), are compared against two modern rainfall products (GPCP 1DD and TRMM 3B42) to discriminate robust effects of superparameterization that emerge across multiple versions. The geographic pattern of annual‐mean rainfall is mostly insensitive to superparameterization, with only slight improvements in the double‐ITCZ bias. However, unfolding intensity distributions reveal several improvements in the character of rainfall simulated by SPCAM. The rainfall rate that delivers the most accumulated rain (i.e., amount mode) is systematically too weak in all versions of CAM relative to TRMM 3B42 and does not improve with horizontal resolution. It is improved by superparameterization though, with higher modes in regions of tropical wave, Madden‐Julian Oscillation, and monsoon activity. Superparameterization produces better representations of extreme rates compared to TRMM 3B42, without sensitivity to horizontal resolution seen in CAM. SPCAM produces more dry days over land and fewer over the ocean. Updates to CAM's low cloud parameterizations have narrowed the frequency peak of light rain, converging toward SPCAM. Poleward of 50°, where more rainfall is produced by resolved‐scale processes in CAM, few differences discriminate the rainfall properties of the two models. These results are discussed in light of their implication for future rainfall changes in response to climate forcing.

The effect of large‐scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM

AbstractThe effect of global climate model (GCM) time step—which also controls how frequently global and embedded cloud resolving scales are coupled—is examined in the Superparameterized Community Atmosphere Model ver 3.0. Systematic bias reductions of time‐mean shortwave cloud forcing (∼10 W/m2) and longwave cloud forcing (∼5 W/m2) occur as scale coupling frequency increases, but with systematically increasing rainfall variance and extremes throughout the tropics. An overarching change in the vertical structure of deep tropical convection, favoring more bottom‐heavy deep convection as a global model time step is reduced may help orchestrate these responses. The weak temperature gradient approximation is more faithfully satisfied when a high scale coupling frequency (a short global model time step) is used. These findings are distinct from the global model time step sensitivities of conventionally parameterized GCMs and have implications for understanding emergent behaviors of multiscale deep convective organization in superparameterized GCMs. The results may also be useful for helping to tune them.

Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM

AbstractThe superparameterized Community Atmosphere Model (SPCAM) is used to investigate the impact and geographic sensitivity of positive Indian Ocean Dipole (+IOD) sea‐surface temperatures (SSTs) on Madden‐Julian oscillation (MJO) propagation. The goal is to clarify potentially appreciable +IOD effects on MJO dynamics detected in prior studies by using a global model with explicit convection representation. Prescribed climatological October SSTs and variants of the SST distribution from October 2006, a +IOD event, force the model. Modest MJO convection weakening over the Maritime Continent occurs when either climatological SSTs, or +IOD SST anomalies restricted to the Indian Ocean, are applied. However, severe MJO weakening occurs when either +IOD SST anomalies are applied globally or restricted to the equatorial Pacific. MJO disruption is associated with time‐mean changes in the zonal wind profile and lower moist static energy (MSE) in subsiding air masses imported from the Subtropics by Rossby‐like gyres. On intraseasonal scales, MJO disruption arises from significantly smaller MSE accumulation, weaker meridional advective moistening, and overactive submonthly eddies that mix drier subtropical air into the path of MJO convection. These results (1) demonstrate that SPCAM reproduces observed time‐mean and intraseasonal changes during +IOD episodes, (2) reaffirm the role that submonthly eddies play in MJO propagation and show that such multiscale interactions are sensitive to interannual SST states, and (3) suggest that boreal fall +IOD SSTs local to the Indian Ocean have a significantly smaller impact on Maritime Continent MJO propagation compared to contemporaneous Pacific SST anomalies which, for October 2006, resemble El Niño‐like conditions.

Mean‐state acceleration of cloud‐resolving models and large eddy simulations

AbstractLarge eddy simulations and cloud‐resolving models (CRMs) are routinely used to simulate boundary layer and deep convective cloud processes, aid in the development of moist physical parameterization for global models, study cloud‐climate feedbacks and cloud‐aerosol interaction, and as the heart of superparameterized climate models. These models are computationally demanding, placing practical constraints on their use in these applications, especially for long, climate‐relevant simulations. In many situations, the horizontal‐mean atmospheric structure evolves slowly compared to the turnover time of the most energetic turbulent eddies. We develop a simple scheme to reduce this time scale separation to accelerate the evolution of the mean state. Using this approach we are able to accelerate the model evolution by a factor of 2–16 or more in idealized stratocumulus, shallow and deep cumulus convection without substantial loss of accuracy in simulating mean cloud statistics and their sensitivity to climate change perturbations. As a culminating test, we apply this technique to accelerate the embedded CRMs in the Superparameterized Community Atmosphere Model by a factor of 2, thereby showing that the method is robust and stable to realistic perturbations across spatial and temporal scales typical in a GCM.

Restricting 32–128 km horizontal scales hardly affects the MJO in the Superparameterized Community Atmosphere Model v.3.0 but the number of cloud‐resolving grid columns constrains vertical mixing

AbstractThe effects of artificially restricting the 32–128 km horizontal scale regime on MJO dynamics in the Superparameterized Community Atmosphere Model v.3.0 have been explored through reducing the extent of its embedded cloud resolving model (CRM) arrays. Two and four‐fold reductions in CRM extent (from 128 to 64 km and 32 km) produce statistical composite MJO signatures with spatial scale, zonal phase speed, and intrinsic wind‐convection anomaly structure that are all remarkably similar to the standard SPCAM's MJO. This suggests that the physics of mesoscale convective organization on 32–128 km scales are not critical to MJO dynamics in SPCAM and that reducing CRM extent may be a viable strategy for 400% more computationally efficient analysis of superparameterized MJO dynamics. However several unexpected basic state responses caution that extreme CRM domain reduction can lead to systematic mean state issues in superparameterized models. We hypothesize that an artificial limit on the efficiency of vertical updraft mixing is set by the number of grid columns available for compensating subsidence in the embedded CRM arrays. This can lead to reduced moisture ventilation supporting too much liquid cloud and thus an overly strong cloud shortwave radiative forcing, particularly in regions of deep convection.

Simulation of the South Asian Monsoon in a Coupled Model with an Embedded Cloud-Resolving Model

Abstract The simulation of the South Asian monsoon by a coupled ocean–atmosphere model with an embedded cloud-resolving model is analyzed on intraseasonal and interannual time scales. The daily modes of variability in the superparameterized Community Climate System Model, version 3 (SP-CCSM), are compared with those in observation, the superparameterized Community Atmospheric Model, version 3 (SP-CAM3), and the control simulation of CCSM (CT-CCSM) with conventional parameterization of convection. The CT-CCSM fails to simulate the observed intraseasonal oscillations but is able to generate the atmospheric El Niño–Southern Oscillation (ENSO) mode, although with regular biennial variability. The dominant modes of variability extracted from daily anomalies of outgoing longwave radiation, precipitation, and low-level horizontal wind in SP-CCSM consist of two intraseasonal oscillations and two seasonally persisting modes, in good agreement with observation. The most significant observed features of the intraseasonal oscillations correctly simulated by the SP-CCSM are the northward propagation of convection, precipitation, and circulation as well as the eastward and westward propagations. The observed spatial structure and the periods of the oscillations are also well captured by the SP-CCSM, although with lesser magnitude. The SP-CCSM is able to simulate the chaotic variability and spatial structure of the seasonally persisting atmospheric ENSO mode, while the evidence for the Indian Ocean dipole mode is inconclusive. The SP-CAM3 simulates two intraseasonal oscillations and the atmospheric ENSO mode. However, the intraseasonal oscillations in SP-CAM3 do not show northward propagation while their periods and spatial structures are not comparable to observation. The results of this study indicate the necessity of coupled models with sufficiently realistic cloud parameterizations.

Moist Static Energy Budget of MJO-like Disturbances in the Atmosphere of a Zonally Symmetric Aquaplanet

Abstract A Madden–Julian oscillation (MJO)-like spectral feature is observed in the time–space spectra of precipitation and column-integrated moist static energy (MSE) for a zonally symmetric aquaplanet simulated with Superparameterized Community Atmospheric Model (SPCAM). This disturbance possesses the basic structural and propagation features of the observed MJO. To explore the processes involved in propagation and maintenance of this disturbance, this study analyzes the MSE budget of the disturbance. The authors observe that the disturbances propagate both eastward and poleward. The column-integrated longwave heating is the only significant source of column-integrated MSE acting to maintain the MJO-like anomaly balanced against the combination of column-integrated horizontal and vertical advection of MSE and latent heat flux. Eastward propagation of the MJO-like disturbance is associated with MSE generated by both column integrated horizontal and vertical advection of MSE, with the column longwave heating generating MSE that retards the propagation. The contribution to the eastward propagation by the column-integrated horizontal advection of MSE is dominated by synoptic eddies. Further decomposition indicates that the advection contribution to the eastward propagation is dominated by meridional advection of MSE by anomalous synoptic eddies caused by the suppression of eddy activity ahead of the MJO convection. This suppression is linked to the barotropic conversion mechanism, with the gradients of the low-frequency wind experienced by the synoptic eddies within the MJO envelope acting to modulate the eddy kinetic energy. The meridional eddy advection’s contribution to poleward propagation is dominated by the mean state’s (meridionally varying) eddy activity acting on the anomalous MSE gradients associated with the MJO.