Parameterization Of Moist Convection Research Articles

The Global Atmosphere-aerosol Model ICON-A-HAM2.3-Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness.

The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

The interaction of deep convection with the general circulation in Titan’s atmosphere. Part 2: Impacts on the climate

The impact of methane convection on the circulation of Titan is investigated in the Titan Atmospheric Model (TAM), using a simplified Betts–Miller (SBM) moist convection parameterization scheme. We vary the reference relative humidity (RHSBM) and relaxation timescale of convection (τ) parameters of the SBM scheme. Titan’s atmosphere is mostly insensitive to changes in τ, but convective instability and precipitation are highly impacted by changes in RHSBM. Convection behavior changes from infrequent (<1 per Titan year), intense events at summer solstice that quickly encompass the entire globe at low RHSBM to near-continuous precipitation at the poles during summer at high RHSBM (85%). The intermediate regime (RHSBM=70%–80%) consists of frequent events (∼10 per Titan year) of moderate intensity that are limited in meridional extent to their respective hemisphere. Using results from the Titan Regional Atmospheric Modeling System (TRAMS) and observations, we tune the parameters of the SBM parameterization with optimum values of RH=80% and τ=28800 s. We present a simulated decadal climatology that qualitatively matches observations of Titan’s humidity and cloud activity and generally resembles previous results with TAM. Comparing this simulation to one without moist convection demonstrates that convection strengthens the meridional circulation, warms the mid-levels and cools the surface at the poles, and magnifies zonal-mean global moisture anomalies.

Kilometer-Scale Climate Models: Prospects and Challenges

AbstractCurrently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limited-area numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

Toward a Dynamical Foundation for Organized Convection Parameterization in GCMs

AbstractA dynamically based parameterization of organized moist convection in the form of multiscale coherent structures and slantwise layer overturning principles provides upscale heat and counter‐gradient momentum transports distinct from diffusive down‐gradient mixing by unorganized cumulus. Implementation of a minimalist version of this new parameterization in a state‐of‐the‐art global climate model (GCM) improves the Madden‐Julian Oscillation and convectively coupled waves and generates large‐scale patterns of tropical precipitation consistent with Tropical Rainfall Measuring Mission satellite measurements. A question on the need to parameterize mesoscale convective organization to mitigate mean state biases is addressed.

A Lagrangian Perspective on Parameterizing Deep Convection

Abstract The parameterization of deep moist convection as a subgrid-scale process in numerical models of the atmosphere is required at resolutions that extend well into the convective “gray zone,” the range of grid spacings over which such convection is partially resolved. However, as model resolution approaches the gray zone, the assumptions upon which most existing convective parameterizations are based begin to break down. We focus here on one aspect of this problem that emerges as the temporal and spatial scales of the model become similar to those of deep convection itself. The common practice of static tendency application over a prescribed adjustment period leads to logical inconsistencies at resolutions approaching the gray zone, while more frequent refreshment of the convective calculations can lead to undesirable intermittent behavior. A proposed parcel-based treatment of convective initiation introduces memory into the system in a manner that is consistent with the underlying physical principles of convective triggering, thus reducing the prevalence of unrealistic gradients in convective activity in an operational model running with a 10 km grid spacing. The subsequent introduction of a framework that considers convective clouds as persistent objects, each possessing unique attributes that describe physically relevant cloud properties, appears to improve convective precipitation patterns by depicting realistic cloud memory, movement, and decay. Combined, this Lagrangian view of convection addresses one aspect of the convective gray zone problem and lays a foundation for more realistic treatments of the convective life cycle in parameterization schemes.

Extratropical Cyclone Clouds in the GFDL Climate Model: Diagnosing Biases and the Associated Causes

AbstractThe clouds in Southern Hemisphere extratropical cyclones generated by the GFDL climate model are analyzed against MODIS, CloudSat, and CALIPSO cloud and precipitation observations. Two model versions are used: one is a developmental version of “AM4,” a model GFDL that will utilize for CMIP6, and the other is the same model with a different parameterization of moist convection. Both model versions predict a realistic top-of-atmosphere cloud cover in the southern oceans, within 5% of the observations. However, an examination of cloud cover transects in extratropical cyclones reveals a tendency in the models to overestimate high-level clouds (by differing amounts) and underestimate cloud cover at low levels (again by differing amounts), especially in the post–cold frontal (PCF) region, when compared with observations. In focusing only on the models, it is seen that their differences in high and midlevel clouds are consistent with their differences in convective activity and relative humidity (RH), but the same is not true for the PCF region. In this region, RH is higher in the model with less cloud fraction. These seemingly contradictory cloud and RH differences can be explained by differences in the cloud-parameterization tuning parameters that ensure radiative balance. In the PCF region, the model cloud differences are smaller than either of the model biases with respect to observations, suggesting that other physics changes are needed to address the bias. The process-oriented analysis used to assess these model differences will soon be automated and shared.

A Unified Eddy-Diffusivity/Mass-Flux Approach for Modeling Atmospheric Convection

Abstract A fully unified parameterization of boundary layer and moist convection (shallow and deep) is presented. The new parameterization is based on the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) approach, which distinguishes between convective plumes and nonconvective mixing. The convective plumes represent both surface-forced updrafts and evaporatively driven downdrafts. The type of convection (i.e., dry, shallow, or deep) represented by the updrafts is not defined a priori, but rather depends on the near-surface updraft properties and the stochastic interactions between the plumes and the environment through lateral entrainment. Consequently, some updrafts may contribute only to the nonlocal transport within the subcloud layer, while others may condense and form shallow or even deep convection. Such a formulation is void of trigger functions and additional closures typical of modular parameterizations. The updrafts are coupled to relatively simple warm-, mixed-, and ice-phase microphysics. Each precipitating updraft forms a downdraft driven by the evaporation of detrained precipitation. The downdrafts control the development of cold pools near the surface that can invigorate convection. The new parameterization is tested in a single-column model against large-eddy simulations (LESs) for cases representing weakly precipitating marine convection and the diurnal cycle of continental deep convection. The results of these EDMF experiments compare well with the LES reference simulations. In particular, the transitions between the different dominant convection regimes are realistically simulated.

Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events

AbstractThe parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high‐resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here we focus on these issues using idealized tests in which an ML‐based parameterization is trained on output from a conventional parameterization and its performance is assessed in simulations with a GCM. We use an ensemble of decision trees (random forest) as the ML algorithm, and this has the advantage that it automatically ensures conservation of energy and nonnegativity of surface precipitation. The GCM with the ML convective parameterization runs stably and accurately captures important climate statistics including precipitation extremes without the need for special training on extremes. Climate change between a control climate and a warm climate is not captured if the ML parameterization is only trained on the control climate, but it is captured if the training includes samples from both climates. Remarkably, climate change is also captured when training only on the warm climate, and this is because the extratropics of the warm climate provides training samples for the tropics of the control climate. In addition to being potentially useful for the simulation of climate, we show that ML parameterizations can be interrogated to provide diagnostics of the interaction between convection and the large‐scale environment.

Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

AbstractStochastic schemes to represent model uncertainty in the European Centre for Medium‐Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud‐resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium‐range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October–November 2011. These are well‐studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large‐scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.

Stochastic Parameterization of Convective Area Fractions with a Multicloud Model Inferred from Observational Data

Abstract Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convection parameterization schemes used in general circulation models.

Spontaneous QBO-like oscillations in an atmospheric model dynamical core

[1] The ability of general circulation models (GCMs) to simulate the quasi-biennial oscillation (QBO) is an important model characteristic. Typically, the moist convective parameterization is believed to be the key GCM component that triggers tropical waves, thereby forcing wave-mean flow interactions. We show that QBO-like oscillations can also be simulated in a dry dynamical core driven by the Held-Suarez forcing. No gravity wave drag parameterization is applied. The simulations utilize the semi-Lagrangian spectral transform dynamical core of National Center for Atmospheric Research's Community Atmosphere Model. The QBO-like signal has a long period between 42–45 months and occurs in the upper stratosphere; different from observations. However, the amplitudes, asymmetries, and meridional extent closely resemble the observed QBO. Wave-number frequency analysis shows that resolved equatorially trapped waves are abundant despite the absence of cumulus convection. A Transformed Eulerian-Mean analysis suggests that the divergence of the Eliassen-Palm flux and vertical advection provide most of the forcing counteracted by diffusion.

Convective Entrainment and Large-Scale Organization of Tropical Precipitation: Sensitivity of the CNRM-CM5 Hierarchy of Models

Abstract The spurious double intertropical convergence zone (ITCZ) is a systematic bias affecting state-of-the-art coupled general circulation models (GCMs). Modeling studies show that the ITCZ structure is very sensitive to moist convection parameterization and in particular, to the vertical profile of convective heating and free-tropospheric moistening. To further explore this sensitivity, the authors focus in this study on the influence of lateral entrainment in convective plumes on the simulated tropical precipitation and large-scale circulation. Sensitivity studies to the entrainment parameter were performed in a hierarchy of models (coupled ocean–atmosphere GCM, atmospheric GCM, and aquaplanet GCM), in order to mitigate the double ITCZ problem in the Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5 (CNRM-CM5). The sensitivity of the ITCZ structure to lateral entrainment is robust across our hierarchy of models. In response to increased entrainment, the realistic simulations exhibit a weakening of the southern side of the double ITCZ over the southeastern Pacific Ocean and a better representation of the South Pacific convergence zone (SPCZ). However, as a result of stronger moisture–convection feedbacks, precipitation is overestimated in the center of convergence zones. The change in ITCZ configuration is associated with a more realistic representation of the large-scale vertical regimes, explained by a decreased frequency of weak-to-moderate ascending regimes and an enhanced frequency of subsidence regimes. Mechanisms at play in this circulation change are examined by analyzing the vertically integrated dry static energy budget. This energetic analysis suggests that the feedback between large-scale dynamics and deep convection is crucial in controlling the probability distribution function (PDF) of midtropospheric vertical wind. This PDF, in turn, controls the precipitation distribution and, in particular, the double ITCZ bias.

Cloud-system resolving simulations with the NASA Goddard Earth Observing System global atmospheric model (GEOS-5)

[1] The NASA Global Modeling and Assimilation Office (GMAO) has developed a global non-hydrostatic cloud-system resolving capability within the NASA Goddard Earth Observing System global atmospheric model version 5 (GEOS-5). Using a non-hydrostatic finite-volume dynamical core coupled with advances in the moist physics and convective parameterization the model has been used to perform cloud-system resolving experiments at resolutions as fine as 3.5- to 14-km globally. An overview of preliminary results highlights the development of mid-latitude cyclones, the overall representation of global tropical convection, intense convective activity within the eye wall and outer rain bands of the 2009 Atlantic hurricane Bill validated by satellite observations, and the seasonal predictability of global tropical cyclone activity with realistic intensities. These preliminary results provide motivation for the use of GEOS-5 to simulate multi-scale convective systems within a global model at cloud resolving resolutions.

Mesoscale spectrum of atmospheric motions investigated in a very fine resolution global general circulation model

The horizontal spectrum of wind variance, conventionally referred to as the kinetic energy spectrum, is examined in experiments conducted with the Atmospheric GCM for the Earth Simulator (AFES) global spectral general circulation model. We find that the control version of AFES run at T639 horizontal spectral resolution simulates a kinetic energy spectrum that compares well at large scales with global observational reanalyses and, at smaller scales, with available aircraft observations at near‐tropopause levels. Specifically there is a roughly −3 power‐law dependence on horizontal wave number for wavelengths between about 5000 and 500 km, transitioning to a shallower mesoscale regime at smaller wavelengths. This is seen for both one‐dimensional spectra and for the two‐dimensional total wave number spectrum based on a spherical harmonic analysis. The simulated spectrum at midtropospheric levels is similar in that there is a transition to a shallower mesoscale regime, but the spectrum in the mesoscale is clearly steeper at midtroposphere than near the tropopause. There seem to be no extensive observations of horizontal spectra available in the midtroposphere, so it is not known whether the contrast seen in the model between upper and mid tropospheric levels is realistic. The dependence of the model simulated variability on the subgrid‐scale moist convection parameterization is examined. The space‐time variability of rainfall is shown to depend strongly on the convection scheme employed. The tropospheric kinetic energy spectrum in the mesoscale seems to be correlated with the precipitation behavior, so that in a version with a more variable precipitation field the kinetic energy in the mesoscale is enhanced. This suggests that the mesoscale motions in the model may be directly forced to a significant extent by the variability in the latent heating field. Experiments were also performed with a dry dynamical core version of the model run at both T639 and T1279 resolutions. This version also simulated a shallow mesoscale range, supporting the view that the mesoscale regime in the atmosphere is energized, at least in part, by a predominantly forward (i.e., downscale) nonlinear spectral cascade. Experiments with various formulations of the hyperdiffusion horizontal mixing parameterization show that the kinetic energy spectrum over about the last half of the resolved wave number range is under strong control by the parameterized mixing. However, the T1279 model simulates almost a decade of the shallow mesoscale regime (i.e., for horizontal wavelengths from about 80 to 500 km) that appears to be fairly independent of the diffusion employed. Finally, experiments are conducted in the dry version to see the effects on the kinetic energy spectrum of changing the thermal Rossby number for the simulations.

Stochastic Nature of Physical Parameterizations in Ensemble Prediction: A Stochastic Convection Approach

Abstract In this paper it is argued that ensemble prediction systems can be devised in such a way that physical parameterizations of subgrid-scale motions are utilized in a stochastic manner, rather than in a deterministic way as is typically done. This can be achieved within the context of current physical parameterization schemes in weather and climate prediction models. Parameterizations are typically used to predict the evolution of grid-mean quantities because of unresolved subgrid-scale processes. However, parameterizations can also provide estimates of higher moments that could be used to constrain the random determination of the future state of a certain variable. The general equations used to estimate the variance of a generic variable are briefly discussed, and a simplified algorithm for a stochastic moist convection parameterization is proposed as a preliminary attempt. Results from the implementation of this stochastic convection scheme in the Navy Operational Global Atmospheric Prediction System (NOGAPS) ensemble are presented. It is shown that this method is able to generate substantial tropical perturbations that grow and “migrate” to the midlatitudes as forecast time progresses while moving from the small scales where the perturbations are forced to the larger synoptic scales. This stochastic convection method is able to produce substantial ensemble spread in the Tropics when compared with results from ensembles created from initial-condition perturbations. Although smaller, there is still a sizeable impact of the stochastic convection method in terms of ensemble spread in the extratropics. Preliminary simulations with initial-condition and stochastic convection perturbations together in the same ensemble system show a promising increase in ensemble spread and a decrease in the number of outliers in the Tropics.

Multiscale Models with Moisture and Systematic Strategies for Superparameterization

Abstract The accurate parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate through numerical models. Superparameterization is a promising recent alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model. Basic scales for cloud-resolving modeling are the microscales on the order of 10 km in space on time scales on the order of 15 min, where vertical and horizontal motions are comparable and moist processes are strongly nonlinear (meso-gamma scale). In this paper, systematic multiscale asymptotic analysis is utilized to develop simplified microscale mesoscale dynamic (MMD) models for interaction between the microscales and spatiotemporal mesoscales on the order of 100 km and 2.5 h (meso-beta scale). The new MMD models lead to a systematic framework for superparameterization for numerical weather prediction (NWP) generalizing the traditional column modeling framework. The MMD formulation also provides a flexible systematic framework for devising new parameterization strategies for NWP intermediate between the two extremes of column modeling and detailed cloud-resolving modeling. It is also established here that these MMD models fit crudely into the recent systematic multiscale framework developed to explain the observed larger-scale statistical self-similarity of tropical convection, and therefore provide a systematic framework for superparameterization. Finally, it is shown that the new MMD models have the structure of a heterogeneous multiscale method so that many numerical techniques recently developed in the applied mathematics literature can be applied to this formulation.

An Analysis of the Potential for Extreme Temperature Change Based on Observations and Model Simulations

Abstract The study analyzes observational climate data for June–August 1977–2004 and simulations of current and future climate scenarios from a nested GCM/regional climate model system to assess the potential for extreme temperature change over the eastern United States. Observational evidence indicates that anomalously warm summers in the eastern United States coincide with anomalously cool eastern Pacific sea surface temperatures, conditions that are conducive to geopotential ridging over the east, less frequent precipitation, and lower accumulated rainfall. The study also found that days following nighttime rain are warmer on average than daytime rain events, emphasizing the importance of the timing of precipitation on the radiation balance. Precipitation frequency and eastern Pacific sea surface temperature anomalies together account for 57% of the 28-yr variance in maximum surface temperature anomalies. Simulation results show the sensitivity of maximum surface air temperature to the moist convection parameterization that is employed, since different schemes produce different diurnal cycles and frequencies of precipitation. The study suggests that, in order to accurately project scenarios of extreme temperature change, models need to realistically simulate changes in the surface energy balance caused by the interannual variation of these precipitation characteristics. The mesoscale model that was realistic in this respect predicted much warmer mean and maximum surface air temperatures for five future summers than the parallel GCM driving simulation.

A Combined Eddy-Diffusivity Mass-Flux Approach for the Convective Boundary Layer

Abstract A better conceptual understanding and more realistic parameterizations of convective boundary layers in climate and weather prediction models have been major challenges in meteorological research. In particular, parameterizations of the dry convective boundary layer, in spite of the absence of water phase-changes and its consequent simplicity as compared to moist convection, typically suffer from problems in attempting to represent realistically the boundary layer growth and what is often referred to as countergradient fluxes. The eddy-diffusivity (ED) approach has been relatively successful in representing some characteristics of neutral boundary layers and surface layers in general. The mass-flux (MF) approach, on the other hand, has been used for the parameterization of shallow and deep moist convection. In this paper, a new approach that relies on a combination of the ED and MF parameterizations (EDMF) is proposed for the dry convective boundary layer. It is shown that the EDMF approach follows naturally from a decomposition of the turbulent fluxes into 1) a part that includes strong organized updrafts, and 2) a remaining turbulent field. At the basis of the EDMF approach is the concept that nonlocal subgrid transport due to the strong updrafts is taken into account by the MF approach, while the remaining transport is taken into account by an ED closure. Large-eddy simulation (LES) results of the dry convective boundary layer are used to support the theoretical framework of this new approach and to determine the parameters of the EDMF model. The performance of the new formulation is evaluated against LES results, and it is shown that the EDMF closure is able to reproduce the main properties of dry convective boundary layers in a realistic manner. Furthermore, it will be shown that this approach has strong advantages over the more traditional countergradient approach, especially in the entrainment layer. As a result, this EDMF approach opens the way to parameterize the clear and cumulus-topped boundary layer in a simple and unified way.

The Promise of Load Balancing the Parameterization of Moist Convection Using a Model Data Load Index

Abstract The parameterization of physical processes in atmospheric general circulation models contributes to load imbalances among individual processors of message-passing distributed-multiprocessor systems. Load imbalances increase the overall time to completion of a model run and should be eliminated or reduced as much as possible. Presented herein is a new technique that shows promise for load balancing the parameterization of moist convection found in the Community Climate System Model's (CCSM's) Community Atmosphere Model version 3 (CAM3). At the heart of this technique is a load index that is a marker for moist convection (called a model data load index). The marker for moist convection correlates directly to the amount of processing time per model grid cell and can therefore be used to effect a load balance. Spatial locality on the model grid and temporal locality between model time steps exist that allow a decomposition from a load-balancing step to be retained for multiple time steps. The analysis in this paper shows that the load balance does not need to be applied at every time step and that the number of steps in which the previous load balance remains effective is large enough for the overhead to be cost effective. Tests performed indicate that this technique is scalable to higher-resolution models as well as to higher processor counts than those presented. Through the use of the Load Balancing and Scheduling Framework (LBSF), this technique shows promise in reducing (by ∼47%) the time of the unbalanced load of one particular subroutine in CAM3 at the T85 spectral truncation. A maximum of 3.75 s of total execution time is saved over a 2430-time-step simulation. When extrapolated to a 1000-yr simulation, this translates to a potential savings of ∼22 h in that subroutine alone. Similar methods applied to remaining subroutines can add up to a significant savings. These results are encouraging in that a fine-grained load-balancing technique using the evolving characteristics of geophysical data paves the way for load balancing a broad range of physical calculations, both in CAM3 and other scientific applications, where more general techniques are not practical.

Interaction of moist convection with zonal jets on Jupiter and Saturn

Observations suggest that moist convection plays an important role in the large-scale dynamics of Jupiter's and Saturn's atmospheres. Here we use a reduced-gravity quasigeostrophic model, with a parameterization of moist convection that is based on observations, to study the interaction between moist convection and zonal jets on Jupiter and Saturn. Stable jets with approximately the same width and strength as observations are generated in the model. The observed zonal jets violate the barotropic stability criterion but the modeled jets do so only if the flow in the deep underlying layer is westward. The model results suggest that a length scale and a velocity scale associated with moist convection control the width and strength of the jets. The length scale and velocity scale offer a possible explanation of why the jets of Saturn are stronger and wider than those of Jupiter.