We compare equilibrium climate sensitivity (ECS) estimates from pairs of long (≥800‐year) control and abruptly quadrupled CO2 simulations with shorter (150‐ and 300‐year) coupled atmosphere‐ocean simulations and slab ocean models (SOMs). Consistent with previous work, ECS estimates from shorter coupled simulations based on annual averages for years 1–150 underestimate those from SOM (−8% ± 13%) and long (−14% ± 8%) simulations. Analysis of only years 21–150 improved agreement with SOM (−2% ± 14%) and long (−8% ± 10%) estimates. Use of pentadal averages for years 51–150 results in improved agreement with long simulations (−4% ± 11%). While ECS estimates from current generation U.S. models based on SOM and coupled annual averages of years 1–150 range from 2.6°C to 5.3°C, estimates based longer simulations of the same models range from 3.2°C to 7.0°C. Such variations between methods argues for caution in comparison and interpretation of ECS estimates across models.
We describe GFDL's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the pre‐industrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasi‐periodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.
This study describes the performance of two Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric general circulation models (AGCMs) in simulating the climatologies of planetary boundary layer (PBL) parameters, with a particular focus on the diurnal cycles. The two models differ solely in the PBL parameterization: one uses a prescribed K-profile PBL (KPP) scheme with an entrainment parameterization, and the other employs a turbulence kinetic energy (TKE) scheme. The models are evaluated through the comparison to the reanalysis ensemble, which is generated from ERA-20C, ERA-Interim, NCEP-CFSR and NASA-MERRA, and the following systematic biases are identified. The models exhibit wide-spread cold biases in the high latitudes, and the biases are smaller when the KPP scheme is used. The diurnal cycle amplitudes are underestimated in most dry regions, and the model with the TKE scheme simulates larger amplitudes. For the near-surface winds, the models underestimate both the daily means and the diurnal amplitudes. The differences between the models are relatively small compared to the biases.
The role of the PBL schemes in simulating the PBL parameters is investigated through the analysis of vertical profiles. The Sahara, which is suitable for focusing on the role of vertical mixing in dry PBLs, is selected for a detailed analysis. It reveals that compared to the KPP scheme, the heat transport is weaker with the TKE scheme in both convective and stable PBLs due to weaker vertical mixing, resulting in larger diurnal amplitudes. Lack of non-local momentum transport from the nocturnal low-level jets to the surfaces appears to explain the underestimation of the near-surface winds in the models.
In this two-part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part I, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode – with prescribed sea surface temperatures (SSTs) and sea ice distribution – is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top-of-atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part II, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.
In Part II of this two-part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part I. Part II provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.
We define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. We find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.
The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.
Schmidt, Gavin A., David Bader, Leo J Donner, G S Elsaesser, and Jean-Christophe Golaz, et al., September 2017: Practice and philosophy of climate model tuning across six U.S. modeling centers. Geoscientific Model Development, 10(9), DOI:10.5194/gmd-10-3207-2017. Abstract
Model calibration (or "tuning") is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major U.S. climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present day radiative imbalance vs. the implied balance in the pre-industrial as a target.
The severity of the double Intertropical Convergence Zone (DI) problem in climate models can be measured by a tropical precipitation asymmetry index (PAI), indicating whether tropical precipitation favors the Northern Hemisphere or the Southern Hemisphere. Examination of 19 Coupled Model Intercomparison Project phase 5 models reveals that the PAI is tightly linked to the tropical sea surface temperature (SST) bias. As one of the factors determining the SST bias, the asymmetry of tropical net surface heat flux in Atmospheric Model Intercomparison Project (AMIP) simulations is identified as a skillful predictor of the PAI change from an AMIP to a coupled simulation, with an intermodel correlation of 0.90. Using tropical top-of-atmosphere (TOA) fluxes, the correlations are lower but still strong. However, the extratropical asymmetries of surface and TOA fluxes in AMIP simulations cannot serve as useful predictors of the PAI change. This study suggests that the largest source of the DI bias is from the tropics and from atmospheric models.
Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that has empirically-set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model which includes large-scale feedbacks for cumulus convection, and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.
Uncertainty in equilibrium climate sensitivity impedes accurate climate projections. While the inter-model spread is known to arise primarily from differences in cloud feedback, the exact processes responsible for the spread remain unclear. To help identify some key sources of uncertainty, we use a developmental version of the next generation Geophysical Fluid Dynamics Laboratory global climate model (GCM) to construct a tightly controlled set of GCMs where only the formulation of convective precipitation is changed. The different models provide simulation of present-day climatology of comparable quality compared to the CMIP5 model ensemble. We demonstrate that model estimates of climate sensitivity can be strongly affected by the manner through which cumulus cloud condensate is converted into precipitation in a model’s convection parameterization, processes that are only crudely accounted for in GCMs. In particular, two commonly used methods for converting cumulus condensate into precipitation can lead to drastically different climate sensitivity, as estimated here with an atmosphere/land model by increasing sea surface temperatures uniformly and examining the response in the top-of-atmosphere energy balance. The effect can be quantified through a bulk convective detrainment efficiency, which measures the ability of cumulus convection to generate condensate per unit precipitation. The model differences, dominated by shortwave feedbacks, come from broad regimes ranging from large-scale ascent to subsidence regions. Given current uncertainties in representing convective precipitation microphysics and our current inability to find a clear observational constraint that favors one version of our model over the others, the implications of this ability to engineer climate sensitivity needs to be considered when estimating the uncertainty in climate projections.
CLUBB (Cloud Layers Unified by Binormals) is a higher-order closure (HOC) method with an assumed joint probability density function (PDF) for the subgrid variations in vertical velocity, temperature, and moisture. CLUBB has been implemented in the GFDL climate model AM3-CLUBB and successfully unifies the treatment of shallow convection, resolved clouds, and planetary boundary layer (PBL). In this study, we further explore the possibility for CLUBB to unify the deep convection in a new configuration referred as AM3-CLUBB+. AM3-CLUBB+ simulations with prescribed sea surface temperature are discussed. Cloud, radiation, and precipitation fields compare favorably with observations and reanalyses. AM3-CLUBB+ successfully captures the transition from stratocumulus to deep convection and the modulated response of liquid water path to aerosols. Simulations of tropical variability and the Madden-Julian oscillation (MJO) are also improved. Deficiencies include excessive tropical water vapor and insufficient ice clouds in the mid-latitudes.
Suzuki, K, Graeme L Stephens, Alejandro Bodas-Salcedo, Minghuai Wang, Jean-Christophe Golaz, T Yokohata, and T Koshiro, October 2015: Evaluation of the Warm Rain Formation Process in Global Models with Satellite Observations. Journal of the Atmospheric Sciences, 72(10), DOI:10.1175/JAS-D-14-0265.1. Abstract
This study examines the warm rain formation process over global ocean in global climate models. Methodologies developed to analyze CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations are employed to investigate the cloud-to-precipitation process of warm clouds and are applied to the model results to examine how the models represent the process for warm stratiform clouds. Despite a limitation of the present study that compares the statistics for stratiform clouds in climate models with those from satellite observations including both stratiform and (shallow) convective clouds, the statistics constructed with the methodologies are compared between the models and satellite observations to expose their similarities and differences. A problem common to some models is that they tend to produce rain at a faster rate than is observed. These model characteristics are further examined in the context of cloud microphysics parameterizations using a simplified one-dimensional model of warm rain formation that isolates key microphysical processes from full interactions with other processes in global climate models. The one-dimensional model equivalent statistics reproduce key characteristics of the global model statistics when corresponding auto-conversion schemes are assumed in the one-dimensional model. The global model characteristics depicted by the statistics are then interpreted as reflecting behaviors of the auto-conversion parameterizations adopted in the models. Comparisons of the one-dimensional model with satellite observations hint at improvements to the formulation of the parameterization scheme, thus offering a novel way of constraining key parameters in auto-conversion schemes of global models.
A unified turbulence and cloud parameterization based on multi-variate probability density functions (PDFs) has been incorporated into the GFDL atmospheric general circulation model AM3. This PDF-based parameterization not only predicts sub-grid variations in vertical velocity, temperature, and total water, which bridge sub-grid scale processes (such as aerosol activation and cloud microphysics) and grid-scale dynamic and thermodynamic fields, but also unifies the treatment of planetary boundary layer (PBL), shallow convection, and cloud macrophysics. This parameterization is the “Cloud Layers Unified By Binormals” (CLUBB) parameterization. With the incorporation of CLUBB in AM3, coupled with a two-moment cloud microphysical scheme, AM3-CLUBB allows for a more physically-based and self-consistent treatment of aerosol activation, cloud micro- and macro-physics, PBL, and shallow convection.
The configuration and performance of AM3-CLUBB are described. Cloud and radiation fields, as well as most basic climate features, are modeled realistically. Relative to AM3, AM3-CLUBB improves the simulation of coastal stratocumulus, a longstanding deficiency in GFDL models, and their seasonal cycle, especially at higher horizontal resolution, but global skill scores deteriorate slightly. Through sensitivity experiments, we show that (1) the two-moment cloud microphysics helps relieve the deficiency of coastal stratocumulus; (2) using the CLUBB sub-grid cloud water variability in the cloud microphysics has a considerable positive impact on global cloudiness; and (3) the impact of adjusting CLUBB parameters is to improve the overall agreement between model and observations.
[1] Climate models incorporate a number of adjustable parameters in their cloud formulations. They arise from uncertainties in cloud processes. These parameters are tuned to achieve a desired radiation balance and to best reproduce the observed climate. A given radiation balance can be achieved by multiple combinations of parameters.
[2] We investigate the impact of cloud tuning in the CMIP5 GFDL CM3 coupled climate model by constructing two alternate configurations. They achieve the desired radiation balance using different, but plausible, combinations of parameters. The present-day climate is nearly indistinguishable among all configurations. However, the magnitude of the aerosol indirect effects differs by as much as 1.2 Wm− 2, resulting in significantly different temperature evolution over the 20th century.
Huang, Jing, E Bou-Zeid, and Jean-Christophe Golaz, June 2013: Turbulence and Vertical Fluxes in the Stable Atmospheric Boundary-Layer II: A Novel Mixing Length Model. Journal of the Atmospheric Sciences, 70(6), DOI:10.1175/JAS-D-12-0168.1. Abstract
This is the second part of a study about turbulence and vertical fluxes in the stable atmospheric boundary-layer. Based on a suite of large-eddy simulations in the first part where the effects of stability on the turbulent structures and kinetic energy are investigated, first-order parameterization schemes are assessed and tested in the GFDL single-column model. The applicability of the gradient-flux hypothesis is first examined and it is found that stable conditions are favorable for that hypothesis. However, the concept of introducing a stability correction function fm as a multiplicative factor into the mixing length used under neutral conditions lN is shown to be problematic because fm computed a-priori from large-eddy simulations tends not be a universal function of stability. With this observation, a novel mixing length model is proposed, which conforms to large-eddy simulation results much better under stable conditions and converges to the classic model under neutral conditions. Test cases imposing steady as well as unsteady forcings are developed to evaluate the performance of the new model. It is found that the new model exhibits robust performance as the stability strength is changed, while other models are sensitive to changes in stability. For cases with unsteady forcings, which are very rarely simulated or tested, the results of the single-column model and large-eddy simulations are also closer when the new model is used, compared to the other models. However, unsteady cases are much more challenging for the turbulence closure formulations than cases with steady surface forcing.
Employing the Geophysical Fluid Dynamics Laboratory (GFDL)'s fully-coupled chemistry-climate (ocean/atmosphere/land/sea ice) model (CM3) with an explicit physical representation of aerosol indirect effects (cloud-water droplet activation), we find that the dramatic emission reductions (35–80%) in anthropogenic aerosols and their precursors projected by Representative Concentration Pathway (RCP) 4.5 result in ~1°C of additional warming and ~0.1 mm day−1 of additional precipitation, both globally averaged, by the end of the 21st century. The impact of these reductions in aerosol emissions on simulated global mean surface temperature and precipitation becomes apparent by mid-21st century. Furthermore, we find that the aerosol emission reductions cause precipitation to increase in East and South Asia by ~1.0 mm day−1 through the 2nd half of the 21st century. Both the simulated temperature and precipitation responses in CM3 are significantly stronger than the previously simulated responses in our earlier climate model (CM2.1) that only considered direct radiative forcing by aerosols. We conclude that sulfate aerosol indirect effects greatly enhance the impacts of aerosols on surface temperature in CM3, while both direct and indirect effects from sulfate aerosols dominate the strong precipitation response, possibly with a small contribution from carbonaceous aerosols. Just as we found with the previous GFDL model, CM3 produces surface warming patterns that are uncorrelated with the spatial distribution of 21stcentury changes in aerosol loading. However, the largest precipitation increases in CM3 are co-located with the region of greatest aerosol decrease, in and downwind of Asia.
A set of GFDL AM2 sensitivity simulations by varying an entrainment threshold rate to control deep convection occurrence are used to investigate how cumulus parameterization impacts tropical cloud and precipitation characteristics. In the Tropics, model convective precipitation (CP) is frequent and light, while large-scale precipitation (LSP) is intermittent and strong. With deep convection inhibited, CP decreases significantly over land and LSP increases prominently over ocean. This results in an overall redistribution of precipitation from land to ocean. A composite analysis reveals that cloud fraction (low and middle) and cloud condensate associated with LSP is substantially larger than those associated with CP. With about the same total precipitation and precipitation frequency distribution over the Tropics, simulations having greater LSP fraction tend to have larger cloud condensate and low and middle cloud fraction.
Simulations having greater LSP fraction tend to be drier and colder in the upper-troposphere. The induced unstable stratification supports strong transient wind perturbations and LSP. Greater LSP also contributes to greater intraseasonal (20-100 day) precipitation variability. Model LSP has a close connection to the low level convergence via the resolved grid-scale dynamics and thus a close coupling with the surface heat flux. Such wind-evaporation feedback is essential to the development and maintenance of LSP and enhances model precipitation variability. LSP has stronger dependence and sensitivity on column moisture than CP. The moisture-convection feedback, critical to tropical intraseasonal variability, is enhanced in simulations with large LSP. Strong precipitation variability accompanied by the worse mean state implies that an optimal precipitation partitioning is critical to model tropical climate simulation.
Suzuki, K, Jean-Christophe Golaz, and Graeme L Stephens, August 2013: Evaluating cloud tuning in a climate model with satellite observations. Geophysical Research Letters, 40(16), DOI:10.1002/grl.50874. Abstract
This study examines the validity of a tunable cloud parameter, the threshold particle radius triggering the warm rain formation, in a climate model. Alternate values of the model's particular parameter within uncertainty have been shown to produce severely different historical temperature trends due to differing magnitude of aerosol indirect forcing. Three different threshold radii are evaluated against satellite observations in terms of the statistics depicting microphysical process signatures of the warm rain formation. The results show that the simulated temperature trend best matches to observed trend when the model adopts the threshold radius that worst reproduces satellite-observed microphysical statistics and vice-versa. This inconsistency between the “bottom-up” process-based constraint and the “top-down” temperature trend constraint implies the presence of compensating errors in the model.
Zhang, M, Jean-Christophe Golaz, and Ming Zhao, et al., December 2013: CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models. Journal of Advances in Modeling Earth Systems, 5(4), DOI:10.1002/2013MS000246. Abstract
CGILS – the CFMIP-GASS Intercomparison of Large Eddy Models (LESs) and Single Column Models (SCMs) – investigates the mechanisms of cloud feedback in SCMs and LESs under idealized climate change perturbation. This paper describes the CGILS results from 15 SCMs and eight LES models. Three cloud regimes over the subtropical oceans are studied: shallow cumulus, stratocumulus, and well-mixed coastal stratus/stratocumulus. In the stratocumulus and coastal stratus regimes, SCMs without activated shallow convection generally simulated negative cloud feedbacks, while models with active shallow convection generally simulated positive cloud feedbacks. In the shallow cumulus regime, this relationship is less clear, likely due to the changes in cloud depth, lateral mixing, and precipitation or a combination of them. The majority of LES models simulated negative cloud feedback in the well-mixed coastal stratus/stratocumulus regime, and positive feedback in the shallow cumulus and stratocumulus regime. A general framework is provided to interpret SCM results: In a warmer climate, the moistening rate of the cloudy layer associated with the surface-based turbulence parameterization is enhanced; together with weaker large-scale subsidence, it causes negative cloud feedback. In contrast, in the warmer climate, the drying rate associated with the shallow convection scheme is enhanced. This causes positive cloud feedback. These mechanisms are summarized as “NESTS-SCOPE” (Negative feedback from Surface Turbulence under weaker Subsidence– Shallow Convection PositivE feedback) with the net cloud feedback depending on how the two opposing effects counteract each other. The LES results are consistent with these interpretations.
Seidel, D J., Y Zhang, A Beljaars, Jean-Christophe Golaz, A R Jacobson, and Brian Medeiros, September 2012: Climatology of the planetary boundary layer over the continental United States and Europe. Journal of Geophysical Research: Atmospheres, 117, D17106, DOI:10.1029/2012JD018143. Abstract
Although boundary layer processes are important in climate, weather and air quality,
boundary layer climatology has received little attention, partly for lack of observational
data sets. We analyze boundary layer climatology over Europe and the continental U.S.
using a measure of boundary layer height based on the bulk Richardson number. Seasonal
and diurnal variations during 1981–2005 are estimated from radiosonde observations, a
reanalysis that assimilates observations, and two contemporary climate models that do not.
Data limitations in vertical profiles introduce height uncertainties that can exceed 50% for
shallow boundary layers (<1 km) but are generally <20% for deeper boundary layers.
Climatological heights are typically <1 km during daytime and <0.5 km at night over both
regions. Seasonal patterns for daytime and nighttime differ; daytime heights are larger in
summer than winter, but nighttime heights are larger in winter. The four data sets show
similar patterns of spatial and seasonal variability but with biases that vary spatially,
seasonally, and diurnally. Compared with radiosonde observations, the reanalysis and the
climate models produce deeper layers due to difficulty simulating stable conditions.
The higher-time-resolution reanalysis reveals the diurnal cycle in height, with maxima in
the afternoon, and with amplitudes that vary seasonally (larger in summer) and regionally
(larger over western U.S. and southern Europe). The lower-time-resolution radiosonde
data and climate model simulations capture diurnal variations better over Europe than
over the U.S., due to differences in local sampling times.
Citation: Seidel, D. J., Y. Zhang, A. Beljaars, J.-C. Golaz, A. R. Jacobson,
Zhang, M, F P Bretherton, Peter N Blossey, Sandrine Bony, F Brient, and Jean-Christophe Golaz, December 2012: The CGILS experimental design to investigate low cloud feedbacks in general circulation models by using single-column and large-eddy simulation models. Journal of Advances in Modeling Earth Systems, 4(4), DOI:10.1029/2012MS000182. Abstract
A surrogate climate change is designed to investigate low cloud feedbacks in the northeastern Pacific by using Single Column Models (SCMs), Cloud Resolving Models (CRMs), and Large Eddy Simulation models (LES), as part of the CGILS study (CFMIP-GASS Intercomparison of LES and SCM models). The constructed large-scale forcing fields, including subsidence and advective tendencies, and their perturbations in the warmer climate are shown to compare well with conditions in General Circulation Models (GCMs), but they are free from the impact of any GCM parameterizations. The forcing fields in the control climate are also shown to resemble the mean conditions in the ECMWF-Interim Reanalysis. Applications of the forcing fields in SCMs are presented. It is shown that the idealized design can offer considerable insight into the mechanisms of cloud feedbacks in the models. Caveats and advantages of the design are also discussed.
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol-cloud interactions, chemistry-climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical-system component of earth-system models and models for decadal prediction in the near-term future, for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model.
Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud-droplet activation by aerosols, sub-grid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with eco-system dynamics and hydrology.
Most basic circulation features in AM3 are simulated as realistically, or more so, than in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks and the intensity distributions of precipitation remain problematic, as in AM2.
The last two decades of the 20th century warm in CM3 by .32°C relative to 1881-1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of .56°C and .52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol cloud interactions, and its warming by late 20th century is somewhat less realistic than in CM2.1, which warmed .66°C but did not include aerosol cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud-aerosol interactions to limit greenhouse gas warming in a way that is consistent with observed global temperature changes.
The recently developed GFDL Atmospheric Model version 3 (AM3), an atmospheric general circulation model (GCM), incorporates a prognostic treatment of cloud drop number to simulate the aerosol indirect effect. Since cloud drop activation depends on cloud-scale vertical velocities, which are not reproduced in present-day GCMs, additional assumptions on the subgrid variability are required to implement a local activation parameterization into a GCM.
This paper describes the subgrid activation assumptions in AM3 and explores sensitivities by constructing alternate configurations. These alternate model configurations exhibit only small differences in their present-day climatology. However, the total anthropogenic radiative flux perturbation (RFP) between present-day and preindustrial conditions varies by ±50% from the reference, because of a large difference in the magnitude of the aerosol indirect effect. The spread in RFP does not originate directly from the subgrid assumptions but indirectly through the cloud retuning necessary to maintain a realistic radiation balance. In particular, the paper shows a linear correlation between the choice of autoconversion threshold radius and the RFP.
Climate sensitivity changes only minimally between the reference and alternate configurations. If implemented in a fully coupled model, these alternate configurations would therefore likely produce substantially different warming from preindustrial to present day.
Successful simulation of aerosol indirect effects in climate models requires parameterizations that capture the full range of cloud-aerosol interactions, including positive and negative liquid water path (LWP) responses to increasing aerosol concentrations, as suggested by large eddy simulations (LESs). A parameterization based on multi-variate probability density functions with dynamics (MVD PDFs) has been incorporated into the single-column version of GFDL AM3, extended to treat aerosol activation, and coupled with a two-moment microphysics scheme. We use it to explore cloud-aerosol interactions. In agreement with LESs, our single-column simulations produce both positive and negative LWP responses to increasing aerosol concentrations, depending on precipitation and free atmosphere relative humidity. We have conducted sensitivity tests to vertical resolution and droplet sedimentation parameterization. The dependence of sedimentation on cloud droplet size is essential to capture the full LWP responses to aerosols. Further analyses reveal that the MVD PDFs are able to represent changes in buoyancy profiles induced by sedimentation as well as enhanced entrainment efficiency with aerosols comparable to LESs.
Zhang, Y, D J Seidel, Jean-Christophe Golaz, Clara Deser, and R A Tomas, October 2011: Climatological characteristics of Arctic and Antarctic surface-based inversions. Journal of Climate, 24(19), DOI:10.1175/2011JCLI4004.1. Abstract
Surface-based inversions (SBI) are frequent features of the Arctic and Antarctic atmospheric boundary layer. They influence vertical mixing of energy, moisture and pollutants, cloud formation, and surface ozone destruction. Their climatic variability is related to that of sea ice and planetary albedo, important factors in climate feedback mechanisms. However, climatological polar SBI properties have not been fully characterized, nor have climate model simulations of SBIs been compared comprehensively to observations. Using 20 yr of twice-daily observations from 39 Arctic and 6 Antarctic radiosonde stations, this study examines the spatial and temporal variability of three SBI characteristics -frequency of occurrence, depth (from the surface to the inversion top), and intensity (temperature difference over the SBI depth) – and relationships among them. In both polar regions, SBIs are more frequent, deeper, and stronger in winter and autumn than in summer and spring. In the Arctic, these tendencies increase from the Norwegian Sea eastward toward the East Siberian Sea, associated both with (seasonal and diurnal) variations in solar elevation angle at the standard radiosonde observation times and with differences between continental and maritime climates. Two state-of-the-art climate models and one reanalysis dataset show similar seasonal patterns and spatial distributions of SBI properties as the radiosonde observations, but with biases in their magnitudes that differ among the models and that are smaller in winter and autumn than in spring and summer. SBI frequency, depth and intensity are positively correlated, both spatially and temporally, and all three are anti-correlated with surface temperature.
Guo, Huan, Jean-Christophe Golaz, Leo J Donner, D P Schanen, and B M Griffin, October 2010: Multi-variate probability density functions with dynamics for cloud droplet activation in large-scale models: Single column tests. Geoscientific Model Development, 3(2), DOI:10.5194/gmd-3-475-2010. Abstract
Successful simulation of cloud-aerosol interactions (indirect aerosol effects) in climate models requires relating grid-scale aerosol, dynamic, and thermodynamic fields to small-scale processes like aerosol activation. A turbulence and cloud parameterization, based on multi-variate probability density functions of sub-grid vertical velocity, temperature, and moisture, has been extended to treat aerosol activation. Multi-variate probability density functions with dynamics (MVD PDFs) offer a solution to the problem of the gap between the resolution of climate models and the scales relevant for aerosol activation and a means to overcome the limitations of diagnostic estimates of cloud droplet number concentration based only on aerosol concentration.
Incorporated into the single-column version of GFDL AM3, the MVD PDFs successfully simulate cloud properties including precipitation for cumulus, stratocumulus, and cumulus-under-stratocumulus. The extension to treat aerosol activation predicts droplet number concentrations in good agreement with large eddy simulations (LES). The droplet number concentrations from the MVD PDFs match LES results more closely than diagnostic relationships between aerosol concentration and droplet concentration.
In the single-column model simulations, as aerosol concentration increases, droplet concentration increases, precipitation decreases, but liquid water path can increase or decrease.
A new stratiform cloud scheme including a two-moment bulk microphysics module, a cloud cover parameterization allowing ice supersaturation, and an ice nucleation parameterization has been implemented into the recently developed GFDL AM3 general circulation model (GCM) as part of an effort to treat aerosol-cloud-radiation interactions more realistically. Unlike the original scheme, the new scheme facilitates the study of cloud-ice-aerosol interactions via influences of dust and sulfate on ice nucleation. While liquid and cloud ice water path associated with stratiform clouds are similar for the new and the original scheme, column integrated droplet numbers and global frequency distributions (PDFs) of droplet effective radii differ significantly. This difference is in part due to a difference in the implementation of the Wegener-Bergeron-Findeisen (WBF) mechanism, which leads to a larger contribution from super-cooled droplets in the original scheme. Clouds are more likely to be either completely glaciated or liquid due to the WBF mechanism in the new scheme. Super-saturations over ice simulated with the new scheme are in qualitative agreement with observations, and PDFs of ice numbers and effective radii appear reasonable in the light of observations. Especially, the temperature dependence of ice numbers qualitatively agrees with in-situ observations. The global average long-wave cloud forcing decreases in comparison to the original scheme as expected when super-saturation over ice is allowed. Anthropogenic aerosols lead to a larger decrease in short-wave absorption (SWABS) in the new model setup, but outgoing long-wave radiation (OLR) decreases as well, so that the net effect of including anthropogenic aerosols on the net radiation at the top of the atmosphere (netradTOA = SWABS-OLR) is of similar magnitude for the new and the original scheme.
Stephens, Graeme L., Tristan L'Ecuyer, J M Forbes, Andrew Gettelman, Jean-Christophe Golaz, Alejandro Bodas-Salcedo, K Suzuki, P Gabriel, and J Haynes, December 2010: Dreary state of precipitation in global models. Journal of Geophysical Research: Atmospheres, 115, D24211, DOI:10.1029/2010JD014532. Abstract
New, definitive measures of precipitation frequency provided by CloudSat are used to
assess the realism of global model precipitation. The character of liquid precipitation
(defined as a combination of accumulation, frequency and intensity) over the global
oceans is significantly different from the character of liquid precipitation produced by
global weather and climate models. Five different models are used in this comparison
representing state of the art weather prediction models, state of the art climate models and
the emerging high-resolution global cloud ‘resolving’ models. The differences between
observed and modeled precipitation are larger than can be explained by observational
retrieval errors or by the inherent sampling differences between observations and models.
We show that the time integrated accumulations of precipitation produced by models
closely match observations when globally composited. However, these models produce
precipitation approximately twice as often as that observed and make rainfall far too
lightly. This finding reinforces similar findings from other studies based on surface
accumulated rainfall measurements. The implications of this dreary state of model
depiction of the real world are discussed.
Xie, Shang-Ping, Jean-Christophe Golaz, and Yanluan Lin, et al., January 2010: CLOUDS AND MORE: ARM Climate Modeling Best Estimate Data. Bulletin of the American Meteorological Society, 91(1), DOI:10.1175/2009BAMS2891.1.
Ackerman, A S., and Jean-Christophe Golaz, et al., March 2009: Large-eddy simulations of a drizzling, stratocumulus-topped marine boundary layer. Monthly Weather Review, 137(3), DOI:10.1175/2008MWR2582.1. Abstract
Cloud water sedimentation and drizzle in a stratocumulus-topped boundary layer are the focus of an intercomparison of large-eddy simulations. The context is an idealized case study of nocturnal stratocumulus under a dry inversion, with embedded pockets of heavily drizzling open cellular convection. Results from 11 groups are used. Two models resolve the size distributions of cloud particles, and the others parameterize cloud water sedimentation and drizzle. For the ensemble of simulations with drizzle and cloud water sedimentation, the mean liquid water path (LWP) is remarkably steady and consistent with the measurements, the mean entrainment rate is at the low end of the measured range, and the ensemble-average maximum vertical wind variance is roughly half that measured. On average, precipitation at the surface and at cloud base is smaller, and the rate of precipitation evaporation greater, than measured. Including drizzle in the simulations reduces convective intensity, increases boundary layer stratification, and decreases LWP for nearly all models. Including cloud water sedimentation substantially decreases entrainment, decreases convective intensity, and increases LWP for most models. In nearly all cases, LWP responds more strongly to cloud water sedimentation than to drizzle. The omission of cloud water sedimentation in simulations is strongly discouraged, regardless of whether or not precipitation is present below cloud base.
Golaz, Jean-Christophe, James D Doyle, and S Wang, July 2009: One-way nested large-eddy simulation over the Askervein Hill. Journal of Advances in Modeling Earth Systems, 1(6), DOI:10.3894/JAMES.2009.1.6. Abstract
Large-eddy simulation (LES) models have been used extensively to study atmospheric boundary layer turbulence over flat surfaces; however, LES applications over topography are less common. We evaluate the ability of an existing model -- COAMPS(R)-LES -- to simulate flow over terrain using data from the Askervein Hill Project. A new approach is suggested for the treatment of the lateral boundaries using one-way grid nesting. LES wind profile and speed-up are compared with observations at various locations around the hill. The COAMPS-LES model performs generally well. This case could serve as a useful benchmark for evaluating LES models for applications over topography.
Haywood, Jim M., Leo J Donner, A Jones, and Jean-Christophe Golaz, March 2009: Global indirect radiative forcing caused by aerosols: IPCC (2007) and beyond In Clouds in the Perturbed Climate System, Jost Heintzenberg and Robert Charlson, eds., MIT Press, 451-467. Abstract
Anthropogenic aerosols are thought to exert a significant indirect radiative forcing because they act as cloud condensation nuclei in warm cloud processes and ice nuclei in cold cloud processes. While IPCC (2007) discuss many of the processes associated with the perturbation of cloud microphysics by anthropogenic aerosols, they only provide full quantification of the radiative forcing due to the first indirect effect (referred to by IPCC (2007) as the cloud albedo effect). Here we explain that this approach is necessary if one is to compare the radiative forcing from the indirect effect of aerosols with those from other radiative forcing components such as that from changes in well-mixed greenhouse gases. We also highlight the problems in assessing the effect of anthropogenic aerosols upon clouds under the strict definitions of radiative forcing of IPCC (2007). Although results from GCMs at their current state of development suggest analyzing indirect aerosol effects in terms of forcing and feedback is possible, a key rationale for IPCC’s definition of radiative forcing, a straightforward scaling between an agent’s forcing and the temperature change it induces, is significantly compromised. Feedbacks from other radiative forcings are responses to radiative perturbations, while feedbacks from indirect aerosol effects are responses to both radiative and cloud microphysical perturbations. This inherent difference in forcing mechanism breaks down the consistency between forcing and temperature response. It is likely that additional characterization, such as climate efficacy, will be required when comparing indirect aerosol effects with other radiative forcings. We suggest using the radiative flux perturbation associated with a change from pre-industrial to present-day composition, calculated in a GCM with fixed sea-surface temperature and sea ice, as a supplement to IPCC forcing.
Klein, Stephen A., and Jean-Christophe Golaz, et al., May 2009: Intercomparisons of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part I: Single layer cloud. Quarterly Journal of the Royal Meteorological Society, 135(641), DOI:10.1002/qj.416. Abstract
Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) programme's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud-top temperature of - 15 °C. The average liquid water path of around 160 g m-2 was about two-thirds of the adiabatic value and far greater than the average mass of ice which when integrated from the surface to cloud top was around 15 g m-2.
Simulations of 17 single-column models (SCMs) and 9 cloud-resolving models (CRMs) are compared. While the simulated ice water path is generally consistent with observed values, the median SCM and CRM liquid water path is a factor-of-three smaller than observed. Results from a sensitivity study in which models removed ice microphysics suggest that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path.
Despite this underestimate, the simulated liquid and ice water paths of several models are consistent with observed values. Furthermore, models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter exists. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics.
Teixeira, João, Bjorn Stevens, Christopher S Bretherton, R T Cederwall, James D Doyle, Jean-Christophe Golaz, A A M Holtslag, Stephen A Klein, J K Lundquist, David A Randall, A P Siebesma, and P M M Soares, April 2008: Parameterization of the atmospheric boundary layer: A view from just above the inversion. Bulletin of the American Meteorological Society, 89(4), DOI:10.1175/BAMS-89-4-453.
Wang, S, Jean-Christophe Golaz, and Q Wang, 2008: Effect of intense wind shear across the inversion on stratocumulus clouds. Geophysical Research Letters, 35, L15814, DOI:10.1029/2008GL033865. Abstract
A large-eddy simulation model is used to examine the impact of the intense cross-inversion wind shear on the stratocumulus cloud structure. The wind shear enhanced entrainment mixing effectively reduces the cloud water and thickens the inversion layer. It leads to a reduction of the turbulence kinetic energy (TKE) production in the cloud layer due to the weakened cloud-top radiative cooling and the formation of a turbulent and cloud free sublayer within the inversion. The thickness of the sublayer increases with the enhanced wind shear intensity. Under the condition of a weaker inversion, the enhanced shear mixing within the inversion layer even lowers the cloud-top height and reduces the entrainment velocity. Finally, increasing wind shear or reducing inversion strength tends to create an inversion layer with a constant bulk Richardson number (∼0.3), suggesting that an equilibrium value of the Richardson number is reached.
Golaz, Jean-Christophe, Vincent E Larson, James A Hansen, D P Schanen, and B M Griffin, 2007: Elucidating Model Inadequacies in a Cloud Parameterization by Use of an Ensemble-Based Calibration Framework. Monthly Weather Review, 135(12), DOI:10.1175/2007MWR2008.1. Abstract
Every cloud parameterization contains structural model errors. The source of these errors is difficult to pinpoint because cloud parameterizations contain nonlinearities and feedbacks. To elucidate these model inadequacies, this paper uses a general-purpose ensemble parameter estimation technique. In principle, the technique is applicable to any parameterization that contains a number of adjustable coefficients. It optimizes or calibrates parameter values by attempting to match predicted fields to reference datasets. Rather than striving to find the single best set of parameter values, the output is instead an ensemble of parameter sets. This ensemble provides a wealth of information. In particular, it can help uncover model deficiencies and structural errors that might not otherwise be easily revealed. The calibration technique is applied to an existing single-column model (SCM) that parameterizes boundary layer clouds. The SCM is a higher-order turbulence closure model. It is closed using a multivariate probability density function (PDF) that represents subgrid-scale variability. Reference datasets are provided by large-eddy simulations (LES) of a variety of cloudy boundary layers. The calibration technique locates some model errors in the SCM. As a result, empirical modifications are suggested. These modifications are evaluated with independent datasets and found to lead to an overall improvement in the SCM’s performance.
Results are presented from the first intercomparison of large-eddy simulation (LES) models for the stable boundary layer (SBL), as part of the Global Energy and Water Cycle Experiment Atmospheric Boundary Layer Study initiative. A moderately stable case is used, based on Arctic observations. All models produce successful simulations, in as much as they generate resolved turbulence and reflect many of the results from local scaling theory and observations. Simulations performed at 1-m and 2-m resolution show only small changes in the mean profiles compared to coarser resolutions. Also, sensitivity to subgrid models for individual models highlights their importance in SBL simulation at moderate resolution (6.25 m). Stability functions are derived from the LES using typical mixing lengths used in numerical weather prediction (NWP) and climate models. The functions have smaller values than those used in NWP. There is also support for the use of K-profile similarity in parametrizations. Thus, the results provide improved understanding and motivate future developments of the parametrization of the SBL.
Larson, Vincent E., Adam J Smith, M J Falk, K E Kotenberg, and Jean-Christophe Golaz, 2006: What determines altocumulus dissipation time?Journal of Geophysical Research, 111, D19207, DOI:10.1029/2005JD007002. Abstract
This paper asks what factors influence the dissipation time of altocumulus clouds. The question is addressed using three-dimensional, large-eddy simulations of a thin, midlevel cloud that was observed by aircraft. The cloud might be aptly described as “altostratocumulus” because it was overcast and contained radiatively driven turbulence. The simulations are used to construct a budget equation of cloud water. This equation allows one to directly compare the four processes that diminish liquid: diffusional growth of ice crystals, large-scale subsidence, radiative heating, and turbulent mixing of dry air into the cloud. Various sensitivity studies are used to find the “equivalent sensitivity” of cloud decay time to changes in various parameters. A change from no sunlight to direct overhead sunlight decreases the lifetime of our simulated cloud as much as increasing subsidence by 1.2 cm s−1, increasing ice number concentration by 780 m−3, or decreasing above-cloud total water mixing ratio by 0.60 g kg−1. Finally, interactions among the terms in the cloud water budget are summarized in a “budget term feedback matrix.” It is able to diagnose, for instance, that in our particular simulations, the diffusional growth of ice is a negative feedback.
The Naval Research Laboratory Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) has been extended to perform as a large-eddy simulation (LES) model. It has been validated with a series of boundary-layer experiments spanning a range of cloud nighttime, and includes a nighttime stratocumulus case, a trade wind cumulus layer, shallow cumulus convection over land, and a mixed regime consisting of cumulus clouds under broken stratocumulus. COAMPS-LES results are in good agreement with other models for all the cases simulated. Exact numerical budgets for the vertical velocity second (w'2) and third moment (w'3) have been derived for the stratocumulus and trade wind cumulus cases. For the w'3 budget in the stratocumulus, the buoyancy contribution from the updraughts and downdraughts largely cancel each other due to their similar magnitudes but opposite signs. In contrast, for the cumulus layer, the negative buoyancy contribution from the environmental downdraughts is negligible and the positive contribution from the updraughts completely dominates due to the conditional instability in the environment. As a result, w'3 is significantly larger in the cumulus than in the stratocumulus layer.
Larson, Vincent E., Jean-Christophe Golaz, H Jiang, and W R Cotton, November 2005: Supplying local microphysics parameterizations with information about subgrid variability: Latin hypercube sampling. Journal of the Atmospheric Sciences, 62(11), DOI:10.1175/JAS3624.1. Abstract
One problem in computing cloud microphysical processes in coarse-resolution numerical models is that many microphysical processes are nonlinear and small in scale. Consequently, there are inaccuracies if microphysics parameterizations are forced with grid box averages of model fields, such as liquid water content. Rather, the model needs to determine information about subgrid variability and input it into the microphysics parameterization.
One possible solution is to assume the shape of the family of probability density functions (PDFs) associated with a grid box and sample it using the Monte Carlo method. In this method, the microphysics subroutine is called repeatedly, once with each sample point. In this way, the Monte Carlo method acts as an interface between the host model’s dynamics and the microphysical parameterization. This avoids the need to rewrite the microphysics subroutines.
A difficulty with the Monte Carlo method is that it introduces into the simulation statistical noise or variance, associated with the finite sample size. If the family of PDFs is tractable, one can sample solely from cloud, thereby improving estimates of in-cloud processes. If one wishes to mitigate the noise further, one needs a method for reduction of variance. One such method is Latin hypercube sampling, which reduces noise by spreading out the sample points in a quasi-random fashion.
This paper formulates a sampling interface based on the Latin hypercube method. The associated family of PDFs is assumed to be a joint normal/lognormal (i.e., Gaussian/lognormal) mixture. This method of variance reduction has a couple of advantages. First, the method is general: the same interface can be used with a wide variety of microphysical parameterizations for various processes. Second, the method is flexible: one can arbitrarily specify the number of hydrometeor categories and the number of calls to the microphysics parameterization per grid box per time step.
This paper performs a preliminary test of Latin hypercube sampling. As a prototypical microphysical formula, this paper uses the Kessler autoconversion formula. The PDFs that are sampled are extracted diagnostically from large-eddy simulations (LES). Both stratocumulus and cumulus boundary layer cases are tested. In this diagnostic test, the Latin hypercube can produce somewhat less noisy time-averaged estimates of Kessler autoconversion than a traditional Monte Carlo estimate, with no additional calls to the microphysics parameterization. However, the instantaneous estimates are no less noisy. This paper leaves unanswered the question of whether the Latin hypercube method will work well in a prognostic, interactive cloud model, but this question will be addressed in a future manuscript.
Larson, Vincent E., and Jean-Christophe Golaz, April 2005: Using probability density functions to derive consistent closure relationships among higher-order moments. Monthly Weather Review, 133(4), DOI:10.1175/MWR2902.1. Abstract
Parameterizations of turbulence often predict several lower-order moments and make closure assumptions for higher-order moments. In principle, the low- and high-order moments share the same probability density function (PDF). One closure assumption, then, is the shape of this family of PDFs. When the higher-order moments involve both velocity and thermodynamic scalars, often the PDF shape has been assumed to be a double or triple delta function. This is equivalent to assuming a mass-flux model with no subplume variability. However, PDF families other than delta functions can be assumed. This is because the assumed PDF methodology is fairly general.
This paper proposes closures for several third- and fourth-order moments. To derive the closures, the moments are assumed to be consistent with a particular PDF family, namely, a mixture of two trivariate Gaussians. (This PDF is also called a double Gaussian or binormal PDF by some authors.) Separately from the PDF assumption, the paper also proposes a simplified relationship between scalar and velocity skewnesses. This PDF family and skewness relationship are simple enough to yield simple, analytic closure formulas relating the moments. If certain conditions hold, this set of moments is specifically realizable. By this it is meant that the set of moments corresponds to a real Gaussian-mixture PDF, one that is normalized and nonnegative everywhere.
This paper compares the new closure formulas with both large eddy simulations (LESs) and closures based on double and triple delta PDFs. This paper does not implement the closures in a single-column model and test them interactively. Rather, the comparisons are diagnostic; that is, low-order moments are extracted from the LES and treated as givens that are input into the closures. This isolates errors in the closures from errors in a single-column model. The test cases are three atmospheric boundary layers: a trade wind cumulus layer, a stratocumulus layer, and a clear convective case. The new closures have shortcomings, but nevertheless are superior to the double or triple delta closures in most of the cases tested.
Stevens, Bjorn, and Jean-Christophe Golaz, et al., June 2005: Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Monthly Weather Review, 133(6), DOI:10.1175/MWR2930.1. Abstract
Data from the first research flight (RF01) of the second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) field study are used to evaluate the fidelity with which large-eddy simulations (LESs) can represent the turbulent structure of stratocumulus-topped boundary layers. The initial data and forcings for this case placed it in an interesting part of parameter space, near the boundary where cloud-top mixing is thought to render the cloud layer unstable on the one hand, or tending toward a decoupled structure on the other hand. The basis of this evaluation consists of sixteen 4-h simulations from 10 modeling centers over grids whose vertical spacing was 5 m at the cloud-top interface and whose horizontal spacing was 35 m. Extensive sensitivity studies of both the configuration of the case and the numerical setup also enhanced the analysis. Overall it was found that (i) if efforts are made to reduce spurious mixing at cloud top, either by refining the vertical grid or limiting the effects of the subgrid model in this region, then the observed turbulent and thermodynamic structure of the layer can be reproduced with some fidelity; (ii) the base, or native configuration of most simulations greatly overestimated mixing at cloud top, tending toward a decoupled layer in which cloud liquid water path and turbulent intensities were grossly underestimated; (iii) the sensitivity of the simulations to the representation of mixing at cloud top is, to a certain extent, amplified by particulars of this case. Overall the results suggest that the use of LESs to map out the behavior of the stratocumulus-topped boundary layer in this interesting region of parameter space requires a more compelling representation of processes at cloud top. In the absence of significant leaps in the understanding of subgrid-scale (SGS) physics, such a representation can only be achieved by a significant refinement in resolution—a refinement that, while conceivable given existing resources, is probably still beyond the reach of most centers.
Zhu, P, and Jean-Christophe Golaz, et al., September 2005: Intercomparison and interpretation of single-column model simulations of a nocturnal stratocumulus-topped marine boundary layer. Monthly Weather Review, 133(9), DOI:10.1175/MWR2997.1. Abstract
Ten single-column models (SCMs) from eight groups are used to simulate a nocturnal nonprecipitating marine stratocumulus-topped mixed layer as part of an intercomparison organized by the Global Energy and Water Cycle Experiment Cloud System Study, Working Group 1. The case is idealized from observations from the Dynamics and Chemistry of Marine Stratocumulus II, Research Flight 1. SCM simulations with operational resolution are supplemented by high-resolution simulations and compared with observations and large-eddy simulations. All participating SCMs are able to maintain a sharp inversion and a mixed cloud-topped layer, although the moisture profiles show a slight gradient in the mixed layer and produce entrainment rates broadly consistent with observations, but the liquid water paths vary by a factor of 10 after only 1 h of simulation at both high and operational resolution. Sensitivity tests show insensitivity to activation of precipitation and shallow convection schemes in most models, as one would observationally expect for this case.
A hierarchy of third-order turbulence closure models are used to simulate boundary layer cumuli in this study. An unrealistically strong liquid water oscillation (LWO) is found in the fully prognostic model, which predicts all third moments. The LWO propagates from cloud base to cloud top with a speed of 1 m s−1. The period of the oscillation is about 1000 s. Liquid water buoyancy (LWB) terms in the third-moment equations contribute to the LWO. The LWO mainly affects the vertical profiles of cloud fraction, mean liquid water mixing ratio, and the fluxes of liquid water potential temperature and total water, but has less impact on the vertical profiles of other second and third moments.
In order to minimize the LWO, a moderately large diffusion coefficient and a large turbulent dissipation at its originating level are needed. However, this approach distorts the vertical distributions of cloud fraction and liquid water mixing ratio. A better approach is to parameterize LWB more reasonably. A minimally prognostic model, which diagnoses all third moments except for the vertical velocity, is shown to produce better results, compared to a fully prognostic model.
Brown, Andy R., and Jean-Christophe Golaz, et al., 2002: Large-eddy simulation of the diurnal cycle of shallow cumulus convection over land. Quarterly Journal of the Royal Meteorological Society, 128(582), DOI:10.1256/003590002320373210. Abstract
Large-eddy simulations of the development of shallow cumulus convection over land are presented. Many characteristics of the cumulus layer previously found in simulations of quasi-steady convection over the sea are found to be reproduced in this more strongly forced, unsteady case. Furthermore, the results are shown to be encouragingly robust, with similar results obtained with eight independent models, and also across a range of numerical resolutions. The datasets produced are already being used in the development and evaluation of parametrizations used in numerical weather-prediction and climate models.
The joint probability density function (PDF) of vertical velocity and conserved scalars is important for at least two reasons. First, the shape of the joint PDF determines the buoyancy flux in partly cloudy layers. Second, the PDF provides a wealth of information about subgrid variability and hence can serve as the foundation of a boundary layer cloud and turbulence parameterization.
This paper analyzes PDFs of stratocumulus, cumulus, and clear boundary layers obtained from both aircraft observations and large eddy simulations. The data are used to fit five families of PDFs: a double delta function, a single Gaussian, and three PDF families based on the sum of two Gaussians.
Overall, the double Gaussian, that is binormal, PDFs perform better than the single Gaussian or double delta function PDFs. In cumulus layers with low cloud fraction, the improvement occurs because typical PDFs are highly skewed, and it is crucial to accurately represent the tail of the distribution, which is where cloud occurs. Since the double delta function has been shown in prior work to be the PDF underlying mass-flux schemes, the data analysis herein hints that mass-flux simulations may be improved upon by using a parameterization built upon a more realistic PDF.
A new single-column model for the cloudy boundary layer, described in a companion paper, is tested for a variety of regimes. To represent the subgrid-scale variability, the model uses a joint probability density function (PDF) of vertical velocity, temperature, and moisture content. Results from four different cases are presented and contrasted with large eddy simulations (LES). The cases include a clear convective layer based on the Wangara experiment, a trade wind cumulus layer from the Barbados Oceanographic and Meteorological Experiment (BOMEX), a case of cumulus clouds over land, and a nocturnal marine stratocumulus boundary layer.
Results from the Wangara experiment show that the model is capable of realistically predicting the diurnal growth of a dry convective layer. Compared to the LES, the layer produced is slightly less well mixed and entrainment is somewhat slower. The cloud cover in the cloudy cases varied widely, ranging from a few percent cloud cover to nearly overcast. In each of the cloudy cases, the parameterization predicted cloud fractions that agree reasonably well with the LES. Typically, cloud fraction values tended to be somewhat smaller in the parameterization, and cloud bases and tops were slightly underestimated. Liquid water content was generally within 40% of the LES-predicted values for a range of values spanning almost two orders of magnitude. This was accomplished without the use of any case-specific adjustments.
The joint probability density function (PDF) of vertical velocity and conserved scalars is important for at least two reasons. First, the shape of the joint PDF determines the buoyancy flux in partly cloudy layers. Second, the PDF provides a wealth of information about subgrid variability and hence can serve as the foundation of a boundary layer cloud and turbulence parameterization.
This paper analyzes PDFs of stratocumulus, cumulus, and clear boundary layers obtained from both aircraft observations and large eddy simulations. The data are used to fit five families of PDFs: a double delta function, a single Gaussian, and three PDF families based on the sum of two Gaussians.
Overall, the double Gaussian, that is binormal, PDFs perform better than the single Gaussian or double delta function PDFs. In cumulus layers with low cloud fraction, the improvement occurs because typical PDFs are highly skewed, and it is crucial to accurately represent the tail of the distribution, which is where cloud occurs. Since the double delta function has been shown in prior work to be the PDF underlying mass-flux schemes, the data analysis herein hints that mass-flux simulations may be improved upon by using a parameterization built upon a more realistic PDF.
A series of large-eddy simulations (LES) of non-precipitating cumulus clouds over land was performed. These simulations were idealized from observed conditions at the Southern Great Plains ARM site on 21 June 1997 and were intended to investigate the effect of initial soil moisture on the structure of the cloudy boundary layer. The surface fluxes were either dominated by latent heat or sensible heat flux, with the transition between one regime and the other occurring over a very narrow soil moisture range. The effect on clouds was mixed. Cloud fraction was nearly identical throughout all experiments. Simulations with dominant sensible heat fluxes led to more turbulent boundary layers and higher cloud bases. Simulations dominated by latent heat flux tended to have fewer but stronger updrafts in the cloud layer.
A grid box in a numerical model that ignores subgrid variability has biases in certain microphysical and thermodynamic quantities relative to the values that would be obtained if subgrid-scale variability were taken into account. The biases are important because they are systematic and hence have cumulative effects. Several types of biases are discussed in this paper. Namely, numerical models that employ convex autoconversion formulas underpredict (or, more precisely, never overpredict) autoconversion rates, and numerical models that use convex functions to diagnose specific liquid water content and temperature underpredict these latter quantities. One may call these biases the “grid box average autoconversion bias,” “grid box average liquid water content bias,” and “grid box average temperature bias,” respectively, because the biases arise when grid box average values are substituted into formulas valid at a point, not over an extended volume. The existence of these biases can be derived from Jensen’s inequality.
To assess the magnitude of the biases, the authors analyze observations of boundary layer clouds. Often the biases are small, but the observations demonstrate that the biases can be large in important cases.
In addition, the authors prove that the average liquid water content and temperature of an isolated, partly cloudy, constant-pressure volume of air cannot increase, even temporarily. The proof assumes that liquid water content can be written as a convex function of conserved variables with equal diffusivities. The temperature decrease is due to evaporative cooling as cloudy and clear air mix. More generally, the authors prove that if an isolated volume of fluid contains conserved scalars with equal diffusivities, then the average of any convex, twice-differentiable function of the conserved scalars cannot increase.
A key to parameterization of subgrid-scale processes is the probability density function (PDF) of conserved scalars. If the appropriate PDF is known, then grid box average cloud fraction, liquid water content, temperature, and autoconversion can be diagnosed. Despite the fundamental role of PDFs in parameterization, there have been few observational studies of conserved-scalar PDFs in clouds. The present work analyzes PDFs from boundary layers containing stratocumulus, cumulus, and cumulus-rising-into-stratocumulus clouds.
Using observational aircraft data, the authors test eight different parameterizations of PDFs, including double delta function, gamma function, Gaussian, and double Gaussian shapes. The Gaussian parameterization, which depends on two parameters, fits most observed PDFs well but fails for large-scale PDFs of cumulus legs. In contrast, three-parameter parameterizations appear to be sufficiently general to model PDFs from a variety of cloudy boundary layers.
If a numerical model ignores subgrid variability, the model has biases in diagnoses of grid box average liquid water content, temperature, and Kessler autoconversion, relative to the values it would obtain if subgrid variability were taken into account. The magnitude of such biases is assessed using observational data. The biases can be largely eliminated by three-parameter PDF parameterizations.
Prior authors have suggested that boundary layer PDFs from short segments are approximately Gaussian. The present authors find that the hypothesis that PDFs of total specific water content are Gaussian can almost always be rejected for segments as small as 1 km.
We computed vibrational and electronic properties of the cage, bowl, and ring isomers of neutral and negatively charged C20, within density-functional theory, using fully optimized local-density and gradient-corrected geometries. Vibrational and electronic spectra exhibit distinctive features, which could be used to identify a given isomer and its charge state in molecular beams or thin films. Notable changes are observed in both the Raman and infrared spectra when going from the neutral to the charged isomers. We also calculated vibrational entropies from harmonic frequencies. Our results indicate that, above a critical temperature, the ring isomer is always stabilized by entropic effects, irrespective of the theoretical model used to compute the internal energy. In particular, gradient-corrected functionals predict both the neutral and charged ring to be the most stable isomer at all temperatures. Molecular-dynamics simulations were performed to study the geometry of the ring at high temperature. Furthermore, we rationalized photoelectron spectra of C2n clusters, n=9–12, in terms of differences in the electronic structure for even and odd n.
