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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Parameterization and Explicit Modeling of Cloud Microphysics: Approaches, Challenges, and Future Directions

Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models (GCMs) but also in various higher-resolution limited-area models such as cloud-resolving models (CRMs) and large-eddy simulation (LES) models. Instead of giving a comprehensive review of existing microphysical parameterizations that have been developed over the years, this study concentrates purposely on several topics that we believe are understudied but hold great potential for further advancing bulk microphysics parameterizations: multi-moment bulk microphysics parameterizations and the role of the spectral shape of hydrometeor size distributions; discrete vs “continuous” representation of hydrometeor types; turbulence-microphysics interactions including turbulent entrainment-mixing processes and stochastic condensation; theoretical foundations for the mathematical expressions used to describe hydrometeor size distributions and hydrometeor morphology; and approaches for developing bulk microphysics parameterizations. Also presented are the spectral bin scheme and particle-based scheme (especially, super-droplet method) for representing explicit microphysics. Their advantages and disadvantages are elucidated for constructing cloud models with detailed microphysics that are essential to developing processes understanding and bulk microphysics parameterizations. Particle-resolved direct numerical simulation (DNS) models are described as an emerging technique to investigate turbulence-microphysics interactions at the most fundamental level by tracking individual particles and resolving the smallest turbulent eddies in turbulent clouds. Outstanding challenges and future research directions are explored as well.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO 2 ). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

How Well Can We Represent the Spectrum of Convective Clouds in a Climate Model? Comparisons between Internal Parameterization Variables and Radar Observations

Current climate models cannot resolve individual convective clouds, and hence parameterizations are needed. The primary goal of convective parameterization is to represent the bulk impact of convection on the gridbox-scale variables. Spectral convective parameterizations also aim to represent the key features of the subgrid-scale convective cloud field such as cloud-top-height distribution and in-cloud vertical velocities in addition to precipitation rates. Ground-based radar retrievals of these quantities have been made available at Darwin, Australia, permitting direct comparisons of internal parameterization variables and providing new observational references for further model development. A spectral convective parameterization [the convective cloud field model (CCFM)] is discussed, and its internal equation of motion is improved. Results from the ECHAM–HAM model in single-column mode using the CCFM and the bulk mass flux Tiedtke–Nordeng scheme are compared with the radar retrievals at Darwin. The CCFM is found to outperform the Tiedtke–Nordeng scheme for cloud-top-height and precipitation-rate distributions. Radar observations are further used to propose a modified CCFM configuration with an aerodynamic drag and reduced entrainment parameter, further improving both the convective cloud-top-height distribution (important for large-scale impact of convection) and the in-cloud vertical velocities (important for aerosol activation). This study provides a new development in the CCFM, improving the representation of convective cloud spectrum characteristics observed in Darwin. This is a step toward an improved representation of convection and ultimately of aerosol effects on convection. It also shows how long-term radar observations of convective cloud properties can help constrain parameters of convective parameterization schemes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Comparison of Gravity Wave Temperature Variances from Ray-Based Spectral Parameterization of Convective Gravity Wave Drag with AIRS Observations

The realism of ray-based spectral parameterization of convective gravity wave drag, which considers the updated moving speed of the convective source and multiple wave propagation directions, is tested against the Atmospheric Infrared Sounder (AIRS) onboard the Aqua satellite. Offline parameterization calculations are performed using the global reanalysis data for January and July 2005, and gravity wave temperature variances (GWTVs) are calculated at z = 2.5 hPa (unfiltered GWTV). AIRS-filtered GWTV, which is directly compared with AIRS, is calculated by applying the AIRS visibility function to the unfiltered GWTV. A comparison between the parameterization calculations and AIRS observations shows that the spatial distribution of the AIRS-filtered GWTV agrees well with that of the AIRS GWTV. However, the magnitude of the AIRS-filtered GWTV is smaller than that of the AIRS GWTV. When an additional cloud top gravity wave momentum flux spectrum with longer horizontal wavelength components that were obtained from the mesoscale simulations is included in the parameterization, both the magnitude and spatial distribution of the AIRS-filtered GWTVs from the parameterization are in good agreement with those of the AIRS GWTVs. The AIRS GWTV can be reproduced reasonably well by the parameterization not only with multiple wave propagation directions but also with two wave propagation directions of 45 degrees (northeast-southwest) and 135 degrees (northwest-southeast), which are optimally chosen for computational efficiency.

convective gravity wave drag↗
NASA NTRS2022

Constraining Clouds and Convective Parameterizations in A Climate Model Using Paleoclimate Data

Cloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof-of-concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere-only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present-day runs using multiple satellite climatologies and by comparing simulated δ 18 O of precipitation (δ 18 O p ), known to be sensitive to parameterization schemes, with a global database of speleothem δ 18 O records covering the Last Glacial Maximum (LGM), mid-Holocene (MH) and pre-industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ 18 O p values across all time periods, along with LGM and MH δ 18 O p anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non-stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations.

cloud and convective parameterization↗
DOE OSTI2023

A riming‐dependent parameterization of scattering by snowflakes using the self‐similar Rayleigh–Gans approximation

Abstract Riming is a key process of precipitation formation in ice‐containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross‐section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass–size, cross‐section area–size, and backscattering cross‐section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross‐section parameterization (the “riming‐dependent parameterization”) is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self‐Similar Rayleigh–Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming‐dependent parameterization leads to significantly smaller biases in terms of backscattering cross‐section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1–10 mm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The impact of different parameterizations on the interpretation of CP violation in neutrino oscillations

CP violation in the lepton mass matrix will be probed with good precision in upcoming experiments. The amount of CP violation present in oscillations can be quantified in numerous ways and is typically parameterized by the complex phase δ PDG in the standard PDG definition of the lepton mixing matrix. There are additional parameterizations of the lepton mixing matrix as well. Through various examples, we explore how, given the current data, different parameterizations can lead to different conclusions when working with parameterization dependent variables, such as δ. We demonstrate how the smallness of |U e3 | governs the scale of these results. We then demonstrate how δ can be misleading and argue that the Jarlskog is the cleanest means of presenting the amount of CP violation in the lepton sector. We also confirm that, among the different parameterizations considered, the standard PDG parameterization has a number of convenient features.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer

Deep neural networks (DNNs) are implemented in Super-Parameterized Energy Exascale Earth System Model (SP-E3SM) to imitate the shortwave and longwave radiative transfer calculations. These DNNs were able to emulate the radiation parameters with an accuracy of 90–95% at a cost of 8–10 times cheaper than the original radiation parameterization. Here, a comparison of time-averaged radiative fluxes and the prognostic variables manifested qualitative and quantitative similarity between the DNN emulation and the original parameterization. It has also been found that the differences between the DNN emulation and the original parameterization are comparable to the internal variability of the original parameterization. Although the DNNs developed in this investigation emulate the radiation parameters for a specific set of initial conditions, the results justify the need of further research to generalize the use of DNNs for the emulations of full model radiation and other parameterization for seasonal predictions and climate simulations.

58 GEOSCIENCES↗
DOE OSTI2025

Implementation and Evaluation of Physics-Driven Dynamic Entrainment-Mixing Parameterization in a Climate Model and Its Impact on Low-Cloud Simulation

The turbulent entrainment-mixing process in the Community Earth System Model version 1.2 (CESM1.2) is assumed to follow the extremely inhomogeneous entrainment-mixing. However, different entrainment-mixing scenarios can occur in real clouds. To address this deficiency, a unifying parameterization that represents different entrainment-mixing processes is implemented and evaluated in CESM1.2. The results indicate that the homogeneous mixing degree values simulated by the new parameterization in CESM1.2 are predominantly greater than 50%, suggesting a tendency toward homogeneous mixing. Compared to the extremely inhomogeneous mixing mechanism, the new parameterization increases the cloud droplet number concentration (Nc). More importantly, the new parameterization improves low-cloud fraction (CLDLOW) simulation in Northwest Pacific (NWP) and Southeast Pacific (SEP) regions, with relative improvements of 2.95% and 4.17%, respectively. Furthermore, the improvements reach up to 44.6% and 16.2% in the NWP and SEP regions, respectively, when considering the relationship between N c and CLDLOW. Further analysis reveals that the new parameterization enhances cloud optical depth, longwave radiative cooling effect, net condensation rate, cloud water mixing ratio, lower-troposphere stability, and CLDLOW by increasing N c . Additionally, these results underscore the importance of improving entrainment-mixing parameterization in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Sensitivity of Wind Turbine Array Downstream Effects to the Parameterization Used in WRF

The Weather Research and Forecasting (WRF) Model has been extensively used for wind energy applications, and current releases include a scheme that can be applied to examine the effects of wind turbine arrays on the atmospheric flow and electricity generation from wind turbines. Herein we present a high-resolution simulation using two different wind farm parameterizations: 1) the “Fitch” parameterization that is included in WRF releases and 2) the recently developed Explicit Wake Parameterization (EWP) scheme. We compare the schemes using a single yearlong simulation for a domain centered on the highest density of current turbine deployments in the contiguous United States (Iowa). Pairwise analyses are applied to diagnose the downstream wake effects and impact of wind turbine arrays on near-surface climate conditions. On average, use of the EWP scheme results in small-magnitude wake effects within wind farm arrays and faster recovery of full WT array wakes. This in turn leads to smaller impacts on near-surface climate variables and reduced array–array interactions, which at a systemwide scale lead to summertime capacity factors (i.e., the electrical power produced relative to nameplate installed capacity) that are 2%–3% higher than those from the more commonly applied Fitch parameterization. It is currently not possible to make recommendations with regard to which wind farm parameterization exhibits higher fidelity or to draw inferences with regard to whether the relative performance may vary with prevailing climate conditions and/or wind turbine deployment configuration. However, the sensitivities documented herein to the wind farm parameterization are of sufficient magnitude to potentially influence wind turbine array siting decisions. Thus, our research findings imply high value in undertaking combined long-term high-fidelity observational studies in support of model validation and verification.

17 WIND ENERGY↗
DOE OSTI2021

A Bimodal Diagnostic Cloud Fraction Parameterization. Part II: Evaluation and Resolution Sensitivity

A wide range of approaches exists to account for subgrid cloud variability in regional simulations of the atmosphere. This paper addresses the following questions: 1) Is there still benefit in representing subgrid variability of cloud in convection-permitting simulations? 2) What is the sensitivity to the cloud fraction parameterization complexity? 3) Are current cloud fraction parameterizations scale-aware across convection-permitting resolutions? These questions are addressed for regional simulations of a 6-week observation campaign in the U.S. southern Great Plains. Particular attention is given to a new diagnostic cloud fraction scheme with a bimodal subgrid saturation-departure PDF, described in Part I. The model evaluation is performed using ground-based remote sensing synergies, satellite-based retrievals, and surface observations. It is shown that not using a cloud fraction parameterization results in underestimated cloud frequency and water content, even for stratocumulus. The use of a cloud fraction parameterization does not guarantee improved cloud property simulations, however. Diagnostic and prognostic cloud schemes with a symmetric subgrid saturation-departure PDF underestimate cloud fraction and cloud optical thickness, and hence overestimate surface shortwave radiation. These schemes require empirical bias-correction techniques to improve the cloud cover. The new cloud fraction parameterization, introduced in Part I, improves cloud cover, liquid water content, cloud-base height, optical thickness, and surface radiation compared to schemes reliant on a symmetric PDF. Furthermore, cloud parameterizations using turbulence-based, rather than prescribed constant subgrid variances, are shown to be more scale-aware across convection-permitting resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Collaborative Project: An Integrated Parameterization of Boundary Layer and Convective Mixing: The Eddy-Diffusivity/Mass-Flux (EDMF) Approach

The key goal of this project was to reduce cloud modeling deficiencies and biases by developing and implementing a new unified boundary layer and convection parameterization based on the multi-plume Eddy-Diffusivity/Mass-Flux (EDMF) approach. This is a turbulence and convection parameterization that can be considered fully unified, in the sense that it is able to represent convective processes from boundary layer convection to deep moist convection with one single parameterization. This project will ultimately lead to more accurate climate predictions, which have a significant societal impact. In general, turbulence and convection in the atmosphere are at the core of key climate prediction problems. For example: i) to reduce uncertainties in climate projections, it is essential to improve predictions of cloud feedbacks (how clouds respond to, and influence, climate change), which are controlled by the interactions between a turbulent flow with water phase transitions and radiation; (ii) to improve extreme weather prediction under climate change, it is essential to improve our understanding of how moist convection responds to a warmer world. A fully unified boundary layer and convection parameterization, such as EDMF, will help better represent and predict boundary layer, cloud and convection processes in the atmosphere during present and future climate. The following are the specific tasks that were completed and addressed the project’s objectives: i) Implementation and evaluation of the multi-plume EDMF parameterization into the Single Column Model (SCM) version of CESM; ii) Development of a fully unified multi-plume (shallow to deep convection) EDMF parameterization – that is also scale-aware; iii) Implementation and evaluation of the moist multi-plume EDMF scheme in the global version of WRF; iv) Extension and evaluation of the LES model to deep moist convection regimes; v) Implementation of EDMF in the SCM version of the LBL dynamical core.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales

The U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 2.1 builds on E3SMv2 with several changes, with the most notable being the addition of the Fox-Kemper et al. (2011) mixed-layer eddy parameterization. This parameterization captures the effect of finite-amplitude, mixed-layer eddies as an overturning streamfunction and has the primary function of restratification. Herein, we outline the changes to the mean climate state of E3SM that were introduced by the addition of this parameterization. Overall, the presence of the submesoscale parameterization improves the fidelity of the v2.1 simulation by reducing the ocean surface biases in the North Atlantic present in v2, as illustrated by changes in the climatological sea surface temperature and salinity and the Arctic sea-ice extent. Other impacts include a slight shoaling of the mixed-layer depths in the North Atlantic and a small improvement in the Atlantic Meridional Overturning Circulation (AMOC). We note that the expected shoaling due to the parameterization is regionally dependent in our coupled configuration. In addition, we investigate why the parameterization and its impacts on mixed-layer depth have little impact on the simulated AMOC: despite increased dense-water formation in the Norwegian Sea, only a small fraction of the water formed makes its way south into the North Atlantic basin. Version 2.1 also exhibits small improvements in the atmospheric climatology, with smaller biases in many notable quantities and modes of variability.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

Development of a two-dimensional zonally averaged statistical-dynamical model. I - The parameterization of moist convection and its role in the general circulation

The moist convection parameterization used in the GISS 3-D GCM is adapted for use in a two-dimensional (2-D) zonally averaged statistical-dynamical model. Experiments with different versions of the parameterization show that its impact on the general circulation in the 2-D model does not parallel its impact in the 3-D model unless the effect of zonal variations is parameterized in the moist convection calculations. A parameterization of the variations in moist static energy is introduced in which the temperature variations are calculated from baroclinic stability theory, and the relative humidity is assumed to be constant. Inclusion of the zonal variations of moist static energy in the 2-D moist convection parameterization allows just a fraction of a latitude circle to be unstable and enhances the amount of deep convection. This leads to a 2-D simulation of the general circulation very similar to that in the 3-D model. The experiments show that the general circulation is sensitive to the parameterized amount of deep convection in the subsident branch of the Hadley cell. The more there is, the weaker are the Hadley cell circulations and the westerly jets. The experiments also confirm the effects of momentum mixing associated with moist convection found by earlier investigators and, in addition, show that the momentum mixing weakens the Ferrel cell. An experiment in which the moist convection was removed while the hydrological cycle was retained and the eddy forcing was held fixed shows that moist convection by itself stabilizes the tropics, reduces the Hadley circulation, and reduces the maximum speeds in the westerly jets.

Yao, Mao-Sung↗
NASA NTRS1990

Development of a two-dimensional zonally averaged statistical-dynamical model. III - The parameterization of the eddy fluxes of heat and moisture

A number of perpetual January simulations are carried out with a two-dimensional zonally averaged model employing various parameterizations of the eddy fluxes of heat (potential temperature) and moisture. The parameterizations are evaluated by comparing these results with the eddy fluxes calculated in a parallel simulation using a three-dimensional general circulation model with zonally symmetric forcing. The three-dimensional model's performance in turn is evaluated by comparing its results using realistic (nonsymmetric) boundary conditions with observations. Branscome's parameterization of the meridional eddy flux of heat and Leovy's parameterization of the meridional eddy flux of moisture simulate the seasonal and latitudinal variations of these fluxes reasonably well, while somewhat underestimating their magnitudes. New parameterizations of the vertical eddy fluxes are developed that take into account the enhancement of the eddy mixing slope in a growing baroclinic wave due to condensation, and also the effect of eddy fluctuations in relative humidity. The new parameterizations, when tested in the two-dimensional model, simulate the seasonal, latitudinal, and vertical variations of the vertical eddy fluxes quite well, when compared with the three-dimensional model, and only underestimate the magnitude of the fluxes by 10 to 20 percent.

Stone, Peter H.↗
NASA NTRS2000

The Influence of Microphysical Cloud Parameterization on Microwave Brightness Temperatures

The microphysical parameterization of clouds and rain-cells plays a central role in atmospheric forward radiative transfer models used in calculating passive microwave brightness temperatures. The absorption and scattering properties of a hydrometeor-laden atmosphere are governed by particle phase, size distribution, aggregate density., shape, and dielectric constant. This study identifies the sensitivity of brightness temperatures with respect to the microphysical cloud parameterization. Cloud parameterizations for wideband (6-410 GHz observations of baseline brightness temperatures were studied for four evolutionary stages of an oceanic convective storm using a five-phase hydrometeor model in a planar-stratified scattering-based radiative transfer model. Five other microphysical cloud parameterizations were compared to the baseline calculations to evaluate brightness temperature sensitivity to gross changes in the hydrometeor size distributions and the ice-air-water ratios in the frozen or partly frozen phase. The comparison shows that, enlarging the rain drop size or adding water to the partly Frozen hydrometeor mix warms brightness temperatures by up to .55 K at 6 GHz. The cooling signature caused by ice scattering intensifies with increasing ice concentrations and at higher frequencies. An additional comparison to measured Convection and Moisture LA Experiment (CAMEX 3) brightness temperatures shows that in general all but, two parameterizations produce calculated T(sub B)'s that fall within the observed clear-air minima and maxima. The exceptions are for parameterizations that, enhance the scattering characteristics of frozen hydrometeors.

Skofronick-Jackson, Gail M.↗
NASA NTRS2003

Parameterization of Cloud Droplet Formation in Global Climate Models

An aerosol activation parameterization has been developed based on a generalized representation of aerosol size and composition within the framework of an ascending adiabatic parcel; this allows for parameterizing the activation of chemically complex aerosol with an arbitrary size distribution and mixing state. The new parameterization introduces the concept of"population splitting", in which the cloud condensation nuclei (CCN) that form droplets are treated as two separate populations; those that have a size close to their critical diameter and those that do not.Explicit consideration of kinetic limitations of droplet growth is introduced. Our treatment of the activation process unravels much of its complexity. As a result of this, a substantial number of conditions of droplet formation can be treated completely free of empirical information or correlations; there are, however, some conditions of droplet activation for which an empirically derived correlation is utilized. Predictions of the parameterization are compared against extensive cloud parcel model simu;lations for a variety of aerosol activation conditions that cover a wide range of chemical variability and CCN concentrations. The parameterization tracks the parcel model simulations closely and robustly. The parameterization presented here is intended to allow for a comprehensive assessment of the aerosol indirect effect in general circulation models.

Nenes, A.↗