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DOE OSTI2023

Evaluating EAMv2 Simulated High Latitude Clouds Using ARM Measurements in the Northern and Southern Hemispheres

Here, this study evaluates high latitude stratiform mixed-phase clouds (SMPC) in the atmosphere model of the Energy Exascale Earth System Model version 2 (EAMv2) by utilizing one-year-long ground-based remote sensing measurements from the Atmospheric Radiation and Measurement (ARM) program. A nudging approach is applied to model simulations for a constrained comparison with the ARM observations. Observed and modeled SMPCs are sampled and collocated to address the difference in data resolution, so that we can consistently evaluate their macrophysical properties at the North Slope of Alaska (NSA) site in the Arctic and the McMurdo (AWR) site in the Antarctic. We found that EAMv2 overestimates SMPC frequency of occurrence at both sites. However, the model captures the observed larger cloud frequency of occurrence at the NSA site. For collocated SMPCs, the annual statistics of observed cloud macrophysics are generally reproduced at the NSA site, while at the AWR site, there are larger biases. Compared to the AWR site, the lower cloud top and cloud base and the warmer cloud top temperature observed at NSA are well simulated. On the other hand, simulated cloud phase is substantially biased. The model largely overestimates liquid water path, and the ice water path is underestimated at NSA, but at AWR, the liquid water path is frequently underestimated due to the dominance of snow in Antarctic SMPCs. As a result, the observed hemispheric difference in cloud phase partitioning is misrepresented in EAMv2. This study implies that additional model development is needed for high latitude mixed-phase clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Vertically Resolved Convective–Stratiform Echo-Type Identification and Convectivity Retrieval for Vertically Pointing Radars

Using data from the airborne HIAPER Cloud Radar (HCR), a partitioning algorithm (ECCO-V) that provides vertically resolved convectivity and convective versus stratiform radar-echo classification is developed for vertically pointing radars. The algorithm is based on the calculation of reflectivity and radial velocity texture fields that measure the horizontal homogeneity of cloud and precipitation features. The texture fields are translated into convectivity, a numerical measure of the convective or stratiform nature of each data point. The convective–stratiform classification is obtained by thresholding the convectivity field. Subcategories of low, mid-, and high stratiform, shallow, mid-, deep, and elevated convective, and mixed echoes are introduced, which are based on the melting-layer and divergence-level altitudes. As the algorithm provides vertically resolved classifications, it is capable of identifying different types of vertically layered echoes, and convective features that are embedded in stratiform cloud layers. Its robustness was tested on data from four HCR field campaigns that took place in different meteorological and climatological regimes. The algorithm was adapted for use in spaceborne and ground-based radars, proving its versatility, as it is adaptable not only to different radar types and wavelengths, but also different research applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

Abstract Cloud‐climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long‐lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud‐base properties a more appealing target for inference and evaluation of model simulations. Here I describe an inverse model for the estimation of cloud base ice precipitation properties over Utqiagvik, North Slope of Alaska, using the synthesis of ground‐based radar and lidar measurements. By leveraging a Markov Chain Monte Carlo algorithm as the core of the inverse model, a wide range of particle size distributions are sampled, and different combinations of ice habit models are examined, both of which are typically fixed in other retrieval methods. Results show intriguing links between different cloud base thermodynamic and ice precipitation properties. Apparent ice number concentration enhancements at temperatures of −5 and −15°C suggest possible secondary ice production (SIP). The analysis alludes to an overestimation of SIP occurrence and intensity, especially in studies relying only on radar or lidar measurements. Finally, reflectivity‐dependent ice precipitation rate and ice water content parameterizations are presented.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Arctic Mixed-Phase Cloud Base Ice Precipitation Properties Over the NSA Site

Cloud-climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long-lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud-base properties a more appealing target for inference and evaluation of model simulations. This data set provides more than 1800 samples of cloud-base ice precipitation properties over Utqiagvik, North Slope of Alaska, all of which were retrieved using the synthesis of ARM radar and lidar measurements. The retrieved ice precipitation variables in this data set include, among others, the ice number concentration, water content, PSD parameters, precipitation rate, mass-weighted fall velocity, vertical air motion, and effective radius, all of which are highly valuable for model evaluation and a general understanding of polar cloud sink processes. Each variable sample includes its mean value and associated uncertainty. Additional variables based on ARM measurements (liquid layer statistics, etc.) are included in this data set. The retrieval algorithm and analysis of this data set are described in Silber (JGR, 2023, https://doi.org/10.1029/2022JD038202).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Arctic Mixed-Phase Cloud Base Ice Precipitation Properties During the M-PACE Field Campaign

Cloud-climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long-lived, and nearly-continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud-base properties a more appealing target for inference and evaluation of model simulations. This dataset provides a set of 25 samples from the M-PACE field campaign, all of which were retrieved using the synthesis of ARM radar and lidar measurements. The retrieved ice precipitation variables in this dataset include, among others, the ice number concentration, water content, PSD parameters, precipitation rate, mass-weighted fall velocity, vertical air motion, and effective radius, all of which are highly valuable for model evaluation and a general understanding of polar cloud sink processes. Each variable sample includes its mean value and associated uncertainty. Additional variables based on ARM measurements (liquid layer statistics, etc.) are included in this dataset as well. The retrieval algorithm and analysis of this dataset are described in Silber (JGR, 2023, https://doi.org/10.1029/2022JD038202).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Application of aerosol-ice nucleating particle closure to establish the leading parameters governing ice crystal number concentration under commonly observed mixed-phase cloud conditions

This is the final technical report for DOE DE-SC0021034 award entitled “Application of aerosol-ice nucleating particle closure to establish the leading parameters governing ice crystal number concentration under commonly observed mixed-phase cloud conditions”. The overarching objective of this project was to gain a predictive understanding of the processes that lead from aerosols to ice-nucleating particles (INPs) to ice crystal number concentrations (ICNCs) in stratiform mixed-phase clouds where supercooled water droplets and ice crystals coexist, and to inform climate model parameterizations. During this four-year project we addressed the three main research goals of this objective: 1) Study the aerosol-INP link; 2) Develop an aerosol-cloud column model (AC-1D); and 3) Study the INP-ICNC link. Those activities significantly advanced our predictive understanding of how to represent INPs in could models and its implications on ice crystal number concentrations. The results have been published in five peer-reviewed publications with three more in review and preparation. It allowed for collaboration resulting in additional fourteen peer-reviewed publications that are concerned with the objective of this research project. This research has been communicated to the scientific community at national and international meetings and conferences by numerous talks, seminars, and posters including fifteen invited presentations. This project allowed training of four graduate students and one postdoctoral research associate resulting in one M.S. thesis and advancement to Ph.D. candidacy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Features of mid- and high-latitude low-level clouds and their relation to strong aerosol effects in the Energy Exascale Earth System Model version 2 (E3SMv2)

The E3SMv2 model, like various other global models that include representations of aerosol–cloud interactions, uses an empirically chosen lower bound on the simulated in-cloud cloud droplet number concentration (CDNC) to help constrain the effective radiative forcing of anthropogenic aerosols, ERFaer. This study identifies where ultra-low CDNCs (i.e., concentrations lower than 10 cm−3) occur in the stratiform and shallow convective clouds simulated by E3SMv2 and which of the occurrences have the strongest impact on ERF aer . Process-level analyses are presented to reveal characteristics of the cloud droplet formation and removal processes associated with impactful ultra-low CDNCs. Simulations performed with present-day emissions show that ultra-low CDNCs are most frequently found over the mid- and high-latitude oceans in both hemispheres, while the occurrences are also frequent in polluted continental regions despite the high aerosol concentrations. Ultra-low CDNCs with the largest impacts on the simulated regional and global mean ERF aer are found in the lower troposphere in the Northern Hemisphere middle and high latitudes. These cases are typically associated with large cloud fractions, strong water vapor condensation, weak turbulence, and lack of cloud droplet nucleation from aerosol activation. Under such atmospheric conditions, boosting aerosol activation and enhancing turbulent mixing of cloud droplet number can increase the simulated CDNCs, although the magnitude of the global mean ERF aer increases undesirably. The reason for this model behavior is discussed. Overall, our study suggests that mid- and high-latitude low-level stratus occurring under weak turbulence is a cloud regime worth further investigating for the purpose of identifying and addressing the root causes of ultra-low CDNCs and strong ERF aer in E3SM.

Aerosol-Cloud Interaction↗

DOE OSTI2022

Three-Dimensional Convective–Stratiform Echo-Type Classification and Convectivity Retrieval from Radar Reflectivity

The Echo Classification from COnvectivity (ECCO) algorithm identifies convective and stratiform types of radar echo in three dimensions. It is based on the calculation of reflectivity texture—a combination of the intensity and the heterogeneity of the radar echoes on each horizontal plane in a 3D Cartesian volume. Reflectivity texture is translated into convectivity, which is designed to be a quantitative measure of the convective nature of each 3D radar grid point. It ranges from 0 (100% stratiform) to 1 (100% convective). By thresholding convectivity, a more traditional qualitative categorization is obtained, which classifies radar echoes as convective, mixed, or stratiform. In contrast to previous algorithms, these echo-type classifications are provided on the full 3D grid of the reflectivity field. The vertically resolved classifications, in combination with temperature data, allow for subclassifications into shallow, mid-, deep, and elevated convective features, and low, mid-, and high stratiform regions—again in three dimensions. The algorithm was validated using datasets collected over the U.S. Great Plains during the PECAN field campaign. An analysis of lightning counts shows ~90% of lightning occurring in regions classified as convective by ECCO. A statistical comparison of ECCO echo types with the well-established GPM radar precipitation-type categories show 84% (88%) of GPM stratiform (convective) echo being classified as stratiform (convective) or mixed by ECCO. ECCO was applied to radar grids for the continental United States, the United Arab Emirates, Australia, and Europe, illustrating its robustness and adaptability to different radar grid characteristics and climatic regions.

Radars/Radar observations↗

DOE OSTI2023

Making a Superbolt: Reconciling Observations of the Optically Brightest Lightning on Earth From Different Satellites

We previously documented geographic distributions of the optically brightest lightning on Earth—known as “superbolts”—using two space-based instruments: the photodiode detector (PDD) on the Fast On-orbit Recording of Transient Events (FORTE) satellite and the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites. In this study, we further examine the superbolts identified by the PDD and GLM to reconcile the differences between their geographic distributions. We find that both the physical extent of the parent flash and the development speed of its leaders are important for making a superbolt. The oceanic PDD superbolts tend to occur early in flashes that rapidly expand laterally into long horizontal “megaflashes.” The top GLM superbolts occur over land at later times in particularly large megaflashes. These land-based flashes grow more slowly until they extend over multiple hundreds of kilometers. The FORTE PDD missed these delayed superbolts due to limitations in its triggering. Coincident Tropical Rainfall Measuring Mission measurements show that the warm season megaflash superbolts detected by Lightning Imaging Sensor/GLM and wintertime oceanic superbolts observed by the PDD occur in otherwise similar thunderstorm environments. Both are marked by: low storm heights (<10 km), widespread precipitation near the surface, small infrared brightness temperature gradients, and low flash rates. We suggest that the vertically compact, stratiform nature of these clouds provides favorable conditions for superbolt production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Hydrometeor Transport and the Stratiform Water Budget in Amazonian Mesoscale Convective Systems

A significant portion of precipitation falls within the stratiform regions of mesoscale convective systems (MCSs). These regions are formed from convective clouds that have either weakened and formed a horizontally stratified structure or have advected hydrometeors to form a trailing-stratiform region, with further precipitation growth promoted by in situ dynamical and microphysical processes. Green Ocean Amazon 2014/5 (GoAmazon2014/5) field campaign observations are used to quantify the stratiform water budget of six MCSs (three each from the wet and dry seasons) utilizing the framework from Leary and Houze, with an emphasis on the transfer of hydrometeors (C A ) from the convective to the stratiform regions. The storms were predominantly leading-line/trailing-stratiform MCSs, but two had embedded convection in the stratiform region, which can complicate water budget calculations. A pulse-like pattern with a mean interval of 21 min occurs in C A during each storm resulting from a lag between the initial convective updrafts and the subsequent hydrometeor transport and sedimentation in the stratiform region. Here, the peaks in C A were followed by peaks in hydrometeor growth associated with the mesoscale updraft (C mu ) in the stratiform region. Averaged over the six storms, the positive contributions of C A and C mu to the stratiform water budget were roughly equal, although storm-to-storm variations existed. Negative contributions from evaporation below cloud base in the mesoscale downdraft and detrainment from the stratiform region into the nonprecipitating anvil cloud and environment accounted for 53% and 21% of the sum of C A and C mu , respectively, resulting in a mean stratiform region precipitation efficiency of 26%.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Overview of the Nephele Perturbed Parameter Ensemble for Aerosol‐Cloud Interactions in E3SMv3

Aerosol-cloud interactions (aci) are the leading source of uncertainty in inferring climate sensitivity from the historical record. Earth system models (ESMs) struggle to represent aci because the processes responsible for these phenomena occur at much finer time and space scales than can be resolved by any ESM. Observational constraints provide key benchmarks to test ESMs, but cannot be used alone to fully understand aci processes except in very specific cases where causality is controlled; some degree of modeling is required to infer aci and estimate radiative forcing. Here, we generate and characterize a perturbed parameter ensemble (PPE) in version 3 of the Energy Exascale ESM (E3SMv3). We perturb 25 parameters that govern aci processes over 250 members and integrate the model over present-day and preindustrial aerosol emissions. We find that the process representation in E3SMv3 is flexible and can generate global-mean effective radiative forcings due to aci (ERFaci) ranging from −3.0 to +0.9 W m −2 . The positive ERFaci values simulated by a portion of the PPE are implausible and result from parameter combinations that produce unrealistic top-of-atmosphere energy fluxes. While global-mean cloud droplet number concentration always increases in response to anthropogenic aerosol, cloud liquid water path can both increase and decrease, suggesting that precipitation suppression is not the only aerosol-cloud adjustment represented by E3SMv3. Analysis of which processes control liquid cloud adjustment in the PPE points toward stratiform precipitation processes and aerosol activation, which is consistent with many previous ESMs, as well as the new two-moment convective cloud microphysics in E3SMv3.

Nugent, Jacqueline M. [Univ. of Wyoming, Laramie, ↗

DOE OSTI2025

Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization

To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm

To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection-permitting resolution. This study spans a 2-month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr –1 ). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Understanding the Vertical Transport and Removal of Aerosols during Deep Convective Events

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Examining Aerosol Vertical Transport and Removal during Deep Convective Events

Atmospheric aerosols affect the global energy budget by scattering and absorbing sunlight (direct effects) and by changing the microphysical structure, lifetime, and coverage of clouds (indirect effects). Globally, the free troposphere is a major source of nucleation- and Aitken-mode aerosols due to the enhanced new particle formation rates at high altitudes. Recent studies have shown deep convective systems are capable of transporting these small aerosols from the free troposphere to the boundary layer by strong convective downdrafts and weaker downward motions in the stratiform regions. These vertically transported aerosols can grow into cloud condensation nuclei (CCN) and play a significant role in the global climate. During the deep convective processes, existing accumulation-mode aerosols that act as coagulation sinks of smaller particles are also removed by wet scavenging. Compared to the vertical transport of these particles by entrainment mixing, which is slower but more prevalent, the deep convective downdraft processes may be more rapid and efficient in the vertical transport of aerosols. However, most of the current climate models do not include this mechanism as a source of CCN, mainly because the frequency of deep convective events varies significantly with geographic location and thus their contributions to CCN are unpredictable. We target this critical gap in understanding the vertical transport and removal of aerosols by deep convections. We proposed to analyze a multi-year, multi-site measurement record available from the U.S. Department of Energy (DOE) ARM program, including the observations from the 2014/15 Observations and Modeling of the Green Ocean Amazon (GoAmazon) field campaign, the 2017/18 Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign, the 2018/19 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign, the 2021/22 Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign, and the long-term measurements collected at the Southern Great Plains (SGP) atmospheric observatory, where deep convective clouds were frequently observed. This project is aimed at the following three objectives: (1) Gaining a detailed and quantitative understanding of the aerosols transported by a convective downdraft and their evolution in the atmosphere; (2) Examining the wet scavenging mechanisms and efficiencies of aerosols at altitudes of deep convective systems based on ground and aircraft measurements; (3) Evaluating the contribution of deep convective systems to CCN as both a source and a sink of atmospheric aerosols and its seasonal variabilities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km‐Scale Simulations

Abstract Global kilometer‐scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model‐observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi‐model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter‐model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process‐oriented diagnostics to guide future model development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Mesoscale Convective Systems in DYAMOND Global Convection‐Permitting Simulations

Abstract This study examines the deep convection populations and mesoscale convective systems (MCSs) simulated in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) winter project. A storm tracking algorithm is applied to six DYAMOND simulations and a global high‐resolution satellite cloud and precipitation data set for comparison. The simulated frequencies of tropical deep convection and organized convective systems vary widely among models and regions, although robust MCSs are generally underestimated. The diurnal cycles of MCS initiation and mature stages are well simulated, but the amplitudes are exaggerated over land. Most models capture the observed MCS lifetime, cloud shield area, rainfall volume and movement speed. However, cloud‐top height and convective rainfall intensity are consistently overestimated, and stratiform rainfall area and amount are consistently underestimated. Possible causes for the model differences compared to observations and implications for future model developments are discussed.

58 GEOSCIENCES↗

DOE OSTI2023

Rainbows and Climate Change: A tutorial on climate model diagnostics and parameterization

Earth System Models (ESMs) must represent processes below the grid scale of a model using representations (parameterizations) of physical and chemical processes. As a tutorial exercise to understand diagnostics and parameterization, this work presents a representation of rainbows for an ESM: the Community Earth System Model version 2 (CESM2). Using the 'state' of the model, basic physical laws, and some assumptions, we generate a representation of this unique optical phenomena as a diagnostic output. Rainbow occurrence and it's possible changes are related to cloud occurrence and rain formation which are critical uncertainties for climate change prediction. The work highlights issues which are typical of many diagnostics parameterizations such as assumptions, uncertain parameters and the difficulty of evaluation against uncertain observations. Results agree qualitatively with limited available global 'observations' of Rainbows. Rainbows are seen in expected locations in the sub-tropics over the ocean where broken clouds and frequent precipitation occurs. The diurnal peak is in the morning over ocean and in the evening over land. The representation of rainbows is found to be quantitatively sensitive to the assumed amount of cloudiness and the amount of stratiform rain. Rainbows are projected to have decreased, mostly in the Northern Hemisphere, due to aerosol pollution effects increasing cloud coverage since 1850. In the future, continued climate change is projected to decrease cloud cover, associated with a positive cloud feedback. As a result the rainbow diagnostic projects that rainbows will increase in the future, with the largest changes at mid-latitudes. The diagnostic may be useful for assessing cloud parameterizations, and is an exercise in how to build and test parameterizations of atmospheric phenomena.

54 ENVIRONMENTAL SCIENCES↗