Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds

Thorough analysis of local droplet-level interactions is crucial to better understand the microphysical processes in clouds and their effect on the global climate. High-accuracy simulations of relevant droplet size distributions from Large Eddy Simulations (LES) of bin microphysics challenge current analysis techniques due to their high dimensionality involving three spatial dimensions, time, and a continuous range of droplet sizes. Utilizing the compact latent representations from Variational Autoencoders (VAEs), we produce novel and intuitive visualizations for the organization of droplet sizes and their evolution over time beyond what is possible with clustering techniques. This greatly improves interpretation and allows us to examine aerosol-cloud interactions by contrasting simulations with different aerosol concentrations. We find that the evolution of the droplet spectrum is similar across aerosol levels but occurs at different paces. This similarity suggests that precipitation initiation processes are alike despite variations in onset times.Comment: 4 pages, 3 figures, accepted at NeurIPS 2023 (Machine Learning and the Physical Sciences Workshop

The Role of Ice Splintering on Microphysics of Deep Convective Clouds Forming Under Different Aerosol Conditions : Simulations Using the Model With Spectral Bin Microphysics

Observations during the Ice in Clouds Experiment-Tropical (ICE-T) field experiment show that the ice particles concentration in a developing deep convective clouds at the level of T = −15 °C reached about 500 L−1, that is, many orders higher than that of ice-nucleating particle. To simulate microphysics of these clouds, the 2-D Hebrew University Cloud model (HUCM) with spectral bin microphysics was used in which two main types of ice multiplication mechanisms were included in addition to the Hallet-Mossop mechanism. In the first ice multiplication mechanism ice splinters form by drop freezing and drop-ice collisions. Ice multiplication of this type dominates during developing stage of cloud evolution, when liquid water content is significant. At later stage when clouds become nearly glaciated, ice crystals are produced largely by ice splintering during ice-ice collisions (the second ice multiplication mechanism). Simulations show that droplet size distributions, as well as size distributions of ice particles, agree well with the measurements during ICE-T. Simulations with different cloud condensation nuclei concentrations show the existence of the “optimum” cloud condensation nuclei concentration (or droplet concentration), at which concentration of ice splinters reaches maximum. In these simulations ice nucleation caused by the direct formation of ice crystals upon ice-nucleating particles, as well as the Hallett-Mossop process, has a negligible contribution to the ice crystal concentration

Cloud‐Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties

In this second part of a cloud microphysics scheme intercomparison study, we focus on biases and variabilities of stratiform precipitation properties for a midlatitude squall line event simulated with a cloud-resolving model implemented with eight cloud microphysics schemes. Most of the microphysics schemes underestimate total stratiform precipitation, mainly due to underestimation of stratiform precipitation area. All schemes underestimate the frequency of moderate stratiform rain rates (2-6mm/hr), which may result from low-biased ice number and mass concentrations for 0.2-2-mm diameter particles in the stratiform ice region. Most simulations overestimate ice water content (IWC) at altitudes above 7km for temperatures colder than -20 degrees C but produce a decrease of IWC approaching the melting level, which is opposite to the trend shown by in situ observations. This leads to general underestimations of stratiform IWC below 5-km altitude and rainwater content above 1-km altitude for a given rain rate. Stratiform precipitation area positively correlates with the convective condensate detrainment flux but is modulated by hydrometeor type, size, and fall speed. Stratiform precipitation area also changes by up to 17%-25% through alterations of the lateral boundary condition forcing frequency. Stratiform precipitation, rain rate, and area across the simulations vary by a factor of 1.5. This large variability is primarily a result of variability in the stratiform downward ice mass flux, which is highly correlated with convective condensate horizontal detrainment strength. The variability of simulated local microphysical processes in the stratiform region plays a secondary role in explaining variability in simulated stratiform rainfall properties. Plain Language Summary This is a unique model intercomparison study about different microphysics parametrizations commonly used, with the purposes of examining model biases and variability as well as identifying major factors/processes leading to bias and variability. The study simulated a well-observed squall line MCS from MC3E field campaign, and focused on the stratiform precipitation, following on our part 1 study focusing on convective part. We employed a more constrained approach compared with past intercomparison studies to better identify processes contributing to the differences. Another unique part is our comprehensive model evaluation, that is, we identify stratiform columns and evaluate vertical evolution of cloud properties including size distribution. We find that most of the microphysics schemes underestimate total stratiform precipitation, mainly due to underestimation of stratiform precipitation area. Moderate stratiform rain rates are underestimated, mainly due to incorrect vertical evolution of ice particles. Stratiform precipitation properties across the simulations vary by a factor of 1.5, primarily a result of variability in detrained condensate amount. In addition, we find that stratiform precipitation area correlates well with detrainment amount and is modulated by the detrained hydrometeor properties. So convective microphysics plays a key role in determining stratiform properties.U.S. Department of Energy (DOE) Atmospheric System Research (ASR) Program; U.S. Department of Energy (DOE) Climate Model Development and Validation (CMDV) program; DOE [DE-AC06-76RLO1830]; Office of Science of the U.S. DOE [DE-AC02-05CH1123]; National Basic Research Program of China [2013CB430105]; National Natural Science Foundation of China [41575130, 41775132]; U.S. DOE ASR grant [DE-SC0008678, DE-SC0008648, DE-SC0016476]; DOE CMDV project at University of Arizona [DE-SC0017015]; U.S. DOE [DE-AC02-98CH10886]; Israel Science Foundation [2027/17]; U.S. National Science Foundation; [DE-SC008811]6 month embargo; published online: 2 January 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Convection-Permitting Simulations With the E3SM Global Atmosphere Model

This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40-day prescribed-sea-surface-temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be (Formula presented.) the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long-standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress