Improving the treatment of subgrid cloud variability in warm rain simulation in CESM2

<p>Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In microphysics schemes, the effects of subgrid cloud variability on warm rain process rates calculated based on mean cloud properties are usually accounted for by scaling process rates by an enhancement factor (EF) that is derived from the subgrid variance of cloud water. In our study, we find that the EF derived from Cloud Layers Unified by Binormals (CLUBB) in Community Earth System Model Version 2 (CESM2) is severely overestimated in most of the oceanic areas, which leads to the strong overestimation in the autoconversion rate. Through an EF formula based on empirical fitting of MODIS, we improve the EF in the liquid phase clouds. Results show that the model has a more reasonable relationship between autoconversion rate, cloud liquid water content (LWC), and droplet number concentration (CDNC) in warm rain simulation. The annual mean liquid cloud fraction (LCF), liquid water path (LWP), and CDNC show obvious increases for marine stratocumulus, where the probability of precipitation (POP) shows an obvious decrease. The annual mean LCF, cloud optical thickness (COT), and shortwave cloud forcing (SWCF) match better with observation. The sensitivity of LWP to aerosol decreases obviously. The sensitivities of LCF, LWP, cloud top droplet effective radius (CER), and COT to aerosol are in better agreement with MODIS, but the model still underestimates the response of cloud albedo to aerosol. These results indicate the importance of representing reasonable subgrid cloud variabilities in the simulation of cloud properties and aerosol-cloud interaction in climate models.</p>

Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models

In the current global climate models (GCMs), the nonlinearity effect of subgrid cloud variations on the parameterization of warm-rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm-rain process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in situ measurements from a recent field campaign and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that tends to make EF < 1. This effect is especially strong at cloud top, where the qc and Nc correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of qc and Nc is significantly smaller than its counterpart that accounts only for the subgrid variation of qc, especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of Nc and its correlation with qc both need to be considered for an accurate simulation of the autoconversion process in GCMs.

Vertical Dependence of Horizontal Variation of Cloud Microphysics: Observations from the ACE-ENA field campaign and implications for warm rain simulation in climate models

In the current global climate models (GCM), the nonlinearity effect of subgrid cloud variations on the parameterization of warm rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm ran process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in-situ measurements from a recent field campaign, and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that makes tends to make EF

A comparison study of aerosol impacts on idealized supercell between two bulk microphysics parameterizations

&lt;p&gt;Considering aerosol effects via microphysics parameterization is an imperative work in high-resolution numerical weather prediction. This paper uses two bulk microphysics parameterizations, Aerosol-Aware Thompson and CLR schemes, with the Weather and Research Forecast model to study the impacts of aerosols and microphysics scheme on an idealized supercell storm. Our results show that the implementation of aerosols can successfully modify the cloud droplet size and influence the subsequent warm-rain, mixed-phase, and accumulated precipitation. It implies that aerosols can make numerous differences to cloud microphysics properties and processes but the uncertainty in the magnitude of aerosol effects is huge because the two schemes are different from each other since the warm-rain process including CCN activation and rainwater formation. On the other hand, it is also found that the two schemes make tremendous differences in the rainfall pattern and storm dynamics due to the presence of graupel below the freezing level. The Thompson scheme has hail-like graupel which can fall below the freezing level to chill the air temperature effectively, intensify the downdraft, and enhance the uplifting on the front of cold pools. The mean graupel size represented by the two schemes plays a much more important role than the fall-speed formula for the dynamical feedbacks. Our results suggest that particle size is the core of a myriad of microphysics processes and highly associated with key cloud and dynamical signatures.&lt;/p&gt;

PySDM: Pythonic particle-based cloud microphysics package

&lt;p&gt;In the poster, we will present a new open-source cloud microphysics simulation package PySDM (https://github.com/atmos-cloud-sim-uj/PySDM). The package core is a Pythonic implementation of the Super-Droplet Method (SDM) Monte-Carlo algorithm for representing aerosol/cloud/rain collisional growth.&lt;/p&gt;&lt;p&gt;PySDM design features separation of a backend layer responsible for number-crunching tasks. The developed backend implementations based on Numba, Pythran and ThrustRTC leverage three different Python acceleration techniques dubbed just-in-time, ahead-of-time and runtime compilation, respectively. As a result, PySDM offers high performance with little trade-offs with respect to such advantages of the Python language as succinct and readable source code and portability (seamless interoperability between Windows, OSX and Linux). We will exemplify further advantages that result from embracement of the Jupyter platform which allowed us to equip PySDM with interactive examples and tutorials swiftly executable via web browser through cloud-computing platforms.&lt;/p&gt;&lt;p&gt;Example simulations of the warm-rain process in a kinematic two-dimensional framework mimicking stratoculumus deck will be presented and used as a basis for scalability analysis and discussion of parallelisation nuances of the SDM algorithm.&lt;/p&gt;

Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

The Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) is used to diagnose model performance and physical processes via an apple-to-apple comparison to satellite measurements. Although the COSP provides useful information about clouds and their climatic impact, outputs that have a subcolumn dimension require large amounts of data. This can cause a bottleneck when conducting sets of sensitivity experiments or multiple model intercomparisons. Here, we incorporate two diagnostics for warm rain microphysical processes into the latest version of the simulator (COSP2). The first one is the occurrence frequency of warm rain regimes (i.e., non-precipitating, drizzling, and precipitating) classified according to CloudSat radar reflectivity, putting the warm rain process diagnostics into the context of the geographical distributions of precipitation. The second diagnostic is the probability density function of radar reflectivity profiles normalized by the in-cloud optical depth, the so-called contoured frequency by optical depth diagram (CFODD), which illustrates how the warm rain processes occur in the vertical dimension using statistics constructed from CloudSat and MODIS simulators. The new diagnostics are designed to produce statistics online along with subcolumn information during the COSP execution, eliminating the need to output subcolumn variables. Users can also readily conduct regional analysis tailored to their particular research interest (e.g., land–ocean differences) using an auxiliary post-process package after the COSP calculation. The inline diagnostics are applied to the MIROC6 general circulation model (GCM) to demonstrate how known biases common among multiple GCMs relative to satellite observations are revealed. The inline multi-sensor diagnostics are intended to serve as a tool that facilitates process-oriented model evaluations in a manner that reduces the burden on modelers for their diagnostics effort.

An Investigation of Hydrometeor Latent Cooling upon Convective Cold Pool Formation, Sustainment, and Properties

Downdrafts extending from convective clouds can produce cold pools that propagate outward, sometimes initiating new convection along their leading edges. Models operating at scales requiring convective parameterizations usually lack representation of this detail, and thus fail to predict this convective regeneration and longer episodes of convective activity. Developing such parameterizations requires an improved understanding of the physical drivers of cold pools, and detailed studies of the roles of all the contributing microphysical processes have been lacking. This study utilizes a set of 12 simulations conducted within a single convective environment, but with variability in the microphysical fields produced by varying parameters influencing warm-rain or ice processes. Time-integrated microphysical budgets quantify the contribution of each hydrometeor type to the total latent cooling occurring in the downdrafts that form and sustain the cold pool. The timing of the onset of the cold pool is earlier in cases with a stronger warm rain process, but both graupel and rain were equally as likely to be the dominant hydrometeor in the downdraft first forming the cold pool. Graupel sublimation is the dominant term in sustaining the cold pool in all simulations, but the evaporation of rain has the strongest correlation to the cold pool expansion rate, depth, and intensity. Reconciling the current results with past studies elucidates the importance of considering: graupel sublimation, the latent cooling only in downdrafts contributing to the cold pool, and latent cooling in those downdrafts at altitudes that may be significantly higher than the melting level.