Recent in situ observations show that haze particles exist in a convection cloud chamber. The microphysics schemes previously used for large‐eddy simulations of the cloud chamber could not fully resolve haze particles and the associated processes, including their activation and deactivation. Specifically, cloud droplet activation was modeled based on Twomey‐type parameterizations, wherein cloud droplets were formed when a critical supersaturation for the available cloud condensation nuclei (CCN) was exceeded and haze particles were not explicitly resolved. Here, we develop and adapt haze‐capable bin and Lagrangian microphysics schemes to properly resolve the activation and deactivation processes. Results are compared with the Twomey‐type CCN‐based bin microphysics scheme in which haze particles are not fully resolved. We find that results from the haze‐capable bin microphysics scheme agree well with those from the Lagrangian microphysics scheme. However, both schemes significantly differ from those from a CCN‐based bin microphysics scheme unless CCN recycling is considered. Haze particles from the recycling of deactivated cloud droplets can strongly enhance cloud droplet number concentration due to a positive feedback in haze‐cloud interactions in the cloud chamber. Haze particle size distributions are more realistic when considering solute and curvature effects that enable representing the complete physics of the activation process. Our study suggests that haze particles and their interactions with cloud droplets may have a strong impact on cloud properties when supersaturation fluctuations are comparable to mean supersaturation, as is the case in the cloud chamber and likely is the case in the atmosphere, especially in polluted conditions.
In the atmosphere,
- Award ID(s):
- 1824243
- NSF-PAR ID:
- 10374566
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 12
- Issue:
- 8
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.
-
more » « less
Abstract Ice crystal habit significantly impacts ice crystal processes such as growth by vapor deposition. Despite this, most bulk microphysical models disregard this natural shape effect and assume ice to grow spherically. This paper focuses on how the evolution of ice crystal shape and choice of ice nucleation parameterization in the adaptive habit microphysics model (AHM) influence the lake-effect storm that occurred during intensive observing period 4 (IOP4) of the Ontario Winter Lake-effect Systems (OWLeS) field campaign. This localized snowstorm produced total accumulated liquid-equivalent precipitation amounts up to 17.92 mm during a 16-h time period, providing a natural laboratory to investigate the ice–liquid partitioning within the cloud, various microphysical process rates, the accumulated precipitation magnitude, and its associated spatial distribution. Two nucleation parameterizations were implemented, and aerosol data from a size-resolved advanced particle microphysics (APM) model were ingested into the AHM for use in parameterizing ice and cloud condensation nuclei. Simulations allowing ice crystals to grow nonspherically produced 1.6%–2.3% greater precipitation while altering the nucleation parameterization changed the type of accumulating hydrometeors. In addition, all simulations were highly sensitive to the domain resolution and the source of initial and boundary conditions. These findings form the foundational understanding of relationships among ice crystal habit, nucleation parameterizations, and resultant cold-season mesoscale precipitation within detailed bulk microphysical models allowing adaptive habit.
-
Key Points Significant sensitivity of mid‐latitude deep convective storm and the associated anvil cirrus cloud to choice of model microphysics schemes Hydrometeor size‐dependent microphysical process are linked with large variability in storm dynamics Six bulk microphysics schemes produced an order of magnitude spread in above‐tropopause water vapor concentrationsmore » « less
-
null (Ed.)Abstract. Ground-based observatories use multisensor observations to characterize cloud and precipitation properties. One of the challenges is how to designstrategies to best use these observations to understand these properties and evaluate weather and climate models. This paper introduces the Cloud-resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution cloud-resolving models (CRMs) to emulate multiwavelength,zenith-pointing, and scanning radar observables and multisensor (radar and lidar) products. CR-SIM allows for direct comparison between an atmosphericmodel simulation and remote-sensing products using a forward-modeling framework consistent with the microphysical assumptions used in the atmosphericmodel. CR-SIM has the flexibility to easily incorporate additional microphysical modules, such as microphysical schemes and scattering calculations,and expand the applications to simulate multisensor retrieval products. In this paper, we present several applications of CR-SIM for evaluating therepresentativeness of cloud microphysics and dynamics in a CRM, quantifying uncertainties in radar–lidar integrated cloud products and multi-Dopplerwind retrievals, and optimizing radar sampling strategy using observing system simulation experiments. These applications demonstrate CR-SIM as a virtual observatory operator on high-resolution model output for a consistent comparison between model results and observations to aidinterpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-basedmeasurements. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are publicly available to the scientificcommunity.more » « less
