Andrew S. Ackerman

Ice particles and their properties (shape, size, etc.) have great potential for influencing cloud lifecycles, from their formation through their growth, and precipitation. Coupled with liquid-phase hydrometeors and associated processes in mixed-phase clouds, ice processes can be critical for understanding the extent of aerosol-cloud interactions and their causal links, which require model simulations across scales. Robust estimates of observed ice properties, therefore, can support the evaluation and rectification of model physics, which could ultimately increase model fidelity. However, the entanglement of various parameterized processes that affect modeled cloud characteristics poses challenges for direct comparisons of model state variables with cloud observations. The use of consistent cloud process metrics can help address some of these difficulties and enable direct comparisons between observations and models. In the case of Arctic mixed-phase clouds, the representation of ice precipitation rates at and below cloud base, which serve as a key cloud water sink, could impact simulated cloud lifecycles and alter cloud feedbacks. These metrics can be robustly retrieved from ground-based measurements, which are considerably less susceptible to uncertainties associated with accurately locating cloud base, tropospheric profiling limitations, and spatial footprint size.Here, we describe the main approaches of ice property retrievals. We then focus on cloud-base ice particle property retrievals using a Markov Chain Monte Carlo (MCMC) algorithm applied to radar and high-spectral-resolution lidar observations from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility site at the North Slope of Alaska (NSA). We describe the analysis results, including a qualitative distinction between ice-number- and size-dominated reflectivity regimes, number-concentration enhancements in certain temperature ranges, and a weak, typical Arctic cloud-base vertical motion. After examining insights derived from those retrievals, they are used in a brief bulk evaluation of mixed-phase cloud ice representation in three different global models: the NASA Goddard Institute for Space Studies ModelE3, the NCAR Community Earth System Model Version 2 (CESM2), and the DOE Energy Exascale Earth System Model Version 1 (E3SMv1). To perform a robust evaluation of Arctic cloud precipitation rates against observations, we process regional model output using the Earth Model Column Collaboratory (EMC²) instrument simulator and subcolumn generator, and compare them with corresponding cloud-base precipitation statistics calculated from the long-term ground-based remote-sensing dataset collected at the ARM NSA site. This brief analysis demonstrates key differences between the models and examines the agreement between model output and observations. Finally, we describe our current effort to generate sub-mixed-phase cloud ice precipitation profiles using a deep neural network emulator of the computationally intensive MCMC algorithm and discuss future plans.

Models are universally challenged to accurately predict the coupled microphysical, turbulent and radiative processes within widespread, long-lived marine cold-air outbreak (CAO) cloud fields, which leads to biases and uncertainties in atmospheric predictions over all time scales. Here we assemble a suite of ground-based and satellite measurements to initialize and constrain large-eddy simulations (LES) of cloud field evolution with distance downwind from the marginal ice zone during a strong, highly supercooled and convective CAO observed during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE). Detailed LES results are compared with large-scale models run in single-column model (SCM) mode, providing an observation-constrained framework for large-scale model evaluation and future improvements. All models reproduce rapid cloud formation off the ice edge, and a monotonic ascent of downwind cloud-top heights that is well correlated with time-integrated surface heat fluxes. LES generally reproduce domain-mean observational targets using a modest test domain (25 x 25 km²), and a larger domain (125 x 125 km²) enables better reproducing the observed growth of convective cell sizes. In realistic mixed-phase LES compared with liquid-only simulations, ice processes lead to thinner, broken cloud decks and substantially reduced cloud radiative effects on top-of-atmosphere longwave fluxes. By contrast, mixed-phase SCM simulations generally underpredict the impact of ice on radiative fluxes, primarily owing to insufficient reduction of cloud cover. Results indicate that cellular cloud structure is qualitatively captured by LES, and thus LES could provide guidance to improvement of large-scale model physics schemes. Follow-on work will extend these results to larger domains, apply objective analysis of mesoscale structure, and include prognostic aerosol properties for droplet and heterogeneous ice formation.

Postfrontal cloud regime transitions often result from strong aerosol‐cloud‐precipitation interaction. Using ACTIVATE measurements from 40 flights, we show a diurnal pattern of evolving aerosol particle size distributions (PSD) in the marine boundary layer upwind of clouds. A nucleation mode grows in size between 12 and 17 LT, likely from new particle formation (NPF), and an accumulation mode increases in concentration during daytime. Selecting the day of strongest size growth, we use quasi‐Lagrangian large‐eddy simulations to examine cloud impacts. With a morning PSD, moderate aerosol hygroscopicity aligns best with satellite and field campaign observational targets. Using a PSD near 17 LT, greater cloud droplet number concentrations delay the precipitation onset and keep albedo elevated for longer, primarily driven by increased accumulation mode concentrations, which facilitate an updraft‐limited regime and inhibit activation of smaller modes despite their growth after NPF. These results constitute a caveat for NPF‐related activation under polluted conditions.

Postfrontal clouds, often appearing as marine cold-air outbreaks (MCAOs) along eastern seaboards, undergo overcast-to-broken cloud regime transitions. Earth system models exhibit diverse radiative biases connected to postfrontal clouds, rendering these marine boundary layer (MBL) clouds a major source of uncertainty in projected global-mean temperature. The recent NASA multi-year campaign Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) therefore dedicated most of its resources to sampling postfrontal MCAOs, deploying 71 flights from 2020 through 2022. We provide an overview of (1) the synoptic context within the parent extratropical cyclone, (2) the meteorological conditions with respect to the season, (3) the suitability of case data and measurements for Lagrangian analysis and modeling studies, and (4) the encountered cloud properties. A proposed subset of flights deemed most suitable for Lagrangian modeling case studies is highlighted throughout. Such flights typically cover a greater fetch range, were better aligned with the MBL wind direction, and revisited sampled air masses when key instruments were operational. Like many other flights, these flights often probed cloud formation and some cloud regime transitions. Surveying cloud properties from remote sensing and in situ probes, we find a great range in cloud-top heights and a relatively large concentration of frozen hydrometeors, which suggest strong free tropospheric entrainment and secondary ice formation, respectively. Both processes are expected to leave marked signatures in cloud evolution, such as strongly ranging cloud droplet number concentrations. ACTIVATE data combined with satellite retrievals can establish observational constraints for future model improvement work.

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top‐of‐atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45‐dimensional parameter configurations are retained to generate radiatively‐balanced, auto‐tuned atmospheres that were used in two E3 submissions to CMIP6.

Postfrontal cloud regime transitions often result from strong aerosol-cloud-precipitation interaction. Using ACTIVATE measurements and focusing on the marine boundary layer upwind of clouds, we show a diurnal pattern of evolving aerosol particle size distributions (PSD): a nucleation mode that grows in size between 12 and 17 LT, likely initiated by new particle formation (NPF), and an accumulation mode that increases in concentration during daytime. Selecting the day of strongest size growth, we examine implications for the downwind cloud deck through quasi-Lagrangian large-eddy simulations. Using an upwind PSD prior to 12 LT, we find a moderate aerosol hygroscopicity to best match downwind cloud observational constraints from flight measurements and satellite retrievals. Switching to a PSD near 17 LT, greater cloud droplet number concentrations delay precipitation onset and keep albedo elevated for longer, primarily driven by increased accumulation mode concentrations, which also inhibit activation of smaller modes despite their growth after NPF.

Postfrontal clouds, often appearing as marine cold-air outbreaks (MCAOs) along Eastern seaboards, undergo overcast-to-broken cloud regime transitions. Earth system models exhibit diverse radiative biases connected to postfrontal clouds, rendering these marine boundary layer (MBL) clouds a major source of uncertainty in projected global-mean temperature. The recent NASA multi-year campaign Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) therefore dedicated most of its resources to sampling postfrontal MCAOs, deploying 71 flights from 2020 through 2022. We provide an overview of (1) the synoptic context within the parent extratropical cyclone, (2) the meteorological conditions with respect to the season, (3) the suitability of case data and measurements for Lagrangian analysis and modeling studies, and (4) the encountered cloud properties. A proposed subset of flights deemed most suitable for Lagrangian modeling case studies is highlighted throughout. Such flights typically cover a greater fetch range, were better aligned with the MBL flow, and revisited sampled air masses, when key instruments were operational. Like many other flights, these flights often probed cloud formation and some cloud regime transitions. Surveying cloud properties from remote sensing and in-situ probes, we find a great range in cloud-top heights and a relatively large concentration of frozen hydrometeors, which suggest strong free tropospheric entrainment and secondary ice formation, respectively. Both processes are expected to leave marked signatures in cloud evolution, such as strongly ranging cloud droplet number concentrations that most cases show. ACTIVATE data combined with satellite retrievals can establish observational constraints for future model improvement work.

Improving parameterizations of convective microphysics in Earth system models (ESMs) requires well-constrained cases suitable for scaling between cloud-resolving models and ESMs. We propose a benchmark large eddy simulation (LES) cumulus congestus case study from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP²Ex) and demonstrate its observational constraints using novel polarimetric retrievals and in situ cloud microphysics measurements. Simulations using bulk and bin microphysics with observed aerosol input are compared to cloud-top retrievals of cloud droplet effective radius (R_eff) and number concentration (N_d) from the airborne Research Scanning Polarimeter (RSP). The bulk scheme reasonably reproduces characteristic profiles of cloud-top N_d that decrease with altitude, while the bin simulation realizes greater discrepancies due to weaker precipitation formation. The N_d profile is strongly sensitive to the collision-coalescence process and the vertically resolved aerosol distribution, but appears well-constrained, whereas a persistent low-bias in R_eff is evident in both schemes. Comparison of simulated and in situ droplet size distributions (DSDs) show that low-biased R_eff originates from a cloud droplet mode that is too narrow relative to observations. Finally, a thermal-tracking framework demonstrates that the dilution of N_d throughout a thermal's lifetime is heavily determined by collision-coalescence and the height-varying aerosol distribution, and that in the absence of these, the impact of entrainment on diluting N_d is largely offset by continuous aerosol activation. Implications for developing warm-phase convective microphysics schemes for ESMs, evaluation of cumulus congestus using single column model versions of ESMs, and translating results to global, space-based polarimetry platforms are discussed.

The evolution of cumulus congestus within tropical oceanic and maritime environments is modulated by the interaction of convective dynamics, liquid- and ice-phase microphysical processes, aerosol loading, and entrainment of ambient environmental air. Characterizing this evolution requires robust observational constraints of aerosol properties and cloud macrophysics and microphysics. The NASA Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign in 2019 targeted growing cumulus congestus clouds using airborne in situ and remote sensing platforms. In situ aircraft microphysical measurements and retrievals from the Research Scanning Polarimeter (RSP) both show that cloud droplet number concentrations decrease and effective radius increases with increasing cloud top height, with droplet size distributions (DSDs) that broaden with height. These observed components are responsive to an active collision-coalescence process that produce millimeter-sized drops, onsetting warm-rain formation. Here, we present an analysis of CAMP2Ex RSP data showing the evolution of droplet number concentrations and DSDs with height and its variation with RSP-retrieved aerosol number concentrations. For one case study (RF14, 9/25/2019), we perform large eddy simulations (LES) at 100-m horizontal grid spacing using bin and bulk microphysics schemes. Detailed multi-modal, vertically resolved aerosol measurements from the Fast Integrated Mobility Spectrometer (FIMS) are used as input. The relative ability of the bulk and bin schemes to produce the observed DSD evolution, from activation to warm-rain production, is evaluated. Sensitivity experiments are performed to assess the roles of height-varying aerosol concentrations, rain-forming collision-coalescence, and entrainment in realizing observed droplet number concentration and effective radius profiles. Additionally, we share the prospect of detailed aerosol properties, droplet number concentrations, and DSDs, similar to as acquired by RSP, becoming available from polarimeters on NASA’s PACE satellite mission, launched in early 2024.