Using ARM observations and large-eddy simulation to constrain cloud processing of CCN in boundary-layer clouds over the Eastern North Atlantic

Correctly representing marine boundary layer (MBL) aerosol–cloud–precipitation interactions (ACPIs) in Earth System Models

earth system model (ESM)

A model that simulates how chemistry, biology, and physical forces work together. These models are similar to, but much more comprehensive than, global climate models.

Source: U.S. Department of Energy Source

(ESMs) remains a challenging problem. Previous research has emphasized how aerosol

aerosols

Small particles or droplets, such as wildfire smoke, volcanic gases, or salty sea spray, that float in the air. They are emitted by both natural events and human activities.

Source: NASA Global Climate Change Source

— particularly microphysically active cloud condensation nuclei

cloud condensation nuclei

Tiny airborne particles suspended in the atmosphere that serve as the seeds around which cloud droplets form.

Source: Source

(CCN) — influences radiative or precipitation processes, but the ways in which precipitation and other processes (e.g., entrainment) in turn influence the CCN field have been comparatively less studied. We propose a coordinated observational and modeling investigation to improve fundamental understanding of cloud processing of aerosol and how these processes are parameterized in models. This proposal is highly aligned with the Funding Opportunity Announcement (FOA) research goal of employing Department of Energy Atmospheric Radiation Measurement Program (DOE ARM) data in observational and modeling studies to improve process-level understanding of ACPIs. Our proposal has two coordinated research thrusts to address this objective:

1.   Observationally estimating CCN processing. The rich array of data from the DOE Eastern North Atlantic (ENA) surface observations and surface-based retrievals, aircraft flights from the recent ACE–ENA field campaign, satellite retrievals, and reanalysis will be combined in a mixed-layer-model (MLM) framework to quantify the dominant terms governing the MBL CCN budget and how this behavior depends upon cloud properties, synoptic context, and season. 

2.   Using large-eddy simulation (LES) with size-resolving (bin) microphysics to evaluate current CCN-processing assumptions and develop new parameterizations. Bin-microphysics LES will be constrained by ARM surface and aircraft observations to quantify aerosol cloud processing rates and the dominant mechanisms governing aerosol evolution. This will be done in a Lagrangian framework and in such a way to promote consistent comparisons with the Eulerian-framework (fixed) ENA observations. Model results will be analyzed by 1. using a MLM framework similar to that applied to the observational data; 2. applying a machine-learning random-forest approach to rank the dominant microphysical properties governing CCN processing; and 3. developing new functional forms (parameterizations) of CCN processing by cloud. 

Objectives 1 and 2 are linked through the application of the stochastic collection equation from the LES on aircraft drop size distributions, and the consistent use of a MLM approach for both observations and LES. This research will make special use of ACE–ENA field observations, the PI team’s well-established approaches for classifying synoptic regime, and extensive experience with spectral-bin LES. The focus on the evaluation of current CCN processing approaches in ESMs, together with an active development of new parameterizations, is likely to yield substantial short-term (< 5 years) impacts for ESM models like DOE ACME (Accelerated Climate Modeling for Energy Project).

The research is aligned with one of the five WCRP grand challenges (Coupling Clouds to Circulations; http://www.wcrp-climate.org/index.php/gc-clouds) and the ongoing DOE Artificial Intelligence

artificial intelligence

A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments.

Source: U.S. Department of State Source

for Earth System Predictability (AI4ESP) effort to apply machine learning

machine learning (ML)

Machine Learning (ML) refers to the field and practice of using algorithms that are able to “learn” by extracting patterns from a large body of data.

Source: Center of Excellence Source

approaches (here, self-organizing maps and regression tree analysis) to large, multi-dimensional observational and modeling datasets. Expected outcomes of the research are 1. improving understanding of the factors governing cloud processing of CCN in the MBL across seasons and synoptic regimes; 2. gaining new process-level insights into the CCN-processing component of ACPIs, as illuminated from LES with spectral representations of droplets and CCN; and 3. developing a new parameterization of CCN processing and uncertainty that is independent of existing autoconversion and accretion parameterizations.