Microphysical-Macrophysical Interactions in Low Cloud Systems over the Eastern North Atlantic

Abstract

How low clouds (tops lower than 3km) respond to changes in atmospheric greenhouse gases and aerosols are major sources of uncertainty that limit the accuracy of predictions of future climate. Low cloud systems over the oceans far from continents are a key challenge because they are poorly represented in climate models, and there have been insufficient long-term observations to constrain the key processes that govern their formation and maintenance. Clouds consist of numerous tiny droplets and ice crystals with sizes between 1 and 1000 micrometers (one micrometer = one millionth of a meter) that together are called microphysical properties. Cloud microphysical processes refer to interactions between individual cloud particles. Examples include the collision and coalescence of droplets and process by which even smaller aerosol particles (macrophysical) play a fundamental role in modulating cloud dynamics, entrainment and precipitation, which all help determine how much sunlight is reflected by clouds. It is now understood that precipitation (rain, drizzle) formation in low clouds plays a critical role in helping to organize cloud systems on scales of tens of kilometers.
The new Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site at Graciosa Island (The Azores, 28°W 39°N) provides unprecedented observations in a remote marine environment dominated by low clouds. The ENA site greatly extends the capabilities of observational datasets collected as part of a previous deployment (The Clouds-Aerosols-Precipitation Marine Boundary Layer, CAP-MBL) of the ARM Mobile Facility (AMF) that took place in 2009-2010. The site straddles the boundary between the subtropical and middle latitudes in the Eastern North Atlantic, and experiences a great diversity of meteorological and cloud conditions. In addition, the ENA site is downwind of the North American continent and is periodically impacted by continental pollution aerosol.
This proposal lays out a strategy to accelerate research focused on understanding the interactions between cloud microphysical and macrophysical processes by maximizing the utility of the observational capabilities of the new ENA site. The proposal team will (a) conduct a program of transformative research focused on understanding how microphysical and macrophysical processes interact with each other; (b) work collaboratively with scientists within the Atmospheric System Research (ASR) program, with the ARM infrastructure, and with scientists outside of ASR to ensure that the full potential of the ENA site is realized.
The proposed research program is organized into three distinct but inter-related themes:
Theme 1. Acquiring observational understanding of how microphysical and macrophysical processes interact at different spatial scales
Theme 2. Understanding how microphysical and macrophysical interactions depend upon and influence the aerosol and meteorological environment
Theme 3. Assessing and improving process and climate model representations of clouds, aerosols and their interactions.
Project methods: The proposed work involves the development of new methods to observe clouds, light precipitation, and vertical air motions that will exploit the capabilities of the new suite of vertically-pointing and scanning radars. These will be used together with models and a variety of other ENA datasets to explore how precipitation impact cloud dynamical processes (motions), how clouds mix with their surroundings, how precipitation helps to organize low cloud systems, and how precipitation impact aerosols and the amount of sunlight reflected by clouds. The proposed work involves using process and climate models together with ENA observations, and also includes a model assessment project that will focus the climate modeling community and help drive model improvements by providing key observational constraints needed to identify model errors.
Project Impacts: The proposed work will tackle key challenges in understanding low cloud processes by providing high quality new datasets that will be used to improve both process and large scale models. The work will lead to an improved understanding of the factors influencing cloud microphysical processes and how this influences cloud macrophysical and radiative properties.