Ice Processes in Antarctica: Identification via multi-wavelength active and passive measurements and model evaluation

Principal Investigator(s):
Alessandro Battaglia, University of Leicester

Co-Investigator(s):
Ann Fridlind, NASA - Goddard Space Flight Center
Stefan Kneifel, University of Cologne, Germany
Frederic Tridon, University of Leicester, Leicester, UK
Andrew Ackerman, NASA Goddard Institute for Space Studies, NY, US

Changes of cloud cover and radiative properties of Antarctic clouds can have ripple effects for the general circulation of the atmosphere and can cause not yet thoroughly understood feedbacks to the climate system, e.g. in enhancing melting events. The deployment at the McMurdo site on the southern tip of Antarctica’s Ross Ice Shelf during the Atmospheric Radiation Measurements West Antarctic Radiation Experiment (AWARE) field campaign of an unprecedented number of multi-wavelength active and passive remote sensing systems offers the unique opportunity of overcoming the scarcity of cloud information at southern high latitudes and of unravelling structures and processes related to cloud and precipitation physics at high temporal and spatial resolution.

We propose analysis and application of data gathered during AWARE to achieve three linked observational and modeling science objectives. 1) By optimally integrating a-priori information and multiple observations from lidar, triple-frequency radar and microwave radiometer instruments deployed at the McMurdo AWARE site we will produce a seamless cloud property characterization from the thinnest supercooled liquid or ice layers to the thickest precipitating ice. Within an enhanced cloud mask, outputs to be archived for public use will be liquid and ice water content and the bulk density and characteristic size of one or more ice types present. 2) We will for the first time add to the microphysics outputs an identification of active ice physical processes (vapor growth/sublimation, aggregation, riming, ice multiplication) within the same cloud mask by fully exploiting multi-frequency Doppler spectra.  3) AWARE data presents a unique opportunity to evaluate and improve climate models since so few Antarctica data sets have been available that are sufficient to provide detailed description of the shallow cloud properties that most commonly contain supercooled liquid.  We will make synergistic use of the observational products to evaluate and improve NASA's ModelE global climate model, which has recently upgraded moist turbulence and two-moment stratiform cloud microphysics schemes, and a state-of-the-art aerosol scheme. We propose to employ an approach of weather state identification in observations at McMurdo and in decade-long current-climate simulations of ModelE at the McMurdo location. The observed and ModelE-simulated cloud-mask based weather state frequencies and cloud properties will be directly compared. Special attention will be placed on the occurrence and radiative consequences of supercooled liquid water, which has been increasingly identified as an important emergent constraint on simulated climate sensitivity, especially its presence at extratropical latitudes.

Finally, two case studies will be selected to advance the twin objectives of improving both ModelE and the relatively new effort to observationally fingerprint cloud processes. The case studies will be developed for single-column model and large-eddy simulation with size-resolved mixed-phase microphysics, and will be subject to forward simulation in a reverse of the retrieval process. Each case study will target a particular common microphysical process (e.g., sublimation, aggregation). Sensitivity test simulations with those processes turned on and off will allow the observation team to test the fingerprinting process by examining signatures revealed; the same sensitivity tests will allow the modeling team to assess the importance of process occurrence and representation in ModelE. The data sets and methods applied here will be equally applicable to other climate models and could serve as the basis for a community model evaluation effort.