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A Tale of Two Poles: Exploring Mixed-Phase Clouds from above and below in Greenland and Antarctica

Overview:  This is an exciting University of Leeds-based PhD opportunity that will give you a chance to delve into the use of state-of-the-art machine learning techniques to answer questions about a poorly understood component of the Earth’s climate system.  You will also possibly have the opportunity to conduct fieldwork in either or both Antarctica and Greenland. As such, this represents a comprehensive opportunity for a motivated individual to become an experiment in remote sensing of clouds.

Scientific Need: Current climate models generally show the most significant biases in surface radiation and surface temperature over the Southern Ocean (e.g., Boda-Salcedo et al., 2014, Kay et al., 2016, Lenaerts et al., 2017 and Hyder et al., 2018). Similar biases also occur over Greenland and the Arctic in general. In both cases, these biases fundamentally impact the ability of climate models to assess the global climate in the past, present and future, including the mass balance of the Greenland and Antarctic ice sheets (Slater et al. 2020). Current literature suggests that errors in representing the polar mixed-phase clouds are accountable for these biases (Hyder et al., 2018). To address these errors, this project will investigate the properties of high-latitude mixed-phase clouds, comparing and contrasting observations from both poles, using a unique and large dataset (Obj. 2) that combines remote sensing observations from several major ground-based observation campaigns and ICESat-2’s atmospheric dataset (e.g., ATL09).

Scientific Objectives:

Objective 1 – Validate the novel  ICESat-2 cloud characteristics dataset with ground-based observations from across the Antarctic and Arctic, focusing on observations provided by the US NSF- and NERC-funded ICECAPS project (see below) the NERC-funded Southern Oceans Cloud Project. This will particularly focus on low-level clouds which have not been investigated in detail with this unique combination of data sources.

Objective 2 – Create a dataset of cloud properties (i.e. height, temperature, phase, liquid water path,  etc.) that combines the best estimate of cloud characteristics from the observations above that can be used to compare and contrast the observations at both poles at regional (focusing on statistical comparisons and satellite-based observations) and local scales (i.e., case studies) of low-level liquid clouds which are known to be important for the surface energy budget.

Objective 3 – Investigate the cloud microphysical processes revealed by the dataset produced in Obj. 2 – such as secondary ice production – that are important in the life cycle of these clouds.

Objective 4 – Compare your observational synthesis to the most recently available model-only and reanalysis assessments of polar clouds.

Methodology: Since 2010, the Integrated Characterization of Energy, Atmospheric state, Clouds and Precipitation at Summit (ICECAPS) project has been operating at Summit at 3250 masl. This project has made the first detailed measurements of atmospheric and cloud properties over Greenland. The instruments deployed at Summit (Shupe et al., 2013) represent a “supersite” for atmospheric measurements that have provided a detailed, process-level understanding of how clouds, precipitation, radiation, and atmospheric structure are coupled over Greenland. You will utilize this dataset along with observations from NERC’s Southern Ocean Clouds project led by BAS, several upcoming campaigns (and there might be the possibility of participation by the student) and observations from regionally distributed ground-based lidars and radars to explore the capabilities and validate the ICESat-2’s ATL09 dataset. With this understanding, you will then dig deeper into the observations to improve our knowledge of polar mixed-phase clouds. To do this, you will utilize both traditional remote sensing techniques and newer advances made with machine learning techniques (e.g., hierarchal clustering techniques, Lukach et al., 2021). Data analysis will be enabled by access to NERC’S JASMIN computing system, which will host copies of all the data.

As part of accomplishing the objectives of this work, there will be several opportunities to get hands-on field experience and collect data with remote sensing technology in Greenland and Antarctica. You will also be embedded into the ICESat-2 team at NASA Goddard’s Cryospheric Sciences Lab for at least three months as part of your training.