About the Project
Marine ice losses around the polar regions are one of the most fundamental physical responses of the Earth system to rising atmospheric greenhouse gas concentrations. Such ice losses influence gas and heat exchange, light penetration, water mixing and photosynthetic carbon capture. There is considerable complexity and disparity within and between marine ice loss in the two polar regions, but most commonality is within the Atlantic sector. Sea ice, ice shelf and glacier losses are all considerable and increasing, albeit in non-linear manner, and one key impact has been to increase the duration, composition and in some cases magnitude of phytoplankton blooms. The nature of bloom increase has been difficult to measure because much of it is near coast and in mixed open water-ice conditions. New techniques of image analysis are extracting more detailed and relevant information from ocean colour but they need strong ground-truthing from relevant directly measured samples – which BAS have as part of the Rothera Time Series project and spot ship-based samples. Extended blooms are increasing carbon capture and the fate of some of this has led to considerable increases in carbon stored in the seafloor (in animal tissues and skeletons). Better analysis of bloom and current dynamics will allow stronger interrogation of our benthic blue carbon data collected over a decade of research cruises in both polar regions. The institute, universities and industry partnership should connect data collectors and holders (BAS) with cutting edge analysis and interpretation techniques (Industry) and process and flux scientists (Leeds & Edinburgh).
Other supervisors; Fleming A & Venables H (BAS), Maerz C (Leeds) & Henley S (Edinburgh).
This project asks how well can remotely sensed ocean colour be used to predict carbon cycling, from capture to seabed storage? How does this vary with season, sea ice levels and geographic factors (eg on vs offshore). EO ocean colour (derived from stacks of Sentinel-3 OLCI and other suitable datasets) & albedo data would be collated from Atlantic sector Arctic and Southern Ocean continental shelves to examine trends in marine ice losses and greening duration. Patterns and trends in colour would be ground-truthed for accuracy /error levels using in situ direct measurements of size-fractionated phytoplankton standing stock where and when they are present. Primary production would be linked to regional oceanography (eg flow/water residence time) and water column biogeochemistry. This key stage of the project would construct a model to link ice loss and primary production - through colour change - to predict levels of phytoplankton biomass reaching the seabed. This would be mapped onto existing data on local and regional standing stocks of zoobenthic blue carbon, by splitting zoobenthos into functional groups to separate suspension, deposit and grazing primary consumers and various categories of higher trophic levels. The last major component would be adding georeferenced organic and inorganic carbon profiles in sediments from sediment cores. This can be done from existing core data and unprocessed cores. The last work phase involves analysis of ocean surface to seabed connectivity in carbon sink, lag phases in time and space, and locations of/reasons for strong connectivity. This last stage will include use of data analytics and statistical/machine learning approaches to establish linkages between available datasets.
The student would be based at BAS, Cambridge, but registered for their PhD at the University of Edinburgh, and undergo training in Edinburgh, Leeds and Southampton. They would have monthly video meetings with their Edinburgh supervisor and and twice-yearly spend physical time at Edinburgh to focus on geochemistry at the seabed and in the water column. The first phase of the studentship would liaise particularly closely with industry to develop and improve cutting edge methods of image analysis and interpretation and detection of wide-scale trends from EO imagery.
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
Barnes et al 2018. Icebergs, sea ice, blue carbon and climate feedbacks. Phil Trans Roy Soc Lond A 376: 20170176 https://doi.org/10.1098/rsta.2017.0176
Duprat et al 2016. Enhanced Southern Ocean marine productivity due to fertilization by giant icebergs. Nature Geoscience, 9 (3). pp. 219-221. ISSN 1752-0894
Rogers et al 2019. Antarctic futures: an assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the Southern Ocean. Annual Reviews of Marine Systems 12
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.
MRC DiMeN Doctoral Training Partnership: Using artificial intelligence to optimise treatment decisions by analysis of retinal images for patients with blinding diabetic eye disease