The Arctic is warming faster than any other land region, and its vegetation is responding, with adjustments to plant cover, phenology and growth. As the Arctic changes, its processing of carbon and links to the earth system also adjust. However, we know that net carbon exchange is strongly buffered against climate interannually by compensating adjustments to photosynthesis and respiration. This compensation has been linked to nitrogen-related leaf traits, indicating the importance of C-N coupling in tundra ecosystems. With warming and accelerating N cycling, this buffering may be overcome, leading to substantive changes in the rates of C cycling and its residence times. These changes could have major implications to the global C cycle due to the large stocks of C held in Arctic soils.
To address the overall question this PhD will focus on researching a set of linked questions that scale from site level studies to the pan Arctic, combining field data, satellite remote sensing, machine learning and modelling. The questions are:
What is the climate sensitivity of measured C sink strengths across the pan-Arctic and how is this linked to phenology?
What is the climate sensitivity of earth system models of C exchange at high latitudes, and how is this sensitivity linked to phenology? How do these model results compare with estimate constructed from (i)?
How does climate sensitivity of C sink strength vary with leaf traits, and what are the thresholds for adjustments to rates of C storage?
What are likely scenarios of future arctic C stocks based on probabilistic estimates of key processes and interactions across the Arctic?
This PhD will address the overall question by combining field observations of net CO2 exchange collected over hundreds of locations across the pan-Arctic, plus eddy flux tower data, with satellite observations of vegetation cover and modelling of C cycling, including canopy phenology, photosynthesis and respiration. The first question will be addressed by combining existing flux tower and chamber flux data with high resolution satellite data and locally calibrated versions of SPA C cycle model. For the second question the student will analyse results from a set of widely used earth system models, investigating their characterisation of C cycling and comparing these with the results generated using direct observations in (i). The third question will have the student undertake sensitivity studies using the SPA model to probe the links between leaf traits, climate, and C cycling, and to understand better the nature of climate buffering of C cycling and its thresholds. Finally, the fourth question will use machine learning and data assimilation methods to constrain forecasts of tundra C cycling using a simple version of the SPA model applied across the Arctic.
In additional the extensive training on satellite data analysis and Machine Learning / AI the student will take part in as part of the SENSE CDT, the student will receive training in ecosystem C cycling and ecophysiological modelling, model-data fusion techniques, and spatio-temporal statistics. There will be a potential for field visits to Arctic sites to gather data for model calibration and testing.
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. http://www.eo-cdt.org
López-Blanco, E. J-F. Exbrayat, M. Lund, T. R. Christensen, M.l P. Tamstorf, D. Slevin, G. Hugelius, A. A. Bloom, and M. Williams (2019). Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system. Earth System Dynamics 10, 233-255.
López-Blanco, E, M Lund, T R Christensen, M P Tamstorf, T L Smallman, D Slevin, A Westergaard-Nielsen, B U Hansen, J Abermann and M Williams (2018), Plant Traits are Key Determinants in Buffering the Meteorological Sensitivity of Net Carbon Exchanges of Arctic Tundra, J. Geophysical Research Biogeosciences,9: 2675-2694.
Shaver, G.R, E.B. Rastetter, V. Salmon, L.E. Street, M.J. van de Weg, M.T. van Wijk, and M. Williams (2013). PanArctic Modeling of Net Ecosystem Exchange of CO2. Phil. Tran. Royal Soc. B. 368: 1624