The Earth’s surface is warming. The forest carbon cycle is sensitive to high temperatures (Hubau et al. 2020, Sullivan et al. 2020) and given that forests store 861 ± 66 petagrams of carbon (Pan et al. 2011), their response to climate change is crucial in understanding future trajectories. Temperature is a key variable governing plant physiological rates, but it is tissue temperature rather than air temperature which is relevant. Leaves are at the interface of the atmosphere and biosphere, and their temperatures often differ from air temperatures, depending on microclimate and species-specific morphology and physiology (Fauset et al. 2018). However, to date there has been little large-scale, in-depth analysis of leaf temperature processes across sites and species, limiting our understanding of forest response to climate change. This project will fill this knowledge gap.
The project will be part of a global network monitoring forest canopy temperatures (netCTF) using infrared cameras at 13 sites across 5 continents. Focal sites for this project will be a tropical semi-deciduous forest in Ghana and a mature oak woodland in the UK (https://www.birmingham.ac.uk/research/bifor/face/index.aspx
). You will analyse responses of forest canopies and tree species to climate fluctuations across the netCTF sites, elucidating whether and why canopy temperature processes differ between sites, with a focus on extreme climate events. Fieldwork at focal sites will produce datasets of plant functional traits (e.g. Fauset et al. 2018) and physiological responses to temperature (e.g. Tiwari et al. 2020) to inform on the mechanisms of temperature control and implications of high temperatures.
You will gain a broad skillset relevant to multiple careers, including leading field campaigns in the UK and Ghana, technical skills in sensor technology, and analysis of big data. You will develop an international network through collaboration with the netCTF team, produce scientific papers of the highest standard and present at international conferences.
A degree in a relevant subject (e.g. environmental science, geography, biology); candidates from numerical disciplines (e.g. physics, engineering, computer science) are also encouraged to apply. Experience of programming, fieldwork and environmental sensors are desirable.