The Arctic has seen extreme heat and fire events. This project investigates the link between heat, drought, fire and carbon release, at present and what this means for the future. It will build on a wealth of data and use modern data science methods. (Arctic wildfires, May 2022).
Scientific Background, motivation and context: Climate change is increasing the intensity and frequency of heat waves, due to both warming and changes in weather patterns. This is particularly true in the Arctic, which is warming four times faster than the rest of the planet and has experienced strong heat waves recently. Research suggests that the 2020 Siberian heat wave would have been almost impossible without human influence on climate (WWA 2020). Increased heat leads to more evaporation and stress on plants, and increases fire danger. On the other hand, warming also leads to greening of the high Arctic (Myers-Smith et al., 2020). As global warming continues, we expect a further increase in heat events, with severe consequences for the provision of ecosystem services and carbon storage in vegetation, soil and permafrost (e.g. Ciais et al., 2005). This project will use modern data analysis methods to learn from a wealth of available data, from satellite data to reanalysis data, anchored by sparse in situ data, and state of the art earth system model data.
Aims and objectives of the project: This project addresses three research questions:
1) Where are Arctic summer heatwaves occurring, and what causes those events?
2) Do summer heatwaves result in changes in plant productivity and increased fire activity, and what are the carbon implications?
3) How will future climate conditions influence the intensity and frequency of Arctic summer heat waves?
Methodology: Data analyses will focus on satellite observations (e.g. from MODIS, Sentinel-2), which provide multiple datasets for climate conditions and detection of vegetation stress (e.g., vegetation productivity indices such as NDVI), recent fire activity, surface temperature and drought (e.g., Jiao et al., 2021). Extreme large scale heat will be detected in satellite data, combined with past weather reanalyses (such as ERA, and the 20th century reanalysis) and the mechanisms responsible for them will be determined. Vegetation changes and fire activity in response to these events will be determined from satellite data. When attributing causes to retrieved vegetation stress and greening, multiple causal factors will be considered, both climatic and non-climatic, using a causal network approach (Runge et al., 2019). A large dataset of earth system model outputs (CMIP6, available from JASMIN) will provide simulations of present conditions and future changes. Analogue methods (Yiou et al., 2017) identify how events similar to those observed are simulated in earth system models and how well these capture their consequences, including change in vegetation and carbon storage (e.g. Treharne et al., 2020). The results of this research will improve constraints on the interaction between heat, drought, fire and vegetation in earth system models, and thus lead to more reliable future predictions of fire and vegetation change, and its carbon implications.
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 and field training. All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
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