Climate Change and Arctic Browning: Understanding the Role of Extreme Weather Events

• Main Author: Memon, Murk
• Format: Thesis
• Language: English
• Published: 2023
• Subjects: Arctic; Browning; Norway; Climate change; Northern Norway
• Online Access: https://etheses.whiterose.ac.uk/33340/; https://etheses.whiterose.ac.uk/33340/1/Memon_corrected_thesis_June2023.pdf

Vegetation browning is the decline in plant biomass and productivity arising from climate change, biotic interactions and disturbance. It is now considered one of the major disruptions in a rapidly changing Arctic landscape. Damaged Arctic vegetation due to extreme winter weather events such as warming events and frost drought conditions, has been shown to change from a sink to a net CO2 source at the peak of the growing season. It is crucial to understand the satellite-based signature of browning events due to the challenging nature of field work in the Arctic and the sporadic nature of such events. It is important to understand how browning events can unfold in the future in response to projections of increased frequency, magnitude and severity of extreme winter weather events in the Arctic. This research is the first to provide a remote sensing and climate modelling based framework to examine Arctic browning. Northern Norway was selected as the study area for this PhD research. The first research objective of this PhD thesis was to understand the satellite-based signature of browning events caused by extreme winter weather conditions. This was achieved through examining the effectiveness of two different MODIS vegetation indices at quantifying the on-record ground observations of vegetation decline in the Norwegian Arctic and sub-Arctic areas. The indices included the Chlorophyll Carotenoid Index (CCI) and the Normalized Difference Vegetation Index (NDVI). The CCI and NDVI were extracted for early, peak and end of the growing season (July-September). Moreover, the average growing season CCI and NDVI were calculated as well. These calculations were conducted for three case study sites in northern Norway. The NDVI presented a more robust signal compared to CCI for detecting decreases in the Gross Primary Productivity (GPP) of dwarf shrub vegetation across different Arctic landscapes. This was concluded to be mainly due to the higher spatial resolution of NDVI (0.25 km) compared to that of CCI (1 km). The second ...