Fanning the flames: past and future links between climate change and fire activity across Siberia.

Siberian forest is shaped by complex interactions between climate, forest fires, insect outbreaks and human activities. The frequency and extent of regional forest fires has increased tenfold in the last 20 years. In 2020 about 10 million hectares of forest had been destroyed by August and followed record-breaking high temperatures during the first half of the year, a phenomenon that has been directly attributed to anthropogenically-forced ‘global warming’. While it appears that general ‘Arctic amplification’, whereby recent warming in near-surface regional temperatures has been more than twice that at lower latitudes, has contributed to the increase in Siberian fire disturbance, there remains a clear need to better understand the range of spatial and temporal scales of the processes and drivers involved in fire-climate interactions. Furthermore, species-specific responses to fire – in relation to fire ecology and determination of fuel load – are important in determining the spatial extent and temporal frequency of fire impacts and the net carbon loss from the ecosystem over the longer-term. This project aims to elucidate these processes/drivers and, using state-of-the-art machine learning techniques, link them to broader-scale atmospheric variability. The project aims to answer the following questions:
Q1.What are the key fire-climate processes and climatological drivers affecting the Siberian boreal forest? Q2.To what extent is recent climate change responsible for observed increases in fire disturbance in Siberia? Q3. What will be the likely impact of projected climate change on the frequency of future fire disturbance in regions of Siberia? This will be achieved by undertaking the following tasks:
T1. Obtain measures of fire activity/danger from observations and remotely-sensed data for selected regions of Siberia, based on the level of recent fire activity and tree species. T2. Obtain high-resolution (~10 km) output of meteorological parameters from existing Arctic CORDEX regional climate model runs for these regions of Siberia. T3. Employ state-of-the-art machine learning techniques to develop non-linear multivariate multitemporal relationships between meteorological variables and fire activity/danger observations. This will answer Q1. T4. Employ state-of-the-art machine learning techniques to develop regional ‘fingerprints’ between the key meteorological variables affecting fire activity, obtained from T3, and the broader-scale atmospheric circulation as derived from an ensemble of relatively coarse (~100 km) GCMs. Based on these fingerprints, the student will be able to efficiently downscale the GCM output to the scale of the fire activity. This will answer Q1. T5. Using the historical GCM model runs, validated against reanalysis data, and the fingerprints derived in T4, estimate the likely contribution that changes in the key meteorological variables have made to observed fire activity. This will answer Q2. T6. Using the output from a range of selected GCMs and future climate scenarios estimate the change in Siberian fire activity during the 21st Century. This will include estimates of the overall uncertainty in the projections based on natural variability, model uncertainty and scenario uncertainty. This will answer Q3.