You will aim 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?
You will achieve this 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. We will collaborate with existing contact Dr Elena Kukavskaya, at the V.N. Sukachev Institute of Forest in Krasnoyarsk, to gain access to data normally unavailable outside Russia. Ideally, this will include a visit to Krasnoyarsk itself but it is not essential for the viability of the project.
T2. Obtain high-resolution (~10 km) output of meteorological parameters from existing Arctic CORDEX regional climate model runs for these regions of Siberia. These existing datasets will be stored and partially analysed using the JASMIN super-data-cluster with some final analysis being undertaken at BAS.
T3. Employ state-of-the-art machine learning techniques to develop non-linear multivariate multitemporal relationships between meteorological variables and fire activity/danger observations: e.g. based on the temporal changes of several meteorological variables prior to the summer fire season. Particular emphasis will be placed upon differentiating the responses of evergreen (spruce/pine) and deciduous (larch) forest. 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, you 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.