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


Siberia extends across northern Eurasia and encompasses more than 13 million square kilometres. Much of it is covered in forest, comprising a small number of tree species, whose distributions are disjunct e.g. larch (deciduous needles) to the east, spruce and pine (evergreen needles) to the west. This boreal forest is the largest on Earth, forming a globally important carbon reservoir and described as the ‘lungs of the Northern Hemisphere’: therefore, any change in its capacity as a carbon sink has the potential to significantly impact the magnitude of future ‘global warming’.

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 (e.g. Kukavskaya et al. 2016). 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’ (World Weather Attribution 2020).

While it appears that general ‘Arctic amplification’, whereby recent warming in near-surface regional temperatures has been more than twice that at lower latitudes (e.g. Overland et al. 2016), 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. For example, at a local scale the length of droughts determines the moisture content of potential fire fuel while the broad-scale modes of atmospheric circulation affect the frequency of anticyclonic conditions (Balzter et al. 2007). 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. Ultimately, you will utilise the output of the General Circulation Models (GCMs), as employed by the Intergovernmental Panel on Climate Change (IPCC), to make projections of likely future changes in climate-related Siberian fire activity.

Person specification
We are looking for enthusiastic, self-reliant, and self-motivated candidates with a strong numerical background in mathematics, physics or the environmental/ecological sciences. Previous programming experience in one of Python, MatLab, IDL or similar computing environment would be advantageous.

Click on an image to expand

Image Captions

1. There were over 150 fires in Siberia on 1st July 2020 (source:
2. Russian scientists characterising the nature of burnt forest areas in Siberia (Photo: R Baxter).


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.

Project Timeline

Year 1

Month 1: Core induction programmes for IAPETUS2 and Biosciences (Durham) plus those at BAS.
Months 1-3: Literature review
Month 3: Literature review and project aims report (3000 words; thesis committee)
Months 2-12: Work relating to tasks T1-3, including a possible visit to the V. N. Sukachev Institute of Forest in Siberia
Month 9: First-year report (mini-thesis format, 5000 words; thesis committee); official formal Progression Review.
Month 12: Attend BAS Student Symposium

Year 2

Months 13-24: Completion of work related to tasks T3 and T4
Month 18: Formal Poster Presentation at postgraduate research day, Biosciences; thesis committee.
Month 21: Meeting with thesis committee for the official Confirmation Review.
Month 24: Poster presentation at the BAS Student Symposium

Year 3

Months 25-36: Undertake work related to tasks T5 and T6.
Month 28: Submission of summary of progress and thesis outline plan; thesis committee.
Month 30: Formal oral presentation at postgraduate research day, Biosciences
Month 33: Meeting with Thesis committee for the official Completion Review and submission of a thesis plan, timetable for completion and submission.
Month 36: Oral presentation at the BAS Student Symposium

Year 3.5

Months 37-42: Thesis writing as appropriate

& Skills

The successful candidate will be registered at the University of Durham in the Biosciences Department but based primarily at the British Antarctic Survey (BAS) in Cambridge, within the Atmospheric, Ice and Climate team. They will join a cohort of about 40 PhD students in total at BAS, spread over five DTP programmes and two CDT programmes.

At Durham, the IAPETUS DTP programme will provide cross-disciplinary scientific training and development. Furthermore, the student will undertake the Biosciences Departmental induction programme and core components of the Postgraduate Training Programme whilst attending the initial IAPETUS induction period. Thereafter they will be assigned a “Thesis committee” within Biosciences who will interact directly with the student throughout the PhD programme. Formal training tasks will be carried out at relevant points of progression (see timeline above). Under the supervision of Bob Baxter, the student will spend additional time at Durham learning the relevant aspects of forest and fire ecology. There will also be an opportunity to travel to Krasnoyarsk in Siberia for further ‘in situ’ training.

At BAS, Gareth Marshall will be the student’s primary supervisor. Here, they will receive additional training specific to the PhD topic, including statistical methods, climate model data analysis and visualisation techniques and machine learning tools for analysing ‘big data’. Co-supervisor Scott Hosking (BAS) will be responsible for guiding the machine-learning work. The student will have the opportunity to present their work at an international conference.

References & further reading

Balzter et al. (2007):

Barrett et al. (2020):

Kukavskaya et al. (2016):

Marshall et al. (2020):

Overland et al. (2016):

World Weather Attribution (2020):

Further Information


Dr Gareth Marshall,, 01223 221309

Dr Bob Baxter,, 0191 3341261

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