Improving PREdictability of circumboREAL forest fire activity and its ecological and socio-economic impacts through multi-proxy data comparisons – PREREAL

The ability to predict forest fire activity at monthly, seasonal, and above-annual time scales is critical to mitigate its impacts, including fire-driven
dynamics of ecosystem and socio-economic services. Fire is the primary driving factor of the ecosystem dynamics in the boreal forest, directly
affecting global carbon balance and atmospheric concentrations of the trace gases including carbon dioxide. Resilience of ocean-atmosphere system
provides potential for advanced detection of upcoming fire season intensity. There is a strong potential in using a large body of paleo- and
dendrochronological reconstructions to improve predictability of weather extremes such periods of regionally increased fire activity. We propose that
joint analyses of historical fire proxies (fire scars and charcoal in the lake sediments) with independently obtained proxies of climate variability and
vegetation cover should contribute towards better knowledge of modern climate drivers of forest fires and predictability of fire activity at multiple
temporal scales. In this project we will identify climatic drivers controlling boreal fire activity and its predictability at monthly, seasonal and annual
timescales by relying on analyses of multiple proxies of modern and historic fire activity, and climate-ocean variability. We will also provide monthly
to century-scale predictions of future fire activity and to translate these into impacts on ecosystem services and metrics of socio-economic
performance. We argue that capitalizing on multi-proxy data comparisons should improve predictability of fire activity via (a) a large overlap between
climate and fire proxies, which dramatically extends the period covered by instrumental observations and improves robustness of analyses, (b) a more
realistic translation of fire hazard metrics into actual fire activity, and (c) a better separation of low vs. high frequency variability in the fire activity, an
important aspect in the modeling of the future trends in fire activity.