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- ItemExtreme fire events are related to previous-year surface moisture conditions in permafrost-underlain larch forests of Siberia(Bristol : IOP Publishing, 2012) Forkel, Matthias; Thonicke, Kirsten; Beer, Christian; Cramer, Wolfgang; Bartalev, Sergey; Schmullius, ChristianeWildfires are a natural and important element in the functioning of boreal forests. However, in some years, fires with extreme spread and severity occur. Such severe fires can degrade the forest, affect human values, emit huge amounts of carbon and aerosols and alter the land surface albedo. Usually, wind, slope and dry air conditions have been recognized as factors determining fire spread. Here we identify surface moisture as an additional important driving factor for the evolution of extreme fire events in the Baikal region. An area of 127 000 km2 burned in this region in 2003, a large part of it in regions underlain by permafrost. Analyses of satellite data for 2002–2009 indicate that previous-summer surface moisture is a better predictor for burned area than precipitation anomalies or fire weather indices for larch forests with continuous permafrost. Our analysis advances the understanding of complex interactions between the atmosphere, vegetation and soil, and how coupled mechanisms can lead to extreme events. These findings emphasize the importance of a mechanistic coupling of soil thermodynamics, hydrology, vegetation functioning, and fire activity in Earth system models for projecting climate change impacts over the next century.
- ItemRecent global and regional trends in burned area and their compensating environmental controls(Bristol : IOP Publishing, 2019) Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Chuvieco, Emilio; Hantson, Stijn; Heil, Angelika; Teubner, Irene; Thonicke, Kirsten; Harrison, Sandy P.The apparent decline in the global incidence of fire between 1996 and 2015, as measured by satellite-observations of burned area, has been related to socioeconomic and land use changes. However, recent decades have also seen changes in climate and vegetation that influence fire and fire-enabled vegetation models do not reproduce the apparent decline. Given that the satellite-derived burned area datasets are still relatively short (<20 years), this raises questions both about the robustness of the apparent decline and what causes it. We use two global satellite-derived burned area datasets and a data-driven fire model to (1) assess the spatio-temporal robustness of the burned area trends and (2) to relate the trends to underlying changes in temperature, precipitation, human population density and vegetation conditions. Although the satellite datasets and simulation all show a decline in global burned area over ~20 years, the trend is not significant and is strongly affected by the start and end year chosen for trend analysis and the year-to-year variability in burned area. The global and regional trends shown by the two satellite datasets are poorly correlated for the common overlapping period (2001–2015) and the fire model simulates changes in global and regional burned area that lie within the uncertainties of the satellite datasets. The model simulations show that recent increases in temperature would lead to increased burned area but this effect is compensated by increasing wetness or increases in population, both of which lead to declining burned area. Increases in vegetation cover and density associated with recent greening trends lead to increased burned area in fuel-limited regions. Our analyses show that global and regional burned area trends result from the interaction of compensating trends in controls of wildfire at regional scales.
- ItemContrasting and interacting changes in simulated spring and summer carbon cycle extremes in European ecosystems(Bristol : IOP Publishing, 2017) Sippel, Sebastian; Forkel, Matthias; Rammig, Anja; Thonicke, Kirsten; Flach, Milan; Heimann, Martin; Otto, Friederike E.L.; Reichstein, Markus; Mahecha, Miguel D.Climate extremes have the potential to cause extreme responses of terrestrial ecosystem functioning. However, it is neither straightforward to quantify and predict extreme ecosystem responses, nor to attribute these responses to specific climate drivers. Here, we construct a factorial experiment based on a large ensemble of process-oriented ecosystem model simulations driven by a regional climate model (12 500 model years in 1985–2010) in six European regions. Our aims are to (1) attribute changes in the intensity and frequency of simulated ecosystem productivity extremes (EPEs) to recent changes in climate extremes, CO2 concentration, and land use, and to (2) assess the effect of timing and seasonal interaction on the intensity of EPEs. Evaluating the ensemble simulations reveals that (1) recent trends in EPEs are seasonally contrasting: spring EPEs show consistent trends towards increased carbon uptake, while trends in summer EPEs are predominantly negative in net ecosystem productivity (i.e. higher net carbon release under drought and heat in summer) and close-to-neutral in gross productivity. While changes in climate and its extremes (mainly warming) and changes in CO2 increase spring productivity, changes in climate extremes decrease summer productivity neutralizing positive effects of CO2. Furthermore, we find that (2) drought or heat wave induced carbon losses in summer (i.e. negative EPEs) can be partly compensated by a higher uptake in the preceding spring in temperate regions. Conversely, however, carry-over effects from spring to summer that arise from depleted soil moisture exacerbate the carbon losses caused by climate extremes in summer, and are thus undoing spring compensatory effects. While the spring-compensation effect is increasing over time, the carry-over effect shows no trend between 1985–2010. The ensemble ecosystem model simulations provide a process-based interpretation and generalization for spring-summer interacting carbon cycle effects caused by climate extremes (i.e. compensatory and carry-over effects). In summary, the ensemble ecosystem modelling approach presented in this paper offers a novel route to scrutinize ecosystem responses to changing climate extremes in a probabilistic framework, and to pinpoint the underlying eco-physiological mechanisms.