[1] In this study we present an assessment of the impact of future climate change on total fire probability, burned area, and carbon (C) emissions from fires in Europe. The analysis was performed with the Community Land Model (CLM) extended with a prognostic treatment of fires that was specifically refined and optimized for application over Europe. Simulations over the 21st century are forced by five different high-resolution Regional Climate Models under the Special Report on Emissions Scenarios A1B. Both original and bias-corrected meteorological forcings is used. Results show that the simulated C emissions over the present period are improved by using bias corrected meteorological forcing, with a reduction of the intermodel variability. In the course of the 21st century, burned area and C emissions from fires are shown to increase in Europe, in particular in the Mediterranean basins, in the Balkan regions and in Eastern Europe. However, the projected increase is lower than in other studies that did not fully account for the effect of climate on ecosystem functioning. We demonstrate that the lower sensitivity of burned area and C emissions to climate change is related to the predicted reduction of the net primary productivity, which is identified as the most important determinant of fire activity in the Mediterranean region after anthropogenic interaction. This behavior, consistent with the intermediate fire-productivity hypothesis, limits the sensitivity of future burned area and C emissions from fires on climate change, providing more conservative estimates of future fire patterns, and demonstrates the importance of coupling fire simulation with a climate driven ecosystem productivity model. Citation: Migliavacca, M., et al. (2013), Modeling biomass burning and related carbon emissions during the 21st century in Europe,

[1] In this study, we present simulations of a burned area at a European scale for the period 1990-2009 conducted with the Community Land Model (CLM). By using statistics on fire counts and mean fire suppression time from the European Fire Database, we refined the parameterization of the functions describing human ignition/suppression, and we modified the description of biomass availability for fires. The results obtained with the modified model show an improvement of the description of the spatial and interannual variability of the burned area: the model bias is reduced by 45%, and the explained variance is increased by about 9% compared to the original parameterization of the model. The observed relationships between burned area, climate (temperature and precipitation), and aboveground biomass are also reproduced more accurately by the modified model. This is particularly relevant for the applicability of the model to simulate future fire regimes under different climate conditions. However, results showed an overestimation of the burned area for some European countries (e.g., Spain and France) and an underestimation in years with an extreme fire season in Mediterranean countries. Our results highlight the need for refining the parameterization of human ignition/suppression and fuel availability for regional application of fire models implemented in land surface models.

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This paper presents a quantitative assessment of adaptation options in the context of forest fires in Europe under projected climate change. A standalone fire model (SFM) based on a state-of-the-art large-scale forest fire modelling algorithm is used to explore fuel removal through prescribed burnings and improved fire suppression as adaptation options. The climate change projections are provided by three climate models reflecting the SRES A2 scenario. The SFM's modelled burned areas for selected test countries in Europe show satisfying agreement with observed data coming from two different sources (European Forest Fire Information System and Global Fire Emissions Database). Our estimation of the potential increase in burned areas in Europe under ''no adaptation'' scenario is about 200 % by 2090 (compared with 2000-2008). The application of prescribed burnings has the potential to keep that increase below 50 %. Improvements in fire suppression might reduce this impact even further, e.g. boosting the probability of putting out a fire within a day by 10 % would result in about a 30 % decrease in annual burned areas. By taking more adaptation options into consideration, such as using agricultural fields as fire breaks, behavioural changes, and long-term options, burned areas can be potentially reduced further than projected in our analysis.

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We propose and explore financial instruments supporting programs for reducing emissions from deforestation and forest degradation (FI-REDD). Within a microeconomic framework we model interactions between an electricity producer (EP), electricity consumer (EC), and forest owner (FO). To keep their profit at a maximum, the EP responds to increasing CO 2 prices by adjusting electricity quantities generated by different technologies and charging a higher electricity price to the EC. The EP can prepare for future high (uncertain) CO 2 prices by employing FI-REDD: they can purchase an amount of offsets under an unknown future CO 2 price and later, when the CO 2 price is discovered, decide how many of these offsets to use for actually offsetting emissions and sell the rest on the market, sharing the revenue with the FO. FI-REDD allows for optional consumption of emission offsets by the EP (any amount up to the initially contracted volume is allowed), and includes a benefit-sharing mechanism between the EP and FO as it regards unused offsets. The modeling results indicate that FI-REDD might help avoid bankruptcy of CO 2 -intensive producers at high levels of CO 2 prices and therefore serve as a stabilizing mechanism during the transition of energy systems to greener technologies. The analytical results demonstrate the limits for potential market size explained by existing uncertainties. We illustrated that when suppliers and consumers of REDD offsets have asymmetric information on future CO 2 prices, benefit-sharing increases the contracted REDD offsets quantity.

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Large-scale wildfires affect millions of hectares of land in Indonesia annually and produce severe smoke haze pollution and carbon emissions, with negative impacts on climate change, health, the economy and biodiversity. In this study, we apply a mechanistic fire model to estimate burned area in Indonesia for the first time. We use the Wildfire Climate Impacts and Adaptation Model (FLAM) that operates with a daily time step on the grid cell of 0.25 arc degrees, the same spatio-temporal resolution as in the Global Fire Emissions Database v4 (GFED). GFED data accumulated from 2000–2009 are used for calibrating spatially-explicit suppression efficiency in FLAM. Very low suppression levels are found in peatland of Kalimantan and Sumatra, where individual fires can burn for very long periods of time despite extensive rains and fire-fighting attempts. For 2010–2016, we validate FLAM estimated burned area temporally and spatially using annual GFED observations. From the validation for burned areas aggregated over Indonesia, we obtain Pearson’s correlation coefficient separately for wildfires and peat fires, which equals 0.988 in both cases. Spatial correlation analysis shows that in areas where around 70% is burned, the correlation coefficients are above 0.6, and in those where 30% is burned, above 0.9.