A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0)

Fire is the primary disturbance factor in many terrestrial ecosystems. Wildfire alters vegetation structure and composition, affects carbon storage and biogeochemical cycling, and results in the release of climatically relevant trace gases including CO2, CO, CH4, NOx, and aerosols. One way of assessing the impacts of global wildfire on centennial to multi-millennial timescales is to use process-based fire models linked to dynamic global vegetation models (DGVMs). Here we present an update to the LPJ-DGVM and a new fire module based on SPITFIRE that includes several improvements to the way in which fire occurrence, behaviour, and the effects of fire on vegetation are simulated. The new LPJ-LMfire model includes explicit calculation of natural ignitions, the representation of multi-day burning and coalescence of fires, and the calculation of rates of spread in different vegetation types. We describe a new representation of anthropogenic biomass burning under preindustrial conditions that distinguishes the different relationships between humans and fire among hunter-gatherers, pastoralists, and farmers. We evaluate our model simulations against remote-sensing-based estimates of burned area at regional and global scale. While wildfire in much of the modern world is largely influenced by anthropogenic suppression and ignitions, in those parts of the world where natural fire is still the dominant process (e.g. in remote areas of the boreal forest and subarctic), our results demonstrate a significant improvement in simulated burned area over the original SPITFIRE. The new fire model we present here is particularly suited for the investigation of climate–human–fire relationships on multi-millennial timescales prior to the Industrial Revolution

End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe

A cradle-to-grave life cycle assessment focused on end-of-life (EoL) was conducted in this study for three configurations of a light-duty commercial vehicle (LDCV): diesel, compressed natural gas (CNG), and battery electric vehicle (BEV). The aim is to investigate the impact of recycling under two EoL scenarios with different allocation methods. The first is based on the traditional avoided burden method, while the second is based on the circular footprint formula (CFF) developed by the European Commission. For each configuration, a detailed multilevel waste management scheme was developed in compliance with the 2000/53/CE directive and ISO22628 standard. The results showed that the global warming potential (GWP) impact under the CFF method is significantly greater when compared to the avoided burden method because of the A-parameter, which allocates the burdens and benefits between the two connected product systems. Furthermore, in all configurations and scenarios, the benefits due to the avoided production of virgin materials compensate for the recycling burdens within GWP impact. The main drivers of GWP reduction are steel recycling for all of the considered LDCVs, platinum, palladium, and rhodium recycling for the diesel and CNG configurations, and Li-ion battery recycling for the BEV configuration. Finally, the EoL stage significantly reduces the environmental impact of those categories other than GWP

The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: Results from a process-based model

A process-based fire regime model (SPITFIRE) has been developed, coupled with ecosystem dynamics in the LPJ Dynamic Global Vegetation Model, and used to explore fire regimes and the current impact of fire on the terrestrial carbon cycle and associated emissions of trace atmospheric constituents. The model estimates an average release of 2.24 Pg C yr−1 as CO2 from biomass burning during the 1980s and 1990s. Comparison with observed active fire counts shows that the model reproduces where fire occurs and can mimic broad geographic patterns in the peak fire season, although the predicted peak is 1–2 months late in some regions. Modelled fire season length is generally overestimated by about one month, but shows a realistic pattern of differences among biomes. Comparisons with remotely sensed burnt-area products indicate that the model reproduces broad geographic patterns of annual fractional burnt area over most regions, including the boreal forest, although interannual variability in the boreal zone is underestimated

Corrigendum to "The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model" published in Biogeosciences, 7, 1991-2011, doi:10.5194/bg-7-1991-2010, 2010

No abstract available

The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model.

A process-based fire regime model (SPITFIRE) has been developed, coupled with ecosystem dynamics in the LPJ Dynamic Global Vegetation Model, and used to explore fire regimes and the current impact of fire on the terrestrial carbon cycle and associated emissions of trace atmospheric constituents. The model estimates an average release of 2.24 Pg C yr−1 as CO2 from biomass burning during the 1980s and 1990s. Comparison with observed active fire counts shows that the model reproduces where fire occurs and can mimic broad geographic patterns in the peak fire season, although the predicted peak is 1–2 months late in some regions. Modelled fire season length is generally overestimated by about one month, but shows a realistic pattern of differences among biomes. Comparisons with remotely sensed burnt-area products indicate that the model reproduces broad geographic patterns of annual fractional burnt area over most regions, including the boreal forest, although interannual variability in the boreal zone is underestimated

Seasonal forecasting of fire over Kalimantan, Indonesia

Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean–atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia’s evolving fire management policy

The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols

The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models

The status and challenge of global fire modelling

This is the final version of the article. Available from European Geosciences Union / Copernicus Publications via the DOI in this record.The discussion paper version of this article was published in Biogeosciences Discussions on 25 January 2016 and is in ORE at http://hdl.handle.net/10871/34451Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.Stijn Hantson and Almut Arneth acknowledge support by the EU FP7 projects BACCHUS (grant agreement no. 603445) and LUC4C (grant agreement no. 603542). This work was supported, in part, by the German Federal Ministry of Education and Research (BMBF), through the Helmholtz Association and its research programme ATMO, and the HGF Impulse and Networking fund. The MC-FIRE model development was supported by the global change research programmes of the Biological Resources Division of the US Geological Survey (CA 12681901,112-), the US Department of Energy (LWT-6212306509), the US Forest Service (PNW96–5I0 9 -2-CA), and funds from the Joint Fire Science Program. I. Colin Prentice is supported by the AXA Research Fund under the Chair Programme in Biosphere and Climate Impacts, part of the Imperial College initiative Grand Challenges in Ecosystems and the Environment. Fang Li was funded by the National Natural Science Foundation (grant agreement no. 41475099 and no. 2010CB951801). Jed O. Kaplan was supported by the European Research Council (COEVOLVE 313797). Sam S. Rabin was funded by the National Science Foundation Graduate Research Fellowship, as well as by the Carbon Mitigation Initiative. Allan Spessa acknowledges funding support provided by the Open University Research Investment Fellowship scheme. FireMIP is a non-funded community initiative and participation is open to all. For more information, contact Stijn Hantson ([email protected])

The status and challenge of global fire modelling

This is the discussion paper version of the article. The final published version was published in Biogeosciences Vol. 13 (1), pp. 3359-3375 and is in ORE at http://hdl.handle.net/10871/22886Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, either using well-founded empirical relationships or process-based models with good predictive skill. A large variety of models exist today and it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project - FireMIP, an international project to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we summarise the current state-of-the-art in fire regime modelling and model evaluation, and outline what lessons may be learned from FireMIP.Stijn Hantson and Almut Arneth acknowledge support by the EU FP7 projects BACCHUS (grant agreement no. 603445) and LUC4C (grant agreement no. 603542). This work was supported, in part, by the German Federal Ministry of Education and Research (BMBF), through the Helmholtz Association and its research programme ATMO, and the HGF Impulse and Networking fund. The MC-FIRE model development was supported by the global change research programmes of the Biological Resources Division of the US Geological Survey (CA 12681901,112-), the US Department of Energy (LWT6212306509), the US Forest Service (PNW96–5I0 9 -2-CA), and funds from the Joint Fire Science Program. I. Colin Prentice is supported by the AXA Research Fund under the Chair Programme in Biosphere and Climate Impacts, part of the Imperial College initiative Grand Challenges in Ecosystems and the Environment. Fang Li was funded by the National Natural Science Foundation (grant agreement no. 41475099 and no. 2010CB951801). Jed O. Kaplan was supported by the European Research Council (COEVOLVE 313797). Sam S. Rabin was funded by the National Science Foundation Graduate Research Fellowship, as well as by the Carbon Mitigation Initiative. Allan Spessa acknowledges funding support provided by the Open University Research Investment Fellowship scheme. FireMIP is a non-funded community initiative and participation is open to all

Introducing GFWED: The Global Fire Weather Database

The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5 latitude by 2-3 longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia,Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DCD1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRAs precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphereocean controls on fire weather, and calibration of FWI-based fire prediction models