Modeling burned area in Europe with the Community Land Model

Migliavacca, Mirco; Dosio, Alessandro; Kloster, Silvia; Ward, D. S.; Camia, Andrea; Houborg, Rasmus; Durrant, Tom; Khabarov, Nikolay; Krasovskiĭ, A. A.; Miguel-Ayanz, Jesús San; Cescatti, Alessandro

doi:10.1002/jgrg.20026

Cited by 35 publications

(52 citation statements)

References 76 publications

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“…A similar study reporting simulations of increasing fire emissions for Europe (Migliavacca et al, 2013a) reports an increase for Europe of about 15 TgCyr −1 until the late 21st century, when measured for the same reference period as here, which is within the ensemble range found in this study. Even though they used the same Community Land Model, their fire parameterisation (Migliavacca et al, 2013b) differed from the one used by Kloster et al (2012).…”

Section: Discussionmentioning

confidence: 99%

Climate, CO<sub>2</sub> and human population impacts on global wildfire emissions

Knorr

Jiang

Arneth³

2016

Biogeosciences

120

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Abstract.Wildfires are by far the largest contributor to global biomass burning and constitute a large global source of atmospheric traces gases and aerosols. Such emissions have a considerable impact on air quality and constitute a major health hazard. Biomass burning also influences the radiative balance of the atmosphere and is thus not only of societal, but also of significant scientific interest. There is a common perception that climate change will lead to an increase in emissions as hot and dry weather events that promote wildfire will become more common. However, even though a few studies have found that the inclusion of CO 2 fertilisation of photosynthesis and changes in human population patterns will tend to somewhat lower predictions of future wildfire emissions, no such study has included full ensemble ranges of both climate predictions and population projections, including the effect of different degrees of urbanisation.Here, we present a series of 124 simulations with the LPJ-GUESS-SIMFIRE global dynamic vegetation-wildfire model, including a semi-empirical formulation for the prediction of burned area based on fire weather, fuel continuity and human population density. The simulations use Climate Model Intercomparison Project 5 (CMIP5) climate predictions from eight Earth system models. These were combined with two Representative Concentration Pathways (RCPs) and five scenarios of future human population density based on the series of Shared Socioeconomic Pathways (SSPs) to assess the sensitivity of emissions to the effect of climate, CO 2 and humans. In addition, two alternative parameterisations of the semi-empirical burned-area model were applied. Contrary to previous work, we find no clear future trend of global wildfire emissions for the moderate emissions and climate change scenario based on the RCP 4.5. Only historical population change introduces a decline by around 15 % since 1900. Future emissions could either increase for low population growth and fast urbanisation, or continue to decline for high population growth and slow urbanisation. Only for high future climate change (RCP8.5), wildfire emissions start to rise again after ca. 2020 but are unlikely to reach the levels of 1900 by the end of the 21st century. We find that climate warming will generally increase the risk of fire, but that this is only one of several equally important factors driving future levels of wildfire emissions, which include population change, CO 2 fertilisation causing woody thickening, increased productivity and fuel load and faster litter turnover in a warmer climate.

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Section: Discussionmentioning

confidence: 99%

Climate, CO<sub>2</sub> and human population impacts on global wildfire emissions

Knorr

Jiang

Arneth³

2016

Biogeosciences

120

View full text Add to dashboard Cite

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“…In our approach we focus on hazard-specific metrics of impact relevance that have been documented in recent literature. Details on the sensitivity analysis and calibration/validation exercises for each single hazard are reported in the references (Rojas et al 2012;Migliavacca et al 2013b;Outten and Esau 2013;Forzieri et al 2014;Cid et al 2014;Russo et al 2014). We recognize that extreme value fitting and kernel density estimators may introduce additional uncertainty in the projections of climate hazards especially at high return periods.…”

Section: Sources Of Uncertaintymentioning

confidence: 99%

Multi-hazard assessment in Europe under climate change

et al. 2016

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While reported losses of climate-related hazards are at historically high levels, climate change is likely to enhance the risk posed by extreme weather events. Several regions are likely to be exposed to multiple climate hazards, yet their modeling in a joint scheme is still at the early stages. A multi-hazard framework to map exposure to multiple climate extremes in Europe along the twenty-first century is hereby presented. Using an ensemble of climate projections, changes in the frequency of heat and cold waves, river and coastal flooding, streamflow droughts, wildfires and windstorms are evaluated. Corresponding variations in expected annual exposure allow for a quantitative comparison of hazards described by different process characteristics and metrics. Projected changes in exposure depict important variations in hazard scenarios, especially those linked to rising temperatures, and spatial patterns largely modulated by local climate conditions. Results show that Europe will likely Climatic Change (2016)

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“…The suppression of fire represents fire-fighting efforts, landscape fragmentation, and other processes which leads to a reduction in area burned and is also modelled as a function of population density. The original fire parametrization is described in Arora and Boer (2005b), which has since been adapted and used in several other DGVMs (Kloster et al, 2010;Li et al, 2012;Migliavacca et al, 2013). The fire module in CTEM v. 2.1 incorporates changes suggested in these studies as well as several new improvements which are summarized in detail in Melton and Arora (2016).…”

Section: Fire Methane Emissionsmentioning

confidence: 99%

An assessment of natural methane fluxes simulated by the CLASS-CTEM model

2018

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Abstract. Natural methane emissions from wetlands and fire, and soil uptake of methane, simulated using the Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem (CLASS-CTEM) modelling framework, over the historical 1850-2008 period, are assessed by using a one-box model of atmospheric methane burden. This one-box model also requires anthropogenic emissions and the methane sink in the atmosphere to simulate the historical evolution of global methane burden. For this purpose, global anthropogenic methane emissions for the period 1850-2008 were reconstructed based on the harmonized representative concentration pathway (RCP) and Emission Database for Global Atmospheric Research (EDGAR) data sets. The methane sink in the atmosphere is represented using biascorrected methane lifetimes from the Canadian Middle Atmosphere Model (CMAM). The resulting evolution of atmospheric methane concentration over the historical period compares reasonably well with observation-based estimates (correlation = 0.99, root mean square error = 35 ppb). The modelled natural emissions are also assessed using an inverse procedure where the methane lifetimes required to reproduce the observed year-to-year increase in atmospheric methane burden are calculated based upon the specified global anthropogenic and modelled natural emissions that we have used here. These calculated methane lifetimes over the historical period fall within the uncertainty range of observation-based estimates. The present-day (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) values of modelled methane emissions from wetlands (169 Tg CH 4 yr −1 ) and fire (27 Tg CH 4 yr −1 ), methane uptake by soil (29 Tg CH 4 yr −1 ), and the budget terms associated with overall anthropogenic and natural emissions are consistent with estimates reported in a recent global methane budget that is based on top-down approaches constrained by observed atmospheric methane burden. The modelled wetland emissions increase over the historical period in response to both increases in precipitation and in atmospheric CO 2 concentration. This increase in wetland emissions over the historical period yields evolution of the atmospheric methane concentration that compares better with observation-based values than the case when wetland emissions are held constant over the historical period.

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Modeling burned area in Europe with the Community Land Model

Cited by 35 publications

References 76 publications

Climate, CO<sub>2</sub> and human population impacts on global wildfire emissions

Climate, CO<sub>2</sub> and human population impacts on global wildfire emissions

Multi-hazard assessment in Europe under climate change

An assessment of natural methane fluxes simulated by the CLASS-CTEM model

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Modeling burned area in Europe with the Community Land Model

Cited by 35 publications

References 76 publications

Climate, CO&lt;sub&gt;2&lt;/sub&gt; and human population impacts on global wildfire emissions

Climate, CO&lt;sub&gt;2&lt;/sub&gt; and human population impacts on global wildfire emissions

Multi-hazard assessment in Europe under climate change

An assessment of natural methane fluxes simulated by the CLASS-CTEM model

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Product

Resources

About

Climate, CO<sub>2</sub> and human population impacts on global wildfire emissions

Climate, CO<sub>2</sub> and human population impacts on global wildfire emissions