Fire dynamics during the 20th century simulated by the Community Land Model

Abstract

Abstract.Fire is an integral Earth System process that interacts with climate in multiple ways. Here we assessed the parametrization of fires in the Community Land Model (CLM-CN) and improved the ability of the model to reproduce contemporary global patterns of burned areas and fire emissions. In addition to wildfires we extended CLM-CN to account for fires related to deforestation. We compared contemporary fire carbon emissions predicted by the model to satellite-based estimates in terms of magnitude and spatial extent as well as interannual and seasonal variability. Long-term trends during the 20th century were compared with historical estimates. Overall we found the best agreement between simulation and observations for the fire parametrization based on the work by Arora and Boer (2005). We obtained substantial improvement when we explicitly considered human caused ignition and fire suppression as a function of population density. Simulated fire carbon emissions ranged between 2.0 and 2.4 Pg C/year for the period 1997-2004. Regionally the simulations had a low bias over Africa and a high bias over South America when compared to satellite-based products. The net terrestrial carbon source due to land use change for the 1990s was 1.2 Pg C/year with 11% stemming from deforestation fires. During 2000-2004 this flux decreased to 0.85 Pg C/year with a similar relative contribution from deforestation fires. Between 1900 and 1960 we predicted a slight downward trend in global Correspondence to: S. Kloster (silvia.kloster@zmaw.de) fire emissions caused by reduced fuels as a consequence of wood harvesting and also by increases in fire suppression. The model predicted an upward trend during the last three decades of the 20th century as a result of climate variations and large burning events associated with ENSOinduced drought conditions.

Potential climate forcing of land use and land cover change

“…Estimates from this study using the future scenarios analyzed in the IPCC (the Representative Concentration Pathway, RCP, scenarios) suggest between 20 and 210 PgC carbon will be released, consistent with Strassmann et al (2008), and at the higher end of the model range reported by Brovkin et al (2013). Our model underpredicts the uptake of land carbon relative to other models (e.g Arora et al, 2013), and unlike other estimates includes the explicit interplay between changes in land use and fires (e.g., Marlon et al, 2008;Kloster et al, 2010). The RCP scenarios were designed to cover a diverse set of pathways and create a broad range in possible outcomes for the next century (Moss et al, 2010).…”

“…This configuration of CLM simulates the complicated interplay between land use, land use change, fires, land carbon uptake and loss, and emissions of volatile organic compounds (Thornton et al, 2009;Kloster et al, 2010;Guenther et al, 2006). To isolate the impacts of LULCC we perform separate…”

“…Individual forcings are computed from the results of terrestrial model simulations forced with historical land cover changes and wood harvesting, and projected land cover changes from five future scenarios. Because the land model used here includes a carbon model, fire module, and emissions of volatile organic compounds, we can uniquely account for the complicated interplay between land use and fire (e.g., Marlon et al, 2008;Kloster et al, 2010;Ward et al, 2012). Four of the future scenarios of land cover change correspond to the four Representative Concentration Pathways (RCPs) that were developed for the Climate Model Intercomparison Project in preparation for the IPCC 5th assessment report (AR5) Hurtt et al, 2011;.…”

Smoke consequences of new wildfire regimes driven by climate change

“…A promising recent trend is the development of fire modules within DGVMs [Arora and Boer, 2005;Lenihan et al, 2008;Kloster et al, 2010;Thornicke et al, 2010;Prentice et al, 2011;Li et al, 2012]. With their relatively coarse time steps and spatial resolution, DGVM-based fire modules are compelled to do enough "averaging" to avoid the pitfalls of trying to pin down a stochastic process too precisely.…”

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

“…Fire, extreme drought, insect outbreaks, land management, and land cover and land use change influence terrestrial C dynamics via (1) altering rate processes, for example, gross primary productivity (GPP), growth, tree mortality, or heterotrophic respiration; (2) modifying microclimatic environments; or (3) transferring C from one pool to another (e.g., from live to dead pools during storms or release to the atmosphere with fire) (Kloster et al, 2010;Thonicke et al, 2010;Luo and Weng, 2011;Prentice et al, 2011;Weng et al, 2012). Those disturbance influences can be represented in terrestrial C cycle models through changes in parameter values, environmental scalars, and/or discrete C transfers among pools of Eq.…”