Interannual variation in biomass burning and fire seasonality derived from geostationary satellite data across the contiguous United States from 1995 to 2011

Abstract

Wildfires exhibit a strong seasonality that is driven by climatic factors and human activities.Although the fire seasonality is commonly determined using burned area and fire frequency, it could also be quantified using biomass consumption estimates that directly represent biomass loss (a combination of the area burned and the fuel loading). Therefore, in this study a data set of long-term biomass consumed was derived from geostationary satellite data to explore the interannual variation in the fire seasonality and the possible impacts of climate change and land management practices across the Contiguous United States (CONUS). Specifically, daily biomass consumed data were derived using the fire radiative power retrieved from Geostationary Operational Environmental Satellites series with a pixel size of 4-10 km from 1995 to 2011. Annual fire seasonality metrics including the fire season duration, the timing of the start, peak, and end of the fire season, and interannual variation and trends were derived from the 17 year biomass consumed record. These metrics were associated with climatic factors to examine drivers and mediators of fire seasonality. The results indicate that biomass consumed significantly increased at a rate of 2.87 Tg/yr; however, the derived fire season duration exhibited a shortening trend in various states over the western CONUS and no significant trend in most other regions. This suggests that the frequency of extreme fire events has increased, which is perhaps associated with an observed increase of extreme weather conditions. Further, both the start and the end of the fire season exhibited an early shift (1.5-5 d/yr) in various eastern states although a late shift occurred in Arizona and Oregon. The interannual variation and trend in the fire seasonality was more strongly related to temperature in the western CONUS and to precipitation in the southeast. The Palmer Drought Severity Index was found to effectively reflect interannual variations in total biomass consumed although it was poorly correlated to the fire seasonality metrics. The results indicate that across the CONUS, the spatial patterns of the start, peak, and end of the fire season shift regularly in various regions in response to latitudinal gradients of temperature variation. IntroductionWildfires have a significant influence on ecosystem structure and function, trace gas emissions, carbon cycle, air quality, energy feedbacks to the climate system, regional socioeconomic conditions, and future land use planning [Chapin et al., 2003;Randerson et al., 2006;Balshi et al., 2007Balshi et al., , 2009. These influences may vary greatly with the seasonal occurrence and activity of wildfires [Russell-Smith et al., 2009;Le Page et al., 2010a;Ge et al., 2013]. The principal characteristics of fires in a region are often referred to as the fire regime, usually characterized in terms of the seasonality, frequency, spread patterns, intensity, fuel consumption, and severity of fires [Bond and Keeley, 2005;Gill, 1975]. As the climate...

Century‐scale patterns and trends of global pyrogenic carbon emissions and fire influences on terrestrial carbon balance

“…The DLEM‐simulated global pyrogenic carbon emissions are 21.5% and 17.1% higher than the GFED3.1 and GFAS estimates, respectively. In some regions (such as the continental U.S. and Russia), fire emissions were recognized to be underestimated by the GFED3.1 product [ Zhang et al , ; Konovalov et al , ]. In 2000, the DLEM‐simulated global vegetation carbon storage is 471.8 Pg C, which is 5.9% lower than the IPCC Tier‐1 result.…”

“…Generally, methods of investigating global burned area could be classified into three categories, i.e., ground-based inventory data interpolation [Mouillot and Field, 2005;Schultz et al, 2008], satellite observation [Giglio et al, 2013;Roy et al, 2008;Tansey et al, 2008], and model simulation [e.g., Li et al, 2012;Pechony and Shindell, 2009;Thonicke et al, 2010;Yang et al, 2014a;Yue et al, 2014b]. Global fire emissions can be either estimated based on burned area, fuel loading, and combustion completeness [e.g., Mouillot et al, 2006;Prentice et al, 2011;Thonicke et al, 2010;van der Werf et al, 2010] or derived directly through satellite-observed fire radiative power [Kaiser et al, 2012;Zhang et al, 2014].…”

Meteorological factors contributing to the interannual variability of midsummer surface ozone in Colorado, Utah, and other western U.S. states

“…Residual values are low for each case but GOTH (which is 191 km southwest of Denver near Aspen), where July of 2003 had a relatively high residual of 2.8 ppb.Wildfire smoke originating in Asia/Siberia caused significant increases in North American CO and O 3 concentrations during the summer of 2003[Jaffe et al, 2004;Bertschi and Jaffe, 2005]. Interrelationships among wildfire frequency and extent, meteorology, smoke, and O 3 are complex[Jaffe and Wigder, 2012;Zhang et al, 2014b]. Wildfires increase in size and frequency during drought conditions, and drought conditions in the western U.S. are associated with persistent 500 hPa ridges.…”

Fire Influences on Atmospheric Composition, Air Quality and Climate

“…For the Black Saturday fires in Australia described above, it was found [87] that GFEDv3 estimates were at least a factor of two greater than FINN v1 estimates for all emitted species and that both estimates were likely too low by at least a factor of 50 % when their burned area was compared to more detailed, ground-truth, surveys. Zhang et al [88] found that over the USA, their mid-infrared GOES-based FRP estimates of fuel consumption were higher than GFED3.1 by a factor of 1.2-3.3 for 1997-2010. Differences or biases in emissions can translate into large spread in modelled composition and discrepancies with trace gas and aerosol measurements.…”

“…For FRP-based estimates, further studies of the dependence of combustion rate on fuel condition will be helpful [88], as will further uncertainty characterisation [98]. The fuel consumption database of van Leeuwen et al [99] is an important resource for validating modelled fuel consumption across different biomes and under different fuel conditions.…”