Angelika Heil

Global wildland fire emissions from 1960 to 2000

In many regions of the world, fires are an important and highly variable source of air pollutant emissions, and they thus constitute a significant if not dominant factor controlling the interannual variability of the atmospheric composition. This paper describes the 41-year inventory of vegetation fire emissions constructed for the Reanalysis of the Tropospheric chemical composition over the past 40 years project (RETRO), a global modeling study to investigate the trends and variability of tropospheric ozone and other air pollutants over the past decades. It is the first attempt to construct a global emissions data set with monthly time resolution over such a long period. The inventory is based on a literature review, on estimates from different satellite products, and on a numerical model with a semiphysical approach to simulate fire occurrence and fire spread. Burned areas, carbon consumption, and total carbon release are estimated for 13 continental-scale regions, including explicit treatment of some major burning events such as Indonesia in 1997 and 1998. Global carbon emissions from this inventory range from 1410 to 3140 Tg C/a with the minimum and maximum occurring in 1974 and 1992, respectively (mean of 2078 Tg C/a). Emissions of other species are also reported (mean CO of 330 Tg/a, NO x of 4.6 Tg N/a, CH 2 O of 3.9 Tg/a, CH 4 of 15.4 Tg/a, BC of 2.2 Tg/a, OC of 17.6 Tg/a, SO 2 of 2.2 Tg/a). The uncertainties of these estimates remain high even for later years where satellite data products are available. Future versions of this inventory may benefit from ongoing analysis of burned areas from satellite data going back to 1982.

On the contribution of biomass burning to POPs (PAHs and PCDDs) in air in Africa.

Forest, savannah, and agricultural fires in the tropics and subtropics are sources for widespread pollution and release many organic substances into the air and soil, including persistent organic pollutants, i.e., polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and polycyclic aromatic hydrocarbons (PAHs). The significance of this source for the exposure of humans and the environment in Africa toward phenanthrene, fluoranthene, pyrene, benzo(a)pyrene, 2,3,7,8-tetrachlorodibenzo-p-dioxin, 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin, and octachlorodibenzo-p-dioxin is studied using daily global emissions from vegetation fires observed by satellite and a global multicompartment chemistry-transport model. Near-ground atmospheric concentrations of model-predicted vegetation fire related concentrations of PAHs and PCDDs were in the 10-1000 and 10(-5)-10(-3) pg m(-3) ranges, respectively. Vegetation fires in Africa are found to emit 180 ± 25 kg yr(-1) of PCDD/Fs. By comparison with observations, it is found that fires explain 1-10% of the PCDD (5% of 2,3,7,8- tetrachlorodibenzo-p-dioxin) concentrations in the rural and background atmosphere of sub-Saharan Africa. The contribution of vegetation fires to exposure to PAH is probably >10%, but cannot be quantified due to lack of knowledge with regard to both emission factors and photochemistry. A sensitivity analysis suggests that the heterogeneous reaction of PAHs with ozone is effectively limiting atmospheric lifetime and long-range transport.

Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models

Abstract. Recent climate changes increases fire-prone weather conditions and likely affects fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled Dynamic Global Vegetation Models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socioeconomic predictor variables and burned area. We applied this approach to monthly burned area time series for the period 2005–2011 from satellite observations and from DGVMs from the Fire Model Inter-comparison Project (FireMIP) that were run using a common protocol and forcing datasets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socioeconomic variables (e.g. human population density). DGVMs broadly reproduce the relationships to climate variables and some models to population density. Interestingly, satellite-derived responses show a strong increase of burned area with previous-season leaf area index and plant productivity in most fire-prone ecosystems which was largely underestimated by most DGVMs. Hence our pattern-oriented model evaluation approach allowed to diagnose that current fire-enabled DGVMs represent some controls on fire to a large extent but processes linking vegetation productivity and fire occurrence need to be improved to accurately simulate the role of fire under global environmental change.