Determinants and predictability of global wildfire emissions

Biomass burning is one of the largest sources of atmospheric trace gases and aerosols globally. These emissions have a major impact on the radiative balance of the atmosphere and on air quality, and are thus of significant scientific and societal interest. Several datasets have been developed that quantify those emissions on a global grid and offered to the atmospheric modelling community. However, no study has yet attempted to systematically quantify the dependence of the inferred pyrogenic emissions on underlying assumptions and input data. Such a sensitivity study is needed for understanding how well we can currently model those emissions and what the factors are that contribute to uncertainties in those emission estimates. <br><br> Here, we combine various satellite-derived burned area products, a terrestrial ecosystem model to simulate fuel loads and the effect of fire on ecosystem dynamics, a model of fuel combustion, and various emission models that relate combusted biomass to the emission of various trace gases and aerosols. We carry out simulations with varying parameters for combustion completeness and fuel decomposition rates within published estimates, four different emissions models and three different global burned-area products. We find that variations in combustion completeness and simulated fuel loads have the largest impact on simulated global emissions for most species, except for some with highly uncertain emission factors. Variation in burned-area estimates also contribute considerably to emission uncertainties. We conclude that global models urgently need more field-based data for better parameterisation of combustion completeness and validation of simulated fuel loads, and that further validation and improvement of burned area information is necessary for accurately modelling global wildfire emissions. The results are important for chemical transport modelling studies, and for simulations of biomass burning impacts on the atmosphere under future climate change scenarios

Climate, CO₂ and demographic impacts on global wildfire emissions

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 CO2 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, CO2 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, CO2 fertilisation causing woody thickening, increased productivity and fuel load and faster litter turnover in a warmer climate

Future challenges of representing land-processes in studies on land-atmosphere interactions

Over recent years, it has become increasingly apparent that climate change and air pollution need to be considered jointly for improved attribution and projections of human-caused changes in the Earth system. Exchange processes at the land surface come into play in this context, because many compounds that either act as greenhouse gases, as pollutant precursors, or both, have not only anthropogenic but also terrestrial sources and sinks. And since the fluxes of multiple gases and particulate matter between the terrestrial biota and the atmosphere are directly or indirectly coupled to vegetation and soil carbon, nutrient and water balances, quantification of their geographic patterns or changes over time requires due consideration of the underlying biological processes. In this review we highlight a number of critical aspects and recent progress in this respect, identifying in particular a number of areas where studies have shown that accounting for ecological process understanding can alter global model projections of land-atmosphere interactions substantially. Specifically, this concerns the improved quantification of uncertainties and dynamic system responses, including acclimation, and the incorporation of exchange processes that so far have been missing from global models even though they are proposed to be of relevance for our understanding of terrestrial biota-climate feedbacks. Progress has also been made regarding studies on the impacts of land use/land cover change on climate change, but the absence of a mechanistically based representation of human responseprocesses in ecosystem models that are coupled to climate models limits our ability to analyse how climate change or air pollution in turn might affect human land use. A more integrated perspective is necessary and should become an active area of research that bridges the socio-economic and biophysical communities

Modelling burned area in Africa

The simulation of current and projected wildfires is essential for predicting crucial aspects of vegetation patterns, biogeochemical cycling as well as pyrogenic emissions across the African continent. This study uses a data-driven approach to parameterize two burned area models applicable to dynamic vegetation models (DVMs) and Earth system models (ESMs). We restricted our analysis to variables for which either projections based on climate scenarios are available, or that are calculated by DVMs, and we consider a spatial scale of one degree as the scale typical for DVMs and ESMs. By using the African continent here as an example, an analogue approach could in principle be adopted for other regions, for global scale dynamic burned area modelling. <br><br> We used 9 years of data (2000–2008) for the variables: precipitation over the last dry season, the last wet season and averaged over the last 2 years, a fire-danger index (the Nesterov index), population density, and annual proportion of area burned derived from the MODIS MCD45A1 product. Two further variables, tree and herb cover were only available for 2001 as a remote sensing product. Since the effect of fires on vegetation depends strongly on burning conditions, the timing of wildfires is of high interest too, and we were able to relate the seasonal occurrence of wildfires to the daily Nesterov index. <br><br> We parameterized two generalized linear models (GLMs), one with the full variable set (model VC) and one considering only climate variables (model C). All introduced variables resulted in an increase in model performance. Model VC correctly predicts the spatial distribution and extent of fire prone areas though the total variability is underrepresented. Model VC has a much lower performance in both aspects (correlation coefficient of predicted and observed ratio of burned area: 0.71 for model VC and 0.58 for model C). We expect the remaining variability to be attributed to additional variables which are not available at a global scale and thus not incorporated in this study as well as its coarse resolution. An application of the models using climate hindcasts and projections ranging from 1980 to 2060 resulted in a strong decrease of burned area of ca. 20–25%. Since wildfires are an integral part of land use practices in Africa, their occurrence is an indicator of areas favourable for food production. In absence of other compensating land use changes, their projected decrease can hence be interpreted as a indicator for future loss of such areas

Impact of human population density on fire frequency at the global scale

Human impact on wildfires, a major earth system component, remains poorly understood. While local studies have found more fires close to settlements and roads, assimilated charcoal records and analyses of regional fire patterns from remote-sensing observations point to a decline in fire frequency with increasing human population. Here, we present a global analysis using three multi-year satellite-based burned-area products combined with a parameter estimation and uncertainty analysis with a non-linear model. We show that at the global scale, the impact of increasing population density is mainly to reduce fire frequency. Only for areas with up to 0.1 people per km2, we find that fire frequency increases by 10 to 20% relative to its value at no population. The results are robust against choice of burned-area data set, and indicate that at only very few places on earth, fire frequency is limited by human ignitions. Applying the results to historical population estimates results in a moderate but accelerating decline of global burned area by around 14% since 1800, with most of the decline since 1950

Why are estimates of global terrestrial isoprene emissions so similar (and why is this not so for monoterpenes)?

Emissions of biogenic volatile organic compounds (BVOC) are a chief uncertainty in calculating the burdens of important atmospheric compounds like tropospheric ozone or secondary organic aerosol, reflecting either imperfect chemical oxidation mechanisms or unreliable emission estimates, or both. To provide a starting point for a more systematic discussion we review here global isoprene and monoterpene emission estimates to-date. We note a surprisingly small variation in the predictions of global isoprene emission rate that is in stark contrast with our lack of process understanding and the small number of observations for model parameterisation and evaluation. Most of the models are based on similar emission algorithms, using fixed values for the emission capacity of various plant functional types. In some cases, these values are very similar but differ substantially in other models. The similarities with regard to the global isoprene emission rate would suggest that the dominant parameters driving the ultimate global estimate, and thus the dominant determinant of model sensitivity, are the specific emission algorithm and isoprene emission capacity. But the models also differ broadly with regard to their representation of net primary productivity, method of biome coverage determination and climate data. Contrary to isoprene, monoterpene estimates show significantly larger model-to-model variation although variation in terms of leaf algorithm, emission capacities, the way of model upscaling, vegetation cover or climatology used in terpene models are comparable to those used for isoprene. From our summary of published studies there appears to be no evidence that the terrestrial modelling community has been any more successful in "resolving unknowns" in the mechanisms that control global isoprene emissions, compared to global monoterpene emissions. Rather, the proliferation of common parameterization schemes within a large variety of model platforms lends the illusion of convergence towards a common estimate of global isoprene emissions. This convergence might be used to provide optimism that the community has reached the "relief phase", the phase when sufficient process understanding and data for evaluation allows models' projections to converge, when applying a recently proposed concept. We argue that there is no basis for this apparent relief phase. Rather, we urge modellers to be bolder in their analysis, and to draw attention to the fact that terrestrial emissions, particularly in the area of biome-specific emission capacities, are unknown rather than uncertain