The Orbiting Carbon Observatory (OCO-2) tracks 2-3 peta-gram increase in carbon release to the atmosphere during the 2014-2016 El Niño

The powerful El Niño event of 2015-2016 - the third most intense since the 1950s - has exerted a large impact on the Earth's natural climate system. The column-averaged CO₂ dry-air mole fraction (XCO₂) observations from satellites and ground-based networks are analyzed together with in situ observations for the period of September 2014 to October 2016. From the differences between satellite (OCO-2) observations and simulations using an atmospheric chemistry-transport model, we estimate that, relative to the mean annual fluxes for 2014, the most recent El Niño has contributed to an excess CO₂ emission from the Earth's surface (land + ocean) to the atmosphere in the range of 2.4 ± 0.2 PgC (1 Pg = 10¹⁵ g) over the period of July 2015 to June 2016. The excess CO₂ flux is resulted primarily from reduction in vegetation uptake due to drought, and to a lesser degree from increased biomass burning. It is about the half of the CO₂ flux anomaly (range: 4.4-6.7 PgC) estimated for the 1997/1998 El Niño. The annual total sink is estimated to be 3.9 ± 0.2 PgC for the assumed fossil fuel emission of 10.1 PgC. The major uncertainty in attribution arise from error in anthropogenic emission trends, satellite data and atmospheric transport.

Investigating the relationship between Aerosol Optical Depth and Precipitation over Southeast Asia with Relative Humidity as an influencing factor

Atmospheric aerosols influence precipitation by changing the earth’s energy budget and cloud properties. A number of studies have reported correlations between aerosol properties and precipitation data. Despite previous research, it is still hard to quantify the overall effects that aerosols have on precipitation as multiple influencing factors such as relative humidity (RH) can distort the observed relationship between aerosols and precipitation. Thus, in this study, both satellite-retrieved and reanalysis data were used to investigate the relationship between aerosols and precipitation in the Southeast Asia region from 2001 to 2015, with RH considered as a possible influencing factor. Different analyses in the study indicate that a positive correlation was present between Aerosol Optical Depth (AOD) and precipitation over northern Southeast Asia region during the autumn and the winter seasons, while a negative correlation was identified over the Maritime Continent during the autumn season. Subsequently, a partial correlation analysis revealed that while RH influences the long-term negative correlations between AOD and precipitation, it did not significantly affect the positive correlations seen in the winter season. The result of this study provides additional evidence with respect to the critical role of RH as an influencing factor in AOD-precipitation relationship over Southeast Asia.

Biomass Burning Smoke Climatology of the United States: Implications for Particulate Matter Air Quality

We utilize the NOAA Hazard Mapping System smoke product for the period of 2005 to 2016 to develop climatology of smoke occurrence over the Continental United States (CONUS) region and to study the impact of wildland fires on particulate matter air quality at the surface. Our results indicate that smoke is most frequently found over the Great Plains and western states during the summer months. Other hotspots of smoke occurrence are found over state and national parks in the southeast during winter and spring, in the Gulf of Mexico southwards of the Texas and Louisiana coastline during spring season and along the Mississippi River Delta during the fall season. A substantial portion (20%) of the 24 h federal standard for particulate pollution exceedance events in the CONUS region occur when smoke is present. If the U.S. Environmental Protection Agency regulations continue to reduce anthropogenic emissions, wildland fire emissions will become the major contributor to particulate pollution and exceedance events. In this context, we show that HMS smoke product is a valuable tool for analysis of exceptional events caused by wildland fires and our results indicate that these tools can be valuable for policy and decision makers.

Fire Behavior Simulation from Global Fuel and Climatic Information

Large-scale fire danger assessment has become increasingly relevant in the past few years, and is usually based on weather information. Still, fuel characteristics also play an important role in fire behavior. This study presents a fire behavior simulation based on a global fuelbed dataset and climatic and topographic information. The simulation was executed using the Fuel Characteristic Classification System (FCCS). The climatic information covered the period 1980–2010, and daily weather parameters were used to calculate the mean monthly fuel moisture content (FMC) and wind speed for the early afternoon period. Also, as the most severe fires occur with extreme environmental conditions, a worst-case scenario was created from the 30 days of each month with the lowest FMC values for the 1980–2010 period. The FMC and wind speed information was grouped into classes, and FCCS was used to simulate the reaction intensity, rate of spread and flame length of the fuelbeds for the average and worst-case monthly conditions. Outputs of the simulations were mapped at global scale, showing the variations in surface fire behavior throughout the year, both due to climatic conditions and fuel characteristics. The surface fire behavior parameters identified the fuels and environmental conditions that produced more severe fire events, as well as those regions where high fire danger only occurs in extreme climatic conditions. The most severe fire events were found in grasslands and shrublands in tropical dry biomes, and corresponding with the worst-case scenario environmental conditions. Also, the results showed the importance of including detailed fuel information into fire danger assessment systems, as the same weather and topographic conditions may have different danger rates, depending on fuel characteristics.

Emissions of Selected Semivolatile Organic Chemicals from Forest and Savannah Fires

The emission factors (EFs) for a broad range of semivolatile organic chemicals (SVOCs) from subtropical eucalypt forest and tropical savannah fires were determined for the first time from in situ investigations. Significantly higher (t test, P < 0.01) EFs (μg kg^-1 dry fuel, gas + particle-associated) for polycyclic aromatic hydrocarbons (∑₁₃ PAHs) were determined from the subtropical forest fire (7,000 ± 170) compared to the tropical savannah fires (1,600 ± 110), due to the approximately 60-fold higher EFs for 3-ring PAHs from the former. EF data for many PAHs from the eucalypt forest fire were comparable with those previously reported from pine and fir forest combustion events. EFs for other SVOCs including polychlorinated biphenyl (PCB), polychlorinated naphthalene (PCN), and polybrominated diphenyl ether (PBDE) congeners as well as some pesticides (e.g., permethrin) were determined from the subtropical eucalypt forest fire. The highest concentrations of total suspended particles, PAHs, PCBs, PCNs, and PBDEs, were typically observed in the flaming phase of combustion. However, concentrations of levoglucosan and some pesticides such as permethrin peaked during the smoldering phase. Along a transect (10-150-350 m) from the forest fire, concentration decrease for PCBs during flaming was faster compared to PAHs, while levoglucosan concentrations increased.

A stratified random sampling design in space and time for regional to global scale burned area product validation

The potential research, policy and management applications of global burned area products place a high priority on rigorous, quantitative assessment of their accuracy. Such an assessment can be achieved by implementing validation methods employing design-based inference in which the independent reference data are selected via a probability sampling design. The majority of global burned area validation exercises use Landsat data to derive the independent reference data. This paper presents a three-dimensional sampling grid that allows for probability sampling of Landsat data in both space and time. To sample the globe in the spatial domain with non-overlapping sampling units, the Thiessen Scene Area (TSA) tessellation of the Landsat path/row geometry is used. The TSA grid is combined in time with the 16-day Landsat acquisition calendar to provide three-dimensional elements (voxels).This allows for implementation of stratified random sampling designs, where not only the location but also the time interval of the independent reference data is explicitly drawn by probability sampling. To illustrate this, we use a stratification methodology based on the Olson global ecoregion map and on the MODIS global active fire product. Using the global MODIS burned area product to establish a hypothetical population of reference data, we show that a sampling scheme based on the proposed stratification with equal sample allocation among strata is effective in reducing the standard errors of accuracy and area estimators compared to simple random sampling. Globally, the standard errors were reduced by 63%, 54%, 22% and 53% for overall accuracy, omission error, commission error and total burned area estimates respectively. By incorporating probability sampling in both the spatial and temporal domains, the present study establishes the foundation for rigorous design-based validation of global burned area products and, more generally, of terrestrial thematic products that have high temporal variability.

Fires in Seasonally Dry Tropical Forest: Testing the Varying Constraints Hypothesis across a Regional Rainfall Gradient

The "varying constraints hypothesis" of fire in natural ecosystems postulates that the extent of fire in an ecosystem would differ according to the relative contribution of fuel load and fuel moisture available, factors that vary globally along a spatial gradient of climatic conditions. We examined if the globally widespread seasonally dry tropical forests (SDTFs) can be placed as a single entity in this framework by analyzing environmental influences on fire extent in a structurally diverse SDTF landscape in the Western Ghats of southern India, representative of similar forests in monsoonal south and southeast Asia. We used logistic regression to model fire extent with factors that represent fuel load and fuel moisture at two levels-the overall landscape and within four defined moisture regimes (between 700 and1700 mm yr-1)-using a dataset of area burnt and seasonal rainfall from 1990 to 2010. The landscape scale model showed that the extent of fire in a given year within this SDTF is dependent on the combined interaction of seasonal rainfall and extent burnt the previous year. Within individual moisture regimes the relative contribution of these factors to the annual extent burnt varied-early dry season rainfall (i.e., fuel moisture) was the predominant factor in the wettest regime, while wet season rainfall (i.e., fuel load) had a large influence on fire extent in the driest regime. Thus, the diverse structural vegetation types associated with SDTFs across a wide range of rainfall regimes would have to be examined at finer regional or local scales to understand the specific environmental drivers of fire. Our results could be extended to investigating fire-climate relationships in STDFs of monsoonal Asia.

Decreasing Aerosol Loading in the North American Monsoon Region

We examine the spatio-temporal variability of aerosol loading in the recent decade (2005-2014) over the North American Monsoon (NAM) region. Emerging patterns are characterized using aerosol optical depth (AOD) retrievals from the NASA Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) instrument along with a suite of satellite retrievals of atmospheric and land-surface properties. We selected 20 aerosol hotspots and classified them into fire, anthropogenic, dust, and NAM alley clusters based on the dominant driver influencing aerosol variability. We then analyzed multivariate statistics of associated anomalies during pre-, monsoon, and post-monsoon periods. Our results show a decrease in aerosol loading for the entire NAM region, confirming previous reports of a declining AOD trend over the continental United States. This is evident during pre-monsoon and monsoon for fire and anthropogenic clusters, which are associated with a decrease in the lower and upper quartile of fire counts and carbon monoxide, respectively. The overall pattern is obfuscated in the NAM alley, especially during monsoon and post-monsoon seasons. While the NAM alley is mostly affected by monsoon precipitation, the frequent occurrence of dust storms in the area modulates this trend. We find that aerosol loading in the dust cluster is associated with observed vegetation index and has only slightly decreased in the recent decade.

A pervasive role for biomass burning in tropical high ozone/low water structures

Air parcels with mixing ratios of high O3 and low H2O (HOLW) are common features in the tropical western Pacific (TWP) mid-troposphere (300-700 hPa). Here, using data collected during aircraft sampling of the TWP in winter 2014, we find strong, positive correlations of O3 with multiple biomass burning tracers in these HOLW structures. Ozone levels in these structures are about a factor of three larger than background. Models, satellite data and aircraft observations are used to show fires in tropical Africa and Southeast Asia are the dominant source of high O3 and that low H2O results from large-scale descent within the tropical troposphere. Previous explanations that attribute HOLW structures to transport from the stratosphere or mid-latitude troposphere are inconsistent with our observations. This study suggest a larger role for biomass burning in the radiative forcing of climate in the remote TWP than is commonly appreciated.

Religious Affiliation Modulates Weekly Cycles of Cropland Burning in Sub-Saharan Africa

Vegetation burning is a common land management practice in Africa, where fire is used for hunting, livestock husbandry, pest control, food gathering, cropland fertilization, and wildfire prevention. Given such strong anthropogenic control of fire, we tested the hypotheses that fire activity displays weekly cycles, and that the week day with the fewest fires depends on regionally predominant religious affiliation. We also analyzed the effect of land use (anthrome) on weekly fire cycle significance. Fire density (fire counts.km-2) observed per week day in each region was modeled using a negative binomial regression model, with fire counts as response variable, region area as offset and a structured random effect to account for spatial dependence. Anthrome (settled, cropland, natural, rangeland), religion (Christian, Muslim, mixed) week day, and their 2-way and 3-way interactions were used as independent variables. Models were also built separately for each anthrome, relating regional fire density with week day and religious affiliation. Analysis revealed a significant interaction between religion and week day, i.e. regions with different religious affiliation (Christian, Muslim) display distinct weekly cycles of burning. However, the religion vs. week day interaction only is significant for croplands, i.e. fire activity in African croplands is significantly lower on Sunday in Christian regions and on Friday in Muslim regions. Magnitude of fire activity does not differ significantly among week days in rangelands and in natural areas, where fire use is under less strict control than in croplands. These findings can contribute towards improved specification of ignition patterns in regional/global vegetation fire models, and may lead to more accurate meteorological and chemical weather forecasting.

Local and global pyrogeographic evidence that indigenous fire management creates pyrodiversity

Despite the challenges wildland fire poses to contemporary resource management, many fire-prone ecosystems have adapted over centuries to millennia to intentional landscape burning by people to maintain resources. We combine fieldwork, modeling, and a literature survey to examine the extent and mechanism by which anthropogenic burning alters the spatial grain of habitat mosaics in fire-prone ecosystems. We survey the distribution of Callitris intratropica, a conifer requiring long fire-free intervals for establishment, as an indicator of long-unburned habitat availability under Aboriginal burning in the savannas of Arnhem Land. We then use cellular automata to simulate the effects of burning identical proportions of the landscape under different fire sizes on the emergent patterns of habitat heterogeneity. Finally, we examine the global extent of intentional burning and diversity of objectives using the scientific literature. The current distribution of Callitris across multiple field sites suggested long-unburnt patches are common and occur at fine scales (<0.5 ha), while modeling revealed smaller, patchy disturbances maximize patch age diversity, creating a favorable habitat matrix for Callitris. The literature search provided evidence for intentional landscape burning across multiple ecosystems on six continents, with the number of identified objectives ranging from two to thirteen per study. The fieldwork and modeling results imply that the occurrence of long-unburnt habitat in fire-prone ecosystems may be an emergent property of patch scaling under fire regimes dominated by smaller fires. These findings provide a model for understanding how anthropogenic burning alters spatial and temporal aspects of habitat heterogeneity, which, as the literature survey strongly suggests, warrant consideration across a diversity of geographies and cultures. Our results clarify how traditional fire management shapes fire-prone ecosystems, which despite diverse objectives, has allowed human societies to cope with fire as a recurrent disturbance.

Seasonality of fire weather strongly influences fire regimes in South Florida savanna-grassland landscapes

Fire seasonality, an important characteristic of fire regimes, commonly is delineated using seasons based on single weather variables (rainfall or temperature). We used nonparametric cluster analyses of a 17-year (1993–2009) data set of weather variables that influence likelihoods and spread of fires (relative humidity, air temperature, solar radiation, wind speed, soil moisture) to explore seasonality of fire in pine savanna-grassland landscapes at the Avon Park Air Force Range in southern Florida. A four-variable, three-season model explained more variation within fire weather variables than models with more seasons. The three-season model also delineated intra-annual timing of fire more accurately than a conventional rainfall-based two-season model. Two seasons coincided roughly with dry and wet seasons based on rainfall. The third season, which we labeled the fire season, occurred between dry and wet seasons and was characterized by fire-promoting conditions present annually: drought, intense solar radiation, low humidity, and warm air temperatures. Fine fuels consisting of variable combinations of pyrogenic pine needles, abundant C4 grasses, and flammable shrubs, coupled with low soil moisture, and lightning ignitions early in the fire season facilitate natural landscape-scale wildfires that burn uplands and across wetlands. We related our three season model to fires with different ignition sources (lightning, military missions, and prescribed fires) over a 13-year period with fire records (1997–2009). Largest wildfires originate from lightning and military ignitions that occur within the early fire season substantially prior to the peak of lightning strikes in the wet season. Prescribed ignitions, in contrast, largely occur outside the fire season. Our delineation of a pronounced fire season provides insight into the extent to which different human-derived fire regimes mimic lightning fire regimes. Delineation of a fire season associated with timing of natural lightning ignitions should be useful as a basis for ecological fire management of humid savanna-grassland landscapes worldwide.

Influence of open vegetation fires on black carbon and ozone variability in the southern Himalayas (NCO-P, 5079 m a.s.l.)

We analysed the variability of equivalent black carbon (BC) and ozone (O3) at the global WMO/GAW station Nepal Climate Observatory-Pyramid (NCO-P, 5079 m a.s.l.) in the southern Himalayas, for evaluating the possible contribution of open vegetation fires to the variability of these short-lived climate forcers/pollutants (SLCF/SLCP) in the Himalayan region. We found that 162 days (9% of the data-set) were characterised by acute pollution events with enhanced BC and O3 in respect to the climatological values. By using satellite observations (MODIS fire products and the USGS Land Use Cover Characterization) and air mass back-trajectories, we deduced that 56% of these events were likely to be affected by emissions from open fires along the Himalayas foothills, the Indian Subcontinent and the Northern Indo-Gangetic Plain. These results suggest that open fire emissions are likely to play an important role in modulating seasonal and inter-annual BC and O3 variability over south Himalayas.

Soil humic-like organic compounds in prescribed fire emissions using nuclear magnetic resonance spectroscopy

Here we present the chemical characterization of the water-soluble organic carbon fraction of atmospheric aerosol collected during a prescribed fire burn in relation to soil organic matter and biomass combustion. Using nuclear magnetic resonance spectroscopy, we observed that humic-like substances in fire emissions have been associated with soil organic matter rather than biomass. Using a chemical mass balance model, we estimated that soil organic matter may contribute up to 41% of organic hydrogen and up to 27% of water-soluble organic carbon in fire emissions. Dust particles, when mixed with fresh combustion emissions, substantially enhances the atmospheric oxidative capacity, particle formation and microphysical properties of clouds influencing the climatic responses of atmospheric aeroso. Owing to the large emissions of combustion aerosol during fires, the release of dust particles from soil surfaces that are subjected to intense heating and shear stress has, so far, been lacking.

Evaluating greenhouse gas emissions inventories for agricultural burning using satellite observations of active fires

Fires in agricultural ecosystems emit greenhouse gases and aerosols that influence climate on multiple spatial and temporal scales. Annex 1 countries of the United Nations Framework Convention on Climate Change (UNFCCC), many of which ratified the Kyoto Protocol, are required to report emissions of CH4 and N2O from these fires annually. In this study, we evaluated several aspects of this reporting system, including the optimality of the crops targeted by the UNFCCC globally and within Annex 1 countries, and the consistency of emissions inventories among different countries. We also evaluated the success of individual countries in capturing interannual variability and long-term trends in agricultural fire activity. In our approach, we combined global high-resolution maps of crop harvest area and production, derived from satellite maps and ground-based census data, with Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of active fires. At a global scale, we found that adding ground nuts (e.g., peanuts), cocoa, cotton and oil palm, and removing potato, oats, rye, and pulse other from the list of 14 crops targeted by the UNFCCC increased the percentage of active fires covered by the reporting system by 9%. Optimization led to a different recommended list for Annex 1 countries, requiring the addition of sunflower, cotton, rapeseed, and alfalfa and the removal of beans, sugarcane, pulse others, and tuber-root others. Extending emissions reporting to all Annex 1 countries (from the current set of 19 countries) would increase the efficacy of the reporting system from 6% to 15%, and further including several non-Annex 1 countries (Argentina, Brazil, China, India, Indonesia, Thailand, Kazakhstan, Mexico, and Nigeria) would capture over 55% of active fires in croplands worldwide. Analyses of interannual trends from the United States and Australia showed the importance of both intensity of fire use and crop production in controlling year-to-year variations in agricultural fire emissions. Remote sensing provides an effective means for evaluating some aspects of the current UNFCCC emissions reporting system; and, if combined with census data, field experiments and expert opinion, has the potential to improve the robustness of the next generation inventory system.

The human dimension of fire regimes on Earth

Humans and their ancestors are unique in being a fire-making species, but 'natural' (i.e. independent of humans) fires have an ancient, geological history on Earth. Natural fires have influenced biological evolution and global biogeochemical cycles, making fire integral to the functioning of some biomes. Globally, debate rages about the impact on ecosystems of prehistoric human-set fires, with views ranging from catastrophic to negligible. Understanding of the diversity of human fire regimes on Earth in the past, present and future remains rudimentary. It remains uncertain how humans have caused a departure from 'natural' background levels that vary with climate change. Available evidence shows that modern humans can increase or decrease background levels of natural fire activity by clearing forests, promoting grazing, dispersing plants, altering ignition patterns and actively suppressing fires, thereby causing substantial ecosystem changes and loss of biodiversity. Some of these contemporary fire regimes cause substantial economic disruptions owing to the destruction of infrastructure, degradation of ecosystem services, loss of life, and smoke-related health effects. These episodic disasters help frame negative public attitudes towards landscape fires, despite the need for burning to sustain some ecosystems. Greenhouse gas-induced warming and changes in the hydrological cycle may increase the occurrence of large, severe fires, with potentially significant feedbacks to the Earth system. Improved understanding of human fire regimes demands: (1) better data on past and current human influences on fire regimes to enable global comparative analyses, (2) a greater understanding of different cultural traditions of landscape burning and their positive and negative social, economic and ecological effects, and (3) more realistic representations of anthropogenic fire in global vegetation and climate change models. We provide an historical framework to promote understanding of the development and diversification of fire regimes, covering the pre-human period, human domestication of fire, and the subsequent transition from subsistence agriculture to industrial economies. All of these phases still occur on Earth, providing opportunities for comparative research.

Temporal variability of forest fires in eastern Amazonia

Widespread occurrence of fires in Amazonian forests is known to be associated with extreme droughts, but historical data on the location and extent of forest fires are fundamental to determining the degree to which climate conditions and droughts have affected fire occurrence in the region. We used remote sensing to derive a 23-year time series of annual landscape-level burn scars in a fragmented forest of the eastern Amazon. Our burn scar data set is based on a new routine developed for the Carnegie Landsat Analysis System (CLAS), called CLAS-BURN, to calculate a physically based burn scar index (BSI) with an overall accuracy of 93% (Kappa coefficient 0.84). This index uses sub-pixel cover fractions of photosynthetic vegetation, non-photosynthetic vegetation, and shade/burn scar spectral end members. From 23 consecutive Landsat images processed with the CLAS-BURN algorithm, we quantified fire frequencies, the variation in fire return intervals, and rates of conversion of burned forest to other land uses in a 32 400 km2 area. From 1983 to 2007, 15% of the forest burned; 38% of these burned forests were subsequently deforested, representing 19% of the area cleared during the period of observation. While 72% of the fire-affected forest burned only once during the 23-year study period, 20% burned twice, 6% burned three times, and 2% burned four or more times, with the maximum of seven times. These frequencies suggest that the current fire return interval is 5-11 times more frequent than the estimated natural fire regime. Our results also quantify the substantial influence of climate and extreme droughts caused by a strong El Niño Southern Oscillation (ENSO) on the extent and likelihood of returning forest fires mainly in fragmented landscapes. These results are an important indication of the role of future warmer climate and deforestation in enhancing emissions from more frequently burned forests in the Amazon.

A comparison of Spectral Angle Mapper and Artificial Neural Network classifiers combined with Landsat TM imagery analysis for obtaining burnt area mapping

Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878) the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795). Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ≈ 1% for ANN and ≈ 6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.