A human-driven decline in global burned area Andela, N.; Morton, D. C.; Giglio, L. ...
Science (American Association for the Advancement of Science),
06/2017, Letnik:
356, Številka:
6345
Journal Article
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Fire is an essential Earth system process that alters ecosystem and atmospheric composition. Here we assessed long-term fire trends using multiple satellite data sets. We found that global burned ...area declined by 24.3 ± 8.8% over the past 18 years. The estimated decrease in burned area remained robust after adjusting for precipitation variability and was largest in savannas. Agricultural expansion and intensification were primary drivers of declining fire activity. Fewer and smaller fires reduced aerosol concentrations, modified vegetation structure, and increased the magnitude of the terrestrial carbon sink. Fire models were unable to reproduce the pattern and magnitude of observed declines, suggesting that they may overestimate fire emissions in future projections. Using economic and demographic variables, we developed a conceptual model for predicting fire in human-dominated landscapes.
Global warming is expected to considerably impact wildfire activity and aerosol emission release in the future. Due to their complexity, the future interactions between climate change, wildfire ...activity, emission release, and atmospheric aerosol processes are still uncertain. Here we use the process‐based fire model SPITFIRE within the global vegetation model JSBACH to simulate wildfire activity for present‐day climate conditions and future Representative Concentration Pathways (RCPs). The modeled fire emission fluxes and fire radiative power serve as input for the aerosol‐climate model ECHAM6‐HAM2, which has been extended by a semiempirical plume height parametrization. Our results indicate a general increase in extratropical and a decrease in tropical wildfire activity at the end of the 21st century. Changes in emission fluxes are most pronounced for the strongest warming scenario RCP8.5 (+49% in the extratropics, −37% in the tropics). Tropospheric black carbon (BC) concentrations are similarly affected by changes in emission fluxes and changes in climate conditions with regional variations of up to −50% to +100%. In the Northern Hemispheric extratropics, we attribute a mean increase in aerosol optical thickness of +0.031±0.002 to changes in wildfire emissions. Due to the compensating effects of fire intensification and more stable atmospheric conditions, global mean emission heights change by at most 0.3 km with only minor influence on BC long‐range transport. The changes in wildfire emission fluxes for the RCP8.5 scenario, however, may largely compensate the projected reduction in anthropogenic BC emissions by the end of the 21st century.
Key Points
Future wildfire activity increases in the extratropics and decreases in the tropics
Changes in wildfire activity compensate decreasing anthropogenic emissions
Future changes in emissions heights are negligible for the climate impact
The presence of multiple stable states has far‐reaching consequences for a system's susceptibility to disturbances, including the possibility of abrupt transitions between stable states. The ...occurrence of multiple stable states of vegetation is supported by ecological theory, models, and observations. Here we describe the occurrence of multiple stable states of tree cover in a global dynamic vegetation model and provide the first global picture on multiple stable states of tree cover due to a fire‐vegetation feedback. The multiple stable states occur in the transition zones between grasslands and forests, mainly in Africa and Asia. By sensitivity simulations and simplifying the relevant model equations we show that the occurrence of multiple states is caused by the sensitivity of the fire disturbance rate to the presence of woody plant types.
Key Points
This is the first study showing the potential of multiple stable states of vegetation globally based on a process‐based vegetation model
We identify the reason for multiple stable states as the sensitivity of fire to tree cover and illustrate it with a conceptual model
We identify the potential of multiple stable states in a region in Asia, which has not been in the focus of previous studies
Eddy covariance data are increasingly used to estimate parameters of ecosystem models. For proper maximum likelihood parameter estimates the error structure in the observed data has to be fully ...characterized. In this study we propose a method to characterize the random error of the eddy covariance flux data, and analyse error distribution, standard deviation, cross- and autocorrelation of CO2 and H2O flux errors at four different European eddy covariance flux sites. Moreover, we examine how the treatment of those errors and additional systematic errors influence statistical estimates of parameters and their associated uncertainties with three models of increasing complexity – a hyperbolic light response curve, a light response curve coupled to water fluxes and the SVAT scheme BETHY. In agreement with previous studies we find that the error standard deviation scales with the flux magnitude. The previously found strongly leptokurtic error distribution is revealed to be largely due to a superposition of almost Gaussian distributions with standard deviations varying by flux magnitude. The crosscorrelations of CO2 and H2O fluxes were in all cases negligible (R2 below 0.2), while the autocorrelation is usually below 0.6 at a lag of 0.5 h and decays rapidly at larger time lags. This implies that in these cases the weighted least squares criterion yields maximum likelihood estimates. To study the influence of the observation errors on model parameter estimates we used synthetic datasets, based on observations of two different sites. We first fitted the respective models to observations and then added the random error estimates described above and the systematic error, respectively, to the model output. This strategy enables us to compare the estimated parameters with true parameters. We illustrate that the correct implementation of the random error standard deviation scaling with flux magnitude significantly reduces the parameter uncertainty and often yields parameter retrievals that are closer to the true value, than by using ordinary least squares. The systematic error leads to systematically biased parameter estimates, but its impact varies by parameter. The parameter uncertainty slightly increases, but the true parameter is not within the uncertainty range of the estimate. This means that the uncertainty is underestimated with current approaches that neglect selective systematic errors in flux data. Hence, we conclude that potential systematic errors in flux data need to be addressed more thoroughly in data assimilation approaches since otherwise uncertainties will be vastly underestimated.
Networks that merge and harmonise eddy-covariance measurements from many different parts of the world have become an important observational resource for ecosystem science. Empirical algorithms have ...been developed which combine direct observations of the net ecosystem exchange of carbon dioxide with simple empirical models to disentangle photosynthetic (GPP) and respiratory fluxes (Reco). The increasing use of these estimates for the analysis of climate sensitivities, model evaluation and calibration demands a thorough understanding of assumptions in the analysis process and the resulting uncertainties of the partitioned fluxes. The semi-empirical models used in flux partitioning algorithms require temperature observations as input, but as respiration takes place in many parts of an ecosystem, it is unclear which temperature input – air, surface, bole, or soil at a specific depth – should be used. This choice is a source of uncertainty and potential biases. In this study, we analysed the correlation between different temperature observations and nighttime NEE (which equals nighttime respiration) across FLUXNET sites to understand the potential of the different temperature observations as input for the flux partitioning model. We found that the differences in the correlation between different temperature data streams and nighttime NEE are small and depend on the selection of sites. We investigated the effects of the choice of the temperature data by running two flux partitioning algorithms with air and soil temperature. We found the time lag (phase shift) between air and soil temperatures explains the differences in the GPP and Reco estimates when using either air or soil temperatures for flux partitioning. The impact of the source of temperature data on other derived ecosystem parameters was estimated, and the strongest impact was found for the temperature sensitivity. Overall, this study suggests that the choice between soil or air temperature must be made on site-by-site basis by analysing the correlation between temperature and nighttime NEE. We recommend using an ensemble of estimates based on different temperature observations to account for the uncertainty due to the choice of temperature and to assure the robustness of the temporal patterns of the derived variables.
The net ecosystem exchange of CO2 (NEE) between the atmosphere and a temperate beech forest showed a significant interannual variation (IAV) and a decadal trend of increasing carbon uptake (Pilegaard ...et al., 2011). The objectives of this study were to evaluate to what extent and at which temporal scale, direct climatic variability and changes in ecosystem functional properties regulated the IAV of the carbon balance at this site. Correlation analysis showed that the sensitivity of carbon fluxes to climatic variability was significantly higher at shorter than at longer time scales and changed seasonally. Ecosystem response anomalies implied that changes in the distribution of climate anomalies during the vegetation period will have stronger impacts on future ecosystem carbon balances than changes in average climate. We improved a published modelling approach which distinguishes the direct climatic effects from changes in ecosystem functioning (Richardson et al., 2007) by employing the semi empirical model published by Lasslop et al. (2010b). Fitting the model in short moving windows enabled large flexibility to adjust the parameters to the seasonal course of the ecosystem functional state. At the annual time scale as much as 80% of the IAV in NEE was attributed to the variation in photosynthesis and respiration related model parameters. Our results suggest that the observed decadal NEE trend at the investigated site was dominated by changes in ecosystem functioning. In general this study showed the importance of understanding the mechanisms of ecosystem functional change. Incorporating ecosystem functional change into process based models will reduce the uncertainties in long-term predictions of ecosystem carbon balances in global climate change projections.
In global fire models, lightning is typically prescribed from observational data with monthly mean temporal resolution while meteorological forcings, such as precipitation or temperature, are ...prescribed in a daily resolution. In this study, we investigate the importance of the temporal resolution of the lightning forcing for the simulation of burned area by varying from daily to monthly and annual mean forcing. For this, we utilize the vegetation fire model JSBACH‐SPITFIRE to simulate burned area, forced with meteorological and lightning data derived from the general circulation model ECHAM6. On a global scale, differences in burned area caused by lightning forcing applied in coarser temporal resolution stay below 0.55% compared to the use of daily mean forcing. Regionally, however, differences reach up to 100%, depending on the region and season. Monthly averaged lightning forcing as well as the monthly lightning climatology cause differences through an interaction between lightning ignitions and fire prone weather conditions, accounted for by the fire danger index. This interaction leads to decreased burned area in the boreal zone and increased burned area in the Tropics and Subtropics under the coarser temporal resolution. The exclusion of interannual variability, when forced with the lightning climatology, has only a minor impact on the simulated burned area. Annually averaged lightning forcing causes differences as a direct result of the eliminated seasonal characteristics of lightning. Burned area is decreased in summer and increased in winter where fuel is available. Regions with little seasonality, such as the Tropics and Subtropics, experience an increase in burned area.
Key Points
Reducing the temporal resolution of lightning from daily to monthly or yearly decreases simulated burned area in the boreal zone
The same leads to an increase of burned area in the Tropics and Subtropics
Excluding the interannual variability of monthly lightning forcing has only a minor effect on simulated burned area
We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site‐level ...gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5° × 0.5° spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross‐validation analyses revealed good performance of MTE in predicting among‐site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 1018 yr−1), H (164 ± 15 J × 1018 yr−1), and GPP (119 ± 6 Pg C yr−1) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr−1) was likely underestimated by 5–10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.
Recent climate changes have increased fire-prone weather conditions in many
regions and have likely affected 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
socio-economic predictor variables and burned area. We applied this approach
to monthly burned area time series for the period from 2005 to 2011 from satellite
observations and from DGVMs from the “Fire Modeling Intercomparison
Project” (FireMIP) that were run using a common protocol and forcing data sets. 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 socio-economic variables (e.g. human population
density). DGVMs broadly reproduce the relationships with climate variables
and, for some models, with population density. Interestingly, satellite-derived responses
show a strong increase in burned area with an increase in 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 us to diagnose that vegetation effects on fire are a main
deficiency regarding fire-enabled dynamic global vegetation models' ability to accurately
simulate the role of fire under global environmental change.
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