Tropical climate–vegetation–fire relationships: multivariate evaluation of the land surface model JSBACH

The interactions between climate, vegetation and fire can strongly influence the future trajectories of vegetation in Earth system models. We evaluate the relationships between tropical climate, vegetation and fire in the global vegetation model JSBACH, using a simple fire scheme and the complex fire model SPITFIRE with the aim to identify potential for model improvement. We use two remote-sensing products (based on MODIS and Landsat) in different resolutions to assess the robustness of the obtained observed relationships. We evaluate the model using a multivariate comparison that allows us to focus on the interactions between climate, vegetation and fire and test the influence of land use change on the modelled patterns. Climate–vegetation–fire relationships are known to differ between continents; we therefore perform the analysis for each continent separately.The observed relationships are similar in the two satellite data sets, but maximum tree cover is reached at higher precipitation values for coarser resolution. This shows that the spatial scale of models and data needs to be consistent for meaningful comparisons. The model captures the broad spatial patterns with regional differences, which are partly due to the climate forcing derived from an Earth system model. Compared to the simple fire scheme, SPITFIRE strongly improves the spatial pattern of burned area and the distribution of burned area along increasing precipitation. The correlation between precipitation and tree cover is higher in the observations than in the largely climate-driven vegetation model, with both fire models. The multivariate comparison identifies excessive tree cover in low-precipitation areas and a too-strong relationship between high fire occurrence and low tree cover for the complex fire model. We therefore suggest that drought effects on tree cover and the impact of burned area on tree cover or the adaptation of trees to fire can be improved.The observed variation in the relationship between precipitation and maximum tree cover between continents is higher than the simulated one. Land use contributes to the intercontinental differences in fire regimes with SPITFIRE and strongly overprints the modelled multimodality of tree cover with SPITFIRE.The multivariate model–data comparison used here has several advantages: it improves the attribution of model–data mismatches to model processes, it reduces the impact of biases in the meteorological forcing on the evaluation and it allows us to evaluate not only a specific target variable but also the interactions.</p

Influences of observation errors in eddy flux data on inverse model parameter estimation

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 CO&lt;sub&gt;2&lt;/sub&gt; and H&lt;sub&gt;2&lt;/sub&gt;O 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 CO&lt;sub&gt;2&lt;/sub&gt; and H&lt;sub&gt;2&lt;/sub&gt;O fluxes were in all cases negligible (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; 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

Influence of fire on the carbon cycle and climate

Purpose of Review: Understanding of how fire affects the carbon cycle and climate is crucial for climate change adaptation and mitigation strategies. As those are often based on Earth system model simulations, we identify recent progress and research needs that can improve the model representation of fire and its impacts. Recent Findings New constraints of fire effects on the carbon cycle and climate are provided by the quantification of the carbon ages and effects of vegetation types and traits. For global scale modelling the low understanding of the human-fire relationship is limiting. Summary Recent developments allow improvements in Earth system models with respect to the influences of vegetation on climate, peatland burning and the pyrogenic carbon cycle. Better understanding of human influences is required. Given the impacts of fire on carbon storage and climate, thorough understanding of the effects of fire in the Earth system is crucial to support climate change mitigation and adaptation

Linking Vegetation-Climate-Fire Relationships in Sub-Saharan Africa to Key Ecological Processes in Two Dynamic Global Vegetation Models

Africa is largely influenced by fires, which play an important ecological role influencing the distribution and structure of grassland, savanna and forest biomes. Here vegetation strongly interacts with climate and other environmental factors, such as herbivory and humans. Fire-enabled Dynamic Global Vegetation Models (DGVMs) display high uncertainty in predicting the distribution of current tropical biomes and the associated transitions, mainly due to the way they represent the main ecological processes and feedbacks related to water and fire. The aim of this study is to evaluate the outcomes of two state-of-the–art DGVMs, LPJ-GUESS and JSBACH, also currently used in two Earth System Models (ESMs), in order to assess which key ecological processes need to be included or improved to represent realistic interactions between vegetation cover, precipitation and fires in sub-Saharan Africa. To this end, we compare models and remote-sensing data, analyzing the relationships between tree and grass cover, mean annual rainfall, average rainfall seasonality and average fire intervals, using generalized linear models, and we compare the patterns of grasslands, savannas, and forests in sub-Saharan Africa. Our analysis suggests that LPJ-GUESS (with a simple fire-model and complex vegetation description) performs well in regions of low precipitation, while in humid and mesic areas the representation of the fire process should probably be improved to obtain more open savannas. JSBACH (with a complex fire-model and a simple vegetation description) can simulate a vegetation-fire feedback that can maintain open savannas at intermediate and high precipitation, although this feedback seems to have stronger effects than observed, while at low precipitation JSBACH needs improvements in the representation of tree-grass competition and drought effects. This comparative process-based analysis permits to highlight the main factors that determine the tropical vegetation distribution in models and observations in sub-Saharan Africa, suggesting possible improvements in DGVMs and, consequently, in ESM simulations for future projections. Given the need to use carbon storage in vegetation as a climate mitigation measure, these models represent a valuable tool to improve our understanding of the sustainability of vegetation carbon pools as a carbon sink and the vulnerability to disturbances such as fire

Recent global and regional trends in burned area and their compensating environmental controls

The apparent decline in the global incidence of fire between 1996 and 2015, as measured by satellite- observations of burned area, has been related to socioeconomic and land use changes. However, recent decades have also seen changes in climate and vegetation that influence fire and fire-enabled vegetation models do not reproduce the apparent decline. Given that the satellite-derived burned area datasets are still relatively short (<20 years), this raises questions both about the robustness of the apparent decline and what causes it. We use two global satellite-derived burned area datasets and a data-driven fire model to (1) assess the spatio-temporal robustness of the burned area trends and (2) to relate the trends to underlying changes in temperature, precipitation, human population density and vegetation conditions. Although the satellite datasets and simulation all show a decline in global burned area over ~20 years, the trend is not significant and is strongly affected by the start and end year chosen for trend analysis and the year-to-year variability in burned area. The global and regional trends shown by the two satellite datasets are poorly correlated for the common overlapping period (2001–2015) and the fire model simulates changes in global and regional burned area that lie within the uncertainties of the satellite datasets. The model simulations show that recent increases in temperature would lead to increased burned area but this effect is compensated by increasing wetness or increases in population, both of which lead to declining burned area. Increases in vegetation cover and density associated with recent greening trends lead to increased burned area in fuel-limited regions. Our analyses show that global and regional burned area trends result from the interaction of compensating trends in controls of wildfire at regional scales

Recent global and regional trends in burned area and their compensating environmental controls

The apparent decline in the global incidence of fire between 1996 and 2015, as measured by satellite-observations of burned area, has been related to socioeconomic and land use changes. However, recent decades have also seen changes in climate and vegetation that influence fire and fire-enabled vegetation models do not reproduce the apparent decline. Given that the satellite-derived burned area datasets are still relatively short (<20 years), this raises questions both about the robustness of the apparent decline and what causes it. We use two global satellite-derived burned area datasets and a data-driven fire model to (1) assess the spatio-temporal robustness of the burned area trends and (2) to relate the trends to underlying changes in temperature, precipitation, human population density and vegetation conditions. Although the satellite datasets and simulation all show a decline in global burned area over ~20 years, the trend is not significant and is strongly affected by the start and end year chosen for trend analysis and the year-to-year variability in burned area. The global and regional trends shown by the two satellite datasets are poorly correlated for the common overlapping period (2001–2015) and the fire model simulates changes in global and regional burned area that lie within the uncertainties of the satellite datasets. The model simulations show that recent increases in temperature would lead to increased burned area but this effect is compensated by increasing wetness or increases in population, both of which lead to declining burned area. Increases in vegetation cover and density associated with recent greening trends lead to increased burned area in fuel-limited regions. Our analyses show that global and regional burned area trends result from the interaction of compensating trends in controls of wildfire at regional scales

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models

An overview of ADSL Homed Nepenthes Honeypots In Western Australia

This paper outlines initial analysis from research in progress into ADSL homed Nepenthes honeypots. One of the Nepenthes honeypots prime objective in this research was the collection of malware for analysis and dissection. A further objective is the analysis of risks that are circulating within ISP networks in Western Australian. What differentiates Nepenthes from many traditional honeypot designs it that is has been engineered from a distributed network philosophy. The program allows distribution of results across a network of sensors and subsequent aggregation of malware statistics readily within a large network environment