Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, ...biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean-atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.
The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning ...in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5° latitude by 2/3° longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA-based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DC = 1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRA's precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.
We describe an implementation of the Ecosystem Demography (ED) concept in the Community Land Model. The structure of CLM(ED) and the physiological and structural modifications applied to the CLM are ...presented. A major motivation of this development is to allow the prediction of biome boundaries directly from plant physiological traits via their competitive interactions. Here we investigate the performance of the model for an example biome boundary in eastern North America. We explore the sensitivity of the predicted biome boundaries and ecosystem properties to the variation of leaf properties using the parameter space defined by the GLOPNET global leaf trait database. Furthermore, we investigate the impact of four sequential alterations to the structural assumptions in the model governing the relative carbon economy of deciduous and evergreen plants. The default assumption is that the costs and benefits of deciduous vs. evergreen leaf strategies, in terms of carbon assimilation and expenditure, can reproduce the geographical structure of biome boundaries and ecosystem functioning. We find some support for this assumption, but only under particular combinations of model traits and structural assumptions. Many questions remain regarding the preferred methods for deployment of plant trait information in land surface models. In some cases, plant traits might best be closely linked to each other, but we also find support for direct linkages to environmental conditions. We advocate intensified study of the costs and benefits of plant life history strategies in different environments and the increased use of parametric and structural ensembles in the development and analysis of complex vegetation models.
We present a computationally efficient modelling system, IMOGEN, designed to undertake global and regional assessment of climate change impacts on the physical and biogeochemical behaviour of the ...land surface. A pattern-scaling approach to climate change drives a gridded land surface and vegetation model MOSES/TRIFFID. The structure allows extrapolation of General Circulation Model (GCM) simulations to different future pathways of greenhouse gases, including rapid first-order assessments of how the land surface and associated biogeochemical cycles might change. Evaluation of how new terrestrial process understanding influences such predictions can also be made with relative ease.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is ...often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.
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In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019–2020 austral spring-summer was an exception, ...with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001–2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires.
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, NOx of 4.6 Tg N/a, CH2O of 3.9 Tg/a, CH4 of 15.4 Tg/a, BC of 2.2 Tg/a, OC of 17.6 Tg/a, SO2 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.
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation ...models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model.
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Question: What are the correlations between the degree of drought stress and temperature, and the adoption of specific adaptive strategies by plants in the Mediterranean region?
Location: 602 sites ...across the Mediterranean region.
Method: We considered 12 plant morphological and phenological traits, and measured their abundance at the sites as trait scores obtained from pollen percentages. We conducted stepwise regression analyses of trait scores as a function of plant available moisture (α) and winter temperature (MTCO).
Results: Patterns in the abundance for the plant traits we considered are clearly determined by α, MTCO or a combination of both. In addition, trends in leaf size, texture, thickness, pubescence and aromatic leaves and other plant level traits such as thorniness and aphylly, vary according to the life form (tree, shrub, forb), the leaf type (broad, needle) and phenology (evergreen, summer‐green).
Conclusions: Despite conducting this study based on pollen data we have identified ecologically plausible trends in the abundance of traits along climatic gradients. Plant traits other than the usual life form, leaf type and leaf phenology carry strong climatic signals. Generally, combinations of plant traits are more climatically diagnostic than individual traits. The qualitative and quantitative relationships between plant traits and climate parameters established here will help to provide an improved basis for modelling the impact of climate changes on vegetation and form a starting point for a global analysis of pollen‐climate relationships.
Aim: To quantify the regional-scale spatio-temporal relationships among rainfall, vegetation and fire frequency in the Australian wet-dry tropics (AWDT). Location: Northern Australia: Cape York ...Peninsula, central Arnhem, central Kimberly, Einasleigh Uplands, Gulf Fall Uplands and northern Kimberley. Methods: Monthly 'fraction of photosynthetic active radiation absorbed by green vegetation' (fAPAR) was decomposed into monthly evergreen (EG) and monthly raingreen (RG) components using time-series techniques applied to monthly normalized difference vegetation index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) imagery. Fire affected areas were independently mapped at the same spatio-temporal resolution from AVHRR imagery. Weather station records were spatially interpolated to create monthly rainfall surfaces. Vegetation structural classes were derived from a digitized map of northern Australian vegetation communities (1:1,000,000). Generalized linear models were used to quantify relationships among the fAPAR, EG and RG signals, vegetation structure, rainfall and fire frequency, for the period November 1996-December 2001. Results: The fAPAR and EG signals are positively correlated with annual rainfall and canopy cover, notably: $EG_{closed forest} > EG_{open heathland} > EG_{open forest} > EG_{woodland} > EG_{open woodland} > EG_{low woodland} > EG_{low open woodland} > EG_{open grassland}$. Vegetation height and fAPAR are positively correlated, excluding the special case of open heathland. The RG signal is highest where intermediate annual rainfall and strong seasonality in rainfall coincide, and is associated with vegetation structure as follows: $RG_{open grassed} > RG_{woodland} > RG_{open forest} > RG_{open heathland} > RG_{low woodland} > RG_{open woodland} > RG_{low open woodland} > RG_{closed forest}$. Monthly RG tracks monthly rainfall. Annual proportion of area burnt (PB) is maximal where high RG coincides with low EG (open grassland, several woodland communities). PB is minimal in vegetation where both RG and EG are low (low open woodland); and in vegetation where EG is high (closed forest, open heathland). Conclusions: The RG-EG scheme successfully reflects digitally mapped tree and grass covers in relation to rainfall. RG-EG patterns are strongly associated with fire frequency patterns. PB is maximal in areas of high RG, where high biomass production during the wet season supports abundant fine fuel during the dry season. PB is minimal in areas with high EG, where relatively moist fuel limits fire ignition; and in areas with low EG and RG, where a relative short supply of fuel limits fire spread.
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