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Land management and deforestation in tropical regions cause wildfires and forest degradation, leading to a loss of ecosystem services and global climate regulation. The objective of the study was to provide a comprehensive assessment of the spatial extent and patterns of burned areas in a new deforestation frontier in the Amazonas state. The methodology applied cross-referenced burned area data from 2003 to 2019 with climate, land cover, private properties and Protected Areas information and performed a series of statistical tests. The influence of the Multivariate ENSO Index (MEI) contributed to a decreasing rainfall anomalies trend and increasing temperature anomalies trend. This process intensified the dry season and increased the extent of annual natural vegetation affected by fires, reaching a peak of 681 km2 in 2019. The results showed that the increased deforestation trend occurred mostly in public lands, mainly after the new forest code, leading to an increase in fires from 66 to 84% in 2019. The methods developed here could identify fire extent, trends, and relationship with land cover change and climate, thus pointing to priority areas for preservation. The conclusion presented that policy decisions affecting the Amazon Forest must include estimates of fire risk and impact under current and projected future climates. [ABSTRACT FROM AUTHOR]

A key challenge in developing flagship climate model configurations is the process of setting uncertain input parameters at values that lead to credible climate simulations. Setting these parameters traditionally relies heavily on insights from those involved in parameterisation of the underlying climate processes. Given the many degrees of freedom and computational expense involved in evaluating such a selection, this can be imperfect leaving open questions about whether any subsequent simulated biases result from mis-set parameters or wider structural model errors (such as missing or partially parameterised processes). Here we present a complementary approach to identifying plausible climate model parameters, with a method of bias correcting subcomponents of a climate model using a Gaussian process emulator that allows credible values of model input parameters to be found even in the presence of a significant model bias. A previous study (McNeall et al., 2016) found that a climate model had to be run using land surface input parameter values from very different, almost non-overlapping parts of parameter space to satisfactorily simulate the Amazon and other forests respectively. As the forest fraction of modelled non-Amazon forests was broadly correct at the default parameter settings and the Amazon too low, that study suggested that the problem most likely lay in the model's treatment of non-plant processes in the Amazon region. This might be due to (1) modelling errors such as missing deep-rooting in the Amazon in the land surface component of the climate model, (2) a warm-dry bias in the Amazon climate of the model, or a combination of both. In this study we bias correct the climate of the Amazon in a climate model using an augmented Gaussian process emulator, where temperature and precipitation, variables usually regarded as model outputs, are treated as model inputs alongside regular land surface input parameters. A sensitivity analysis finds that the forest fraction is nearly as sensitive to climate variables as changes in its land surface parameter values. Bias correcting the climate in the Amazon region using the emulator corrects the forest fraction to tolerable levels in the Amazon at many candidates for land surface input parameter values, including the default ones, and increases the valid input space shared with the other forests. We need not invoke a structural model error in the land surface model, beyond having too dry and hot a climate in the Amazon region. The augmented emulator allows bias correction of an ensemble of climate model runs and reduces the risk of choosing poor parameter values because of an error in a subcomponent of the model. We discuss the potential of the augmented emulator to act as a translational layer between model subcomponents, simplifying the process of model tuning when there are compensating errors, and helping model developers discover and prioritise model errors to target. [ABSTRACT FROM AUTHOR]

The article discusses the potential impact of PULSE-Brazil platform for understanding the long-term sustainability of ecosystems and human health in Amazonia rainforest in Brazil. Topics discussed include the design and production of PULSE-Brazil, the PULSE-Brazil platform designed as a web application using the open-source technologies and visualizing climate impacts using the platform.

Deforestation in Amazonia seems likely to continue in the 21st century, and fire risk could increase as a result of global climate change. With deforestation activities providing a source of ignition, the combination of this with increased fire risk could lead to widespread impacts on Amazonian ecosystems. This would also contribute further to climate change through feedbacks on the global carbon cycle. This study uses the McArthur Forest Fire Danger Index and an ensemble of variants of the HadCM3 climate model to simulate potential changes to fire risk in Amazonia over the 21st century. Significant increases in central and eastem Amazonia are simulated by 2020, and high fire danger for over 50% of the forest is simulated in all model runs by 2080. By 2080, as much as 25% of Amazonia shows high levels of agreement in projections of combined fire risk and deforestation. The areas simulated with high fire danger are also those projected to be impacted most significantly by deforestation. If the regional climate changes simulated for Amazonia by HadCM3 are realistic, the risk of significant forest damage could become high, and deforestation activities could, therefore, have a greater potential for impacting on wider areas of forest. [ABSTRACT FROM AUTHOR]

The Amazon rainforest plays a crucial role in the climate system, helping to drive atmospheric circulations in the tropics by absorbing energy and recycling about half of the rainfall that falls on it. This region (Amazonia) is also estimated to contain about one-tenth of the total carbon stored in land ecosystems, and to account for one-tenth of global, net primary productivity. The resilience of the forest to the combined pressures of deforestation and global warming is therefore of great concern, especially as some general circulation models (GCMs) predict a severe drying of Amazonia in the twenty-first century. Here we analyse these climate projections with reference to the 2005 drought in western Amazonia, which was associated with unusually warm North Atlantic sea surface temperatures (SSTs). We show that reduction of dry-season (July–October) rainfall in western Amazonia correlates well with an index of the north–south SST gradient across the equatorial Atlantic (the ‘Atlantic N–S gradient’). Our climate model is unusual among current GCMs in that it is able to reproduce this relationship and also the observed twentieth-century multidecadal variability in the Atlantic N–S gradient, provided that the effects of aerosols are included in the model. Simulations for the twenty-first century using the same model show a strong tendency for the SST conditions associated with the 2005 drought to become much more common, owing to continuing reductions in reflective aerosol pollution in the Northern Hemisphere. [ABSTRACT FROM AUTHOR]

The forest biome of Amazonia is one of Earth's greatest biological treasures and a major component of the Earth system. This century, it faces the dual threats of deforestation and stress from climate change. Here, we summarize some of the latest findings and thinking on these threats, explore the consequences for the forest ecosystem and its human residents, and outline options for the future of Amazonia. We also discuss the implications of new proposals to finance preservation of Amazonian forests. [ABSTRACT FROM AUTHOR]