Modelling burned area in Africa

The simulation of current and projected wildfires is essential for predicting crucial aspects of vegetation patterns, biogeochemical cycling as well as pyrogenic emissions across the African continent. This study uses a data-driven approach to parameterize two burned area models applicable to dynamic vegetation models (DVMs) and Earth system models (ESMs). We restricted our analysis to variables for which either projections based on climate scenarios are available, or that are calculated by DVMs, and we consider a spatial scale of one degree as the scale typical for DVMs and ESMs. By using the African continent here as an example, an analogue approach could in principle be adopted for other regions, for global scale dynamic burned area modelling. <br><br> We used 9 years of data (2000–2008) for the variables: precipitation over the last dry season, the last wet season and averaged over the last 2 years, a fire-danger index (the Nesterov index), population density, and annual proportion of area burned derived from the MODIS MCD45A1 product. Two further variables, tree and herb cover were only available for 2001 as a remote sensing product. Since the effect of fires on vegetation depends strongly on burning conditions, the timing of wildfires is of high interest too, and we were able to relate the seasonal occurrence of wildfires to the daily Nesterov index. <br><br> We parameterized two generalized linear models (GLMs), one with the full variable set (model VC) and one considering only climate variables (model C). All introduced variables resulted in an increase in model performance. Model VC correctly predicts the spatial distribution and extent of fire prone areas though the total variability is underrepresented. Model VC has a much lower performance in both aspects (correlation coefficient of predicted and observed ratio of burned area: 0.71 for model VC and 0.58 for model C). We expect the remaining variability to be attributed to additional variables which are not available at a global scale and thus not incorporated in this study as well as its coarse resolution. An application of the models using climate hindcasts and projections ranging from 1980 to 2060 resulted in a strong decrease of burned area of ca. 20–25%. Since wildfires are an integral part of land use practices in Africa, their occurrence is an indicator of areas favourable for food production. In absence of other compensating land use changes, their projected decrease can hence be interpreted as a indicator for future loss of such areas

Why are estimates of global isoprene emissions so similar (and why is this not so for monoterpenes)?

International audienceEmissions of biogenic volatile organic compounds (BVOC) are a chief uncertainty in calculating the burdens of important atmospheric compounds like tropospheric ozone or secondary organic aerosol, reflecting either imperfect chemical oxidation mechanisms or unreliable emission estimates, or both. To provide a starting point for a more systematic discussion we review here global isoprene and monoterpene emission estimates to-date. We note a surprisingly small variation in the predictions of global isoprene emission rate that is in stark contrast with our lack of process understanding and the small number of observations for model parameterisation and evaluation. Most of the models are based on similar emission algorithms, using fixed values for the emission capacity of various plant functional types. In some studies these values are very similar, but they differ substantially in others. The models differ also broadly with regard to their representation of net primary productivity, method of biome coverage determination and climate data. Their similarities with regard to the global isoprene emission rate would suggest that the dominant parameters driving the ultimate global estimate, and thus the dominant determinant of model sensitivity, are the specific emission algorithm and isoprene emission capacity. Contrary to isoprene, monoterpene estimates show significantly larger model-to-model variation although variation in terms of leaf algorithm, emission capacities, the way of model upscaling, vegetation cover or climatology used in terpene models are comparable to those used for isoprene. From our summary of published studies there appears to be no evidence that the terrestrial modelling community has been any more successful in "resolving unknowns" in the mechanisms that control global isoprene emissions, compared to global monoterpene emissions. Rather, the proliferation of common parameterization schemes within a large variety of model platforms lends the illusion of convergence towards a common estimate of global isoprene emissions. This convergence might be used to provide optimism that the community has reached the "relief phase", the phase when sufficient process understanding and data for evaluation allows for models to converge, when applying a recently proposed concept. We argue that there is no basis for this apparent "relief" phase. Rather, we urge modellers to be bolder in their analysis to draw attention to the fact that terrestrial emissions, particularly in the area of biome-specific emission capacities, are unknown rather than uncertain

How do variations in the temporal distribution of rainfall events affect ecosystem fluxes in seasonally water-limited Northern Hemisphere shrublands and forests?

As a result of climate change, rainfall regimes became more extreme over the course of the 20th century, characterised by fewer and larger rainfall events. Such changes are expected to continue throughout the current century. The effect of changes in the 5 temporal distribution of rainfall on ecosystem carbon fluxes is poorly understood, with most available information coming from experimental studies of grassland ecosystems. Here, continuous measurements of ecosystem carbon fluxes and precipitation from the worldwide FLUXNET network of eddy-covariance sites are exploited to investigate the effects of differences in rainfall distribution on the carbon balance of seasonally water10 limited shrubland and forest sites. Once the strong dependence of ecosystem fluxes on total annual rainfall amount is accounted for, results show that sites with more extreme rainfall distributions have significantly lower gross productivity, slightly lower ecosystem respiration and consequently a smaller net ecosystem productivity.JRC.H.7-Climate Risk Managemen

Can a “state of the art” chemistry transport model simulate Amazonian tropospheric chemistry?

We present an evaluation of a nested high-resolution Goddard Earth Observing System (GEOS)-Chem chemistry transport model simulation of tropospheric chemistry over tropical South America. The model has been constrained with two isoprene emission inventories: (1) the canopy-scale Model of Emissions of Gases and Aerosols from Nature (MEGAN) and (2) a leaf-scale algorithm coupled to the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model, and the model has been run using two different chemical mechanisms that contain alternative treatments of isoprene photo-oxidation. Large differences of up to 100 Tg C yr^(−1) exist between the isoprene emissions predicted by each inventory, with MEGAN emissions generally higher. Based on our simulations we estimate that tropical South America (30–85°W, 14°N–25°S) contributes about 15–35% of total global isoprene emissions. We have quantified the model sensitivity to changes in isoprene emissions, chemistry, boundary layer mixing, and soil NO_x emissions using ground-based and airborne observations. We find GEOS-Chem has difficulty reproducing several observed chemical species; typically hydroxyl concentrations are underestimated, whilst mixing ratios of isoprene and its oxidation products are overestimated. The magnitude of model formaldehyde (HCHO) columns are most sensitive to the choice of chemical mechanism and isoprene emission inventory. We find GEOS-Chem exhibits a significant positive bias (10–100%) when compared with HCHO columns from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI) for the study year 2006. Simulations that use the more detailed chemical mechanism and/or lowest isoprene emissions provide the best agreement to the satellite data, since they result in lower-HCHO columns

Applying Occam's razor to global agricultural land use change

We present a parsimonious agricultural land-use model that is designed to replicate global land-use change while allowing the exploration of uncertainties in input parameters. At the global scale, the modelled uncertainty range of agricultural land-use change covers observed land-use change. Spatial patterns of cropland change at the country level are simulated less satisfactorily, but temporal trends of cropland change in large agricultural nations were replicated by the model. A variance-based global sensitivity analysis showed that uncertainties in the input parameters representing to consumption preferences are important for changes in global agricultural areas. However, uncertainties in technological change had the largest effect on cereal yields and changes in global agricultural area. Uncertainties related to technological change in developing countries were most important for modelling the extent of cropland. The performance of the model suggests that highly generalised representations of socio-economic processes can be used to replicate global land-use change

Efectos biofísicos sobre la temperatura y la precipitación debido al cambio de cobertura terrestre

Anthropogenic land cover changes (LCC) affect regional and global climate through biophysical variations of the surface energy budget mediated by albedo, evapotranspiration, and roughness. This change in surface energy budget may exacerbate or counteract biogeochemical greenhouse gas effects of LCC, with a large body of emerging assessments being produced, sometimes apparently contradictory. We reviewed the existing scientific literature with the objective to provide an overview of the state-of-the-knowledge of the biophysical LCC climate effects, in support of the assessment of mitigation/adaptation land policies. Out of the published studies that were analyzed, 28 papers fulfilled the eligibility criteria, providing surface air temperature and/or precipitation change with respect to LCC regionally and/or globally. We provide a synthesis of the signal, magnitude and uncertainty of temperature and precipitation changes in response to LCC biophysical effects by climate region (boreal/temperate/tropical) and by key land cover transitions. Model results indicate that a modification of biophysical processes at the land surface has a strong regional climate effect, and non-negligible global impact on temperature. Simulation experiments of large-scale (i.e. complete) regional deforestation lead to a mean reduction in precipitation in all regions, while air surface temperature increases in the tropics and decreases in boreal regions. The net global climate effects of regional deforestation are less certain. There is an overall consensus in the model experiments that the average global biophysical climate response to complete global deforestation is atmospheric cooling and drying. Observed estimates of temperature change following deforestation indicate a smaller effect than model-based regional estimates in boreal regions, comparable results in the tropics, and contrasting results in temperate regions. Regional/local biophysical effects following LCC are important for local climate, water cycle, ecosystems, their productivity and biodiversity, and thus important to consider in the formulation of adaptation policy. However before considering the inclusion of biophysical climate effects of LCC under the UNFCCC, science has to provide robust tools and methods for estimation of both country and global level effects

Global emissions of terpenoid VOCs from terrestrial vegetation in the last millennium

Peer reviewe

Post-2020 biodiversity targets need to embrace climate change

Recent assessment reports by the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) have highlighted the risks to humanity arising from the unsustainable use of natural resources. Thus far, land, freshwater, and ocean exploitation have been the chief causes of biodiversity loss. Climate change is projected to be a rapidly increasing additional driver for biodiversity loss. Since climate change and biodiversity loss impact human societies everywhere, bold solutions are required that integrate environmental and societal objectives. As yet, most existing international biodiversity targets have overlooked climate change impacts. At the same time, climate change mitigation measures themselves may harm biodiversity directly. The Convention on Biological Diversity's post-2020 framework offers the important opportunity to address the interactions between climate change and biodiversity and revise biodiversity targets accordingly by better aligning these with the United Nations Framework Convention on Climate Change Paris Agreement and the Sustainable Development Goals. We identify the considerable number of existing and proposed post- 2020 biodiversity targets that risk being severely compromised due to climate change, even if other barriers to their achievement were removed. Our analysis suggests that the next set of biodiversity targets explicitly addresses climate change-related risks since many aspirational goals will not be feasible under even lower-end projections of future warming. Adopting more flexible and dynamic approaches to conservation, rather than static goals, would allow us to respond flexibly to changes in habitats, genetic resources, species composition, and ecosystem functioning and leverage biodiversity's capacity to contribute to climate change mitigation and adaptation

The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols

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 two 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. Here 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