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Biological responses to climate change have been widely documented across taxa and regions, but it remains unclear whether species are maintaining a good match between phenotype and environment, i.e. whether observed trait changes are adaptive. Here we reviewed 10,090 abstracts and extracted data from 71 studies reported in 58 relevant publications, to assess quantitatively whether phenotypic trait changes associated with climate change are adaptive in animals. A meta-analysis focussing on birds, the taxon best represented in our dataset, suggests that global warming has not systematically affected morphological traits, but has advanced phenological traits. We demonstrate that these advances are adaptive for some species, but imperfect as evidenced by the observed consistent selection for earlier timing. Application of a theoretical model indicates that the evolutionary load imposed by incomplete adaptive responses to ongoing climate change may already be threatening the persistence of species.

Climate change and land-use change are two major drivers of biome shifts causing habitat and biodiversity loss. What is missing is a continental-scale future projection of the estimated relative impacts of both drivers on biome shifts over the course of this century. Here, we provide such a projection for the biodiverse region of Latin America under four socio-economic development scenarios. We find that across all scenarios 5-6% of the total area will undergo biome shifts that can be attributed to climate change until 2099. The relative impact of climate change on biome shifts may overtake land-use change even under an optimistic climate scenario, if land-use expansion is halted by the mid-century. We suggest that constraining land-use change and preserving the remaining natural vegetation early during this century creates opportunities to mitigate climate-change impacts during the second half of this century. Our results may guide the evaluation of socio-economic scenarios in terms of their potential for biome conservation under global change., (© 2016 John Wiley & Sons Ltd.)

Atmospheric monitoring of high northern latitudes (above 40°N) has shown an enhanced seasonal cycle of carbon dioxide (CO2) since the 1960s, but the underlying mechanisms are not yet fully understood. The much stronger increase in high latitudes relative to low ones suggests that northern ecosystems are experiencing large changes in vegetation and carbon cycle dynamics. We found that the latitudinal gradient of the increasing CO2 amplitude is mainly driven by positive trends in photosynthetic carbon uptake caused by recent climate change and mediated by changing vegetation cover in northern ecosystems. Our results underscore the importance of climate-vegetation-carbon cycle feedbacks at high latitudes; moreover, they indicate that in recent decades, photosynthetic carbon uptake has reacted much more strongly to warming than have carbon release processes., (Copyright © 2016, American Association for the Advancement of Science.)

Identifying the relative importance of climatic and other environmental controls on the interannual variability and trends in global land surface phenology and greenness is challenging. Firstly, quantifications of land surface phenology and greenness dynamics are impaired by differences between satellite data sets and phenology detection methods. Secondly, dynamic global vegetation models (DGVMs) that can be used to diagnose controls still reveal structural limitations and contrasting sensitivities to environmental drivers. Thus, we assessed the performance of a new developed phenology module within the LPJmL (Lund-Potsdam-Jena managed Lands) DGVM with a comprehensive ensemble of three satellite data sets of vegetation greenness and ten phenology detection methods, thereby thoroughly accounting for observational uncertainties. The improved and tested model allows us quantifying the relative importance of environmental controls on interannual variability and trends of land surface phenology and greenness at regional and global scales. We found that start of growing season interannual variability and trends are in addition to cold temperature mainly controlled by incoming radiation and water availability in temperate and boreal forests. Warming-induced prolongations of the growing season in high latitudes are dampened by a limited availability of light. For peak greenness, interannual variability and trends are dominantly controlled by water availability and land-use and land-cover change (LULCC) in all regions. Stronger greening trends in boreal forests of Siberia than in North America are associated with a stronger increase in water availability from melting permafrost soils. Our findings emphasize that in addition to cold temperatures, water availability is a codominant control for start of growing season and peak greenness trends at the global scale., (© 2015 John Wiley & Sons Ltd.)

Extreme droughts, heat waves, frosts, precipitation, wind storms and other climate extremes may impact the structure, composition and functioning of terrestrial ecosystems, and thus carbon cycling and its feedbacks to the climate system. Yet, the interconnected avenues through which climate extremes drive ecological and physiological processes and alter the carbon balance are poorly understood. Here, we review the literature on carbon cycle relevant responses of ecosystems to extreme climatic events. Given that impacts of climate extremes are considered disturbances, we assume the respective general disturbance-induced mechanisms and processes to also operate in an extreme context. The paucity of well-defined studies currently renders a quantitative meta-analysis impossible, but permits us to develop a deductive framework for identifying the main mechanisms (and coupling thereof) through which climate extremes may act on the carbon cycle. We find that ecosystem responses can exceed the duration of the climate impacts via lagged effects on the carbon cycle. The expected regional impacts of future climate extremes will depend on changes in the probability and severity of their occurrence, on the compound effects and timing of different climate extremes, and on the vulnerability of each land-cover type modulated by management. Although processes and sensitivities differ among biomes, based on expert opinion, we expect forests to exhibit the largest net effect of extremes due to their large carbon pools and fluxes, potentially large indirect and lagged impacts, and long recovery time to regain previous stocks. At the global scale, we presume that droughts have the strongest and most widespread effects on terrestrial carbon cycling. Comparing impacts of climate extremes identified via remote sensing vs. ground-based observational case studies reveals that many regions in the (sub-)tropics are understudied. Hence, regional investigations are needed to allow a global upscaling of the impacts of climate extremes on global carbon-climate feedbacks., (© 2015 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

The terrestrial biosphere is a key component of the global carbon cycle and its carbon balance is strongly influenced by climate. Continuing environmental changes are thought to increase global terrestrial carbon uptake. But evidence is mounting that climate extremes such as droughts or storms can lead to a decrease in regional ecosystem carbon stocks and therefore have the potential to negate an expected increase in terrestrial carbon uptake. Here we explore the mechanisms and impacts of climate extremes on the terrestrial carbon cycle, and propose a pathway to improve our understanding of present and future impacts of climate extremes on the terrestrial carbon budget.

Forests, critical components of global ecosystems, face unprecedented challenges due to climate change. This study investigates the influence of functional diversity—as a component of biodiversity—to enhance long‐term biomass of European forests in the context of changing climatic conditions. Using the next‐generation flexible trait‐based vegetation model, LPJmL‐FIT, we explored the impact of functional diversity on long‐term forest biomass under three different climate change scenarios (video abstract: https://www.pik‐potsdam.de/~billing/video/2023/video%5fabstract%5fbilling%5fet%5fal%5fLPJmLFIT.mp4). Four model set‐ups were tested with varying degrees of functional diversity and best‐suited functional traits. Our results show that functional diversity positively influences long‐term forest biomass, particularly when climate warming is low (RCP2.6). Under these conditions, high‐diversity simulations led to an approximately 18.2% increase in biomass compared to low‐diversity experiments. However, as climate change intensity increased, the benefits of functional diversity diminished (RCP8.5). A Bayesian multilevel analysis revealed that both full leaf trait diversity and diversity of plant functional types contributed significantly to biomass enhancement under low warming scenarios in our model simulations. Under strong climate change, the presence of a mixture of different functional groups (e.g. summergreen and evergreen broad‐leaved trees) was found more beneficial than the diversity of leaf traits within a functional group (e.g. broad‐leaved summergreen trees). Ultimately, this research challenges the notion that planting only the most productive and climate‐suited trees guarantees the highest future biomass and carbon sequestration. We underscore the importance of high functional diversity and the potential benefits of fostering a mixture of tree functional types to enhance long‐term forest biomass in the face of climate change. [ABSTRACT FROM AUTHOR]

This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk. [ABSTRACT FROM AUTHOR]

Climate change heavily threatens forest ecosystems worldwide and there is urgent need to understand what controls tree survival and forests stability. There is evidence that biodiversity can enhance ecosystem stability (Loreau and de Mazancourt in Ecol Lett 16:106–115, 2013; McCann in Nature 405:228–233, 2000), however it remains largely unclear whether this also holds for climate change and what aspects of biodiversity might be most important. Here we apply machine learning to outputs of a flexible-trait Dynamic Global Vegetation Model to unravel the effects of enhanced functional tree trait diversity and its sub-components on climate-change resistance of temperate forests (http://www.pik-potsdam.de/~billing/video/Forest%5fResistance%5fLPJmLFIT.mp4). We find that functional tree trait diversity enhances forest resistance. We explain this with 1. stronger complementarity effects (~ 25% importance) especially improving the survival of trees in the understorey of up to + 16.8% (± 1.6%) and 2. environmental and competitive filtering of trees better adapted to future climate (40–87% importance). We conclude that forests containing functionally diverse trees better resist and adapt to future conditions. In this context, we especially highlight the role of functionally diverse understorey trees as they provide the fundament for better survival of young trees and filtering of resistant tree individuals in the future. [ABSTRACT FROM AUTHOR]

The increasing impacts of climate change require strategies for climate adaptation. Dynamic Global Vegetation Models (DGVMs) are one type of multi-sectorial impact models with which the effects of multiple interacting processes in the terrestrial biosphere under climate change can be studied. The complexity of DGVMs is increasing as more and more processes, especially for plant physiology, are implemented. Therefore, there is a growing demand for increasing the computational performance of the underlying algorithms as well as ensuring their numerical accuracy. One way to approach this issue is to analyse the routines which have the potential for improved computational efficiency and/or increased accuracy when applying sophisticated mathematical methods. In this paper, the Farquhar-Collatz photosynthesis model under water stress as implemented in the Lund-Potsdam-Jena managed Land DGVM (4.0.002) was examined.We found that the numerical solution of a nonlinear equation, so far solved with the Bisection method, could be significantly improved by using Newton's method instead. The latter requires the computation of the derivative of the underlying function which is presented. Model simulations show a significant lower number of iterations to solve the equation numerically and an overall run time reduction of the model of about 16 % depending on the chosen accuracy. The Farquhar-Collatz photosynthesis model forms the core component in many DGVMs and land-surface models. An update in the numerical solution of the nonlinear equation can therefore be applied to similar photosynthesis models. Furthermore, this exercise can serve as an example for improving computationally costly routines while improving their mathematical accuracy. [ABSTRACT FROM AUTHOR]

Enhancing the capacity of social‐ecological systems (SES) to adapt to climate change is of crucial importance. While gradual climate change impacts have been the main focus of much recent research, much less is known about how SES are impacted by climate extremes and how they adapt. Here, based on an advanced conceptualization of social‐ecological resilience, performed by an interdisciplinary group of scientists, we outline three major challenges for operationalizing the resilience concept with particular focus on climate extremes. First, we discuss the necessary steps required to identify and measure relevant variables for capturing the full response spectrum of the coupled social and ecological components of SES. Second, we examine how climate extreme impacts on coupling flows in SES can be quantified by learning from past societal transitions or adaptations to climate extremes and resulting changes in ecosystem service supply. Last, we explore how to identify management options for maintaining and enhancing social‐ecological resilience under a changing regime of climate extremes. We conclude that multiple pathways within adaptation and mitigation strategies which enhance the adaptive capacity of SES to absorb climate extremes will open the way toward a sustainable future. Plain Language Summary: Ecosystems and society are closely coupled and are both affected by climate change. Climate extremes are expected to occur more often and/or get more intense under climate change. We ask the following question: How can ecosystems and society, which can be described as so‐called social‐ecological systems, withstand climate extremes and can therefore become more resilient? To achieve this, we use the concept of social‐ecological resilience and identify three challenges that scientists, decision makers, and practitioners need to work on to improve the adaptive capacity of social‐ecological systems to climate extremes. We need to describe and measure the main drivers of climate extremes that impact ecosystems and society and those variables that describe the adaptive capacity and all possible responses of ecosystems and society. Ecosystems and society are coupled: Ecosystems provide ecosystem services to society, and society manages ecosystems. These coupling flows also change under the impact of climate extremes. We still do not fully understand how climate extremes impact these coupling flows or how they can be measured. Because society has influenced ecosystems for many centuries and millennia in many regions of the world, these coupling flows also often have a long history; as such we cannot expect ecosystems and society to adapt to climate extremes separately. We can learn about impacts from past extreme events to continuously improve the management of ecosystems and increase the adaptive capacity of social‐ecological systems. Such management options range from adaptations of land management to institutional practices which are often necessary in order to be useful in helping the affected region immediately after a climate extreme event. Key Points: Climate extremes impact the resilience of social‐ecological systems; their adaptation requires knowledge on ecological and social mechanismsRelevant ecological and social variables document the impact of climate extremes on the coupling flows, enabling management optionsThree challenges remain to advance our understanding on adapting social‐ecological systems for a resilient and sustainable future [ABSTRACT FROM AUTHOR]

Ecosystem service (ES) models can only inform policy design adequately if they incorporate ecological processes. We used the Lund-Potsdam-Jena managed Land (LPJmL) model, to address following questions for Mexico, Bolivia and Brazilian Amazon: (i) How different are C stocks and C sequestration quantifications under standard (when soil and litter C and heterotrophic respiration are not considered) and comprehensive (including all C stock and heterotrophic respiration) approach? and (ii)Howdoes the valuation of C stock and C sequestration differ in national payments for ES and global C funds or marketswhen comparing both approach?Wefound that up to 65%of C stocks have not been taken into account by neglecting to include C stored in soil and litter, resulting in gross underpayments (up to 500 times lower). Since emissions from heterotrophic respiration of organic material offset a large proportion of C gained through growth of living matter, we found thatmarkets and decision-makers are inadvertently overestimating up to 100 times C sequestrated. Newapproaches formodelling C services relevant ecological process-based can help accounting for C in soil, litter and heterotrophic respiration and become important for the operationalization of agreements on climate change mitigation following the COP21 in 2015. [ABSTRACT FROM AUTHOR]

Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under- and overestimate aboveground biomass by 25 % and up to 60 %, respectively. Our comparison metrics provide a quantitative measure for model–data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biomass distribution are necessary to improve DGVMs. Our approach provides robust statistical measures for any pixel-to-point comparison, which is applicable for evaluation of models and remote-sensing products. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates - internally consistently - the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process. [ABSTRACT FROM AUTHOR]

Impacts of climate change require that society urgently develops ways to reduce amounts of carbon in the atmosphere. Tropical forests present an important opportunity, as they take up and store large amounts of carbon. It is often suggested that forests with high biodiversity have large stocks and high rates of carbon uptake. Evidence is, however, scattered across geographic areas and scales, and it remains unclear whether biodiversity is just a co-benefit or also a requirement for the maintenance of carbon stocks and uptake. Here, we perform a quantitative review of empirical studies that analyzed the relationships between plant biodiversity attributes and carbon stocks and carbon uptake in tropical forests. Our results show that biodiversity attributes related to species, traits or structure significantly affect carbon stocks or uptake in 64% of the evaluated relationships. Average vegetation attributes (community-mean traits and structural attributes) are more important for carbon stocks, whereas variability in vegetation attributes ( i.e., taxonomic diversity) is important for both carbon stocks and uptake. Thus, different attributes of biodiversity have complementary effects on carbon stocks and uptake. These biodiversity effects tend to be more often significant in mature forests at broad spatial scales than in disturbed forests at local spatial scales. Biodiversity effects are also more often significant when confounding variables are not included in the analyses, highlighting the importance of performing a comprehensive analysis that adequately accounts for environmental drivers. In summary, biodiversity is not only a co-benefit, but also a requirement for short- and long-term maintenance of carbon stocks and enhancement of uptake. Climate change policies should therefore include the maintenance of multiple attributes of biodiversity as an essential requirement to achieve long-term climate change mitigation goals. [ABSTRACT FROM AUTHOR]

Deforestation in Amazon is expected to decrease evapotranspiration (ET) and to increase soil moisture and river discharge under prevailing energy-limited conditions. The magnitude and sign of the response of ET to deforestation depend both on the magnitude and regional patterns of land-cover change (LCC), as well as on climate change and CO2 levels. On the one hand, elevated CO2 decreases leaf-scale transpiration, but this effect could be offset by increased foliar area density. Using three regional LCC scenarios specifically established for the Brazilian and Bolivian Amazon, we investigate the impacts of climate change and deforestation on the surface hydrology of the Amazon Basin for this century, taking 2009 as a reference. For each LCC scenario, three land surface models (LSMs), LPJmL-DGVM, INLAND-DGVM and ORCHIDEE, are forced by bias-corrected climate simulated by three general circulation models (GCMs) of the IPCC 4th Assessment Report (AR4). On average, over the Amazon Basin with no deforestation, the GCM results indicate a temperature increase of 3.3 °C by 2100 which drives up the evaporative demand, whereby precipitation increases by 8.5%, with a large uncertainty across GCMs. In the case of no deforestation, we found that ET and runoff increase by 5.0 and 14 %, respectively. However, in south-east Amazonia, precipitation decreases by 10%at the end of the dry season and the three LSMs produce a 6% decrease of ET, which is less than precipitation, so that runoff decreases by 22%. For instance, the minimum river discharge of the Rio Tapajós is reduced by 31% in 2100. To study the additional effect of deforestation, we prescribed to the LSMs three contrasted LCC scenarios, with a forest decline going from 7 to 34% over this century. All three scenarios partly offset the climate-induced increase of ET, and runoff increases over the entire Amazon. In the southeast, however, deforestation amplifies the decrease of ET at the end of dry season, leading to a large increase of runoff (up to +27% in the extreme deforestation case), offsetting the negative effect of climate change, thus balancing the decrease of low flows in the Rio Tapajós. These projections are associated with large uncertainties, which we attribute separately to the differences in LSMs, GCMs and to the uncertain range of deforestation. At the subcatchment scale, the uncertainty range on ET changes is shown to first depend on GCMs, while the uncertainty of runoff projections is predominantly induced by LSM structural differences. By contrast, we found that the uncertainty in both ET and runoff changes attributable to uncertain future deforestation is low. [ABSTRACT FROM AUTHOR]

Aim Drivers of biodiversity loss are increasingly broad in scale, requiring conservation planning to move towards range-wide assessments. This is especially challenging for migratory species, such as reindeer or caribou ( Rangifer tarandus), which use only a small portion of their range at a given point in time, and for which some parts of their range, such as calving grounds, may be much more important than others. Our aim was to identify potential calving ground habitat of wild tundra reindeer populations throughout Russia, where scarce knowledge about seasonal reindeer habitat is an obstacle for conservation planning, and to assess possible impacts from oil and gas development and climate change. Location Northern Eurasia. Method We used occurrence data from known reindeer calving grounds using species distribution models to first assess calving grounds characteristics and second predict their distribution across the Russian Arctic. We then compared our calving ground map with maps of oil and gas development, and a range of climate change indicators. Results We found areas throughout the Russian Arctic that are suitable for calving, including for some wild reindeer populations where calving ground locations are unknown. Variables relating to resource availability in spring and predator avoidance were the strongest predictors in our model. Oil and gas development affects calving grounds especially in the Barents Sea region and in south-western Siberia, whereas climate change affects calving grounds on Taymyr, Chukotka, and Kamchatka. Main conclusions We conducted the first assessment of calving grounds of Russia's wild reindeer populations, highlighting the spatial heterogeneity of the threats that they may face. Given the potentially strong impact of oil and gas development and climate change, conservation planning should aim for designing resilient conservation networks that would allow Arctic biodiversity to freely move in time and space and thus to adapt to changing environments. [ABSTRACT FROM AUTHOR]

Trends of summer precipitation and summer temperature and their influence on trends in summer drought and area burned in British Columbia (BC) were investigated for the period 1920-2000. The complexity imposed by topography was taken into account by incorporating high spatial resolution climate and fire data. Considerable regional variation in trends and in climate-fire relationships was observed. A weak but significant increase in summer temperature was detected in northeastern and coastal BC, whereas summer precipitation increased significantly in all regions-by up to 45.9 %. A significant decrease in province-wide area burned and at the level of sub-units was strongly related to increasing precipitation, more so than to changing temperature or drought severity. A stronger dependence of area burned on precipitation, a variable difficult to predict, implies that projected changes in future area burned in this region may yield higher uncertainties than in regions where temperature is predominantly the limiting factor for fire activity. We argue that analyses of fire-climate relationships must be undertaken at a sufficiently high resolution such that spatial variability in limiting factors on area burned like precipitation, temperature, and drought is captured within units. [ABSTRACT FROM AUTHOR]

Within the changing fire regimes of Portugal, the relative importance of humans and climatic variability for regional fire statistics remains poorly understood. This work investigates the statistical relationship between temporal dynamics of fire events in Portugal and a set of socioeconomic, landscape, and climatic variables for the time periods of 1980-1990, 1991-2000, and extreme fires years. For 10 of 15 districts, it was possible to observe moderate shifts in the significance of fire drivers for the first two decadal periods. For others, pronounced changes of the significance of fire drivers were found across time. Results point toward a dynamic (perhaps highly non-linear) behavior of socioeconomic and landscape fire drivers, especially during the occurrence of extreme fire years of 2003 and 2005. At country level, population density alone explained 42% of the inter-annual and inter-district deviance in number of fires. At the same temporal and spatial scale, the explanatory power of temperature anomalies proved to explain 43% of area burnt. We highlight the necessity of including a broad set of socioeconomic and landscape fire drivers in order to account for potential significance shifts. In addition, although climate does trigger broad favorable fire conditions across Portugal mainland, socioeconomic and landscape factors proved to determine much of the complex fire patterns at a subnational scale. [ABSTRACT FROM AUTHOR]

Global change includes multiple stressors to natural ecosystems ranging from direct climate and land-use impacts to indirect degradation processes resulting from fire. Humid tropical forests are vulnerable to projected climate change and possible synergistic interactions with deforestation and fire, which may initiate a positive feedback to rising atmospheric CO2. Here, we present results from a multifactorial impact analysis that combined an ensemble of climate change models with feedbacks from deforestation and accidental fires to quantify changes in Amazon Basin carbon cycling. Using the LPJmL Dynamic Global Vegetation Model, we modelled spatio-temporal changes in net biome production (NBP); the difference between carbon fluxes from fire, deforestation, soil respiration and net primary production. By 2050, deforestation and fire (with no CO2 increase or climate change) resulted in carbon losses of 7.4–20.3 Pg C with the range of uncertainty depending on socio-economic storyline. During the same time period, interactions between climate and land use either compensated for carbon losses due to wetter climate and CO2 fertilization or exacerbated carbon losses from drought-induced forest mortality (−20.1 to +4.3 Pg C). By the end of the 21st century, depending on climate projection and the rate of deforestation (including its interaction with fire), carbon stocks either increased (+12.6 Pg C) or decreased (−40.6 Pg C). The synergistic effect of deforestation and fire with climate change contributed up to 26–36 Pg C of the overall decrease in carbon stocks. Agreement between climate projections ( n=9), not accounting for deforestation and fire, in 2050 and 2098 was relatively low for the directional change in basin-wide NBP (19–37%) and aboveground live biomass (13–24%). The largest uncertainty resulted from climate projections, followed by implementation of ecosystem dynamics and deforestation. Our analysis partitions the drivers of tropical ecosystem change and is relevant for guiding mitigation and adaptation policy related to global change. [ABSTRACT FROM AUTHOR]

Owing to large climatic and orographic variation, British Columbia covers a variety of ecosystems extending from temperate rainforests on the Pacific coast to boreal forests in the north-east. The aim of this study is to investigate the spatial variation of trends in wildfire activity and their relationship to summer drought for the entire province of British Columbia. Time series of annual wildfire extent and occurrence, summer self-calibrating Palmer Drought Severity Index and summer Aridity Index were derived from spatially explicit data. Sixteen landscape regions according to the provincial Biogeoclimatic Ecosystem Classification system served as spatial reference. The regional series for 1920-2000 were subjected to trend analysis. Correlations between area burned and summer drought were assessed and tested for significance. The observed decrease in wildfire activity is significantly related to wetter summers with the strength of the relationship considerably varying between British Columbia's landscapes. Our results suggest that aggregated statistics for large regions with complex topography and climate can hide the spatial variation in direction and strength of changes and may accordingly obscure the relationship between fire and drought. Based on high-spatial-resolution data, our study is the first to provide a differentiated picture for British Columbia. [ABSTRACT FROM AUTHOR]

Vegetation fires control global vegetation distribution, ecosystem functioning, and globalcarbon cycling. Specifically in South America, changes in fire occurrence together with landuse change could accelerate forest fragmentation and increase the vulnerability of tropicalforests and savannas to climate change. Dynamic Global Vegetation Models (DGVMs) arevaluable tools to estimate the effects of fire on ecosystem functioning and carbon cyclingunder future climate changes. However, fire-enabled DGVMs have partly poor performancesin capturing the magnitude, spatial patterns, and temporal dynamics of burned area asobserved by satellites. As fire is controlled by the interplay of weather conditions, vegetationproperties and human activities, fire modules in DGVMs can be improved in variousaspects.As a starting point, we here focus on improving the controls of climate on fire danger andhence fuel moisture content in the LPJmL4-SPITFIRE DGVM in South America andespecially for the Brazilian fire-prone ecoregions Caatinga and Cerrado. We therefore test twoalternative model formulations (Nesterov index and water vapor pressure deficit) for climateeffects on fire danger within a formal model-data integration setup where we estimate modelparameters against satellite datasets of burned area (GFED4) and above ground biomass oftrees.Our results show that an optimized version of the fire danger indices improves the modelperformance significantly especially in the Cerrado/Caatinga region. In addition, the firecontrolled vegetation patterns improve in terms of plant functional types (PFT) distributionand above ground biomass. While both optimized fire danger parameterizationsimproved the model performance, we obtained the best results by using the watervapor pressure deficit (VPD) for the calculation of fire danger. The VPD includes incomparison to the Nesterov index a representation of the air humidity and the vegetationdensity.This work shows the successful application of a model-data integration setup, as well as theintegration of a new fire danger formulation in order to optimize a process based fire model. Itfurther highlights the potential of this approach to be used in a global setting in order toachieve a new level of accuracy in comprehensive fire modeling and prediction. [ABSTRACT FROM AUTHOR]

Understanding the effect of climate on the distribution and composition of vegetation is crucial to investigate its response to climate change. However, process-based vegetation models usually use comparably coarse-grain climatic data, which underestimate topographic effects on local climate.To evaluate potential effects of topoclimate on vegetation redistribution predicted by the process-based vegetation model LPJmL, we compared modelled vegetation based on different climate inputs, namely coarse-grained climatic data, data corrected for temperature lapse rate according to local elevation and data incorporating topographic effects responsible for cool-air pooling and anisotropic heat load in a hilly region in the Czech Republic.The results showed the pronounced effect of terrain topography as well the large discrepancy between coarse-scale climate and locally adapted climate. Our results thus give a hint on how sensitive modelled vegetation distribution is to climate input correction. We found that despite future climate change, including higher temperatures and less precipitation, the current vegetation might be able to remain in some local refuges, which are however not captured by coarse-scale climatic dataset. Therefore, the inclusion of topographic effects is crucial for realistic estimates of the vegetation redistribution in the response to climate change. [ABSTRACT FROM AUTHOR]

The response of land ecosystems to future climate change is among the largest uncertainties in the climate/carbon cycle feedback. Dynamic global vegetation models (DGVMs) are a key tool to assess the impacts of climate change on vegetation distribution, biomass, and land carbon exchange. Carbon cycle uncertainties in DGVMs depend partly on the simulated distribution and temporal changes in vegetation cover and its interactions with photosynthesis, biomass allocation and turnover. The present-day availability of multiple satellite observations can potentially help to constrain DGVMs. Here we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced florescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain phenology, productivity, and vegetation dynamic parameters in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle. However, the optimized model better reproduces observed spatial patterns of biomass and tree cover. The optimized model shows regionally-dependent increases and decreases in vegetation and ecosystem carbon turnover times. In summary, optimizations of dynamic global vegetation models against multiple satellite observation improve regional simulations of vegetation and carbon cycle dynamics. [ABSTRACT FROM AUTHOR]