Fire emissions are critical for carbon and nutrient cycles, climate, and air quality. Dynamic Global Vegetation Models (DGVMs) with interactive fire modeling provide important estimates for long-term ...and large-scale changes of fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700-2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-the-art global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most up-to-date fire emission factor table from field and laboratory studies over various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. Evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products, and can capture the high emissions over the tropical savannas, low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most of the models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Historically, all models show only a weak trend in global fire emissions before ~1850s, consistent with multi-source merged historical reconstructions. The long-term trends among DGVMs are quite different for the 20th century, with some models showing an increase and others a decrease in fire emissions, mainly as a result of the discrepancy in their simulated responses to human population density change and land-use and land-cover change (LULCC). Our study provides a basic dataset for developing regional and global multi-source merged historical reconstructions and merging methods, and analyzing historical changes of fire emissions and their uncertainties as well as their role in the Earth system. It also highlights the importance of accurately modeling the responses of fire emissions to LULCC and population density change in reducing uncertainties in historical reconstructions of fire emissions and providing more reliable future projections.
Abstract
Climate change is expected to increase fire risk in many forested regions, posing a potential threat to forest functioning (i.e. carbon pools and fluxes). At the same time, expansion of the ...wildland-urban interface threatens to bring more and more people, property, and infrastructure into contact with wildfire events. It is critical that fire be managed in a way that minimizes risk to human health and well-being and maintains forest climate change mitigation potential without affecting the important ecological role fire plays in many ecosystems. Dynamic global vegetation models (DGVMs) simulate processes over large geographic regions and long time periods and could provide information that supports fire and fuel management programs by assessing performance of such measures under different climate change scenarios in different regions. However, thus far DGVMs have not been put to this use. In this work, we introduce a novel prescribed burning (PB) module to the LPJ-GUESS DGVM. Focusing on two regions (Eastern Europe and the Iberian Peninsula), we compare the effectiveness of PB and mechanical thinning on various aspects of the fire regime under two climate change scenarios through the end of the 21st century. We find that PB and thinning, by reducing fuel load, reduce fireline intensity; this suggests that what wildfires do occur could be more easily controlled. While this would reduce risks to human health and well-being, PB comes with the tradeoff of increased fire emissions, which could contribute to respiratory problems. Mechanical thinning reduces fireline intensity by as much or more while also reducing emissions. While net primary production remained unaffected by fire management, cumulative net biome production until the end of the 21st century declined especially under the influence of thinning. While these results are based on stylized management treatments, this work shows the potential of DGVMs in exploring fire management options.
We propose a model wherein chronic stress results in glucocorticoid receptor resistance (GCR) that, in turn, results in failure to down-regulate inflammatory response. Here we test the model in two ...viral-challenge studies. In study 1, we assessed stressful life events, GCR, and control variables including baseline antibody to the challenge virus, age, body mass index (BMI), season, race, sex, education, and virus type in 276 healthy adult volunteers. The volunteers were subsequently quarantined, exposed to one of two rhinoviruses, and followed for 5 d with nasal washes for viral isolation and assessment of signs/symptoms of a common cold. In study 2, we assessed the same control variables and GCR in 79 subjects who were subsequently exposed to a rhinovirus and monitored at baseline and for 5 d after viral challenge for the production of local (in nasal secretions) proinflammatory cytokines (IL-1β, TNF-α, and IL-6). Study 1: After covarying the control variables, those with recent exposure to a long-term threatening stressful experience demonstrated GCR; and those with GCR were at higher risk of subsequently developing a cold. Study 2: With the same controls used in study 1, greater GCR predicted the production of more local proinflammatory cytokines among infected subjects. These data provide support for a model suggesting that prolonged stressors result in GCR, which, in turn, interferes with appropriate regulation of inflammation. Because inflammation plays an important role in the onset and progression of a wide range of diseases, this model may have broad implications for understanding the role of stress in health.
Lung carcinogenesis is a complex and stepwise process involving accumulation of genetic mutations in signaling and oncogenic pathways via interactions with environmental factors and host ...susceptibility. Tobacco exposure is the leading cause of lung cancer, but its relationship to clinically relevant mutations and the composite tumor mutation burden (TMB) has not been fully elucidated. In this study, we investigated the dose-response relationship in a retrospective observational study of 931 patients treated for advanced-stage non-small cell lung cancer (NSCLC) between April 2013 and February 2020 at the Dana Farber Cancer Institute and Brigham and Women's Hospital. Doubling smoking pack-years was associated with increased
and less frequent
and
mutations, whereas doubling smoking-free months was associated with more frequent
. In advanced lung adenocarcinoma, doubling smoking pack-years was associated with an increase in TMB, whereas doubling smoking-free months was associated with a decrease in TMB, after controlling for age, gender, and stage. There is a significant dose-response association of smoking history with genetic alterations in cancer-related pathways and TMB in advanced lung adenocarcinoma. SIGNIFICANCE: This study clarifies the relationship between smoking history and clinically relevant mutations in non-small cell lung cancer, revealing the potential of smoking history as a surrogate for tumor mutation burden.
We use a variational formulation incorporating the full Navier–Stokes equations to identify initial perturbations with finite kinetic energy
${E}_{0} $
which generate the largest gain in perturbation ...kinetic energy at some time
$T$
later for plane Couette flow. Using the flow geometry originally used by Butler & Farrell (Phys. Fluids A, vol. 4, 1992, pp. 1637–1650) to identify the linear transient optimal perturbations for
${E}_{0} \ensuremath{\rightarrow} 0$
and incorporating
$T$
as part of the optimization procedure, we show how the addition of nonlinearity smoothly changes the result as
${E}_{0} $
increases from zero until a small but finite
${E}_{c} $
is reached. At this point, the variational algorithm is able to identify an initial condition of completely different form which triggers turbulence – called the minimal seed for turbulence. If instead
$T$
is fixed at some asymptotically large value, as suggested by Pringle, Willis & Kerswell (J. Fluid Mech., vol. 703, 2012, pp. 415–443), a fundamentally different ‘final’ optimal perturbation emerges from our algorithm above some threshold initial energy
${E}_{f} \in (0, {E}_{c} )$
which shows signs of localization. This nonlinear optimal perturbation clearly approaches the structure of the minimal seed as
${E}_{0} \ensuremath{\rightarrow} { E}_{c}^{\ensuremath{-} } $
, although for
${E}_{0} \lt {E}_{c} $
, its maximum gain over all time intervals is always less than the equivalent maximum gain for the ‘quasi-linear optimal perturbation’, i.e. the finite-amplitude manifestation of the underlying linear optimal perturbation. We also consider a wider flow geometry recently studied by Monokrousos et al. (Phys. Rev. Lett., vol. 106, 2011, 134502) and present evidence that the critical energy for transition
${E}_{c} $
they found by using total dissipation over a time interval as the optimizing functional is recovered using energy gain at a fixed target time as the optimizing functional, with the same associated minimal seed emerging. This emphasizes that the precise form of the functional does not appear to be important for identifying
${E}_{c} $
provided it takes heightened values for turbulent flows, as postulated by Pringle, Willis & Kerswell (J. Fluid Mech., vol. 703, 2012, pp. 415–443). All our results highlight the irrelevance of the linear energy gain optimal perturbation for predicting or describing the lowest-energy flow structure which triggers turbulence.
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.
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.
Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle and to infer relationships between climate, land use and fire. However, ...differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, in order to improve projections of fire characteristics and fire impacts on ecosystems and human societies in the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent interannual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the Northern Hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions, and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears to correlate with a better representation of burnt area. The two older fire models included in the FireMIP ensemble (LPJ–GUESS–GlobFIRM, MC2) clearly perform less well globally than other models, but it is difficult to distinguish between the remaining ensemble members; some of these models are better at representing certain aspects of the fire regime; none clearly outperforms all other models across the full range of variables assessed.
Objectives: A Randomized Controlled Trial was conducted to evaluate the effectiveness of the Hebrew adaptation of the Program for the Education and Enrichment of Relational Skills (PEERS®), a ...parent-assisted intervention. Parental sensitivity (PS), measured in conflict and support contexts, was assessed as a predictor of adolescents' intervention-related outcomes.
Design: Eighty-two Hebrew-speaking adolescents (9 females), aged 12-17 years, and their parents (62 mothers), were randomly allocated into immediate intervention (II; n = 40) or delayed intervention control (DI; n = 42) groups. Participants were tested at three time-points (Pre-Post-Follow Up for II, Pre-Pre-Post for DI). Outcome measures included behavioral assessments of adolescents' social communication (SC), a social-skills knowledge test, and self, parent, and teacher reported questionnaires. PS was assessed using support and conflict parent-adolescent interactions. Repeated measures ANOVAs were used to assess intervention effectiveness. SEM was used to examine PS pre- and post-intervention as predictors of adolescents' immediate and follow-up outcomes.
Results: The II group improved on adolescents' measured SC and social knowledge, on parent-(but not teacher-) reported social skills, and on self-reported empathy. Gains maintained at follow-up. The DI group showed similar gains following their intervention. Adolescents' intervention-related SC gains were negatively predicted by pre-intervention PS, and positively predicted by intervention-related PS changes in the support context. Pre-intervention PS in the conflict context positively predicted adolescent SC at follow-up.
Conclusions: The Hebrew-adapted PEERS® is an effective intervention for adolescents with ASD. PS plays an important role in the promotion of SC in adolescents with ASD and should receive clinical attention.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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