Global environmental controls of wildfire burnt area, size and intensity.

Fire is an important influence on the global patterns of vegetation structure and composition. Wildfire is included as a distinct process in many dynamic global vegetation models but limited current understanding of fire regimes restricts these models' ability to reproduce more than the broadest geographic patterns. Here we present a statistical analysis of the global controls of remotely sensed burnt area (BA), fire size (FS), and a derived metric related to fire intensity (FI). Separate generalized linear models were fitted to observed monthly fractional BA from the Global Fire Emissions Database (GFEDv4), median FS from the Global Fire Atlas, and median fire radiative power from the MCD14ML dataset normalized by the square root of median FS. The three models were initially constructed from a common set of 16 predictors; only the strongest predictors for each model were retained in the final models. It is shown that BA is primarily driven by fuel availability and dryness; FS by conditions promoting fire spread; and FI by fractional tree cover and road density. Both BA and FS are constrained by landscape fragmentation, whereas FI is constrained by fuel moisture. Ignition sources (lightning and human population) were positively related to BA (after accounting for road density), but negatively to FI. These findings imply that the different controls on BA, FS and FI need to be considered in process-based models. They highlight the need to include measures of landscape fragmentation as well as fuel load and dryness, and to pay close attention to the controls of fire spread

Accounting for atmospheric carbon dioxide variations in pollen-based reconstructions of past hydroclimates.

Changes in atmospheric carbon dioxide (CO2) concentration directly influence the ratio of stomatal water loss to carbon uptake. This ratio (e) is a fundamental quantity for terrestrial ecosystems, as it defines the water requirement for plant growth. Statistical and analogue-based methods used to reconstruct past hydroclimate variables from fossil pollen assemblages do not take account of the effect of CO2 variations on e. Here we present a general, globally applicable method to correct for this effect. The method involves solving an equation that relates e to a climatic moisture index (MI, the ratio of mean annual precipitation to mean annual potential evapotranspiration), mean growing-season temperature, and ambient CO2. The equation is based on the least-cost optimality hypothesis, which predicts how the ratio (χ) of leaf-internal to ambient CO2 varies with vapour pressure deficit (vpd), growing-season temperature and atmospheric pressure, combined with experimental evidence on the response of χ to the CO2 level at which plants have been grown. An empirical relationship based on global climate data is used to relate vpd to MI and growing-season temperature. The solution to the equation allows past MI to be estimated from pollen-reconstructed MI, given past CO2 and temperature. This MI value can be used to estimate mean annual precipitation, accounting for the effects of orbital variations, temperature and cloud cover (inferred from MI) on potential evapotranspiration. A pollen record from semi-arid Spain that spans the last glacial interval is used to illustrate the method. Low CO2 leads to estimated MI being larger than reconstructed MI during glacial times. The CO2 effect on inferred precipitation was partly offset by increased cloud cover; nonetheless, inferred precipitation was greater than present almost throughout the glacial period. This method allows a more robust reconstruction of past hydroclimatic variations than currently available tools

High-pressure phases of FeTiO3 from first principles

Published versio

Leaf morphological traits as adaptations to multiple climate gradients

1. Leaf morphological traits vary systematically along climatic gradients. However, recent studies in plant functional ecology have mainly analysed quantitative traits, while numerical models of species distributions and vegetation function have focused on traits associated with resource acquisition; both ignore the wider functional significance of leaf morphology. 2. A data set comprising 22 leaf morphological traits for 662 woody species from 92 sites, representing all biomes present in China, was subjected to multivariate analysis in order to identify leading dimensions of trait covariation (correspondence analysis), quantify climatic and phylogenetic contributions (canonical correspondence analysis with variation partitioning), and characterize co-occurring trait syndromes (k-means clustering) and their climatic preferences. 3. Three axes accounted for > 20% of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing-season temperature accounted for 8–10% of trait variation; family 15–32%. Microphyll or larger, mid- to dark green leaves with drip-tips in wetter climates contrasted with nanophyll or smaller glaucous leaves without drip-tips in drier climates. Thick, entire leaves in less seasonal climates contrasted with thin, marginal dissected, aromatic, and involute/revolute leaves in more seasonal climates. Thick, involute, hairy leaves in colder climates contrasted with thin leaves with marked surface structures (surface patterning) in warmer climates. Distinctive trait clusters were linked to the driest and most seasonal climates, for example the clustering of picophyll, fleshy and succulent leaves in the driest climates and leptophyll, linear, dissected, revolute or involute, and aromatic leaves in regions with highly seasonal rainfall. Several trait clusters co-occurred in wetter climates, including clusters characterised by microphyll, moderately thick, patent, and entire leaves or notophyll, waxy, dark green leaves. 4. Synthesis. The plastic response of size, shape, color and other leaf morphological traits to climate is muted, thus their apparent shift along climate gradients reflects plant adaptations to environment at a community-level as determined by species replacement. Information on leaf morphological traits, widely available in floras, could be used to strengthen predictive models of species distribution and vegetation function

Optimality-based modelling of wheat sowing dates globally

CONTEXT Sowing dates are currently an essential input for crop models. However, in the future, the optimal sowing time will be affected by climate changes and human adaptations to these changes. A better understanding of what determines the choice of wheat type and sowing dates is required to be able to predict future crop yields reliably. OBJECTIVE This study was conducted to understand how climate conditions affect the choice of wheat types and sowing dates globally. METHODS We develop a model integrating optimality concepts for simulating gross primary production (GPP) with climate constraints on wheat phenology to predict sowing dates. We assume that wheat could be sown at any time with suitable climate conditions and farmers would select a sowing date that maximises yields. The model is run starting on every possible climatically suitable day, determined by climate constraints associated with low temperature and intense precipitation. The optimal sowing date is the day which gives the highest yield in each location. We evaluate the simulated optimal sowing dates with data on observed sowing dates created by merging census-based datasets and local agronomic information, then predict their changes under future climate scenarios to gain insight into the impacts of climate change. RESULTS AND CONCLUSIONS Cold-season temperatures are the major determinant of sowing dates in the extra-tropics, whereas the seasonal cycle of monsoon rainfall is important in the tropics. Our model captures the timing of reported sowing dates, with differences of less than one month over much of the world; maximum errors of up to two months occur in tropical regions with large altitudinal gradients. Discrepancies between predictions and observations are larger in tropical regions than temperate and cold regions. Slight warming is shown to promote earlier sowing in wet areas but later in dry areas; larger warming leads to delayed sowing in most regions. These predictions arise due to the interactions of several influences on yield, including the effects of warming on growing-season length, the need for sufficient moisture during key phenological stages, and the temperature threshold for vernalization of winter wheat. SIGNIFICANCE The integration of optimality concepts for simulating GPP with climate constraints on phenology provides realistic predictions of wheat type and sowing dates. The model thus provides a basis for predicting how crop calendars might change under future climate change. It can also be used to investigate potential changes in management to mitigate the negative impacts of climate change

Controversies in the management of primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) remains a rare but significant disease, which affects mainly young males in association with inflammatory bowel disease. There have been few advances in the understanding of the pathogenesis of the condition and no therapeutics with proven mortality benefit aside from liver transplantation. There remain areas of controversy in the management of PSC which include the differentiation from other cholangiopathies, in particular immunoglobulin G4 related sclerosing cholangitis, the management of dominant biliary strictures, and the role of ursodeoxycholic acid. In addition, the timing of liver transplantation in PSC remains difficult to predict with standard liver severity scores. In this review, we address these controversies and highlight the latest evidence base in the management of PSC

Optimality principles explaining divergent responses of alpine vegetation to environmental change

Recent increases in vegetation greenness over much of the world reflect increasing CO2 globally and warming in cold areas. However, the strength of the response to both CO2 and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite-observed peak season absorbed photosynthetically active radiation (Fmax) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year−1 in drier regions and a browning of 0.12 ± 0.08% year−1 in wetter regions). Using an eco-evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in Fmax with a correlation coefficient (r) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year−1) and browning (0.07 ± 0.06% year−1). We also predicted the observed reduced sensitivities of Fmax to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces Fmax in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO2 stimulates vegetation growth by enhancing water-use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change

Fluid and Diffusion Limits for Bike Sharing Systems

Bike sharing systems have rapidly developed around the world, and they are served as a promising strategy to improve urban traffic congestion and to decrease polluting gas emissions. So far performance analysis of bike sharing systems always exists many difficulties and challenges under some more general factors. In this paper, a more general large-scale bike sharing system is discussed by means of heavy traffic approximation of multiclass closed queueing networks with non-exponential factors. Based on this, the fluid scaled equations and the diffusion scaled equations are established by means of the numbers of bikes both at the stations and on the roads, respectively. Furthermore, the scaling processes for the numbers of bikes both at the stations and on the roads are proved to converge in distribution to a semimartingale reflecting Brownian motion (SRBM) in a $N^{2}$-dimensional box, and also the fluid and diffusion limit theorems are obtained. Furthermore, performance analysis of the bike sharing system is provided. Thus the results and methodology of this paper provide new highlight in the study of more general large-scale bike sharing systems.Comment: 34 pages, 1 figure

Ecosystem photosynthesis in land-surface models: a first-principles approach

Vegetation regulates land-atmosphere water and energy exchanges and is an essential component of land-surface models (LSMs). However, LSMs have been handicapped by assumptions that equate acclimated photosynthetic responses to environment with fast responses observable in the laboratory. These time scales can be distinguished by including specific representations of acclimation, but at the cost of further increasing parameter requirements. Here we develop an alternative approach based on optimality principles that predict the acclimation of carboxylation and electron-transport capacities, and a variable controlling the response of leaf-level carbon dioxide drawdown to vapour pressure deficit (VPD), to variations in growth conditions on a weekly to monthly time scale. In the “P model”, an optimality-based light-use efficiency model for gross primary production (GPP) on this time scale, these acclimated responses are implicit. Here they are made explicit, allowing fast and slow response time-scales to be separated and GPP to be simulated at sub-daily timesteps. The resulting model mimics diurnal cycles of GPP recorded by eddy-covariance flux towers in a temperate grassland and boreal, temperate and tropical forests, with no parameter changes between biomes. Best performance is achieved when biochemical capacities are adjusted to match recent midday conditions. This model suggests a simple and parameter-sparse method to include both instantaneous and acclimated responses within an LSM framework, with many potential applications in weather, climate and carbon - cycle modelling

Improved simulation of fire-vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1)

The Land surface Processes and eXchanges (LPX) model is a fire-enabled dynamic global vegetation model that performs well globally but has problems representing fire regimes and vegetative mix in savannas. Here we focus on improving the fire module. To improve the representation of ignitions, we introduced a reatment of lightning that allows the fraction of ground strikes to vary spatially and seasonally, realistically partitions strike distribution between wet and dry days, and varies the number of dry days with strikes. Fuel availability and moisture content were improved by implementing decomposition rates specific to individual plant functional types and litter classes, and litter drying rates driven by atmospheric water content. To improve water extraction by grasses, we use realistic plant-specific treatments of deep roots. To improve fire responses, we introduced adaptive bark thickness and post-fire resprouting for tropical and temperate broadleaf trees. All improvements are based on extensive analyses of relevant observational data sets. We test model performance for Australia, first evaluating parameterisations separately and then measuring overall behaviour against standard benchmarks. Changes to the lightning parameterisation produce a more realistic simulation of fires in southeastern and central Australia. Implementation of PFT-specific decomposition rates enhances performance in central Australia. Changes in fuel drying improve fire in northern Australia, while changes in rooting depth produce a more realistic simulation of fuel availability and structure in central and northern Australia. The introduction of adaptive bark thickness and resprouting produces more realistic fire regimes in Australian savannas. We also show that the model simulates biomass recovery rates consistent with observations from several different regions of the world characterised by resprouting vegetation. The new model (LPX-Mv1) produces an improved simulation of observed vegetation composition and mean annual burnt area, by 33 and 18% respectively compared to LPX