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The sapwood area supporting a given leaf area (vH) reflects a coordinated coupling between carbon uptake, water transport and loss at a whole plant level. Worldwide variation in vH reflects diverse plants strategies adapt to prevailing environments, and impact the evolution of global carbon and water cycles. Why such a variation has not been convincingly explained yet, thus hinder its representation in Earth System Models. Here we hypothese that optimal vH tends to mediate between sapwood conductance and climates so that leaf water loss matches both sapwood hydraulics and leaf photosynthesis. By compiling and testing against two extensive datasets, we show that our hypothesis explains nearly 60% of vH variation responding to light, vapor pressure deficit, temperature, and sapwood conductance in a quantitively predictable manner. Sapwood conductance or warming-enhanced hydraulic efficiency reduces the demand on sapwood area for a given total leaf area and, whereas brightening and air dryness enhance photosynthetic capacities consequently increasing the demand. This knowledge can enrich Earth System Models where carbon allocation and water hydraulics play key roles in predicting future climate-carbon feedback.

Climate change and atmospheric CO2 levels can influence wildfire properties through separate and potentially contrasting impacts on vegetation and climate. One way to examine the sensitivity of global wildfire properties to changes in climate and CO2 levels is using an out-of-sample experiment, such as the Last Glacial Maximum (LGM; 21 ka BP). Charcoal records show reduced burning at the LGM, when CO2 levels were ~ 185 ppm and the climate was cooler and drier. In this analysis, we isolated out the potential effects of LGM CO2 levels and LGM climate on the spatial patterns of global wildfire properties.Using three statistical models, we conducted simulations of the spatial distribution of global burnt area, fire size and fire intensity under four scenarios: modern climate/modern CO2 levels, LGM climate/LGM CO2 levels, modern climate/LGM CO2 levels and LGM/ modern CO2 levels. We used outputs from three coupled ocean–atmosphere models representative of the range of simulated LGM climates. The ecophysiological effect of CO2 levels was explicitly accounted for through vegetation inputs. Gross primary productivity (GPP) and land cover were derived for the LGM and modern climate keeping either CO2 levels at 395 ppm (modern), or setting them to 185 ppm, using the P Model, a first-principles model of GPP which allows continuous acclimation of photosynthetic parameters to environmental variations, and the BIOME4 equilibrium global vegetation model.Our results show a reduction in burnt area under LGM CO2 levels, both with modern and LGM climate inputs. In the case of the warmest of the LGM climate scenarios, this reduction was of the same magnitude as the combined LGM climate/LGM CO2 levels scenario. However, the driest and coldest LGM climate scenario produced a reduction in burnt area even with modern CO2 levels, and the largest reduction in burnt area with LGM CO2. The reduction was primarily driven by changes in vapour pressure deficit (VPD). Fire size increased under LGM climates, due to changes in wind and VPD. The lower CO2 values at the LGM had no impact on fire size. Fire intensity increased under LGM climates and LGM CO2 levels, with both effects of similar amplitude and changes driven primarily by VPD, GPP and diurnal temperature range. We compared our outputs with sedimentary charcoal records from the Reading Palaeofire Database (RPD). Overall, the burnt area LGM CO2 levels/LGM climate scenario showed the greatest agreement, though depending on how cold and dry the LGM climate was, this agreement was either equal to LGM CO2 levels or LGM climate alone. These results suggest that whilst there was reduced global burning at the LGM, there may have been larger and more intense fires. They also highlight the importance of the ecophysiological effect of CO2 levels on fuels, a major control of burnt area and fire intensity regardless of climate. They point to the importance of including this effect in process-based fire models, as well as the importance of accurately estimating the amplitude of projected change for different climate variables in order to increase the reliability of future projections.

Representations of interactions between the C and N cycles in terrestrial ecosystems are now implemented in a majority of state-of-the-art Dynamic Global Vegetation Models (C-N models). Standard models for simulating the response of individual processes to changes in N availability have not yet emerged and widely used models have not been tested against the full diversity of empirical data. Large remaining model structural uncertainty has important implications for projections and hindcasts of the land C uptake.Here, we summarise the current state of global land C balance simulations by comparing C-N models to C-only models; summarise data from field surveys and experiments to elucidate the role of soil N in controlling photosynthesis and its acclimation, stoichiometry, allocation, and growth; and demonstrate how optimality principles can guide the representation of acclimation and allocation for simulating ecosystem responses to experimental treatments of CO2 and soil N – consistent with observations. Promising model results are achieved by assuming that the atmospheric environment, including CO2, is the principal driver for photosynthetic capacities and leaf N following optimality theory of photosynthetic acclimation (Prentice et al., 2014). In turn, the functional balance hypothesis (Bloom et al., 1985) yields accurate predictions for how soil N availability and CO2 influence allocation and growth in different tissues.Our results show how confronting new theoretical approaches to simulating ecosystem C-N interactions against the collective constraints from diverse types of observations can guide model development and potentially reduce the large uncertainty in global carbon cycle projections.ReferencesBloom, Arnold J, F Stuart Chapin, and Harold A Mooney. “Resource Limitation in Plants--An Economic Analogy” Annual Review of Ecology and Systematics, 16, no. 1 (1985): 363–92. https://doi.org/10.1146/annurev.es.16.110185.002051.Prentice, I. Colin, Ning Dong, Sean M. Gleason, Vincent Maire, and Ian J. Wright. “Balancing the Costs of Carbon Gain and Water Transport: Testing a New Theoretical Framework for Plant Functional Ecology.” Ecology Letters 17, 1 (2014): 82–91. https://doi.org/10.1111/ele.12211.

Spatio-temporal quantification of vegetation diversity and structure is important for accurate monitoring of terrestrial ecosystems from space. Landsat data have been employed to provide estimates of vegetation biophysical properties due to their medium (30 m) spatial resolution, sufficient (16 days) temporal resolution, and spectral sensitivity to biophysical properties. This study has explored the use of Landsat-derived vegetation indices (VIs) related to greenness, moisture content and fire severity, recorded during the peak and late growing seasons, to estimate in situ observed species number, basal area and aboveground biomass (AGB) in tropical biomes that are affected by fire, and were surveyed at various stages of post-fire recovery. Linear and logarithmic regressions and coefficients of determination (R2) were computed to assess the relationships of species number, basal area and AGB with ten broadband VIs, with goodness of fit measured by root mean squared error (RMSE). Best fits were obtained using peak-season Green Chlorophyll Index (CI Green), Normalized Difference Moisture Index (NDMI) and Normalized Burn Ratio (NBR2) for species number (R2 = 0.50–0.68, RMSE = 3–4), basal area (R2 = 0.23–0.37, RMSE = 1.0–1.1 m2 ha–1) and AGB (R2 = 0.66–0.74, RMSE = 1.1–1.2 Mg ha-1) in open savanna and savanna forest. Late-season Normalized Difference Vegetation Index (NDVI), NDMI and NBR showed stronger relationships for species number (R2 = 0.88, RMSE = 5.72), basal area (R2 = 0.24–0.68, RMSE = 0.03–9.7 m2 ha–1) and AGB (R2 = 0.20–0.73, RMSE = 1.4–19.2 Mg ha–1) in most of the more complex forest biomes. These results are promising for the wider application of Landsat data especially from Landsat-8 Operational Land Imager (OLI) multispectral sensor to infer post-fire vegetation recovery in tropical ecosystems.

Climate and fuel availability jointly control the incidence of wildfires. The effects of atmospheric CO2 on plant growth influence fuel availability independently of climate; but the relative importance of each in driving large-scale changes in wildfire regimes cannot easily be quantified from observations alone. Here, we use previously developed empirical models to simulate the global spatial pattern of burnt area, fire size and fire intensity for modern and Last Glacial Maximum (LGM; ~ 21,000 ka) conditions using both realistic changes in climate and CO2 and sensitivity experiments to separate their effects. Three different LGM scenarios are used to represent the range of modelled LGM climates. We show large, modelled reductions in burnt area at the LGM compared to the recent period, consistent with the sedimentary charcoal record. This reduction was predominantly driven by the effect of low CO2 on vegetation productivity. The amplitude of the reduction under low CO2 conditions was similar regardless of the LGM climate scenario and was not observed in any LGM scenario when only climate effects were considered, with one LGM climate scenario showing increased burning under these conditions. Fire intensity showed a similar sensitivity to CO2 across different climates but was also sensitive to changes in vapour pressure deficit (VPD). Modelled fire size was reduced under LGM CO2 in many regions but increased under LGM climates because of changes in wind strength, dryness (DD) and diurnal temperature range (DTR). This increase was offset under the coldest LGM climate in the northern latitudes because of a large reduction in VPD. These results emphasis the fact that the relative magnitudes of changes in different climate variables influence the wildfire regime and that different aspects of climate change can have opposing effects. The importance of CO2 effects imply that future projections of wildfire must take rising CO2 into account.

Recent variability in West African monsoon rainfall has been shown to be influenced by multiple ocean-atmosphere modes, including the El Niño Southern Oscillation, Atlantic Multidecadal Oscillation and the Interdecadal Pacific Oscillation. How these modes will change in response to long term forcing is less well understood. Here we use four transient simulations driven by changes in orbital forcing and greenhouse gas concentrations over the past 6,000 years to examine the relationship between West African monsoon rainfall multiscale variability and changes in the modes associated with this variability. All four models show a near linear decline in monsoon rainfall over the past 6,000 years in response to the gradual weakening of the interhemispheric gradient in sea surface temperatures. The only modes that show a long-term trend are those associated with the strengthening of the El Niño Southern Oscillation from the mid-Holocene onwards. There are marked multi-centennial oscillations superimposed on the long-term trend in monsoon rainfall which are strongly associated with multi-centennial oscillations in the Atlantic Multidecadal Oscillation and in tropical Atlantic sea surface temperatures in all models. However, the influence of different modes on interannual to multi-decadal rainfall variability is not consistent across the models. This is driven in one case by the Atlantic Multidecadal Oscillation and changes in Mediterranean sea surface temperatures, by tropical sea surface temperature changes in another, and in the fourth model by a combination of the Atlantic Multidecadal Oscillation and the El Niño Southern Oscillation. Furthermore, multiple inter-basin teleconnections are associated with significant rainfall anomalies in each model.

This research has been supported by the European Research Council (GC2.0 (grant no. 694481)), the European Research Council (REALM (grant no. 787203)), Imperial College through the Lee Family Scholarship, the Leverhulme Centre for Wildfires, Environment and Society (grant no. RC-2018-023), and the REDISCO (grant no. HAR2017-88035-P) project., Charcoal accumulated in lake, bog or other anoxic sediments through time has been used to document the geographical patterns in changes in fire regimes. Such reconstructions are useful to explore the impact of climate and vegetation changes on fire during periods when human influence was less prevalent than today. However, charcoal records only provide semi-quantitative estimates of change in biomass burning. Here we derive quantitative estimates of burnt area from vegetation data in two stages. First, we relate the modern charcoal abundance to burnt area using a conversion factor derived from a generalised linear model of burnt area probability based on eight environmental predictors. Then, we establish the relationship between fossil pollen assemblages and burnt area using tolerance-weighted weighted averaging partial least-squares regression with a sampling frequency correction (fxTWA-PLS). We test this approach using the Iberian Peninsula as a case study because it is a fire-prone region with abundant pollen and charcoal records covering the Holocene. We derive the vegetation-burnt area relationship using the 31 records that have both modern and fossil charcoal and pollen data and then reconstruct palaeoburnt area for the 113 records with Holocene pollen records. The pollen data predict charcoal-derived burnt area relatively well (R-2 = 0.44), and the changes in reconstructed burnt area are synchronous with known climate changes through the Holocene. This new method opens up the possibility of reconstructing changes in fire regimes quantitatively from pollen records, after regional calibration of the vegetation-burnt area relationship, in regions where pollen records are more abundant than charcoal records., European Research Council (ERC) European Commission 694481 787203, Imperial College through the Lee Family Scholarship, Leverhulme Centre for Wildfires, Environment and Society RC-2018-023 REDISCO HAR2017-88035-P

Vegetation cover regulates the exchanges of energy, water and carbon between land and atmosphere. Remotely-sensed fractional absorbed photosynthetically active radiation (fAPAR), a land-surface greenness metric, depends on carbon allocation to foliage while also controlling photon flux for photosynthesis. Greenness is thus both a driver and an outcome of gross primary production (GPP). An equation with just two (globally) fitted parameters describes annual maximum fAPAR (fAPARmax) as the smaller of a water-limited value, transpiring a constant fraction of annual precipitation, and an energy-limited value, maximizing annual plant growth. This minimalist description reproduces global greenness patterns, and the consistent temporal trends among remote-sensing products, as accurately as the best-performing dynamic global vegetation models. Widely observed greening is attributed to the influence of rising carbon dioxide on the light- and water-use efficiencies of GPP, augmented by wetting in some dry regions and warming in high latitudes. Limited regions show browning, attributed to drying.

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.

Wheat sowing dates are currently used as an input for crop models that simulated wheat production. However, the optimal time for planting wheat will be affected by climate changes and human adaptations to these changes. In this paper, we present an optimality-based modelling approach, with additional constraints from low temperature and precipitation intensity, to estimate wheat sowing dates globally. This approach assumes that wheat could be sown at any time when the climate conditions are suitable, but the optimal sowing date that would be adopted by farmers would be that which maximises overall grain yields. We therefore run the model starting on every possible sowing date as determined by the climate constraints and then select the date which gives the highest yield in each location. We compare the modelled optimal sowing dates with an updated version of observed sowing dates created by merging census-based datasets and local agronomic information. Cold season temperatures are the major determinant of sowing dates in the extra-tropics, whereas the seasonal cycle of monsoon rainfall plays an important role in determining sowing dates in the tropics. The model captures the timing of reported sowing dates, with difference between estimated and observed sowing dates of less than one month (< 30 days) over much of the world; maximum errors in tropical regions with large altitudinal gradients, such as Ethiopia, Bolivia and Peru, are up to two months. Discrepancies between the predictions and observations are larger in tropical regions than temperate and cold regions. Our approach for estimating optimal wheat sowing dates provides a way to examine human management decisions could mitigate the impacts of climate change on crop systems.

Fire-enabled vegetation models are an important component of earth system modelling. Understanding the sensitivity of vegetation and wildfire to climate change benefits from out-of-sample experiments, of which the Last Glacial Maximum (LGM; 21 ka BP) is a preferred test. Here, we compared wildfire simulations for the LGM made with four fire-enabled vegetation models using a standardized protocol and driven by a climate-model simulation of the response to known LGM changes in ice-sheet extent, atmospheric composition and insolation. We compare the resulting model output with inferred changes in fire based on charcoal records from the Reading Palaeofire Database (RPD).All four models show a global decrease in fire at the LGM compared to the present day, consistent with the charcoal records which also record less fire. The simulated change in fire is driven principally by changes in vegetation cover at the LGM, particularly the shift from forest to more open vegetation. The simulated reduction in forest cover is consistent with pollen-based reconstructions of LGM vegetation. Despite this general agreement among models, there are differences between the simulated fire anomalies at a regional scale. The largest differences between the models occur in equatorial Africa, South America and East Asia where the amplitude and spatial extent of regions of increased fire (driven principally by the replacement of tropical trees by grassland); in some regions even the direction of change is not consistent. Comparison of the simulated changes with charcoal records from these regions identifies which model(s) perform best, but also make it clear that there is no one model that simulates observed patterns of change in fire across all of the regions.

The probability of wildfire occurrence has been successfully predicted on coarse spatial scales (~0.5°) based on empirical modelling of burned area. This method has limited scope for predicting the wildfire hazard at local scales and in the near-term, both necessary for wildfire management. A high-resolution, practically applicable hazard model can be built through quantifying the probability of fire as a function of site-specific present and antecedent climate and vegetation variables. Here we apply the known Poisson and Pareto distributions of wildfire occurrence and fire size in a two-step hazard model, where the probability of a location being affected by wildfire is approximated using multiple climate and vegetation parameters. In addition, we examine other predictor variables that have been used for modelling fire at coarse resolution, e.g. road density, to determine at what spatial scale they lose predictive power. The study focuses exclusively on the contiguous United States, due to its comparatively long and high-resolution record of wildfire events.

Plants take up water from the soil via roots and release it into the atmosphere through stomata; uptake of CO2 from the atmosphere also proceeds through the stomata, implying tight coupling of transpiration and photosynthesis. We distinguish leaf-level (biochemical and stomatal) responses to external stimuli on different timescales: fast responses taking place over seconds to hours, and longer-term (acclimation) responses taking place over weeks to months. Typically, land-surface models (LSMs) have focused on the fast responses, and have not accounted for acclimation responses, although these can be different in magnitude and even in sign. We have developed a method that explicitly separates these two timescales in order to implement an existing optimality-based model, the P model, with a sub-daily timestep; and, thereby, to include acclimated responses within an LSM framework. The resulting model, compared to flux-tower gross primary production (GPP) data in five “well-watered” biomes from boreal to tropical, correctly reproduces diurnal cycles of GPP throughout the growing season. No changes of parameters are required between biomes, because optimality ensures that current parameter values are always adapted to the local environment. This is a clear practical advantage because it eliminates the need to specify different parameter values for different plant functional types. However, in areas with large seasonal variations in moisture variability, the model does not perform well. Here we address the issue of soil-moisture controls on GPP, which is a challenging issue for LSMs in general. We note two problems: an error in magnitude, and an error in shape. The model tends to overestimate GPP in dry areas because it does not consider the effect of low soil moisture (as opposed to atmospheric dryness) on photosynthesis; and it does not simulate the ‘midday depression’ that is observed under very high vapour pressure deficits. Moving beyond commonly used (empirical) water-stress formulations, we have incorporated soil moisture limitation on photosynthesis in the sub-daily P model. The main idea is to control GPP via hydraulic limitation. The revised model firstly assesses the “demand”—the transpiration that would take place under well-watered conditions—then constrains the actual transpiration at a rate that does not exceed the canopy’s estimated hydraulic capacity. This transpiration rate is then used to obtain revised rates of stomatal conductance and GPP, “corrected” for water stress. Preliminary results evaluating the revised model’s performances against flux tower measurements at dry sites are encouraging, suggesting a route towards a parameter-sparse and globally applicable LSM.

Fire modifies vegetation spectral reflectances in the optical, thermal and microwave domains due to the changes it induces in vegetation canopy components (leaves, needles, branches) and in soil properties. Freely available satellite-derived (Landsat) Vegetation Indices (VIs) and PALSAR Mosaic backscatter measurements (known to be sensitive to vegetation structure) were used to help understand vegetation properties (species richness, basal area) in relation to fire return time (FRT) across a range of tropical biomes (open savanna, savanna forest, evergreen forest, peat-swamp forest) in Mato Grosso (Brazil), Kruger National Park (South Africa) and Central Kalimantan (Indonesia).For each site, we combined: (i) post-fire Landsat imagery (30 m) to derive VIs sensitive to vegetation diversity with (ii) PALSAR (25 m) backscatter that employes a longer wavelength (21 cm) and dual polarisation (Horizontal-Horizontal, Horizontal-Vertical) enabling the capture of strong backscattering of signal by branches and trunks.Most of the Landsat VI values showed greater variability in forests compared to open savanna, reflecting the greater diversity in species’ composition and growth form. A strong positive relationship was found between VIs and FRT across biomes and especially in forests. The amount of vegetation burned per fire as recorded by the magnitude of changes in these VIs, was highest in annual burn regimes (FRT = 1 year). Green and red-edge bands provided better discrimination of vegetation species richness and basal area. A significant positive relationship to basal area in response to fire return time was also found using PALSAR data due to its deeper canopy penetration level and strong backscattering from woody components. The observed responses of VI- and PALSAR-inferred species’ richness and basal area in response to FRT in different tropical biomes suggest that the green and red-edge channels from optical and longer wavelength HV-backscatter are useful metrics to quantify post-fire tropical vegetation dynamics.

Wildfires are fundamental for maintaining ecosystem structure and functioning, and thus it is important to know how projected climate and land use changes will affect wildfire regimes globally. Fire-enabled vegetation models can be used to predict changes in fire regime but are still far from perfect since many of the processes that control different aspects of the fire regime are still relatively poorly understood. In this work, we investigated the underlying relationships between potential controls of different fire properties, including fire size, intensity and burnt area, at a global scale. We fitted three generalized linear models (GLM) to monthly data from 2010 to 2015 for fractional burnt area from the Global Fire Emissions Database version 4.1 (GFEDv4), fire size from the Global Fire Atlas database and median fire radiative power divided by square root of median fire size (as a proxy for fire intensity) from the MODIS MCD14ML dataset. We used partial residual plots between each predictor and each response variable to show the underlying linear relationships fitted by each model. We show that there are different controls on burnt area, on fire size and on fire intensity. Specifically, whilst burnt area is driven mainly by fuel availability and dryness, fire size is driven primarily by wind speed and fire intensity by tree cover and road density. Land fragmentation was highly limiting for fire size and burnt area whereas dryness was limiting for fire intensity. These findings suggest that it is possible to develop empirical models of multiple aspects of fire regimes which could be used to predict how these will change in the future. Furthermore, they highlight the importance of including landscape fragmentation as a control on fire explicitly within process-based fire models. Additionally, the limiting nature of dryness on fire intensity could be due to antecedent vegetation conditions and highlights the need for better representation of these conditions and their effect on fuel load. Finally, these results also suggest that the current treatment of ignition sources as an important driver in these models is unnecessary.

Leaf dark respiration (Rd) accounts for approximately 50% of plant respiration. The acclimation of plant respiration to temperature weakens the positive feedback to global warming. Most existing land surface models (LSMs) adopt an empirical leaf respiration scheme with a constant Rd25 (leaf dark respiration rate at 25°C) for each vegetation type, since there is no acceptable theory of Rd acclimation and how it varies temporally and spatially. Here we propose that Rd25 adjusts to prior nighttime temperature (Tnight) to maintain the ratio of Rd to photosynthesis capacity (Vcmax) approximately constant. To test this hypothesis and explore the time scale of acclimation, we predict Rd25 over different time windows and evaluate these predictions using data from 14 sites from two datasets (Boreal Forest Warming at an Ecotone in Danger (B4WarmED) experiment and Leaf Carbon Exchange dataset (LCE)), one of which provides measurements through time and the other across spatial gradients. Predictions that account for the combined effects of Vcmax and Tnight have better predictive power for all species (mean R2=0.4) than considering the effect of one factor alone. Predictions of acclimation on different timescales show that Vcmax and Tnight averaged over the past fortnight explain the most variation in observed Rd25. These results could provide an alternative solution to the leaf respiration schemes used in LSMs.

The Eastern Mediterranean-Black Sea Caspian Corridor (EMBSeCBIO) region (33°–49°N, 20°–60°E ), is characterized by strong temperature and precipitation gradients and topographic heterogeneity, resulting in the clear patterns in biome distribution within a relatively limited geographic space. The complexity of this area is a challenge for reconstructing the dynamics of the vegetation throughout the Holocene. In this study, we apply a recently developed method to reconstruct past vegetation changes. The method uses a large dataset of modern pollen samples assigned to biomes based on potential natural vegetation cover data to characterize biomes according to the means and standard deviations of the abundances of each taxon. We use this characterization to calculate an index of dissimilarity between any given pollen sample and each biome, and thus evaluate the probability that a pollen sample belongs to a particular biome. The method also allows us to identify non-analogue vegetation types, when the scores for fossil samples are outside the range of modern scores. The vegetation reconstructions were used to produce point-to-area interpolated maps for 300 years windows. We identify periods of relative ecological stability for mapping, and also periods of rapid environmental change, by analysing high resolution (=

Land-surface shortwave albedo is an important quantity in the energy budget of the Earth. Remotely sensed snow cover, maximum tree height and maximum fractional absorbed photosynthetically active radiation (fAPAR) explain 87% of the variation in present-day annual mean land surface albedo (weighted by the seasonal cycle of shortwave radiation) in a generalized linear model. We can therefore apply this model during Dansgaard-Oeschger (D-O) warming events during the last glacial period. We have already used these repeated, rapid (50–200 year), near-global climate-change events to provide new quantifications of Earth system feedbacks involving atmospheric CO2, CH4 and N2O. We now reconstruct maximum tree height and maximum fAPAR based on a new global compilation of pollen data covering the relevant time interval, combined with snow cover changes during simulated D-O events, in order to reconstruct global changes in radiative forcing due to changes in vegetation and snow cover – and thereby quantify the global land-surface albedo feedback.

Climate changes have been invoked to explain recent changes in wildfire regimes in Mediterranean regions, and climate projections suggest that there will be an increase in fire weather during the 21st century. However, humans influence natural fire regimes today directly by supressing or igniting fires, and indirectly by changing fuel types and fuel structure through land use changes. Recent observations provide only a limited basis for determining the relative importance of climate and human activities for fire. The diachronous introduction of agriculture during the Neolithic provides an opportunity to examine the potential impact of human activities on fire regimes. We reconstruct fire history using sedimentary charcoal records and population change based on summed probability distributions of radiocarbon dates on archaeological material, focusing on the interval between 10,000 and 3,500 cal. BP. The archaeological radiocarbon dates are also used to map the onset of agriculture through time across the region. For Iberia as a whole, we identify two periods of rapid population growth, centred on ca. 7,400 and ca. 5,400 cal. BP. However, these periods of rapid population growth are not synchronous with changes in charcoal accumulation. Changes in charcoal accumulation are not aligned with the time-transgressive dates for the introduction of agriculture across the region; charcoal accumulation was already increasing ca. 400 years prior to the onset of agriculture and continues to increase for ca. 200 years afterwards. There is also no consistent correlative relationship between population and fire across the period of analysis. Our analyses show that there are no direct links between the introduction of agriculture or subsequent increases in population and changes to fire regimes in Iberia in the early to mid-Holocene, suggesting that changes in fire regimes were largely driven by climate changes.

This research has been supported by the Leverhulme Trust (grant no. RC-2018-023), the European Research Council (grant no. 694481), the German Research Foundation (grant no. FE-1096/6-1), the Swiss Government Excellence Postdoctoral Scholarships (grant no. FIRECO 2016.0310), the National Science Centre of Poland (grant no. 2015/17/B/ST10/01656), the SCIEX Scholarship Fund (grant no. PSPB-013/2010), and the Estonian Research Council (grant no. MOBJD313)., Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345., Leverhulme Trust RC-2018-023, European Research Council (ERC) European Commission 694481, German Research Foundation (DFG) FE-1096/6-1, Swiss Government Excellence Postdoctoral Scholarships FIRECO 2016.0310, SCIEX Scholarship Fund PSPB-013/2010, Estonian Research Council MOBJD313

The mid-Holocene (6000 years ago) is a standard time period for the evaluation of the simulated response of global climate models using palaeoclimate reconstructions. The latest mid-Holocene simulations are a palaeoclimate entry card for the Palaeoclimate Model Intercomparison Project (PMIP4) component of the current phase of the Coupled Model Intercomparison Project (CMIP6) – hereafter referred to as PMIP4-CMIP6. Here we provide an initial analysis and evaluation of the results of the experiment for the mid-Holocene. We show that state-of-the-art models produce climate changes that are broadly consistent with theory and observations, including increased summer warming of the Northern Hemisphere and associated shifts in tropical rainfall. Many features of the PMIP4-CMIP6 simulations were present in the previous generation (PMIP3-CMIP5) of simulations. The PMIP4-CMIP6 ensemble for the mid-Holocene has a global mean temperature change of −0.3 K, which is −0.2 K cooler than the PMIP3-CMIP5 simulations predominantly as a result of the prescription of realistic greenhouse gas concentrations in PMIP4-CMIP6. Biases in the magnitude and the sign of regional responses identified in PMIP3-CMIP5, such as the amplification of the northern African monsoon, precipitation changes over Europe, and simulated aridity in mid-Eurasia, are still present in the PMIP4-CMIP6 simulations. Despite these issues, PMIP4-CMIP6 and the mid-Holocene provide an opportunity both for quantitative evaluation and derivation of emergent constraints on the hydrological cycle, feedback strength, and potentially climate sensitivity.

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.\ud \ud 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.\ud \ud 3. Three axes accounted for > 20% of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing-season temperature explained 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.\ud \ud 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.

Modern observations show considerable interannual to interdecadal variability in monsoon precipitation. However, there are few reconstructions of variability at this timescale through the Holocene, and there is therefore less understanding of how changes in external forcing might have affected monsoon variability in the past. Here, we reconstruct the evolution of the amplitude of interannual to interdecadal variability (IADV) in the East Asian, Indian and South American monsoon regions through the Holocene using a global network of high-resolution speleothem oxygen isotope (δ 18O) records. We reconstruct changes in IADV for individual speleothem records using the standard deviation of δ 18O values in sliding time windows after correcting for the influence of confounding factors such as variable sampling resolution, growth rates and mean climate. We then create composites of IADV changes for each monsoon region. We show that there is an overall increase in δ 18O IADV in the Indian monsoon region through the Holocene, with an abrupt change to present-day variability at ∼2 ka. In the East Asian monsoon, there is an overall decrease in δ 18O IADV through the Holocene, with an abrupt shift also seen at ∼2 ka. The South American monsoon is characterised by large multi-centennial shifts in δ 18O IADV through the early and mid-Holocene, although there is no overall change in variability across the Holocene. Our regional IADV reconstructions are broadly reproduced by transient climate-model simulations of the last 6 000 years. These analyses indicate that there is no straightforward link between IADV and changes in mean precipitation, or between IADV and orbital forcing, at a regional scale.

© 2021 The Author(s)., Recent extreme wildfire seasons in several regions have been associated with exceptionally hot, dry conditions, made more probable by climate change. Much research has focused on extreme fire weather and its drivers, but natural wildfire regimes—and their interactions with human activities—are far from being comprehensively understood. There is a lack of clarity about the 'causes' of wildfire, and about how ecosystems could be managed for the co-existence of wildfire and people. We present evidence supporting an ecosystem-centred framework for improved understanding and modelling of wildfire. Wildfire has a long geological history and is a pervasive natural process in contemporary plant communities. In some biomes, wildfire would be more frequent without human settlement; in others they would be unchanged or less frequent. A world without fire would have greater forest cover, especially in present-day savannas. Many species would be missing, because fire regimes have co-evolved with plant traits that resist, adapt to or promote wildfire. Certain plant traits are favoured by different fire frequencies, and may be missing in ecosystems that are normally fire-free. For example, post-fire resprouting is more common among woody plants in high-frequency fire regimes than where fire is infrequent. The impact of habitat fragmentation on wildfire crucially depends on whether the ecosystem is fire-adapted. In normally fire-free ecosystems, fragmentation facilitates wildfire starts and is detrimental to biodiversity. In fire-adapted ecosystems, fragmentation inhibits fires from spreading and fire suppression is detrimental to biodiversity. This interpretation explains observed, counterintuitive patterns of spatial correlation between wildfire and potential ignition sources. Lightning correlates positively with burnt area only in open ecosystems with frequent fire. Human population correlates positively with burnt area only in densely forested regions. Models for vegetation-fire interactions must be informed by insights from fire ecology to make credible future projections in a changing climate., We gratefully acknowledge support from the Leverhulme Centre for Wildfires, Environment and Society, who organized the virtual mini-workshop which initiated the writing of this paper. RKN is supported by the Leverhulme Centre. SPH and YS acknowledge support from the ERC-funded project GC2.0 (Global Change 2.0: Unlocking the past for a clearer future, Grant Number 694481). ICP, KJB and ND acknowledge support from the ERC-funded project REALM (Re-inventing Ecosystem And Land-surface Models, Grant Number 787203). JCH acknowledges funding from the ERC project SCATAPNUT (Grant Number 681885). This work is a contribution to the LEMONTREE (Land Ecosystem Models based On New Theory, obseRvations and ExperimEnts) project, funded through the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program (SPH, YS and ICP).

Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using INTCAL2020 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1681 records (entities) from 1477 sites worldwide. The database (DOI: 10.17864/1947.319) is available from https://researchdata.reading.ac.uk/id/eprint/319.

International audience; Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studiesthat demonstrate how EEO generate parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration, and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust toenvironmental change.

Most catchments in tropical regions are ungauged and data deficient, complicating the simulation of water quantity and quality. Yet, developing and testing hydrological models in data-poor regions ...

The Last Glacial Maximum (LGM, ∼ 21 000 years ago) has been a major focus for evaluating how well state-of-the-art climate models simulate climate changes as large as those expected in the future using paleoclimate reconstructions. A new generation of climate models has been used to generate LGM simulations as part of the Paleoclimate Modelling Intercomparison Project (PMIP) contribution to the Coupled Model Intercomparison Project (CMIP). Here, we provide a preliminary analysis and evaluation of the results of these LGM experiments (PMIP4, most of which are PMIP4-CMIP6) and compare them with the previous generation of simulations (PMIP3, most of which are PMIP3-CMIP5). We show that the global averages of the PMIP4 simulations span a larger range in terms of mean annual surface air temperature and mean annual precipitation compared to the PMIP3-CMIP5 simulations, with some PMIP4 simulations reaching a globally colder and drier state. However, the multi-model global cooling average is similar for the PMIP4 and PMIP3 ensembles, while the multi-model PMIP4 mean annual precipitation average is drier than the PMIP3 one. There are important differences in both atmospheric and oceanic circulations between the two sets of experiments, with the northern and southern jet streams being more poleward and the changes in the Atlantic Meridional Overturning Circulation being less pronounced in the PMIP4-CMIP6 simulations than in the PMIP3-CMIP5 simulations. Changes in simulated precipitation patterns are influenced by both temperature and circulation changes. Differences in simulated climate between individual models remain large. Therefore, although there are differences in the average behaviour across the two ensembles, the new simulation results are not fundamentally different from the PMIP3-CMIP5 results. Evaluation of large-scale climate features, such as land–sea contrast and polar amplification, confirms that the models capture these well and within the uncertainty of the paleoclimate reconstructions. Nevertheless, regional climate changes are less well simulated: the models underestimate extratropical cooling, particularly in winter, and precipitation changes. These results point to the utility of using paleoclimate simulations to understand the mechanisms of climate change and evaluate model performance.

The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R 2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.

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.Plain Language SummaryVegetation regulates the exchanges of energy, water and carbon dioxide between the land and the atmosphere. Numerical climate models represent these processes, focusing mainly on their rapid variations in response to changes in the environment (including temperature and light) on timescales of seconds to hours. However, plants also adjust their physiology to environmental changes over longer periods within the season. Here we have adapted a simple model that formulates plant behaviour in terms of optimal trade-offs between different processes, so it simulates processes on both time scales. This model correctly reproduces the daily cycle of carbon dioxide uptake by plants, as recorded in different kinds of vegetation. We show that plants optimize their behaviour for midday conditions, when the light is greatest, and adjust to longer-term environmental variations on a timescale of about a week to a month. The model conveniently avoids the need to give specific, fixed values to physiological variables (such as photosynthetic capacity) for different types of plants. The optimality assumptions mean that the model gives equally good results in tropical, temperate and boreal forests, and in grasslands, using the same equations, and a very small number of input variables that are constant across the world.Key PointsOptimality theory is used to develop a simple model incorporating fast and acclimated responses of photosynthesis and stomatal conductanceBiogeochemical photosynthetic capacities adjust to midday light conditionsThe new model simulates gross primary production on sub-daily timesteps across a range of different vegetation types and climate