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Wildfire is a global scale ecosystem phenomenon with substantial impact on the carbon cycle, climate warming, and ecosystem resilience. Fire and the hydrological cycle are strongly interlinked, with water availability determining the amount and combustibility of fuel, and fire influencing infiltration, runoff rates and evapotranspiration. Consequently, understanding soil moisture (SM) and vegetation water content (VWC) dynamics pre- and post-fire is fundamental for predicting fire occurrence, fire severity, and ecosystem recovery. Fire can modulate SM and VWC dynamics by influencing interception of rainfall, soil porosity, plant water uptake, and runoff; however, much evidence for fire effects on the hydrological cycle is obtained at the field- to watershed-scale. Therefore, we ask the following research question: What are the effects of large-scale fire events on SM and VWC dynamics across biomes globally?Here we use over six years of global SM, VWC and vapor pressure deficit (VPD) derived from different remote sensing datasets to investigate the effects of large-scale fires on SM and VWC dynamics. We apply a dry down framework, only analyzing consecutive observations of decreasing soil moisture, to describe post-fire response rates for SM, VWC and VPD relative to a pre-fire reference state.We find large scale evidence that the post-fire rate of change of SM over time is more negative, indicating faster water loss. Vegetation recovery, indicated by a positive change in VWC over time, exceeds the pre-fire reference state, which suggests that post-fire recovery is predominantly faster than undisturbed seasonal vegetation growth, likely due to succession of fast-growing plant species. Furthermore, fire affects ecosystem hydrology on shorter timescales as well, reducing diurnal VWC variation over a wide range of SM and VWC conditions. Our findings confirm several trends previously only observed at smaller scales and suggest global remote sensing of SM and VWC can substantially contribute to understanding the dynamics of post-fire plant and soil water status.

The data requirements of vegetation models are changing. For more than a decade, the community has been developing “next-generation” models that should be globally applicable and at the same time incorporate great process detail. The individual tree emerges from this development as the finest scale at which carbon, water, and nutrient dynamics can be realistically simulated. As such, precise tree-level observations of the relevant processes would ideally be available from across all forested biomes to inform and evaluate tree-centered vegetation models. This is not the case. Instead, we note a growing discrepancy between the demand for and the availability of highly-resolved measurements of carbon allocation in trees and forests.To exemplify this discrepancy, we conducted a survey at 90 flux-tower sites from around the world that revealed priorities and deficiencies in existing data collections. We found that forest structure and aboveground carbon stocks have been ubiquitously inventoried, and that tree growth and foliage turnover have also been measured at many sites. By contrast, detailed information on water cycling, volume increment, and wood formation processes (especially belowground) are less common, as are records of tree mortality or terrestrial and airborne LiDAR that could help scale local observations. In addition, we found that the temporal resolution and length of existing time-series vary substantially across the current flux-tower network. Weighing the strengths and limitations of this and many other ecological monitoring networks, we conclude that the present data basis is insufficient to support accelerating vegetation model development.Looking forward, we anticipate that not only the amount of tree-level observations needs to be increased – especially in tropical and boreal systems – but that the consistency, scalability, and predictability of forest carbon cycle observations needs to be improved. We also propose that intensive long-term monitoring sites be strategically paired with manipulative experiments at comparable sites to better connect past, present, and expected future dynamics. For this, we propose a versatile experimental framework and call for a community-wide discussion on the “yield on cost” of various field observations. We also list a number of key questions on how to best build and maintain cross-scale data archives in support of tree-centered vegetation modelling.

During the Last Glacial Maximum (LGM), atmospheric CO2 was around 90 ppmv lower than during the pre-industrial period. The reasons for this decrease are most often elucidated through factorial experiments testing the impact of individual mechanisms. Due to uncertainty in our understanding of the real system, however, the different models used to conduct the experiments inevitably take on different parameter values and different structures. In this paper, the objective is therefore to take an uncertainty-based approach to investigating the LGM CO2 drop by simulating it with a large ensemble of parameter sets, designed to allow for a wide range of large-scale feedback response strengths. Our aim is not to definitely explain the causes of the CO2 drop but rather explore the range of possible responses. We find that the LGM CO2 decrease tends to predominantly be associated with decreasing sea surface temperatures (SSTs), increasing sea ice area, a weakening of the Atlantic Meridional Overturning Circulation (AMOC), a strengthening of the Antarctic Bottom Water (AABW) cell in the Atlantic Ocean, a decreasing ocean biological productivity, an increasing CaCO3 weathering flux and an increasing deep-sea CaCO3 burial flux. The majority of our simulations also predict an increase in terrestrial carbon, coupled with a decrease in ocean and increase in lithospheric carbon. We attribute the increase in terrestrial carbon to a slower soil respiration rate, as well as the preservation rather than destruction of carbon by the LGM ice sheets. An initial comparison of these dominant changes with observations and paleoproxies other than carbon isotope and oxygen data (not evaluated directly in this study) suggests broad agreement. However, we advise more detailed comparisons in the future, and also note that, conceptually at least, our results can only be reconciled with carbon isotope and oxygen data if additional processes not included in our model are brought into play.

Despite its importance for the study of past climates, as well as its significance for carbon sequestration, we lack a mechanistic explanation for how temperature controls wood anatomy. A model of xylogenesis is presented and used to analyse observed tree ring anatomy-temperature relationships in Scots pine (Pinus sylvestris). The model treats the daily proliferation of new cells in the cambium and their subequent differentiation through expansion and secondary wall thickening phases. Control on size at division in the cambium follows recent work on the Arabidopsis shoot apical meristem, and cell enlargement rates in the cambium and enlargement zone are controlled by temperature. The duration of post-cambial enlargement is partially controlled by the rate at which cells pass through the enlargement zone, and partially by the size of this zone, which is controlled by daylength. This set of assumptions is sufficient to generate observed profiles of cell sizes across radial files, with characteristic transitions from earlywood to latewood. After they leave the enlarging zone, cells enter the wall thickening zone, the width of which is also dependent on daylength. A temperature-dependent rate of wall material deposition is insufficient to reproduce the observed gradient in mass density across the radial file, and fails to fully capture the observed seasonality of the correlation between maximum latewood density and temperature. Inclusion of a control on the rate of wall deposition from substrate (sugar) supply, diffusing from the phloem across the radial file, corrects these deficiencies. The resulting model provides a mechanistic explanation of temperature-tree ring relationships, and has the potential to underpin understanding of how climate and CO2 interact in determining the amount of carbon sequestered in trees.

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.

During the Last Glacial Maximum (LGM), atmospheric CO2 was around 90 ppmv lower than during the preindustrial period. Despite years of research, however, the exact mechanisms leading to the glacial atmospheric CO2 drop are still not entirely understood. Here, a large (471-member) ensemble of GENIE-1 simulations is used to simulate the equilibrium LGM minus preindustrial atmospheric CO2 concentration difference (ΔCO2). The ensemble has previously been weakly constrained with modern observations and was designed to allow for a wide range of large-scale feedback response strengths. Out of the 471 simulations, 315 complete without evidence of numerical instability, and with a ΔCO2 that centres around −20 ppmv. Roughly a quarter of the 315 runs predict a more significant atmospheric CO2 drop, between ~ 30 and 90 ppmv. This range captures the error in the model's process representations and the impact of processes which may be important for ΔCO2 but are not included in the model. These runs jointly constitute what we refer to as the plausible glacial atmospheric CO2 change-filtered (PGACF) ensemble. Our analyses suggest that decreasing LGM atmospheric CO2 tends to be associated with decreasing SSTs, increasing sea ice area, a weakening of the Atlantic Meridional Overturning Circulation (AMOC), a strengthening of the Antarctic Bottom Water (AABW) cell in the Atlantic Ocean, a decreasing ocean biological productivity, an increasing CaCO3 weathering flux, an increasing terrestrial biosphere carbon inventory and an increasing deep-sea CaCO3 burial flux. The increases in terrestrial biosphere carbon are predominantly due to our choice to preserve rather than destroy carbon in ice sheet areas. However, the ensemble soil respiration also tends to decrease significantly more than net photosynthesis, resulting in relatively large increases in non-burial carbon. In a majority of simulations, the terrestrial biosphere carbon increases are also accompanied by decreases in ocean carbon and increases in lithospheric carbon. In total, however, we find there are 5 different ways of achieving a plausible ΔCO2 in terms of the sign of individual carbon reservoir changes. The PGACF ensemble members also predict both positive and negative changes in global particulate organic carbon (POC) flux, AMOC and AABW cell strengths, and global CaCO3 burial flux. Comparison of the PGACF ensemble results against observations suggests that the simulated LGM physical climate and biogeochemical changes are mostly of the right sign and magnitude or within the range of observational error, except for the change in global deep-sea CaCO3 burial flux – which tends to be overestimated. We note that changing CaCO3 weathering flux is a variable parameter (included to account for variation in both the CaCO3 weathering rate and the un-modelled CaCO3 shallow water deposition flux), and this parameter is strongly associated with changes in global CaCO3 burial rate. The increasing terrestrial carbon inventory is also likely to have contributed to the LGM increase in deep-sea CaCO3 burial flux via the process of carbonate compensation. However, we do not yet rule out either of these processes as causes of ΔCO2 since missing processes such as Si fertilisation, Si leakage and the effect of decreasing SSTs on CaCO3 production may have introduced a high LGM global CaCO3 burial rate bias. Including these processes would, all else held constant, lower the rain ratio seen by the sediments and result in a decrease in atmospheric CO2 and increase in ocean carbon. Despite not modelling Δ14C(atm (DIC)) and δ13C(atm (DIC)), we also highlight some ways in which our results may potentially be reconciled with these records.

Water stress is a defining characteristic of Mediterranean ecosystems, and is likely to become more severe in the coming decades. Simulation models are key tools for making predictions, but our current understanding of how soil moisture controls ecosystem functioning is not sufficient to adequately constrain parameterisations. Canopy-scale flux data from four forest ecosystems with Mediterranean-type climates were used in order to analyse the physiological controls on carbon and water flues through the year. Significant non-stomatal limitations on photosynthesis were detected, along with lesser changes in the conductance-assimilation relationship. New model parameterisations were derived and implemented in two contrasting modelling approaches. The effectiveness of two models, one a dynamic global vegetation model ("ORCHIDEE"), and the other a forest growth model particularly developed for Mediterranean simulations ("GOTILWA+"), was assessed and modelled canopy responses to seasonal changes in soil moisture were analysed in comparison with in situ flux measurements. In contrast to commonly held assumptions, we find that changing the ratio of conductance to assimilation under natural, seasonally-developing, soil moisture stress is not sufficient to reproduce forest canopy CO2 and water fluxes. However, accurate predictions of both CO2 and water fluxes under all soil moisture levels encountered in the field are obtained if photosynthetic capacity is assumed to vary with soil moisture. This new parameterisation has important consequences for simulated responses of carbon and water fluxes to seasonal soil moisture stress, and should greatly improve our ability to anticipate future impacts of climate changes on the functioning of ecosystems in Mediterranean-type climates.

A biogenic emissions scheme is incorporated in the global dynamic vegetation model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic EcosystEms) in order to calculate global biogenic emissions of isoprene, monoterpenes, methanol, acetone, acetaldehyde, formaldehyde and formic and acetic acids. Important parameters such as the leaf area index are fully determined by the global vegetation model and the influences of light extinction (for isoprene emissions) and leaf age (for isoprene and methanol emissions) are also taken into account. We study the interannual variability of biogenic emissions using the satellite-based climate forcing ISLSCP-II as well as relevant CO2 atmospheric levels, for the 1983–1995 period. Mean global emissions of 460 TgC/year for isoprene, 117 TgC/year for monoterpenes, 106 TgC/year for methanol and 42 TgC/year for acetone are predicted. The mean global emission of all biogenic compounds is 752±16 TgC/yr with extremes ranging from 717 TgC/yr in 1986 to 778 TgC/yr in 1995, that is a 8.5% increase between both. This variability differs significantly from one region to another and among the regions studied, biogenic emissions anomalies were the most variable in Europe and the least variable in Indonesia (isoprene and monoterpenes) and North America (methanol). Year-to-year variability also reveals a strong correlation of emissions in tropical regions with El Niño events, particularly for isoprene, for which the tropical regions are a major source. Two scenarios of land use changes are considered using the 1983 climate and atmospheric CO2 conditions, to study the sensitivity of biogenic emissions to vegetation alteration, namely tropical deforestation and European afforestation. Global biogenic emissions are highly affected by tropical deforestation, with a 29% decrease in isoprene emission and a 22% increase in methanol emission. Global emissions are not significantly affected by European afforestation, but on a European scale, total biogenic VOCs emissions increase by 54%.

In order to achieve climate change mitigation, long-term decisions are required that must be reconciled with other societal goals that draw on the same resources. For example, ensuring food security for a growing population may require an expansion of crop land, thereby reducing natural carbon sinks or the area available for bio-energy production. Here, we show that current impact-model uncertainties pose an important challenge to long-term mitigation planning and propose a new risk-assessment and decision framework that accounts for competing interests. Based on cross-sectorally consistent simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) we discuss potential gains and limitations of additional irrigation and trade-offs of the expansion of agricultural land as two possible response measures to climate change and growing food demand. We describe an illustrative example in which the combination of both measures may close the supply demand gap while leading to a loss of approximately half of all natural carbon sinks. We highlight current limitations of available simulations and additional steps required for a comprehensive risk assessment.

Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climate–carbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable and no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and may play a key role in biospheric feedback to elevated atmospheric carbon dioxide (CO2) in the warmer future world. We examined seven biome models with climate projections forced by four representative-concentration-pathways (RCPs)-based atmospheric concentration scenarios. The goal was to specify uncertainty in global SOC stock projections from global and regional perspectives. Our simulations showed that SOC stocks among the biome models varied from 1090 to 2650 Pg C even in historical periods (ca. 2000). In a higher forcing scenario (RCP8.5), inconsistent estimates of impact on the total SOC (2099–2000) were obtained from different model simulations, ranging from a net sink of 347 Pg C to a net source of 122 Pg C. In all models, the elevated atmospheric CO2 concentration in the RCP8.5 scenario considerably contributed to carbon accumulation in SOC. However, magnitudes varied from 93 to 264 Pg C by the end of the 21st century. Using time-series data of total global SOC estimated by biome biome model, we statistically analyzed the sensitivity of the global SOC stock to global mean temperature and global precipitation anomalies (ΔT and ΔP respectively) in each biome model using a state-space model. This analysis suggests that ΔT explained global SOC stock changes in most models with a resolution of 1–2 °C, and the magnitude of global SOC decomposition from a 2 °C rise ranged from almost 0 Pg C yr−1 to 3.53 Pg C yr−1 among the biome models. On the other hand, ΔP had a negligible impact on change in the global SOC changes. Spatial heterogeneity was evident and inconsistent among the changes in SOC estimated by the biome models, especially in boreal to arctic regions. Our study revealed considerable climate change impact uncertainty in SOC decomposition among biome models. Further research is required to improve our understanding and ability to estimate biospheric feedback through SOC-relevant processes as well as vegetation processes.