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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).

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.

Changes in lake status are often interpreted as palaeoclimate indicators due to their dependence on precipitation and evaporation. The Global Lake Status Database (GLSDB) has since long provided a standardised synopsis of qualitative lake status over the last 30,000 14C years. Potential sources of uncertainty however are not recorded in the GLSDB. Here we present an updated and improved relational-database framework that incorporates uncertainty in both chronology and the interpretation of palaeoenvironmental data. The database uses peer-reviewed palaeolimnological studies to produce a consensus on qualitative lake-status histories, whose chronologies are revised and standardized through the recalibration of radiocarbon dates and the application of Bayesian age-depth modelling for stratigraphic archives. Quantitative information on absolute water-level elevation is preserved if available from geomorphological sources. We also propose a new probabilistic analytical framework that accounts for these uncertainties to reconstruct synoptic, integrated environmental signals. The process is based on a Monte Carlo algorithm that iteratively samples individual lake-status histories within the limits of their uncertainties to produce many possible scenarios. We then use Recursively-Subtracted Empirical Orthogonal Function analysis to extract dominant patterns of lake-status variability from these scenarios. As a proof of concept, we apply this framework to 67 sites in eastern and southern Africa whose lake-status histories cover part of the late Pleistocene and/or Holocene. We show that, despite the sometimes large temporal and interpretation uncertainties, and the inclusion of highly discontinuous lake-status time series, identifying the major known millennial-scale climatic phases during the last 20,000 years is possible. Our framework was also able to identify an antiphased response between the lake basins in eastern and interior southern Africa to these changes. We propose that our new database and methodology framework serves as a template for efficient lake-status data synthesis, encourages the incorporation of lake-status data in palaeoclimate syntheses, and expands the possibilities for the use of such data in the evaluation of climate models.

In the 12,000 years preceding the Industrial Revolution, human activities led to significant changes in land cover, plant and animal distributions, surface hydrology, and biochemical cycles. Earth system models suggest that this anthropogenic land cover change influenced regional and global climate. However, the representation of past land use in earth system models is currently oversimplified. As a result, there are large uncertainties in the current understanding of the past and current state of the earth system. In order to improve representation of the variety and scale of impacts that past land use had on the earth system, a global effort is underway to aggregate and synthesize archaeological and historical evidence of land use systems. Here we present a simple, hierarchical classification of land use systems designed to be used with archaeological and historical data at a global scale and a schema of codes that identify land use practices common to a range of systems, both implemented in a geospatial database. The classification scheme and database resulted from an extensive process of consultation with researchers worldwide. Our scheme is designed to deliver consistent, empirically robust data for the improvement of land use models, while simultaneously allowing for a comparative, detailed mapping of land use relevant to the needs of historical scholars. To illustrate the benefits of the classification scheme and methods for mapping historical land use, we apply it to Mesopotamia and Arabia at 6 kya (c. 4000 BCE). The scheme will be used to describe land use by the Past Global Changes (PAGES) LandCover6k working group, an international project comprised of archaeologists, historians, geographers, paleoecologists, and modelers. Beyond this, the scheme has a wide utility for creating a common language between research and policy communities, linking archaeologists with climate modelers, biodiversity conservation workers and initiatives. Fil: Morrison, Kathleen D.. University of Pennsylvania; Estados Unidos Fil: Hammer, Emily. University of Pennsylvania; Estados Unidos Fil: Boles, Oliver. University of Pennsylvania; Estados Unidos Fil: Madella, Marco. University of the Witwatersrand; Sudáfrica. Universitat Pompeu Fabra; España Fil: Whitehouse, Nicola. University of Glasgow; Reino Unido Fil: Gaillard, Marie-Jose. Linnaeus University; Suecia Fil: Bates, Jennifer. University of Pennsylvania; Estados Unidos Fil: Linden, Marc Vander. Bournemouth University; Sudáfrica Fil: Merlo, Stefania. University of the Witwatersrand; Sudáfrica Fil: Yao, Alice. University of Chicago; Estados Unidos Fil: Popova, Laura. Arizona State University; Estados Unidos Fil: Hill, Austin Chad. University of Pennsylvania; Estados Unidos Fil: Antolin, Ferran. Universiat Basel; Suiza Fil: Bauer, Andrew. University of Stanford; Estados Unidos Fil: Biagetti, Stefano. University of the Witwatersrand; Sudáfrica. Universitat Pompeu Fabra; España Fil: Bishop, Rosie R.. University of Stavanger; Noruega Fil: Buckland, Phillip. Universidad de Umea; Suecia Fil: Cruz, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades. Universidad Nacional de Jujuy. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades; Argentina Fil: Dreslerová, Dagmar. Institute Of Archaeology Of The Academy Of Sciences Of The Czech Republic; Hungría Fil: Dusseldorp, Gerrit. Leiden University; Países Bajos Fil: Ellis, Erle C.. University of Maryland; Estados Unidos. University Of Johannesburg; Sudáfrica Fil: Filipovich, Ruben Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Bio y Geociencias del NOA. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Museo de Ciencias Naturales. Instituto de Bio y Geociencias del NOA; Argentina Fil: Foster, Thomas. University of Tulsa; Estados Unidos Fil: Hannaford, Matthew J.. Lincoln University.; Nueva Zelanda Fil: Harrison, Sandy P.. University of Reading; Reino Unido Fil: Hazarika, Manjil. Cotton University; India Fil: Herold, Hajnalka. University of Exeter; Reino Unido Fil: Hilpert, Johanna. Universitat zu Köln; Alemania Fil: Kaplan, Jed O.. The University Of Hong Kong; Hong Kong Fil: Kay, Andrea. Max Planck Institute For The Science Of Human History; Alemania Fil: Goldewijk, Kees Klein. Utrecht University; Países Bajos Fil: Kolár, Jan. Masaryk University; República Checa Fil: Kyazike, Elizabeth. Kyambogo University; Uganda Fil: Laabs, Julian. University of Bern; Suiza Fil: Lancelotti, Carla. Universitat Pompeu Fabra; España Fil: Lane, Paul. University of the Witwatersrand; Sudáfrica. University of Cambridge; Estados Unidos Fil: Lawrence, Dan. University of Durham; Reino Unido Fil: Lewis, Krista. University of Arkansas; Estados Unidos Fil: Lombardo, Umberto. University of Bern; Suiza Fil: Lucarini, Giulio. Consiglio Nazionale delle Ricerche; Italia. University of Naples; Italia Fil: Arroyo Kalin, Manuel. Colegio Universitario de Londres; Reino Unido Fil: Marchant, Rob. University of York; Reino Unido Fil: Mayle, Francis. University of Reading; Reino Unido Fil: McClatchie, Meriel. University College; Irlanda Fil: McLeester, Madeleine. Dartmouth College; Estados Unidos Fil: Mooney, Scott. Unsw Sydney; Australia Fil: Moskal-Del Hoyo, Magdalena. Wladyslaw Szafer Institute Of Botany Of The Polish Academy Of Sciences; Polonia Fil: Navarrete, Vanessa. Universitat Autònoma de Barcelona; España Fil: Ndiema, Emmanuel. National Museums Of Kenya; Kenia Fil: Neves, Eduardo Góes. Universidade de Sao Paulo; Brasil Fil: Nowak, Marek. Jagiellonian University; Polonia Fil: Out, Welmoed A.. Moesgaard Museum; Dinamarca Fil: Petrie, Cameron. Universitat Pompeu Fabra; España. University of Cambridge; Estados Unidos Fil: Phelps, Leanne N.. Royal Botanic Gardens; Reino Unido. University of Edinburgh; Reino Unido Fil: Szakonyi, Zsolt. Eötvös University; Hungría Fil: Rostain, Stéphen. Centre National de la Recherche Scientifique; Francia Fil: Russell, Thembi. University of the Witwatersrand; Sudáfrica Fil: Sluyter, Andrew. Louisiana State University; Estados Unidos Fil: Styring, Amy K.. University of Oxford; Reino Unido Fil: Tamanaha, Eduardo. The Mamirauá Sustainable Development Institute; Brasil Fil: Thomas, Evert. The Alliance of Bioversity International and CIAT; Perú Fil: Veerasamy, Selvakumar. Tamil University; India Fil: Welton, Lynn. University of the Witwatersrand; Sudáfrica Fil: Zanon, Marco. Institute of Pre- and Protohistoric Archaeology; Alemania

Most techniques for pollen-based quantitative climate reconstruction use modern assemblages as a reference data set. We examine the implication of methodological choices in the selection and treatment of the reference data set for climate reconstructions using Weighted Averaging Partial Least Squares (WA-PLS) regression and records of the last glacial period from Europe. We show that the training data set used is important because it determines the climate space sampled. The range and continuity of sampling along the climate gradient is more important than sampling density. Reconstruction uncertainties are generally reduced when more taxa are included, but combining related taxa that are poorly sampled in the data set to a higher taxonomic level provides more stable reconstructions. Excluding taxa that are climatically insensitive, or systematically overrepresented in fossil pollen assemblages because of known biases in pollen production or transport, makes no significant difference to the reconstructions. However, the exclusion of taxa overrepresented because of preservation issues does produce an improvement. These findings are relevant not only for WA-PLS reconstructions but also for similar approaches using modern assemblage reference data. There is no universal solution to these issues, but we propose a number of checks to evaluate the robustness of pollen-based reconstructions.

Reconstruction of past changes in monsoon climate from speleothem oxygen isotope (δ18O) records is complex because δ18O signals can be influenced by multiple factors including changes in precipitation, precipitation recycling over land, temperature at the moisture source and changes in the moisture source region and transport pathway. Here, we analyse > 150 speleothem records from version 2 of the Speleothem Isotopes Synthesis and Analysis (SISAL) database to produce composite regional trends in δ18O in monsoon regions; compositing minimises the influence of site-specific karst and cave processes that can influence individual site records. We compare speleothem δ18O observations with isotope-enabled climate model simulations to investigate the specific climatic factors causing these regional trends. We focus on differences in δ18O signals between interglacial (mid-Holocene and Last Interglacial) and glacial (Last Glacial Maximum) states, and on δ18O evolution through the Holocene. Differences in speleothem δ18O between the mid-Holocene and Last Interglacial in the East Asian and Indian monsoons are small, despite the larger summer insolation values during the Last Interglacial. Last Glacial Maximum δ18O values are significantly less negative than interglacial values. Comparison with simulated glacial-interglacial δ18O shows that changes are principally driven by global shifts in temperature and regional precipitation. Holocene speleothem δ18O records show distinct and coherent regional trends. Trends are similar to summer insolation in India, China and southwestern South America, but different in the Indonesian-Australian region. Redundancy analysis shows that 37 % of Holocene variability can be accounted for by latitude and longitude, supporting the differentiation of records into individual monsoon regions. Regression analysis of simulated precipitation δ18O and climate variables show that global Holocene monsoon δ18O trends are driven by changes in precipitation, atmospheric circulation and (to a lesser extent) source area temperature, whilst precipitation recycling is non-significant. However, there are differences in regional scale mechanisms; there are clear relationships between changes in precipitation and in δ18O for India, southwestern South America and the Indonesian-Australian regions, but not for the East Asian monsoon. Changes in atmospheric circulation contributes to δ18O trends in the East Asian, Indian and Indonesian-Australian monsoons, and a weak source area temperature effect is observed over southern and central America and Asia. Precipitation recycling is influential in southwestern South America and southern Africa. Overall, our analyses show that it is possible to differentiate the impacts of specific climatic mechanisms influencing precipitation δ18O and use this analysis to interpret changes in speleothem δ18O.

In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.

Plants and vegetation play a critical-but largely unpredictable-role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.Integrating natural selection and other organizing principles into next-generation vegetation models could render them more theoretically sound and useful for earth system applications and modelling climate impacts.

Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea )), and photosynthetic capacities (Vcmax , Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

Aim\ud \ud Leaf and air temperatures are seldom equal, but many vegetation models assume that they are. Land-surface models calculate canopy temperatures, but how well they do so is unknown. We encourage consideration of the leaf- and canopy-to-air temperature difference (ΔΤ) as a benchmark for land-surface modelling and an important feature of plant and ecosystem function.\ud Location\ud \ud Tropical SW China.\ud Time period\ud \ud 2013.\ud Major Taxa studies\ud \ud Tropical trees.\ud Methods\ud \ud We illustrate diurnal cycles of leaf- and canopy-to-air temperature difference (ΔΤ) with field measurements in a tropical dry woodland and with continuous monitoring data in a tropical seasonal forest. The Priestley–Taylor (PT) and Penman–Monteith (PM) approaches to evapotranspiration are used to provide insights into the interpretation and prediction of ΔT. Field measurements are also compared with land-surface model results obtained with the Joint U.K. Land Environment Simulator (JULES) set up for the conditions of the site.\ud Results\ud \ud The ΔT followed a consistent diurnal cycle, with negative values at night (attributable to negative net radiation) becoming positive in the morning, reaching a plateau and becoming negative again when air temperature exceeded a ‘crossover’ in the 24–29 °C range. Daily time courses of ΔT could be approximated by either the PT or the PM model, but JULES tended to underestimate the magnitude of negative ΔT.\ud Main conclusions\ud \ud Leaves with adequate water supply are partly buffered against air-temperature variations, through a passive biophysical mechanism. This is likely to be important for optimal leaf function, and land-surface and vegetation models should aim to reproduce it.

We present a new global reconstruction of seasonal climates at the Last Glacial Maximum (LGM, 21 000 years BP) made using 3-D variational data assimilation with pollen-based site reconstructions of six climate variables and the ensemble average of the PMIP3—CMIP5 simulations as a prior (initial estimate of LGM climate). We assume that the correlation matrix of the uncertainties in the prior is both spatially and temporally Gaussian, in order to produce a climate reconstruction that is smoothed both from month to month and from grid cell to grid cell. The pollen-based reconstructions include mean annual temperature (MAT), mean temperature of the coldest month (MTCO), mean temperature of the warmest month (MTWA), growing season warmth as measured by growing degree days above a baseline of 5 ∘C (GDD5), mean annual precipitation (MAP), and a moisture index (MI), which is the ratio of MAP to mean annual potential evapotranspiration. Different variables are reconstructed at different sites, but our approach both preserves seasonal relationships and allows a more complete set of seasonal climate variables to be derived at each location. We further account for the ecophysiological effects of low atmospheric carbon dioxide concentration on vegetation in making reconstructions of MAP and MI. This adjustment results in the reconstruction of wetter climates than might otherwise be inferred from the vegetation composition. Finally, by comparing the uncertainty contribution to the final reconstruction, we provide confidence intervals on these reconstructions and delimit geographical regions for which the palaeodata provide no information to constrain the climate reconstructions. The new reconstructions will provide a benchmark created using clear and defined mathematical procedures that can be used for evaluation of the PMIP4–CMIP6 entry-card LGM simulations and are available at https://doi.org/10.17864/1947.244 (Cleator et al., 2020b).

Pollen data are widely used to reconstruct past climate changes, using relationships between modern pollen abundance in surface samples and climate at the surface-sample sites as a calibration. Visualization of modern pollen data in multidimensional climate space provides a way to establish that taxon abundances are well behaved before using them in climate reconstructions. Visualization is also helpful for ecological interpretation of variations in pollen abundance in space and time. Here, we present Generalized Additive Models for the distribution of 195 European pollen and pteridophyte spore taxa in a bioclimate space defined by seasonal temperatures (as mean temperature of the coldest month and annual growing degree days) and an annual moisture index. These models can be used to explore the realized climate niche of pollen taxa and to build statistical models for palaeoclimate reconstruction. The data set is released under a Creative Commons BY license. When using the data set, we kindly request that you cite this article.

Anthropogenic changes in land use and land cover (LULC) during the pre-industrial Holocene could have affected regional and global climate. Current LULC scenarios are based on relatively simple assumptions and highly uncertain estimates of population changes through time. Archaeological and palaeoenvironmental reconstructions have the potential to refine these assumptions and estimates. The Past Global Changes (PAGES) LandCover6k initiative is working towards improved reconstructions of LULC globally. In this paper, we document the types of archaeological data that are being collated and how they will be used to improve LULC reconstructions. Given the large methodological uncertainties involved, we propose methods to evaluate the revised scenarios by using independent pollen-based reconstructions of land cover and of climate. A further test involves carbon-cycle simulations to determine whether the LULC reconstructions are consistent with constraints provided by ice-core records of CO2 evolution and modern-day LULC. Finally, we outline a protocol for using the improved LULC reconstructions in palaeoclimate simulations within the framework of the Palaeoclimate Modelling Intercomparison Project in order to quantify the magnitude of anthropogenic impacts on climate through time and ultimately to improve the realism of Holocene climate simulations., Geoscientific Model Development Discussions, ISSN:1991-962X, ISSN:1991-9611

We present a new global reconstruction of seasonal climates at the Last Glacial Maximum (LGM, 21,000 yr BP) made using 3-D variational data assimilation with pollen-based site reconstructions of six climate variables and the ensemble average of the PMIP3/CMIP5 simulations as a prior. We assume that the correlation matrix of the errors of the prior both spatially and temporally is Gaussian, in order to produce a climate reconstruction that is smoothed both from month to month and from grid cell to grid cell. The pollen-based reconstructions include mean annual temperature (MAT), mean temperature of the coldest month (MTCO), mean temperature of the warmest month (MTWA), growing season warmth as measured by growing degree days above a baseline of 5 °C (GDD5), mean annual precipitation (MAP) and a moisture index (MI), which is the ratio of MAP to mean annual potential evapotranspiration. Different variables are reconstructed at different sites, but our approach both preserves seasonal relationships and allows a more complete set of seasonal climate variables to be derived at each location. We further account for the ecophysiological effects of low atmospheric carbon dioxide concentration on vegetation in making reconstructions of MAP and MI. This adjustment results in the reconstruction of wetter climates than might otherwise be inferred by the vegetation composition. Finally, by comparing the error contribution to the final reconstruction, we provide confidence intervals on these reconstructions and delimit geographical regions for which the palaeodata provide no information to constrain the climate reconstructions. The new reconstructions will provide a robust benchmark for evaluation of the PMIP4/CMIP6 entry-card LGM simulations.

Before tree-ring series can be used to quantify climatic influences on growth, ontogenetic and microenvironmental effects must be removed. Existing statistical detrending methods struggle to eliminate bias, caused by the fact that older/larger trees are nearly always more abundantly sampled during the most recent decades – which happens also to have seen the strongest environmental changes. Here we develop a new approach to derive a productivity index (P*) from tree-ring series. The critical stem diameter, when an initial rapid increase in stem radial growth gives way to a gradual decrease, is estimated using a theoretical approximation; previous growth rings are removed from analysis. The subsequent dynamics of stem radial growth are assumed to be determined by: tree diameter and height; P* (gross primary production per unit leaf area, discounted by a "tax" due to the respiration and turnover of leaves and fine roots); and a quantity proportional to sapwood specific respiration (r1). The term r1 depends not only on the growth rate but also on tree height, because a given leaf area requires a greater volume of living sapwood to be maintained in taller trees. Height-diameter relationships were estimated from independent observations. P* values were then estimated from tree ring-width measurements on multiple trees, using a non-linear mixed-effects model in which the random effect of individual tree identity accounts for the impact of local environmental variability, due to soil or hydrological conditions, and canopy position (i.e. shading and competition). Year-by-year P* at a site should then represent the influence of year-by-year changes in environment, independently of the growth trend in individual trees. This approach was applied to tree-ring records from two genera (Picea and Pinus) at 492 sites across the Northern Hemisphere extratropics. Using a multiple linear mixed-effects regression with site as a random effect, it was found that estimated annual P* values for both genera show consistent, temporally stable positive responses of P* to total photosynthetically photon flux density during the growing season (PPFD5) and soil moisture availability (indexed by an estimate of the ratio of actual to potential evapotranspiration). The partial effect of mean temperature during the growing season (mGGD5) however was shown to follow a unimodal curve, being positive in climates with mGGD5

Widespread forest dieback is a phenomenon of global concern that requires an improved understanding of the relationship between tree growth and climate to support conservation efforts. One priority for conservation is the Atlas cedar (Cedrus atlantica), an endangered species exhibiting dieback throughout its North African range. In this study, we evaluate the long-term context for recent dieback and develop a projection of future C. atlantica growth by exploring the periodic variability of its growth through time. First, we present a new C. atlantica tree- ring chronology (1150–2013 CE) from the Middle Atlas mountains, Morocco. We then compare the new chronology to existing C. atlantica chronologies in Morocco and use principal components analysis (PCA) to isolate the common periodic signal from the seven longest available records (PCA7, 1271–1984 CE) in the Middle and High Atlas portions of the C. atlantica range. PCA7 captures 55.7% of the variance and contains significant multidecadal ( ̃95yr, ̃57yr, ̃21yr) periodic components, revealed through spectral and wavelet analyses. Parallel analyses of historical climate data (1901–2016 CE) suggests that the multidecadal growth signal ori- ginates primarily in growing season (spring and summer) precipitation variability, compounded by slow- changing components of summer and winter temperatures. Finally, we model the long-term growth patterns between 1271–1984 CE using a small number (three to four) of harmonic components, illustrating that sup- pressed growth since the 1970s – a factor implicated in the dieback of this species – is consistent with recurrent climatically-driven growth declines. Forward projection of this model suggests two climatically-favourable periods for growth in the 21st century that may enhance current conservation actions for the long-term survival of the C. atlantica in the Middle and High Atlas mountains.

The apparent decline in the global incidence of fire between 1996 and 2015, as measured by satellite-observations of burned area, has been related to socioeconomic and land use changes. However, recent decades have also seen changes in climate and vegetation that influence fire and fire-enabled vegetation models do not reproduce the apparent decline. Given that the satellite-derived burned area datasets are still relatively short (

Two interglacial epochs are included in the suite of paleoclimate simulations in the present phase of the Coupled Model Intercomparison Project (CMIP6). Equilibrium simulations of the mid-Holocene (midHolocene, 6000 years before present) and the Last Interglacial (lig127k, 127,000 years before present) are designed to examine the impact of changes in orbital forcing at times when atmospheric greenhouse gas levels were similar to those of the preindustrial period and the continental configurations were almost identical to modern. These simulations test our understanding of the interplay between radiative forcing and atmospheric circulation, and the connections among large-scale and regional climate changes giving rise to phenomena such as land-sea contrast and high-latitude amplification in temperature changes, and responses of the monsoons, as compared to today. They also provide an opportunity, through carefully designed additional sensitivity experiments as part of the Paleoclimate Modeling Intercomparison Project (PMIP4), to quantify the strength of atmosphere, ocean, cryosphere, and land-surface feedbacks. Sensitivity experiments are proposed to investigate the role of freshwater forcing in triggering abrupt climate changes within interglacial epochs. These feedback experiments naturally lead to a focus on climate evolution during interglacial periods, which will be examined through transient experiments. Analyses of the sensitivity simulations will also focus on interactions between extratropical and tropical circulation, and the relationship between changes in mean climate state and climate variability on annual to multi-decadal timescales. The comparative abundance of paleoenvironmental data and of quantitative climate reconstructions for the Holocene and Last Interglacial make these two epochs ideal candidates for systematic evaluation of model performance, and such comparisons will shed new light on the importance of external feedbacks (e.g., vegetation, dust) and the ability of state-of-the-art models to simulate climate changes realistically.

Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location non-impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing Machine Learning Algorithms (MLA) for operational mapping of Potential Natural Vegetation (PNV). The following MLA were considered: neural networks (nnet package), random forest (ranger), gradient boosting (gmb), K-nearest neighborhood (class) and cubist. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief and lithologic variables, were used as explanatory variables. The overall results show that random forest gives the overall best performance. he highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial Cross Validation) and 68% (simple random subsetting), with the most important predictors being total annual precipitation, monthly temperatures and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers and month of the year, respectively. Further developments of PNV mapping could include using GBIF records to map global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at 1 km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK.

Temperature exerts strong controls on the incidence and severity of fire. All else equal, warming is expected to increase fire-related carbon emissions, and thereby atmospheric CO2. But the magnitude of this feedback is very poorly known. We use a single-box model of the land biosphere to quantify this positive feedback from satellite-based estimates of biomass burning emissions for 2000–2014CE and from sedimentary charcoal records for the millennium before the industrial period. We derive an estimate of the centennial-scale feedback strength of 6.5±3.4ppmCO2 per degree of land temperature increase, based on the satellite data. However, this estimate is poorly constrained, and is largely driven by the well-documented dependence of tropical deforestation and peat fires (primarily anthropogenic) on climate variability patterns linked to the El Niño–Southern Oscillation. Palaeo-data from pre-industrial times provide the opportunity to assess the fire-related climate–carbon-cycle feedback over a longer period, with less pervasive human impacts. Past biomass burning can be quantified based on variations in either the concentration and isotopic composition of methane in ice cores (with assumptions about the isotopic signatures of different methane sources) or the abundances of charcoal preserved in sediments, which reflect landscape-scale changes in burnt biomass. These two data sources are shown here to be coherent with one another. The more numerous data from sedimentary charcoal, expressed as normalized anomalies (fractional deviations from the long-term mean), are then used – together with an estimate of mean biomass burning derived from methane isotope data – to infer a feedback strength of 5.6±3.2ppmCO2 per degree of land temperature and (for a climate sensitivity of 2.8K) a gain of 0.09±0.05. This finding indicates that the positive carbon cycle feedback from increased fire provides a substantial contribution to the overall climate–carbon-cycle feedback on centennial timescales. Although the feedback estimates from palaeo- and satellite-era data are in agreement, this is likely fortuitous because of the pervasive influence of human activities on fire regimes during recent decades.

International audience; Aim: To evaluate the biomization technique for reconstructing past vegetation in the Eastern Mediterranean-Black Sea-Caspian-Corridor using an extensive modern pollen data set and comparing reconstructions to potential vegetation and observed land cover data. Location: The region between 28-48 degrees N and 22-62 degrees E. Methods: We apply the biomization technique to 1,387 modern pollen samples, representing 1,107 entities, to reconstruct the distribution of 13 broad vegetation categories (biomes). We assess the results using estimates of potential natural vegetation from the European Vegetation Map and the Physico-Geographic Atlas of the World. We test whether anthropogenic disturbance affects reconstruction quality using land use information from the Global Land Cover data set. Results: The biomization scheme successfully predicts the broadscale patterns of vegetation across the region, including changes with elevation. The technique discriminates deserts from shrublands, the prevalence of woodlands in moister lowland sites, and the presence of temperate and mixed forests at higher elevations. Quantitative assessment of the reconstructions is less satisfactory: the biome is predicted correctly at 44% of the sites in Europe and 33% of the sites overall. The low success rate is not a reflection of anthropogenic impacts: only 33% of the samples are correctly assigned after the removal of sites in anthropogenically altered environments. Open vegetation is less successfully predicted (33%) than forest types (73%), reflecting the under-representation of herbaceous taxa in pollen assemblages and the impact of long-distance pollen transport into open environments. Samples from small basins (

Explanations of leaf size variation commonly focus on water availability, yet leaf size also varies with latitude and elevation in environments where water is not strongly limiting. We provide the first conclusive test of a prediction of leaf energy balance theory that may explain this pattern: large leaves are more vulnerable to night-time chilling, because their thick boundary layers impede convective exchange with the surrounding air. Seedlings of 15 New Zealand evergreens spanning 12-fold variation in leaf width were exposed to clear night skies, and leaf temperatures were measured with thermocouples. We then used a global dataset to assess several climate variables as predictors of leaf size in forest assemblages. Leaf minus air temperature was strongly correlated with leaf width, ranging from −0.9 to −3.2°C in the smallest- and largest-leaved species, respectively. Mean annual temperature and frost-free period were good predictors of evergreen angiosperm leaf size in forest assemblages, but no climate variable predicted deciduous leaf size. Although winter deciduousness makes large leaves possible in strongly seasonal climates, large-leaved evergreens are largely confined to frost-free climates because of their susceptibility to radiative cooling. Evergreen leaf size data can therefore be used to enhance vegetation models, and to infer palaeotemperatures from fossil leaf assemblages.

Dynamic global vegetation models (DGVMs) typically rely on plant functional types (PFTs), which are assigned distinct environmental tolerances and replace one another progressively along environmental gradients. Fixed values of traits are assigned to each PFT; modelled trait variation along gradients is thus driven by PFT replacement. But empirical studies have revealed "universal" scaling relationships (quantitative trait variations with climate that are similar within and between species, PFTs and communities); and continuous, adaptive trait variation has been proposed to replace PFTs as the basis for next-generation DGVMs. Here we analyse quantitative leaf-trait variation on long temperature and moisture gradients in China with a view to understanding the relative importance of PFT replacement vs. continuous adaptive variation within PFTs. Leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC) and nitrogen content of dry matter were measured on all species at 80 sites ranging from temperate to tropical climates and from dense forests to deserts. Chlorophyll fluorescence traits and carbon, phosphorus and potassium contents were measured at 47 sites. Generalized linear models were used to relate log-transformed trait values to growing-season temperature and moisture indices, with or without PFT identity as a predictor, and to test for differences in trait responses among PFTs. Continuous trait variation was found to be ubiquitous. Responses to moisture availability were generally similar within and between PFTs, but biophysical traits (LA, SLA and LDMC) of forbs and grasses responded differently from woody plants. SLA and LDMC responses to temperature were dominated by the prevalence of evergreen PFTs with thick, dense leaves at the warm end of the gradient. Nutrient (N, P and K) responses to climate gradients were generally similar within all PFTs. Area-based nutrients generally declined with moisture; Narea and Karea declined with temperature, but Parea increased with temperature. Although the adaptive nature of many of these trait-climate relationships is understood qualitatively, a key challenge for modelling is to predict them quantitatively. Models must take into account that community-level responses to climatic gradients can be influenced by shifts in PFT composition, such as the replacement of deciduous by evergreen trees, which may run either parallel or counter to trait variation within PFTs. The importance of PFT shifts varies among traits, being important for biophysical traits but less so for physiological and chemical traits. Finally, models should take account of the diversity of trait values that is found in all sites and PFTs, representing the "pool" of variation that is locally available for the natural adaptation of ecosystem function to environmental change.

Isotopic measurements on junipers growing in southern California during the last glacial, when the ambient atmospheric [CO2] (ca) was ~180 ppm, show the leaf-internal [CO2] (ci) was approaching the modern CO2 compensation point for C3 plants. Despite this, stem growth rates were similar to today. Using a coupled light-use efficiency and tree growth model, we show that it is possible to maintain a stable ci/ca ratio because both vapour pressure deficit and temperature were decreased under glacial conditions at La Brea, and these have compensating effects on the ci/ca ratio. Reduced photorespiration at lower temperatures would partly mitigate the effect of low ci on gross primary production, but maintenance of present-day radial growth also requires a ~27% reduction in the ratio of fine root mass to leaf area. Such a shift was possible due to reduced drought stress under glacial conditions at La Brea. The necessity for changes in allocation in response to changes in [CO2] is consistent with increased below-ground allocation, and the apparent homoeostasis of radial growth, as ca increases today.

The Last Glacial Maximum (LGM, 21 000 years ago) is one of the suite of paleoclimate simulations included in the current phase of the Coupled Model Intercomparison Project (CMIP6). It is an interval when insolation was similar to the present, but global ice volume was at a maximum, eustatic sea level was at or close to a minimum, greenhouse gas concentrations were lower, atmospheric aerosol loadings were higher than today, and vegetation and land-surface characteristics were different from today. The LGM has been a focus for the Paleoclimate Modelling Intercomparison Project (PMIP) since its inception, and thus many of the problems that might be associated with simulating such a radically different climate are well documented. The LGM state provides an ideal case study for evaluating climate model performance because the changes in forcing and temperature between the LGM and pre-industrial are of the same order of magnitude as those projected for the end of the 21st century. Thus, the CMIP6 LGM experiment could provide additional information that can be used to constrain estimates of climate sensitivity. The design of the Tier 1 LGM experiment (lgm) includes an assessment of uncertainties in boundary conditions, in particular through the use of different reconstructions of the ice sheets and of the change in dust forcing. Additional (Tier 2) sensitivity experiments have been designed to quantify feedbacks associated with land-surface changes and aerosol loadings, and to isolate the role of individual forcings. Model analysis and evaluation will capitalize on the relative abundance of paleoenvironmental observations and quantitative climate reconstructions already available for the LGM.

Climate controls fire regimes through its influence on the amount and types of fuel present and their dryness. CO2 concentration constrains primary production by limiting photosynthetic activity in plants. However, although fuel accumulation depends on biomass production, and hence on CO2 concentration, the quantitative relationship between atmospheric CO2 concentration and biomass burning is not well understood. Here a fire-enabled dynamic global vegetation model (the Land surface Processes and eXchanges model, LPX) is used to attribute glacial–interglacial changes in biomass burning to an increase in CO2, which would be expected to increase primary production and therefore fuel loads even in the absence of climate change, vs. climate change effects. Four general circulation models provided last glacial maximum (LGM) climate anomalies – that is, differences from the pre-industrial (PI) control climate – from the Palaeoclimate Modelling Intercomparison Project Phase~2, allowing the construction of four scenarios for LGM climate. Modelled carbon fluxes from biomass burning were corrected for the model's observed prediction biases in contemporary regional average values for biomes. With LGM climate and low CO2 (185 ppm) effects included, the modelled global flux at the LGM was in the range of 1.0–1.4 Pg C year-1, about a third less than that modelled for PI time. LGM climate with pre-industrial CO2 (280 ppm) yielded unrealistic results, with global biomass burning fluxes similar to or even greater than in the pre-industrial climate. It is inferred that a substantial part of the increase in biomass burning after the LGM must be attributed to the effect of increasing CO2 concentration on primary production and fuel load. Today, by analogy, both rising CO2 and global warming must be considered as risk factors for increasing biomass burning. Both effects need to be included in models to project future fire risks.

The mineralogy of airborne dust affects the impact of dust particles on direct and indirect radiative forcing, on atmospheric chemistry and on biogeochemical cycling. It is determined partly by the mineralogy of the dust-source regions and partly by size-dependent fractionation during erosion and transport. Here we present a data set that characterizes the clay and silt sized fractions of global soil units in terms of the abundance of 12 minerals that are important for dust-climate interactions: quartz, feldspars, illite, smectite, kaolinite, chlorite, vermiculite, mica, calcite, gypsum, hematite and goethite. The basic mineralogical information is derived from the literature, and is then expanded following explicit rules, in order to characterize as many soil units as possible. We present three alternative realisations of the mineralogical maps that account for the uncertainties in the mineralogical data. We examine the implications of the new database for calculations of the single scattering albedo of airborne dust and thus for dust radiative forcing.

Peatlands are a major terrestrial carbon store and a persistent natural carbon sink during the Holocene, but there is considerable uncertainty over the fate of peatland carbon in a changing climate. It is generally assumed that higher temperatures will increase peat decay, causing a positive feedback to climate warming and contributing to the global positive carbon cycle feedback. Here we use a new extensive database of peat profiles across northern high latitudes to examine spatial and temporal patterns of carbon accumulation over the past millennium. Opposite to expectations, our results indicate a small negative carbon cycle feedback from past changes in the long-term accumulation rates of northern peatlands. Total carbon accumulated over the last 1000 yr is linearly related to contemporary growing season length and photosynthetically active radiation, suggesting that variability in net primary productivity is more important than decomposition in determining long-term carbon accumulation. Furthermore, northern peatland carbon sequestration rate declined over the climate transition from the Medieval Climate Anomaly (MCA) to the Little Ice Age (LIA), probably because of lower LIA temperatures combined with increased cloudiness suppressing net primary productivity. Other factors including changing moisture status, peatland distribution, fire, nitrogen deposition, permafrost thaw and methane emissions will also influence future peatland carbon cycle feedbacks, but our data suggest that the carbon sequestration rate could increase over many areas of northern peatlands in a warmer future.

A new global synthesis and biomization of long (> 40 kyr) pollen-data records is presented and used with simulations from the HadCM3 and FAMOUS climate models and the BIOME4 vegetation model to analyse the dynamics of the global terrestrial biosphere and carbon storage over the last glacial–interglacial cycle. Simulated biome distributions using BIOME4 driven by HadCM3 and FAMOUS at the global scale over time generally agree well with those inferred from pollen data. Global average areas of grassland and dry shrubland, desert, and tundra biomes show large-scale increases during the Last Glacial Maximum, between ca. 64 and 74 ka BP and cool substages of Marine Isotope Stage 5, at the expense of the tropical forest, warm-temperate forest, and temperate forest biomes. These changes are reflected in BIOME4 simulations of global net primary productivity, showing good agreement between the two models. Such changes are likely to affect terrestrial carbon storage, which in turn influences the stable carbon isotopic composition of seawater as terrestrial carbon is depleted in 13C.

Le Quére, Corinne ... et al.-- 23 pages, 13 figures, 4 tables, supplement https://dx.doi.org/10.5194/bg-13-4111-2016-supplement, Global ocean biogeochemistry models currently employed in climate change projections use highly simplified representations of pelagic food webs. These food webs do not necessarily include critical pathways by which ecosystems interact with ocean biogeochemistry and climate. Here we present a global biogeochemical model which incorporates ecosystem dynamics based on the representation of ten plankton functional types (PFTs): six types of phytoplankton, three types of zooplankton, and heterotrophic procaryotes. We improved the representation of zooplankton dynamics in our model through (a) the explicit inclusion of large, slow-growing macrozooplankton (e.g. krill), and (b) the introduction of trophic cascades among the three zooplankton types. We use the model to quantitatively assess the relative roles of iron vs. grazing in determining phytoplankton biomass in the Southern Ocean high-nutrient lowchlorophyll (HNLC) region during summer. When model simulations do not include macrozooplankton grazing explicitly, they systematically overestimate Southern Ocean chlorophyll biomass during the summer, even when there is no iron deposition from dust. When model simulations include a slow-growing macrozooplankton and trophic cascades among three zooplankton types, the high-chlorophyll summer bias in the Southern Ocean HNLC region largely disappears. Our model results suggest that the observed low phytoplankton biomass in the Southern Ocean during summer is primarily explained by the dynamics of the Southern Ocean zooplankton community, despite iron limitation of phytoplankton community growth rates. This result has implications for the representation of global biogeochemical cycles in models as zooplankton faecal pellets sink rapidly and partly control the carbon export to the intermediate and deep ocean, C. Le Quéré and E. T. Buitenhuis were funded by UK-NERC projects NE/C516079/1 and NE/K001302/1, and European Commission project EMBRACE 282672. R. Moriarty was funded by EU FAASIS project MEST/CT/2004/514159. M. Vogt was funded by the Marie Curie Research and Training Network GREENCYCLES project MC-RTN-512464 and EUR-OCEANS project 282672

Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.

We have incorporated a semi-mechanistic isoprene emission module into the JULES land-surface scheme, as a first step towards a modelling tool that can be applied for studies of vegetation – atmospheric chemistry interactions, including chemistry-climate feedbacks. Here, we evaluate the coupled model against local above-canopy isoprene emission flux measurements from six flux tower sites as well as satellite-derived estimates of isoprene emission over tropical South America and east and south Asia. The model simulates diurnal variability well: correlation coefficients are significant (at the 95 % level) for all flux tower sites. The model reproduces day-to-day variability with significant correlations (at the 95 % confidence level) at four of the six flux tower sites. At the UMBS site, a complete set of seasonal observations is available for two years (2000 and 2002). The model reproduces the seasonal pattern of emission during 2002, but does less well in the year 2000. The model overestimates observed emissions at all sites, which is partially because it does not include isoprene loss through the canopy. Comparison with the satellite-derived isoprene-emission estimates suggests that the model simulates the main spatial patterns, seasonal and inter-annual variability over tropical regions. The model yields a global annual isoprene emission of 535 ± 9 TgC yr−1 during the 1990s, 78 % of which from forested areas.

The terrestrial biosphere is a key regulator of atmospheric chemistry and climate. Total positive radiative forcing resulting from biogeochemical feedbacks between the terrestrial biosphere and atmosphere could be equally as important as that resulting from physical feedbacks. The terrestrial biosphere is a key regulator of atmospheric chemistry and climate. During past periods of climate change, vegetation cover and interactions between the terrestrial biosphere and atmosphere changed within decades. Modern observations show a similar responsiveness of terrestrial biogeochemistry to anthropogenically forced climate change and air pollution. Although interactions between the carbon cycle and climate have been a central focus, other biogeochemical feedbacks could be as important in modulating future climate change. Total positive radiative forcings resulting from feedbacks between the terrestrial biosphere and the atmosphere are estimated to reach up to 0.9 or 1.5 W m−2 K−1 towards the end of the twenty-first century, depending on the extent to which interactions with the nitrogen cycle stimulate or limit carbon sequestration. This substantially reduces and potentially even eliminates the cooling effect owing to carbon dioxide fertilization of the terrestrial biota. The overall magnitude of the biogeochemical feedbacks could potentially be similar to that of feedbacks in the physical climate system, but there are large uncertainties in the magnitude of individual estimates and in accounting for synergies between these effects.

The biomisation method is used to reconstruct Latin American vegetation at 6000±500 and 18 000±1000 radiocarbon years before present (14C yr BP) from pollen data. Tests using modern pollen data from 381 samples derived from 287 locations broadly reproduce potential natural vegetation. The strong temperature gradient associated with the Andes is recorded by a transition from high altitude cool grass/shrubland and cool mixed forest to mid-altitude cool temperate rain forest, to tropical dry, seasonal and rain forest at low altitudes. Reconstructed biomes from a number of sites do not match the potential vegetation due to local factors such as human impact, methodological artefacts and mechanisms of pollen representivity of the parent vegetation. At 6000±500 14C yr BP 255 samples are analysed from 127 sites. Differences between the modern and the 6000±500 14C yr BP reconstruction are comparatively small; change relative to the modern reconstruction are mainly to biomes characteristic of drier climate in the north of the region with a slight more mesic shift in the south. Cool temperate rain forest remains dominant in western South America. In northwestern South America a number of sites record transitions from tropical seasonal forest to tropical dry forest and tropical rain forest to tropical seasonal forest. Sites in Central America show a change in biome assignment, but to more mesic vegetation, indicative of greater plant available moisture, e.g. on the Yucatán peninsula sites record warm evergreen forest, replacing tropical dry forest and warm mixed forest presently recorded. At 18 000±1000 14C yr BP 61 samples from 34 sites record vegetation reflecting a generally cool and dry environment. Cool grass/shrubland is prevalent in southeast Brazil whereas Amazonian sites record tropical dry forest, warm temperate rain forest and tropical seasonal forest. Southernmost South America is dominated by cool grass/shrubland, a single site retains cool temperate rain forest indicating that forest was present at some locations at the LGM. Some sites in Central Mexico and lowland Colombia remain unchanged in the biome assignments of warm mixed forest and tropical dry forest respectively, although the affinities that these sites have to different biomes do change between 18 000±1000 14C yr BP and present. The "unresponsive" nature of these sites results from their location and the impact of local edaphic influence.

The objective biomization method developed by Prentice et al. (1996) for Europe was extended using modern pollen samples from Beringia and then applied to fossil pollen data to reconstruct palaeovegetation patterns at 6000 and 18,000 14C yr bp. The predicted modern distribution of tundra, taiga and cool conifer forests in Alaska and north-western Canada generally corresponds well to actual vegetation patterns, although sites in regions characterized today by a mosaic of forest and tundra vegetation tend to be preferentially assigned to tundra. Siberian larch forests are delimited less well, probably due to the extreme under-representation of Larix in pollen spectra. The biome distribution across Beringia at 6000 14C yr bp was broadly similar to today, with little change in the northern forest limit, except for a possible northward advance in the Mackenzie delta region. The western forest limit in Alaska was probably east of its modern position. At 18,000 14C yr bp the whole of Beringia was covered by tundra. However, the importance of the various plant functional types varied from site to site, supporting the idea that the vegetation cover was a mosaic of different tundra types.

14 C-dated pollen and lake-level data from Europe are used to assess the spatial patterns of climate change between 6000 yr BP and present, as simulated by the NCAR CCM1 (National Center for Atmospheric Research, Community Climate Model, version 1) in response to the change in the Earth’s orbital parameters during this perod. First, reconstructed 6000 yr BP values of bioclimate variables obtained from pollen and lake-level data with the constrained-analogue technique are compared with simulated values. Then a 6000 yr BP biome map obtained from pollen data with an objective biome reconstruction (biomization) technique is compared with BIOME model results derived from the same simulation. Data and simulations agree in some features: warmer-than-present growing seasons in N and C Europe allowed forests to extend further north and to higher elevations than today, and warmer winters in C and E Europe prevented boreal conifers from spreading west. More generally, however, the agreement is poor. Predominantly deciduous forest types in Fennoscandia imply warmer winters than the model allows. The model fails to simulate winters cold enough, or summers wet enough, to allow temperate deciduous forests their former extended distribution in S Europe, and it incorrectly simulates a much expanded area of steppe vegetation in SE Europe. Similar errors have also been noted in numerous 6000 yr BP simulations with prescribed modern sea surface temperatures. These errors are evidently not resolved by the inclusion of interactive sea-surface conditions in the CCM1. Accurate representation of mid-Holocene climates in Europe may require the inclusion of dynamical ocean–atmosphere and/or vegetation–atmosphere interactions that most palaeoclimate model simulations have so far disregarded.

We synthesize existing sedimentary charcoal records to reconstruct Holocene fire history at regional, continental and global scales. The reconstructions are compared with the two potential controls of burning at these broad scales – changes in climate and human activities – to assess their relative importance on trends in biomass burning. Here we consider several hypotheses that have been advanced to explain the Holocene record of fire, including climate, human activities and synergies between the two. Our results suggest that 1) episodes of high fire activity were relatively common in the early Holocene and were consistent with climate changes despite low global temperatures and low levels of biomass burning globally; 2) there is little evidence from the paleofire record to support the Early Anthropocene Hypothesis of human modification of the global carbon cycle; 3) there was a nearly-global increase in fire activity from 3 to 2 ka that is difficult to explain with either climate or humans, but the widespread and synchronous nature of the increase suggests at least a partial climate forcing; and 4) burning during the past century generally decreased but was spatially variable; it declined sharply in many areas, but there were also large increases (e.g., Australia and parts of Europe). Our analysis does not exclude an important role for human activities on global biomass burning during the Holocene, but instead provides evidence for a pervasive influence of climate across multiple spatial and temporal scales.

There is large uncertainty about the magnitude of warming and how rainfall patterns will change in response to any given scenario of future changes in atmospheric composition and land use. The models used for future climate projections were developed and calibrated using climate observations from the past 40 years. The geologic record of environmental responses to climate changes provides a unique opportunity to test model performance outside this limited climate range. Evaluation of model simulations against palaeodata shows that models reproduce the direction and large-scale patterns of past changes in climate, but tend to underestimate the magnitude of regional changes. As part of the effort to reduce model-related uncertainty and produce more reliable estimates of twenty-first century climate, the Palaeoclimate Modelling Intercomparison Project is systematically applying palaeoevaluation techniques to simulations of the past run with the models used to make future projections. This evaluation will provide assessments of model performance, including whether a model is sufficiently sensitive to changes in atmospheric composition, as well as providing estimates of the strength of biosphere and other feedbacks that could amplify the model response to these changes and modify the characteristics of climate variability.

International audience; During each of the late Pleistocene glacial–interglacial transitions, atmospheric carbon dioxide concentrations rose by almost 100 ppm. The sources of this carbon are unclear, and efforts to identify them are hampered by uncertainties in the magnitude of carbon reservoirs and fluxes under glacial conditions. Here we use oxygen isotope measurements from air trapped in ice cores and ocean carbon-cycle modelling to estimate terrestrial and oceanic gross primary productivity during the Last Glacial Maximum. We find that the rate of gross terrestrial primary production during the Last Glacial Maximum was about 40 ± 10 Pg C yr−1 , half that of the pre-industrial Holocene. Despite the low levels of photosynthesis, we estimate that the late glacial terrestrial biosphere contained only 330 Pg less carbon than pre-industrial levels. We infer that the area covered by carbon-rich but unproductive biomes such as tundra and cold steppes was significantly larger during the Last Glacial Maximum, consistent with palaeoecological data. Our data also indicate the presence of an inert carbon pool of 2,300 Pg C, about 700 Pg larger than the inert carbon locked in permafrost today. We suggest that the disappearance of this carbon pool at the end of the Last Glacial Maximum may have contributed to the deglacial rise in atmospheric carbon dioxide concentrations.

[1] Four CO2 concentration inversions and the Global Fire Emissions Database (GFED) versions 2.1 and 3 are used to provide benchmarks for climate-driven modeling of the global land-atmosphere CO2 flux and the contribution of wildfire to this flux. The Land surface Processes and exchanges (LPX) model is introduced. LPX is based on the Lund-Potsdam-Jena Spread and Intensity of FIRE (LPJ-SPITFIRE) model with amended fire probability calculations. LPX omits human ignition sources yet simulates many aspects of global fire adequately. It captures the major features of observed geographic pattern in burnt area and its seasonal timing and the unimodal relationship of burnt area to precipitation. It simulates features of geographic variation in the sign of the interannual correlations of burnt area with antecedent dryness and precipitation. It simulates well the interannual variability of the global total land-atmosphere CO2 flux. There are differences among the global burnt area time series from GFED2.1, GFED3 and LPX, but some features are common to all. GFED3 fire CO2 fluxes account for only about 1/3 of the variation in total CO2 flux during 1997–2005. This relationship appears to be dominated by the strong climatic dependence of deforestation fires. The relationship of LPX-modeled fire CO2 fluxes to total CO2 fluxes is weak. Observed and modeled total CO2 fluxes track the El Nino–Southern Oscillation (ENSO) closely; GFED3 burnt area and global fire CO2 flux track the ENSO much less so. The GFED3 fire CO2 flux-ENSO connection is most prominent for the El Nino of 1997–1998, which produced exceptional burning conditions in several regions, especially equatorial Asia. The sign of the observed relationship between ENSO and fire varies regionally, and LPX captures the broad features of this variation. These complexities underscore the need for process-based modeling to assess the consequences of global change for fire and its implications for the carbon cycle.

The leaf carbon isotope ratio (δ(13) C) of C3 plants is inversely related to the drawdown of CO2 concentration during photosynthesis, which increases towards drier environments. We aimed to discriminate between the hypothesis of universal scaling, which predicts between-species responses of δ(13) C to aridity similar to within-species responses, and biotic homoeostasis, which predicts offsets in the δ(13) C of species occupying adjacent ranges. The Northeast China Transect spans 130-900 mm annual precipitation within a narrow latitude and temperature range. Leaves of 171 species were sampled at 33 sites along the transect (18 at ≥ 5 sites) for dry matter, carbon (C) and nitrogen (N) content, specific leaf area (SLA) and δ(13) C. The δ(13) C of species generally followed a common relationship with the climatic moisture index (MI). Offsets between adjacent species were not observed. Trees and forbs diverged slightly at high MI. In C3 plants, δ(13) C predicted N per unit leaf area (Narea) better than MI. The δ(13) C of C4 plants was invariant with MI. SLA declined and Narea increased towards low MI in both C3 and C4 plants. The data are consistent with optimal stomatal regulation with respect to atmospheric dryness. They provide evidence for universal scaling of CO2 drawdown with aridity in C3 plants.

International audience; Sedimentary charcoal records document changes in fire regime. We have identified 67 sites (30 sites with better than millennial resolution) which have records for some part of the Last Glacial to analyse changes in global fire regimes. Fire was consistently lower during the glacial than during the Eemian and Holocene. Within the glacial, Marine Isotope Stage (MIS) 3 is characterised globally by more fire than MIS 2. The signal for MIS 4 is less clear: there is more fire in the Northern Hemisphere and less fire in the Southern Hemisphere than during MIS 2 and 3. The records, most particularly records from the northern extratropics, show millennial-scale variability in fire regimes corresponding to the rapid climate changes associated with Dansgaard-Oeschger (D-O) cycles. Most of the DO cycles during the Last Glacial and all of the Heinrich stadials are apparent in the composite global record of fire regime: fire increases during DO warming events and decreases during intervals of rapid cooling. Our analyses show that fire regimes show a lagged response to rapid climate changes of ca 100-200 years in the case of DO warming events, ca 0-100 years in the case of DO cooling events and ca 200 years in the case of Heinrich Stadials. The Strong climatic variability experienced during the glacial resulted in important changes in fire regimes even though the base level of biomass burning was less than today.

†Background and Aims The collection of field data on plant traits is time consuming and this makes it difficult to examine changing patterns of traits along large-scale climate gradients. The present study tests whether trait information derived from regional floras can be used in conjunction with pre-existing quadrat data on species presence to derive meaningful relationships between specific morphometric traits and climate. †Methods Quadrat records were obtained for 867 species in 404 sites from northern China (38‐ 498N, 82 ‐1328E) together with information on the presence/absence of key traits from floras. Bioclimate parameters for each site were calculated using the BIOME3 model. Principal component analysis and correlation analysis were conducted to determine the most important climate factors. The Akaike Information Criterion was used to select the best relationship between each trait and climate. Canonical correspondence analysis was used to explore the relationships between climate and trait occurrence. †Key Results The changing abundance of life form, leaf type, phenology, photosynthetic pathway, leaf size and several other morphometric traits are determined by gradients in plant-available moisture (as measured by the ratio of actual to potential evapotranspiration: a), growing-season temperature (as measured by growing degree-days on a 08 base: GDD0) or a combination of these. Different plant functional types (PFTs, as defined by life form, leaf type and phenology) reach maximum abundance in distinct areas of this climate space: for example, evergreen trees occur in the coldest, wettest environments (GDD0 , 25008Cd, a . 0.38), and deciduous scale-leaved trees occur in drier, warmer environments than deciduous broad-leaved trees. Most leaf-level traits show similar relationships with climate independently of PFT: for example, leaf size in all PFTs increases as the environment becomes wetter and cooler. However, some traits (e.g. petiole length) display different relationships with climate in different PFTs. †Conclusions Based on presence/absence species data and flora-based trait assignments, the present study demonstrates ecologically plausible trends in the occurrence of key plant traits along climate gradients in northern China. Life form, leaf type, phenology, photosynthetic pathway, leaf size and other key traits reflect climate. The success of these analyses opens the possibility of using quadrat- and flora-based trait analyses to examine climate ‐trait relationships in other regions of the world.

International audience; Question: What plant properties might define plant functional types (PFTs) for the analysis of global vegetation responses to climate change, and what aspects of the physical environment might be expected to predict the distributions of PFTs?Methods: We review principles to explain the distribution of key plant traits as a function of bioclimatic variables. We focus on those whole-plant and leaf traits that are commonly used to define biomes and PFTs in global maps and models.Results: Raunkiær's plant life forms (underlying most later classifications) describe different adaptive strategies for surviving low temperature or drought, while satisfying requirements for reproduction and growth. Simple conceptual models and published observations are used to quantify the adaptive significance of leaf size for temperature regulation, leaf consistency for maintaining transpiration under drought, and phenology for the optimization of annual carbon balance. A new compilation of experimental data supports the functional definition of tropical, warm-temperate, temperate and boreal phanerophytes based on mechanisms for withstanding low temperature extremes. Chilling requirements are less well quantified, but are a necessary adjunct to cold tolerance. Functional traits generally confer both advantages and restrictions; the existence of trade-offs contributes to the diversity of plants along bioclimatic gradients.Conclusions: Quantitative analysis of plant trait distributions against bioclimatic variables is becoming possible; this opens up new opportunities for PFT classification. A PFT classification based on bioclimatic responses will need to be enhanced by information on traits related to competition, successional dynamics and disturbance.

We have used the BIOME4 biogeography-biochemistry model and comparison with palaeovegetation data to evaluate the response of six ocean-atmosphere general circulation models to mid-Holocene changes in orbital forcing in the mid- to high-latitudes of the northern hemisphere. All the models produce: (a) a northward shift of the northern limit of boreal forest, in response to simulated summer warming in high-latitudes. The northward shift is markedly asymmetric, with larger shifts in Eurasia than in North America; (b) an expansion of xerophytic vegetation in mid-continental North America and Eurasia, in response to increased temperatures during the growing season; (c) a northward expansion of temperate forests in eastern North America, in response to simulated winter warming. The northward shift of the northern limit of boreal forest and the northward expansion of temperate forests in North America are supported by palaeovegetation data. The expansion of xerophytic vegetation in mid-continental North America is consistent with palaeodata, although the extent may be over-estimated. The simulated expansion of xerophytic vegetation in Eurasia is not supported by the data. Analysis of an asynchronous coupling of one model to an equilibrium-vegetation model suggests vegetation feedback exacerbates this mid-continental drying and produces conditions more unlike the observations. Not all features of the simulations are robust: some models produce winter warming over Europe while others produce winter cooling. As a result, some models show a northward shift of temperate forests (consistent with, though less marked than, the expansion shown by data) and others produce a reduction in temperate forests. Elucidation of the cause of such differences is a focus of the current phase of the Palaeoclimate Modelling Intercomparison Project.

International audience; Question: What are the correlations between the degree of drought stress and temperature, and the adoption of specific adaptive strategies by plants in the Mediterranean region? Location: 602 sites across the Mediterranean region. Method: We considered 12 plant morphological and phenological traits, and measured their abundance at the sites as trait scores obtained from pollen percentages. We conducted stepwise regression analyses of trait scores as a function of plant available moisture (alpha) and winter temperature (MTCO). Results: Patterns in the abundance for the plant traits we considered are clearly determined by alpha, MTCO or a combination of both. In addition, trends in leaf size, texture, thickness, pubescence and aromatic leaves and other plant level traits such as thorniness and aphylly, vary according to the life form (tree, shrub, forb), the leaf type (broad, needle) and phenology (evergreen, summer-green). Conclusions: Despite conducting this study based on pollen data we have identified ecologically plausible trends in the abundance of traits along climatic gradients. Plant traits other than the usual life form, leaf type and leaf phenology carry strong climatic signals. Generally, combinations of plant traits are more climatically diagnostic than individual traits. The qualitative and quantitative relationships between plant traits and climate parameters established here will help to provide an improved basis for modelling the impact of climate changes on vegetation and form a starting point for a global analysis of pollen-climate relationships.

[1] The current consensus is that up to half of the modern atmospheric dust load originates from anthropogenically-disturbed soils. Here, we estimate the contribution to the atmospheric dust load from agricultural areas by calibrating a dust-source model with emission indices derived from dust-storm observations. Our results indicate that dust from agricultural areas contributes

Simulations with the IPSL atmosphere–ocean model asynchronously coupled with the BIOME1 vegetation model show the impact of ocean and vegetation feedbacks, and their synergy, on mid- and high-latitude (>40°N) climate in response to orbitally-induced changes in mid-Holocene insolation. The atmospheric response to orbital forcing produces a +1.2 °C warming over the continents in summer and a cooling during the rest of the year. Ocean feedback reinforces the cooling in spring but counteracts the autumn and winter cooling. Vegetation feedback produces warming in all seasons, with largest changes (+1 °C) in spring. Synergy between ocean and vegetation feedbacks leads to further warming, which can be as large as the independent impact of these feedbacks. The combination of these effects causes the high northern latitudes to be warmer throughout the year in the ocean–atmosphere-vegetation simulation. Simulated vegetation changes resulting from this year-round warming are consistent with observed mid-Holocene vegetation patterns. Feedbacks also impact on precipitation. The atmospheric response to orbital-forcing reduces precipitation throughout the year; the most marked changes occur in the mid-latitudes in summer. Ocean feedback reduces aridity during autumn, winter and spring, but does not affect summer precipitation. Vegetation feedback increases spring precipitation but amplifies summer drying. Synergy between the feedbacks increases precipitation in autumn, winter and spring, and reduces precipitation in summer. The combined changes amplify the seasonal contrast in precipitation in the ocean–atmosphere-vegetation simulation. Enhanced summer drought produces an unrealistically large expansion of temperate grasslands, particularly in mid-latitude Eurasia.

A unified scheme to assign pollen samples to vegetation types was used to reconstruct vegetation patterns north of 55°N at the last glacial maximum (LGM) and mid-Holocene (6000 years B.P.). The pollen data set assembled for this purpose represents a comprehensive compilation based on the work of many projects and research groups. Five tundra types (cushion forb tundra, graminoid and forb tundra, prostrate dwarf-shrub tundra, erect dwarf-shrub tundra, and low- and high-shrub tundra) were distinguished and mapped on the basis of modern pollen surface samples. The tundra-forest boundary and the distributions of boreal and temperate forest types today were realistically reconstructed. During the mid-Holocene the tundra-forest boundary was north of its present position in some regions, but the pattern of this shift was strongly asymmetrical around the pole, with the largest northward shift in central Siberia (∼200 km), little change in Beringia, and a southward shift in Keewatin and Labrador (∼200 km). Low- and high-shrub tundra extended farther north than today. At the LGM, forests were absent from high latitudes. Graminoid and forb tundra abutted on temperate steppe in northwestern Eurasia while prostrate dwarf-shrub, erect dwarf-shrub, and graminoid and forb tundra formed a mosaic in Beringia. Graminoid and forb tundra is restricted today and does not form a large continuous biome, but the pollen data show that it was far more extensive at the LGM, while low- and high-shrub tundra were greatly reduced, illustrating the potential for climate change to dramatically alter the relative areas occupied by different vegetation types.