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Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution. ISSN:1354-1013 ISSN:1365-2486

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests., Global Change Biology, 28 (23), ISSN:1354-1013, ISSN:1365-2486

Under the Paris Agreement, countries are encouraged to preserve and enhance existing carbon sinks, especially forests, thereby including the LULUCF (Land Use, Land Use Change and Forestry) sector in international climate mitigation targets. In particular, Europe has set the target to reach climate neutrality, i.e. a balance between anthropogenic emissions by sources and removals by sinks, by 2050. A prerequisite to reach these goals is an accurate and credible estimation of both these large fluxes. However, recent works highlighted the uncertainty related to the quantification of the land sector mitigation potential, one of the most challenging emission sectors. Moreover, the definition of climate mitigation policies often occur at the local level, where details on CO2 removals from forests and other land uses are traditionally lacking. Indeed, local authorities (e.g. cities and Regions) can be more effective in the transition to a sustainable economy compared to higher level authorities such as Nations. In this study, we tested a data-driven method based on eddy covariance (EC) data to quantify the current LULUCF role to the regional carbon sink of the Aosta Valley Region (Italy), by the integration of different approaches. Our model is based on eddy covariance measurements of CO2 fluxes, MODIS NDVI (250m), daily gridded meteorological variables at 100m spatial resolution, and a land cover map at 250m spatial resolution. A Random Forest model was used to up-scale the point eddy covariance data to the Regional level, by testing different sets of drivers (air temperature, VPD, Snow (presence/absence), NDVI, solar radiation,...). Our model was then compared to independent data derived from the National Forest Inventory (NFI), and a process-based model. Preliminary results show that forests and other ecosystems of the Region remove nearly 70% of the total anthropogenic emissions in this area. The discrepancies between the different methods will be discussed by exploring the different advantages and flaws and the spatio-temporal variability of the different approaches. Such an assessment of the local carbon budget and its uncertainties will provide a solid base for Climate-smart management of the territory and thus for reaching the carbon neutrality targets.

The prediction of atmospheric CO2 concentrations is limited by the high interannual variability (IAV) of terrestrial gross primary productivity (GPP). However, there are large uncertainties in the drivers of GPP IAV among Earth system models (ESMs). Here, we evaluate the impact of these uncertainties on the predictability of atmospheric CO2 in six ESMs. We use regression analysis to determine the role of environmental drivers on (i) the patterns of GPP IAV, and (ii) the predictability of GPP. There are large uncertainties in the spatial distribution of GPP IAV. Although all ESMs agree on the high IAV in the tropics, several ESMs have unique hotspots of GPP IAV. The main driver of GPP IAV is temperature in the ESMs using the Community Land Model, and soil moisture in IPSL-CM6A-LR and MPI-ESM-LR, revealing underlying differences in the source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable one year ahead, with four out of six ESMs between 19 % and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, while only 7 % to 13 % of the GPP IAV induced by radiation. The results show that while ESMs are fairly similar in their ability to predict themselves, their predicted contribution to the atmospheric CO2 variability originates from different regions and is caused by different drivers. A higher coherence in atmospheric CO2 predictability could be achieved by reducing uncertainties of GPP sensitivity to soil moisture, and by accurate observational products for GPP IAV.

To assess the degree to which it has met its commitments under the Paris Agreement, Morocco is called upon to carry out carbon assessments and transparent evaluations. Within the forestry sector, little is known about the role of Morocco’s forests in contributing to carbon uptake. With this aim, we applied for the first time in literature the 3-PG model to Cedrus atlantica ((Endl.) Manetti ex Carrière, 1855), which represents about 131.800 ha of Morocco’s forest area (i.e. Azrou forest). Through the Differential Evolution - Markov Chains (DE-MC) we tested and assessed the sensitivity and calibrated the 3-PG model. This process-based model provided significant results regarding the carbon sequestration capacity. The results showed the following: i- Parameters related to stand properties, canopy structure, and processes, as well as biomass partitioning, are the most important or sensitive for the performance of the model; ii- The DE-MC method optimized the values of the 3-PG parameters which was confirmed by the means of Gelman-Rubin convergence test; iii- According to the predictions of the calibrated 3-PG, the Net Primary Production in the pure Azrou forest varies between 0.32 and 7.88 tC.ha−1.yr−1, it is equal in average to 4.9 tC.ha−1.yr−1, which given the total area corresponds to 7078 tC.yr−1.

Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio‐temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches – species distribution models, individual‐based forest models, and dynamic global vegetation models – as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change., Forest models can help understanding the processes that shape forest functioning, structure and diversity, since they can can simulate forest dynamics over spatio‐temporal scales unreachable by most empirical investigations. Here we describe the development of three widely applied but contrasting forest mo−delling approaches — species distribution models, individual‐based models and dynamic global vegetation models. We provide an overview of recent model applications and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.

Mediterranean pine plantations provide several ecosystem services but are vulnerable to climate change. Forest management might play a strategic role in the adaptation of Mediterranean forests, but the joint effect of climate change and diverse management options have seldom been investigated together. Here, we simulated the development of a Laricio pine (Pinus nigra subsp. laricio) stand in the Bonis watershed (southern Italy) from its establishment in 1958 up to 2095 using a state-of-the-science process-based forest model. The model was run under three climate scenarios corresponding to increasing levels of atmospheric CO

Mediterranean pine plantations provide several ecosystem services but are particularly sensitive to climate change. Forest management practices might play a strategic role in the long-term adaptation of Mediterranean forests, but the joint effect of climate change and alternative management options in the near and far future have seldom been investigated together. Here, we developed a portfolio of management options and simulated the development of a Laricio pine (Pinus nigra subsp. laricio) stand in the Bonis watershed (southern Italy) from its establishment in 1958 up to 2095 using a state-of-the-science process-based forest model. The model was run under three climate change scenarios corresponding to increasing levels of atmospheric CO2 concentration, and seven management options with different goals, including post-disturbance management, wood production and renaturalization purposes. We analyzed the effect of climate change on annual carbon fluxes (i.e., gross and net primary production) and stocks (i.e., basal area and potential carbon woody stocks), as well as the impact of different management options compared to no management. Results show that, while climate change (i.e., warming and enriched atmospheric CO2 concentration) seems to increase carbon fluxes and stocks in the first half of the century, both show a substantial decrease in the second half, along with higher temperatures (+3 to +5 °C) and lower precipitation (−20% to −22%). When compared to no management, alternative options had a moderate effect on carbon fluxes over the whole simulation (between −6% and +7%) but overall carbon stocks were maximized by thinning interventions and the shelterwood system (+54% to +55%). We demonstrate that the choice of management exerts greater effects on the features of Laricio pine plantations than climate change alone. Therefore, silvicultural strategies might enhance potential stocks and improve forest conditions, with cascading positive effects on the provision of ecosystem services in Mediterranean pine plantations.HighlightsWe simulated the development of a Laricio pine stand over 137 years under three different climatic scenarios and seven management options.Carbon fluxes and stocks benefit from climate change (i.e., warming and enriched atmospheric CO2 concentration) in the first half of the century but show a marked decrease in the longer-term.Forest management exerts a much stronger effect on these features than climate change alone.Silvicultural options aimed at reducing stand density preserve and enhance carbon fluxes and stocks over the simulated time period.

Surface ozone (O3) is a threat to forests by decreasing photosynthesis and, consequently, influencing the strength of land carbon sink. However, due to the lack of continuous surface O3 measurements, observational-based assessments of O3 impacts on forests are largely missing at hemispheric to global scales. Currently, some metrics are used for regulatory purposes by governments or national agencies to protect forests against the negative impacts of ozone: in particular, both Europe and United States (US) makes use of two different exposure-based metrics, i.e. AOT40 and W126, respectively. However, because of some limitations in these metrics, a new standard is under consideration by the European Union (EU) to replace the current exposure metric. We analyse here the different air quality standards set or proposed for use in Europe and in the US to protect forests from O3 and to evaluate their spatial and temporal consistency while assessing their effectiveness in protecting northern-hemisphere forests. Then, we compare their results with the information obtained from a complex land surface model (ORCHIDEE). We find that present O3 uptake decreases gross primary production (GPP) in 37.7% of the NH forested area of northern hemisphere with a mean loss of 2.4% year-1. We show how the proposed US (W126) and the currently used European (AOT40) air quality standards substantially overestimate the extension of potential vulnerable regions, predicting that 46% and 61% of the Northern Hemisphere (NH) forested area are at risk of O3 pollution. Conversely, the new proposed European standard (POD1) identifies lower extension of vulnerability regions (39.6%).

Forest management interventions can act as value-based agents to remove CO2 from the atmosphere and slow anthropogenic climate change and thus might play a strategic role in the framework of the EU forestry-based mitigation strategy. To what extent diversified management actions could lead to quantitatively important changes in carbon sequestration potential and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to assess effects of multiple alternative forest management scenarios on plant net primary productivity (NPP) and potential carbon woody stocks (pCWS) under differing scenarios of climate change. The experiments indicated that the capacity of trees to assimilate and store atmospheric CO2 in recalcitrant standing woody tissue is already being attained as its optimum under business-as-usual forest management conditions regardless of the different climate change scenarios investigated. Nevertheless, on the long-term and under increasing atmospheric CO2 concentration and warming, managed forests show both higher productivity and a larger pool of stored carbon than unmanaged ones as long as forest thinning and tree harvesting are of moderate intensity.

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately. ispartof: GLOBAL CHANGE BIOLOGY vol:27 issue:23 pages:6307-6319 ispartof: location:England status: published

Kelly's commentary regarding our paper1, claiming that nutrient scarcity cannot select for masting behaviour in plants, initiated a fruitful discussion on traditionally settled hypotheses about the evolution of reproductive behaviour in plants. In his commentary, Prof. Kelly raises support for a contrasting hypothesis explaining our observation that temporally variable seed production is more pronounced under nutrient scarcity, namely that nutrient scarcity does not directly cause seed production variability, but instead increases variability induced by economies of scale. The commentary hinges mainly on the argument that an economy of scale (EOS) is necessary to select for highly variable seed production. It also points out that there are no mechanisms by which nutrient scarcity would select for that particular trait over generations. In reply to the stimulating comment, we 1) propose a mechanism by which nutrient scarcity may select for highly variable seed production, with larger-scale weather patterns inducing masting synchrony across populations; 2) we further discuss why wind-pollination and predator satiation, the EOS suggested by Kelly, cannot be the only selective pressures that select for highly variable reproduction.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-flled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the frst time in this paper. In addition, 206 of these sites are for the frst time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible., European Union (EU), United States Department of Energy (DOE)

Mast seeding is one of the most intriguing reproductive traits in nature. Despite its potential drawbacks in terms of fitness, the widespread existence of this phenomenon suggests that it should have evolutionary advantages under certain circumstances. Using a global dataset of seed production time series for 219 plant species from all of the continents, we tested whether masting behaviour appears predominantly in species with low foliar nitrogen and phosphorus concentrations when controlling for local climate and productivity. Here, we show that masting intensity is higher in species with low foliar N and P concentrations, and especially in those with imbalanced N/P ratios, and that the evolutionary history of masting behaviour has been linked to that of nutrient economy. Our results support the hypothesis that masting is stronger in species growing under limiting conditions and suggest that this reproductive behaviour might have evolved as an adaptation to nutrient limitations and imbalances.

Extreme weather events are increasing in frequency and intensity due to global climate change. We hypothesized that tree carbon reserves are crucial for resilience of beech, buffering the source–sink imbalance due to late frosts and summer droughts, and that different components of non-structural carbohydrates (NSCs) play specific roles in coping with stressful situations. To assess the compound effects on mature trees of two extreme weather events, first a late frost in spring 2016 and then a drought in summer 2017, we monitored the phenology, radial growth and the dynamics of starch and soluble sugars in a Mediterranean beech forest. A growth reduction of 85% was observed after the spring late frost, yet not after the drought event. We observed a strong impact of late frost on starch, which also affected its dynamic at the beginning of the subsequent vegetative season. In 2017, the increase of soluble sugars, associated with starch hydrolysis, played a crucial role in coping with the severe summer drought. Non-structural carbohydrates helped to counteract the negative effects of both events, supporting plant survival and buffering source–sink imbalances under stressful conditions. Our findings indicate a strong trade-off between growth and NSC storage in trees. Overall, our results highlight the key role of NSCs on beech trees, response to extreme weather events, confirming the resilience of this species to highly stressful events. These insights are useful for assessing how forests may respond to the potential impacts of climate change on ecosystem processes in the Mediterranean area.

Terrestrial ecosystems, and forests in particular, are important components of land processes because of their key role in reducing atmospheric greenhouse gas concentrations by storing a large amount of carbon in tree biomass and soils. Increasing attention is being paid to forestland area, which accounts for 30% of the total land surface and acts as the main C store in the Earth system. In their life cycle, plants uptake, process, allocate (i.e., the distribution of net primary production among the different plant organs), and remobilize the product [...]

In recent decades, droughts, deforestation and wildfires have become recurring phenomena that have heavily affected both human activities and natural ecosystems in Amazonia. The time needed for an ecosystem to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, little is known about the impacts of these disturbances, alone and synergistically, on forest recovery time and the resulting spatiotemporal patterns at the regional scale. In this study, we combined the 3-PG forest growth model, remote sensing and field derived equations, to map the Amazonia-wide (3 km of spatial resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our results indicate that AGB decreases by 4%, 19% and 46% in forests affected by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: (1) the recovery time of the forests takes longer in the southeastern part of the basin, and, (2) as droughts and wildfires become more frequent&mdash, since the intervals between the disturbances are getting shorter than the rate of forest regeneration&mdash, the long lasting damage they cause potentially results in a permanent and increasing carbon losses from these fragile ecosystems.

In the last decades droughts, deforestation and wildfires have become recurring phenomena that have affected both human activities and natural ecosystems in Amazonia. The time an ecosystem requires to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, the factors influencing and controlling the recovery time and its spatiotemporal patterns at the regional scale are still poorly understood. In this study, we combined forest growth model, remote sensing and field plots, to map Amazonia-wide (300-ha resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our simulated results indicate that AGB decreases by 4%, 19% and 46% in forests disturbed by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: 1) the recovery time of the forests takes longer in the southeastern part of the basin, and, 2) as droughts and wildfires become more frequent – since the intervals between the disturbances is getting shorter than forest regeneration – potentially causing a long-lasting damage in these fragile ecosystems and a permanent degradation.

This study aimed to simulate oak and beech forest growth under various scenarios of climate change and to evaluate how the forest response depends on site properties and particularly on stand characteristics using the individual process-based model HETEROFOR. First, this model was evaluated on a wide range of site conditions. We used data from 36 long-term forest monitoring plots to initialize, calibrate, and evaluate HETEROFOR. This evaluation showed that HETEROFOR predicts individual tree radial growth and height increment reasonably well under different growing conditions when evaluated on independent sites. In our simulations under constant CO2 concentration ([CO2]cst) for the 2071-2100 period, climate change induced a moderate net primary production (NPP) gain in continental and mountainous zones and no change in the oceanic zone. The NPP changes were negatively affected by air temperature during the vegetation period and by the annual rainfall decrease. To a lower extent, they were influenced by soil extractable water reserve and stand characteristics. These NPP changes were positively affected by longer vegetation periods and negatively by drought for beech and larger autotrophic respiration costs for oak. For both species, the NPP gain was much larger with rising CO2 concentration ([CO2]var) mainly due to the CO2 fertilisation effect. Even if the species composition and structure had a limited influence on the forest response to climate change, they explained a large part of the NPP variability (44% and 34% for [CO2]cst and [CO2]var, respectively) compared to the climate change scenario (5% and 29%) and the inter-annual climate variability (20% and 16%). This gives the forester the possibility to act on the productivity of broadleaved forests and prepare them for possible adverse effects of climate change by reinforcing their resilience.

Forest conservation strategies and plans can be unsuccessful if the new habitat conditions determined by climate change are not considered. Our work aims at investigating the likelihood of future suitability, distribution and diversity for some common European forest species under the projected changes in climate, focusing on Southern Europe. We combine an Ensemble Platform for Species Distribution Models (SDMs) to five Global Circulation Models (GCMs) driven by two Representative Concentration Pathways (RCPs), to produce maps of future climate‐driven habitat suitability for ten categories of forest species and two time horizons. For each forest category and time horizon, ten maps of future distribution (5 GCMs by 2 RCPs) are thus combined in a single suitability map supplied with information about the “likelihood” adopting the IPCC terminology based on consensus among projections. Then, the statistical significance of spatially aggregated changes in forest composition at local and regional level is analyzed. Finally, we discuss the importance, among SDMs, that environmental predictors seem to have in influencing forest distribution. Future impacts of climate change appear to be diversified across forest categories. A strong change in forest regional distribution and local diversity is projected to take place, as some forest categories will find more suitable conditions in previously unsuitable locations, while for other categories the same new conditions will become less suited. A decrease in species diversity is projected in most of the area, with Alpine region showing the potentiality to become a refuge for species migration.

Models of trees and stands exist since many decades, what nowadays has changed is our focus towards integrated forest ecosystem functioning. Managing forests in the 21st century requires efforts that go beyond empiricism. Annikki Makela and Harry T. Valentine’s new book “Models of tree and stand dynamics” (Springer Nature, Cham, 2020) shows the state-of-art for knowledge applied within forest models with useful practical applications. It is a textbook for graduates and scientists interested in process-based models of stand dynamics, growth and forest management. Models represent valuable tools for tackling issues like ecosystem carbon sequestration, helping us understand the economic implications of silvicultural interventions and providing insights on the impacts of climate change on forests. The textbook is a compendium of applied research that non-pure mathematicians can understand, interiorize and take advantage of for getting precious knowledge on forest functioning.

Climate change likely represents the major modifying agents of functional and structural processes in terrestrial and marine ecosystems [...]

The long-term research and monitoring site of Collelongo - Selva Piana has been established in 1991 (Abruzzo Region, Central Italy, 1560 m elevation) in the framework of a project on ecology and silviculture of European beech. In 1993, the site was the first forest in Europe where canopy fluxes started to be measured with the eddy covariance technique. Since then, the site has been involved in several of the most important networks and projects, including ICP Forests, Euroflux, CarboEurope-IP, FluxNet and, in 2006, joined the Long Term Ecological Research network (LTER).Measurements at the Collelongo site bridge two centuries, starting right at the end of the WMO and IPCC climate reference period (1961-1990) and extending, in continuous, over a period of great changes (the three warmest ever decades occurred since then), including a number of extreme events (heat waves, droughts, late frost). During these thirty years, more than 50 researchers from different parts of the world performed direct measurements at the site, flux datasets from the site (code IT-Col) had more than 1500 unique downloads and it is estimated that more than 300 people used the data for producing more than 150 ISI papers.We will briefly present the main results on different ecosystem processes (Phenology, Net Primary Production, Carbon Allocation, Net Ecosystem Exchange, Nutrient and Water Cycling) emphasizing responses to drivers, including legacies from the past. Over these thirty years, the growing season length increased significantly (1.33 day yr-1 from 2000 to 2015), the studied beech forests absorbed between 120 and 150 tC ha-1 and showing plasticity and resilience to changing climatic conditions. However, increasing warming, drought and extreme events may impair adaptation capacity. In this respect, modelling offers a tool to evaluate long-term responses, including possible management options to increase both adaptation and resilience capacities.

Forest production efficiency (FPE) metric describes how efficiently the assimilated carbon is partitioned into plants organs (biomass production, BP) or—more generally—for the production of organic matter (net primary production, NPP). We present a global analysis of the relationship of FPE to stand-age and climate, based on a large compilation of data on gross primary production and either BP or NPP. FPE is important for both forest production and atmospheric carbon dioxide uptake. We find that FPE increases with absolute latitude, precipitation and (all else equal) with temperature. Earlier findings—FPE declining with age—are also supported by this analysis. However, the temperature effect is opposite to what would be expected based on the short-term physiological response of respiration rates to temperature, implying a top-down regulation of carbon loss, perhaps reflecting the higher carbon costs of nutrient acquisition in colder climates. Current ecosystem models do not reproduce this phenomenon. They consistently predict lower FPE in warmer climates, and are therefore likely to overestimate carbon losses in a warming climate., Many models assume a universal carbon use efficiency across forest biomes, in contrast to assumptions of other process-based models. Here the authors analyse forest production efficiency across a wide range of climates to show a positive relationship with annual temperature and precipitation, indicating that ecosystem models are overestimating forest carbon losses under warming.

Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand-level, as well as remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction, and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a SQLite relational database or ASCII flat file version (at https://doi.org/10.5880/PIK.2019.008). The data policies of the individual, contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R-package (https://github.com/COST-FP1304-PROFOUND/ProfoundData), which provides basic functions to explore, plot, and extract the data for model set-up, calibration and evaluation.

SummaryThe effects of short-term extreme events on tree functioning and physiology are still rather elusive. European beech is one of the most sensitive species to late frost and water shortage. We investigated the intra-annual C dynamics in stems under such conditions.Wood formation and stem CO2efflux were monitored in a Mediterranean beech forest for three years (2015–2017), including a late frost (2016) and a summer drought (2017).The late frost reduced radial growth and, consequently, the amount of carbon fixed in the stem biomass by 80%. Stem carbon efflux in 2016 was reduced by 25%, which can be attributed to the reduction of effluxes due to growth respiration. Counter to our expectations, we found no effects of the 2017 summer drought on radial growth and stem carbon efflux.The studied extreme weather events had various effects on tree growth. Even though late spring frost had a devastating impact on beech radial growth in the current year, trees fully recovered in the following growing season, indicating high resilience of beech to this stressful event.

Vegetation phenology and its variability have substantial influence on land-atmosphere interaction, and changes in growing season length are additional indicators of climate change impacts on ecosystems. For these reasons, global land surface models are routinely evaluated in order to assess their ability to reproduce the observed phenological variability. This work presents a new approach that integrates a wider spectrum of growing season modes in order to better describe the observed variability in vegetation growing season onset and offset, as well as assess the ability of state-of-the-art land surface models to capture this variability at the global scale. The land surface models results are validated against the LAI3g satellite-observation. The comparison between data and model outputs shows the ability of models in reproducing the growing season features in the boreal forests, but also displays their limitations located, for example, in areas where water availability acts as the main driver of vegetation phenological activity. In general, the new methodology expands the area of analysis from northern mid- and high-latitudes to the global continental areas, and allows to assess the vegetation response to the on-going climate change in a larger variety of ecosystems, ranging from rain forests to semi-arid regions, passing through boreal evergreen and temperate deciduous forests. This effort is part of the EU-funded CRESCENDO project. Keywords: Phenology, modeling, vegetation growing season.

The future trajectory of atmospheric CO2 concentration depends on the development of the terrestrial carbon sink, which in turn is influenced by forest dynamics under changing environmental conditions. An in‐depth understanding of model sensitivities and uncertainties in non‐steady‐state conditions is necessary for reliable and robust projections of forest development and under scenarios of global warming and CO2 enrichment. Here, we systematically assessed if a biogeochemical process‐based model (3D‐CMCC‐CNR), which embeds similarities with many other vegetation models, applied in simulating net primary productivity (NPP) and standing woody biomass (SWB), maintained a consistent sensitivity to its 55 input parameters through time, during forest ageing and structuring as well as under climate change scenarios. Overall, the model applied at three contrasting European forests showed low sensitivity to the majority of its parameters. Interestingly, model sensitivity to parameters varied through the course of >100 yr of simulations. In particular, the model showed a large responsiveness to the allometric parameters used for initialize forest carbon and nitrogen pools early in forest simulation (i.e., for NPP up to ~37%, 256 g C·m−2·yr−1 and for SWB up to ~90%, 65 Mg C/ha, when compared to standard simulation), with this sensitivity decreasing sharply during forest development. At medium to longer time scales, and under climate change scenarios, the model became increasingly more sensitive to additional and/or different parameters controlling biomass accumulation and autotrophic respiration (i.e., for NPP up to ~30%, 167 g C·m−2·yr−1 and for SWB up to ~24%, 64 Mg C/ha, when compared to standard simulation). Interestingly, model outputs were shown to be more sensitive to parameters and processes controlling stand development rather than to climate change (i.e., warming and changes in atmospheric CO2 concentration) itself although model sensitivities were generally higher under climate change scenarios. Our results suggest the need for sensitivity and uncertainty analyses that cover multiple temporal scales along forest developmental stages to better assess the potential of future forests to act as a global terrestrial carbon sink.

Plants can respond differently to different biotic or abiotic factors and their combinations. Their traits are integrated into vegetation models as parameters and their values are defined by measurements and/or by expert judgment and then reported in literature. One of the aims of the models is to represent the processes by mathematical formulations and to assess the response of ecosystems to biotic or abiotic drivers. To estimate the olive trees traits we have combined Bayesian calibration, model and different climatic conditions. Olive tree (Olea europaea L.) has been chosen because is one of the most widespread cultivated arboreal species in the Mediterranean basin and the olive production has recently expanded into some non-traditional areas (South Africa, New Zealand, Australia, Chile, China, etc.). Furthermore the olive trees are important to the positive contribution of the carbon sequestration, mitigation and adaptation to climate change recognizing the long-term carbon storage capacity in soil and woody compartments (Nieto et al., 2010). The Bayesian calibration is one of the most utilized statistical tool to define the best set of the parameters (posteriors) using: a set of initial parameter values (prior) and their range of variability; a set of data measured; a likelihood function to link the prior and posterior; a model to simulate the data measured; and an input dataset to launch the simulations. In this work we have applied the Bayesian calibration framework on the 3D-CMCC-OLIVE model (a modified version of the 3D-CMCC-CNR model (Collalti et al., 2018a; 2018b) calibrated for olive trees and which also includes pruning and irrigation schemes). The 3D-CMCC-OLIVE model parameters was calculated by the Bayesian calibration and using the daily Net Primary Production (NPP; g C m-2 day-1), simulated by a different model in a olive grove located in Central Italy (42 o 56 0 N, 10 o 46 0 E) (Brilli, et al., 2018). Meteorological conditions in the olive grove differed in the three studied years (2010-2012). The first year was markedly wetter than the second, the third year was intermediate to the two previous. To estimate the effects of different climatic conditions in the model parameters (and olive trees traits), the calibration is applied with different datasets: one-year-at-time, two-years-at-time, all time series. At the end of the calibrations each set of posterior has been applied and the performance of the 3D-CMCC-OLIVE was evaluated.

Gross primary production (GPP) is partitioned to autotrophic respiration (Ra) and net primary production (NPP), the latter being used to build plant tissues and synthesize non-structural and secondary compounds. Waring et al. (1998; Net primary production of forests: a constant fraction of gross primary production? Tree Physiol 18:129–134) suggested that a NPP:GPP ratio of 0.47 ± 0.04 (SD) is universal across biomes, tree species and stand ages. Representing NPP in models as a fixed fraction of GPP, they argued, would be both simpler and more accurate than trying to simulate Ra mechanistically. This paper reviews progress in understanding the NPP:GPP ratio in forests during the 20 years since the Waring et al. paper. Research has confirmed the existence of pervasive acclimation mechanisms that tend to stabilize the NPP:GPP ratio and indicates that Ra should not be modelled independently of GPP. Nonetheless, studies indicate that the value of this ratio is influenced by environmental factors, stand age and management. The average NPP:GPP ratio in over 200 studies, representing different biomes, species and forest stand ages, was found to be 0.46, consistent with the central value that Waring et al. proposed but with a much larger standard deviation (±0.12) and a total range (0.22–0.79) that is too large to be disregarded.

The issue of climate changes is one of the main threats to the earth of the last decades, with the raise of the mean global temperature, due to increment of carbon dioxide concentration in the atmosphere, and the consequently twisting of forest ecosystem dynamics. On the other hand, forests provide responses to the effects of climate changes, playing a key role in the mitigation of the problem. However, studying these responses is intricate since there are many factors interacting with each other. Among them, the cycle of nutrients in the soil, in particular carbon, nitrogen and phosphorous, takes on great importance. On this issue, many questions without an answer still persist. The analysis and the studies of the last years have revealed that the increase of atmospheric CO 2 con- centration facilitates forest productivity. Nevertheless, this more pronounced forest growth should be supported by an high availability of nutrients in the soil. On the other hand, the increase of global temperature could accelerates the soil organic matter decomposition, causing the rise of nutrients that could be used by the plants. To study the evolution of these dynamics and to give answers to this kind of questions, a new simulation model of biogeochemical cycles in the soil, the 3D-CMCC-SOIL, is in implementation. Five different phases (atmosphere, plant, litter, soil and anthropogenic activity) are defined, determining the main carbon, nitrogen and phosphorous pools in every phases and the fluxes between them. In the litter and soil, the organic pools are subdivided in labile, intermediate and recalcitrant, on the base of the chemical composition of the dead biomass. The soil mineral nitrogen and phosphorous are exchanged with plants, by uptake process, and atmosphere, by the transformation in gases forms.

In literature a very large range of available DSS tools can be found for facing agriculture, forestry and environmental issues. However, these are typically designed to tackle a specific problem/scale/end-user group. The objective of LANDSUPPORT (H2020 project) is the construction of a web-based smart geoSpatial Decision Support System (S-DSS), which shall provide a powerful set of tools devoted to: (i) support sustainable agriculture and forestry; (ii) evaluate trade-off between land uses; (iii) contribute to implementation, impact and delivery of about 20 European land policies and also selected 2030 UN Sustainable Development Goals including climate change resilience goals. Specifically, a family of tools is under development to support sustainable forestry as required by EU Forestry Strategy designed for forest owners and policy makers to adopt best forest practices. LANDSUPPORT will use EO data and simulation modelling in order to: (i) map and monitor forest productivity; (ii) (iii) (iv) identify forestry best practices to increase climate change resilience; assess the impact of selected forest bestpractices indicators and criteria with modelling applications; quantify forest ecosystem services through the development of relevant indicators for a preliminary FES estimation also according to forest types. LANDSUPPORT will also establish a cooperative network with EIP-Agri aiming to contribute to EIP work "Forest practices and Climate change group". The above-mentioned objectives will be achieved through the integration of existing available databases and the development of high performance modelling chains and engines. Starting from sustainable forestry management policies, LULUCF regulation will also be taken into account. The forestry tool will work at different geographic scales including Europe, EU Member States (Italy, Hungary,) and two European Regions in IT and HU (Campania Region and Keszthely), plus one local test-site in Slovenia. LANDSUPPORT aims at providing an integrated and operational set of tools that could be useful to foresters, municipality officers and land owners, to support and foster decision-making process in forest planning and land management.

International audience; Masting is the highly variable and synchronous production of seeds by plants. Masting can have cascading effects on plant population dynamics and forest properties such as tree growth, carbon stocks, regeneration, nutrient cycling, or future species composition. However, masting has often been missing from forest models. Those few that simulate masting have done so using relatively simple empirical rules, and lack an implementation of process-based mechanisms that control such events. Here we review more than 200 published papers on mechanistic formulations of masting, and summarize how the main processes involved in masting and their related patterns can be incorporated in forest models at different degrees of complexity. Our review showed that, of all proximate causes of masting, resource acquisition, storage and allocation were the processes studied most often. Hormonal and genetic regulation of bud formation, floral induction, and anthesis were less frequently addressed. We outline the building blocks of a general process-based model of masting that can be used to improve the oversimplified functions in different types of forest models, and to implement them where missing. A complete implementation of masting in forest models should include functions for resource allocation and depletion, and for pollination, as regulated by both forest structure and weather in the years prior to seed production. When models operate at spatio-temporal scales mismatched with the main masting processes, or if calibration data are not available, simulation can be based on parameterizing masting patterns (variability, synchrony, or frequency). Also, observed masting patterns have the potential to be used as “reality checks” for more process-based forest models wishing to accurately reproduce masting as an emergent phenomenon.

Forests represent about the 90% of global biomass of carbon and the capability of plants to fixed carbon from atmospheric CO2 via photosynthesis is influenced by competitive interaction for light, water and nutrient. The competition can be regulated by the stand dynamics and the terms to describe interactions are: plant species, tree sizes, age distribution, spatial (vertical and horizontal) arrangement and tree canopy layering. The main silvicultural practices (e.g. thinning, harvest and replanting) heavily affect the stand structure and influence the nutrient cycling, decomposition rates, regeneration patterns, but also on stand structure dynamics. Due to the long lifespan of forest stands and lack in experiment covering such long time period, a very useful tool to understand the effect of silvicultural practices on stand structure and carbon dynamic are the forest models. The forest models involve the abstraction of real forest dynamics in a conceptual environment which allows the systems behavior to be reproduced. The models are the only tool for estimating the effect of such different types of silvicultural practices under current climate as well as under future climate change scenarios. To simulate the effects of different management practices and climate change on Standing Woody Biomass (SWB) we apply the model 3D-CMCC-CNR FEM (v.5.4 BGC) at three different even-aged, managed, European forests: an adult (80 years old) Danish temperate European beech forest (Fagus sylvatica L.; Sorø); a moderately young (28 years old) Finnish boreal Scots pine forest (Pinus sylvestris L.; Hyytiälä) and a young (16 years old) Czech temperate-humid Norway spruce forest (Picea abies Karst.; Bílý K?í?). The study sites were selected because the three species are representative of the most common types of European deciduous and coniferous species and involved into the Inter-Sectoral Impact Model Intercomparison (ISIMIP) Project. To estimate climate change effects the simulations consider a subset of climate change scenarios data (ESM: HadGEM2-ES; RCP 2.6 and RCP 8.5) from the ISIMIP Fast Track initiative (https://www.isimip.org, Warszawski et al., 2014). We simulate the business-as-usual (BAU) management and different thinning and harvesting regimes and their combination ( rotation BAU +/- 10 years; thinning interval BAU +/- 5 years; thinning intensity BAU +/- 10%) plus the "unmanaged" forest. The effect of climate change was simulated for a period of about 100 years (1997-2099) and the total runs for each sites was about 56 (3 rotation * 3 thinning interval * 3 thinning intensity * 2 RCP plus 2 RCP in "unmanaged" forest). The simulations of different management practices under current climate condition can help the stakeholders to increase the knowledge of ecosystem response to human activity and to establish the effect of their decision in the carbon cycle and the biomass in the short period. The simulations under climate change can suggest the effects of the management in the maintain, reduce or increase the sink/source service of forest in the medium and long period. The changes in regimes of thinning, harvest and thinning intensity are mandatory to the sustainable forests and to increase the quality and increments of the woody biomass increments.

In the mitigation of climate changes by forest ecosystems, soil plays a key role, storing more than 50% of global carbon stocks. Soil dynamics have a significant influence on the evolution of the forests and, consequently, on their capability to mitigate climate alterations. Some of these dynamics are related to soil nutrient availability for the plants, in particular nitrogen (N) and phosphorous (P), hence linked to biogeochemical cycles that incorporate the transformation processes of organic, inorganic and gaseous compounds of carbon and nutrients, as well as the mechanisms of nutrient losses from the soil. Given the importance of soil biogeochemical cycles under climate change, it's crucial to have tools for a complete and detailed study of biogeochemical processes. For this purpose, the simulation models represent a fundamental tool to simulate future scenarios on the evolution of forest ecosystems and, consequently, on their capability to contrast climate alterations. However, the models show some lacks in the simulation of biogeochemical cycles. Many studies highlight how some processes, especially under climate changes, play a not negligible weight to determine soil nutrient availability for the plants. These mechanisms are not simulated by the models or, in some cases, are reproduced with extremely simplified approaches. An example is constituted by nitrogen transformation between different mineral forms (Dissimilatory Nitrate Reduction to Ammonium, DNRA) (Silver et al., 2001) and by inorganic nitrogen lost in the atmosphere (anammox) (Xi et al., 2016). Other dynamics are linked to root exudates production and to mycorrhizae (Coskun et al.,2017; Phillips et al., 2011), as well as to the effects of these mechanisms on the main biogeochemical processes (Achat et al., 2016). With the aim to overcome the lacks of models to simulate the cycle of nutrients in the soil, a new model, the 3D-CMCC-SOIL, is in implementation. It will integrate the 3D-CMCC BGC (Collalti et al., 2014, 2016; Marconi et al., 2017), a simulation model of forest dynamics. The 3D-CMCC-SOIL implementation has started from a litter and soil scheme constituted by the main organic, inorganic and gaseous pools of carbon, nitrogen and phosphorous and also by nutrient exchange fluxes between the different pools. Based on this structure, a sensitivity analysis is ongoing on the approaches used by the models to simulate the main biogeochemical processes. These mechanisms are represented by the decomposition of organic nutrients (litter and Soil Organic Matter (SOM) decomposition), nitrogen and phosphorous conversion in mineral compounds (mineralization), transformation in recalcitrant organic compounds (immobilization), Symbiotic Biological Nitrogen Fixation (SBNF) and the nutrient losses from the soil due to heterotrophic respiration, plant uptake, denitrification and leaching. The goal of the analysis is to find the best combination between the different approaches that will constitute the basic scheme of 3D-CMCC-SOIL. The scheme will be subsequently integrated by the processes not yet simulated by the models and related to nitrogen transformation between different mineral compounds, root exudates production and mycorrhizae. The new model will also simulate the forest management practices with the aim to study their effects on soil nutrient availability. The 3D-CMCC-SOIL will be a tool for the study of biogeochemical cycles in the soil under climate change and it will contribute to give answer to the questions on the soil nutrient availability as limiting factor for forest growth and also on the role of forest ecosystems in the mitigation of climate change.

Vegetation phenology and its intra-annual variability have a large influence on land-atmosphere feedbacks. For this reason, global land surface models are routinely evaluated in order to assess their ability in reproducing the observed phenological variability. To do so, many state-of-the-art global land surface models simulate explicitly carbon-nitrogen biogeochemical cycles and dynamical vegetation evolution. Since changes in vegetation growing season are indicators of climate change impacts on ecosystems, we aim at analyzing the observed variability in vegetation growing season onset and offset and the ability of a state-of-the-art land surface model to capture this variability at global scale. To do so, we apply an updated version of the method presented in Murray-Tortarolo et al. (2013) intended to represent the three main types of observed growing season behavior: (1) single growing season with a normal distribution shape; (2) summer dormancy; (3) double growing season (i.e. secondary peak after summer drought). This analysis uses Leaf Area Index (LAI) seasonal amplitude variations as proxy for the vegetation phenology variability. We apply our method both to satellite-observations and to the Community Land Model version 4.5 (CLM4.5) simulations. The comparison of observations and simulations provides an insight on the ability of the CLM4.5 in capturing the observed growing season features. Moreover, an analysis at Plant Functional Type (PFT) level is performed in order to produce further information on the sensitivity of each PFT to the ongoing climate change. In general, our method expands the area of study going from high-latitude northern hemisphere regions to global, giving the possibility to understand the vegetation response to the ongoing climate change in a larger variety of ecosystems.

Forest ecosystems are characterized by high spatial heterogeneity, often related to complex composition and vertical structure which is a challenge in many process-based models. The need to expand process-based models (PBMs) to take in account such structural complexity led to development and testing of a new approach into Forest Ecosystem Models (FEMs), named 3D-CMCC-FEM, able to investigate carbon and water fluxes, including biomass pools and their partitioning, for complex multi-layer forests. 3D-CMCC FEM integrates several characteristics of the functional structural tree models and the robustness of the light use efficiency CLUE) approach to investigate forest growth patterns and yield processes. The modelling approach was tested by simulating the effects of competition for light and water, growth and yield of a two-layered deciduous forest dominated by Turkey Oak in central Italy for a period of eight years. The model outputs were validated against a series of independently measured data for the major biomass pools, the inter-annual stem increments and above-ground net primary productivity of the overstorey and understorey, respectively. The comparison of Leaf Area Index, Gross Primary Production, and evapotranspiration produced by the model against MODIS data showed agreement in results. In addition, the multi-layered model approach was evaluated against a series of simplified versions to determine whether the enhanced complexity of the model positively contributed to its predictive ability. The proposed model reduced the error in the estimates of forest productivity (e.g. NPP) and dynamics (e.g. growth, mortality) and indicates the importance of considering, as far as possible, the structural complexity in PBMs. (C) 2013 Elsevier B.V. All rights reserved.

Scientific community and policy-makers share the common interest in identifying and evaluating potential impacts of climate change on ecosystems, relying mainly on probabilistic methods of exploring the risks. In this perspective, the concept of ensemble forecasting makes possible to handle uncertainties associated with climate risk analysis by focusing on a range of potential or probable impact scenarios rather than actualizing a single case. In this paper, an ensemble of simulations based on the Lund-Potsdam-Jena (LPJ) model was used to investigate the uncertainty upon predictions of the future Euro-Mediterranean vegetation distribution, carbon dynamics, and water budget. Twenty simulations from past to future were based on the combination of different climate inputs, vegetation model parameterizations, and configurations. The evaluation of results combined the separate deterministic future projections from the LPJ model into a single probabilistic projection, associating a likelihood degree in accordance with the most recent Intergovernmental Panel on Climate Change terminology. Results projected a general critical situation in terms of water availability, made more serious if considering that also the occurrence of extreme-related events, e.g., fires, is expected to become more frequent as favored by more recurrent drought episodes. Although more uncomfortable climate conditions were projected for vegetation, net primary production (NPP) was predicted to increase due to the potential enrichment of CO2 in atmosphere and its fertilization effects on vegetation. The combination of rising NPP and fire frequency may shape the carbon cycle components, as the carbon losses by fire also were projected to increase.