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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. [ABSTRACT FROM AUTHOR]

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species' occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long‐term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention. [ABSTRACT FROM AUTHOR]

Autumnal leaf senescence signals the end of photosynthetic activities in temperate deciduous trees and consequently exerts a strong control on various ecological processes. Predicting leaf senescence dates (LSD) with high accuracy is thus a prerequisite for better understanding the climate–ecosystem interactions. However, modeling LSD at large spatial and temporal scales is challenging. In this study, first, we used 19972 site‐year records (848 sites and four deciduous tree species) from the PAN European Phenology network to calibrate and evaluate six leaf senescence models during the period 1980–2013. Second, we extended the spatial analysis by repeating the procedure across Europe using satellite‐derived end of growing season and a forest map. Overall, we found that models that considered photoperiod and temperature interactions outperformed models using simple temperature or photoperiod thresholds for Betula pendula, Fagus sylvatica and Quercus robur. On the contrary, no model displayed reasonable predictions for Aesculus hippocastanum. This inter‐model comparison indicates that, contrary to expectation, photoperiod does not significantly modulate the accumulation of cooling degree days (CDD). On the other hand, considering the carryover effect of leaf unfolding date could promote the models' predictability. The CDD models generally matched the observed LSD at species level and its interannual variation, but were limited in explaining the inter‐site variations, indicating that other environmental cues need to be considered in future model development. The discrepancies remaining between model simulations and observations highlight the need of manipulation studies to elucidate the mechanisms behind the leaf senescence process and to make current models more realistic. [ABSTRACT FROM AUTHOR]

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above‐ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model‐data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB95) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB95, particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB. [ABSTRACT FROM AUTHOR]

The magnitude of the nitrogen (N) limitation of terrestrial carbon (C) storage over the 21st century is highly uncertain because of the complex interactions between the terrestrial C and N cycles. We use an ensemble approach to quantify and attribute process‐level uncertainty in C‐cycle projections by analysing a 30‐member ensemble representing published alternative representations of key N cycle processes (stoichiometry, biological nitrogen fixation (BNF) and ecosystem N losses) within the framework of one terrestrial biosphere model. Despite large differences in the simulated present‐day N cycle, primarily affecting simulated productivity north of 40°N, ensemble members generally conform with global C‐cycle benchmarks for present‐day conditions. Ensemble projections for two representative concentration pathways (RCP 2.6 and RCP 8.5) show that the increase in land C storage due to CO2 fertilization is reduced by 24 ± 15% due to N constraints, whereas terrestrial C losses associated with climate change are attenuated by 19 ± 20%. As a result, N cycling reduces projected land C uptake for the years 2006–2099 by 19% (37% decrease to 3% increase) for RCP 2.6, and by 21% (40% decrease to 9% increase) for RCP 8.5. Most of the ensemble spread results from uncertainty in temperate and boreal forests, and is dominated by uncertainty in BNF (10% decrease to 50% increase for RCP 2.6, 5% decrease to 100% increase for RCP 8.5). However, choices about the flexibility of ecosystem C:N ratios and processes controlling ecosystem N losses regionally also play important roles. The findings of this study demonstrate clearly the need for an ensemble approach to quantify likely future terrestrial C–N cycle trajectories. Present‐day C‐cycle observations only weakly constrain the future ensemble spread, highlighting the need for better observational constraints on large‐scale N cycling, and N cycle process responses to global change. [ABSTRACT FROM AUTHOR]

Abstract: Changes in the spring onset of vegetation growth in response to climate change can profoundly impact climate–biosphere interactions. Thus, robust simulation of spring onset is essential to accurately predict ecosystem responses and feedback to ongoing climate change. To date, the ability of vegetation phenology models to reproduce spatiotemporal patterns of spring onset at larger scales has not been thoroughly investigated. In this study, we took advantage of phenology observations via remote sensing to calibrate and evaluated six models, including both one‐phase (considering only forcing temperatures) and two‐phase (involving forcing, chilling, and photoperiod) models across the Northern Hemisphere between 1982 and 2012. Overall, we found that the model that integrated the photoperiod effect performed best at capturing spatiotemporal patterns of spring phenology in boreal and temperate forests. By contrast, all of the models performed poorly in simulating the onset of growth in grasslands. These results suggest that the photoperiod plays a role in controlling the onset of growth in most Northern Hemisphere forests, whereas other environmental factors (e.g., precipitation) should be considered when simulating the onset of growth in grasslands. We also found that the one‐phase model performed as well as the two‐phase models in boreal forests, which implies that the chilling requirement is probably fulfilled across most of the boreal zone. Conversely, two‐phase models performed better in temperate forests than the one‐phase model, suggesting that photoperiod and chilling play important roles in these temperate forests. Our results highlight the significance of including chilling and photoperiod effects in models of the spring onset of forest growth at large scales, and indicate that the consideration of additional drivers may be required for grasslands. [ABSTRACT FROM AUTHOR]

Higher temperatures caused by future climate change will bring more frequent heat stress events and pose an increasing risk to global wheat production. Crop models have been widely used to simulate future crop productivity but are rarely tested with observed heat stress experimental datasets. Four wheat models ( DSSAT- CERES-Wheat, DSSAT-Nwheat, APSIM-Wheat, and WheatGrow) were evaluated with 4 years of environment-controlled phytotron experimental datasets with two wheat cultivars under heat stress at anthesis and grain filling stages. Heat stress at anthesis reduced observed grain numbers per unit area and individual grain size, while heat stress during grain filling mainly decreased the size of the individual grains. The observed impact of heat stress on grain filling duration, total aboveground biomass, grain yield, and grain protein concentration ( GPC) varied depending on cultivar and accumulated heat stress. For every unit increase of heat degree days ( HDD, degree days over 30 °C), grain filling duration was reduced by 0.30-0.60%, total aboveground biomass was reduced by 0.37-0.43%, and grain yield was reduced by 1.0-1.6%, but GPC was increased by 0.50% for cv Yangmai16 and 0.80% for cv Xumai30. The tested crop simulation models could reproduce some of the observed reductions in grain filling duration, final total aboveground biomass, and grain yield, as well as the observed increase in GPC due to heat stress. Most of the crop models tended to reproduce heat stress impacts better during grain filling than at anthesis. Some of the tested models require improvements in the response to heat stress during grain filling, but all models need improvements in simulating heat stress effects on grain set during anthesis. The observed significant genetic variability in the response of wheat to heat stress needs to be considered through cultivar parameters in future simulation studies. [ABSTRACT FROM AUTHOR]

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space ( i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our 'best' model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. [ABSTRACT FROM AUTHOR]

Several studies have shown that satellite retrievals of solar-induced chlorophyll fluorescence (SIF) provide useful information on terrestrial photosynthesis or gross primary production (GPP). Here, we have incorporated equations coupling SIF to photosynthesis in a land surface model, the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4), and have demonstrated its use as a diagnostic tool for evaluating the calculation of photosynthesis, a key process in a land surface model that strongly influences the carbon, water, and energy cycles. By comparing forward simulations of SIF, essentially as a byproduct of photosynthesis, in CLM4 with observations of actual SIF, it is possible to check whether the model is accurately representing photosynthesis and the processes coupled to it. We provide some background on how SIF is coupled to photosynthesis, describe how SIF was incorporated into CLM4, and demonstrate that our simulated relationship between SIF and GPP values are reasonable when compared with satellite (Greenhouse gases Observing SATellite; GOSAT) and in situ flux-tower measurements. CLM4 overestimates SIF in tropical forests, and we show that this error can be corrected by adjusting the maximum carboxylation rate (Vmax) specified for tropical forests in CLM4. Our study confirms that SIF has the potential to improve photosynthesis simulation and thereby can play a critical role in improving land surface and carbon cycle models. [ABSTRACT FROM AUTHOR]

Identifying the relative importance of climatic and other environmental controls on the interannual variability and trends in global land surface phenology and greenness is challenging. Firstly, quantifications of land surface phenology and greenness dynamics are impaired by differences between satellite data sets and phenology detection methods. Secondly, dynamic global vegetation models (DGVMs) that can be used to diagnose controls still reveal structural limitations and contrasting sensitivities to environmental drivers. Thus, we assessed the performance of a new developed phenology module within the LPJmL (Lund-Potsdam-Jena managed Lands) DGVM with a comprehensive ensemble of three satellite data sets of vegetation greenness and ten phenology detection methods, thereby thoroughly accounting for observational uncertainties. The improved and tested model allows us quantifying the relative importance of environmental controls on interannual variability and trends of land surface phenology and greenness at regional and global scales. We found that start of growing season interannual variability and trends are in addition to cold temperature mainly controlled by incoming radiation and water availability in temperate and boreal forests. Warming-induced prolongations of the growing season in high latitudes are dampened by a limited availability of light. For peak greenness, interannual variability and trends are dominantly controlled by water availability and land-use and land-cover change (LULCC) in all regions. Stronger greening trends in boreal forests of Siberia than in North America are associated with a stronger increase in water availability from melting permafrost soils. Our findings emphasize that in addition to cold temperatures, water availability is a codominant control for start of growing season and peak greenness trends at the global scale. [ABSTRACT FROM AUTHOR]

The representation of the nitrogen (N) cycle in Earth system models ( ESMs) is strongly motivated by the constraint N poses on the sequestration of anthropogenic carbon (C). Models typically implement a stoichiometric relationship between C and N in which external supply and assimilation by organisms are adjusted to maintain their internal stoichiometry. N limitation of primary productivity thus occurs if the N supply from uptake and fixation cannot keep up with the construction of tissues allowed by C assimilation. This basic approach, however, presents considerable challenges in how to faithfully represent N limitation. Here, we review how N limitation is currently implemented and evaluated in ESMs and highlight challenges and opportunities in their future development. At or near steady state, N limitation is governed by the magnitude of losses from the plant-unavailable pool vs. N fixation and there are considerable differences in how models treat both processes. In nonsteady-state systems, the accumulation of N in pools with slow turnover rates reduces N available for plant uptake and can be challenging to represent when initializing ESM simulations. Transactional N limitation occurs when N is incorporated into various vegetation and soil pools and becomes available to plants only after it is mineralized, the dynamics of which depends on how ESMs represent decomposition processes in soils. Other challenges for ESMs emerge when considering seasonal to interannual climatic oscillations as they create asynchronies between C and N demand, leading to transient alternations between N surplus and deficit. Proper evaluation of N dynamics in ESMs requires conceptual understanding of the main levers that trigger N limitation, and we highlight key measurements and observations that can help constrain these levers. Two of the biggest challenges are the mechanistic representation of plant controls on N availability and turnover, including N fixation and organic matter decomposition processes. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate 10 process-based terrestrial biosphere models that were used for the IPCC fifth Assessment Report. The simulated gross primary productivity ( GPP) is compared with flux-tower-based estimates by Jung et al. [ Journal of Geophysical Research 116 (2011) G00J07] ( JU11). The net primary productivity ( NPP) apparent sensitivity to climate variability and atmospheric CO2 trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO2 sensitivity of NPP is compared to the results from four Free-Air CO2 Enrichment ( FACE) experiments. The simulated global net biome productivity ( NBP) is compared with the residual land sink ( RLS) of the global carbon budget from Friedlingstein et al. [ Nature Geoscience 3 (2010) 811] ( FR10). We found that models produce a higher GPP (133 ± 15 Pg C yr−1) than JU11 (118 ± 6 Pg C yr−1). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5-20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100 ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 ± 0.8 Pg C yr−1 is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr−1). The interannual variability in modeled NBP is significantly correlated with that of RLS for the period 1980-2009. Both model-to-model and interannual variation in model GPP is larger than that in model NBP due to the strong coupling causing a positive correlation between ecosystem respiration and GPP in the model. The average linear regression slope of global NBP vs. temperature across the 10 models is −3.0 ± 1.5 Pg C yr−1 °C−1, within the uncertainty of what derived from RLS (−3.9 ± 1.1 Pg C yr−1 °C−1). However, 9 of 10 models overestimate the regression slope of NBP vs. precipitation, compared with the slope of the observed RLS vs. precipitation. With most models lacking processes that control GPP and NBP in addition to CO2 and climate, the agreement between modeled and observation-based GPP and NBP can be fortuitous. Carbon-nitrogen interactions (only separable in one model) significantly influence the simulated response of carbon cycle to temperature and atmospheric CO2 concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models. [ABSTRACT FROM AUTHOR]