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Abstract: Dynamic vegetation models are useful tools for analysing terrestrial ecosystem processes and their interactions with climate through variations in carbon and water exchange. Long-term changes in structure and composition (vegetation dynamics) caused by altered competitive strength between plant functional types (PFTs) are attracting increasing attention as controls on ecosystem functioning and potential feedbacks to climate. Imperfect process knowledge and limited observational data restrict the possibility to parameterise these processes adequately and potentially contribute to uncertainty in model results. This study addresses uncertainty among parameters scaling vegetation dynamic processes in a process-based ecosystem model, LPJ-GUESS, designed for regional-scale studies, with the objective to assess the extent to which this uncertainty propagates to additional uncertainty in the tree community structure (in terms of the tree functional types present and their relative abundance) and thus to ecosystem functioning (carbon storage and fluxes). The results clearly indicate that the uncertainties in parameterisation can lead to a shift in competitive balance, most strikingly among deciduous tree PFTs, with dominance of either shade-tolerant or shade-intolerant PFTs being possible, depending on the choice of plausible parameter values. Despite this uncertainty, our results indicate that the resulting effect on ecosystem functioning is low. Since the vegetation dynamics in LPJ-GUESS are representative for the more complex Earth system models now being applied within ecosystem and climate research, we assume that our findings will be of general relevance. We suggest that, in terms of carbon storage and fluxes, the heavier parameterisation requirement of the processes involved does not widen the overall uncertainty in model predictions. [Copyright &y& Elsevier]

Biogeochemical models are often used for making projections of future carbon dynamics under scenarios of global change. The aim of this study was to assess the accuracy of the process-based biogeochemical model Biome-BGC for application in central European forests from the lowlands to upper treeline as a pre-requisite for environmental impact assessments. We analyzed model behavior along an altitudinal gradient across the alpine treeline, which provided insights on the sensitivity of simulated average carbon pools to changes in environmental factors. A second set of tests included medium-term (30 years) simulations of carbon fluxes, and a third set of tests focused on daily carbon and water fluxes. Model results were compared to aboveground biomass measurements, leaf area index recordings as well as net ecosystem exchange (NEE) and actual evapotranspiration (AET) measurements. The simulated medium-term forest growth agreed well with measured data. Also daily NEE fluxes were simulated adequately in most cases. Problems were detected when simulating ecosystems close to the upper timberline (overestimation of measured growth and pool sizes), and when simulating daily AET fluxes (overestimation of measured fluxes). The results showed that future applications of Biome-BGC could benefit much from an improvement of model algorithms (e.g., the Q10 model for respiration) as well as from a detailed analysis of the ecological significance of crucial parameters (e.g., the canopy water interception coefficient). [ABSTRACT FROM AUTHOR]

Process-based models can be classified into: (a) terrestrial biogeochemical models (TBMs), which simulate fluxes of carbon, water and nitrogen coupled within terrestrial ecosystems, and (b) dynamic global vegetation models (DGVMs), which further couple these processes interactively with changes in slow ecosystem processes depending on resource competition, establishment, growth and mortality of different vegetation types. In this study, four models – RHESSys, GOTILWA+, LPJ-GUESS and ORCHIDEE – representing both modelling approaches were compared and evaluated against benchmarks provided by eddy-covariance measurements of carbon and water fluxes at 15 forest sites within the EUROFLUX project. Overall, model-measurement agreement varied greatly among sites. Both modelling approaches have somewhat different strengths, but there was no model among those tested that universally performed well on the two variables evaluated. Small biases and errors suggest that ORCHIDEE and GOTILWA+ performed better in simulating carbon fluxes while LPJ-GUESS and RHESSys did a better job in simulating water fluxes. In general, the models can be considered as useful tools for studies of climate change impacts on carbon and water cycling in forests. However, the various sources of variation among models simulations and between models simulations and observed data described in this study place some constraints on the results and to some extent reduce their reliability. For example, at most sites in the Mediterranean region all models generally performed poorly most likely because of problems in the representation of water stress effects on both carbon uptake by photosynthesis and carbon release by heterotrophic respiration ( Rh). The use of flux data as a means of assessing key processes in models of this type is an important approach to improving model performance. Our results show that the models have value but that further model development is necessary with regard to the representation of the some of the key ecosystem processes. [ABSTRACT FROM AUTHOR]

The net ecosystem exchange (NEE) of forests represents the balance of gross primary productivity (GPP) and respiration (R). Methods to estimate these two components from eddy covariance flux measurements are usually based on a functional relationship between respiration and temperature that is calibrated for night-time (respiration) fluxes and subsequently extrapolated using daytime temperature measurements. However, respiration fluxes originate from different parts of the ecosystem, each of which experiences its own course of temperature. Moreover, if the temperature-respiration function is fitted to combined data from different stages of biological development or seasons, a spurious temperature effect may be included that will lead to overestimation of the direct effect of temperature and therefore to overestimates of daytime respiration. We used the EUROFLUX eddy covariance data set for 15 European forests and pooled data per site, month and for conditions of low and sufficient soil moisture, respectively. We found that using air temperature (measured above the canopy) rather than soil temperature (measured 5 cm below the surface) yielded the most reliable and consistent exponential (Q10) temperature-respiration relationship. A fundamental difference in air temperature-based Q10 values for different sites, times of year or soil moisture conditions could not be established; all were in the range 1.6–2.5. However, base respiration (R0, i.e. respiration rate scaled to 0 °C) did vary significantly among sites and over the course of the year, with increased base respiration rates during the growing season. We used the overall mean Q10 of 2.0 to estimate annual GPP and R. Testing suggested that the uncertainty in total GPP and R associated with the method of separation was generally well within 15%. For the sites investigated, we found a positive relationship between GPP and R, indicating that there is a latitudinal trend in NEE because the absolute decrease in GPP towards the pole is greater than in R. [ABSTRACT FROM AUTHOR]

Abstract The current emphasis on global climate studies has led the scientific community to set up a number of sites for measuring long-term biospheric fluxes, and to develop a wide range of biosphere–atmosphere exchange models. This paper presents a new model of this type, which has been developed for a pine forest canopy. In most coniferous species the canopy layer is well separated from the understorey and several cohorts of needles coexist. It was therefore found necessary to distinguish several vegetation layers and, in each layer, several leaf classes defined not only by their light regime and wetness status but also by their age. This model, named MuSICA , is a multilayer, multileaf process-based model. Each submodel is first independently parameterized using data collected at a EUROFLUX site near Bordeaux (Southwestern France). Particular care is brought to identify the seasonal variations in the various physiological parameters. The full model is then evaluated using a two-year long data set, split up into 12 day-type classes defined by the season, the weather type and the soil water status. Beyond the good overall agreement obtained between measured and modelled values at various time scales, several points of further improvement are identified. They concern the seasonal variations in the stomatal response of needles and the soil/litter respiration, as well as their interaction with soil or litter moisture. A sensitivity analysis to some of the model features (in-canopy turbulent transfer scheme, leaf age classes, water retention, distinction between shaded and sunlit leaves, number of layers) is finally performed in order to evaluate whether significant simplifications can be brought to such a model with little loss in its predictive quality. The distinction between several leaf classes is crucial if one is to compute biospheric fluxes accurately. It is also evidenced that accounting for in-canopy turbulent transfer leads to better estimates of... [ABSTRACT FROM AUTHOR]

As length and timing of the growing season are major factors explaining differences in carbon exchange of ecosystems, we analyzed seasonal patterns of net ecosystem carbon exchange (FNEE) using eddy covariance data of the FLUXNET data base (http://www-eosdis.ornl.gov/FLUXNET). The study included boreal and temperate, deciduous and coniferous forests, Mediterranean evergreen systems, rainforest, native and managed temperate grasslands, tundra, and C3 and C4 crops. Generalization of seasonal patterns are useful for identifying functional vegetation types for global dynamic vegetation models, as well as for global inversion studies, and can help improve phenological modules in SVAT or biogeochemical models. The results of this study have important validation potential for global carbon cycle modeling.The phasing of respiratory and assimilatory capacity differed within forest types: for temperate coniferous forests seasonal uptake and release capacities are in phase, for temperate deciduous and boreal coniferous forests, release was delayed compared to uptake. According to seasonal pattern of maximum nighttime release (evaluated over 15-day periods, Fmax) the study sites can be grouped in four classes: (1) boreal and high altitude conifers and grasslands; (2) temperate deciduous and temperate conifers; (3) tundra and crops; (4) evergreen Mediterranean and tropical forests. Similar results are found for maximum daytime uptake (Fmin) and the integral net carbon flux, but temperate deciduous forests fall into class 1.For forests, seasonal amplitudes of Fmax and Fmin increased in the order tropical and temperate coniferous deciduous and boreal forests, and the pattern seems relatively stable for these groups. The seasonal amplitudes of Fmax and Fmin are largest for managed grasslands and crops. Largest observed values of Fmin varied between −48 and −2 μmol m−2 s−1, decreasing in the order C4-crops>C3-crops>temperate deciduous forests>temperate conifers>boreal conifers>tundra ecosystems.Due to data restrictions, our analysis centered mainly on Northern Hemisphere temperate and boreal forest ecosystems. Grasslands, crops, Mediterranean ecosystems, and rainforests are under-represented, as are savanna systems, wooded grassland, shrubland, or year-round measurements in tundra systems. For regional or global estimates of carbon sequestration potentials, future investigations of eddy covariance should expand in these systems. [Copyright &y& Elsevier]

Differences in the seasonal pattern of assimilatory and respiratory processes are responsible for divergences in seasonal net carbon exchange among ecosystems. Using FLUXNET data (http://www.eosdis.ornl.gov/FLUXNET) we have analyzed seasonal patterns of gross primary productivity (FGPP), and ecosystem respiration (FRE) of boreal and temperate, deciduous and coniferous forests, Mediterranean evergreen systems, a rainforest, temperate grasslands, and C3 and C4 crops. Based on generalized seasonal patterns classifications of ecosystems into vegetation functional types can be evaluated for use in global productivity and climate change models. The results of this study contribute to our understanding of respiratory costs of assimilated carbon in various ecosystems.Seasonal variability of FGPP and FRE of the investigated sites increased in the order tropical. Together with the boreal forest sites, the managed grasslands and crops show the largest seasonal variability. In the temperate coniferous forests, seasonal patterns of FGPP and FRE are in phase, in the temperate deciduous and boreal coniferous forests FRE was delayed compared to FGPP, resulting in the greatest imbalance between respiratory and assimilatory fluxes early in the growing season.FGPP adjusted for the length of the carbon uptake period decreased at the sampling sites across functional types in the order C4 crops, temperate and boreal deciduous forests (7.5–8.3 g C m−2 per day)>temperate conifers, C3 grassland and crops (5.7–6.9 g C m−2 per day)>boreal conifers (4.6 g C m−2 per day). Annual FGPP and net ecosystem productivity (FNEP) decreased across climate zones in the order tropical>temperate>boreal. However, the decrease in FNEP with latitude was greater than the decrease in FGPP, indicating a larger contribution of respiratory (especially heterotrophic) processes in boreal systems. [Copyright &y& Elsevier]

Summary This paper presents CO2 flux data from 18 forest ecosystems, studied in the European Union funded EUROFLUX project. Overall, mean annual gross primary productivity (GPP, the total amount of carbon (C) fixed during photosynthesis) of these forests was 1380 ± 330 gC m-2 y-1 (mean ±SD). On average, 80% of GPP was respired by autotrophs and heterotrophs and released back into the atmosphere (total ecosystem respiration, TER = 1100 ± 260 gC m-2 y-1 ). Mean annual soil respiration (SR) was 760 ± 340 gC m-2 y-1 (55% of GPP and 69% of TER). Among the investigated forests, large differences were observed in annual SR and TER that were not correlated with mean annual temperature. However, a significant correlation was observed between annual SR and TER and GPP among the relatively undisturbed forests. On the assumption that (i) root respiration is constrained by the allocation of photosynthates to the roots, which is coupled to productivity, and that (ii) the largest fraction of heterotrophic soil respiration originates from decomposition of young organic matter (leaves, fine roots), whose availability also depends on primary productivity, it is hypothesized that differences in SR among forests are likely to depend more on productivity than on temperature. At sites where soil disturbance has occurred (e.g. ploughing, drainage), soil espiration was a larger component of the ecosystem C budget and deviated from the relationship between annual SR (and TER) and GPP observed among the less-disturbed forests. At one particular forest, carbon losses from the soil were so large, that in some years the site became a net source of carbon to the atmosphere. Excluding the disturbed sites from the present analysis reduced mean SR to 660 ± 290 gC m-2 y-1 , representing 49% of GPP and 63% of TER in the relatively undisturbed forest ecosystems. [ABSTRACT FROM AUTHOR]

Dynamic Global Vegetation Models (DGVMs) are commonly used to describe the land biogeochemical processes and regulate carbon and water pools. However, the simulation efficiency and validation of DGVMs are limited to varying temporal and spatial resolutions. Additionally, the uncertainties caused by different interpolation methods used in DGVMs are still not clear. In this study, we employ Socio-Economic and natural Vegetation ExpeRimental (SEVER) DGVM to simulate Net Ecosystem Exchange (NEE) flux with large scale National Centers for Environmental Prediction (NCEP) daily climate data as inputs for the years 1997–2000 at 14 Euroflux sites. It is shown that daily local NEE flux on chosen sites can be reasonably simulated, and daily temperature and shortwave radiation are the most essential inputs for daily NEE simulation compared with precipitation and the ratio of sunshine hours. Different running means (1 to 30 days) methods are analysed for each Euroflux site, and the best results of both averaged regression coefficient and averaged slope of regression are discovered by using 5 days running mean method. SEVER DGVM, driven by linearly interpolated daily climate data is compared at the monthly time step with Lund-Potsdam-Jena (LPJ) DGVM, which combines the linear interpolation of daily temperature with stochastic generation of daily precipitation. The comparison demonstrates that the stochastic generation of daily precipitation provides an acceptable fit to local observed NEE, but with a slight decrease in accuracy. Simulation experiments with SEVER DGVM demonstrate that daily local NEE flux inside a grid cell for a region as large as Europe can be modelled by DGVMs, using only large scale climate data as inputs. Unlabelled Image • Temperature and shortwave radiation are essential inputs for daily NEE simulation • Stochastic generation of daily precipitation provides a good fit to local NEE • DGVMs can model local daily grid cell-based NEE using large scale climate data • Running mean of 5 days is the best averaging comparison window for local NEE • Large potential exists to improve formal descriptions of underlying GPP and Rh [ABSTRACT FROM AUTHOR]