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Author(s): R. Valentini (corresponding author) [1]; G. Matteucci [1]; A. J. Dolman [2]; E.-D. Schulze [3, 4]; C. Rebmann [3, 4]; E. J. Moors [2]; A. Granier [5]; P. Gross [...]

The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m² m-2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0-54.2%), crop yield, as well as of the daily carbon and energy fluxes with NRMSE of ~9.0-20.1 and ~9.4-22.3 % for NEE, and sensible and latent heat fluxes, respectively. The model data mistfit for energy fluxes are within uncertainties of the measurements, which themselves show an incomplete energy balance closure within the range 80.6-86.3 %. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of management events. In addition, ORCHIDEE-CROP (v0) is shown to have the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, sensible heat fluxes and latent heat fluxes, across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and of management, is expected to improve its predictive ability to simulate croplands in an Earth System Model. [ABSTRACT FROM AUTHOR]

Concentrations of atmospheric carbon dioxide (CO2) have continued to increase whereas atmospheric deposition of sulphur and nitrogen has declined in Europe and the USA during recent decades. Using time series of flux observations from 23 forests distributed throughout Europe and the USA, and generalised mixed models, we found that forest-level net ecosystem production and gross primary production have increased by 1% annually from 1995 to 2011. Statistical models indicated that increasing atmospheric CO2 was the most important factor driving the increasing strength of carbon sinks in these forests. We also found that the reduction of sulphur deposition in Europe and the USA lead to higher recovery in ecosystem respiration than in gross primary production, thus limiting the increase of carbon sequestration. By contrast, trends in climate and nitrogen deposition did not significantly contribute to changing carbon fluxes during the studied period. Our findings support the hypothesis of a general CO2-fertilization effect on vegetation growth and suggest that, so far unknown, sulphur deposition plays a significant role in the carbon balance of forests in industrialized regions. Our results show the need to include the effects of changing atmospheric composition, beyond CO2, to assess future dynamics of carbon-climate feedbacks not currently considered in earth system/climate modelling.

Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET "La Thuile" data set, which includes several times more sites (144) and site years (422) than previous efforts have used. Our results show that remotely sensed proxies and modeled GPP are able to capture statistically significant amounts of spatial variation in mean annual GPP in every biome except croplands, but that the total variance explained differed substantially across biomes (R² ≈ 0.1 - 0.8). The ability of remotely sensed proxies and models to explain interannual variability GPP was even more limited. Remotely sensed proxies explained 40-60% of interannual variance in annual GPP in moisture-limited biomes including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, and deciduous broadleaf forests. Because important factors that affect year-to-year variation in GPP are not explicitly captured or included in the remote sensing proxies and models we examined (e.g., interactions between biotic and abiotic conditions, and lagged ecosystems responses to environmental process), our results are not surprising. Nevertheless, robust and repeatable characterization of interannual variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. As larger and more comprehensive data sets derived from the FLUXNET community become available, additional systematic assessment and refinement of remote sensing-based methods for monitoring annual GPP is warranted. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) emissions are highly variable in time, with high peak emissions lasting a few days to several weeks and low background emissions. This temporal variability is poorly understood which hampers the simulation of daily N2O emissions. In structured soils, like clay and peat, aggregates hamper the diffusion of oxygen, which leads to anaerobic microsites in the soil, favourable for denitrification. Diffusion of N2O out of the aggregates is also hampered, which leads to delayed emissions and increased reduction of N2O to N2. In this model simulation study we investigate the effect of aggregates in soils on the N2O emissions. We present a parameterization to simulate the effects of aggregates on N2O production by denitrification and on N2O reduction. The parameterization is based on the mobile-immobile model concept. It was implemented in a field-scale hydrological-biogeochemical model combination. We compared the simulated fluxes with observed fluxes from a fertilized and drained peat soil under grass. The results of this study show that aggregates strongly affect the simulated N2O emissions: peak emissions are lower, whereas the background emissions are slightly higher. Including the effect of aggregates caused a 40% decrease in the simulated annual emissions relative to the simulations without accounting for the effects of aggregates. The new parameterization significantly improved the model performance regarding simulation of observed daily N2O fluxes; r² and RMSE improved from 0.11 and 198 g N2O-N ha-1 d-1 to 0.41 and 40 g N2O-N ha-1 d-1, respectively. Our analyses of the model results show that aggregates have a larger impact on the reduction than on the production of N2O. Reduction of N2O is more sensitive to changes in the drivers than production of N2O and is in that sense the key to understanding N2O emissions from denitrification. The effects of changing environmental conditions on reduction of N2O relative to N2O production strongly depend on the NO3 content of the soil. More anaerobic conditions have hardly any effect on the ratio of production to reduction if NO3 is abundant, but will decrease this ratio if NO3 is limiting. In the first case the emissions will increase, whereas in the second case the emissions will decrease. This study suggests that the current knowledge of the hydrological, biogeochemical and physical processes may be sufficient to understand the observed N2O fluxes from a fertilized clayey peatland. Further research is needed to test how aggregates affect the N2O fluxes from other soils or soils with different fertilization regimes. [ABSTRACT FROM AUTHOR]

Most countries use a 1-year-resolution emission factor approach (Tier 1 or 2) to estimate terrestrial N2O emissions as part of their national greenhouse gas inventory. Little attention has so far been paid to the effect of the temporal resolution of the approach (e.g., day, season, and year) on N2O emission estimates. The effect of lumping temporal variation can be very large because of daily or seasonal variations of processes causing N2O emissions. Therefore, we compared atmual N2O emissions from a model with daily time steps (DNDC) with those of a model with annual time steps (INITIATOR). Emissions were simulated for two intensively managed grassland plots in the Dutch fen meadow landscape. Annual N2O emissions from the investigated grasslands were sensitive to rainfall distribution within the year, especially to summer rainfall. We recommend that Tier 2 N2O emission estimates for intensively managed grasslands on peat soils in the temperate climate zone are adjusted for relative summer rainfall. [ABSTRACT FROM AUTHOR]

Chambers are widely used to measure surface fluxes of nitrous oxide (N2O). Usually linear regression is used to calculate the fluxes from the chamber data. Non-linearity in the chamber data can result in an underestimation of the flux. Non-linear regression models are available for these data, but are not commonly used. In this study we compared the fit of linear and non-linear regression models to determine significant non-linearity in the chamber data. We assessed the influence of this significant nonlinearity on the annual fluxes. For a two year dataset from an automatic chamber we calculated the fluxes with linear and non-linear regression methods. Based on the fit of the methods 32% of the data was defined significant non-linear. Significant non-linearity was not recognized by the goodness of fit of the linear regression alone. Using non-linear regression for these data and linear regression for the rest, increases the annual flux with 21% to 53% compared to the flux determined from linear regression alone. We suggest that differences this large are due to leakage through the soil. Macropores or a coarse textured soil can add to fast leakage from the chamber. Yet, also for chambers without leakage non-linearity in the chamber data is unavoidable, due to feedback from the increasing concentration in the chamber. To prevent a possibly small, but systematic underestimation of the flux, we recommend comparing the fit of a linear regression model with a non-linear regression model. The non-linear regression model should be used if the fit is significantly better. Open questions are how macropores affect chamber measurements and how optimization of chamber design can prevent this. [ABSTRACT FROM AUTHOR]

Water-use efficiency (WUE) has been recognized as an important characteristic of vegetation productivity in various natural scientific disciplines for decades, but only recently at the ecosystem level, where different ways exist to characterize water-use efficiency. Hence, the objective of this research was (a) to systematically compare different ways of calculating ecosystem water-use efficiency (WUEe) from eddy-covariance measurements, (b) quantify the diurnal, seasonal and interannual variability of WUEe in relation to meteorological conditions, and (c) analyse between-site variability of WUEe as affected by vegetation type and climatic conditions, across sites in European forest ecosystems. Day-to-day variability of gross primary productivity (GPP) and evapotranspiration (ET) were more strongly coupled than net ecosystem production (NEP) and ET, obviously because NEP also depends on the respiration that is not heavily coupled to water fluxes. However, the slope of daytime NEP versus ET (mNEP) from half-hourly measurements of a single day may also be used as a WUEe-estimate giving very similar results to those of the GPP-ET slope (mGPP), since the diurnal variation is dominated by GPP. Since ET is the sum of transpiration (linked to GPP) and evaporation from wet vegetation and soil surfaces (not linked to GPP) we expected that WUEe is increasing when days after rain are excluded from the analysis. However only very minor changes were found, justifying an analysis of WUEe related to vegetation type. In most of the studied ecosystems the instantaneous WUEGPP was quite sensitive to diurnally varying meteorological conditions and tended to decline from the morning to the afternoon by more than 50% because of increasing vapour pressure deficits (VPD). Seasonally, WUEGPP increased with a rising monthly precipitation sum and rising average monthly temperatures up to a threshold of 11, 14 and 18°C in boreal, temperate and Mediterranean ecosystems, respectively. Across all sites, the highest monthly WUEGPP-values were detected at times of positive anomalies of summer-precipitation. During drought periods with high temperatures, high VPD, little precipitation and low soil water content, the water-use efficiency of gross carbon uptake (WUEGPP) tended to decrease in all forest types because of a stronger decline of GPP compared to ET. However the largest variation of growing season WUEGPP was found between sites and significantly related to vegetation type: WUEGPP was highest in ecosystems dominated by deciduous trees ranging from 5.0 gCO2 kgH2O-1 for temperate broad leaved deciduous forests (TD), to 4.5 for temperate mixed forests (TM), 3.5 for temperate evergreen conifers (TC), 3.4 for Mediterranean broad-leaved deciduous forests (MD), 3.3 for Mediterranean broad-leaved evergreen forests (Mbeg), 3.1 for Mediterranean evergreen conifers (MC), 2.9 for boreal evergreen conifers (BC) and only 1.2 g CO2 kgH2O-1 for a boreal wetland site (BT). Although vegetation type and meteorology co-vary, the WUEGPP variation was hardly related to meteorology, as we could show by comparing similar meteorological conditions only. Furthermore we compared across-site WUEGPP only under conditions when the 10% high GPP rates were exhibited. The between site differences remained, and at all sites ecosystem reached higher WUEGPP levels under this condition. This means when vegetation is most productive usually it also maximises the amount of carbon gained per water lost. Overall our results show that water-use efficiency exhibits a strong time-scale dependency in the sense that at longer time-scale meteorological conditions play a smaller role compared to shorter time scale. Moreover, we highlight the role of vegetation in determining carbon-water relation at ecosystem level. Consequently, all predictions of changing carbon-water cycle under changing climate should take into this role and the differences between vegetation types. These results show the strong time-scale dependency of water-use efficiency. [ABSTRACT FROM AUTHOR]

An intercomparison is made of the Net Ecosystem Exchange of CO2, NEE, for eight Dutch grassland sites; four natural grasslands, two production grasslands and two meteorological stations within a rotational grassland region. At all sites the NEE was determined during at least 10 months per site, using the eddy-covariance (EC) technique, but in different years. The photosynthesis-light response analysis technique is used along with the respiration-temperature response technique to partition NEE among Gross Primary Production (GPP) and Ecosystem Respiration (Re) and to obtain the eco-physiological characteristics of the sites at the field scale. Annual sums of NEE, GPP and Re are then estimated using the fitted response curves with observed radiation and air temperature from a meteorological site in the centre of The Netherlands as drivers. These calculations are carried out for four years (2002-2005). The estimated annual Re for all individual sites is more or less constant per site and the average for all sites amounts to 1390±30 gCm² a-1. The narrow uncertainty band (±2%) reflects the small differences in the mean annual air temperature. The mean annual GPP was estimated to be 1325 gCm-2 a-1, and displays a much higher standard deviation, of ±100 gCm-2 a-1 (8%), which reflects the relatively large variation in annual solar radiation. The mean annual NEE amounts to -65±85 gCm-2 a-1, which implies that on average the grasslands act as a source, with a relatively large standard deviation. From two sites, four-year records of CO2 flux were available and analyzed (2002- 2005). Using the weather record of 2005 with optimizations from the other years, standard deviation of annual GPP was estimated to be 171-206 gCm-2 a-1 (8-14%), of annual Re 227-247 gCm-2 a-1 (14-16%) and of annual NEE 176-276 gCm-2 a-1. The inter-site standard deviation was higher for GPP and Re, 534 gCm-2 a-1 (37.3%) and 486 gCm-2 a-1 (34.8%), respectively. However, the inter-site standard deviation of NEE was similar to the interannual one, amounting to 207 gCm-2 a-1. Large differences occur due to soil type. The grasslands on organic (peat) soils show a mean net release of CO2 of 220±90 gCm-2 a-1 while the grasslands on mineral (clay and sand) soils show a mean net uptake of CO2of 90±90 gCm-2 a-1. If a weighing with the fraction of grassland on organic (20%) and mineral soils (80%) is applied, an average NEE of 28±gCm-2 a-1 is found, which means that on average the Dutch grasslands behave like a small sink for CO2. The results from the analysis illustrate the need for regionally specific and spatially explicit CO2 emission estimates from grassland. [ABSTRACT FROM AUTHOR]