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Wang T.,CEA Saclay Nuclear Research Center | Brender P.,CEA Saclay Nuclear Research Center | Brender P.,Agro ParisTech | Ciais P.,CEA Saclay Nuclear Research Center | And 19 more authors.
Ecological Modelling | Year: 2012

Characterization of state-dependent model biases in land surface models can highlight model deficiencies, and provide new insights into model development. In this study, artificial neural networks (ANNs) are used to estimate the state-dependent biases of a land surface model (ORCHIDEE: ORganising Carbon and Hydrology in Dynamic EcosystEms). To characterize state-dependent biases in ORCHIDEE, we use multi-year flux measurements made at 125 eddy covariance sites that cover 7 different plant functional types (PFTs) and 5 climate groups. We determine whether the state-dependent model biases in five flux variables (H: sensible heat, LE: latent heat, NEE: net ecosystem exchange, GPP: gross primary productivity and R eco: ecosystem respiration) are transferable within and between three different timescales (diurnal, seasonal-annual and interannual), and between sites (categorized by PFTs and climate groups). For each flux variable at each site, the spectral decomposition method (singular system analysis) was used to reconstruct time series on the three different timescales.At the site level, we found that the share of state-dependent model biases (hereafter called " error transferability" ) is larger for seasonal-annual and interannual timescales than for the diurnal timescale, but little error transferability was found between timescales in all flux variables. Thus, performing model evaluations at multiple timescales is essential for diagnostics and future development. For all PFTs, climate groups and timescale components, the state-dependent model biases are found to be transferable between sites within the same PFT and climate group, suggesting that specific model developments and improvements based on specific eddy covariance sites can be used to enhance the model performance at other sites within the same PFT-climate group. This also supports the legitimacy of upscaling from the ecosystem scale of eddy covariance sites to the regional scale based on the similarity of PFT and climate group. However, the transferability of state-dependent model biases between PFTs or climate groups is not always found on the seasonal-annual and interannual timescales, which is contrary to transferability found on the diurnal timescale and the original time series. © 2012 Elsevier B.V. Source

Wang T.,LSCE IPSL | Ciais P.,LSCE IPSL | Piao S.L.,Peking University | Ottle C.,LSCE IPSL | And 28 more authors.
Biogeosciences | Year: 2011

Winter CO 2 fluxes represent an important component of the annual carbon budget in northern ecosystems. Understanding winter respiration processes and their responses to climate change is also central to our ability to assess terrestrial carbon cycle and climate feedbacks in the future. However, the factors influencing the spatial and temporal patterns of winter ecosystem respiration (R eco) of northern ecosystems are poorly understood. For this reason, we analyzed eddy covariance flux data from 57 ecosystem sites ranging from ∼35° N to ∼70° N. Deciduous forests were characterized by the highest winter R eco rates (0.90 ± 0.39 g C m -2 d -1), when winter is defined as the period during which daily air temperature remains below 0 °C. By contrast, arctic wetlands had the lowest winter R eco rates (0.02 ± 0.02 g C m -2 d -1). Mixed forests, evergreen needle-leaved forests, grasslands, croplands and boreal wetlands were characterized by intermediate winter R eco rates (g C m -2 d -1) of 0.70(±0.33), 0.60(±0.38), 0.62(±0.43), 0.49(±0.22) and 0.27(±0.08), respectively. Our cross site analysis showed that winter air (T air) and soil (T soil) temperature played a dominating role in determining the spatial patterns of winter R eco in both forest and managed ecosystems (grasslands and croplands). Besides temperature, the seasonal amplitude of the leaf area index (LAI), inferred from satellite observation, or growing season gross primary productivity, which we use here as a proxy for the amount of recent carbon available for R eco in the subsequent winter, played a marginal role in winter CO 2 emissions from forest ecosystems. We found that winter R eco sensitivity to temperature variation across space (Q S) was higher than the one over time (interannual, Q T). This can be expected because Q S not only accounts for climate gradients across sites but also for (positively correlated) the spatial variability of substrate quantity. Thus, if the models estimate future warming impacts on R eco based on Q S rather than Q T, this could overestimate the impact of temperature changes. © Author(s) 2011. Source

Balzarolo M.,University of Tuscia | Anderson K.,University of Exeter | Nichol C.,University of Edinburgh | Rossini M.,University of Milan Bicocca | And 26 more authors.
Sensors | Year: 2011

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903 "Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe" that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety ofoptical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites. © 2011 by the authors; licensee MDPI, Basel, Switzerland. Source

Peltoniemi M.,University of Helsinki | Peltoniemi M.,Finnish Forest Research Institute | Pulkkinen M.,University of Helsinki | Kolari P.,University of Helsinki | And 13 more authors.
Tree Physiology | Year: 2012

The maximum light use efficiency (LUE = gross primary production (GPP)/absorbed photosynthetic photon flux density (aPPFD)) of plant canopies has been reported to vary spatially and some of this variation has previously been attributed to plant species differences. The canopy nitrogen concentration [N] can potentially explain some of this spatial variation. However, the current paradigm of the N-effect on photosynthesis is largely based on the relationship between photosynthetic capacity (Amax) and [N], i.e., the effects of [N] on photosynthesis rates appear under high PPFD. A maximum LUE-[N] relationship, if it existed, would influence photosynthesis in the whole range of PPFD. We estimated maximum LUE for 14 eddy-covariance forest sites, examined its [N] dependency and investigated how the [N]-maximum LUE dependency could be incorporated into a GPP model. In the model, maximum LUE corresponds to LUE under optimal environmental conditions before light saturation takes place (the slope of GPP vs. PPFD under low PPFD). Maximum LUE was higher in deciduous/mixed than in coniferous sites, and correlated significantly with canopy mean [N]. Correlations between maximum LUE and canopy [N] existed regardless of daily PPFD, although we expected the correlation to disappear under low PPFD when LUE was also highest. Despite these correlations, including [N] in the model of GPP only marginally decreased the root mean squared error. Our results suggest that maximum LUE correlates linearly with canopy [N], but that a larger body of data is required before we can include this relationship into a GPP model. Gross primary production will therefore positively correlate with [N] already at low PPFD, and not only at high PPFD as is suggested by the prevailing paradigm of leaf-level Amax-[N] relationships. This finding has consequences for modelling GPP driven by temporal changes or spatial variation in canopy [N]. © The Author 2011. Source

Groenendijk M.,VU University Amsterdam | Groenendijk M.,University of Exeter | Dolman A.J.,VU University Amsterdam | Ammann C.,Federal Research Station Agroscope ART | And 22 more authors.
Journal of Geophysical Research: Biogeosciences | Year: 2011

Global vegetation models require the photosynthetic parameters, maximum carboxylation capacity (Vcm), and quantum yield (α) to parameterize their plant functional types (PFTs). The purpose of this work is to determine how much the scaling of the parameters from leaf to ecosystem level through a seasonally varying leaf area index (LAI) explains the parameter variation within and between PFTs. Using Fluxnet data, we simulate a seasonally variable LAIF for a large range of sites, comparable to the LAI M derived from MODIS. There are discrepancies when LAIF reach zero levels and LAIM still provides a small positive value. We find that temperature is the most common constraint for LAIF in 55% of the simulations, while global radiation and vapor pressure deficit are the key constraints for 18% and 27% of the simulations, respectively, while large differences in this forcing still exist when looking at specific PFTs. Despite these differences, the annual photosynthesis simulations are comparable when using LAIF or LAIM (r2 = 0.89). We investigated further the seasonal variation of ecosystem-scale parameters derived with LAIF. Vcm has the largest seasonal variation. This holds for all vegetation types and climates. The parameter α is less variable. By including ecosystem-scale parameter seasonality we can explain a considerable part of the ecosystem-scale parameter variation between PFTs. The remaining unexplained leaf-scale PFT variation still needs further work, including elucidating the precise role of leaf and soil level nitrogen. Copyright 2011 by the American Geophysical Union. Source

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