Vargas R.,University of California at Berkeley |
Baldocchi D.D.,University of California at Berkeley |
Querejeta J.I.,CSIC - Center of Edafology and Applied Biology of the Segura |
Curtis P.S.,Ohio State University |
And 5 more authors.
New Phytologist | Year: 2010
Here, we explore how interannual variations in environmental factors (i.e. temperature, precipitation and light) influence CO2 fluxes (gross primary production and ecosystem respiration) in terrestrial ecosystems classified by vegetation type and the mycorrhizal type of dominant plants (arbuscular mycorrhizal (AM) or ectomycorrhizal (EM)). We combined 236 site-year measurements of terrestrial ecosystem CO2 fluxes and environmental factors from 50 eddy-covariance flux tower sites with information about climate, vegetation type and dominant plant species. Across large geographical distances, interannual variations in ecosystem CO2 fluxes for EM-dominated sites were primarily controlled by interannual variations in mean annual temperature. By contrast, interannual variations in ecosystem CO 2 fluxes at AM-dominated sites were primarily controlled by interannual variations in precipitation. This study represents the first large-scale assessment of terrestrial CO2 fluxes in multiple vegetation types classified according to dominant mycorrhizal association. Our results support and complement the hypothesis that bioclimatic conditions influence the distribution of AM and EM systems across large geographical distances, which leads to important differences in the major climatic factors controlling ecosystem CO2 fluxes. © 2009 New Phytologist.
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.
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.
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.
Richardson A.D.,Harvard University |
Black T.A.,University of British Columbia |
Ciais P.,CEA Saclay Nuclear Research Center |
Delbart N.,CEA Saclay Nuclear Research Center |
And 19 more authors.
Philosophical Transactions of the Royal Society B: Biological Sciences | Year: 2010
We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an 'extra' day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands. © 2010 The Royal Society.
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.
Teuling A.J.,ETH Zurich |
Teuling A.J.,Wageningen University |
Seneviratne S.I.,ETH Zurich |
Stockli R.,Climate Science |
And 17 more authors.
Nature Geoscience | Year: 2010
Recent European heatwaves have raised interest in the impact of land cover conditions on temperature extremes. At present, it is believed that such extremes are enhanced by stronger surface heating of the atmosphere, when soil moisture content is below average. However, the impact of land cover on the exchange of water and energy and the interaction of this exchange with the soil water balance during heatwaves is largely unknown. Here we analyse observations from an extensive network of flux towers in Europe that reveal a difference between the temporal responses of forest and grassland ecosystems during heatwaves. We find that initially, surface heating is twice as high over forest than over grassland. Over grass, heating is suppressed by increased evaporation in response to increased solar radiation and temperature. Ultimately, however, this process accelerates soil moisture depletion and induces a critical shift in the regional climate system that leads to increased heating. We propose that this mechanism may explain the extreme temperatures in August 2003. We conclude that the conservative water use of forest contributes to increased temperatures in the short term, but mitigates the impact of the most extreme heat and/or long-lasting events. © 2010 Macmillan Publishers Limited. All rights reserved.
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.
Melaas E.K.,Boston University |
Richardson A.D.,Harvard University |
Friedl M.A.,Boston University |
Dragoni D.,Indiana University |
And 5 more authors.
Agricultural and Forest Meteorology | Year: 2013
Vegetation phenology is sensitive to climate change and variability, and is a first order control on the carbon budget of forest ecosystems. Robust representation of phenology is therefore needed to support model-based projections of how climate change will affect ecosystem function. A variety of models have been developed to predict species or site-specific phenology of trees. However, extension of these models to other sites or species has proven difficult. Using meteorological and eddy covariance data for 29 forest sites (encompassing 173 site-years), we evaluated the accuracy with which 11 different models were able to simulate, as a function of air temperature and photoperiod, spatial and temporal variability in the onset of spring photosynthetic activity. In parallel, we also evaluated the accuracy with which dynamics in remotely sensed vegetation indices from MODIS captured the timing of spring onset. To do this, we used a subset of sites in the FLUXNET La Thuile database located in evergreen needleleaf and deciduous broadleaf forests with distinct active and dormant seasons and where temperature is the primary driver of seasonality. As part of this analysis we evaluated predictions from refined versions of the 11 original models that include parameterizations for geographic variation in both thermal and photoperiod constraints on phenology. Results from cross-validation analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than the original models. Estimates for the timing of spring onset from MODIS were highly correlated with the onset of photosynthesis derived from flux measurements, but were biased late for needleleaf sites. Our results demonstrate that simple phenology models can be used to predict the timing of spring photosynthetic onset both across sites and across years at individual sites. By extension, these models provide an improved basis for predicting how the phenology and carbon budgets of temperature-limited forest ecosystems may change in the coming decades. © 2012 Elsevier B.V.