Publications (8)111.94 Total impact
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Article: Semiempirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites
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ABSTRACT: In this study we examined ecosystem respiration (RECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of RECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of RECO failed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of RECO. The maximum seasonal leaf area index (LAIMAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature Tref=15 °C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r2=0.52, P<0.001, n=104) even within each PFT. Besides LAIMAX, we found that reference respiration may be explained partially by total soil carbon content (SoilC). For undisturbed temperate and boreal forests a negative control of total nitrogen deposition (Ndepo) on reference respiration was also identified. We developed a new semiempirical model incorporating abiotic factors (climate), recent productivity (daily GPP), general site productivity and canopy structure (LAIMAX) which performed well in predicting the spatio-temporal variability of RECO, explaining >70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands.Global Change Biology 12/2010; 17(1):390 - 409. · 6.86 Impact Factor -
Article: Influence of spring and autumn phenological transitions on forest ecosystem productivity.
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ABSTRACT: 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.Philosophical Transactions of The Royal Society B Biological Sciences 10/2010; 365(1555):3227-46. · 6.40 Impact Factor -
Article: Recent decline in the global land evapotranspiration trend due to limited moisture supply.
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ABSTRACT: More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land−a key diagnostic criterion of the effects of climate change and variability−remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.Nature 10/2010; 467(7318):951-4. · 36.28 Impact Factor -
Article: Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate.
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ABSTRACT: Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.Science 08/2010; 329(5993):834-8. · 31.20 Impact Factor -
Article: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate
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ABSTRACT: Terrestrial gross primary production (GPP) is the largest global CO2 flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 ± 8 petagrams of carbon per year (Pg C year−1) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP’s latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate–carbon cycle process models.Science 08/2010; 329(5993):834-838. · 31.20 Impact Factor -
Article: On the use of integrating FLUXNET eddy covariance and remote sensing data for model evaluation
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ABSTRACT: The current FLUXNET database (www.fluxdata.org) of CO2, water and energy exchange between the terrestrial biosphere and the atmosphere contains almost 1000 site-years with data from more than 250 sites, encompassing all major biomes of the world and being processed in a standardized way (1-3). In this presentation we show that the information in the data is sufficient to derive generalized empirical relationships between vegetation/respective remote sensing information, climate and the biosphere-atmosphere exchanges across global biomes. These empirical patterns are used to generate global grids of the respective fluxes and derived properties (e.g. radiation and water-use efficiencies or climate sensitivities in general, bowen-ratio, AET/PET ratio). For example we revisit global 'text-book' numbers such as global Gross Primary Productivity (GPP) estimated since the 70's as ca. 120PgC (4), or global evapotranspiration (ET) estimated at 65km3/yr-1 (5) - for the first time with a more solid and direct empirical basis. Evaluation against independent data at regional to global scale (e.g. atmospheric CO2 inversions, runoff data) lends support to the validity of our almost purely empirical up-scaling approaches. Moreover climate factors such as radiation, temperature and water balance are identified as driving factors for variations and trends of carbon and water fluxes, with distinctly different sensitivities between different vegetation types. Hence, these global fields of biosphere-atmosphere exchange and the inferred relations between climate, vegetation type and fluxes should be used for evaluation or benchmarking of climate models or their land-surface components, while overcoming scale-issues with classical point-to-grid-cell comparisons. 1. M. Reichstein et al., Global Change Biology 11, 1424 (2005). 2. D. Baldocchi, Australian Journal of Botany 56, 1 (2008). 3. D. Papale et al., Biogeosciences 3, 571 (2006). 4. D. E. Alexander, R. W. Fairbridge, Encyclopedia of Environmental Science (Springer, Heidelberg, 1999), pp. 741. 5. T. Oki, S. Kanae, Science 313, 1068 (Aug 25, 2006)04/2010; 12:6708. -
Article: The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data
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ABSTRACT: We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals <110 gC m−2 year−1 for the synthetic case. Annual C flux estimates generated by participants generally agreed with gap-filling approaches using half-hourly data. The estimation of ecosystem respiration and GPP through MDF agreed well with outputs from partitioning studies using half-hourly data. Confidence limits on annual NEE increased by an average of 88% in the prediction year compared to the previous year, when data were available. Confidence intervals on annual NEE increased by 30% when observed data were used instead of synthetic data, reflecting and quantifying the addition of model error. Finally, our analyses indicated that incorporating additional constraints, using data on C pools (wood, soil and fine roots) would help to reduce uncertainties for model parameters poorly served by eddy covariance data.Agricultural and Forest Meteorology. -
Article: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate
[show abstract] [hide abstract]
ABSTRACT: Terrestrial gross primary production (GPP) is the largest global CO2 flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 {+/-} 8 petagrams of carbon per year (Pg C year-1) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.Science. 329(5993):834-838.
Top Journals
Institutions
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2010
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Università degli Studi della Tuscia
Viterbo, Latium, Italy -
Max Planck Institute for Biogeochemistry Jena
- Group of Model-Data Integration
Jena, Thuringia, Germany
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