Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate

By C. Beer, Markus Reichstein, E. Tomelleri, Philippe Ciais, Martin Jung, N. Carvalhais, C. Rödenbeck, M. A. Arain, Dennis D. Baldocchi, Gordon Bonan and 14 more. Science, 2010

2,639 citations recorded by the source corpus.

Subfields: Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/984e7af1bdd7e28a6456fdd5ad9072e22fb92017

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In Global and Planetary Change

Citations in the corpus

Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought (2011, background): “Similarly, water availability has recently been described as a major determinant of terrestrial gross carbon dioxide uptake in Mediterranean and temperate regions (41).”
The productivity, metabolism and carbon cycle of tropical forest vegetation (2012, neutral): “Beer et al. (2010) used data-driven scaling approaches to scale up from the eddy covariance measurements to the globe (Fig.”
Permafrost carbon-climate feedbacks accelerate global warming (2011, result): “ORCHIDEE shows a very high sensitivity to CO2 fertilization at high latitudes; this high sensitivity is likely due to a number of biases in the model: (i) there is no limitation by N in the model, and thus increases in CO2 directly allow increases in productivity; and (ii) the baseline productivity ”
Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence (2014, methodology): “…Joseph A. Berry4, Christian Frankenberg5, Alfredo Huete6, Pablo Zarco-Tejada7, Jung-Eun Lee8, M. Susan Moran9, Guillermo Ponce-Campos9, Christian Beer10, Gustavo Camps-Valls11, Nina Buchmann12, Damiano Gianelle13, Katja Klumpp14, Alessandro Cescatti15, John M. Baker16, and Timothy J.…”
A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2018, background): “1 Introduction Obtaining a spatio-temporal continuous photosynthetic carbon fixation or gross primary production (GPP) dataset is crucial to food security, ecosystem service and health evaluation, and global carbon cycle studies (Beer et al., 2010).”
Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems (2020, background): “The development of data products (including those scaled from site level observations to gridded products, e.g., Beer et al., 2010) should bear in mind, in particular, the likely future trajectory of land surface model developments in future years.”
Correlations among leaf traits provide a significant constraint on the estimate of global gross primary production (2012, methodology): “46 years as the default values in CABLE that was calibrated using the GPP estimates of Beer et al. [2010]. The relative differences between the default values in CABLE and the means of those three leaf traits fromWright et al. [2004] often are largest for t. In general, the default values of t in CA”
Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model (2011, background): “…with its low thermal conductivity and high heat capacity modulating the transfer of energy down into the soil during spring and summer and out of the soil during fall and winter, typically leading to cooler soil temperatures than would be apparent for pure mineral soils (Bonan and Shugart 1989).”
TRY – a global database of plant traits (2011, background): “Similarly, patterns of spatially interpolated global fields of biosphere–atmosphere exchange (Beer et al., 2010; Jung et al., 2010a) may be related to spatialized plant traits in order to detect a biotic imprint on the global carbon and water cycles.”
Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. (2014, background): “Although the LUE approach provides a theoretical basis for constructing and calibrating models, there are no corresponding benchmarks to evaluate model performance at large geographical scales, and model uncertainty remains high (Beer et al., 2010).”
Time‐lag effects of global vegetation responses to climate change (2015, background): “In general, vegetation growth primarily depends on external climatic factors: temperature, precipitation, and radiation (Bonan, 2008; Beer et al., 2010; Wang et al., 2011; Craine et al., 2012).”
Leaf chlorophyll content as a proxy for leaf photosynthetic capacity (2017, background): “Terrestrial biosphere models (TBMs) are the principal means for providing regional and global estimates of terrestrial carbon cycling (Beer et al., 2010).”