Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation

Law, B. E.; Falge, Eva; Gu, Lianhong; Baldocchi, Dennis; Bakwin, Peter S.; Berbigier, Paul; Davis, Kenneth J.; Dolman, A. J.; Falk, Matthias; Fuentes, José D.; Goldstein, Allen H.; Granier, André; Grelle, Achim; Hollinger, D. Y.; Janssens, Ivan A.; Jarvis, P. G.; Jensen, N.O.; Katul, Gabriel G.; Mahli, Y.; Matteucci, Giorgio; Meyers, Tilden P.; Monson, Russell K.; Munger, J. William; Oechel, Walter C.; Olson, Richard J.; Pilegaard, Kim; Thorgeirsson, Halldór; Валентини, Р.; Verma, Shashi B.; Vesala, Timo; Wilson, K.; Wofsy, Steven C.

doi:10.1016/s0168-1923(02)00104-1

Cited by 1,199 publications

(899 citation statements)

References 81 publications

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“…One therefore has to limit model validation to interannual and seasonal variations of carbon stocks and fluxes using the model at equilibrium. This was also done here, yet it is well established that most FluxNet sites are net carbon sinks [e.g., Law et al, 2002]. Simulated half-hourly CO 2 fluxes were therefore corrected in order to account for these sinks.…”

Section: B3 Initialization Of the Modelmentioning

confidence: 99%

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

Krinner

Viovy

Noblet‐Ducoudré

et al. 2005

Global Biogeochemical Cycles

2,071

2,063

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[1] This work presents a new dynamic global vegetation model designed as an extension of an existing surface-vegetation-atmosphere transfer scheme which is included in a coupled ocean-atmosphere general circulation model. The new dynamic global vegetation model simulates the principal processes of the continental biosphere influencing the global carbon cycle (photosynthesis, autotrophic and heterotrophic respiration of plants and in soils, fire, etc.) as well as latent, sensible, and kinetic energy exchanges at the surface of soils and plants. As a dynamic vegetation model, it explicitly represents competitive processes such as light competition, sapling establishment, etc. It can thus be used in simulations for the study of feedbacks between transient climate and vegetation cover changes, but it can also be used with a prescribed vegetation distribution. The whole seasonal phenological cycle is prognostically calculated without any prescribed dates or use of satellite data. The model is coupled to the IPSL-CM4 coupled atmosphereocean-vegetation model. Carbon and surface energy fluxes from the coupled hydrologyvegetation model compare well with observations at FluxNet sites. Simulated vegetation distribution and leaf density in a global simulation are evaluated against observations, and carbon stocks and fluxes are compared to available estimates, with satisfying results.

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Section: B3 Initialization Of the Modelmentioning

confidence: 99%

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

Krinner

Viovy

Noblet‐Ducoudré

et al. 2005

Global Biogeochemical Cycles

2,071

2,063

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“…Previous studies have demonstrated that the spatial variation in LAI could affect the spatial patterns of different components of AET. For example, evaporation of canopy interception depended on LAI (Jung et al, 2011), and the proportion of AET that evaporated from soil surfaces increased as LAI decreased (Law et al, 2002). Thus, it is seemly that LAI regulates the spatial patterns of AET mainly by influencing the proportion of different AET components (i.e., vegetation transpiration, soil evaporation and evaporation of canopy interception).…”

Section: Effects Of Climate and Vegetation On The Spatial Variation Imentioning

confidence: 99%

“…Given the pronounced covariation or interactive effects among climatic factors (e.g., MAT vs. R n , MAT vs, VPD, MAP vs. RH) and vegetation attributes (e.g., MAT vs. LAI, MAP vs. LAI) (Law et al, 2002), it is not surprised that the spatial patterns of AET significantly correlated with that of R n , VPD, RH and LAI. To better understand the climate and vegetation controls on the spatial variation in AET, it is important to comprehensively understand the roles of different potential influencing factors, especially vegetation attributes, in regulating the spatial patterns of AET.…”

Section: Effects Of Climate and Vegetation On The Spatial Variation Imentioning

confidence: 99%

Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements

Zheng

Wang

et al. 2016

J. Geogr. Sci.

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Abstract:Understanding the spatial variation in annual actual evapotranspiration (AET) and its influencing factors is crucial for a better understanding of hydrological processes and water resources management. By synthesizing ecosystem-level observations of eddy-covariance flux sites in China (a total of 61 sites), we constructed the most complete AET dataset in China up to now. Based on this dataset, we quantified the statistic characteristics of AET and water budgets (defined as the ratio of AET to annual mean precipitation (MAP), AET/MAP) of terrestrial ecosystems in China. Results showed that AET differed significantly among both different vegetation types and climate types in China, with overall mean AET of 534.7±232.8 mm yr -1 . AET/MAP also differed significantly among different climate types, but there were no distinct differences in AET/MAP values across vegetation types, with mean AET/MAP of 0.82±0.28 for non-irrigated ecosystems. We further investigated how the main climatic factors and vegetation attributes control the spatial variation in AET. Our findings revealed that the spatial variation of AET in China was closely correlated with the geographical patterns of climate and vegetation, in which the effects of total annual net radiation (R n ), MAP and mean annual air temperature (MAT) were dominant. Thus, we proposed an empirical equation to describe the spatial patterns of AET in China, which could explain about 84% of the spatial variation in AET of terrestrial ecosystems in China. Based on the constructed dataset, we also evaluated the uncertainties of five published global evapotranspiration products in simulating 1392 Journal of Geographical Sciences site-specific AET in China. Results showed that large biases in site-specific AET values existed for all five global evapotranspiration products, which indicated that it is necessary to involve more observation data of China in their parameterization or validation, while our AET dataset would provide a data source for it.

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“…Here, we use over 40 site-years of data from the AmeriFlux network (Law et al, 2002) to examine forest, crop, and grassland albedo parameterizations of several LSMs and use inverse methods to suggest several improved parameterizations. This is a far more comprehensive dataset than used in previous estimates of albedo, providing continuous replication within functional types and across years.…”

Section: Introductionmentioning

confidence: 99%

Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration

Hollinger

Ollinger

Richardson

et al. 2010

Global Change Biology

154

106

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; Schmid, H. P.; Stoy, P. C.; Suyker, Andrew E.; and Verma, Shashi, "Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration" (2010 Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration AbstractVegetation albedo is a critical component of the Earth's climate system, yet efforts to evaluate and improve albedo parameterizations in climate models have lagged relative to other aspects of model development. Here, we calculated growing season albedos for deciduous and evergreen forests, crops, and grasslands based on over 40 site-years of data from the AmeriFlux network and compared them with estimates presently used in the land surface formulations of a variety of climate models. Generally, the albedo estimates used in land surface models agreed well with this data compilation. However, a variety of models using fixed seasonal estimates of albedo overestimated the growing season albedo of northerly evergreen trees. In contrast, climate models that rely on a common two-stream albedo submodel provided accurate predictions of boreal needle-leaf evergreen albedo but overestimated grassland albedos. Inverse analysis showed that parameters of the two-stream model were highly correlated. Consistent with recent observations based on remotely sensed albedo, the AmeriFlux dataset demonstrated a tight linear relationship between canopy albedo and foliage nitrogen concentration (for forest vegetation: albedo 5 0.01 1 0.071%N, r 2 5 0.91; forests, grassland, and maize: albedo 5 0.02 1 0.067%N, r 2 5 0.80). However, this relationship saturated at the higher nitrogen concentrations displayed by soybean foliage. We developed similar relationships between a foliar parameter used in the two-stream albedo model and foliage nitrogen concentration. These nitrogen-based relationships can serve as the basis for a new approach to land surface albedo modeling that simplifies albedo estimation while providing a link to other important ecosystem processes.

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Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation

Cited by 1,199 publications

References 81 publications

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements

Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration

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