Historical variations in terrestrial biospheric carbon storage

Post, Wilfred M.; King, A. W.; Wullschleger, Stan D.

doi:10.1029/96gb03942

Cited by 71 publications

(41 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This scheme has subsequently been extended and used widely in a range of global C-cycle models [Goudrian, 1992;King et al, 1997;Post et al, 1997;Polglase and Wang, 1992;Meuller and Kramer, 1994;Wang and Polglase, 1995;Ciais et al, 1999]. Based on the observed allocation coefficients of Gower et al [1999], Kucharick et al [2000] devised an allocation scheme having similar values to Goudrian and Ketner [1984] for a range of biomes.…”

Section: A33 Allocation Of Npp To Plant Tissuesmentioning

confidence: 99%

“…Based on the observed allocation coefficients of Gower et al [1999], Kucharick et al [2000] devised an allocation scheme having similar values to Goudrian and Ketner [1984] for a range of biomes. In VAST, the three Australian biomes were matched to the extended set of global biomes from Post et al [1997] and the allocation coefficients for leaf and stem (+ branch) were used as the equality constraints for a L and a W in the initial parameterization run. However, these values were found to lead to inconsistencies in the parameterization to be reported elsewhere.…”

Section: A33 Allocation Of Npp To Plant Tissuesmentioning

confidence: 99%

See 1 more Smart Citation

Steady state turnover time of carbon in the Australian terrestrial biosphere

Barrett¹

2002

Global Biogeochemical Cycles

119

127

View full text Add to dashboard Cite

[1] The turnover time of terrestrial carbon was estimated using a multiobjective parameterization method that combined data sets of plant production, biomass, litter and soil-C observations in the calibration of a C-cycle model for the Australian continent (VAST1.1; Vegetation and Soil carbon Transfer). The method employed a genetic algorithm to minimize model-data deviations and maximize consistency between estimated model parameters and all available data. Based on the parameterization, the turnover time of biosphere C for Australia was estimated to be 78 years which is longer than global C-turnover estimates (of 26-60 years) due entirely to slower turnover of C in the upper 20 cm of soil. Turnover times of litter and deeper soil-C were similar to global values. By splitting total C in the upper 20 cm between labile and nonlabile fractions (based on published data) the turnover time of the labile pool was at least 44 years which is still longer than global estimates (9-25 years). Longer C-turnover in Australian surface soils was attributed to (1) limited soil moisture slowing decomposition more than net primary production, (2) frequent fires leading to a large fraction of nonlabile charcoal C in soil, and (3) strong adsorbing capacity for organic-C in these highly weathered soils. It was found that >89% of the C flux to the atmosphere from decomposition of organic matter originated from fine litter, coarse woody debris and the upper 20 cm of soil in all biomes.

show abstract

Section: A33 Allocation Of Npp To Plant Tissuesmentioning

confidence: 99%

Section: A33 Allocation Of Npp To Plant Tissuesmentioning

confidence: 99%

Steady state turnover time of carbon in the Australian terrestrial biosphere

Barrett¹

2002

Global Biogeochemical Cycles

119

127

View full text Add to dashboard Cite

show abstract

“…The dynamics of the carbon pools are integrated with an annual time step. Net primary productivity (NPP), , is simulated on an annual basis following the semi-empirical parameterization of Lieth (1975), which is often used for first-guess estimates on a global scale (Post et al, 1997). To find out the NPP per model grid cell, the NPP per square meter is multiplied by the vegetation fraction v, which is in most cases equal to one, and the land area.…”

Section: Vecode: the Continental Biosphere Componentmentioning

confidence: 99%

Description of the Earth system model of intermediate complexity LOVECLIM version 1.2

et al. 2010

View full text Add to dashboard Cite

Abstract. The main characteristics of the new version 1.2 of the three-dimensional Earth system model of intermediate complexity LOVECLIM are briefly described. LOVE-CLIM 1.2 includes representations of the atmosphere, the ocean and sea ice, the land surface (including vegetation), the ice sheets, the icebergs and the carbon cycle. The atmospheric component is ECBilt2, a T21, 3-level quasigeostrophic model. The ocean component is CLIO3, which consists of an ocean general circulation model coupled to a comprehensive thermodynamic-dynamic sea-ice model. Its horizontal resolution is of 3 • by 3 • , and there are 20 levels in the ocean. ECBilt-CLIO is coupled to VECODE, a vegetation model that simulates the dynamics of two main terrestrial plant functional types, trees and grasses, as well as desert. VECODE also simulates the evolution of the carbon cycle over land while the ocean carbon cycle is represented by LOCH, a comprehensive model that takes into acCorrespondence to: H. Goosse (hugues.goosse@uclouvain.be) count both the solubility and biological pumps. The ice sheet component AGISM is made up of a three-dimensional thermomechanical model of the ice sheet flow, a visco-elastic bedrock model and a model of the mass balance at the iceatmosphere and ice-ocean interfaces. For both the Greenland and Antarctic ice sheets, calculations are made on a 10 km by 10 km resolution grid with 31 sigma levels. LOVECLIM1.2 reproduces well the major characteristics of the observed climate both for present-day conditions and for key past periods such as the last millennium, the mid-Holocene and the Last Glacial Maximum. However, despite some improvements compared to earlier versions, some biases are still present in the model. The most serious ones are mainly located at low latitudes with an overestimation of the temperature there, a too symmetric distribution of precipitation between the two hemispheres, and an overestimation of precipitation and vegetation cover in the subtropics. In addition, the atmospheric circulation is too weak. The model also tends to underestimate the surface temperature changes (mainly at low latitudes) and to overestimate the ocean heat uptake observed over the last decades.

show abstract

“…We wanted to determine under what conditions the models accurately simulated monthly runoff and under what conditions the models performed poorly. We evaluated the behavior of six VEMAP models-Biome-BGC (BGC ϭ BioGeochemical Cycles; Running 1992, Running andHunt 1993), Century (Parton et al 1987(Parton et al , 1988(Parton et al , 1993, Global Terrestrial Ecosystem Carbon Model (GTEC; Post et al 1997), Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ; Haxeltine andPrentice 1996, Sitch 2003), MC1 Dynamic Global Vegetation Model (MC ϭ modified Century; Daly et al 2000), and Terrestrial Ecosystem Model (TEM; McGuire et al 1992, Melillo et al 1993, Tian et al 2000-by comparing simulated runoff from the VEMAP Phase 2 historical (20th century) experiments to gauged streamflow from the Hydro-Climatic Data Network (HCDN; Slack and Landwehr 1992). These streamflow observations cover much of the 20th century, providing a lengthy record of monthly flows against which to validate the models.…”

Section: Introductionmentioning

confidence: 99%

Validation of Simulated Runoff From Six Terrestrial Ecosystem Models: Results From Vemap

Gordon¹,

Famiglietti

Fowler

et al. 2004

Ecological Applications

View full text Add to dashboard Cite

Abstract. Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 model experiments investigated the response of biogeochemical and dynamic global vegetation models (DGVMs) to differences in climate over the conterminous United States. This was accomplished by simulating ecosystem processes using historical climate and atmospheric CO 2 records from 1895-1993. We evaluated the behavior of six models (Biome-BGC, Century, GTEC, LPJ, MC1, and TEM) by comparing simulated runoff in 13 watersheds to gauged streamflow from the Hydro-Climatic Data Network. Metrics used to assess the ''goodness of fit'' between simulated and observed values were: (1) Pearson's r to evaluate the overall data set, (2) Kendall's to gauge seasonality trends as derived from a timeseries analysis of monthly runoff, and (3) three measures of absolute and relative error.We found small differences in performance among the six models over all watersheds. However, the models yielded highly divergent results depending upon the watershed analyzed. Performance of the ensemble of models in a watershed was positively correlated with observed streamflow: models in the wettest watersheds in this study were associated with the highest model correlations and largest absolute errors, and models in the driest watersheds were associated with the lowest correlations and smallest absolute errors. Mean relative error was small and nearly constant across watersheds. A bias estimator showed that the models tended to underestimate runoff in wet watersheds and overestimate runoff in dry watersheds. Analysis of long-term trends in runoff using a moving-average approach demonstrated the ability of the models to reproduce temporal variation in observed data, even though quantitative differences among models were large.Models relying on prescribed vegetation (Biome-BGC, Century, and TEM) outperformed the two DGVMs (LPJ and MC1); GTEC gave the poorest fit to observations due to the absence of an evaporation function and a snow routine. Across all 13 watersheds, TEM ranked the highest in model performance. The validation results presented here suggest that improvements in the simulation of hydrologic processes in land-surface models will come, in part, from a more realistic representation of subgrid-scale soil moisture and from a more detailed understanding and representation of subsurface processes.

show abstract

Historical variations in terrestrial biospheric carbon storage

Cited by 71 publications

References 35 publications

Steady state turnover time of carbon in the Australian terrestrial biosphere

Steady state turnover time of carbon in the Australian terrestrial biosphere

Description of the Earth system model of intermediate complexity LOVECLIM version 1.2

Validation of Simulated Runoff From Six Terrestrial Ecosystem Models: Results From Vemap

Contact Info

Product

Resources

About