Evaluation of modeled global vegetation carbon dynamics: Analysis based on global carbon flux and above-ground biomass data

Dynamic global vegetation models are useful tools for the simulation of global carbon cycle. However, most models are hampered by the poor availability of global aboveground biomass (AGB) data, which is necessary for the model calibration process. Here, taking the integrated biosphere simulator model (IBIS) as an example, we evaluated the modeled carbon dynamics, including gross primary production (GPP) and potential AGB, at the global scale. The IBIS model was constrained by both in situ GPP and plot-level AGB data collected from the literature. Model results showed that IBIS could reproduce GPP with acceptable accuracy in monthly and annual scales. At the global scale, the IBIS-simulated total AGB was similar to those obtained in other studies. However, discrepancies were observed between the model-derived and observed AGB for pan-tropical forests. The bias in modeled AGB was mainly caused by the unchanged parameters over the global scale for a specific plant functional type. This study also showed that different meteorological inputs can introduce substantial differences in modeled AGB in the global scale, although this difference is small compared with parameter-induced differences. The conclusions of our research highlight the necessity of considering the heterogeneity of key model physiological parameters in modeling global AGB.

[1]  A. Denning,et al.  Remote sensing data assimilation for a prognostic phenology model , 2008 .

[2]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[3]  Dan Liu,et al.  The contribution of China’s Grain to Green Program to carbon sequestration , 2014, Landscape Ecology.

[4]  Nathan M. Urban,et al.  Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates , 2011 .

[5]  H. Tian,et al.  Contribution of increasing CO2 and climate change to the carbon cycle in China's ecosystems , 2008 .

[6]  F. Woodward,et al.  Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models , 2001 .

[7]  Stephen Sitch,et al.  Simulated resilience of tropical rainforests to CO2-induced climate change , 2013 .

[8]  Stephen Sitch,et al.  Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change. , 2010, The New phytologist.

[9]  Atul K. Jain,et al.  Climate‐driven uncertainties in modeling terrestrial energy and water fluxes: a site‐level to global‐scale analysis , 2014, Global change biology.

[10]  Yadvinder Malhi,et al.  The productivity, metabolism and carbon cycle of tropical forest vegetation , 2012 .

[11]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

[12]  P. Jones,et al.  Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .

[13]  Markus Reichstein,et al.  Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models , 2007 .

[14]  Jin Liu,et al.  Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data , 2016, Remote. Sens..

[15]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[16]  D. Pollard,et al.  A Global Climate Model (GENESIS) with a Land-Surface Transfer Scheme (LSX). Part II: CO2 Sensitivity , 1995 .

[17]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[18]  Michael T. Coe,et al.  Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure , 2000 .

[19]  Ryuichi Hirata,et al.  The role of carbon flux and biometric observations in constraining a terrestrial ecosystem model: a case study in disturbed forests in East Asia , 2013, Ecological Research.

[20]  G. Powell,et al.  High-resolution forest carbon stocks and emissions in the Amazon , 2010, Proceedings of the National Academy of Sciences.

[21]  O. Phillips,et al.  An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR) , 2002 .

[22]  David G. Victor,et al.  Increased Carbon Sink in Temperate and Boreal Forests , 2003 .

[23]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[24]  Philippe Ciais,et al.  High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region , 2014, Proceedings of the National Academy of Sciences.

[25]  R. K. Dixon,et al.  Carbon Pools and Flux of Global Forest Ecosystems , 1994, Science.

[26]  I. C. Prentice,et al.  Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) , 2008 .

[27]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[28]  Arief Wijaya,et al.  An integrated pan‐tropical biomass map using multiple reference datasets , 2016, Global change biology.

[29]  K. Davis,et al.  Uncertainty in model parameters and regional carbon fluxes: A model-data fusion approach , 2014 .

[30]  Christopher B. Field,et al.  Forest biomass allometry in global land surface models , 2011 .

[31]  P. Ciais,et al.  Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model , 2010 .

[32]  Pierre Friedlingstein,et al.  A global prognostic scheme of leaf onset using satellite data , 2000 .

[33]  Michael Obersteiner,et al.  Global cost estimates of reducing carbon emissions through avoided deforestation , 2008, Proceedings of the National Academy of Sciences.

[34]  I. C. Prentice,et al.  Carbon balance of the terrestrial biosphere in the Twentieth Century: Analyses of CO2, climate and land use effects with four process‐based ecosystem models , 2001 .

[35]  C. Peng,et al.  Evaluating the effects of future climate change and elevated CO2 on the water use efficiency in terrestrial ecosystems of China , 2011 .

[36]  David B. Lindenmayer,et al.  Re-evaluation of forest biomass carbon stocks and lessons from the world's most carbon-dense forests , 2009, Proceedings of the National Academy of Sciences.

[37]  P. Ciais,et al.  Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model , 2010 .

[38]  I. C. Prentice,et al.  An integrated biosphere model of land surface processes , 1996 .

[39]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[40]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[41]  Ramakrishna R. Nemani,et al.  A generalized, bioclimatic index to predict foliar phenology in response to climate , 2004 .

[42]  Christopher B. Field,et al.  FOREST CARBON SINKS IN THE NORTHERN HEMISPHERE , 2002 .

[43]  S. Pacala,et al.  Predictive Models of Forest Dynamics , 2008, Science.

[44]  Y. Malhi,et al.  Improving simulated Amazon forest biomass and productivity by including spatial variation in biophysical parameters , 2013 .

[45]  B. Kruijt,et al.  Modeling forest dynamics along climate gradients in Bolivia , 2014 .

[46]  W. Cohen,et al.  Estimates of forest canopy height and aboveground biomass using ICESat , 2005 .

[47]  Eileen H. Helmer,et al.  Root biomass allocation in the world's upland forests , 1997, Oecologia.

[48]  J. Terborgh,et al.  Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites , 2014, Global ecology and biogeography : a journal of macroecology.

[49]  Markus Reichstein,et al.  CO2 balance of boreal, temperate, and tropical forests derived from a global database , 2007 .

[50]  Kyoichi Otsuki,et al.  Influences of canopy structure and physiological traits on flux partitioning between understory and overstory in an eastern Siberian boreal larch forest , 2011 .

[51]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[52]  Stephen Sitch,et al.  Towards quantifying uncertainty in predictions of Amazon ‘dieback’ , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[53]  Maggi Kelly,et al.  Airborne Lidar-derived volume metrics for aboveground biomass estimation: A comparative assessment for conifer stands , 2014 .

[54]  P. Alton How useful are plant functional types in global simulations of the carbon, water, and energy cycles? , 2011 .

[55]  C. Schmullius,et al.  Carbon stock and density of northern boreal and temperate forests , 2014 .

[56]  D. Pollard,et al.  A Global Climate Model (GENESIS) with a Land-Surface Transfer Scheme (LSX). Part I: Present Climate Simulation. , 1995 .

[57]  Atul K. Jain,et al.  Climate‐driven uncertainties in modeling terrestrial gross primary production: a site level to global‐scale analysis , 2014, Global change biology.

[58]  A. Di Fiore,et al.  Variation in wood density determines spatial patterns inAmazonian forest biomass , 2004 .

[59]  Mizue Ohashi,et al.  Modeling CO2 exchange over a Bornean tropical rain forest using measured vertical and horizontal variations in leaf-level physiological parameters and leaf area densities , 2006 .

[60]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[61]  I. E. Woodrow,et al.  A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .