Regional contribution to variability and trends of global gross primary productivity

Chen, Min; Rafique, Rashid; Asrar, Ghassem R.; Bond-Lamberty, Ben; Ciais, Philippe; Zhao, Fang; Reyer, Christopher P. O.; Ostberg, Sebastian; Chang, Jinfeng; Ito, Akihiko; Yang, Jia; Zeng, Ning; Kalnay, Eugenia; West, Tristram; Leng, Guoyong; Francois, Louis; Munhoven, Guy; Henrot, Alexandra; Tian, Hanqin; Pan, Shufen; Nishina, Kazuya; Viovy, Nicolas; Morfopoulos, Catherine; Betts, Richard; Schaphoff, Sibyll; Steinkamp, Jörg; Hickler, Thomas

doi:10.1088/1748-9326/aa8978

Title: Regional contribution to variability and trends of global gross primary productivity

Full Record
Other Related Research

Abstract

Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000-2010 total global GPP estimated from the model ensemble to be 117±13 Pg C yr-1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr-1), and close to the MTE (120 Pg C yr-1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However,more »« less

Authors:

Chen, Min; Rafique, Rashid; Asrar, Ghassem R.;

; Ciais, Philippe; Zhao, Fang; Reyer, Christopher P. O.; Ostberg, Sebastian; Chang, Jinfeng; Ito, Akihiko; Yang, Jia; Zeng, Ning; Kalnay, Eugenia; West, Tristram; Leng, Guoyong; Francois, Louis; Munhoven, Guy; Henrot, Alexandra; Tian, Hanqin; Pan, Shufen more »

Publication Date:: Thu Sep 28 00:00:00 EDT 2017

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1414522

Report Number(s):: PNNL-SA-128641
Journal ID: ISSN 1748-9326

DOE Contract Number:: AC05-76RL01830

Resource Type:: Journal Article

Journal Name:: Environmental Research Letters

Additional Journal Information:: Journal Volume: 12; Journal Issue: 10; Journal ID: ISSN 1748-9326

Publisher:: IOP Publishing

Country of Publication:: United States

Language:: English

Citation Formats


                    Chen, Min, Rafique, Rashid, Asrar, Ghassem R., Bond-Lamberty, Ben, Ciais, Philippe, Zhao, Fang, Reyer, Christopher P. O., Ostberg, Sebastian, Chang, Jinfeng, Ito, Akihiko, Yang, Jia, Zeng, Ning, Kalnay, Eugenia, West, Tristram, Leng, Guoyong, Francois, Louis, Munhoven, Guy, Henrot, Alexandra, Tian, Hanqin, Pan, Shufen, Nishina, Kazuya, Viovy, Nicolas, Morfopoulos, Catherine, Betts, Richard, Schaphoff, Sibyll, Steinkamp, Jörg, and Hickler, Thomas. Regional contribution to variability and trends of global gross primary productivity.  United States: N. p., 2017. 
        Web.  doi:10.1088/1748-9326/aa8978.

Copy to clipboard


                    Chen, Min, Rafique, Rashid, Asrar, Ghassem R., Bond-Lamberty, Ben, Ciais, Philippe, Zhao, Fang, Reyer, Christopher P. O., Ostberg, Sebastian, Chang, Jinfeng, Ito, Akihiko, Yang, Jia, Zeng, Ning, Kalnay, Eugenia, West, Tristram, Leng, Guoyong, Francois, Louis, Munhoven, Guy, Henrot, Alexandra, Tian, Hanqin, Pan, Shufen, Nishina, Kazuya, Viovy, Nicolas, Morfopoulos, Catherine, Betts, Richard, Schaphoff, Sibyll, Steinkamp, Jörg, & Hickler, Thomas. Regional contribution to variability and trends of global gross primary productivity.  United States.  https://doi.org/10.1088/1748-9326/aa8978

Copy to clipboard


                    Chen, Min, Rafique, Rashid, Asrar, Ghassem R., Bond-Lamberty, Ben, Ciais, Philippe, Zhao, Fang, Reyer, Christopher P. O., Ostberg, Sebastian, Chang, Jinfeng, Ito, Akihiko, Yang, Jia, Zeng, Ning, Kalnay, Eugenia, West, Tristram, Leng, Guoyong, Francois, Louis, Munhoven, Guy, Henrot, Alexandra, Tian, Hanqin, Pan, Shufen, Nishina, Kazuya, Viovy, Nicolas, Morfopoulos, Catherine, Betts, Richard, Schaphoff, Sibyll, Steinkamp, Jörg, and Hickler, Thomas. 2017.  
        "Regional contribution to variability and trends of global gross primary productivity".  United States.  https://doi.org/10.1088/1748-9326/aa8978.

Copy to clipboard


                    
@article{osti_1414522,

  title        = {Regional contribution to variability and trends of global gross primary productivity},

  author       = {Chen, Min and Rafique, Rashid and Asrar, Ghassem R. and Bond-Lamberty, Ben and Ciais, Philippe and Zhao, Fang and Reyer, Christopher P. O. and Ostberg, Sebastian and Chang, Jinfeng and Ito, Akihiko and Yang, Jia and Zeng, Ning and Kalnay, Eugenia and West, Tristram and Leng, Guoyong and Francois, Louis and Munhoven, Guy and Henrot, Alexandra and Tian, Hanqin and Pan, Shufen and Nishina, Kazuya and Viovy, Nicolas and Morfopoulos, Catherine and Betts, Richard and Schaphoff, Sibyll and Steinkamp, Jörg and Hickler, Thomas},

  abstractNote = {Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000-2010 total global GPP estimated from the model ensemble to be 117±13 Pg C yr-1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr-1), and close to the MTE (120 Pg C yr-1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models’ ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.},

  doi          = {10.1088/1748-9326/aa8978},

  url          = {https://www.osti.gov/biblio/1414522},
  journal      = {Environmental Research Letters},

  issn         = {1748-9326},

  number       = 10,

  volume       = 12,

  place        = {United States},

  year         = {Thu Sep 28 00:00:00 EDT 2017},

  month        = {Thu Sep 28 00:00:00 EDT 2017}

}

Copy to clipboard

Journal Article:

https://doi.org/10.1088/1748-9326/aa8978

Other availability

Find in Google Scholar

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

Remote sensing evaluation of CLM4 GPP for the period 2000 to 2009

Journal Article Mao, Jiafu; Thornton, Peter; Shi, Xiaoying; ... - Journal of Climate

The ability of a process-based ecosystem model like Version 4 of the Community Land Model (CLM4) to provide accurate estimates of CO2 flux is a top priority for researchers, modelers and policy makers. Remote sensing can provide long-term and large scale products suitable for ecosystem model evaluation. Global estimations of gross primary production (GPP) at the 1 km spatial resolution from years 2000 to 2009 from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor offer a unique opportunity for evaluating the temporal and spatial patterns of global GPP and its relationship with climate for CLM4. We compare monthly GPP simulated bymore »« less
https://doi.org/10.1175/JCLI-D-11-00401.1
Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach

Journal Article Jung, Martin; Schwalm, Christopher; Migliavacca, Mirco; ... - Biogeosciences (Online)

FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed.more »« less
https://doi.org/10.5194/bg-17-1343-2020

Full Text Available
Assessing the ability of $$\mathrm{MODIS}$$ $$\mathrm{EVI}$$ to estimate terrestrial ecosystem gross primary production of multiple land cover types

Journal Article Shi, Hao; Li, Longhui; Eamus, Derek; ... - Ecological Indicators

Terrestrial ecosystem gross primary production (GPP) is the largest component in the global carbon cycle. The enhanced vegetation index (EVI) has been proven to be strongly correlated with annual GPP within several biomes. However, the annual GPP-EVI relationship and associated environmental regulations have not yet been comprehensively investigated across biomes at the global scale. In this report we explored relationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, where GPP was predicted with a log-log model: 1n(GPP) = a x 1n(iEVI) + b. iEVI was computed from MODIS monthly EVI products following removal of valuesmore »« less
Cited by 42
https://doi.org/10.1016/j.ecolind.2016.08.022

Full Text Available
Benchmarking carbon fluxes of the ISIMIP2a biome models

Journal Article Chang, Jinfeng; Ciais, Philippe; Wang, Xuhui; ... - Environmental Research Letters

The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). Here, we evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions (E_LUC) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al (2015), and 2) the land CO₂ fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F_Jena and F_CAMS respectively. Themore »« less
Cited by 31
https://doi.org/10.1088/1748-9326/aa63fa

Full Text Available
Variability and quasi-decadal changes in the methane budget over the period 2000–2012

Journal Article Saunois, Marielle; Bousquet, Philippe; Poulter, Ben; ... - Atmospheric Chemistry and Physics (Online)

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH₄) budget over 2000–2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH₄ emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in totalmore »« less
Cited by 73
https://doi.org/10.5194/acp-17-11135-2017

Full Text Available

Similar Records