TY - JOUR
T1 - Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production
AU - Melnikova, Irina
AU - Sasai, Takahiro
N1 - Funding Information:
We thank the members of the Tohoku University atmospheric science laboratory, H. Sato from the Institute of Arctic Climate and Environment Research, the Japan Agency for Marine-Earth Science and Technology, H. Shibata of Hokkaido University, and V. Brovkin of the Max Planck Institute for Meteorology for valuable discussions and technical advice on this research. Thanks to Deborah Huntzinger, Northern Arizona University, and two anonymous reviewers for providing peer review of the manuscript. This research was supported by the Integrated Research Program for Advancing Climate Models (TOUGOU program) of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, the Social Implementation Program on Climate Change Adaptation Technology (SI-CAT) of MEXT, and the Environment Research and Technology Development Fund (S-15) of the Ministry of the Environment, Japan?and by?JSPS KAKENHI Grant Number 17H01516.
Funding Information:
This study used the database for Policy Decision making for Future climate change (d4PDF) provided through the server by the Data Integration and Analysis System ( https://www.diasjp.net ) and eddy covariance data acquired and shared by the FLUXNET community ( http://daac.ornl.gov/FLUXNET/ ), including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet‐Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux‐TERN, TCOS‐Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and the Fluxdata project of FLUXNET, with the support of CDIAC and the ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux, and AsiaFlux offices. All data for this paper are properly cited and referred to in the reference list. We thank the FLUXNET PIs for providing their data openly: Vincenzo Magliulo (IT‐BCi DOI: 10.18140/FLX/1440166 ), Benjamin Loubet (FR‐Gri DOI: 10.18140/FLX/1440162 ), Christian Brümmer (DE‐Geb DOI: 10.18140/FLX/1440146 ), Sebastien Biraud (US‐ARM DOI: 10.18140/FLX/1440066 ), Andy Suyker (US‐Ne1 DOI: 10.18140/FLX/1440084 , US‐Ne2 DOI: 10.18140/FLX/1440085 , US‐Ne3 DOI: 10.18140/FLX/1440086 ), Nina Buchmann (CH‐Oe2 DOI: 10.18140/FLX/1440136 ), Christian Bernhofer (DE‐Kli DOI: 10.18140/FLX/1440149 ), Mike Goulden (BR‐Sa3 DOI: 10.18140/FLX/1440033 ), Satoru Takanashi (MY‐PSO DOI: 10.18140/FLX/1440240 ), J. William Munger (US‐Ha1 DOI: 10.18140/FLX/1440071 ), Alexander Knohl (DE‐Hai DOI: 10.18140/FLX/1440148 ), Ankur Desai (US‐WCr DOI: 10.18140/FLX/1440095 ), Jason Beringer (AU‐DaS DOI: 10.18140/FLX/1440122 ), Lutz Merbold (RU‐Che DOI: 10.18140/FLX/1440181 ), Han Dolman (RU‐Cok DOI: 10.18140/FLX/1440182 ), Andrej Varlagin (RU‐Fyo DOI: 10.18140/FLX/1440183 ), and Dario Papale (RU‐Ha1 DOI: 10.18140/FLX/1440184 ). We downloaded GPP of MTE‐GPP and FLUXCOM from BGI data portal ( https://www.bgc‐jena.mpg.de ) and MsTMIP from Oak Ridge National Laboratory Distributed Active Archive Center, ORNL DAAC ( https://daac.ornl.gov ). LAI data of PhenoAnalysis were provided by Reto Stöckli ( ftp://ftp.cscs.ch/out/stockli/phenoanalysis/Global‐Prediction/ ), GIMMS3g by Ranga Myneni, MODIS by the Land‐Atmosphere Interaction Research Group ( http://globalchange.bnu.edu.cn ), SPOT by the Copernicus Global Land Service, CGLS ( https://land.copernicus.eu ), GLOBMAP ( http://www.globalmapping.org ), and GLASS ver.4 by Global Land Cover Facility (GLCF), University of Maryland ( http://ftp.glcf.umd.edu/ ). Climate data of Terrestrial Hydrology Research Group of Princeton University were available online ( http://hydrology.princeton.edu/data/ ), 20CRV2c, GPCC and GPCP, NCEP NCAR, and University of Delaware 4.0 were available from NOAA Earth System Research Laboratory's Physical Sciences Division ( https://www.esrl.noaa.gov ), CRU TS 4.00 from the NERC or STFC through the Centre for Environmental Data Analysis (CEDA; http://data.ceda.ac.uk ), CRU‐NCEP from North American Carbon Program ORNL DAAC ( ftp://nacp.ornl.gov ), ERA‐Interim from the ECMWF Data Server ( http://apps.ecmwf.int/datasets/ ), JRA‐55 and NCEP CFSR from the NCAR CISL‐RDA server ( http://rda.ucar.edu ), MERRA‐2 from the NASA the Goddard Earth Sciences Data, and Information Services Center, GES DISC ( https://disc.gsfc.nasa.gov/ ). We used CO concentration by the Institute for Atmospheric and Climate Science, Zürich, Switzerland ( ftp://data.iac.ethz.ch ), Multivariate ENSO Index data ( https://www.esrl.noaa.gov ), and volcano aerosol data ( http://www.juergen‐grieser.de ). We are grateful to the data set producers and their collaborators. The data generated in this study, including large‐ensemble GPP estimates and sensitivity experiment outputs, are available through Zenodo with this DOI ( 10.5281/zenodo.3663075 ), and large‐ensemble LAI and FAPAR estimates are available with this DOI ( 10.5281/zenodo.3663243 ). 2
Funding Information:
We thank the members of the Tohoku University atmospheric science laboratory, H. Sato from the Institute of Arctic Climate and Environment Research, the Japan Agency for Marine‐Earth Science and Technology, H. Shibata of Hokkaido University, and V. Brovkin of the Max Planck Institute for Meteorology for valuable discussions and technical advice on this research. Thanks to Deborah Huntzinger, Northern Arizona University, and two anonymous reviewers for providing peer review of the manuscript. This research was supported by the Integrated Research Program for Advancing Climate Models (TOUGOU program) of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, the Social Implementation Program on Climate Change Adaptation Technology (SI‐CAT) of MEXT, and the Environment Research and Technology Development Fund (S‐15) of the Ministry of the Environment, Japan and by JSPS KAKENHI Grant Number 17H01516.
Publisher Copyright:
©2020. American Geophysical Union. All Rights Reserved.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Gross primary production (GPP) has been identified as the largest terrestrial carbon flux and the major driver of the growing biosphere uptake of carbon. Factorial simulations using several biosphere models have been used to estimate the effects of long-term (>50 years) climate change on global terrestrial GPP. However, no study has integrated large-ensemble climate simulation data into a biosphere model to realistically estimate global terrestrial GPP with associated uncertainty. Here we present a novel approach to estimate the global terrestrial long-term GPP with associated climate data-induced uncertainty that combines a diagnostic-type biosphere model with a large-ensemble climate simulation data set. We distinguish the effects of recent anthropogenic activity on global GPP (the anthropogenic GPP effect) from the effects of interannual climate variability (the natural GPP effect) by comparing GPP model estimates forced by historical and “nonwarming” climate data. We provide evidence for an increasing anthropogenic effect on global terrestrial GPP. The anthropogenic GPP effect is driven by CO2 fertilization, which is projected to weaken or saturate by 2050–2150, depending on the representative concentration pathway scenario used. Model results suggest that shortwave radiation couples with El Niño–Southern Oscillation conditions and volcanic eruptions to drive the natural GPP effect. Because shortwave radiation at the surface is related to cloud cover, we encourage future studies to focus on cloud-radiation feedbacks on the carbon cycle.
AB - Gross primary production (GPP) has been identified as the largest terrestrial carbon flux and the major driver of the growing biosphere uptake of carbon. Factorial simulations using several biosphere models have been used to estimate the effects of long-term (>50 years) climate change on global terrestrial GPP. However, no study has integrated large-ensemble climate simulation data into a biosphere model to realistically estimate global terrestrial GPP with associated uncertainty. Here we present a novel approach to estimate the global terrestrial long-term GPP with associated climate data-induced uncertainty that combines a diagnostic-type biosphere model with a large-ensemble climate simulation data set. We distinguish the effects of recent anthropogenic activity on global GPP (the anthropogenic GPP effect) from the effects of interannual climate variability (the natural GPP effect) by comparing GPP model estimates forced by historical and “nonwarming” climate data. We provide evidence for an increasing anthropogenic effect on global terrestrial GPP. The anthropogenic GPP effect is driven by CO2 fertilization, which is projected to weaken or saturate by 2050–2150, depending on the representative concentration pathway scenario used. Model results suggest that shortwave radiation couples with El Niño–Southern Oscillation conditions and volcanic eruptions to drive the natural GPP effect. Because shortwave radiation at the surface is related to cloud cover, we encourage future studies to focus on cloud-radiation feedbacks on the carbon cycle.
KW - BEAMS
KW - Gross primary production
KW - anthropogenic effect
KW - climate change
KW - d4PDF
KW - large ensemble
UR - http://www.scopus.com/inward/record.url?scp=85082415158&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082415158&partnerID=8YFLogxK
U2 - 10.1029/2019JG005403
DO - 10.1029/2019JG005403
M3 - Article
AN - SCOPUS:85082415158
VL - 125
JO - Journal of Geophysical Research: Biogeosciences
JF - Journal of Geophysical Research: Biogeosciences
SN - 2169-8961
IS - 3
M1 - e2019JG005403
ER -