Climate-Driven Variability and Trends in Plant Productivity Over Recent Decades Based on Three Global Products.

carbon climate gross primary productivity interannual variability trends

Journal

Global biogeochemical cycles
ISSN: 0886-6236
Titre abrégé: Global Biogeochem Cycles
Pays: United States
ID NLM: 100971590

Informations de publication

Date de publication:
Dec 2020
Historique:
received: 21 03 2020
revised: 17 11 2020
accepted: 22 11 2020
entrez: 31 12 2020
pubmed: 1 1 2021
medline: 1 1 2021
Statut: ppublish

Résumé

Variability in climate exerts a strong influence on vegetation productivity (gross primary productivity; GPP), and therefore has a large impact on the land carbon sink. However, no direct observations of global GPP exist, and estimates rely on models that are constrained by observations at various spatial and temporal scales. Here, we assess the consistency in GPP from global products which extend for more than three decades; two observation-based approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing derived light use efficiency model (RS-LUE), and from a suite of terrestrial biosphere models (TRENDYv6). At local scales, we find high correlations in annual GPP among the products, with exceptions in tropical and high northern latitudes. On longer time scales, the products agree on the direction of trends over 58% of the land, with large increases across northern latitudes driven by warming trends. Further, tropical regions exhibit the largest interannual variability in GPP, with both rainforests and savannas contributing substantially. Variability in savanna GPP is likely predominantly driven by water availability, although temperature could play a role via soil moisture-atmosphere feedbacks. There is, however, no consensus on the magnitude and driver of variability of tropical forests, which suggest uncertainties in process representations and underlying observations remain. These results emphasize the need for more direct long-term observations of GPP along with an extension of in situ networks in underrepresented regions (e.g., tropical forests). Such capabilities would support efforts to better validate relevant processes in models, to more accurately estimate GPP.

Identifiants

pubmed: 33380772
doi: 10.1029/2020GB006613
pii: GBC21073
pmc: PMC7757257
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e2020GB006613

Informations de copyright

©2020. The Authors.

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Auteurs

Michael O'Sullivan (M)

College of Engineering, Mathematics and Physical Sciences University of Exeter Exeter UK.

William K Smith (WK)

School of Natural Resources and the Environment University of Arizona Tucson AZ USA.

Stephen Sitch (S)

College of Life and Environmental Sciences University of Exeter Exeter UK.

Pierre Friedlingstein (P)

College of Engineering, Mathematics and Physical Sciences University of Exeter Exeter UK.
LMD/IPSL, ENS, PSL Université, École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université, CNRS Paris France.

Vivek K Arora (VK)

Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada University of Victoria Victoria British Columbia Canada.

Vanessa Haverd (V)

CSIRO Oceans and Atmosphere Canberra ACT Australia.

Atul K Jain (AK)

Department of Atmospheric Sciences University of Illinois Urbana IL USA.

Etsushi Kato (E)

Institute of Applied Energy (IAE) Minato Japan.

Markus Kautz (M)

Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU) Karlsruhe Institute of Technology (KIT) Garmisch-Partenkirchen Germany.
Forest Research Institute Baden-Württemberg Freiburg Germany.

Danica Lombardozzi (D)

Climate and Global Dynamics Division National Center for Atmospheric Research Boulder CO USA.

Julia E M S Nabel (JEMS)

Max Planck Institute for Meteorology Hamburg Germany.

Hanqin Tian (H)

International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences Auburn University Auburn AL USA.

Nicolas Vuichard (N)

Laboratoire des Sciences du Climat et de l'Environnement, UMR8212 CEA-CNRS-UVSQ, Université Paris-Saclay, IPSL Gif-sur-Yvette France.

Andy Wiltshire (A)

Met Office Hadley Centre Exeter UK.

Dan Zhu (D)

Laboratoire des Sciences du Climat et de l'Environnement, UMR8212 CEA-CNRS-UVSQ, Université Paris-Saclay, IPSL Gif-sur-Yvette France.

Wolfgang Buermann (W)

Institute of Geography Augsburg University Augsburg Germany.
Institute of the Environment and Sustainability University of California, Los Angeles Los Angeles CA USA.

Classifications MeSH