SwePub
Sök i LIBRIS databas

  Extended search

onr:"swepub:oai:DiVA.org:su-123351"
 

Search: onr:"swepub:oai:DiVA.org:su-123351" > Spatiotemporal patt...

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist
  • Anav, Alessandro (author)

Spatiotemporal patterns of terrestrial gross primary production : A review

  • Article/chapterEnglish2015

Publisher, publication year, extent ...

  • 2015
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:su-123351
  • https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-123351URI
  • https://doi.org/10.1002/2015RG000483DOI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:for swepub-publicationtype

Notes

  • Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990-2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Friedlingstein, Pierre (author)
  • Beer, ChristianStockholms universitet,Institutionen för tillämpad miljövetenskap (ITM)(Swepub:su)cbeer (author)
  • Ciais, Philippe (author)
  • Harper, Anna (author)
  • Jones, Chris (author)
  • Murray-Tortarolo, Guillermo (author)
  • Papale, Dario (author)
  • Parazoo, Nicholas C. (author)
  • Peylin, Philippe (author)
  • Piao, Shilong (author)
  • Sitch, Stephen (author)
  • Viovy, Nicolas (author)
  • Wiltshire, Andy (author)
  • Zhao, Maosheng (author)
  • Stockholms universitetInstitutionen för tillämpad miljövetenskap (ITM) (creator_code:org_t)

Related titles

  • In:Reviews of geophysics53:3, s. 785-8188755-12091944-9208

Internet link

Find in a library

To the university's database

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view