Linked Life Data

20553387

Source:http://linkedlifedata.com/resource/pubmed/id/20553387

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-7-27
pubmed:abstractText
*Climate change will very likely affect most forests in Amazonia during the course of the 21st century, but the direction and intensity of the change are uncertain, in part because of differences in rainfall projections. In order to constrain this uncertainty, we estimate the probability for biomass change in Amazonia on the basis of rainfall projections that are weighted by climate model performance for current conditions. *We estimate the risk of forest dieback by using weighted rainfall projections from 24 general circulation models (GCMs) to create probability density functions (PDFs) for future forest biomass changes simulated by a dynamic vegetation model (LPJmL). *Our probabilistic assessment of biomass change suggests a likely shift towards increasing biomass compared with nonweighted results. Biomass estimates range between a gain of 6.2 and a loss of 2.7 kg carbon m(-2) for the Amazon region, depending on the strength of CO(2) fertilization. *The uncertainty associated with the long-term effect of CO(2) is much larger than that associated with precipitation change. This underlines the importance of reducing uncertainties in the direct effects of CO(2) on tropical ecosystems.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1469-8137
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
187
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
694-706
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Estimating the risk of Amazonian forest dieback.
pubmed:affiliation
Potsdam Institute for Climate Impact Research, Telegraphenberg A62, Potsdam, Germany. anja.rammig@pik-potsdam.de
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't