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dc.contributor.authorChen, W
dc.contributor.authorZhu, D
dc.contributor.authorHuang, C
dc.contributor.authorCiais, P
dc.contributor.authorYao, Y
dc.contributor.authorFriedlingstein, P
dc.contributor.authorSitch, S
dc.contributor.authorHaverd, V
dc.contributor.authorJain, AK
dc.contributor.authorKato, E
dc.contributor.authorKautz, M
dc.contributor.authorLienert, S
dc.contributor.authorLombardozzi, D
dc.contributor.authorPoulter, B
dc.contributor.authorTian, H
dc.contributor.authorVuichard, N
dc.contributor.authorWalker, AP
dc.contributor.authorZeng, N
dc.date.accessioned2019-11-21T14:43:32Z
dc.date.issued2019-05-21
dc.description.abstractClimate extremes have remarkable impacts on ecosystems and are expected to increase with future global warming. However, only few studies have focused on the ecological extreme events and their drivers in China. In this study, we carried out an analysis of negative extreme events in gross primary productivity (GPP)in China and the sub-regions during 1982–2015, using monthly GPP simulated by 12 process-based models (TRENDYv6)and an observation-based model (Yao-GPP). Extremes were defined as the negative 5th percentile of GPP anomalies, which were further merged into individual extreme events using a three-dimensional contiguous algorithm. Spatio-temporal patterns of negative GPP anomalies were analyzed by taking the 1000 largest extreme events into consideration. Results showed that the effects of extreme events decreased annual GPP by 2.8% (i.e. 208 TgC year−1)in TRENDY models and 2.3% (i.e. 151 TgC year−1)in Yao-GPP. Hotspots of extreme GPP deficits were mainly observed in North China (−53 gC m−2 year−1)in TRENDY models and Northeast China (−42 gC m−2 year−1)in Yao-GPP. For China as a whole, attribution analyses suggested that extreme low precipitation was associated with 40%–50% of extreme negative GPP events. Most events in northern and western China could be explained by meteorological droughts (i.e. low precipitation)while GPP extreme events in southern China were more associated with temperature extremes, in particular with cold spells. GPP was revealed to be much more sensitive to heat/drought than to cold/wet extreme events. Combined with projected changes in climate extremes in China, GPP negative anomalies caused by drought events in northern China and by temperature extremes in southern China might be more prominent in the future.en_GB
dc.description.sponsorshipNatural Science Foundation for Distinguished Young Scholars of Hubei Province of Chinaen_GB
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_GB
dc.description.sponsorship111 Projecten_GB
dc.description.sponsorshipChina Scholarship Councilen_GB
dc.description.sponsorshipSwiss National Science Foundationen_GB
dc.identifier.citationVol. 275 (15), pp. 47 - 58en_GB
dc.identifier.doi10.1016/j.agrformet.2019.05.002
dc.identifier.grantnumber2016CFA051en_GB
dc.identifier.grantnumber41772029en_GB
dc.identifier.grantnumber41322013en_GB
dc.identifier.grantnumberB14031en_GB
dc.identifier.grantnumberB08030en_GB
dc.identifier.grantnumber201806410044en_GB
dc.identifier.grantnumber20020_172476en_GB
dc.identifier.urihttp://hdl.handle.net/10871/39687
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.rights.embargoreasonPublisher policy.en_GB
dc.rights© 2019. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/  en_GB
dc.subjectClimate changeen_GB
dc.subjectExtreme eventsen_GB
dc.subjectGross primary productionen_GB
dc.subjectPower law distributionen_GB
dc.subjectChinaen_GB
dc.titleNegative extreme events in gross primary productivity and their drivers in China during the past three decadesen_GB
dc.typeArticleen_GB
dc.date.available2019-11-21T14:43:32Z
dc.identifier.issn0168-1923
dc.descriptionThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.en_GB
dc.identifier.journalAgricultural and Forest Meteorologyen_GB
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
dcterms.dateAccepted2019-05-04
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2019-05-04
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2019-11-21T14:38:19Z
refterms.versionFCDAM
refterms.panelBen_GB


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© 2019. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/  
Except where otherwise noted, this item's licence is described as © 2019. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/