| |
|
|
| Registros recuperados : 5 | |
| Registros recuperados : 5 | |
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Amazônia Oriental. |
|
Data corrente: |
02/01/2017 |
|
Data da última atualização: |
20/05/2022 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
RESTREPO-COUPE, N.; LEVINE, N. M.; CHRISTOFFERSEN, B. O.; ALBERT, L. P.; WU, J.; COSTA, M. H.; GALBRAITH, D.; IMBUZEIRO, H.; MARTINS, G.; ARAUJO, A. C. da; MALHI, Y. S.; ZENG, X.; MOORCROFT, P.; SALESKA, S. R. |
|
Afiliação: |
NATALIA RESTREPO-COUPE, University of Technology Sydney / University of Arizona; NAOMI M. LEVINE, University of Southern California / Harvard University; BRADLEY O. CHRISTOFFERSEN, University of Arizona / Los Alamos National Laboratory; LOREN P. ALBERT, University of Arizona; JIN WU, University of Arizona / Brookhaven National Lab; MARCOS H. COSTA, UFV; DAVID GALBRAITH, University of Leeds; HEWLLEY IMBUZEIRO, UFV; GIORDANE MARTINS, INPA; ALESSANDRO CARIOCA DE ARAUJO, CPATU; YADVINDER S. MALHI, University of Oxford; XUBIN ZENG, University of Arizona; PAUL MOORCROFT, Harvard University; SCOTT R. SALESKA, University of Arizona. |
|
Título: |
Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. |
|
Ano de publicação: |
2017 |
|
Fonte/Imprenta: |
Global Change Biology, v. 23, n. 1, p. 191-208, Jan. 2017. |
|
DOI: |
10.1111/gcb.13442 |
|
Idioma: |
Inglês |
|
Conteúdo: |
To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ?soil water stress? and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments. MenosTo predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ?soil water stress? and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consis... Mostrar Tudo |
|
Palavras-Chave: |
Carbon dynamics; Dinâmica do carbono; Dynamic global vegetation models; Ecosystem–climate interactions; Florestas tropicais; Modelos dinâmicos de vegetação; Sazonalidade; Seasonality; Tropical forests phenology. |
|
Thesagro: |
Fenologia. |
|
Thesaurus NAL: |
Amazonia; eddy covariance. |
|
Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
|
Marc: |
LEADER 03473naa a2200433 a 4500
001 2059770
005 2022-05-20
008 2017 bl uuuu u00u1 u #d
024 7 $a10.1111/gcb.13442$2DOI
100 1 $aRESTREPO-COUPE, N.
245 $aDo dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison.$h[electronic resource]
260 $c2017
520 $aTo predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ?soil water stress? and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.
650 $aAmazonia
650 $aeddy covariance
650 $aFenologia
653 $aCarbon dynamics
653 $aDinâmica do carbono
653 $aDynamic global vegetation models
653 $aEcosystem–climate interactions
653 $aFlorestas tropicais
653 $aModelos dinâmicos de vegetação
653 $aSazonalidade
653 $aSeasonality
653 $aTropical forests phenology
700 1 $aLEVINE, N. M.
700 1 $aCHRISTOFFERSEN, B. O.
700 1 $aALBERT, L. P.
700 1 $aWU, J.
700 1 $aCOSTA, M. H.
700 1 $aGALBRAITH, D.
700 1 $aIMBUZEIRO, H.
700 1 $aMARTINS, G.
700 1 $aARAUJO, A. C. da
700 1 $aMALHI, Y. S.
700 1 $aZENG, X.
700 1 $aMOORCROFT, P.
700 1 $aSALESKA, S. R.
773 $tGlobal Change Biology$gv. 23, n. 1, p. 191-208, Jan. 2017.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Amazônia Oriental (CPATU) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
