A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices

Simple item page
dc.contributor.authorSulca Jota, Juan Carlos
dc.contributor.authorTakahashi, Ken
dc.contributor.authorEspinoza, Jhan-Carlo
dc.contributor.authorTacza, José
dc.contributor.authorZubieta Barragán, Ricardo
dc.contributor.authorMosquera Vásquez, Kobi Alberto
dc.contributor.authorApaéstegui Campos, James Emiliano
dc.date.accessioned2024-01-18T21:06:24Z
dc.date.available2024-01-18T21:06:24Z
dc.date.issued2024-01-02
dc.description.abstractThe northwestern Peruvian Amazon (NWPA) basin (78.4–75.8° W, 7.9–5.4° S) is an important region for coffee and rice production in Peru. Currently, no prediction models are available for estimating rainfall in advance during the wet season (January–February–March, JFM). Hence, we developed multiple linear regression (MLR) models using predictors derived from sea surface temperature (SST) indices of the Pacific, Atlantic, and Indian Oceans, including central El Niño (C), eastern El Niño (E), tropical South Atlantic (tSATL), tropical North Atlantic (tNATL), extratropical North Atlantic (eNATL), and Indian Ocean basin-wide with E and C removed (IOBW*) indices. Additionally, we utilized large-scale convection indices, namely, the eastern Pacific intertropical convergence zone (ITCZe) and South American Monsoon System (SAMSi) indices, for the 1981–2018 period. Rainfall in the lowland NWPA exhibits a bimodal annual cycle, whereas rainfall in the highland NWPA exhibits a unimodal annual cycle. The MLR model can be used to accurately capture the interannual variability during the wet season in the highland NWPA by utilizing predictors derived from the C and SAMSi indices. In contrast, regarding rainfall in the lowland NWPA, the Pacific SST variability, SAMS and tropical North Atlantic index were relevant. For long lead times, the MLR model provided reliable forecasts of JFM rainfall anomalies in the highlands (R3, approximately 2700 m asl) as these regions are governed by Pacific variability. However, the MLR model exhibited limitations in accurately estimating the wettest JFM season in the highlands due to the absence of a predictor for the amplified effect of the Madden–Julian Oscillation on rainfall.es_ES
dc.description.peer-reviewPor pareses_ES
dc.formatapplication/pdfes_ES
dc.identifier.citationSulca, J., Takahashi, K., Espinoza, J. C., Tacza, J., Zubieta, R., Mosquera, K., & Apaestegui, J. (2024). A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices.==$Climate Dynamics.$== https://doi.org/10.1007/s00382-023-07044-7es_ES
dc.identifier.doihttps://doi.org/10.1007/s00382-023-07044-7es_ES
dc.identifier.govdocindex-oti2018
dc.identifier.journalClimate Dynamicses_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12816/5504
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.ispartofurn:issn:1432-0894
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.subjectNorthwestern Peruvian Amazon basines_ES
dc.subjectBimodal rainfall regimees_ES
dc.subjectSeason-ahead rainfall predictiones_ES
dc.subjectCentral ENSO (C)es_ES
dc.subjectTropical North Atlantices_ES
dc.subjectSouth American monsoon system (SAMS)es_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.05.09es_ES
dc.titleA multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indiceses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Sulca_et_al_2024_Climate_Dynamics.pdf
Size:
341.35 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Arbitrados