Abstract
The 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.
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Data availability
The monthly ERA5 data used in this study were downloaded from https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset. The rain-gauge station data must be requested from the Servicio Nacional de Meteorología e Hidrología (SENAMHI) in Peru.
Change history
09 February 2024
A Correction to this paper has been published: https://doi.org/10.1007/s00382-024-07135-z
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Acknowledgements
This study was performed using computational resources, including the HPC-Linux-Cluster, from the Laboratorio de Dinámica de Fluidos Geofísicos Computacionales at the Instituto Geofísico del Perú (Grant 101-2014-FONDECYT). JCE received partial support from the AMANECER-MOPGA project funded by the ANR and IRD (ref. ANR- 18-MPGA-0008). Finally, we are very grateful to the three anonymous reviewers who provided valuable comments, which helped us significantly advance our results and substantially improve the manuscript.
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Conceptualization, JS; methodology, JS, KT, JCE, JT, RZ, and KM. formal analysis, JS, KT, JCE, JT, RZ, and KM; investigation, JS, KT, JCE, JT, RZ, and KM; writing—original draft preparation, JS, KT, JCE, JT, and RZ; writing—revised draft preparation, JS, KT, JCE, JT, RZ, KM and JA.
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Sulca, J., Takahashi, K., Espinoza, JC. et al. A multiple linear regression model for the prediction of summer rainfall in the northwestern Peruvian Amazon using large-scale indices. Clim Dyn (2024). https://doi.org/10.1007/s00382-023-07044-7
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DOI: https://doi.org/10.1007/s00382-023-07044-7