Climatology and spectral characteristics of 10-day rainfall variability in Manokwari, West Papua (1991–2022)

¹ Physics Department, Faculty of Mathematics and Natural Sciences, University of Papua, 98314, Manokwari, Indonesia
² Meteorology Study Program, Sekolah Tinggi Meteorologi Klimatologi dan Geofisika (STMKG), 15119, Tangerang City, Indonesia
³ Climatology Study Program, Sekolah Tinggi Meteorologi Klimatologi dan Geofisika (STMKG), 15119, Tangerang City, Indonesia

^* Corresponding: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Rainfall variability in the Indo-Pacific warm pool arises from coupled interactions between the Intertropical Convergence Zone (ITCZ), the Asian-Australian monsoon, and large-scale ocean-atmosphere teleconnections. However, eastern Indonesia, particularly West Papua, remains underrepresented in climatological research. This study examined rainfall dynamics in Manokwari, West Papua, using 32 years (1991-2022) of BMKG station observations. Daily rainfall was aggregated into dekadal (10-day) totals to capture variability from intraseasonal to interannual timescales. We first characterized the annual and seasonal rainfall cycles using descriptive statistics and then diagnosed the dominant periodicities through Fast Fourier Transform (FFT) spectral analysis. To assess large-scale climate control, we quantified relationships between dekadal rainfall and key climate drivers, El Nino-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Australian monsoon, using correlation and partial correlation analyses to isolate each driver's independent association amid overlapping signals. Together, these diagnostics establish a robust dekadal climatology for an equatorial maritime region with limited prior analysis, supporting seasonal monitoring and climate-sensitive decision making. Beyond site-specific findings, this study introduces a replicable central-local co-production approach: standardized diagnostics developed centrally are paired with locally owned station data and locally contextualized interpretation by forecasters in the field. Applied across stations, this framework can strengthen the local analytical capacity in remote regions while improving the consistency and depth of Indonesia's climate information services for adaptation and operational forecasting, with ENSO emerging as the most robust large-scale modulator in both partial-correlation and phase-composite evidence.