Temporal variability of meteorological and oceanographic parameters in Tuban Waters, Indonesia, and their relationships with ENSO and IOD
DOI:
https://doi.org/10.29080/mrcm.v7i2.2552Keywords:
chlorophyll-a, ENSO, IOD, meteorological and oceanographic variability, sea surface temperatureAbstract
Global climate variability influences meteorological and oceanographic conditions in Indonesian coastal waters, including Tuban Waters. This study aimed to analyze the temporal variability and anomalies of meteorological and oceanographic parameters and to evaluate the relationships between the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) with environmental variability in Tuban Waters during 2015–2024. Meteorological and oceanographic data were obtained from BMKG, NOAA, and NASA Ocean Color, including rainfall, sea surface temperature (SST), chlorophyll-a concentration, wind speed, wave height, current velocity, tidal elevation, the Oceanic Niño Index (ONI), and the Dipole Mode Index (DMI). Trend analysis, anomaly analysis, and Spearman correlation analysis were applied to assess temporal variability and climate–environment relationships. The results showed decreasing trends in wind speed, wave height, and rainfall, whereas current velocity exhibited moderate variability without a clear long-term trend. Rainfall variability was significantly negatively correlated with both DMI (ρ = −0.353, p < 0.01) and ONI (ρ = −0.293, p < 0.01), indicating that positive IOD and El Niño conditions were generally associated with reduced rainfall. SST anomalies were significantly negatively correlated with DMI (ρ = −0.332, p < 0.01), whereas their relationship with ONI was weak and not statistically significant. Chlorophyll-a anomalies exhibited weak correlations with both climate indices, suggesting that local oceanographic processes, particularly nutrient dynamics and water-column mixing, played a greater role in regulating biological variability. Overall, the results indicate that rainfall variability in Tuban Waters is influenced by both ENSO and IOD, whereas SST variability is more closely associated with IOD and regional oceanographic processes. In contrast, chlorophyll-a variability is primarily controlled by local oceanographic conditions, with only limited influence from large-scale climate variability. These findings provide an integrated understanding of meteorological and oceanographic variability and their interactions with climate variability in Tuban Waters, supporting coastal environmental monitoring and management.
Downloads
References
J. Sprintall, A. L. Gordon, S. E. Wijffels, M. Feng, and S. Hu. 2019. Detecting Change in the Indonesian Seas. Front. Mar. Sci. 6: 257. https://doi.org/10.3389/fmars.2019.00257
R. Suwarman, E. Riawan, Y. S. M. Simanjuntak, and D. E. Irawan. 2022. Kajian Perubahan Iklim di Pesisir Jakarta Berdasarkan Data Curah Hujan dan Temperatur. Bul. Oseanografi Mar. 11(1): 99–110. https://doi.org/10.14710/buloma.v11i1.42749
M. N. Nur’utami and R. Hidayat. 2016. Influences of IOD and ENSO to Indonesian Rainfall Variability: Role of Atmosphere-ocean Interaction in the Indo-pacific Sector. Procedia Environ. Sci. 33: 196–203. https://doi.org/10.1016/j.proenv.2016.03.070
Khalifah, N., Asmadin, A., & Palupi, R. 2024. Pengukuran Data Pasang Surut Menggunakan Kearifan Lokal Etno-Oseanografi (Suku Sama) dan Metode Admiralty di Mola Raya, Wakatobi. Jurnal Laut Pulau: Hasil Penelitian Kelautan. 3(2): 21-28. https://doi.org/10.30598/jlpvol3iss2pp21-28
L. F. Ichsari, G. Handoyo, H. Setiyono, A. Ismanto, J. Marwoto, and M. Yusuf. 2020. Studi Komparasi Hasil Pengolahan Pasang Surut dengan 3 Metode (Admiralty, Least Square dan Fast Fourier Transform) di Pelabuhan Malahayati, Banda Aceh. Indonesian Journal of Oceanography. 2(1): 3–6. https://doi.org/10.14710/ijoce.v2i2.7985
J. Sprintall, et al. 2014. The Indonesian Seas and Their Role in the Coupled Ocean-Climate System. Nat. Publ. Gr. 7. https://doi.org/10.1038/ngeo2188
Pasaribu, P. M., Tubalawony, S., & Masrikat, J. A. N. 2026. The Correlation Between Sea Surface Temperature Variability and Convective Cloud Characteristics over Ambon Island Coastal Waters. Jurnal Meteorologi dan Geofisika. 26(2): 145–155. https://doi.org/10.31172/jmg.v26i2.1085
I. S. Cabral, I. R. Young, and A. Toffoli. 2022. Long-Term and Seasonal Variability of Wind and Wave Extremes in the Arctic Ocean. Front. Mar. Sci. 9: 1–13. https://doi.org/10.3389/fmars.2022.802022
F. R. Amanulloh, A. Romdani, Y. Ramdani, and P. Irawan. 2025. Does ENSO-driven climate variability increase coastal flooding? Insights from the Brebes coastal area Indonesia. Nat. Hazards Res. https://doi.org/10.1016/j.nhres.2025.10.004
A. Rahman, L. Alfaris, A. Wahyudi, and A. N. Firdaus. 2025. Seasonal Variability of Surface Ocean Currents in the Southern Java Offshore based on Global Reanalysis Data. Geosfera: Jurnal Penelitian Geografi. 4(2): 610–621. https://doi.org/10.37905/geojpg.v4i2.37281
R. S. Nerem, B. D. Beckley, J. T. Fasullo, B. D. Hamlington, D. Masters, and G. T. Mitchum. 2018. Climate-change–driven accelerated sea-level rise detected in the altimeter era. Earth, Atmospheric, and Planetary Sciences. 115 (9): 2022-2025. https://doi.org/10.1073/pnas.1717312115
B. Nababan, S. Hadianti, and N. Natih. 2015. Dinamika Anomali Paras Laut Perairan Indonesia. J. Ilmu dan Teknol. Kelaut. Trop. 7(1): 259–272.
Tsoulis, D., and K. Patlakis. 2013. A spectral assessment review of current satellite-only and combined Earth gravity models. Rev. Geophys. 51: 186–243. https://doi.org/10.1002/rog.20012
S. A. Grodsky, J. A. Carton, and F. O. Bryan. 2014. A curious local surface salinity maximum in the northwestern tropical Atlantic. J. Geophys. Res. Oceans. 119(1): 484–495. https://doi.org/10.1002/2013JC009450
C. Wenju, S. Agus, C. Matthew, D. Boris, and K. Christina. 2021. Changing El Niño – Southern Oscillation in a warming climate. Archimer. 2(9): 628–644. https://doi.org/10.1038/s43017-021-00199-z
C. E. Bulgin, C. J. Merchant, and D. Ferreira. 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci. Rep. 1–13. https://doi.org/10.1038/s41598-020-64785-9
Dunstan, P.K., Foster, S.D., King, E. et al. 2018. Global patterns of change and variation in sea surface temperature and chlorophyll-a. Sci. Rep. 8: 14624. https://doi.org/10.1038/s41598-018-33057-y
F. Serva, S. Marullo, and R. Iacono. 2025. Detection of Satellite Sea Surface Temperature Extremes: Low Frequency Variability and Climate Change. J. Geophys. Res. Ocean. 130(10). https://doi.org/10.1029/2025JC022886
W. Schneider, D. Donoso, J. Garcés-vargas, and R. Escribano. 2017. Progress in Oceanography Water-column cooling and sea surface salinity increase in the upwelling region off central-south Chile driven by a poleward displacement of the South Pacific High. Prog. Oceanogr. 151: 38–48. https://doi.org/10.1016/j.pocean.2016.11.004
J. Guo and J. Lu. 2022. Variability of Chlorophyll-a and Secchi Disk Depth (1997 – 2019) in the Bohai Sea Based on Monthly Cloud-Free Satellite Data Reconstructions. Remote Sens. 14: 639.
Xu Z, Ji F, Liu B, et al. 2021. Long-term evolution of global sea surface temperature trend. Int J Climatol. 41: 4494–4508. https://doi.org/10.1002/joc.7082
R. Dwi, et al. 2024. Chlorophyl-a Distribution and Sea Surface Temperature. Journal Kelautan. 17(1): 74–82.
M. Ahmed, A. Naser Mahmood, and J. Hu. 2016. A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60: 19–31. https://doi.org/10.1016/j.jnca.2015.11.016
Cianelli, D., D’Alelio, D., Uttieri, M. et al. 2017. Disentangling physical and biological drivers of phytoplankton dynamics in a coastal system. Sci Rep. 7: 15868. https://doi.org/10.1038/s41598-017-15880-x
D. Rosalina et al. 2024. Chlorophyl-a Distribution and Sea Surface Temperature Using Modis Aqua Image Data in Bone Bay Waters. J. Kelaut. 17(1): 74–82. http://doi.org/10.21107/jk.v17i1.24100
M. Ariska, Supari, M. Irfan, and I. Iskandar. 2023. Pemodelan numerik hubungan pola curah hujan wilayah equatorial di Pulau Sumatera terhadap fenomena ENSO dan IOD menggunakan metode Simulated Annealing. J. Teor. dan Apl. Fis. 11(2): 95–106.
A. Santoso, H. Hendon, and A. Watkins. 2019. Dynamics and Predictability of El Niño–Southern Oscillation: An Australian Perspective on Progress and Challenges. Am. Meteorol. Soc. 100(3): 403–420. https://doi.org/10.1175/BAMS-D-18-0057.1
Liguori, G., McGregor, S., Singh, M., Arblaster, J., & Di Lorenzo, E. 2022. Revisiting ENSO and IOD contributions to Australian precipitation. Geophysical Research Letters. 49: e2021GL094295. https://doi.org/10.1029/2021GL094295
C. Wang. 2019. Three‑ocean interactions and climate variability: a review and perspective. Clim. Dyn. 53(7): 5119–5136. https://doi.org/10.1007/s00382-019-04930-x
Wang, S., Waitzmann, D., Ummenhofer, C. C., & Oppo, D. W. 2026. Remote and regional drivers of the Indonesian Throughflow under future warming: Implications for inter-basin freshwater transport. Geophysical Research Letters. 53: e2025GL119514. https://doi.org/10.1029/2025GL119514
Kunarso, K., Ismunarti, D. H., Rifai, A., Munandar, B., Wirasatriya, A., and Susanto, R. D. 2023. Effect of Extreme ENSO and IOD on the Variability of Chlorophyll-a and Sea Surface Temperature in the North and South of Central Java Province. ILMU KELAUTAN: Indonesian Journal of Marine Sciences. 28(1): 1-11. https://doi.org/10.14710/ik.ijms.28.1.1-11
C. Res, N. H. Saji, and T. Yamagata. 2003. Possible impacts of Indian Ocean Dipole mode events on global climate. Climate Research. 25: 151–169.
B. Sukresno. 2018. Analisis Multilayer Variabilitas Upwelling di Perairan Selatan Jawa. J. Kelaut. Nas. 1(1). https://doi.org/10.15578/jkn.v1i1.6619
B. Nababan et al. 2021. Variability of Sea Surface Temperature and Chlorophyll-a Concentration in the Northeast Indian Ocean, West of Sumatra. J. Teknol. Perikan. dan Kelaut. 12(2): 143–159.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Elsa Anggilia, Rodhi Naufal Ma’arif, Asri Sawiji, Eko Hadi Santoso
This work is licensed under a Creative Commons Attribution 4.0 International License.
