Latent Heat Fluxes Trend and their Response to El Niño Southern Oscillation at the Global Scale

• Autorzy: As-Syakur Abd. Rahman

Identyfikatory

DOI

10.12912/27197050/190162

• Języki publikacji: EN

Abstrakty

EN

This study employed the Japanese Ocean flux data sets with use of remote sensing observations version 2 (JOFURO2) to examine global-scale seasonal variations and trends in Latent heat flux (LHF) over a 19-year period. Furthermore, additional analysis has been conducted to determine the response of LHF to the El Niño Southern Oscillation (ENSO) phenomenon. To assess variability, trends, and strength of relationships with ENSO, statistical score analysis was employed using seasonal means, standard deviations, linear trends, and linear correlations, respectively. In this study, the seasons were classified as December-January-February (DJF), March-April-May (MAM), June-July-August (JJA), and September-October-November (SON). The result of the study revealed that the highest LHF values tracked the annual movement of the sun. In the Northern Hemisphere, the highest spatial trends occurred during DJF, while JJA exhibited the peak values in the Southern Hemisphere. This spatial pattern aligns with the seasonal means of LHF, where the highest and lowest standard deviations and trends coincide with the corresponding regions of high and low LHF. This finding suggests that the standard deviation patterns support the observed variability in seasonal LHF means. The strongest spatial correlations between LHF and ENSO were observed over the Indian Ocean during the SON season. In contrast, the correlations between LHF and ENSO in the Atlantic Ocean exhibited spatial heterogeneity, with a significant correlation only during the DJF season. In general, the seasonal spatio-temporal patterns suggest a dynamic link between LHF and ENSO, potentially linked to large-scale monsoon system changes, the specific locations and distributions of positive/negative trends and standard deviations in LHF reveal a spatial response that appears independent of the ENSO phenomenon.

Słowa kluczowe

EN

heat flow; climate change; seasonal variability; spatial variability; satellite-based estimation

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2024
• Tom: Vol. 25, iss. 9
• Strony: 328--335
• Opis fizyczny: Bibliogr. 39 poz., rys.

Twórcy

autor

As-Syakur Abd. Rahman

• Marine Science Department, Faculty of Marine and Fisheries, Udayana University, Kampus Bukit Jimbaran, Bali 80361, Indonesia

• https://orcid.org/0000-0003-3881-634X

Bibliografia

• 1. Alexander, M.A., Bladé, I., Newman, M., Lanzante, J.R., Lau, N.C., Scott, J.D. 2002. The atmospheric bridge: The influence of ENSO teleconnections on air-sea interaction over the global oceans. Journal of Climate, 15(16), 2205–2231. https://doi.org/10.1175/1520-0442(2002)0152.0.CO;2
• 2. Ashok, K., Yamagata, T. 2009. The El Niño with a difference. Nature, 461, 481–484. https://doi.org/10.1038/461481a
• 3. Baosen, L. 1989. The latent and sensible heat fluxes over the western tropical Pacific and its relationship to ENSO. Advances in atmospheric sciences, 6(4), 467-474, https://doi.org/10.1007/BF02659080
• 4. Brown, G.W. 1982. Standard deviation, standard error: Which’standard’should we use?. American Journal of Diseases of Children, 136(10), 937–941.
• 5. Chou, S-H., Nelkin, E., Ardizzone, J., Atlas, R.M. 2004. A Comparison of latent heat fluxes over global oceans for four flux products. Journal of climate, 17, 3973–3989. https://doi.org/10.1175/1520-0442(2004)0172.0.CO;2
• 6. Congbin, F., Diaz, H., Huijun, F. 1992. Variability in latent heat flux over the tropical pacific in association with recent two ENSO events. Advances in atmospheric sciences, 9(3), 351–358. https://doi.org/10.1007/BF02656945
• 7. Crewell, S., Ruprecht, E., Simmer, C. 2009. Latent heat flux over the north atlantic ocean - A case study. Journal of applied meteorology, 30, 1627–1635. https://doi.org/10.1175/1520-0450(1991)0302.0.CO;2
• 8. Curtis, S. 2008. The El Niño–Southern Oscillation and Global Precipitation. Geography Compass, 2/3, 600–619. https://doi.org/10.1111/j.1749-8198.2008.00105.x
• 9. Enfield, D.B., Mestas-Nuñez, A.M. 2000. Global modes of ENSO and non-ENSO sea surface temperature variability and their associations with climate. in Diaz, H.F., and Markgraf, V. (Eds). El Niño and the Southern Oscillation: multiscale variability and global and regional impacts. Cambridge University Press, Cambridge, UK, 89–112.
• 10. Fairall, C.W., Bradley, E.F., Hare, J.E., Grachev, A.A., Edson, J.B. 2003. Bulk parameterization of airsea fluxes: Updates and verification for the COARE algorithm. Journal of climate, 17, 571–591. https://doi.org/10.1175/1520-0442(2003)016%3C0571:BPOASF%3E2.0.CO;2
• 11. Good, S.A., Corlett, G.K., Remedios, J.J., Noyes, E.J., Llewellyn-Jones, D.T. 2007. The global trend in sea surface temperature from 20 years of advanced very high resolution radiometer data. Journal of climate, 20(7), 1255–1264. https://doi.org/10.1175/JCLI4049.1
• 12. Gravetter, F., and Wallnau, L. 2006. Statistics for the behavioral sciences, 8th edition. Wadsworth, Cengage Learning. Belmont, California, USA.
• 13. Grodsky, S.A., Bentamy, A., Carton, J.A., Pinker, R.T. 2009. intraseasonal latent heat flux based on satellite observations. Journal of climate, 22, 4539– 4556. https://doi.org/10.1175/2009JCLI2901.1
• 14. Kondo, J. 1975. Air-sea bulk transfer coefficients in diabatic conditions. BoundaryLayer Meteorology, 9(1), 91–112. https://doi.org/10.1175/1520-0442(1999)012%3C0917:RSSTVD%3E2.0.CO;2
• 15. Kubota, M., Tomita, H. 2007. Introduction of JOFURO latent heat flux version 2, in: Proceedings of the Joint 2007 EUMETSAT Meteorological Satellite Conference and the 15th Satellite Meteorology and Oceanography Conference of the American Meteorological Society, Amsterdam, The Netherlands, 24–28 September.
• 16. Kubota, M., Tomita, H., Iwasaki, S. 2006. Introduction of J-OFURO version 2. In 36th COSPAR Scientific Assembly 36, 2297). Held 16–23 July 2006, in Beijing, China. COSPAR2006-A-02297
• 17. Kubota, M., Kano, A., Muramatsu, H., Tomita, H. 2003. Intercomparison of various surface latent heat flux fields. Journal of climate, 16(4), 670–678. https://doi.org/10.1175/1520-0442(2003)016%3C0 670:IOVSLH%3E2.0.CO;2
• 18. Kubota, M., Iwasaka, N., Kizu, S., Konda, M., Kutsuwada, K. 2002. Japanese ocean flux data sets with use of remote sensing observations (J-OFURO). Journal of Oceanography, 58(1), 213–225. https://doi.org/10.1023/A:1015845321836
• 19. Kubota, M., Hiroyuki, T., Kutsuwada, K., Akiyama, M., Iwasaki, S. 2010. Introduction of Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO) Version 2 in Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Annex), Venice, Italy, 21–25 September 2009, Hall, J., Harrison, D.E., Stammer, D., Eds., ESA Publication WPP-306, doi:10.5270/OceanObs09.
• 20. Li, G., Ren, B., Yang, C., Zheng, J. 2011. Revisiting the trend of the tropical and subtropical Pacific surface latent heat flux during 1977–2006. Journal of Geophysical Research: Atmospheres, 116, D10115, https://doi.org/10.1029/2010JD015444
• 21. Liou, Y.A., Le, M.S., Chien, H. 2018. Normalized difference latent heat index for remote sensing of land surface energy fluxes. IEEE Transactions on Geoscience and Remote Sensing, 57(3), 1423-1433, https://doi.org/10.1109/TGRS.2018.2866555
• 22. Liu, J., Curry, J.A. 2006. Variability of the tropical and subtropical ocean surface latent heat flux during 1989–2000. Geophysical Research Letters, 33, L05706-1–L05706-5. https://doi.org/10.1029/2005GL024809
• 23. Liu, W.T. 1988. Moisture and latent heat flux variabilities in the tropical Pacific derived from satellite data. Journal of Geophysical Research: Oceans, 93(C6), 6749–6760, https://doi.org/10.1029/JC093iC06p06749
• 24. Liu, Z., Zhou, L.T. 2018. Evolution features of the surface latent heat flux anomalies over the tropical Pacific associated with two types of ENSO events. Theoretical and Applied Climatology, 134, 721– 737, https://doi.org/10.1007/s00704-017-2264-9
• 25. Ropelewski, C.F., Halpert, M.S. 1987. Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Monthly weather review, 115(8), 1606–1626. https://doi.org/10.1175/1520-0493(1987)115%3C1606:GAR SPP%3E2.0.CO;2
• 26. Sakurai, T., Yukio, K., Kuragano, T. 2005. Merged satellite and in-situ data global daily SST. In Geoscience and Remote Sensing Symposium, 2005. IGARSS’05. Proceedings. 2005 IEEE International, 4, 2606–2608. https://doi.org/10.1109/IGARSS.2005.1525519
• 27. Swain, D., Ghose, S.K. 2020. Latent and Sensible heat flux variation in north Indian Ocean during ENSO and Indian Ocean dipole years. In IEEE 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 1–3. https://doi.org/10.23919/URSIGASS49373.2020.9232005
• 28. Tomita, H., Kubota, M. 2006. An analysis of the accuracy of Japanese Ocean Flux data sets with Use of Remote sensing Observations (J‐OFURO) satellite‐derived latent heat flux using moored buoy data. Journal of Geophysical Research: Oceans, 111(C7), https://doi.org/10.1029/2005JC003013
• 29. Tomita, H., Kubota, M., Cronin, M.F., Iwasaki, S., Konda, M., Ichikawa, H. 2010. An assessment of surface heat fluxes from J-OFURO2 at the KEO and JKEO sites. Journal of Geophysical Research, 115, C03018-1– C03018-13. https://doi.org/10.1029/2009JC005545
• 30. Tomita, H., Hihara, T., Kako, S. I., Kubota, M., Kutsuwada, K. 2019. An introduction to J-OFURO3, a third-generation Japanese ocean flux data set using remote-sensing observations. Journal of Oceanography, 75(2), 171–194, https://doi.org/10.1007/s10872-018-0493-x
• 31. Tomita, H., Kutsuwada, K., Kubota, M., Hihara, T. 2021. Advances in the estimation of global surface net heat flux based on satellite observation: J-OFURO3 V1. 1. Frontiers in Marine Science, 8, 612361. https://doi.org/10.3389/fmars.2021.612361
• 32. Trenberth, K.E. 1997. The definition of El Niño. Bulletin of the American Meteorological Society, 78:2771–2777. https://doi.org/10.1175/1520-0477(1997)078%3C2771:TDOENO%3E2.0.CO;2
• 33. van Oldenborgh, G.J., Philip, S.Y., Collins, M. 2005. El Niño in a changing climate: a multi-model study. Ocean Science, 1, 81–95. https://doi.org/10.5194/os-1-81-2005
• 34. von Storch, H., Zwiers, F.W. 1999. Statistical Analysis in Climate Research. Cambridge University Press, 484: Cambridge, UK.
• 35. Yang, H., Liu, J., Lohmann, G., Shi, X., Hu, Y., Chen, X. 2016. Ocean-atmosphere dynamics changes associated with prominent ocean surface turbulent heat fluxes trends during 1958–2013. Ocean Dynamics, 66, 353–365, https://doi.org/10.1007/s10236-016-0925-3
• 36. Yu, L., Weller, R.A. 2007. Objectively analyzed air–sea heat fluxes for the global ice-free oceans (1981–2005). Bulletin of the American Meteorological Society, 88, 527–539. https://doi.org/10.1175/BAMS-88-4-527
• 37. Yu, L., Jin, X., Weller, R.A. 2007. Annual, seasonal, and interannual variability of air–sea heat fluxes in the Indian Ocean. Journal of climate, 20(13), 3190– 3209, https://doi.org/10.1175/JCLI4163.1
• 38. Zheng, C., Zhou, L., Huang, C., Shi, Y., Li, J., Li, J. 2013. The long-term trend of the sea surface wind speed and the wave height (wind wave, swell, mixed wave) in global ocean during the last 44 a. Acta Oceanologica Sinica, 32(10), 1–4. https://doi.org/10.1007/s13131-013-0358-5
• 39. Zong, H., Liu, Y., Xiu, P., Xu, Q., Rong, Z. 2010. Interannual variability of latent and sensible heat fluxes in the South China Sea. Chinese Journal of Oceanology and Limnology, 28, 153–159. https://doi.org/10.1007/s00343-010-9241-9