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  • 1
    Publication Date: 2022-05-27
    Description: Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 34(7), (2021): 2473-2490, https://doi.org/10.1175/JCLI-D-20-0625.1.
    Description: This study uses sea surface salinity (SSS) as an additional precursor for improving the prediction of summer [December–February (DJF)] rainfall over northeastern Australia. From a singular value decomposition between SSS of prior seasons and DJF rainfall, we note that SSS of the Indo-Pacific warm pool region [SSSP (150°E–165°W and 10°S–10°N) and SSSI (50°–95°E and 10°S–10°N)] covaries with Australian rainfall, particularly in the northeast region. Composite analysis that is based on high or low SSS events in the SSSP and SSSI regions is performed to understand the physical links between the SSS and the atmospheric moisture originating from the regions of anomalously high or low, respectively, SSS and precipitation over Australia. The composites show the signature of co-occurring La Niña and negative Indian Ocean dipole with anomalously wet conditions over Australia and conversely show the signature of co-occurring El Niño and positive Indian Ocean dipole with anomalously dry conditions there. During the high SSS events of the SSSP and SSSI regions, the convergence of incoming moisture flux results in anomalously wet conditions over Australia with a positive soil moisture anomaly. Conversely, during the low SSS events of the SSSP and SSSI regions, the divergence of incoming moisture flux results in anomalously dry conditions over Australia with a negative soil moisture anomaly. We show from the random-forest regression analysis that the local soil moisture, El Niño–Southern Oscillation (ENSO), and SSSP are the most important precursors for the northeast Australian rainfall whereas for the Brisbane region ENSO, SSSP, and the Indian Ocean dipole are the most important. The prediction of Australian rainfall using random-forest regression shows an improvement by including SSS from the prior season. This evidence suggests that sustained observations of SSS can improve the monitoring of the Australian regional hydrological cycle.
    Description: This research is funded through the Earth System and Climate Change Hub of the Australian government’s National Environmental Science Programme. The assistance of computing resources from the National Computational Infrastructure supported by the Australian Government is acknowledged. Author Ummenhofer acknowledges support from the U.S. National Science Foundation under Grant OCE-1663704. Author Feng was supported by the Centre for Southern Hemisphere Oceans Research (CSHOR), which is a joint initiative between the Qingdao National Laboratory for Marine Science and Technology (QNLM), CSIRO, University of New South Wales, and the University of Tasmania. The authors also acknowledge Dr. Manali Pal for technical discussion on machine learning.
    Description: 2021-09-01
    Keywords: ENSO ; Flood events ; Hydrologic cycle ; Machine learning ; Rainfall ; Salinity ; Seasonal forecasting ; Soil moisture
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sprintall, J., Gordon, A. L., Wijffels, S. E., Feng, M., Hu, S., Koch-Larrouy, A., Phillips, H., Nugroho, D., Napitu, A., Pujiana, K., Susanto, R. D., Sloyan, B., Yuan, D., Riama, N. F., Siswanto, S., Kuswardani, A., Arifin, Z., Wahyudi, A. J., Zhou, H., Nagai, T., Ansong, J. K., Bourdalle-Badie, R., Chanuts, J., Lyard, F., Arbic, B. K., Ramdhani, A., & Setiawan, A. Detecting change in the Indonesian Seas. Frontiers in Marine Science, 6, (2019):257, doi:10.3389/fmars.2019.00257.
    Description: The Indonesian seas play a fundamental role in the coupled ocean and climate system with the Indonesian Throughflow (ITF) providing the only tropical pathway connecting the global oceans. Pacific warm pool waters passing through the Indonesian seas are cooled and freshened by strong air-sea fluxes and mixing from internal tides to form a unique water mass that can be tracked across the Indian Ocean basin and beyond. The Indonesian seas lie at the climatological center of the atmospheric deep convection associated with the ascending branch of the Walker Circulation. Regional SST variations cause changes in the surface winds that can shift the center of atmospheric deep convection, subsequently altering the precipitation and ocean circulation patterns within the entire Indo-Pacific region. Recent multi-decadal changes in the wind and buoyancy forcing over the tropical Indo-Pacific have directly affected the vertical profile, strength, and the heat and freshwater transports of the ITF. These changes influence the large-scale sea level, SST, precipitation and wind patterns. Observing long-term changes in mass, heat and freshwater within the Indonesian seas is central to understanding the variability and predictability of the global coupled climate system. Although substantial progress has been made over the past decade in measuring and modeling the physical and biogeochemical variability within the Indonesian seas, large uncertainties remain. A comprehensive strategy is needed for measuring the temporal and spatial scales of variability that govern the various water mass transport streams of the ITF, its connection with the circulation and heat and freshwater inventories and associated air-sea fluxes of the regional and global oceans. This white paper puts forward the design of an observational array using multi-platforms combined with high-resolution models aimed at increasing our quantitative understanding of water mass transformation rates and advection within the Indonesian seas and their impacts on the air-sea climate system. Introduction
    Description: JS acknowledges funding to support her effort by the National Science Foundation under Grant Number OCE-1736285 and NOAA’s Climate Program Office, Climate Variability and Predictability Program under Award Number NA17OAR4310257. SH was supported by the National Natural Science Foundation of China (Grant 41776018) and the Key Research Program of Frontier Sciences, CAS (QYZDB-SSW-SYS023). HP acknowledges support from the Australian Government’s National Environmental Science Programme. HZ acknowledges support from National Science Foundation under Grant No. 41876009. RS was supported by National Science Foundation Grant No. OCE-07-25935; Office of Naval Research Grant No. N00014-08-01-0618 and National Aeronautics and Space Administration Grant No. 80NSSC18K0777. SW, MF, and BS were supported by Center for Southern Hemisphere Oceans Research (CSHOR), which is a joint initiative between the Qingdao National Laboratory for Marine Science and Technology (QNLM), CSIRO, University of New South Wales and University of Tasmania.
    Keywords: Indonesian throughflow ; Observing system ; Intraseasonal ; ENSO ; Transport variability ; Planetary waves
    Repository Name: Woods Hole Open Access Server
    Type: Article
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