Improving Australian Rainfall Prediction Using Sea Surface Salinity

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  • 1 Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia, and ARC Centre of Excellence for Climate System Science, Hobart, Australia
  • 2 Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia, and ARC Centre of Excellence for Climate Extremes, Hobart, Australia, and CSIRO Oceans and Atmosphere, Hobart, Australia, and Australian Antarctic Program Partnership, Hobart, Australia
  • 3 Woods Hole Oceanographic Institution, Woods Hole, MA, USA, and ARC Centre of Excellence for Climate Extremes, Sydney, Australia
  • 4 Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia, and ARC Centre of Excellence for Climate Extremes, Hobart, Australia
  • 5 CSIRO Oceans and Atmosphere, Indian Ocean Marine Research Centre, Crawley, WA, Australia, and Centre for Southern Hemisphere Oceans Research, CSIRO, Hobart, Australia
  • 6 Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, India
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Abstract

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°E-95°E and 10°S-10°N)] co-varies with Australian rainfall, particularly in the northeast region. Composite analysis based on high (low) SSS events in SSSP and SSSI region is performed to understand the physical links between the SSS and the atmospheric 31 moisture originating from the regions of anomalously high (low) SSS and precipitation over Australia. The composites show the signature of co-occurring La Niña and negative Indian Ocean dipole (co-occurring El Niño and positive Indian Ocean dipole) with anomalously wet (dry) conditions over Australia. During the high (low) SSS events of SSSP and SSSI regions, the convergence (divergence) of incoming moisture flux results in anomalously wet (dry) conditions over Australia with a positive (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 Indian Ocean Dipole (IOD) 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.

Corresponding author: Email:- saurabh.rathore@utas.edu.au

Abstract

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°E-95°E and 10°S-10°N)] co-varies with Australian rainfall, particularly in the northeast region. Composite analysis based on high (low) SSS events in SSSP and SSSI region is performed to understand the physical links between the SSS and the atmospheric 31 moisture originating from the regions of anomalously high (low) SSS and precipitation over Australia. The composites show the signature of co-occurring La Niña and negative Indian Ocean dipole (co-occurring El Niño and positive Indian Ocean dipole) with anomalously wet (dry) conditions over Australia. During the high (low) SSS events of SSSP and SSSI regions, the convergence (divergence) of incoming moisture flux results in anomalously wet (dry) conditions over Australia with a positive (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 Indian Ocean Dipole (IOD) 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.

Corresponding author: Email:- saurabh.rathore@utas.edu.au
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