Improving Australian Rainfall Prediction Using Sea Surface Salinity

Saurabh Rathore

doi:10.1175/JCLI-D-20-0625.1

Improving Australian Rainfall Prediction Using Sea Surface Salinity

2021-04-01

5

OA

AI

Caroline C. Ummenhofer

DOI：10.1175/JCLI-D-20-0625.1

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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 degrees E-165 degrees W and 10 degrees S-10 degrees N) and SSSI (50 degrees-95 degrees E and 10 degrees S-10 degrees 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 Nina and negative Indian Ocean dipole with anomalously wet conditions over Australia and conversely show the signature of co-occurring El Nino 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 Nino-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.

Keyword:

ENSO

Flood events

Hydrologic cycle

Machine learning

Rainfall

Salinity

Seasonal forecasting

Soil moisture

Improving Australian Rainfall Prediction Using Sea Surface Salinity

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