Improving Australian Rainfall Prediction Using Sea Surface Salinity

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.

Skillful Long‐Lead Prediction of Summertime Heavy Rainfall in the US Midwest From Sea Surface Salinity

“…In addition, the oceanic moisture export in the form of latent heat fuels atmospheric teleconnection, an important source of predictability for regional climate (Scaife et al., 2017). Encouraging skill of SSSA‐based seasonal rainfall forecasts was shown for the African Sahel (Li et al., 2016a), the United States (Li et al., 2016b; T. Liu et al., 2018), China (Zeng et al., 2019), and Australia (Rathore et al., 2021). Here, we present an additional line of evidence that springtime SSSA in the tropical Pacific and subtropical North Atlantic are skillful predictors of summertime heavy rainfall in the US Midwest.…”

Niño 4 West (Niño‐4W) Sea Surface Temperature Variability

“…The West Pacific SST gradient (WPG), defined as the standardized difference between area‐averaged SST over the central Pacific Ocean (Niño‐4 region) and west Pacific Ocean (0°–10°N, 130°–150°E), has been found to significantly modulate Indo‐Pacific climate (Hoell & Funk, 2013) and Ningaloo Niño variability (Zinke et al., 2015). Moisture fluxes from the Niño‐4W region, especially in austral winter‐spring, may have a great influence on the northern Australian rainfall during austral summer (Rathore et al., 2020, 2021). An attempt to distinguish El Niño Modokis according to their influence on southern China rainfall also identified one type of El Niño Modoki with warming in the eastern half of Niño 4 versus a second type of asymmetric warming with a footprint in the western half of Niño 4 (Wang & Wang, 2013).…”

“…The West Pacific SST gradient (WPG), defined as the standardized difference between area-averaged SST over the central Pacific Ocean (Niño-4 region) and west Pacific Ocean (0°-10°N, 130°-150°E), has been found to significantly modulate Indo-Pacific climate (Hoell & Funk, 2013) and Ningaloo Niño variability (Zinke et al, 2015). Moisture fluxes from the Niño-4W region, especially in austral winter-spring, may have a great influence on the northern Australian rainfall during austral summer (Rathore et al, 2020(Rathore et al, , 2021. and c) top two empirical orthogonal function (EOF) modes of the tropical Pacific SST anomalies average during November-February, explaining 64% and 10% of the total variance, respectively.…”