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Summertime heavy rainfall and its resultant floods are among the most harmful natural hazards in the US Midwest, one of the world's primary crop production areas. However, seasonal forecasts of heavy rain, currently based on preseason sea surface temperature anomalies (SSTAs), remain unsatisfactory. Here, we present evidence that sea surface salinity anomalies (SSSAs) over the tropical western Pacific and subtropical North Atlantic are skillful predictors of summer time heavy rainfall one season ahead. A one standard deviation change in tropical western Pacific SSSA is associated with a 1.8 mm day⁻¹increase in local precipitation, which excites a teleconnection pattern to extratropical North Pacific. Via extratropical air‐sea interaction and long memory of midlatitude SSTA, a wave train favorable for US Midwest heavy rain is induced. Combined with soil moisture feedbacks bridging the springtime North Atlantic salinity, the SSSA‐based statistical prediction model improves Midwest heavy rainfall forecasts by 92%, complementing existing SSTA‐based frameworks.

 

Research on Atmospheric Rivers (ARs) has focused primarily on AR (thermo)dynamics and hydrological impacts over land. However, the evolution and potential role of nearshore air‐sea fluxes during landfalling ARs are not well documented. Here, we examine synoptic evolutions of nearshore latent heat flux (LHF) during strong late‐winter landfalling ARs (1979–2017) using 138 overshelf buoys along the U. S. west coast. Composite evolutions show that ARs typically receive upward (absolute) LHF from the coastal ocean. LHF is small during landfall due to weak air‐sea humidity gradients but is strongest (30–50 W/m²along the coast) 1–3 days before/after landfall. During El Niño winters, southern‐coastal LHF strengthens, coincident with stronger ARs. A decomposition of LHF reveals that sea surface temperature (SST) anomalies modulated by the El Niño Southern Oscillation dominate interannual LHF variations under ARs, suggesting a potential role for nearshore SST and LHF influencing the intensity of landfalling ARs.

 

Stalagmites and climate models reveal ITCZ shifts drove concurrent changes in Australian tropical cyclone and monsoon rainfall. 

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°–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. 

The long-term trend of sea surface salinity (SSS) reveals an intensification of the global hydrological cycle due to human-induced climate change. This study demonstrates that SSS variability can also be used as a measure of terrestrial precipitation on inter-seasonal to inter-annual time scales, and to locate the source of moisture. Seasonal composites during El Niño Southern Oscillation/Indian Ocean Dipole (ENSO/IOD) events are used to understand the variations of moisture transport and precipitation over Australia, and their association with SSS variability. As ENSO/IOD events evolve, patterns of positive or negative SSS anomaly emerge in the Indo-Pacific warm pool region and are accompanied by atmospheric moisture transport anomalies towards Australia. During co-occurring La Niña and negative-IOD events, salty anomalies around the maritime continent (north of Australia) indicate freshwater export and are associated with a significant moisture transport that converges over Australia to create anomalous wet conditions. In contrast, during co-occurring El Niño and positive IOD events, there is the moisture transport divergence anomaly over Australia and results in anomalous dry conditions. The relationship between SSS and atmospheric moisture transport also holds for pure ENSO/IOD events but varies in magnitude and spatial pattern. The significant pattern correlation between the moisture flux divergence and SSS anomaly during the ENSO/IOD events highlights the associated ocean-atmosphere coupling. A case study of the extreme hydroclimatic events of Australia (e.g. 2010-11 Brisbane flood) demonstrates that the changes in SSS occur before the peak of ENSO/IOD events. This raises the prospect that tracking of SSS variability could aid the prediction of Australian rainfall.