Air-Sea Heat Flux Variability in the Southeast Indian Ocean and Its Relation With Ningaloo Niño

Abstract

Previous studies suggest that both air-sea heat flux anomalies and heat advection caused by an anomalous Leeuwin Current play an important role in modulating the sea surface temperature (SST) variability associated with the Ningaloo Niño. However, the estimates of surface heat fluxes vary substantially with the datasets, and the uncertainties largely depend on the time scale and locations. This study investigates air-sea flux variability associated with the Ningaloo Niño using multiple datasets of surface fluxes. The climatological net surface heat flux off the west coast of Australia from six major air-sea flux products shows large uncertainties, which exceeds 80 W m −2 , especially in the austral summer when the Ningaloo Niño develops. These uncertainties stem mainly from those in shortwave radiation and latent heat flux. The use of different bulk flux algorithms and uncertainties of bulk atmospheric variables (wind speed and air specific humidity) are mostly responsible for the difference in latent heat flux climatology between the datasets. The composite evolution of air-sea heat fluxes over the life cycle of Ningaloo Niño indicates that the anomalous latent heat flux is dominant for the net surface heat flux variations, and that the uncertainties in latent heat flux anomaly largely depend on the phase of the Ningaloo Niño. During the recovery period of Ningaloo Niño, large negative latent heat flux anomalies (cooling the ocean) are evident in all datasets and thus significantly contribute to the SST cooling. Because the recovery of winds occurs earlier than SST, high SST and strong winds favor large evaporative cooling during the recovery phase. In contrast, the role of latent heat flux during the developing phase is not clear, because the sign of the anomalies depends on the datasets in this period. The use of high-resolution SST data, which can adequately represent SST variations produced by the anomalous Leeuwin Current, could largely reduce the errors in latent heat flux anomalies during the onset and peak phases.

Depth Structure of Ningaloo Niño/Niña Events and Associated Drivers

“…The definition is based on daily SST data but was modified here to use monthly output fields from the global ocean simulations. The adapted approach employed the Ningaloo Niño index (NNI), which has been used previously to study MHWs off the coast of WA (Kataoka et al 2014(Kataoka et al , 2017(Kataoka et al , 2018Marshall et al 2015;Feng et al 2015;Feng and Shinoda 2019). We adapt the NNI definition by Marshall et al (2015), who use the average SST anomalies over the box region 1108-1168E, 228-328S.…”

A Systematic Review of How Multiple Stressors From an Extreme Event Drove Ecosystem-Wide Loss of Resilience in an Iconic Seagrass Community

“…During La Niña years (e.g., 1999-2000, 2011-2012) the LC flows stronger resulting in transport of elevated ocean temperatures down the west coast (Feng et al, 2003). The region is typically dominated by strong southerly wind patterns through the summer months that oppose the LC, contributing to its seasonality, and also acting to moderate heating of coastal ocean temperatures through upwelling (Woo et al, 2006;Rossi et al, 2013) and air-sea heat flux (e.g., evaporative cooling) processes (Feng and Shinoda, 2019). However, relaxation or reversal of the southerly winds can further enhance heating, as occurred during the La Niña event of 2010-2011, when weak, northerly winds combined with an unusually strong summer Leeuwin Current to elevate summer maximum sea temperatures by 2-4 • C in the region.…”

Progress in understanding of Indian Ocean circulation, variability, air–sea exchange, and impacts on biogeochemistry

“…The Agulhas Current has a seasonal cycle and is strongest in summer (Krug and Tournadre, 2012;Beal and Elipot, 2016) and tied to a baroclinic adjustment of near-field winds (Hutchinson et al, 2018). Seasonal changes in the Agulhas retroflection region (Lutjeharms and van Ballegooyen, 1988;Quartly and Srokosz, 1993) and in the southwest Indian Ocean (Ffield et al, 1997) have been suggested from hydrographic and satellite data (Krug and Tournadre, 2012), but with weak statistical significance due to a lack of a sufficiently long time series.…”