Dramatic reduction and quick recovery of the South Indian Ocean heat content and sea level in 2014-2019

2020 ◽  
Author(s):  
Denis Volkov ◽  
Michael Rudko ◽  
Sang-Ki Lee
Keyword(s):  
Sea Level ◽  
Rossby Waves ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Wind Forcing ◽  
Upper Ocean ◽  
Local Wind ◽  
Upper Ocean Heat Content ◽  
Local Wind Forcing ◽  
And Sea Level

<p>The interannual-to-decadal variability of heat content and sea level in the South Indian Ocean (SIO) is strongly influenced by its connection with the Pacific and large-scale climatic forcing in the Indo-Pacific region primarily associated with El Niño-Southern Oscillation (ENSO). Besides the advection by the Indonesian Throughflow, signals generated in the Pacific can enter the SIO as coastally trapped Kelvin waves and propagate along the coast of Western Australia. In the southeast tropical and subtropical Indian Ocean, these signals along the eastern boundary can radiate westward as Rossby waves and eventually impact sea level and heat content in the SIO interior and near the western boundary. Local wind forcing, through Ekman pumping over the open ocean and coastal upwelling, is also able to generate Rossby waves and/or modify those emanated from the eastern boundary.</p><p>As measured by Argo floats and satellite altimetry, a decade-long increase of the upper-ocean heat content and sea level in the SIO in 2004-2013 ended with a remarkable drop returning to the initial values in 2004. This basin-wide heat release was associated with one of the strongest on record El Niño events in 2014-2016. Surprisingly, the basin-averaged heat content and sea level quickly recovered during the weak La Niña event in 2017-2019. Here we present an analysis of the evolution and mechanisms of 2014-2016 cooling and subsequent warming in the SIO subtropical gyre. We show that the 2014-2016 El Niño did contribute to the reduced heat content in the eastern SIO, while the local wind forcing (via increased Ekman upwelling) largely contributed to the heat reduction in the western SIO. We find no evidence to support that the 2017-2018 warming was forced by the weak La Niña, because the upper-ocean heat content in eastern SIO was still below normal during 2016-2018. The recovery largely occurred in the western SIO due to local wind forcing (via increased Ekman downwelling) primarily associated with changes in the strength of the southeasterly trade winds.</p><p>Because sea level is a good proxy for the oceanic heat content in the SIO, we extend our analysis back to 1993 using satellite altimetry records. Using a simple model of wind-forced Rossby waves, we estimate the relative contributions of sea level signals propagating from the eastern boundary, the origin of which is strongly linked to ENSO, and the local wind forcing in the SIO interior to the observed sea level variability. The local wind forcing appears to dominate the sea level (and, hence, the upper-ocean heat content) variability in the western SIO, especially in 2013-2019, while the ENSO-related signals are dominant in the eastern SIO. The local wind forcing over the SIO interior effectively suppressed the cooling associated with the most recent 2014-2016 El Niño event. In contrast, the cooling associated with the strongest on record 1997-1998 El Niño was amplified by the local wind forcing in the basin’s interior.</p>

scholarly journals Interannual variability of sea level in the South Indian Ocean: Local versus remote forcing mechanisms

2021 ◽  
Author(s):  
Marion Kersalé ◽  
Denis L. Volkov ◽  
Kandaga Pujiana ◽  
Hong Zhang
Keyword(s):  
Indian Ocean ◽  
Interannual Variability ◽  
Sea Level ◽  
Heat Content ◽  
Wind Forcing ◽  
Local Wind ◽  
South Indian Ocean ◽  
South Indian ◽  
Local Wind Forcing ◽  
And Sea Level

Abstract. The subtropical South Indian Ocean (SIO) has been described as one of the world's largest heat accumulators due to its remarkable warming during the past two decades. However, the relative contributions of the remote (of Pacific origin) forcing and local wind forcing to the variability of heat content and sea level in the SIO have not been fully attributed. Here, we combine a general circulation model, an analytic linear reduced gravity model, and observations to disentangle the spatial and temporal inputs of each forcing component on interannual to decadal timescales. A sensitivity experiment is conducted with artificially closed Indonesian straits to physically isolate the Indian and Pacific Oceans, thus, intentionally removing the Indonesian throughflow (ITF) influence on the Indian Ocean heat content and sea level variability. We show that the relative contribution of the signals originating in the equatorial Pacific versus signals caused by local wind forcing to the interannual variability of sea level and heat content in the SIO is dependent on location within the basin (low vs. mid latitude; western vs. eastern side of the basin). The closure of the ITF in the numerical experiment reduces the amplitude of interannual-to-decadal sea level changes compared to the simulation with a realistic ITF. However, the spatial and temporal evolution of sea level patterns in the two simulations remain similar and correlated with El Nino Southern Oscillation (ENSO). This suggests that these patterns are mostly determined by local wind forcing and oceanic processes, linked to ENSO via the ‘atmospheric bridge’ effect. We conclude that local wind forcing is an important driver for the interannual changes of sea level, heat content, and meridional transports in the SIO subtropical gyre, while oceanic signals originating in the Pacific amplify locally-forced signals.

Variability of Sea Level and Upper-Ocean Heat Content in the Indian Ocean: Effects of Subtropical Indian Ocean Dipole and ENSO

2019 ◽  
Vol 32 (21) ◽  
pp. 7227-7245 ◽  
Author(s):  
Lei Zhang ◽  
Weiqing Han ◽  
Yuanlong Li ◽  
Nicole S. Lovenduski
Keyword(s):  
Indian Ocean ◽  
Sea Level ◽  
Indian Ocean Dipole ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Upper Ocean ◽  
Thermocline Depth ◽  
Upper Ocean Heat Content ◽  
South Indian ◽  
The Indian Ocean

Abstract In this study, the Indian Ocean upper-ocean variability associated with the subtropical Indian Ocean dipole (SIOD) is investigated. We find that the SIOD is associated with a prominent southwest–northeast sea level anomaly (SLA) dipole over the western-central south Indian Ocean, with the north pole located in the Seychelles–Chagos thermocline ridge (SCTR) and the south pole at southeast of Madagascar, which is different from the distribution of the sea surface temperature anomaly (SSTA). While the thermocline depth and upper-ocean heat content anomalies mirror SLAs, the air–sea CO2 flux anomalies associated with SIOD are controlled by SSTA. In the SCTR region, the westward propagation of oceanic Rossby waves generated by anomalous winds over the eastern tropical Indian Ocean is the major cause for the SLAs, with cyclonic wind causing negative SLAs during positive SIOD (pSIOD). Local wind forcing is the primary driver for the SLAs southeast of Madagascar, with anticyclonic winds causing positive SLAs. Since the SIOD is correlated with ENSO, the relative roles of the SIOD and ENSO are examined. We find that while ENSO can induce significant SLAs in the SCTR region through an atmospheric bridge, it has negligible impact on the SLA to the southeast of Madagascar. By contrast, the SIOD with ENSO influence removed is associated with an opposite SLA in the SCTR and southeast of Madagascar, corresponding to the SLA dipole identified above. A new subtropical dipole mode index (SDMI) is proposed, which is uncorrelated with ENSO and thus better represents the pure SIOD effect.

Oceanic processes of upper ocean heat content associated with two types of ENSO

2017 ◽  
Vol 74 (2) ◽  
pp. 219-238 ◽  
Author(s):  
Junqiao Feng ◽  
Fei-fei Jin ◽  
Dunxin Hu ◽  
Shoude Guan
Keyword(s):  
Heat Content ◽  
Ocean Heat Content ◽  
Upper Ocean ◽  
Upper Ocean Heat Content

How Well Do Climate Models Reproduce North Atlantic Subtropical Mode Water?

2013 ◽  
Vol 43 (10) ◽  
pp. 2230-2244 ◽  
Author(s):  
Shenfu Dong ◽  
Kathryn A. Kelly
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
North Atlantic ◽  
Climate Models ◽  
Formation Rate ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Upper Ocean ◽  
Upper Ocean Heat Content ◽  
Mode Water

Abstract Formation and the subsequent evolution of the subtropical mode water (STMW) involve various dynamic and thermodynamic processes. Proper representation of mode water variability and contributions from various processes in climate models is important in order to predict future climate change under changing forcings. The North Atlantic STMW, often referred to as Eighteen Degree Water (EDW), in three coupled models, both with data assimilation [GFDL coupled data assimilation (GFDL CDA)] and without data assimilation [GFDL Climate Model, version 2.1 (GFDL CM2.1), and NCAR Community Climate System Model, version 3 (CCSM3)], is analyzed to evaluate how well EDW processes are simulated in those models and to examine whether data assimilation alters the model response to forcing. In comparison with estimates from observations, the data-assimilating model gives a better representation of the formation rate, the spatial distribution of EDW, and its thickness, with the largest EDW variability along the Gulf Stream (GS) path. The EDW formation rate in GFDL CM2.1 is very weak because of weak heat loss from the ocean in the model. Unlike the observed dominant southward movement of the EDW, the EDW in GFDL CM2.1 and CCSM3 moves eastward after formation in the excessively wide GS in the models. However, the GFDL CDA does not capture the observed thermal response of the overlying atmosphere to the ocean. Observations show a robust anticorrelation between the upper-ocean heat content and air–sea heat flux, with upper-ocean heat content leading air–sea heat flux by a few months. This anticorrelation is well captured by GFDL CM2.1 and CCSM3 but not by GFDL CDA. Only GFDL CM2.1 captures the observed anticorrelation between the upper-ocean heat content and EDW volume. This suggests that, although data assimilation corrects the readily observed variables, it degrades the model thermodynamic response to forcing.

Sign in / Sign up

Export Citation Format

Share Document