Search for: All records

A previous study demonstrated that atmospheric rivers (ARs) generate substantial air-sea fluxes in the northeast Pacific. Since the southeast Indian Ocean is one of the active regions of ARs, similar air-sea fluxes could be produced. However, the spatial pattern of sea surface temperature (SST) in the southeast Indian Ocean, especially along the west coast of Australia, is different from that in the northeast Pacific because of the poleward flowing Leeuwin Current, which may cause different air-sea fluxes. This study investigates AR-associated air-sea fluxes in the southeast Indian Ocean and their relation with SST variability. The large-scale spatial pattern of latent heat flux (evaporation) associated with ARs in the southeast Indian Ocean is similar to that in the northeast Pacific. A significant difference is however found near the coastal area where relatively warm SSTs are maintained in all seasons. While AR-induced latent heat flux is close to zero around the west coast of North America where the equatorward flowing coastal current and upwelling generate relatively cold SSTs, a significant latent heat flux induced by ARs is evident along the west coast of Australia due to the relatively warm surface waters. Temporal variations of coastal air-sea fluxes associated with landfalling ARs are investigated based on the composite analysis. While the moisture advection reduces the latent heat during landfalling, the reduction of air humidity with strong winds enhances large evaporative cooling (latent heat flux) after a few days of the landfalling. A significant SST cooling along the coast is found due to the enhanced latent heat flux.

 

Abstract The subtropical Indian Ocean dipole (SIOD) and Ningaloo Niño are the two dominant modes of interannual climate variability in the subtropical south Indian Ocean. Observations show that the SIOD has been weakening in the recent decades, while Ningaloo Niño has been strengthening. In this study, we investigate the causes for such changes by analyzing climate model experiments using the NCAR Community Earth System Model, version 1 (CESM1). Ensemble-mean results from CESM1 large-ensemble (CESM1-LE) show that the external forcing causes negligible changes in the amplitudes of the SIOD and Ningaloo Niño, suggesting a dominant role of internal climate variability. Meanwhile, results from CESM1 pacemaker experiments reveal that the observed changes in the two climate modes cannot be attributed to the effect of sea surface temperature anomalies (SSTA) in either the eastern tropical Pacific Ocean or tropical Indian Ocean. By further comparing different ensemble members from the CESM1-LE, we find that a warm pool dipole mode of decadal variability, with opposite SSTA in the southeast Indian Ocean and the western-central tropical Pacific Ocean plays an important role in driving the observed changes in the SIOD and Ningaloo Niño. These changes in the two climate modes have considerable impacts on precipitation and sea level variabilities in the south Indian Ocean region. 

Atlantic Niño is the Atlantic equivalent of El Niño-Southern Oscillation (ENSO), and it has prominent impacts on regional and global climate. Existing studies suggest that the Atlantic Niño may arise from local atmosphere-ocean interaction and is sometimes triggered by the Atlantic Meridional Mode (AMM), with overall weak ENSO contribution. By analyzing observational datasets and performing numerical model experiments, here we show that the Atlantic Niño can be induced by the Indian Ocean Dipole (IOD). We find that the enhanced rainfall in the western tropical Indian Ocean during positive IOD weakens the easterly trade winds over the tropical Atlantic, causing warm anomalies in the central and eastern equatorial Atlantic basin and therefore triggering the Atlantic Niño. Our finding suggests that the cross-basin impact from the tropical Indian Ocean plays a more important role in affecting interannual climate variability than previously thought.

 

Low-lying island nations like Indonesia are vulnerable to sea level Height EXtremes (HEXs). When compounded by marine heatwaves, HEXs have larger ecological and societal impact. Here we combine observations with model simulations, to investigate the HEXs and Compound Height-Heat Extremes (CHHEXs) along the Indian Ocean coast of Indonesia in recent decades. We find that anthropogenic sea level rise combined with decadal climate variability causes increased occurrence of HEXs during 2010–2017. Both HEXs and CHHEXs are driven by equatorial westerly and longshore northwesterly wind anomalies. For most HEXs, which occur during December-March, downwelling favorable northwest monsoon winds are enhanced but enhanced vertical mixing limits surface warming. For most CHHEXs, wind anomalies associated with a negative Indian Ocean Dipole (IOD) and co-occurring La Niña weaken the southeasterlies and cooling from coastal upwelling during May-June and November-December. Our findings emphasize the important interplay between anthropogenic warming and climate variability in affecting regional extremes.

 

Abstract Understanding the impact of the Indian Ocean Dipole (IOD) on El Niño-Southern Oscillation (ENSO) is important for climate prediction. By analyzing observational data and performing Indian and Pacific Ocean pacemaker experiments using a state-of-the-art climate model, we find that a positive IOD (pIOD) can favor both cold and warm sea surface temperature anomalies (SSTA) in the tropical Pacific, in contrast to the previously identified pIOD-El Niño connection. The diverse impacts of the pIOD on ENSO are related to SSTA in the Seychelles-Chagos thermocline ridge (SCTR; 60°E-85°E and 7°S-15°S) as part of the warm pole of the pIOD. Specifically, a pIOD with SCTR warming can cause warm SSTA in the southeast Indian Ocean, which induces La Niña-like conditions in the tropical Pacific through interbasin interaction processes associated with a recently identified climate phenomenon dubbed the “Warm Pool Dipole”. This study identifies a new pIOD-ENSO relationship and examines the associated mechanisms. 

Abstract An unprecedented extreme positive Indian Ocean Dipole event (pIOD) occurred in 2019, which has caused widespread disastrous impacts on countries bordering the Indian Ocean, including the East African floods and vast bushfires in Australia. Here we investigate the causes for the 2019 pIOD by analyzing multiple observational datasets and performing numerical model experiments. We find that the 2019 pIOD is triggered in May by easterly wind bursts over the tropical Indian Ocean associated with the dry phase of the boreal summer intraseasonal oscillation, and sustained by the local atmosphere-ocean interaction thereafter. During September-November, warm sea surface temperature anomalies (SSTA) in the central-western tropical Pacific further enhance the Indian Ocean’s easterly winds, bringing the pIOD to an extreme magnitude. The central-western tropical Pacific warm SSTA is strengthened by two consecutive Madden Julian Oscillation (MJO) events that originate from the tropical Indian Ocean. Our results highlight the important roles of cross-basin and cross-timescale interactions in generating extreme IOD events. The lack of accurate representation of these interactions may be the root for a short lead time in predicting this extreme pIOD with a state-of-the-art climate forecast model. 

Abstract The Indian Ocean has received increasing attention for its large impacts on regional and global climate. However, sea surface temperature (SST) variability arising from Indian Ocean internal processes has not been well understood particularly on decadal and longer timescales, and the external influence from the Tropical Pacific has not been quantified. This paper analyzes the interannual-to-decadal SST variability in the Tropical Indian Ocean in observations and explores the external influence from the Pacific versus internal processes within the Indian Ocean using a Linear Inverse Model (LIM). Coupling between Indian Ocean and tropical Pacific SST anomalies (SSTAs) is assessed both within the LIM dynamical operator and the unpredictable stochastic noise that forces the system. Results show that the observed Indian Ocean Basin (IOB)-wide SSTA pattern is largely a response to the Pacific ENSO forcing, although it in turn has a damping effect on ENSO especially on annual and decadal timescales. On the other hand, the Indian Ocean Dipole (IOD) is an Indian Ocean internal mode that can actively affect ENSO; ENSO also has a returning effect on the IOD, which is rather weak on decadal timescale. The third mode is partly associated with the Subtropical Indian Ocean Dipole (SIOD), and it is primarily generated by Indian Ocean internal processes, although a small component of it is coupled with ENSO. Overall, the amplitude of Indian Ocean internally generated SST variability is comparable to that forced by ENSO, and the Indian Ocean tends to actively influence the tropical Pacific. These results suggest that the Indian-Pacific Ocean interaction is a two-way process. 

Abstract Multi-time-scale variabilities of the Indian Ocean (IO) temperature over 0–700 m are revisited from the perspective of vertical structure. Analysis of historical data for 1955–2018 identifies two dominant types of vertical structures that account for respectively 70.5% and 21.2% of the total variance on interannual-to-interdecadal time scales with the linear trend and seasonal cycle removed. The leading type manifests as vertically coherent warming/cooling with the maximal amplitude at ~100 m and exhibits evident interdecadal variations. The second type shows a vertical dipole structure between the surface (0–60 m) and subsurface (60–400 m) layers and interannual-to-decadal fluctuations. Ocean model experiments were performed to gain insights into underlying processes. The vertically coherent, basinwide warming/cooling of the IO on an interdecadal time scale is caused by changes of the Indonesian Throughflow (ITF) controlled by Pacific climate and anomalous surface heat fluxes partly originating from external forcing. Enhanced changes in the subtropical southern IO arise from positive air–sea feedback among sea surface temperature, winds, turbulent heat flux, cloud cover, and shortwave radiation. Regarding dipole-type variability, the basinwide surface warming is induced by surface heat flux forcing, and the subsurface cooling occurs only in the eastern IO. The cooling in the southeast IO is generated by the weakened ITF, whereas that in the northeast IO is caused by equatorial easterly winds through upwelling oceanic waves. Both El Niño–Southern Oscillation (ENSO) and IO dipole (IOD) events are favorable for the generation of such vertical dipole anomalies. 

Despite the well‐recognized initial value nature of the subseasonal forecasts, the role of subsurface ocean initialization in subseasonal forecasts remains underexplored. Using observing system experiments, this study investigates the impact of ocean in situ data assimilation on the propagation of Madden–Julian Oscillation (MJO) events across the Maritime Continent in the European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal forecast system. Two sets of twin experiments are analyzed, which only differ on the use or not of in situ ocean observations in the initial conditions. Besides using the Real‐time Multivariate MJO Index (RMMI) to evaluate the forecast performance, we also develop a new MJO tracking method based on outgoing longwave radiation anomalies (OLRa) for forecast evaluation. We find that the ocean initialization with in situ data assimilation, though having an impact on the forecasted ocean mean state, does not improve the relatively low MJO forecast skill across the Maritime Continent. Moist static energy budget analysis further suggests that a significant underestimation in the meridional moisture advection in the model forecast may hinder the potential role played by the ocean state differences associated with data assimilation. Bias of the intraseasonal meridional winds in the model is a more important factor for such underestimation than the mean state moisture biases. This finding suggests that atmospheric model biases dominate the forecast error growth, and the atmospheric circulation bias is one of the major sources of the MJO prediction error and should be a target for improving the ECMWF subseasonal forecast model.