scholarly journals Using Indicators of ENSO, IOD, and SAM to Improve Lead Time and Accuracy of Tropical Cyclone Outlooks for Australia

2020 ◽  
Vol 59 (11) ◽  
pp. 1901-1917
Author(s):  
Andrew D. Magee ◽  
Anthony S. Kiem
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Lead Time ◽  
Southern Oscillation ◽  
Pearson Correlation ◽  
Southern Annular Mode ◽  
Sea Surface ◽  
Selection Algorithm ◽  
Statistical Framework ◽  
The Indian Ocean

AbstractCatastrophic impacts associated with tropical cyclone (TC) activity mean that the accurate and timely provision of TC outlooks are important to people, places, and numerous sectors in Australia and beyond. In this study, we apply a Poisson regression statistical framework to predict TC counts in the Australian region (AR; 5°–40°S, 90°–160°E) and its four subregions. We test 10 unique covariate models, each using different representations of the influence of El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and southern annular mode (SAM) and use an automated covariate selection algorithm to select the optimum combination of predictors. The performance of preseason TC count outlooks generated between April and October for the AR TC season (November–April) and in-season TC count outlooks generated between November and January for the remaining AR TC season are tested. Results demonstrate that skillful TC count outlooks can be generated in April (i.e., 7 months prior to the start of the AR TC season), with Pearson correlation coefficient values between r = 0.59 and 0.78 and covariates explaining between 35% and 60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly preseason and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.

scholarly journals Triggering the Indian Ocean Dipole from the Southern Hemisphere

2021 ◽  
Author(s):  
Lian-Yi Zhang ◽  
Yan Du ◽  
Wenju Cai ◽  
Zesheng Chen ◽  
Tomoki Tozuka ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Southern Hemisphere ◽  
Indian Ocean Dipole ◽  
Southern Oscillation ◽  
Southern Annular Mode ◽  
Triggering Mechanism ◽  
Phase Development ◽  
The Indian Ocean ◽  
High System ◽  
The Tropics

<p>This study identifies a new triggering mechanism of the Indian Ocean Dipole (IOD) from the Southern Hemisphere. This mechanism is independent from the El Niño/Southern Oscillation (ENSO) and tends to induce the IOD before its canonical peak season. The joint effects of this mechanism and ENSO may explain different lifetimes and strengths of the IOD. During its positive phase, development of sea surface temperature cold anomalies commences in the southern Indian Ocean, accompanied by an anomalous subtropical high system and anomalous southeasterly winds. The eastward movement of these anomalies enhances the monsoon off Sumatra-Java during May-August, leading to an early positive IOD onset. The pressure variability in the subtropical area is related with the Southern Annular Mode, suggesting a teleconnection between high-latitude and mid-latitude climate that can further affect the tropics. To include the subtropical signals may help model prediction of the IOD event.</p>

scholarly journals The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean

2007 ◽  
Vol 20 (13) ◽  
pp. 2872-2880 ◽  
Author(s):  
Gary Meyers ◽  
Peter McIntosh ◽  
Lidia Pigot ◽  
Mike Pook
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
El Nino Southern Oscillation ◽  
La Nina ◽  
The Indian Ocean

Abstract The Indian Ocean zonal dipole is a mode of variability in sea surface temperature that seriously affects the climate of many nations around the Indian Ocean rim, as well as the global climate system. It has been the subject of increasing research, and sometimes of scientific debate concerning its existence/nonexistence and dependence/independence on/from the El Niño–Southern Oscillation, since it was first clearly identified in Nature in 1999. Much of the debate occurred because people did not agree on what years are the El Niño or La Niña years, not to mention the newly defined years of the positive or negative dipole. A method that identifies when the positive or negative extrema of the El Niño–Southern Oscillation and Indian Ocean dipole occur is proposed, and this method is used to classify each year from 1876 to 1999. The method is statistical in nature, but has a strong basis on the oceanic physical mechanisms that control the variability of the near-equatorial Indo-Pacific basin. Early in the study it was found that some years could not be clearly classified due to strong decadal variation; these years also must be recognized, along with the reason for their ambiguity. The sensitivity of the classification of years is tested by calculating composite maps of the Indo-Pacific sea surface temperature anomaly and the probability of below median Australian rainfall for different categories of the El Niño–Indian Ocean relationship.

The relationship between Indian Ocean sea–surface temperature and East African rainfall

2005 ◽  
Vol 363 (1826) ◽  
pp. 43-47 ◽  
Author(s):  
Emily Black
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Sea Surface ◽  
East African ◽  
African Rainfall ◽  
The Pacific ◽  
The Indian Ocean ◽  
The Relationship

Knowledge of the processes that control East African rainfall is essential for the development of seasonal forecasting systems, which may mitigate the effects of flood and drought. This study uses observational data to unravel the relationship between the Indian Ocean Dipole (IOD), the El Niño Southern Oscillation (ENSO) and rainy autumns in East Africa. Analysis of sea–surface temperature data shows that strong East African rainfall is associated with warming in the Pacific and Western Indian Oceans and cooling in the Eastern Indian Ocean. The resemblance of this pattern to that which develops during IOD events implies a link between the IOD and strong East African rainfall. Further investigation suggests that the observed teleconnection between East African rainfall and ENSO is a manifestation of a link between ENSO and the IOD.

scholarly journals Correlations between Interannual SST Oscillations and Modeled Swell Impacts on Turbulent Mixing*

2015 ◽  
Vol 29 (1) ◽  
pp. 293-311 ◽  
Author(s):  
Yalin Fan ◽  
W. Erick Rogers ◽  
Tommy G. Jensen
Keyword(s):  
Indian Ocean ◽  
Southern Ocean ◽  
Turbulent Mixing ◽  
Southern Oscillation ◽  
Wind Waves ◽  
Sea Surface ◽  
Interannual Variations ◽  
The Indian Ocean ◽  
Wave Models ◽  
Sst Anomalies

Abstract The possibility of teleconnections between Southern Ocean swells and sea surface temperature (SST) anomalies on interannual time scales in the eastern Pacific Niño-3 region and southeastern Indian Ocean is investigated using numerical wave models. Two alternative parameterizations for swell dissipation are used. It is found that swell dissipation in the models is not directly correlated with large interannual variations such as El Niño–Southern Oscillation (ENSO) or the Indian Ocean dipole (IOD). However, using one of the two swell dissipation parameterizations, a correlation is found between observed SST anomalies and the modification of turbulent kinetic energy flux (TKEF) by Southern Ocean swells due to the damping of short wind waves: modeled reduction of TKEF is opposite in phase to the SST anomalies in the Niño-3 region, indicating a potential positive feedback. The modeled bimonthly averaged TKEF reduction in the southeastern Indian Ocean is also well correlated with the IOD mode.

scholarly journals Impact of the southern annular mode on extreme changes in indian rainfall during the early 1990s

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pao-Wei Huang ◽  
Yong-Fu Lin ◽  
Chau-Ron Wu
Keyword(s):  
Indian Ocean ◽  
Southern Oscillation ◽  
Social Systems ◽  
Vertical Motion ◽  
Southern Annular Mode ◽  
Annular Mode ◽  
The Past ◽  
Indian Rainfall ◽  
The Indian Ocean ◽  
Primary Pattern

AbstractThe variability in rainfall amounts in India draws much attention because it strongly influences the country’s ecological and social systems. Indian rainfall is associated with climate factors, including El Niño/Southern Oscillation and the Indian Ocean Dipole. Here we identified the Southern Annular Mode (SAM), the primary pattern of climate variability in the Southern Hemisphere, as the ultimate forcing leading to decadal changes in Indian rainfall. Through statistical analyses using observational data covering the period from 1979 to 2015, we show an increase in the decadal rainfall amount in the early 1990s over the Indian region. Examining atmospheric environmental conditions, we demonstrate that conditions have become more favorable over the past few decades. Specifically, during the positive SAM phase since the early 1990s, changes in the atmospheric fields have evoked anomalous vertical motion over the continent and the Indian Ocean, enhancing the southerly cross-equatorial flow by increased land–sea thermal contrast, thereby increasing decadal rainfall in the region.

scholarly journals LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean <i>p</i>CO<sub>2</sub> over the global ocean

2019 ◽  
Vol 12 (5) ◽  
pp. 2091-2105 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Marion Gehlen ◽  
Mathieu Vrac ◽  
Carlos Mejia
Keyword(s):  
Neural Network ◽  
Indian Ocean ◽  
Interannual Variability ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Global Ocean ◽  
Co2 Fluxes ◽  
Surface Ocean ◽  
The Indian Ocean ◽  
Data Coverage

Abstract. A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) the reconstruction of pCO2 climatology, and (2) the reconstruction of pCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step (2) augmented by their anomalies. During each step, the FFNN model reconstructs the nonlinear relationships between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1∘×1∘ grid for the period from 2001 to 2016. Global ocean pCO2 was reconstructed with satisfying accuracy compared with independent observational data from SOCAT. However, errors were larger in regions with poor data coverage (e.g., the Indian Ocean, the Southern Ocean and the subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO2 with reasonable skill over the equatorial Pacific associated with ENSO (the El Niño–Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variability between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern Hemisphere and, in particular, in the southern Pacific and the Indian Ocean, as these regions suffer from poor data coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea–air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.

scholarly journals The Curious Case of Indian Ocean Warming*,+

2014 ◽  
Vol 27 (22) ◽  
pp. 8501-8509 ◽  
Author(s):  
Mathew Koll Roxy ◽  
Kapoor Ritika ◽  
Pascal Terray ◽  
Sébastien Masson
Keyword(s):  
Indian Ocean ◽  
Southern Oscillation ◽  
Tropical Indian Ocean ◽  
Western Indian Ocean ◽  
Warming Trend ◽  
Warm Pool ◽  
Sea Surface ◽  
Monsoon Circulation ◽  
Mean Sea Surface ◽  
The Indian Ocean

Abstract Recent studies have pointed out an increased warming over the Indian Ocean warm pool (the central-eastern Indian Ocean characterized by sea surface temperatures greater than 28.0°C) during the past half-century, although the reasons behind this monotonous warming are still debated. The results here reveal a larger picture—namely, that the western tropical Indian Ocean has been warming for more than a century, at a rate faster than any other region of the tropical oceans, and turns out to be the largest contributor to the overall trend in the global mean sea surface temperature (SST). During 1901–2012, while the Indian Ocean warm pool went through an increase of 0.7°C, the western Indian Ocean experienced anomalous warming of 1.2°C in summer SSTs. The warming of the generally cool western Indian Ocean against the rest of the tropical warm pool region alters the zonal SST gradients, and has the potential to change the Asian monsoon circulation and rainfall, as well as alter the marine food webs in this biologically productive region. The current study using observations and global coupled ocean–atmosphere model simulations gives compelling evidence that, besides direct contribution from greenhouse warming, the long-term warming trend over the western Indian Ocean during summer is highly dependent on the asymmetry in the El Niño–Southern Oscillation (ENSO) teleconnection, and the positive SST skewness associated with ENSO during recent decades.

scholarly journals The Modulating Influence of Indian Ocean Sea Surface Temperatures on Australian Region Seasonal Tropical Cyclone Counts

2017 ◽  
Vol 30 (13) ◽  
pp. 4843-4856 ◽  
Author(s):  
H. A. Ramsay ◽  
M. B. Richman ◽  
L. M. Leslie
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Relative Vorticity ◽  
Sea Surface ◽  
Late Winter ◽  
Australian Region ◽  
Predictive Skill

The Australian region seasonal tropical cyclone count (TCC) maintained a robust statistical relationship with El Niño–Southern Oscillation (ENSO), with skillful forecasts of above (below) average TCC during La Niña (El Niño) years from 1969 until about 1998, weakening thereafter. The current study identifies an additional climate driver that mitigates the loss of predictive skill for Australian TCC after about 1998. It is found that the seasonal Australian TCC is strongly modulated by a southwest-to-northeast-oriented dipole in Indian Ocean sea surface temperature anomalies (SSTAs), referred to here as the transverse Indian Ocean dipole (TIOD). The TIOD emerges as the leading mode of detrended Indian Ocean SSTAs in the Southern Hemisphere during late winter and spring. Active (inactive) TC seasons are linked to positive (negative) TIOD phases, most notably during August–October immediately preceding the TC season, when SSTAs northwest of Australia, in the northeast pole of the TIOD, are positive (negative). To provide a physical interpretation of the TIOD–TCC relationship, 850-hPa zonal winds, 850-hPa relative vorticity, and 600-hPa relative humidity are composited for positive and negative TIOD phases, providing results consistent with observed TCC modulation. Correlations between ENSO and TCC weaken from 1998 onward, becoming statistically insignificant, whereas the TIOD–TCC correlation remains statistically significant until 2003. Overall, TIOD outperforms Niño-4 SSTA as a TCC predictor (46% skill increase since about 1998), when used individually or with Niño-4. The combination of TIOD and Niño 4 provide a skill increase (up to 33%) over climatology, demonstrating reliably accurate seasonal predictions of Australian region TCC.

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