EI Niño/La Niña Status
Updated on 20 July 2017
Sea-surface temperatures (SSTs) over the equatorial Pacific Ocean is warmer than average (Figure A) and the Nino3.4 index was 0.5 for June 2017. The 3-month average (April to June) Nino3.4 value was 0.5 (Figure B). Although this value has hit the El Niño threshold, the SST patterns over the tropical Pacific Ocean do not indicate developing El Niño conditions. Atmospheric conditions, such as trade winds and cloudiness, over the equatorial Pacific also remain neutral. For regional SSTs, the cool anomalies in the eastern Indian Ocean is subsiding (Figure A). The cool anomalies in the eastern Indian Ocean, coupled with the warm anomalies in the western Indian Ocean, are associated to a phenomenon known as the positive Indian Ocean Dipole (IOD) which is known to lead to drier conditions in the region. However, the values are still neutral. Furthermore, in the latest outlook, only a limited number of the climate models assessed predict continued development of significant positive IOD in the coming months.
For the tropical Pacific, most models indicate that the observed warming will subside and remain neutral in the second half of 2017 (Figure C). For July-September 2017 season, latest experts’ consensus favours neutral conditions over El Niño (Figure D).
Impact of El Niño/La Niña on Singapore
Singapore would normally experience drier and warmer conditions during El Niño events, especially during the Southwest Monsoon period (June – September), including October (Figure E and Figure F). The opposite, i.e. wetter conditions over Singapore, usually occurs during La Niña events. Outside this season, the impact of El Niño/La Niña is less significant for Singapore. For example during the Northeast Monsoon season (December to early March), the impact on rainfall from El Niño/La Niña is less pronounced (Figure E and Figure F).
No two El Niño events or two La Niña events are alike in terms of their impact on Singapore’s rainfall and temperature. Furthermore, the strength of events and the corresponding impact do not always scale. For example, there were years where relatively weaker El Niño/La Niña events induced more significant changes in rainfall during the Southwest Monsoon season than the stronger events (Figure G).
For El Niño/La Niña updates, MSS assesses information provided by the World Meteorological Organization (WMO) and various international climate centres, such as the Climate Prediction Center (CPC) US, the Bureau of Meteorology (BoM) Australia, as well information from the International Research Institute for Climate and Society (IRI) which contains model outputs from various other centres around the world.
Figure A: Sea-surface temperature (SST) anomalies for June 2017 with respect to 1981-2010 climatology. Warm shades show regions of relative warming, while cool shades show regions of relative cooling. On average, the tropical Pacific Ocean Nino3.4 region (solid red box, 120°W-170°W and 5°S-5°N) was warmer than normal in June, but the warming patterns are not consistent with a developing El Niño. Closer to the region, the western Indian Ocean, WTIO (solid black box, 50°E-70°E and 10°S-10°N) is warmer relative to the south-eastern Indian Ocean, SETIO (dotted black box, 90°E-110°E and 10°S-0°N), which makes the Indian Ocean Dipole Mode index (WTIO minus SETIO) positive, but still within neutral. Data source: ERSSTv4 from NOAA.
Figure B: The Nino3.4 index using three-month running mean of SST anomalies (against 1981-2010 base period) in the Nino3.4 region bounded by 5°N to 5°S and 170°W to 120°W. Warm anomalies (red line) correspond to El Niño conditions while cold anomalies (blue line) correspond to La Niña conditions; otherwise neutral (grey line). The horizontal axis is labelled with the first letters of the 3-month seasons, e.g. JFM refers to January, February and March seasonal average. Data source: ERSSTv4 from NOAA.
Figure C: Forecasts of Nino3.4 index’s strength for 2017 from various seasonal prediction models of international climate centres. Values above 0.5°C indicate El Niño conditions, below -0.5°C indicate La Niña conditions, and in between indicate neutral conditions, i.e. neither El Niño nor La Niña. Models predict the warming will subside and for conditions to remain neutral for the rest of 2017. (image credit: IRI-CPC).
Figure D: Probability of El Niño (red), La Niña (blue) and neutral conditions (green) for 2017. Neutral conditions are favoured over El Niño for Jul-September (JAS) 2017 with decreasing chance of El Niño developing for the rest of 2017 (image credit: IRI-CPC).
Figure E: Correlation between total monthly rainfall (averaged over 28 Singapore stations) and Nino3.4 index from 1980-2013. It shows statistically significant (red) negative correlations between local rainfall and Nino3.4 in July, September and October, which suggest that warmer temperatures in the Nino3.4 region lead to significantly less rainfall over Singapore and vice versa. In other months, where the correlations are weaker or insignificant, the relationship is not as established.
Figure F: Correlation between total seasonal rainfall (averaged over 28 Singapore stations) and seasonal Nino3.4 index (also known as Oceanic Niño Index, ONI) from 1980-2013. It shows statistically significant (red) negative correlations between local rainfall and the ONI during JAS and ASO, which suggest that warmer temperatures in the Nino3.4 region lead to significantly less rainfall over Singapore and vice versa during these seasons. In other seasons, where the correlations are weaker or insignificant, the relationship is not as established.
Figure G: Singapore rainfall anomalies for June-September (as percentage of departure from long-term rainfall average) arranged in the order from strong La Niña (left) to strong El Niño (right). Warm shades denote El Niño years, cool shades denote La Niña years (La Niña is the opposite of El Niño) and white denotes neutral years. WL, ML and SL refer to weak, moderate and strong La Niña respectively, while WE, ME and SE refer to weak, moderate and strong El Niño respectively.