November 2018 Climate Forecast Discussion
The SST forecast shows moderate El Nino conditions (>1.0ºC and <1.5ºC for the Nino3.4 SST index) during Dec-Feb and Jan-Mar, becoming weak (>0.5ºC and <1.0ºC) for Feb-Apr and Mar-May.
Read more →Research notes and updates from the team.
The SST forecast shows moderate El Nino conditions (>1.0ºC and <1.5ºC for the Nino3.4 SST index) during Dec-Feb and Jan-Mar, becoming weak (>0.5ºC and <1.0ºC) for Feb-Apr and Mar-May.
Read more →Note: The SST anomalies cited below refer to the OISSTv2 SST data set, and not ERSSTv4. OISSTv2 is often used for real-time analysis and model initialization, while ERSSTv4 is used for retrospective official ENSO diagnosis because it is more homogeneous over time, allowing for more accurate comparisons among ENSO events that are years apart.
Read more →The SST forecast shows moderate El Nino conditions (>1.0C and <1.5C for the Nino3.4 SST index) developing by the initial Nov-Jan season, continuing through Feb-Apr. Much of the subtropical and extratropical North Atlantic and North Pacific SSTs, as well as the SSTs in much of the mid- to high-latitude Southern Hemisphere, are predicted to be above-average during all four forecast seasons.
Read more →Note: The SST anomalies cited below refer to the OISSTv2 SST data set, and not ERSSTv4. OISSTv2 is often used for real-time analysis and model initialization, while ERSSTv4 is used for retrospective official ENSO diagnosis because it is more homogeneous over time, allowing for more accurate comparisons among ENSO events that are years apart.
Read more →The SST forecast shows weak El Nino conditions developing by the initial Oct-Dec season, most likely remaining at weak strength (>0.5C and <1.0C for the Nino3.4 index) through Jan-Mar, with a smaller chance for moderate strength (>1.0C).
Read more →Note: The SST anomalies cited below refer to the OISSTv2 SST data set, and not ERSSTv4. OISSTv2 is often used for real-time analysis and model initialization, while ERSSTv4 is used for retrospective official ENSO diagnosis because it is more homogeneous over time, allowing for more accurate comparisons among ENSO events that are years apart.
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