The El-Niño Southern Oscillation (ENSO) is the preeminent mode of global climate variability on timescales of months to several years. Thus, the state of ENSO tends to be the primary predictor for remote local variability in climate on seasonal (e.g., 3-month average) timescales.
There are many research groups and organizations that do ENSO forecasts for the Pacific Ocean but in order to be useful to the general public, these forecasts require conversion into relevant climate conditions at geographic locations where people actually live. That is the purpose of the Simple ENSO Regression Forecast (SERF) below.
The SERF is based on an ensemble of dynamical and statistical model forecasts of the future state of ENSO, combined with the historical relationships between the state of ENSO and concurrent local surface air temperature departures from average (e.g., see here).
At ClimateAI, we are developing considerably more sophisticated machine learning techniques for application to seasonal forecasting that are able to achieve enhanced skill over this simple method. Nevertheless, this simple method is transparent and serves as a useful benchmark for more sophisticated methods to be compared to.
Below is the Simple ENSO Regression Forecast (SERF) for May-June-July 2019, centered on the US.
Elevated NINO3.4 sea surface temperatures are expected to persist through the May-June-July forecast period which has historically translated into warmer than average temperatures for the North American coasts and cooler than average temperatures for the northern interior of the continent. Los Angeles and New York each have about a 60% chance of experiencing a warmer than average May-June-July.
Compare the SERF forecast above to the forecast from NOAA’s Climate Prediction Center here.