The El-Niño Southern Oscillation (ENSO) is the preeminent mode of global climate variability on timescales of months to several years. El Niño events cause temporary elevations in global average temperatures, and in the context of background global warming from increasing greenhouse gas concentrations, El Niño events are often associated with setting new global temperature records. El Niños cause warmer than typical global average temperatures because they are associated with a great amount of heat release from the equatorial Pacific to the atmosphere which is then distributed globally. This release of heat also imprints on the structure of the atmosphere and shifts the tendencies of typical atmospheric circulations. In certain locations, advection from climatologically colder locations (e.g., flow from the north in the Northern Hemisphere) becomes more prominent than normal during El Niño events which can cause a local tendency for temperatures to cool during El Niños, despite elevated temperatures globally. The large scale atmospheric circulation is also influenced by the state of ENSO differently depending on the time of the year.
This all means that if you want to translate the state of ENSO into a seasonal forecast (e.g., a forecast for 3-month average temperatures) at a particular location, you have to be careful to examine both the specific relationship between ENSO and climate variability at the location you are interested in as well as how that relationship depends on the time of the year. This is the purpose of the Simple ENSO Regression Forecast (SERF).
The SERF is based on an ensemble of dynamical and statistical model forecasts that predict the future state of ENSO, combined with the historical relationships between the state of ENSO and concurrent local surface air temperature departures from average (as a function of location and time of the year).
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 the 2019 Northern Hemisphere summer and Southern Hemisphere winter (June-July-August 2019). A weak El-Niño like state is expected to persist throughout the upcoming season. This translates into an expectation for below normal temperatures over northern/central Canada, the US upper Midwest and much of Russia. Above average temperatures are expected over the US Pacific Northwest, Mexico, much of South America, Africa, India, the Middle East and Europe (see Figure 1 and Figure 2 below). One reason that the tropics shows more consistent warming is that the background global warming has a higher signal-to-noise ratio there which means it is more likely that any given season will be above its 1971-2000 average, regardless of the state of ENSO.
Figure 1. Top) SERF forecast of the average temperature for June-July-August 2019 relative to the long term average (from 1971-2000) for each location. Bottom) Chance that the average temperature over June-July-August will be above the long term average (from 1971-2000) for June-July-August at that location.
Figure 2. Same as the bottom of figure 1 but zoomed in to particular regions.