The basic idea is to estimate the trend component, by smoothing the data or by fitting a regression model, and then estimate the seasonal component, by averaging the de-trended seasonal data points (e.g., the December
seasonal effect comes from the data points for all Decembers in the series).
Most all of
the seasonal effect comes from kittens.
Not exact matches
Unsurprisingly the biggest
seasonal effects on sea level
came during the fall and winter months, when El Niño events typically reach their peak.
Seasonal produce
comes with nutrients and properties that the body needs to battle the change in season and to withstand the adverse
effects of the weather.
At best, maybe jetfuel would be on to something if the change in
seasonal ice / snow cover in Canada is measurably altering the albedo, as scaddenp notes, but I doubt we'll see jetfuel
come up with any evidence showing the existence or magnitude of such an
effect.
«A climate pattern may
come in the form of a regular cycle, like the diurnal cycle or the
seasonal cycle; a quasi periodic event, like El Niño; or a highly irregular event, such as a volcanic winter... A mode of variability is a climate pattern with identifiable characteristics, specific regional
effects, and often oscillatory behavior... the mode of variability with the greatest
effect on climates worldwide is the
seasonal cycle, followed by El Niño - Southern Oscillation, followed by thermohaline circulation.»