People familiar with the chaos that is weather will agree that seasonal forecasts based merely on computer simulations are highly speculative and involve much guesswork. The quality of their output leaves little to be desired.
As much as some of these forecasts may be presented with authoritative tones, in the end they all come with a fine print disclaimer concerning certainty, and so they are nothing one can take to the bank.
The average of a series of WAGs
Even the most powerful super-computers using conventional simulations struggle to predict the weather 10 days out, let alone what an entire a winter will be like in terms of temperature and precipitation. The simulations used by the US weather agencies, for example, simply perform a number of data crunching runs — each using different start conditions — to generate a set of scenarios (i.e. wild-ass guesses) and then average them out to create an “ensemble”. Under the bottom line, the ensemble is based on a set of wild ass guesses, and so is correspondingly unreliable.
Not surprisingly, Germany’s flagship daily Frankfurter Allgemeine Zeitung (FAZ) also finds that the ensembles leave very little to be desired and point out that the seasonal forecasts issued by weather agencies such as the NOAA are hugely uncertain, and that clearly the computer simulation methodology is in dire need of improvement.
Even farmers seem to have greater success.
So what can be done to improve forecasting performance?
The FAZ writes that a whole new methodology has been developed by German scientists and that this new methodology promises to vastly improve seasonal forecasts. The FAZ writes that earlier this year a study by the University of Hamburg was published in the Geophysical Research Letters. The press release announced: “Possible for the first time: reliable three-month forecasts for European winters“.
80% winter season accuracy
According to the FAZ, oceanographer Dr. Mikhail Dobrynin of the Hamburg University Institute of Oceanography, Center for Earth System Research and Sustainability and his colleagues have developed a method that works with 80% success rate. Instead of solely relying on a super computer crunching data using a variety of start conditions and then averaging them out, Dobrynin says their method relies also on “teleconnections”, i.e. finding “signals amid the chaos“.
The FAZ reports that for winter forecasts, Dr. Dobrynin has identified four factors: “the snow depth in Siberia, Arctic polar vortex, extent of Arctic sea ice and the Atlantic ocean temperature”, which allow “the path of certain air masses going to Europe to be predicted”. The most critical of these is the snow cover over Siberia in fall.
The NAO index is one key factor in seasonal winter forecasts for Europe. Source: Geophysical Research Letters.
The scientists say that conditions in different geographical regions over the Atlantic “can act as a switch that steers winter weather in Europe” and that focusing on how these interact and behave can allow greatly improved forecasting.
According to the conclusion of the study, the method shows vastly improved performance:
For the real forecast test from 2001 to 2017 the prediction skill of the winter NAO is increased from 0.42 for the full ensemble mean to 0.86 for the subsampled ensemble mean. As a result of a better representation of the winter NAO, the prediction skill for the winter surface temperature, total precipitation, and SLP is improved for considerable parts of the NH.”
NOAA forecasts “hardly better” than guessing
The FAZ writes that “without considering teleconnections, winter in Europe has been virtually unpredictable”.
Dr. Mikhail Dobrynin added that results of forecasts made by the NOAA “were hardly better” than if they had guessed. The new method should thus improve the winter season forecasts considerably. However, he warns that the uncertainty will always exist and we must remain wary of any predictions dealing with seasonal weather.