Harnessing Butterflies: Theory and Practice of the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

Abstract

The atmosphere is governed by continuum mechanics and thermodynamics yet simultaneously obeys statistical turbulence laws. Up until its deterministic predictability limit (τ _w ≈ 10 days), only general circulation models (GCMs) have been used for prediction; the turbulent laws being still too difficult to exploit. However, beyond τ _w —in macroweather—the GCMs effectively become stochastic with internal variability fluctuating about the model—not the real world—climate and their predictions are poor. In contrast, the turbulent macroweather laws become advantageously notable due to (a) low macroweather intermittency that allows for a Gaussian approximation, and (b) thanks to a statistical space-time factorization symmetry that (for predictions) allows much decoupling of the strongly correlated spatial degrees of freedom. The laws imply new stochastic predictability limits. We show that pure macroweather—such as in GCMs without external forcings (control runs)—can be forecast nearly to these limits by the ScaLIng Macroweather Model (SLIMM) that exploits huge system memory that forces the forecasts to converge to the real world climate.

To apply SLIMM to the real world requires pre-processing to take into account anthropogenic and other low frequency external forcings. We compare the overall Stochastic Seasonal to Interannual Prediction System (StocSIPS, operational since April 2016) with a classical GCM (CanSIPS) showing that StocSIPS is superior for forecasts 2 months and further in the future, particularly over land. In addition, the relative advantage of StocSIPS increases with forecast lead time.

In this chapter we review the science behind StocSIPS and give some details of its implementation and we evaluate its skill both absolute and relative to CanSIPS.