Measuring the potential predictability of seasonal climate predictions

Measuring the potential predictability of seasonal climate predictions Michael Tippett, IRI Richard Kleeman and Youmin Tang CIMS, NYU

Predictability • Climatology pdf of seasonal precipitation. • Forecast probability distribution based on additional information: – Initial conditions; – Boundary conditions (SST; soil); – ENSO state • If the two distributions are the same: – No additional information in forecast. • Greater difference, more information in forecast.

Measuring predictability • Relative entropy measures the difference between forecast p and climatology q pdfs. • Measures change in mean and higher order moments. • Nice properties. • Invariant under nonlinear transformations. – Taking log or square-root does not change R. – Useful for non-Gaussian pdfs. Gaussian

Outline • Measure relative entropy in two GCM simulations forced by observed SST. – “Perfect model” potential predictabilty – Time-series in three locations – JFM North America precipitation. • How does relative entropy depend on – Ensemble mean? – Ensemble variance?

South Florida DJF

Kenya OND

NE Brazil MAM

North America JFM precipitation

North America JFM precipitation

Summary • Relative entropy measures the difference between forecast and climatology pdfs. – changes in mean, variance, higher order moments. • For seasonal precipitation total: – RE is more closely related to changes in mean than variance • Model dependence. • Future questions: – Differing utility of predictions during warm/cold events.