The goal of this study is to take advantage of current understanding of predictability to determine a metric that maximize the signal-to-noise ratio as we move from weather to subseasonal scales. To achieve this, we use Average Predictability time (ATP) analysis (Delsole and Tippet, 2009) as a metric to diagnose predictability on multiple time scales. APT is applied to a large suite of retrospective forecasts using the NASA's Global Modeling and Assimilation Office (GMAO) subseasonal-to-seasonal (S2S) v2 forecasts produced by the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation model. We demonstrate how APT can be used as a tool to study smooth transitions between consecutive averaging intervals within the framework of retrospective forecasts. Our analysis will highlight the potential shifts in predictability corresponding to different states or regimes, spanning from weather to subseasonal and that from subseasonal to seasonal scales, without the application of time averaging.
Reference:
DelSole, T., and M. K. Tippett, 2009: Average Predictability Time. Part I: Theory. J. Atmos. Sci., 66, 1172–1187, https://doi.org/10.1175/2008JAS2868.1.

