Abstract ID: 245
State-dependent Forecast Skill in S2S Forecasts
Lead Author: Abigail Jaye
NCAR, United States of America
Keywords: State-dependence, Forecast Skill, Model evaluation
Abstract: Recently, there has been much interest in forecasts on the sub-seasonal to seasonal (S2S) timescale. Their skill is generally very small except during selected “”windows of opportunity”” during which they are skillful. These windows of opportunity are typically associated with large-scale modes of variability such as the Pacific North American pattern, the North Atlantic Oscillation or forcing from the tropics such as those associated with the El Nino-Southern Oscillation or the Madden-Julian Oscillation.
Here, we will investigate the state-dependent forecast skill across different S2S models including ECMWF, CESM2 and NCEP. We will report on various probabilistic and deterministic skill measures for 2m-temperature and geopotential height in 500hPa and for different geographical regions.
We will use the perfect model framework to point to differences in the potential predictability in different S2S models and link those to differences in the teleconnections patterns and ensemble spread.
Co-authors:
Judith Berner (National Center for Atmospheric Research, Boulder, CO)
