Abstract ID: 246
Impact of Stochastic Parameterization on S2S Forecasts with CESM
Lead Author: Judith Berner
NCAR, United States of America
Keywords: Stochastic parameterization, Forecast Skill, Ensemble spread, CESM2
Abstract: There has been increasing interest on generating skillful forecasts on the sub-seasonal to seasonal timescale which fills the gap between weather and seasonal climate forecasts.
On this timescale, ensemble systems tend to be unreliable and overconfident, i.e. they underestimate the uncertainty of a particular forecast. In addition, forecasts are limited by large systematic model errors.
Stochastic parameterization schemes are used routinely to remedy the problem of unreliability, but also have the potential to reduce systematic model errors.
Here, we study the impact of adding a stochastic parameterization scheme in initialized coupled simulations with the climate model CESM2. We will give details of the impact on precipitation, 2m-temperature and geopotential height in 500hPa skill for different geographical regions.
Adding a stochastic scheme results in more reliable forecasts, but generally leads to a decrease in deterministic skill. In particular, tercile forecasts for precipitation and temperature for weeks 3-4 and weeks 5-6 are significantly improved.
Co-authors:
Abigail Jaye (National Center for Atmospheric Research, Boulder, CO)
