Quantifying sources of subseasonal prediction skill in CESM2 in a perfect modeling framework

Recently, Richter at al. 2024 investigated the sources of predictability from initializing the ocean, atmosphere and land components and verifying S2S predictions against observations. They find that ocean initialization adds little skill in weeks 4-6 and land initializations deteriorate skill in week 1-2. These results point to possible problems with spin-up and coupled model drift. Here we will revisit these results, but in a perfect modeling framework which eliminates model error. For the perfect model, we find that land initializations do contribute to skill, especially in the summer hemisphere. By studying the evolution of the lead-time dependent bias in the actual and perfect predictions, we attempt to disentangle initialization error from coupled model drift.

 

Richter, J.H., Glanville, A.A., King, T. et al. Quantifying sources of subseasonal prediction skill in CESM2. npj Clim Atmos Sci 7, 59 (2024). https://doi.org/10.1038/s41612-024-00595-4