Missing eddy feedback may explain weak signal-to-noise ratios in climate predictions - Steven C. Hardiman; Nick J. Dunstone; Adam A. Scaife; Doug M. Smith; Ruth Comer; Yu Nie; Hong-Li Ren (npj Climate and Atmospheric Science, 2022)
Abstract The signal-to-noise paradox that climate models are better at predicting the real world than their own ensemble forecast members highlights a serious and currently unresolved model error, adversely affecting climate predictions and introducing uncertainty into climate projections. By computing the magnitude of feedback between transient eddies and large-scale flow anomalies in multiple seasonal forecast systems, this study shows that current systems underestimate this positive eddy feedback, and that this deficiency is strongly linked to weak signal-to-noise ratios in ensemble mean predictions. Improved eddy feedback is further shown to be linked to greater teleconnection strength between the El Niño Southern Oscillation and the Arctic Oscillation and to stronger predictable signals. We also present a technique to estimate the potential gain in skill that may come from eliminating eddy feedback deficiency, showing that skill could double in some extratropical regions, significantly improving predictions of the Arctic Oscillation.