Tuesday, 9 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Recent studies have shown skillful model predictions of seasonal tropical cyclone (TC) activity using persisted sea surface temperature (SST) anomalies, especially in the Atlantic basin. To better understand and potentially improve seasonal TC prediction, we performed several sets of seasonal TC hindcasts to quantify the TC predictability using a two-tiered forecast approach. In this approach, SST anomalies were first derived from NCAR CESM decadal prediction (DP) simulations and then used to force a TC-permitting atmospheric-only tropical channel model (TCM) in the global tropics. We conducted ensembles of 6-month hindcast runs, in which the daily CESM DP SSTs with a six-month lead time were used to force the TCM. Two El Niño events, i.e., 1997/1998 and 2015/2016, were used to test the model’s forecast skill, as El Niño-Southern Oscillation (ENSO) is one of the strongest climate modes that influence tropical TC activity. By utilizing the two-tiered approach, we were able to separately investigate the impact of SST forcing uncertainty and the uncertainty due to atmospheric internal variability on TC predictability. We performed 10-member ensemble experiments for each simulation year forced by: (1) the ensemble-mean DP SST and different atmospheric initial conditions, and (2) individual ensemble members of DP SST, with an identical atmospheric initial condition. These DP SST-forced runs were then compared to the control runs forced with observed SST. The results show potential skill of seasonal TC prediction using DP SSTs, and also quantify the relative importance of uncertainties in predicted SST versus atmospheric internal variability in seasonal TC prediction.
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