Skillful decadal prediction for Northwest Pacific tropical cyclone activity

The Northwest Pacific (NWP) tropical cyclone (TC) activity exhibits significant decadal variations with alternating active and inactive periods. However, it remains unknown whether such kinds of decadal variations are predictable. Here, we develop a dynamic-statistic model for the decadal predictions of the tropical cyclone genesis frequency (TCGF) and accumulated cyclone energy (ACE) index of the NWP TCs. The dynamic-statistic model is a combination of decadal prediction experiments by coupled general circulation models (CGCM) from the CMIP6 Decadal Climate Prediction Project (DCPP) and multiple linear regression models based on the correlation relationships between the NWP TCGF (ACE) and large-scale variability modes of sea surface temperature (SST) anomalies in observations. For the TCGF, we first calculate anomalous SST intensities associated with Atlantic multidecadal variability (AMV), Pacific decadal oscillation (PDO), and global mean SST (GMSST) predicted by the decadal prediction experiments. Then, they are substituted into the regression model trained by the historical observational TCs and SST to predict the NWP TCGF. For the ACE, one more predictor, viz. the anomalous SST in the NWP, is involved in its regression model. The dynamic-statistic model can be applied for both deterministic and probabilistic predictions with multi-model ensemble mean, and individual members of the decadal prediction experiments used, respectively. Retrospective predictions for the past 50 years show that the correlation skill of the deterministic predictions for the NWP TCGF (ACE) in the future 2-5 and 6-9 years reach 0.71 and 0.59 (0.59 and 0.41), respectively. The results of this dynamic-statistic model will provide decision-makers of the western Pacific Rim countries with valuable information to adapt to variations in NWP TC activity over the next 10 years.