High-resolution atmosphere–ocean coupled models are the primary tool for subseasonal to seasonal-scale (S2S) prediction. However, seasonal-scale sea surface temperature (SST) drift is inevitable due to the imbalance between the model components, which may deteriorate the prediction skill. Here, we investigate the performance of a simple flux adjustment method specifically designed to suppress seasonal-scale SST drift through case studies. The Nonhydrostatic Icosahedral Atmospheric Model (NICAM)–Center for Climate System Research Ocean Component Model (COCO) coupled weather/climate model, referred to as NICOCO, was used for wintertime 40-day integrations with a horizontal resolution of 14 km for the atmosphere and 0.25° for the ocean components. The coupled model with no flux adjustment suffers SST drift of typically −1.5–2°C in 40 days over the tropical, subtropical, and Antarctic regions. Simple flux adjustment was found to sufficiently suppress the SST drift. Nevertheless, the lead–lag correlation analysis revealed that air-sea interactions are likely to be appropriately represented under flux adjustment. Thus, high-resolution coupled models with flux adjustment can significantly improve S2S prediction.

Typhoon Krosa (2019) formed in the eastern part of the Philippine Sea and ~ 1400 km east of another typhoon, Lekima, on 6 August and made landfall in the western part of Japan's mainland on 15 August. The operational global model forecasts, which were initialized just after Krosa's formation, showed a very large uncertainty and completely failed to predict the actual track of Krosa. In this study, we investigated the causes of this large uncertainty through 101-member ensemble forecast experiments using a 28-km mesh global nonhydrostatic model. The experiments initialized at 1200 UTC 6 August showed a large uncertainty. An ensemble-based lagged correlation analysis indicated that the western North Pacific subtropical high (WNPSH) retreated further east in the members with large track forecast errors than in the members with small errors. For the members with a large track forecast error for Krosa, Krosa and Lekima approached each other by 250 km, and Krosa moved northward faster than the observation in 36 h from the initialization time. For the members with a small track forecast error for Krosa, the two typhoons approached each other by only 50 km, and the northward moving speed was comparable with that of the observation. The typhoon-center relative composite analysis exhibited that at the initialization time, the members with a large Krosa track forecast error had a larger horizontal size of Krosa, and the difference in Krosa's size was kept during the forecast period. This difference in size led to a stronger interaction between the two typhoons and the retreatment of the WNPSH, thus resulting in a fast northward moving speed for the members with a large Krosa track error.

This study aimed to systematically and globally evaluate the monthly precipitation forecasts of Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System ver. 2 (JMA/MRI-CPS2), a dynamical seasonal climate forecast (Dyn-SCF) system operated by the Japan Meteorological Agency, by comparing its forecasts with those of a statistical SCF (St-SCF) system using climate indices. We developed a new global St-SCF system using 17 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, the skill of JMA/MRI-CPS2 was determined to be globally higher than that of the St-SCF for zero-month lead forecasts. Contrarily, for forecasts made with a lead time of 1 month or longer, the deterministic skill of JMA/MRI-CPS2 was comparable to that of the St-SCF, and the probabilistic skill of JMA/MRI-CPS2 remained slightly higher. In addition to evaluating the skill of JMA/MRI-CPS2, we identified several regions and seasons, for which JMA/MRI-CPS2 exhibited a low forecast skill, compared with the St-SCF. This indicated that JMA/MRI-CPS2 cannot sufficiently reproduce certain dynamics. In conclusion, comparing Dyn-SCFs with St-SCFs can elucidate the potential regions and seasons to improve the forecast skill of Dyn-SCFs.