GloSea5 모형의 6개월 장기 기후 예측성 검증

• Published in: Atmosphere
• Main Authors: 정명일, 손석우, 최정, 강현석
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 한국기상학회 2018
• Subjects: ARCTIC OSCILLATION; SUMMER MONSOON; INDIAN-OCEAN; EAST-ASIA; WINTER; PREDICTABILITY; PRECIPITATION; ENSO; TELECONNECTIONS; CLIMATOLOGY; GloSea5; seasonal prediction; climate index; Arctic; Indian; Pacific
• Online Access: https://hdl.handle.net/10371/207191; https://doi.org/10.14191/Atmos.2015.25.2.323

This study explores the 6-month lead prediction skill of several climate indices that influence on East Asian climate in the GloSea5 hindcast experiment. Such indices include Nino3.4, Indian Ocean Diploe (IOD), Arctic Oscillation (AO), various summer and winter Asian monsoon indices. The models prediction skill of these indices is evaluated by computing the anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) for ensemble mean values over the period of 1996~2009. In general, climate indices that have low seasonal variability are predicted well. For example, in terms of ACC, Nino3.4 index is predicted well at least 6 months in advance. The IOD index is also well predicted in late summer and autumn. This contrasts with the prediction skill of AO index which shows essentially no skill beyond a few months except in February and August. Both summer and winter Asian monsoon indices are also poorly predicted. An exception is the Western North Pacific Monsoon (WNPM) index that exhibits a prediction skill up to 4- to 6-month lead time. However, when MSSS is considered, most climate indices, except Nino3.4 index, show a negligible prediction skill, indicating that conditional bias is significant in the model. These results are only weakly sensitive to the number of ensemble members. N 2