Abstract
This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6- and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño–Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content.
Highlights
In recent decades, it has been proven that tailored seasonal climate forecasts are more beneficial than climatology for decision-making in many sectors of society, e.g. energy, agriculture, transport, insurance and water resource management (Soares and Dessai 2015)
This study clearly demonstrates that assimilating sea surface temperature (SST) observations with an advanced DA method can be as or even more competitive than the current state of the systems that assimilate more data
The global SST prediction skill (ACC and root mean square error (RMSE)) of Norwegian Climate Prediction Model (NorCPM) at 6- and 12-month lead times is generally higher than the averaged skill of 13 North American Multimodel Ensemble (NMME) systems, especially in the tropical western Atlantic and the region extending from the Iceland Basin to the Barents Sea
Summary
It has been proven that tailored seasonal climate forecasts are more beneficial than climatology for decision-making in many sectors of society, e.g. energy, agriculture, transport, insurance and water resource management (Soares and Dessai 2015). Most current ocean initialisations used for seasonal forecasting systems (Balmaseda et al 2009) assimilate sea surface temperature (SST) observations, since SST plays a key role in influencing atmospheric circulations (Shukla 1998). They assimilate many other oceanic observation types, e.g. subsurface temperature and salinity and altimeter data that have been demonstrated to improve seasonal predictions (Balmaseda and Anderson 2009). Zhu et al (2017) even showed that such simple initialisation scheme could achieve a comparable seasonal SST prediction skill to the averaged skill of the NMME models that use more data (e.g., oceanic subsurface data and atmospheric data) and/or more advanced DA methods for initialisation.
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