A Simple Statistical Postprocessing Scheme for Enhancing the Skill of Seasonal SST Predictions in the Tropics

• Published in: Monthly weather review Vol. 152; no. 4; pp. 1039 - 1056
• Main Authors: Richter, Ingo, Ratnam, Jayanthi V., Martineau, Patrick, Oettli, Pascal, Doi, Takeshi, Ogata, Tomomichi, Kataoka, Takahito, Counillon, François
• Format: Journal Article
• Language: English
• Published: Washington American Meteorological Society 01.04.2024
• Subjects: Climate; Climate prediction; Climate science; Climate variability; Correlation analysis; Current prediction; Data assimilation; El Nino; El Nino phenomena; Forecasting skill; General circulation models; Lead time; Ocean temperature; Oceans; Parameters; Prediction models; Sea surface temperature; Statistics; Surface temperature; Systematic errors; Temperature distribution; Temperature fields; Tropical environments; Variability; Indian Ocean
• ISSN: 0027-0644; 1520-0493
• DOI: 10.1175/MWR-D-23-0266.1

Seasonal prediction systems are subject to systematic errors, including those introduced during the initialization procedure, that may degrade the forecast skill. Here we use a novel statistical postprocessing correction scheme that is based on canonical correlation analysis (CCA) to relate errors in ocean temperature arising during initialization with errors in the predicted sea surface temperature fields at 1–12-month lead time. In addition, the scheme uses CCA of simultaneous SST fields from the prediction and corresponding observations to correct pattern errors. Finally, simple scaling is used to mitigate systematic location and phasing errors as a function of lead time and calendar month. Applying this scheme to an ensemble of seven seasonal prediction models suggests that moderate improvement of prediction skill is achievable in the tropical Atlantic and, to a lesser extent, in the tropical Pacific and Indian Ocean. The scheme possesses several adjustable parameters, including the number of CCA modes retained, and the regions of the left and right CCA patterns. These parameters are selected using a simple tuning procedure based on the average of four skill metrics. The results of the present study indicate that errors in ocean temperature fields due to imperfect initialization and SST variability errors can have a sizable negative impact on SST prediction skill. Further development of prediction systems may be able to remedy these impacts to some extent.