Improvement of initialized decadal predictions over the North Pacific Ocean by systematic anomaly pattern correction

Hye Mi Kim, Yoo Geun Ham, Adam A. Scaife

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The prediction skill and errors in surface temperature anomalies in initialized decadal hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are assessed using six ocean-atmosphere coupled models initialized every year from 1961 to 2008. The initialized hindcasts show relatively high prediction skill over the regions where external forcing dominates, indicating that a large portion of the prediction skill is dueto the long-term trend. After removing the linear trend, high prediction skill is shown near the centers of action of the dominant decadal climate oscillations, such as the Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO). Low prediction skill appears over the tropical and eastern North Pacific Ocean where the predicted anomaly patterns associated with the PDO are systematically different in model and observations. By statistically correcting those systematic errors using a stepwise pattern projectionmethod (SPPM) based on the data in an independent training period, the prediction skill of sea surfacetemperature (SST) is greatly enhanced over the North Pacific Ocean. The SST prediction skill over the North Pacific Ocean after the SPPM error correction is as high as that over the North Atlantic Ocean. In addition, the prediction skill in a single model after correction exceeds the skill of the multimodel ensemble (MME) mean before correction, implying that the MME method is not as effective in addressing systematic errors as the SPPM correction.

Original languageEnglish
Pages (from-to)5148-5162
Number of pages15
JournalJournal of Climate
Issue number13
StatePublished - 2014


  • Coupled models
  • Model comparison
  • Model errors
  • Model evaluation/performance
  • North Pacific Oscillation
  • Pacific decadal oscillation


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