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An Attribution Analysis of CPC Seasonal Surface Temperature Forecast Skill

This study examines the skill of seasonal surface temperature forecasts from 1995-2007, analyzing dominant predictors and individual forecast tools. It concludes that temperature trend and the Optimal Climate Normal tool are major contributors to forecast skill, and suggests future improvements in the Climate Forecast System and multi-model ensembles.

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An Attribution Analysis of CPC Seasonal Surface Temperature Forecast Skill

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  1. An Attribution Analysis of CPC Seasonal Surface Temperature Forecast Skill Peitao Peng Arun Kumar Climate Prediction Center 32nd CDPW, Tallahassee, FL Acknowledgments: M. Halpert, D. Unger and B. Jha

  2. Outline • Heidke skill score (HSS) of seasonal surface temperature forecast for the period of 1995-2007; • Analysis of dominant predictors; • Analysis of individual tools and their consolidation; • Thoughts on the future skill improvement.

  3. Heidke Skill Score (HSS) of CPC Seasonal Temperature Forecast (95-07) (0-mon Lead) HSS=100x(H-N/3)/(N-N/3) H: hit number N: total case number

  4. How are the skill variations related to dominant predictors (i.e., tropical SST and recent trend in surface temperature)?

  5. Nino3.4 and its amplitude index

  6. HSS vs Nino3.4

  7. HSS vs US T

  8. Conclusion 1: Temperature trend dominates over the tropical SSTs in explaining the skill.

  9. More evidence about the dominance of the trend: An analysis of the HSS over the southwest region of US

  10. Skill Analysis for the Southwest US HSS vs Nino3.4: COR=0.05 HSS vs sfcT: COR=0.83

  11. Temperature trend The similarity of the trend map (right) with the HSS map shows that most skill indeed comes form the trend prediction

  12. Analysis of individual tools • CFS: NCEP climate forecast system • OCN: optimal climate normal • CCA: canonical correlation analysis • SMLR: screening multiple linear regression

  13. HSS of Individual Models (temporal variation) CCA: 9.3 OCN: 19.5 SMLR: 8.4 CFS: 12.7

  14. HSS of Individual Models (spatial distribution) OCN CCA SMLR CFS

  15. Individual Model HSS vs Nino3.4 Amplitude OCN: cor=0.2 CCA: cor=0.2 SMLR: cor=0.27 CFS: cor=0.25

  16. Individual model HSS vs US T CCA: cor=0.48 OCN: cor=0.84 SMLR: cor=0.44 CFS: cor=0.77

  17. HSS of Consolidation

  18. Consolidated vs Official: Temporal variation of HSS

  19. Consolidated vs Official: spatial distribution of HSS Consolidation Official

  20. Consolidated vs individual tools Skills: CONS(18.1); OCN(19.5); CFS(12.7); CCA(9.3); SMLR(8.4)

  21. Conclusion 2: • Recent temperature trendis a major skill source for all the forecast tools analyzed here; • OCN dominates over other tools in US seasonal forecast; • CFS can catch the trend information as well, Land initial conditions? Something else? • The skill of the consolidation toolmostly from the OCN.

  22. Perspectives on forecast skill improvement in the future • Next generation CFS with improved ICs is expected to be more skillful in forecasting ENSO and variability related to other physical process; • Improvement of OCN with some techniques (EOCN, EMD); • Multi-model ensemble (MME); optimal schemes for MME.

  23. Seasonal Cycle of HSS

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