# Presentation: 398066

## 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

## 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.

## Heidke Skill Score (HSS) of CPC Seasonal Temperature Forecast (95-07)

***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

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

## Nino3.4 and its amplitude index

- _Nino3.4 and its amplitude index_

## HSS vs Nino3.4

_**HSS vs Nino3.4**_

## HSS vs US T

- _HSS vs US T_

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

- 	Temperature trend dominates over the tropical SSTs in explaining the skill.

## More evidence about the dominance of 
the trend:

An analysis of the HSS over the southwest region of US

- the trend:
- An analysis of the HSS over the southwest region of US

## Slide 10

## Skill Analysis for the Southwest US

_**Skill Analysis for the Southwest US**_

- HSS vs Nino3.4: COR=0.05

- HSS vs sfcT: COR=0.83

## Temperature trend

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

## Analysis of individual tools

- CFS: NCEP climate forecast system
- OCN: optimal climate normal
- CCA: canonical correlation analysis
- SMLR: screening multiple linear regression

## HSS of Individual Models (temporal variation)

- CCA: 9.3

- SMLR: 8.4

- OCN: 19.5

- CFS: 12.7

## HSS of Individual Models (spatial distribution) 

- CCA

- SMLR

- OCN

- CFS

## Individual Model HSS vs Nino3.4 Amplitude

- CCA: cor=0.2

- SMLR: cor=0.27

- OCN: cor=0.2

- CFS: cor=0.25

## Slide 17

## Individual model HSS vs US T

- CCA: cor=0.48

- SMLR: cor=0.44

- OCN: cor=0.84

- CFS: cor=0.77

## HSS of Consolidation

## Consolidated vs Official: Temporal variation of HSS

- _Consolidated vs Official: Temporal variation of HSS_

## Consolidated vs Official: spatial distribution of HSS

- Consolidation

- Official

- _Consolidated vs Official: spatial distribution of HSS_

## Consolidated vs individual tools

- _Consolidated vs individual tools_

- Skills: CONS(18.1); OCN(19.5); CFS(12.7); CCA(9.3); SMLR(8.4)

## Conclusion 2:

Recent temperature trend is 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 tool mostly from the OCN.   

- Recent temperature trend is 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 tool mostly from the OCN.

## 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.

## Seasonal Cycle of HSS

_**Seasonal Cycle of HSS**_