Bias in ocean data assimilation Two-stage bias correction algorithm Bias model

1. Forecast model bias correction in ocean data assimilationG. Chepurin, Jim Carton, and D. Dee*Univ. MD and *GSFC • Bias in ocean data assimilation • Two-stage bias correction algorithm • Bias model • Results from a series of 30-yr assimilation experiments Manuscript available: {http://www.atmos.umd.edu/~carton/bias}

2. Bias is the difference between the state forecast and the true state

3. Time-mean bias along equator “Cold tongue is too cold, while the thermocline in the central basin is too diffuse” 20C

4. Annual cycle of mixed layer bias in subtropics (10N-30N) Dec June “Too hot in summer, too cold in winter”

5. 100m Mixed layer Time-evolution of forecast error along equator “Forecast error is episodic, linked to ENSO” Time 

6. Two stage algorithm to correct systematic aspects of forecast error Stage I Stage II

7. Three-term bias forecast model ENSO-linked bias Annual cycle bias Time-mean bias

8. along Pacific Eq Correcting time-mean bias 20C This is business as usual This is what results when time-mean bias is modeled 20C

9. Corr time-mean bias Correcting time-mean bias

10. Correcting annual cycle bias Dec June Business as usual Annual cycle bias correction

11. Annual cycle of forecast error before correction

12. Annual cycle of forecast error after correction After Before

13. Correcting ENSO bias CorEOF1,SOI = 0.7 before after

14. Thermocline depth ML temp Summary of the impact of bias correction time mean +annual cycle +ENSO variability RMS (fcst-obs)

15. Conclusions • Half of the {forecast – observation} differences in high variability regions are due to bias. The largest contribution is time-mean followed by annual cycle and interannual variability. • Two-stage correction works well in addressing these. Manuscript available: {http://www.atmos.umd.edu/~carton/bias}

16. The End