1. Yuiko Ichikawa and Masaru Inatsu Hokkaido University, Japan 1 Systematic bias and forecast spread in JMA one-month forecast projected on the MJO phase space

2. Introduction 2 Assessing the MJO prediction is important for understanding the mechanism of MJO and improvement of forecast. The dynamics of the MJO involves atmospheric planetary-scale circulations and its interaction with mesoscale convective activities. It is very difficult to simulate the MJO correctly by state-of-the-art global weather prediction models and global climate models (Zhang 2005).

3. Introduction 3 Previous studies have used MJO phase spaces to assess prediction ． From Kim et al. (2014) Phase-dependency of the MJO forecast in ECMWF’s VarEPS.

4. Introduction 4 Synthetic method to investigate spatial structure of error on the phase space is needed. From Rashid et al. (2012) From Lin et al. (2008)

5. Purpose: Make an assessment of the global distribution of bias and spread on the MJO phase space. Fair comparison of forecast models Evaluate behavior of model MJO in contrast of reanalysis Introduction 5

6. Data fcst length/frequency 34 days / week (262 weeks) ensemble members49 resolutionT106L40 (about 110 km) periodJan 2008 to Dec 2012 variables850 hPa, 200 hPa zonal wind 6 松枝聡子・高谷祐平による「 1 か月予報モデルにおける MJO の予測精 度」より The JRA-25/JCDAS reanalysis was used as the true value and to define phase space following Wheeler and Hendon (2004). Forecast model assessed is the Japan Meteorological Agency’s one-month forecast

7. MJO phase space 7 EOF1 PC2 of combined EOF EOF2 Zonal wind at 200 hPa Zonal wind at 850 hPa Phase space is defined from leading modes of tropical intraseasonal variability almost following Wheeler and Hendon (2004)

8. Method 8 Reanalysis Ensemble forecast projected on the MJO phase space Bias: distance between ensemble mean and reanalysis EOF1 EOF2

9. Results 9 Reanalysis Spread: Standard deviation among ensemble members Initial-value dependency is estimated from forecast spread.

10. Method 10 After obtaining bias and spread for each week’s forecast, calculate average of variables among forecasts initialized at same grid point. Forecast initialized on June 20th May 1st Dec 27th EOF1 EOF2

11. 21 days of potential predictability estimated using Waliser et al. (2003)’s measure. 21 days of potential 11 Results Spread distributed almost homogenously on the phase space. Spread × √2 Amplitude

12. Results Bias is high in Indonesia and low in Africa and in the western Indian Ocean. Bias is about ４ times of spread. 12 Indian Ocean Pacific Maritime Continent Africa & Western Indian

13. Results 13

14. Results 14 １ 5 10 The centroid of PDF shifts leftward (toward Phase 8-1). Shrinking around the centroid indicates dumping of the MJO signal. Indian Ocean Pacific Maritime Continent Africa & Western Indian The probability density function for day lead-time.

15. 15 Zonal wind at 200 hPa EOF1 minus Seasonally averaged bias in velocity potential at 200 hPa From Sato et al. (2015) Verification. JMA seasonal forecast textbook 2014

16. Results 16 Almost axisymmetric. Counterclockwise rotation.

17. Results 17 Somehow rotates around (-1, 0) Has slight inward component

18. Conclusion The global distribution of bias and spread in JMA’s one-month forecast was evaluated on the MJO phase space. 18 The bias in one-month forecast had strong phase dependency and its main cause was leftward PDF-shift related to systematic model bias. Spread was spatially homogeneous and inferred 21 day of potential predictability, which is consistent with previous researches.

19. Conclusion 19 Gained overview of variables on phase space helps understanding the behavior of model-MJO Statistical relation between model and reanalysis moving speed could be used for bias correction