Seasonal Prediction Skills and Systematic Errors in Climate Models

1. How are seasonal prediction skills related to models’ systematic error? June-Yi Lee and Bin Wang IPRC, University of Hawaii, USA In-Sik Kang, Seoul National University, Korea J. Shukla, George Mason University, USA C.-K. Park, APCC, Korea

2. CliPAS: Climate Prediction and Its Application to Society The international project, the CliPAS, in support of APCC is aimed at establishing well-validated multi-model ensemble (MME) prediction systems for climate prediction and developing economic and societal applications. Acknowledge contributions from the following CliPAS/APCC Investigators BMRC: O. Alves CES/SNU: I.-S. Kang, J.-S. Kug COLA/GMU: J. Shukla, B. Kirtman, J. Kinter, K. Jin FSU: T. Krishnamurti, S. Cocke, FRCGC/JAMSTEC: J. Luo, T. Yamagata (UT) IAP/CAS: T. Zhou, B. Wang KMA: W.-T. Yun NASA/GSFC: M. Suarez, S. Schubert, W. Lau NOAA/GFDL: N.-C. Lau, T. Rosati, W. Stern NOAA/NCEP: J. Schemm, A. Kumar UH/IPRC/ICCS: B. Wang, J.-Y. Lee, P. Liu, L. X. Fu

3. The Current Status of HFP Production One-Tier systems Two-Tier systems Statistical-Dynamical SST prediction (SNU) AGCM CGCM FSU 79-04, 2 times NASA 80-04,2 times CFS (NCEP) 81-04,12 times GFDL 79-04, 2 times CAM2 (UH) 79-03, 4 times SINTEX-F 82-04, 12 times SNU 80-02, 4 times SNU/KMA 79-02, 12 times ECHAM(UH) 79-03, 2 times IAP 79-04, 4 times UH 82-03, 4 times GFDL 79-05,12 times *NCEP 81-04,4 times POAMA(BMRC) 80-02, 12 times * NCEP two-tier prediction was forced by CFS SST prediction

4. Climate Prediction Models Multi-Model Ensemble Climate Prediction 13 coupled model retrospective forecasts for 1981-2001 targeting seasonal climate prediction with 4 initial conditions starting from February 1st, May 1st, August 1st, and November 1st APCC/CliPAS One Tier APCC/CliPAS Two Tier DEMETER NCEP/CFS FSU CERFACS Meteo-France FRCGC/ SINTEX-F GFDL ECMWF SNU SNU Met Office INVG Comparison GFDL NCEP GFS MPI LODYC POAMA/ BMRC IAP UH UH 1 NASA UH 2

5. Topics Objective: To identify the strengths and weaknesses of the seasonal prediction models, especially coupled models, in predicting seasonal monsoon climate. (1) The impact of the models’ systematic errors in mean state on its performance on seasonal precipitation prediction The fidelity of a model simulation of interannual variability has a close link to its ability in simulation of climatology (Shukla 1984; Fennessy et al. 1994, Sperber and Palmer 1996; Kang et al. 2002; Wang et al. 2004)and seasonal migration of rain belt (Gadgil and Sajani 1998). (2) The impact of the systematic errors on ENSO-monsoon relationship Improvements in a coupled model’s mean climatology generally lead to a more realistic simulation of ENSO-monsoon teleconnection (Lau and Nath 2000; Annamalai and Liu 2005; Turner et al. 2005; Annamalai et al. 2007)

6. 13 Coupled Climate Models

7. Reconstruction of Annual Cycle in Climate Prediction Annual cycle of prediction is reconstructed using retrospective forecasts for 4 initial conditions starting from 1 February, 1 May, 1 August, and 1 November. Thus, each month has different forecast lead time. 2-month forecast is used for March, June, September, and December, 3-month forecast for April, July, October, and January, and 4-month forecast for May, August, November, and February. Reconstruction of annual cycle using different forecast lead time for each month Feb Feb Jan Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 1mon 2mon 3mon 4mon Spring forecast Integrating from February 1st Forecast lead time 1mon 2mon 3mon 4mon 1mon 2mon 3mon 4mon Summer forecast Integrating from May 1st Fall forecast Integrating from August 1st 1mon 2mon 3mon 4mon Winter forecast Integrating from November 1st

8. Current Status of Prediction of Seasonal Precipitation : Temporal Correlation Skill for 13 Coupled Model MME(81-01) • The prediction skills for precipitation vary with space and season. • The variations in the spatial patterns and the seasonality of the correlation skills suggest that ENSO variability is the primary source of the global seasonal prediction skill. • Prediction in DJF, SON and MAM is evidently better than JJA due to the model’s capacity in capturing the ENSO teleconnections around the mature phases of ENSO.Precipitation predictions over land and local summer monsoon region have little skills.

9. One-Tier vs Two-Tier MME Prediction of JJA Precp. /Anomaly Pattern Correlation & Normalized RMSE A-AM Region ENSO Region • It is documented that the prediction skill of one-tier systems is better than the two-tier seasonal prediction system in boreal summer over both A-AM [40-160E, 30S-30N] and ENSO [160-280E, 30S-30N] regions in terms of anomaly pattern correlation skill and normalized RMS error.

10. mmday-1 mmday-1 Performance on Annual Mean MME prediction reproduces the observed features which include (1) the major oceanic convergence zones over the Tropics, (2) the Major precipitation zones in the extratropical Pacific and Atlantic and (3) remarkable longitudinal and latitudinal asymmetries • Underestimation over ocean convergence zone • (2) Overestimation over Maritime continents and high elevated terrains where the wind-terrain interaction influences annual rainfall.

11. Equinox asymmetric mode (13%) (AM minus ON mean precipitation) Forecast Skill mm/day mm/day Performance on Annual Cycle Solstice global monsoon mode (71%) (JJAS minus DJFM mean precipitation) The spring-fall asymmetry is exaggerated over the entire Indian Ocean, East Asia and South China Sea-Western North Pacific regions. The MME predicted a weaker-than-observed Asian summer monsoon.

12. Systematic Errors in JJA Monsoon Climate Reduced precipitation over BoB, SCS, WNP, and East Asia Enhance precipitation over MC, WIO and TP Strong warm bias over land and cold bias over ocean enhancing the zonal and meridional land-sea thermal contrast Enhanced AC over IO and MC and northward shifted AC over NP Strong low level div. over India and weakening of V over BoB and SCS, Strong conv. over MC Weakening of divergence and anti cyclonic circulation in upper level monsoon flow (a) Precipitation ( mmday-1) (2) 2m air temperature (degree) (3) stream function (shading, 1x106m2s-1) and wind (vector,ms-1) at 850 hPa, (d) stream function (shading) and velocity potential (contour, 2x106m2s-1)

13. Performance on Mean States and its Linkage with Seasonal Prediction Pattern Correlation over Global Tropics [30S – 30N] Combined annual cycle skill of the 1st and 2nd EOF modes by weighting their eigenvalues The seasonal prediction skills are positively correlated with their performances on both the annual mean and annual cycle in the coupled climate models. The MME prediction has much better skill than individual model predictions for all metrics

14. Annual Mode vs Seasonal Precipitation Prediction/ One-Tier vs Two-Tier MME (a) Climatology vs IAV (b) 1st Annual Cycle vs IAV NCEP CFS NCEP CFS NCEP T2 NCEP T2 Metric: Anomaly pattern correlation skill over 0-360E, 30S-30N

15. One Tier vs Two Tier / The 1st Annual Cycle Mode Mean biases against CMAP precipitation Model spread against multi-model ensemble mean The spatial distribution of mean biases in one-tier MME is quite similar to that in two-tier MME except few regions, although the biases are much alleviated. The common biases in the two types of systems may arise from uncertain model physics and problematic land surface processes.

16. Source of Seasonal Predictability of Precipitation in Couple Model MME SEOF Modes for Precipitation over Global Tropics [0-360E, 30S-30N] • How many modes are predictable?

17. Systematic and Anomaly Errors of JJA SST Forecast • The errors in El Nino amplitude, phase, and maximum location of variability in coupled models are related with mean state errors such as colder equatorial Pacific SST and stronger easterly wind over western equatorial Pacific.

18. ENSO Composite / Precipitation (Shaded) & SST (Contoured) (Normalized anomaly field) • The breaking relationship between ENSO and Indian monsoon is evident in observation, whilst the MME produce clear negative relationship. • The anomalous precipitation and circulation are predicted better in the ENSO decaying JJA than ENSO developing JJA.

19. (63,68,72) (82,91,97) ENSO Composite /Velocity Potential at 850 (shaded) and 200 hPa (contoured) Divergence (Dashed line) Convergence (Solid line) • The shift of variability centers in onset summers and exaggerated variability in decay summers are evident in the atmospheric circulation field.

20. Summary The skills of one-month lead MME prediction of seasonal mean precipitation vary with space and season. The variations in the spatial patterns and the seasonality of the correlation skills suggest that ENSO variability is the primary source of the global seasonal prediction skill. Prediction in DJF, SON, and MAM is evidently better than JJA due to the model’s capacity in capturing the ENSO teleconnections around the mature phases of ENSO. 1 The state-of-the art coupled models can reproduce realistically the observed features of long-term annual mean precipitation. However, these models have common biases over the oceanic convergence zones where SST bias exists and the regions where the wind-terrain interaction is likely to produce annual rainfall. 2 The seasonal prediction skills are positively correlated with their performances on mean states in the coupled climate models. The MME prediction has much better skill than individual model predictions. 3 The errors in amplitude, phase, and maximum location of El Nino variability in model are associated with mean state errors such as colder equatorial Pacific SST and stronger easterly wind over western equatorial Pacific, resulting in errors in ENSO-Monsoon teleconnection. The breaking relationship between ENSO and Indian monsoon is evident in observation, whilst the MME produce clear negative relationship. 4

21. Thank You !

22. Model Descriptions of CliPAS System APCC/CliPAS Tier-1 Models APCC/CliPAS Tier-2 Models

23. Current Status of ENSO Prediction / Correlation Skill of Nino 3.4 SST

24. ENSO Composite (Velocity Potential) (ECMWF model) Divergence (dashed line) Convergence (solid line)

25. MME predicts weaker-than-observed monsoon precipitation Systematic Bias of Model in JJA • Strong warm bias over land and cold bias over ocean enhance the zonal and meridional land-sea thermal contrast in the prediction models. • Oceanic anticyclones are enhanced especially over Indian Ocean and maritime continent. North Pacific anticyclone is shifted northward. • Associated with enhanced anticyclones, cross-equatorial meridional wind is weaken over east of maritime continent and South China Sea. Meridional wind over Bay of Bengal is also weaken. • Precipitation is reduced over Bay of Bengal, SCS, WNP, and east Asian monsoon region and enhanced over maritime continent and western North Indian Ocean. • Reduced precipitation over SCS-WNP region results in weakening of divergence over same region and anticyclone over Indian Ocean at 200 hPa.

26. Annual Cycle of NCEP Models SST Precipitation

27. Source of Predictability and Error Indian Monsoon • MME system predicts realistic annual cycle of precipitation over the Indian monsoon region, while it has no skill in seasonal anomaly prediction of precipitation. • Systematic Bias: Cold bias of SST over the entire North Indian Ocean Weak upper level easterly • Major error source: Systematic bias in ENSO-Indian monsoon teleconnection SCS-WNP Monsoon • MME system has large systematic bias in annual cycle of precipitation, it has moderate skill in seasonal anomaly prediction • Systematic Bias: Cold bias of SST Enhance precipitation in cold seasons and reduced one in warm season Weak mean precipitation and its variance in JJA Weak upper level divergence • Predictability source: ENSO (MME reproduce realistic ENSO-WNP relationship) • Error source: unrealistic simulation of ISO in models is related to weak mean precipitation and its weak variance

28. Monsoon Domain (red) The definition of monsoon domain The regions in which the annual range (summer mean minus winter mean) exceeds 2mm/day and the local summer monsoon precipitation exceeds 35% of annual rainfall. Here, summer means JJA in the NH and DJF in the SH (Wang and Ding 2006).

29. Temporal Correlation and Normalized RMSE of Precipitation Prediction Figure 4. Temporal correlation coefficients (upper panels) and normalized RMSE (lower panels) of precipitation between observation and one-month lead seasonal prediction obtained from APCC/CliPAS MME system in summer (left-hand panels) and winter (right-hand panels) seasons, respectively. In (a) and (b), dashed line is for 0.3 and solid line is for 0.5 correlation coefficient. Solid contour indicates 0.9 in (b) and (d).

30. Performance on Mean States and its Linkage with Seasonal Prediction Pattern Correlation skill over the A-AM Region [40-160E, 30S-30N]

31. Performance on Mean States and its Linkage with Seasonal Prediction Pattern Correlation skill over the global Tropics [30S-30N]