MJO Prediction Skill of the S2S Prediction Models

Transcription

1 6th International Workshop on Monsoons, ovember 15, 017 MJO Prediction Skill of the SS Prediction Models Yuna Lim 1, Seok-Woo Son 1, Daehyun Kim 1 Seoul ational University, Republic of Korea University of Washington, U.S.A.

2 Madden-Julian Oscillation (MJO) MJO is a large scale, convective disturbance that propagates eastward with a period of days (Madden and Julian, 1971; 197). MJO is a major source of subseasonal-to-seasonal prediction.

3 SS models Modeling center Model resolution Reforecast period Reforecast frequency Total number of reforecasts Reforecast length Ensemble size BoM + T47 L six times/month 990 Days CMA* + T106 L six times/month 600 Days CR-ISAC 0.75 x0.56 L every 5 days 900 Days CRM + T55 L twice/month 10 Days ECCC x0.45 L weekly 418 Days ECMWF + T639/319 L twice/week 70 Days HMCR 1.1 X1.4 L weekly 546 Days JMA + T319 L three times/month 450 Days CEP* + T16 L six times/month 360 Days Key Questions UKMO 16 L four times/month 80 Days How long is the MJO prediction skill in the latest operational models? How can be the MJO prediction skill improved?

4 MJO evaluation metrics Reanalysis and observation data: ERA-Interim and OAA s OLR Real-time Multivariate MJO (RMM) index: Wheeler and Hendon (004) O 1 t, O t : Observation s RMM indices M 1 t, τ, M t, τ : Model s RMM indices A O t, φ O t : Observation s MJO amplitude and phase A M t, φ M t : Model s MJO amplitude and phase Wintertime (ov.-mar.) MJO events, which have larger amplitudes than 1.0, are evaluated. MJO prediction skills: BCOR τ = BMSE τ AE τ = 1 = 1 (O 1 t M 1 t, τ + O t M t, τ ) t A M t, τ A O MJO amplitude and phase errors: AE τ = 1 M 1 t, τ O 1 t + M t, τ O t A M t, τ A O t, PE τ = 1 Mean-squared amplitude and phase errors: A M t, τ A O t, PE τ = 1 φ M t, τ φ O t φ M t, τ φ O t. Rashid et al. (011) Lim et al. (017, in revision). Rashid et al. (011) Lim et al. (017, in revision)

5 BMSE BCOR MJO prediction skills Bivariate correlation, BCOR Forecast lead (days) Bivariate mean-squared error, BMSE BCOR = 0.5 (0.7), BMSE =.0 BoM 7 (15), 8 CMA 18 (1), 0 CR-ISAC 15 (11), 13 CRM 0 (14), 18 ECCC 17 (1), 19 ECMWF 36 (3), 40 HMCR 1 (6), 11 JMA 17 (1), 18 CEP 4 (1), 17 UKMO 5 (16), 31 MMM 1.1±7.0 (13.3±4.3), 1.5±8.9 Forecast lead (days) The MJO prediction skill of the SS models ranges from 1 to 36 days with a large intermodal spread. On average, MJO prediction skill is about 3 weeks.

6 AE PE Mean errors: AE and PE Amplitude error, AE Phase error, PE AE τ = 1 A M t, τ A O t PE τ = 1 φ M t, τ φ O t Forecast lead (days) Forecast lead (days) The SS models generally show weak MJO amplitude and slow propagation speed.

7 Mean-squared errors: AE and PE Mean-squared amplitude error, AE Mean-squared phase error, PE AE τ = 1 A M t, τ A O t PE τ = 1 φ M t, τ φ O t AE PE Forecast lead (days) Forecast lead (days) AE is saturated at about 3 weeks and PE increases continuously with forecast lead time. The phase error is more important than amplitude error for the improvement of MJO prediction skill.

8 Moisture mode view: Mean-moisture field MJO growth, decay and propagation are explained by those of anomalous moisture. Column-integrated moisture budget (intraseasonal) q t = u q x v q y ω q p P + E = u q x v q y + i) Mean-moisture field : Small gradient of mean moisture Weak moistening Weak MJO propagation (Kiranmayi and Maloney 011; Andersen and Kuang 01; Kim et al. 014; Adames and Wallace 015; Kim 017; Jiang 017)

9 Moisture mode view: Cloud-radiation feedback MJO growth, decay and propagation are explained by those of anomalous moisture. Column-integrated moisture budget (intraseasonal) q t = u q x v q y ω q p P + E ii) Cloud-radiation feedback : Small clouds and moisture Increase in longwave cooling Cooling is balanced by downward motion Weak MJO maintenance (Kiranmayi and Maloney 011; Andersen and Kuang 01; Kim et al. 015; Adames and Kim 016)

10 Moisture mode view MJO growth, decay and propagation are explained by those of anomalous moisture. Column-integrated moisture budget (intraseasonal) q t = u q x v q y ω q p P + E i) Mean-moisture field : Small gradient of mean moisture Weak moistening Weak MJO propagation ii) Cloud-radiation feedback : Small clouds and moisture Increase in longwave cooling Cooling is balanced by downward motion Weak MJO maintenance Hypothesis I: models with a more realistic low-level mean moisture pattern (hence gradient of it) would produce a better MJO Hypothesis II: models with a more realistic cloud-radiation feedback would produce a better MJO

11 Hypothesis 1: Mean-moisture field Observation (SSMI-TMI) BoM Model Observation (dry moist) CMA CR-ISAC ECCC ECMWF JMA Column-integrated CEP water vapor (CWV) exhibits a dry bias around the Maritime Continent. In subtropics, most models also show dry bias or wet bias.

12 Hypothesis 1: Mean-moisture field ECMWF (model obs.) 1 1 q x : q y : 1 1 q x vs. BCOR q y vs. BCOR Pattern correlation (5 일평균 smoothing, filtering) -week predictions of MJO phases -3 q x q y All models underestimate the horizontal gradient of background moisture. Model with a smaller bias in gradient of moisture has a higher MJO prediction skill.

13 Hypothesis : Cloud-radiation feedback Observation BoM Model Observation (weak strong) CMA CR-ISAC ECCC ECMWF JMA CEP Most models exhibit weak cloud-radiation feedbacks (CLW) over the Indo- Pacific warm pool.

14 Hypothesis : Cloud-radiation feedback ECMWF (model obs.) CLW vs. BCOR -week predictions of MJO phases -3 The MJO prediction skill can be linked to the CLW feedback biases.

15 Summary MJO prediction skill in the latest operational model is about 3 weeks. MJO amplitude is still weak and propagation speed is slow. MJO phase error is more important than amplitude error.

16 Summary Improvement of the mean-moisture field and cloud-radiation feedback biases can develop the MJO prediction skill. Even though the ECMWF shows the high performance, this will give an insight for why the ECMWF has a better prediction skill. Cloud-radiation feedback (CLW) bias vs. BCOR Meridional gradient of moisture (dcwv/dy) bias vs. BCOR q y

17 Reference Lim, Y., S.-W. Son, and D. Kim, 017: MJO prediction skill of the subseasonal-to seasonal (SS) prediction models, in revision.

18 Thank you