# Presentation: 916236

## Seasonal predictability experiment by JMA AGCM

***Summary***
- ・To estimate seasonal predictability, the seasonal simulation experiment has been carried out.
- ・We revalidate that the predictabilities over tropics mark high level (***Fig.1***). The skill scores over tropics are also high (***Figs.2***).
- ・The predictabilities and skill scores over Pacific are slightly better than those over Atlantic (***Figs.1 and 3***).
- ・The high predictability area decreases with increasing lead-time (***Figs.4***). It indicates that the first season predictability owes largely to the signal of initial condition.
- ・The signal of initial conditions over the stratosphere lasts longer than that of the troposphere(***Figs.5***).

***Seasonal predictability experiment by JMA AGCM***
- Chiaki KOBAYASHI, Kiyoharu TAKANO, Tosiyuki NAKAEGAWA
- (Meteorological Research Institute, Japan Meteorological Agency)

***1.Introduction***

***6. Time dependence of Variance-ratio distribution (R)***

***Fig.4 ****Variance-ratio distribution of 500hPa height (90 day mean)**.** a) DJF(day 1-90), b) JFM(day 31-120), c) FMA(day 61-150), d) MAM(day 91-180). Contour intervals are 0.1.*

***7. Signal of initial condition***

***Fig.5 ****Variance-ratio distribution of 500hPa height (90day mean)**.** a1) DJF(day1-90), a2) DJF(day91-180), a3)(a1)minus(a2), b1) DJF’(day16-105), b2) DJF’(day106-195), b3)(b1)minus(b2), c1) Variance-ratio distribution of 30hPa height (90day mean)**.** DJF’(day16-105), c2) DJF’(day106-195), c3)(c1)minus(c2). Contour intervals are 0.1.*

***Fig.1 ****Variance-ratio distribution of 500hPa height day 31-120 (90 day mean)**.** a) AMJ, b) JAS, c) OND, d) JFM. Contour intervals are 0.1.*

***4.Variance-ratio distribution (R)***

***5. Skill score (Anomaly Correlation)***

***Fig.3 ****Variance-ratio distribution and anomaly correlation (AC) pattern of 500hPa height inter annual variability (day 31-120, 90 day mean, JFM)** over Northern Hemisphere. **a) Variance-ratio, b) ACC. Contour intervals are 0.1.*

***Fig.2 ****Skill score (ACC) of each case for day 31-120 (90 day mean). a) 500hPa height **over Northern Hemisphere**, b) 500hPa height **over tropics. c) precipitation over tropics**. d) velocity potential at 200hPa over tropics. *

**The high predictability area decreases with increasing lead-time. **It indicates that the first season predictability owes largely to the signal of initial conditions, which decreases with increasing  lead-time.

**  ****The signals in the tropics are stronger than in extra-tropics. **
**  ****The predictabilities over the Pacific are slightly better than those over the Atlantic.**

**  ****The skill scores over tropics show higher levels. **This model has a basic ability as a seasonal forecast model.

- The predictability distribution is roughly consistent with the anomaly correlation pattern. There is an area that the skill score shows low level although predictability is high over the eastern North Pacific.

**  **To estimate the influence of initial values on the predictability, we compared the first season predictability with the second season predictability started from previous season.
-   We consider that the differences between (***Fig5 a1***) and (***Fig5 a2***) shows the influence of initial value on the predictability.
-   If we take a 15-day longer lead-time, the influence of initial values become very small in the troposphere (***Fig5 b3***).
-  The influence of initial value of the stratosphere is larger than that of the troposphere. If we take lead-time 15day, the signals still remain.(***Fig5 c3***).
-   **The signal of initial conditions over stratosphere lasts longer than that of the troposphere.**

-    Japan Meteorological Agency (JMA) plans to start dynamical seasonal forecast with the use of T63L40 JMA AGCM from March 2003. To estimate the potential predictability of this AGCM, the seasonal predictability experiment forced by observed sea surface temperature (SST) has been carried out, although the operational integrations will use the predicted SST, through the integration period.*** ***
***  ***With this experiment, we will participate in SMIP2 (Seasonal Prediction Model intercomparison project 2).

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***e-mail: ckobayas@mri-jma.go.jp***