1. Taiwan summer climate variability in the CWB GFS ensemble simulation Jau-Ming Chen 、 Ching-Feng Shih 、 Jyh-Wen Hwu Central Weather Bureau, Taiwan

2. By analyzing a 10-member ensemble climate (1950-2000) simulation with the CWB GFS, we attempt to investigate the summer (JJA) climate variability in Taiwan region simulated by the GFS, emphasizing on simulation accuracy and physical processes responsible for systematic error; predictability and its associated determining mechanism.

3. horizontal resolution: GFS(T42, L18) ~2.8 0  2.5 0 grid Grid distribution Simulated climate Mean value of the 4 grids in the red box is used to represent the simulated Taiwan climate. Values from the 16 grids in the green box are used to compute the anomaly pattern correlation (APC) which is employed to estimate the predictability.

4. Taipei Hsinchu Taichung Tainan Kaohsiung Hengchun Ilan Hualien Chengkung Taitung The averaged value of the 10 major stations is used to represent the observed Taiwan climate. Observation

5. T(JJA) P(JJA) GFS OBS GFS-OBS Correlation: T: 0.81 P: -0.23 Climate change: mean OBS GFS 1950-1977 27.8 28.1 1979-2000 28.2 28.4 ΔT +0.4 +0.3 P-T correlation: OBS: -0.56 (P-T out of phase) GFS: 0.45 (P-T in phase) Simulation result

6. Why can the GFS have good skills in the simulation of Taiwan summer T variability, but no skill in the simulation of rainfall variability?

7. Correlation maps: T(OBS) as the index SST T(GFS) P(GFS) X850(GFS) S850(GFS) anomalous summer warming in Taiwan corresponds to : anomalous warm SST  (GFS) convergence  Rossby wave  warm and moist south wind  anomalous warm and wet GFS summer climate mechanism in the simulation T(OBS) and the surrounding SST anomalies are highly correlated.

8. Correlation maps: P(OBS) as the index SSTT(GFS) X850(GFS) P(GFS) S850(GFS) anomalous wet Taiwan summer climate corresponds to : anomalous cold SST  (GFS) divergence  Rossby wave  cold and dry north wind  anomalous cold and dry GFS summer climate Based upon correlation analysis, SST anomalies in the vicinity of Taiwan are the major mechanism affecting the simulation of Taiwan summer climate variability in the GFS ensemble experiment.

9. Systematic error in the Taiwan climate simulation and associated physical processes : In the GFS simulation, thermal forcing regulates Taiwan rainfall variability, leading to an in-phase P-T relationship (warm-wet; cold-dry).  ocean-type climate In observation, Taiwan T variability is affected by the surrounding SSTA and rainfall processes, resulting in a mainly out-of-phase P-T relationship (dry-warm; wet-cold).  island-type climate  GFS can simulate Taiwan summer T variability pretty well, but the mechanism is not quite right.  model simulates an ocean-type climate in Taiwan region  GFS(T42) portrays Taiwan region as an ocean domain, instead of a land domain.  The simulated T variability thus follows closely with SST variability in the surrounding oceans to obtain a highly accurate T simulation.

10. Correction: 1.to increase the horizontal resolution of the GFS to a level higher enough for the GFS to detect the existence of the Taiwan island. 2. modify physical scheme?

11. Predictability of Taiwan summer T variability 20-30% = 45 APCs Mean=0.75 S.D. = 0.16

12. Cases in different APC types

13. What mechanism is more important? Thermal or dynamic ? Total heating850 mb circulation APC+ APC- HT S850 APC+ 50+% 40+% APC- 30-% 40-%

14. How does the thermal mechanism affect the predictability?

15. Composite analysis: comparison of APC anomalies (APC+, T+)(APC-, T+) SST Surf. T(GFS) HT↓(GFS) The APC + years are associated with stronger SST anomalies and GFS thermal variability, compared with the APC- years. T+ anomaly  Warm SSTA  net upward heating

16. (APC+, T+)(APC-, T+) LH SW↓ P stronger SST anomalies  stronger heating anomalies  stronger rainfall variability  higher predictability (APC) net upward surface heating  decrease of SW↓, increase of LH  increase of P highly resembling patterns  mechanism directly driven by thermal forcing

17. area-mean values in the green box.

18. Summary: In the GFS simulation, strength of the climate anomalies is of importance in determining the predictability of Taiwan summer T variability. Stronger SST anomaly in the vicinity of Taiwan  higher predictability for Taiwan summer climate variability in the GFS simulation. Based upon the accuracy and predictability analyses, we find that SST variability in the oceans surround Taiwan is an importance mechanism to affect Taiwan summer climate variability in the GFS simulation.