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Taiwan summer climate variability in the CWB GFS ensemble simulation Jau-Ming Chen 、 Ching-Feng Shih 、 Jyh-Wen Hwu Central Weather Bureau, Taiwan. - PowerPoint PPT Presentation
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Taiwan summer climate variability in
the CWB GFS ensemble simulation
Jau-Ming Chen、 Ching-Feng Shih、 Jyh-Wen HwuCentral Weather Bureau, Taiwan
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.
horizontal resolution: GFS(T42, L18) ~2.80 2.50 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.
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
T(JJA)
P(JJA)
GFS
OBS
GFS-OBS Correlation:T: 0.81P: -0.23
Climate change: mean OBS GFS1950-1977 27.8 28.11979-2000 28.2 28.4ΔT +0.4 +0.3
P-T correlation:OBS: -0.56(P-T out of phase)
GFS: 0.45(P-T in phase)
Simulation result
Why can the GFS have good skills in the simulation of Taiwan summer T variability,
but no skill in the simulation of rainfall variability?
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.
Correlation maps: P(OBS) as the index
SST T(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.
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.
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?
Predictability of Taiwan summer T variability
20-30%
2/1 22/1 2
bjaj
bjaj
AA
AAAPC
!2!8
!10102 C = 45 APCs
Mean=0.75S.D. = 0.16
Type
1954 0.91 0. 211970 0.89 0.271983 0.89 0.34
T+ 1989 0.97 0.21993 0.96 0.33
APC+ 1994 0.88 0.231998 0.94 0.8
AVG 0.92 0.34
1957 0.92 -0.34
T- 1958 0.93 -0.381997 0.99 -0.18
AVG 0.95 -0.3
1952 0.58 0.071953 0.56 0.02
T+ 1959 0.52 01996 0.61 0.21
AVG 0.57 0.07
APC- 1950 0.29 -0.341965 0.2 -0.4
T- 1974 0.58 -0.211976 0.52 -0.581982 0.59 -0.33
AVG 0.44 -0.37
Predi ctabi l i ty
T(GFS)
anomal i es Year APC
(T-,APC-)
T(C)
(T+,APC+)
(T-,APC+)
(T+,APC-)
Cases in different APC types
What mechanism is more important? Thermal or dynamic ?
Total heating 850 mb circulationAPC+
APC-
HT S850APC+ 50+% 40+%APC- 30-% 40-%
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
(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 Phighly resembling patterns mechanism directly driven by thermal forcing
anomaly predictability SST HT↓ SW↓ LW↓ LH SH PAPC+ 0.32 -21.4 -12.7 -2.9 6.1 -0.2 1.8APC- 0.07 -8.8 -8.5 -1.4 -0.4 -0.7 0.8
APC+ -0.28 27.4 16.8 2.6 -7.9 0 -1.8APC- -0.24 16.9 11.7 3 -2.8 0.5 -1.4
T+
T-
area-mean values in the green box.
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.