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 indexSST 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!1010

2 C = 45 APCsMean=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 0.8AVG 0.92 0.34

1957 0.92 -0.34T- 1958 0.93 -0.38

1997 0.99 -0.18AVG 0.95 -0.3

1952 0.58 0.071953 0.56 0.02

T+ 1959 0.52 01996 0.61 0.21AVG 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 -0.33AVG 0.44 -0.37

Predi ctabi l i tyT(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-%

How does the thermal mechanism affect the predictability?

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.