Examining Taiwan Weekly Rainfall Examining Taiwan Weekly Rainfall Examining Taiwan Weekly Rainfall Examining Taiwan Weekly Rainfall

in in in in the Phases of MJOsthe Phases of MJOsthe Phases of MJOsthe Phases of MJOs

Yun-Lan Chen

Central Weather Bureau, Taipei, Taiwan

Conference on East Asia and Western Pacific Meteorology and Climate

cum 25th Anniversary of Hong Kong Meteorological Society (HKMMS25)

RMM1 RMM2 Phase Amplitude

RMM index ( Wheeler and Hendon , 2004 ) :

An objectively-defined index to monitor and predict variations related to the MJO

RMM index applications : ( CHEN, 2004, CWB internal website)

Helping monitoring on Monsoon and Tropical cyclone activities

Understanding the possible influence on local weather/climate

Probabilities of abnormal weekly rainfall in Taiwan in the 8 phases of the RMM indexExcess rainfall : weekly rainfall > 70% historical ranking

RMM index amplitude > 1.0 Statistics base on 1979 ~ 2012

High prob.Low prob.

Are there different types in a same MJO phase ?

Phase 4 ( cases wet in Taiwan)Phase 4 ( cases dry in Taiwan)

Take FMA for example

Focus on Phase 4

OLR(Ano) & W850(Ano)

composite by RMM index

OLR Ano(+)OLR Ano(-)

High prob.Low prob.

Can we sift the information for a better local forecast application ?

Maybe a bottom up strategy would do…..

EX :

For a specific MJO phase,

1. Distinguish the key differences by Taiwan wet/dry cases

2. Categorize the phase by conditions favor local wetness

Distinguish the key differences by Taiwan wet/dry cases 1.

���� The difference over Philippines to M.C. is evident at lag days.

Lag 6~10 days

Lag 11~15 days

Lag 16~20 days

Lag 0

Cases DRY in TaiwanCases WET in Taiwan

Phase 4

Divided to two typesType A (positive aolr at lag days)

Type B ( Others )

Phase 4

P4 (B)

P4 (A)

Categorize the phase by conditions favor local wetness

( In this case, OLR over Phillippines to M.C. is significant positive at lag days. )2. Type A 14-day Trajectory

Type B 14-day Trajectory

Prob. of wet ness from type

A Phase 4 data samples.

The probabilities have

increased to over 40%,

some reach to 50%.

���� Not all MJO phase 4s follow by a earlier strong anticyclonic high over Phillippines to M.C. area.

For those do, Taiwan will have more chances to get wet than the other type, which also imply there

are different predictabilities for Taiwan rainfall in the two MJO types.

Lag 6~10 days

Lag 6~10 days

� RMM index is capable of capturing the tropical large scale eastward propagating waves, as well as

the northward. It has been well adapted in many operation centers as an important index for

monitoring and forecasting the sub-seasonal climate variations. The index has also been one of the

form that routinely output from the numerical forecast models. It will be with practical values to

check how local key weather elements change with the phase of MJO index.

� The statistical result of the probabilities of wetness in Taiwan during each MJO phase shows

difference in seasons. Islandwide excess rainfall in the winter and spring seasons tend to happen when

the tropical convections still sit on Indian ocean(before phase 4), while when moving to warm

seasons, in which the weathers in Taiwan are more dominated by SW monsoon and the northwestern

Pacific tropical convection systems, wet situations are relatively more seen after phase 5 .

� Even only the cases with RMM amplitude greater than 1 were selected in this study, the composite

maps and the statistics for the same specific phase may come from different type of MJOs. Some

differences , although still catch the large scale MJO phase major pattern, might bring opposite

changes to local weathers.

� This study take FMA season for example, using the bottom up strategy, dividing the MJOs to 2 group

according to the key features that distinguish wet or dry conditions in Taiwan. The result shows an

earlier strong anticyclonic system over Philippines to M.C. area is an important sign that tend to bring

Taiwan abundant rainfall later.

� Further examining the two groups of phase 4 cases, it reveals the type B have more strong La Nina

events. It might be the reason that the earlier strong anticyclone, which is usually seen before phase 4

, is suppressed by the more active western Pacific convections during La Nina years. This also

suggests the forecast information interpreted from MJOs should not be all taken as the same.

� Forecasters might need to distinguish the key air-sea features that affect the local weathers, and also

do researches to get its robustness or variations associated with the MJO cycle. We might need to try

not just relying on the MJO cycle prediction information, but should also monitoring carefully the

potential precursor to do a better judgment for the forecast.

Summary

氣象局預報中心陳雲蘭

e-mail : yunlan.chen@mfc.cwb.gov.tw

DJF (Phase 3)

MJ (phase 6)

JAS (phase 6)