Regional Climate Downscaling Intercomparison over the Philippines 

J.­H. Qian, A.W. Robertson, M. Tippett, L. Sun, S. Mason 

International Research Institute (IRI) for Climate and Society 

Acknowledgement: PAGASA, the Philippines

Analysis of r a i n f a l l f l u c t u a t i o n s in the P h i l i p p i n e s 237

Legend: 1st Type Two pronounced seasons;Dry from November to April;Wet during the r e s t of the year . 2nd Type No dry season with a very pronounced maximum rainfall from November to January.

3rd Type Seasons not very pronounced relatively dry from November to April,wet during the rest of the year .

4th Type Rainfall more or less evenly distributed throughout the year.

LEGASPI

VISAYAS

MINDANAO

DAVAO

0 -| 1

300 km

Figure 1 Climatological map (after "Philippines Water Resources", 1976). Vigan, Legaspi, Zamboanga and Davao are the stations selected for analysis.

occurring more or less alternately. These abnormal rainfall patterns may last for one or two years.

In the same figure, the arrowed peaks above the threshold identify the time of occurrences of abrupt changes in model parameters. As can be observed in this figure, these changes coincide with occurrences of abnormal rainfall patterns, indicating that the occurrence of an abnormal rainfall pattern may induce significant rainfall fluctuation. For each sequence, the abrupt changes in model parameters identify three rainfall periods with average duration of 11 years. The estimates of the parameters by AKF are shown to be

Analysis of r a i n f a l l f l u c t u a t i o n s in the P h i l i p p i n e s 239

MM 700-1

FIRST TYPE

VIGAN

j m i a j j n u n

SECOND TYPE

LEGASPI

MM 5 0 0 -

J U I I J J I S 1 K D

MM 200 -

THIRD TYPE

ZAMBOANGA

±£ J F F3 A H J J A S û H D

MM 2 0 0 -

FOURTH TYPE

DAVAO

J I H I I J J t S O l D

Figure 2 Mean monthly rainfall. First type, second type, third type and fourth type are the types of climates in the Philippines.

Table 2 Results of the Chow test for parameter change. (p is number of parameters, n and m are the numbers of observations in the two- periods.)

Station Pair of F-statistic periods (critical value)

Degrees of freedom p, n + m - 2p

Vigan

Legaspi

Zamboanga

Davao

I vs II II vs III

I vs II II vs III

I vs II II vs III

I vs II II vs III

2.202 (1.92) 1.110 (1.92)

2.218 (1.83) 0.605 (1.82)

1.487 (1.83) 0.456 (1.83)

0.823 (1.91) 1.266 (1.95)

9, 239 9, 260

11, 251 11, 293

11, 195 11, 283

9, 296 9, 125

amounts to an increase of 32.7% in mean rainfall corresponding to an increase of 854 mm (45.6%) in the annual rainfall during 1961-72 over that of the previous ten years (1951-60). In contrast, the shift from period II to period III, which is also initiated by a Type B abnormal pattern, is characterized by a decrease in My, A]̂ and B^and an increase in A2 and B2. Although the F-statistic finds these

Introduction 

•Regional climate downscaling intercomparison over the Philippines in April­Jun (AMJ) 

•77 PAGASA station daily precipitation data 

•ECHAM4 GCM vs a regional climate model RegCM3 ­ raw model outputs interpolate to stations and compared to PAGASA station data 

•Statistical vs dynamical downscaling – MOS, SST, GCM and RCM comparison 

•Precipitation intensity vs frequency – compare statistically and dynamically downscaled rainy days, dry days, wet and dry spells

Regional climate downscaling skills are seasonal dependent (usually high in the dry season, low in wet season)

The Philippines: RegCM3 skill over the Philippines is high in winter monsoon seasons and low in summer monsoon season

RPSS of 12 three­monthly seasons (based on 30­year ECHAM4­RegCM3 simulation from 1971­2000), RPSS are higher in boreal winter than in boreal summer

Regional climate downscaling skills are high in ENSO years than normal years, implying strong SST influence on interannual variability

The Philippines: RegCM3 downscaling results show wet anomaly in JAS & dry anomaly in OND in El Nino years

RPSS for all years RPSS for ENSO years (higher skill than the RPSS for all years

Compare dynamical (regional climate) downscaling skills with statistical downscaling from GCM and SST 

For comparison, GCM and RCM simulated precipitations on model grids are interpolated to observational grids (of GPCC) and meteorological stations (of the 77 PAGASA stations).

PAGASA avg pcp 

RCM avg pcp 

Correlation between PAGASA and RCM raw pcp 

Interpolate RCM simulated precipitation to PAGASA stations, then calculate correlation with PAGASA rain gauge observation (AMJ)

Correlation between PAGASA 

& ECHAM raw pcp Correlation between PAGASA 

& RCM raw pcp 

Interpolate RCM and GCM simulated precipitation to the PAGASA stations, then calculate correlation with the PAGASA rain gauge observation (AMJ). The correlation with PAGASA station data are similar for the RCM and GCM.

PAGASA 

RCM 

Rain intensity (avg over rainy days) 

Corr of rain intensity between PAGASA and RCM 

The correlation of precipitation intensity is low

Correlation of wet day frequency between PAGASA and RCM 

Corr of freq of >medium rain >5mm/day 

Corr of freq of heavy rain >10mm/day 

Corr of freq >1mm/day 

The correlation of precipitation frequency is very high for wet day (>1 mm/day) frequency, and less high for medium and heavy rain frequencies

Correlation of dry day (<1 mm/day) frequency between PAGASA and RegCM 

Correlation of dry spell (>5 days) between PAGASA and RegCM 

The correlation of dry day frequency between RCM and station observation is high (but is low for frequency of long dry spells (> 5 days)

Anomaly correlation of AMJ GPCC precipitation with SST MOS­ corrected and raw model data 

MOS improves skill 

GCM and RCM raw data have similar skills. 

intensity

Anom corr of AMJ PAGASA stn pcp with SST MOS­ corrected and raw model data 

MOS improves skill (GCM MOS skills are higher than RCM MOS skills, because more information (global) is used in GCM 

GCM and RCM raw data have similar skill 

intensity

Anom corr of AMJ PAGASA stn pcp number of days with rainfall > 1 mm with statistically predicted values. 

Missing values at stations with fewer than 20% missing are imputed using regularized expectation maximization. 

Frequency has higher skill than intensity! 

frequency

Anom corr of AMJ PAGASA stn pcp number of days with rainfall > 5 mm with statistically predicted values. 

Missing values at stations with fewer than 20% missing are imputed using regularized expectation maximization. 

Skills of Precipitation frequency are higher than those of precipitation intensity! 

RCM has higher skill in northern Luzon. 

frequency

Anom corr of AMJ PAGASA stn pcp number of days with rainfall > 10 mm with statistically predicted values. 

Missing values at stations with fewer than 20% missing are imputed using regularized expectation maximization. 

Skills of Precipitation frequency are higher than those of precipitation intensity! 

RCM has higher skill in northern Luzon. 

frequency

Conclusion 

•Statistical and dynamical downscaling methods are compared over the Philippines, focused in the AMJ season. 

•Downscaling skill is high in boreal winter (dry) season and low in boreal summer (wet) season 

•Downscaling skill is higher in ENSO years than in normal years, implying importance of SST forcing 

•In terms of precipitation intensity, statistical downscaling by using SST and GCM data are as high as, if not higher than, the RCM dynamical downscaling in AMJ, probably because of more data (global) are used in the GCM downscaling than RCM in a limited domain 

•As for precipitation frequency (rainy or dry days), statistical and dynamical downscaling has similar skill. RCM has higher skill over some local regions such as northern Luzon.