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As the results in this initial study suggest, more specific
climate information provided in advance of a particular
planting or harvest season will be of great help to those who
make specific decisions in the agriculture sector...What is
important is to be able to determine which forecasting system
will be able to yield better results depending on various
variables like season, location, time of year, lead time, and
the status of ENSO.
decreases from 40 to 20 percent from September to March.
• The seasonal forecast skill in Malaybalay and
Tacloban is statistically significant starting the month of
October up to March.
• Meanwhile, like in Tacloban, the percent chance of
exceeding the median rainfall in Tuguegarao is increased
from 60–80 percent during a cooler Pacific Ocean while
the chance of getting this level is reduced during a hotter
Pacific Ocean.
For the Malaybalay site, meanwhile, since the
available climate record is about 79 years, the same site
was used. On the other hand, no evaluation was done as
yet for the Cebu site.
As mentioned, the SST phase system using the Pacific
Ocean effects was the one applied in evaluating the
impact on the study sites. In particular, the following main
effects of the Pacific Ocean were tested: (1) cooler Pacific
Ocean pattern where phases 1, 4, and 7 (which are
associated with wetter than normal rainfall condition in
the Philippines) were combined; (2) neutral Pacific Ocean
pattern where phases 2, 5, and 8 (wherein neutral
conditions indicate that there is an equal chance of
getting above normal or below normal rainfall in the
Philippines) were combined; and (3) hotter Pacific Ocean
pattern where phases 3, 6, and 9 (which are associated
with drier than normal rainfall condition in the Philippines)
were combined.
Results of analysis
The following are the key results of the analysis/
evaluation:
• An analysis of the historical data (from 1919–2004)
in Malaybalay found that there is a 70 percent chance or
probability of having the rains exceed the median rainfall
during a cooler Pacific Ocean from September to February
while there is a lower chance—at 20 to 30 percent—of
getting a median rainfall during a hotter Pacific Ocean
from September to March.
• For the study site in Tacloban, analysis of historical
data showed that for a constant lead time (0 month)
before a three-month rainfall period, there is a 60–80
percent chance of exceeding the median rainfall starting
the month of November up to March during a cooler
Pacific Ocean. During Phases 3, 6, 9 of the hotter Pacific
Ocean pattern, the chance of getting above median rainfall
What do the above results mean
Simply told, the impact of ENSO on the Philippines varies
with season and location. Generally, the forecast skill is
higher for October to March.
With regard to the status of the ENSO, the results
indicate that during the onset of El Niño and La Niña
(hotter Pacific Ocean and cooler Pacific Ocean
occurrences, respectively), the trends established in the
chances of getting lesser (for the El Niño period) or more
(for La Niña period) amounts of rain than the median
rainfall are more distinct.
Unfortunately, however, there are also neutral
conditions when there is an equal chance of getting
above or below normal rainfall in the country. During this
period, the forecast skill is not statistically significant and
decisionmakers need to use the long-term climate
record.
Conclusion
As the results in this initial study suggest, more specific
climate information provided in advance of a particular
planting or harvest season will be of great help to those
who make specific decisions in the agriculture sector.
For this study, focus was on the use of the SST phase
system as an ENSO indicator at zero lead time. There are,
however, other features in Rainman that can look, for
instance, at the seasonal forecast skill using various lead
times like, say, 30–60 days before a harvest season.
In this regard, Rainman will be used and tested in
the coming months to provide better answers to the
specific needs of the users.
What is important is to be able to determine which
forecasting system will be able to yield better results
depending on various variables like season, location, time
of year, lead time, and the status of ENSO. (SCF Project
Updates, December 2005)