SCF
Project Updates
Bridging the gap between seasonal climate forecasts (SCFs) and decisionmakers in agriculture
Vol. 1 No. 2 (December 2005)
Assessing rainfall variability in Philippine study sites: the Rainman application
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ecause of its geographical location, the Philippines is prone to extreme weather and climate events. Floods and droughts have, for instance, been common occurrences in the country especially in the recent past resulting in massive destruction of property, loss of life, diseases, and food shortages. Sectors of the economy, including agriculture and water resources, have likewise been severely affected by these weather/climate events. The Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) monitors weather and climate conditions from both local and global perspectives. It has a network of weather stations strategically located all over the country that monitor meteorological and weather elements. These parameters are then analyzed using various statistical techniques and procedures to come up with weather or climate forecasts. Provision of these forecasts and early warnings of potential crop failure due to drought, with a lead time of 3060 days before harvest, is important because it enables policy/decisionmakers to implement alternative courses of action to mitigate potential damages to the agricultural sector. Seasonal forecasting
is an attempt to provide information on the likely conditions of the weather several months in advance. The Climate Information, Monitoring and Prediction Center (CLIMPC), one of the sections of the Climatology and Agrometeorology Branch (CAB) of PAGASA, is responsible for the issuance and dissemination of seasonal climate forecasts and advisories. With the recent advancement in the understanding of the El NiĂąo southern oscillation (ENSO) phenomenon and climate prediction, seasonal to interannual prediction has made it possible to predict climate with improved accuracy and with lead times ranging from one season to over a year in advance. This improvement means that impending extreme climate events can be predicted with greater accuracy. Predictability of the climate from season-to-season and year-to-year arises from the interaction of the ocean and the atmosphere. The best-known example is the ENSO phenomenon. The combination of the slowly changing temperature of the oceans and their interactions with the atmosphere provides a degree of predictability for seasonal climate in many regions of the world. Based on global studies, ENSO and other sea surface temperature anomalies are known to in-
Contents 5 Making the most out of seasonal climate forecasts (SCFs) 7 Tale of two surveys: feedback to PAGASA’s Climate Information Products and Services
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fluence global climate, altering rainfall and other climate variables throughout much of the tropics and subtropics and in a few locations in mid-latitudes. Seasonal climate prediction is based on the expectation of the effects of these influences in the coming season. In this regard, climate forecasters normally ask two basic questions: (1) what will the sea surface temperature anomalies be in the coming season? and (2) how will they impact on global climate? There are models available which can evaluate the effects of ENSO on seasonal climatic patterns and on the variability of rainfall in the Philippines. One of these is Rainman. This brief writeup focuses on the use of this program in evaluating the effects of the ENSO phenomenon on seasonal climatic patterns and variability of rainfall in three selected study sites of the Philippines— Isabela in the island of Luzon; Baybay (Leyte) in the Visayas; and Malaybalay (Bukidnon) in Mindanao.
Statistical test results on forecasts of rainfall in Southeast Asia (Analysis of historical data–1903 to 1995–using SST Phase forecast in September for rainfall period: Oct to Dec, leadtime of 0 months)
The Rainman Program: providing an enhanced method of forecasting ENSO effects on rainfall Rainman is an integrated package about rainfall and streamflow information developed by the Queensland Department of Primary Industry, Australia in a previous ACIAR-funded project. A unique feature of Rainman is the seasonal rainfall analysis which may be done with monthly data and also daily data where they are available. Here, one can see what influence either the southern oscillation index (SOI) or the sea surface temperature (SST) may have on rainfall, using any length of season (1-12 months), up to the coming year. This prediction or forecast is helpful for those making management decisions in a highly variable climate. The initial results of the seasonal climate forecasts for 12 overlapping seasons (i.e., December-January-February; January-February-March; FebruaryMarch-April; and so on) at zero lead time (meaning that for forecasts for, say, February-March-April, the data used are those for January) in the three study sites earlier mentioned are presented here. The statistical skills of these forecasts were evaluated using the SST forecast phase system (the Pacific effects) of Rainman to indicate whether changes in rainfall pattern as predicted or forecasted are real or are due to chance. The Philippine study sites and the test applications The Philippine component of the ACIAR project on seasonal climate forecasts selected four sites for its case studies, namely: Isabela in the island of Luzon; Baybay (Leyte) and Cebu in the Visayas; and Malaybalay (Bukidnon) in Mindanao. For this particular study, however, certain considerations were taken into account and some changes/ substitutes were made.
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In particular, the significance of the test results is sensitive to the number of years of data; the more years (minimum is 30 years), the better. In this light, the absence of longer climate record for the stations in Baybay, Leyte and in Isabela influenced this study’s use instead of the climate data in nearby areas (Tacloban for Baybay and Tuguegarao in Cagayan Valley for Isabela) that have the same climate types as the original study sites. 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
Rainman is an integrated package about rainfall and streamflow information. A unique feature of Rainman is the seasonal rainfall analysis which may be done with monthly data and also daily data where they are available. Here, one can see what influence either the southern oscillation index (SOI) or the sea surface temperature (SST) may have on rainfall, using any length of season (1-12 months), up to the coming year. This prediction or forecast is helpful for those making management decisions in a highly variable climate.
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 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. 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 (hot-
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ter Pacific Ocean and cooler Pacific Ocean occurrences, respectively), the trends established in the chances of getting lesser (for the El Ni単o period) or more (for La Ni単a 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 The Philippine study sites
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. b RdG and JPTL
*Data substituted for Isabela
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Making the most out of seasonal climate forecasts (SCFs)
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o one can tell for sure what the next season will be like. Even when a climate in a particular place or region is generally predictable, there is a varying difference in the yearly duration, intensity and timing of rainy and dry periods. Thus, it is important to know and understand SCF and how it may benefit the population. Global changes in weather and climate are largely brought about by the cycle of atmospheric and pattern changes in the Pacific Ocean called the El Niño southern oscillation (ENSO). This usually occurs in December; hence, the term “El Niño” for the “Christ Child,” and usually has a cycle duration of four years. The ENSO is a complex process but basically it involves the unusual warming and cooling of the ocean’s surface sea temperature. The El Niño is the warm phase of the ENSO while La Niña is the cool phase. The changes in temperature that these phases bring affect weather and climate in many parts of the world, even those that are far from the Pacific Ocean. With advances in science and technology, people’s knowledge on seasonal climate changes such as ENSO has grown considerably. A seasonal climate forecast is an estimate of how rainfall or temperature in a coming season is likely to be different from the prevailing average climate. SCFs use dynamical (based on laws of physics) or statistical (based on historical patterns) methods to predict the climate. They usually forecast “above median” or “below median” rainfall. Seasonal climate forecasting is usually done three months to a year in advance or longer.
Why it is important to understand SCFs Weather and climate are significant forces in people’s lives. Important and not-so-important decisions are made depending on the weather or ensuing climate. Planning for social and economic benefits would be greatly enhanced by being able to forecast seasonal conditions in the months ahead. Conversely, it can mean human lives and incomes lost when changes in climate are not anticipated. Thus, knowing and understanding SCFs can save lives, lessen the costs and present opportunities to various sectors for better planning and decisionmaking. The agriculture sector would naturally be the major beneficiary of SCF. For the Philippines, it is one of the country’s top industries which accounted for about 18 percent of the total gross domestic product (GDP) and whose labor force reached 11.38 million in 2004. Since agriculture is vulnerable to climate variability, farmers may sometimes benefit from SCFs by being able to choose what crops to plant and when to plant them. While the risks may not
Weather and climate are significant forces in people’s lives. Important and not-so-important decisions are made depending on the weather or ensuing climate. Planning for social and economic benefits would be greatly enhanced by being able to forecast seasonal conditions in the months ahead. Conversely, it can mean human lives and incomes lost when changes in climate are not anticipated. Thus, knowing and understanding SCFs can save lives, lessen the costs and present opportunities to various sectors for better planning and decisionmaking.
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be completely eliminated, information from SCFs can lessen the costs that would have been incurred and may even enable farmers to make substantial yields and higher incomes. Other end users of SCFs include the energy sector—suppliers of electricity and natural gas which benefit from forecasts to help them plan energy usage and make operations run efficiently. The tourism industry is likewise a logical beneficiary as travel agents and event organizers are able to put together vacation packages and schedule occasions at appropriate times. Retailers and other businesses can also benefit from valuable climate forecasts as they will be able to time their procurement of stocks that may be in demand once the weather changes. National and local governments can strengthen their civil defense programs by being able to stock up on supplies and train for emergency disaster operations and drought relief activities. Limitations of SCFs: bridging the gap Certainly, SCF is still an imperfect science even with the advancement of technology and research. The accuracy of the forecasts is the primary concern which may fluctuate over a period of time and with successive forecasts. It is not known what percentage of farmers in the Philippines rely on SCFs in their decisionmaking. It is said that the use of SCFs in the country and in Australia is “hampered by the lack of robust means of showing the economic value of SCF-specific decisions.” Some of the major concerns regarding SCFs are their accuracy and timeliness, the difficulties encountered in applying them to farm management decisions and the apparent lack of evidence of their economic value to reduce the risks associated with their adoption. In view of this, the application of SCFs in
decisionmaking has been more difficult than initially thought. Ground level: reaching out to end users for SCF information To ensure that the SCFs are rendered useful to their beneficiaries, it is important that they reach them in a timely fashion and that they contain the needed information for the decisionmakers. Thus, information like when the rains will come, how frequently they will occur and how much rainfall is to be expected must be delivered in the clearest, simplest and most accurate manner. This may be achieved by conducting frequent information blitzes to the farmers on the basics of weather, climate and seasonal forecasts, issuing frequent weather and climate analyses in popular mass media, and making information readily available and accessible to the farmers and other end users. A study on the usage of SCFs in Zimbabwe found that farmers complained of receiving climate forecasts after they have made planting decisions. They also did not understand nor trusted the forecasts. Thus, any seasonal climate forecast communications system that will be developed by any country should involve the active participation of farmers and other stakeholders. In so doing, SCFs would have greater relevance, credibility and legitimacy. b BFG Sources SCF Project Updates Vol. 1, June 2005 “Valuing seasonal climate forecasts” by Dr. John Mullen www.nscb.gov.ph www.ksg.harvard.edu/sed/docs/k4dev/ lemos_k4dev_031002.pp www.census.gov.ph/data http://iri.columbia.edu/outreach/meeting/ MediaWS2001/Glossary.html http://www.bas.gov.ph/agri_dev.php
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Tale of two surveys: feedback to PAGASA’s Climate Information Products and Services
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n June 30 and December 1, 2005, seminar-workshops on “Toward bridging the gap between seasonal climate forecasts and decisionmakers in agriculture” were held in Baybay, Leyte and Malaybalay, Bukidnon, respectively. These seminars were part of the dissemination program of the four-year project with the above title sponsored by the Australian Centre for International Agricultural Research (ACIAR) and were jointly conducted by the Philippine project implementing institutions, namely, the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA), the Philippine Institute for Development Studies (PIDS) and the Leyte State University (LSU). The purpose of these seminar-workshops was to introduce the project to various local government units, members of academe and farmer groups in terms of its objectives, plan of activities, expected outputs and possible utility in the decisionmaking and risk management of stakeholders/decisionmakers in agriculture. Some basic concepts relating to the project like the El Niño southern oscillation phenomenon, tropical cyclones, climate outlook and local forecasts, and other useful meteorological terms and information were also explained. Participants in these two seminars were from LGUs (mostly municipal agriculturists), the academe and a few groups representing farmers. To help PAGASA in its goal of improving its service delivery, especially in terms of its climate information products and services, to the agriculture sector and other related stakeholders, the
participants were asked to answer a survey questionnaire during the seminars. The questionnaire had two parts. The first referred to the participants’ profile which identified the respondents’ designations and sector/category representation. The second referred to the participants’ feedback which had 11 questions on what the respondents thought about PAGASA’s products and services. Aside from directly offering information to PAGASA, the responses to the questionnaire may also provide some insights to the project team members on how decisionmakers in agriculture source and make use of information regarding climate, including seasonal forecasts.
Members of the Philippine project team interview a municipal agriculturist in Bukidnon on her area’s need and use of climate forecasts in making farm decisions.
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SCF Project Updates is a semestral newsletter of the project on Bridging the gap between seasonal climate forecasts and decisionmakers in agriculture. It is published with financial support from the Australian Centre for International Agricultural Research (ACIAR). Apart from this semestral newsletter and papers released occasionally, information and updates about the project, its research and research-related activities may be accessed through the project website at http:// dirp3.pids.gov.ph/ACIAR. For inquiries, please call or email the SCF Project Updates editorial staff: 1. Dr. John Mullen Tel: 02 6391 3608 Fax: 02 6391 3650 Email: john.mullen@dpi.nsw.gov.au 2. Ms. Jennifer P.T. Liguton Tel: 632 893 5705 Fax: 632 893 9589 Email: jliguton@pidsnet.pids.gov.ph
Editorial Staff John Mullen/Jennifer P.T. Liguton Editors-in-Chief Genna J. Estrabon, Managing Editor Christian D. Mina, Barbara F. Gualvez, Rosalinda de Guzman, Ernesto R. Verceles, Contributors Jane C. Alcantara, Design/Layout
Findings Majority of the respondents were municipal agriculturists and members of the academe with a few members of farmers’ groups. All of them considered weather/climate as a factor in planning and decisionmaking in their work/source of livelihood, with the majority claiming it is a critical factor. Radio/tv were cited as the sources of information about weather/climate used by the majority of the respondents, with PAGASA stations coming in second and the rest a split among local practices/beliefs, broadsheets/tabloids, advisories from head offices and associates and extension workers. In terms of awareness of PAGASA’s products and services, majority of the respondents in Leyte were aware while less than half of the respondents in Bukidnon were. Those who were aware and went on to rate these products and services gave generally good assessments. Suggestions given by the respondents on how to improve PAGASA’s products and services were basically the same as gleaned from the two surveys. Essentially, what the respondents want is for PAGASA to have a stronger presence in their municipalities and establish a stronger linkage with them. There was also a clamor for publications that are easier to understand, preferably in the vernacular, and more information and education campaign (IEC) activities, trainings and seminars from PAGASA. Establishment of agromet and weather stations in their local government units (LGUs) was also shared by many of the respondents as well as the improvement of PAGASA’s facilities. Perhaps owing to the difference in the sector they belong to, the members of the academe in Leyte have access to the internet and thus wanted weather/climate data available online.
The respondents in Leyte recommended a closer link between PAGASA and LGUs in order that the LGUs themselves could request the kind of information that are more suited to their constituents and localities. They also cited the need for more site-specific data that the local weather stations could regularly disseminate to the community. Those in Bukidnon, on the other hand, basically wanted to have agromet stations and rain collector systems facilities in addition to more related publications and trainings from PAGASA. Implications and recommendations It is clear that PAGASA needs to do more to reach the people who use its products and services to make decisions that affect their work, especially since these people are in the rural areas and far from information-rich metropolises. In order to achieve this, the weather bureau needs to have more partners in nongovernment organizations (NGOs), LGUs, the academic research community and individual experts who can help disseminate and explain weather/climate information. The more vigorous partnership with these groups and individuals would help establish better communication among the stakeholders and help make the receivers of information inform PAGASA of the data they need in their localities. More effort must also be made in making the information more understandable and more accessible to the clients. This would indeed be challenging since scientific data are difficult to translate to local dialects and so it is necessary to have more seminars and lectures by PAGASA particularly in the regions. Lastly, the mass media should be tapped not just to report weather forecasts that are usually steeped in weather jargon but also to explain basic concepts in order to reach more people. b BFG