APN Report

1pt; text-align:center’>BUILDING SCIENTIFIC CAPACITY IN SEASONAL CLIMATE FORECASTING (SCF) FOR IMPROVED RISK MANAGEMENT DECISIONS IN A CHANGING CLIMATE (1st draft only)

 1. Introduction and background:

The use of Seasonal Climate Forecasting (SCF) in risk management decisions in the developing countries such Indonesia, which are most vulnerable to the impacts of climate variability and climate change, has not been widely applied yet.  The major limitations are: the limited national capacity for climate monitoring and forecasting; low levels of awareness among decision makers to the local and regional impact of climate variability (e.g. ENSO); and lack of effective policy responses to climate variability and climate change.  Provision of training to develop in-country scientific capacity and practical skills in the use of SCF in decision making is an important step for effective development of adaptive capacity for farmers and government agencies to manage seasonal variability enhance preparedness for possible climate change impacts, developing resilient climate systems and improvements in food and water security.

2. Participating agencies:

Indonesia is one of the participating countries (among Australia, Bangladesh and The Phillipines), and the agencies involved are from the Bureau of Meteorology (Meteorological Climatoloigical and Geophysical Agency) and Water Resources Department from Nusa Tenggara Barat Province.

3. Objectives:

The aims of the project include:

• Improved awareness of climate variability and climate change impacts amongst policy makers, researchers, government agencies and the farming community;
• to conduct a validation study (regional workshop) of different climate drivers that affect the climate of Asia Pacific region
• to build capacity and network in the areas of climate forecasting, climate variability and change.
• Innovative ways of communicating climate uncertainty and variability tailored to the different communication skills and needs of policy makers, government and non-government staff and small holder farmers; and
• An effective dissemination pathway and the packaging of seasonal forecast information into farmer friendly advisories through local capacity building.

A.      BRIEF REPORT ON CLIMATE PATTERN AND EVALUATION OF RAINFALL-SOI USING SCOPIC SOFTWARE

5.1. Brief climate situation in Indonesia / NTB Province is as follows:

In general, Indonesia is divided by three distinc climate regions (rainfall types): Monsoon, Semi-monsoon, and Anti Monsoon (see the map/attachment).

Province of Nusa Tenggara Barat, in general, are the driest part of Indo­nesia, but the climate varies with the location of east-west stretch between islands and the elevation within the same is­land. The wet season, generally November and March. The mean annual rainfall is also featured by the wide variation ranging from 700 to 2,500 mm between and within the islands. Throughout the islands, the average sunshine hours are 4 to 5 hr/day during the wet season and increase to 7 to 8 hr/day in the dry season, and the mean annual temperature range from 26⁰C at sea level to 15⁰C at mountains of 3,000 m in height.

The climate patterns peculiar to the province has been change as follows: (Graph attached)

Average rainfall from 1970’st until 2010 has changes:

Average Temperatures:

Temperatures has been increased 0,5 degree during the last decade (see tables bellows)

	YEAR
NORMAL TEMP’S 1971-2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	INCREASING TEMP’S2001-2010
	26.5	26.5	26.4	26.6	26.8	26.5	26.5	26.4	26.7	27.1
26.1	0.4	0.4	0.3	0.5	0.7	0.4	0.4	0.3	0.6	1.0	0.5

Maximum and minimum temperatures; humidity, and wind speed have also been changed significantly during the past 30 years records. In Bali-Nusra, one month delays in rainy season has been decreasing rice production by 7-18% (Naylor et al, 2006)

5.2. Evaluation And Analysis Of Seasonal Rainfall In Indonesia, using SCOPIC Software

Seasonal rainfall patterns in the three distinc regions in correlation with the SOI has been analysis using SCOPIC Softwares. The Meteorological stations from each region have been chossen randomly based on the longest data availabilities (see the map “Rainfall Distribution” bellows).

After been analysed using SCOPIC Software, the of Concurrent Relationship between SOI and Rainfall in Indonesia can be summarised that the SOI has good correlation in equator and southern equator.

B.      BRIEF REPORT OF THE LOCAL WORKSHOP IN MATARAM, NTB PROVINCE INDONESIA

Three days warkshop and trainning has been conducted (26-28 August 2011) at BMKG Office in Kediri. The aims of the training were:

–          To discuss about Seasonal Climate Forecasting and introduce SCOPIC’ software
–          To equip BMKGs with better understanding of global/regional/localised climate forecasting;
–          To equip BMKGs with better understanding  of several SCO applications in a number of user sectors;
–          To prepare BMKGs for Phase II pilot projects; and
–          To inform BMKGs on the Bureau’s ClimSoft project

 The Meeting and training ware very valuable and success (agenda and participants are listed in the separate attachments)