Abstract
Climate forecasts have shown the potential for improving the resilience of agriculture to climate change. The usefulness of climate forecasts for applications in agriculture can be enhanced if the forecasts are translated into agricultural outlooks, in which the information is targeted for decision-making. The sequence of dry periods is necessary for successful crop management decisions, especially in dry season planting. This paper investigates how well the Climate Forecast System version 2 (CFSv2) seasonal forecasts predict the dry spell (DS) over the Indonesia region. The seasonal forecasts were downscaled using the constructed analogue method and, in turn, were corrected with TRMM 3B42 rainfall data to match daily precipitation totals. The DS is defined as rainfall less than 5 mm day−1 for ≥10 consecutive days. Accuracy of the DS prediction was assessed using the Brier Score (BS) method for December-January-February (DJF) and June-July-August (JJA) periods. The results demonstrate that the highest accuracy of the DS forecast occurred in JJA in southern part Indonesia with a range of the BS value between 0-0.2 (>80%). The operational DS seasonal forecasts are needed to manage agriculture practices for the upcoming planting season such as the choice of a crop/variety, supplementary irrigation, and crop water requirement.
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