Applying subseasonal-to-seasonal predictions to improve disaster risk reduction in South-East Asia

Applying subseasonal-to-seasonal predictions to improve disaster risk reduction in South-East Asia

Date: 
Monday, August 26, 2019
Type: 
Public information and advocacy materials
Abstract

Strategies for managing disaster risk currently rely on weather forecasts (daily to 10 days) and seasonal predictions (three to six months). Until recently, the subseasonal scale (defined as timescale from two weeks to two months) has been considered a "predictability desert" because it has not been possible to provide accurate predictions for this timescale. As a result, many preparedness activities are held off until short-range weather forecasts indicate that a hazard is imminent and the exact location is known, so that resources are not wasted in the case of a false alarm. By then, it is often too late to prepare once a rapid-onset disaster materializes, or a slow-onset disaster intensifies.

However recent scientific research shows that it might now be possible to produce subseasonal-to-seasonal (S2S) predictions of heavy rainfall events that could result in flooding, the development of tropical cyclones, heatwaves and coldwaves, as well as the sudden intensification or reprieve of slow-onset drought, and the waxing and waning of monsoon precipitation. This could bridge the gap between weather forecasts and seasonal predictions, by combining a higher precision than seasonal predictions with a longer lead time than weather forecasts.

This has transformative potential for Disaster Risk Reduction (DRR). S2S predictions can help shift from reactive to pro-active disaster risk management, by providing more time for preparedness activities, such as activating institutional processes, raising public awareness, preparing evacuation routes, or moving people out of harm’s way. S2S predictions can also support key innovative approaches to DRR that are currently being pioneered, such as forecast based financing and adaptive social protection.

While S2S products are still largely experimental at this stage, South-East Asia is one of the regions that is most likely to benefit from these advances in the generation of S2S predictions. It has some of the highest skill at the subseasonal timescale, due to sources of predictability such as the El Niño–Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO).

The United Nations, Economic and Social Commission for Asia and the Pacific (ESCAP), the Association of Southeast Asian Nations (ASEAN), Specialized Meteorological Centre (ASMC), and the Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES) are joining forces to ensure that South-East Asian countries can capitalize on the potential applications of S2S products in conjunction with forecasts for the more established weather and seasonal timescales.

This primer has been informed by such collaboration. It outlines the types of S2S products that are available for South-East Asia, and their potential applications across a range of preparedness activities. Recent case studies in South-East Asian countries are used to demonstrate how S2S predictions could have been applied to reduce the impacts of the disasters. The primer then offers ways forward for leveraging the opportunities presented by S2S predictions. End users working in National Disaster Management Organisations (NDMOs) are advised to collaborate with National Hydrometeorological Services (NHMSs) to ensure that their information needs are met based on the best available science, and to combine S2S predictions with existing seasonal predictions and weather forecasts within a comprehensive decision-making framework. This will allow S2S predictions to support innovative approaches to disaster risk reduction such as forecast-based financing and adaptive social protection, thereby facilitating coherence between development and disaster risk reduction. Finally, the primer outlines the ongoing efforts that are underway to prepare sectors to use S2S predictions to improve risk management.

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