Seasonal forecasting of fire over Kalimantan, Indonesia
- 1Department Environment, Earth & Ecosystems, Open University, Milton Keynes, UK
- 2Department Applied Physics & Applied Mathematics, Columbia University, New York, USA
- 3NASA Goddard Institute for Space Studies, New York, USA
- 4European Centre for Medium-Range Weather Forecasts, Reading, UK
- 5School of Geographical Sciences, University of Bristol, Bristol, UK
- 6College of Hydrology and Water Resources, Hohai University, Nanjing, China
- 7Forest Resources & Climate Unit, Institute for Environment & Sustainability, EC-Joint Research Centre, Ispra, Italy
- 8Remote Sensing Solutions GmbH, Munich, Germany
- 9Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
- 10Department Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany
- 11Department of Geography, King's College London, London, UK
- 12College of Engineering, Mathematics & Physical Sciences, Exeter University, Exeter, UK
- 13GeoBio Center, Ludwigs-Maximilians-University, Munich, Germany
Abstract. Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities.
In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean–atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire).
The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.