Abstract
After several decades of intensive research, steady improvements in understanding and modeling the climate system have led to the development of the first generation of operational health early warning systems in the era of climate services. These schemes are based on collaborations across scientific disciplines, bringing together real-time climate and health data collection, state-of-the-art seasonal climate predictions, epidemiological impact models based on historical data, and an understanding of end user and stakeholder needs. In this review, we discuss the challenges and opportunities of this complex, multidisciplinary collaboration, with a focus on the factors limiting seasonal forecasting as a source of predictability for climate impact models.
| Original language | English |
|---|---|
| Pages (from-to) | 8-20 |
| Number of pages | 13 |
| Journal | Annals of the New York Academy of Sciences |
| Volume | 1382 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2016 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Climate services
- Climate variability
- Health impacts
- Seasonal forecasting
- Statistical models
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