Geophysical Research Letters

Multimodel seasonal forecasting of global drought onset

Authors

  • Xing Yuan,

    Corresponding author
    1. Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
    • Corresponding author: X. Yuan, Department of Civil and Environmental Engineering, Princeton University, E318 Engineering Quadrangle, Princeton, NJ 08544, USA. (xingy@princeton.edu)

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  • Eric F. Wood

    1. Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
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Abstract

[1] The capability of seasonal forecasting of global drought onset at local scales (1°) has been investigated using multiple climate models with 110 realizations. Climate models increase the global mean probability of drought onset detection from the climatology forecast by 31%–81%, but only increase equitable threat score by 21%–50% due to a high false alarm ratio. The multimodel ensemble increases the drought detectability over some tropical areas where individual models have better performance, but cannot help more over most extratropical regions. On average, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with low potential predictability and weak antecedent El Niño–Southern Oscillation signal. Given the high false alarms, the reliability is very important for a skillful probabilistic drought onset forecast. This raises the question of whether seasonal forecasting of global drought onset is essentially a stochastic forecasting problem.

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