The Soil Moisture–Surface Flux Relationship as a Factor for Extreme Heat Predictability in Subseasonal to Seasonal Forecasts
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The Soil Moisture–Surface Flux Relationship as a Factor for Extreme Heat Predictability in Subseasonal to Seasonal Forecasts

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  • Journal Title:
    Journal of Climate
  • Personal Author:
  • Description:
    Thresholds of soil moisture exist below which the atmosphere becomes hypersensitive to land surface drying, inducing thermal feedbacks that can exacerbate heatwaves. Realistic representation of threshold transitions in forecast models could improve extreme heat predictability and understanding of the role of land–atmosphere coupling. This study evaluates the performance of several forecast models from the Subseasonal Experiment (SubX) and several prototype versions of the Unified Forecast System (UFS) in their representation of threshold transitions by validation against reanalysis data. A metric of skill (true skill score) is applied to soil moisture breakpoint values, which mark the transition to heatwave hypersensitivity for drying soils. Forecast models have poor skill at being initialized on the correct side of the breakpoint, but show improvement when normalized to account for deficiencies in their soil moisture climatologies. Regionally, models performed best in the U.S. Northwest and worst in the Southwest. They effectively capture the tendency of western regions to spend more summer days in the hypersensitive regime than the eastern United States. Models represent well extreme heat as a consequence of atmospheric initial state for the first week of the forecast, but struggle to represent the soil moisture feedback regime. Forecast models generally perform better at extreme heat prediction when they are already dry and in the hypersensitive regime, even when erroneously so, implying that errors or biases exist in model parameterizations. Nevertheless, composite analysis shows encouraging model performance of the “hit” category, suggesting that an improvement in soil moisture initialization could further improve extreme heat forecast skill.
  • Keywords:
  • Source:
    J. Climate, 36, 6375–6392
  • DOI:
  • Format:
    PDF
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA16OAR4310095
  • Rights Information:
    Other
  • Compliance:
    Submitted
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