Impact of land-atmospheric coupling in CFSv2 on drought prediction

Joshua K. Roundy, Craig R. Ferguson, Eric F. Wood

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


Recent summers in the United States have been plagued by intense droughts that have caused significant damage to crops and have had a large impact on society. The ability to forecasts such events would allow for preparations that could help reduce the impact on society. Coupled land-atmosphere-ocean models were created to provide such forecasts but there are large uncertainties associated with their predictions. The predictive skill of these models is particularly low during the convective season due to the weaker connections with the oceans and an increase in the land-atmosphere interactions. To better understand the degradation of forecasts skill during the summer months and its connection to the land-atmosphere interactions we analyze National Centers for Environmental Prediction's Climate Forecast System Version 2 (CFSv2) in terms of its climatological land-atmosphere interactions. To do this we use a recently developed classification of land-atmosphere interactions and other diagnostic variables to compare the reanalysis from the Climate Forecast System (CFSR) with CFSv2 re-forecasts (CFSRR) over the period 1982-2009. Coupling in the CFSRR tends toward the wet coupling regime for most areas east of the Rocky Mountains. Although the specific mechanism driving CFSRR to wet coupling state varies by region, the overall cause is enhanced vegetation rooting depth, originally implemented to address a near-surface warm bias in CFSR. The long-term tendency to wet coupling precludes the forecast model from consistently predicting and maintaining drought over the continental US.

Original languageEnglish (US)
Pages (from-to)421-434
Number of pages14
JournalClimate Dynamics
Issue number1-2
StatePublished - Jun 2014

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


  • CFS
  • Drought
  • Land-atmosphere interactions
  • Seasonal forecasts


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