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Experimental High-Resolution Winter Seasonal Climate Reforecasts for Florida

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  • 1 a Center for Ocean–Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida
  • | 2 b Florida Climate Institute, Florida State University, Tallahassee, Florida
  • | 3 c Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida
  • | 4 d Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida
  • | 5 e Planning and Systems Decision Support, Tampa Bay Water, Clearwater, Florida
  • | 6 f Patel College of Global Sustainability, University of South Florida, Tampa, Florida
  • | 7 g South Florida Water Management District, Broward County, Florida
  • | 8 h Manatee County Utilities, Bradenton, Florida
  • | 9 i St. Johns River Water Management District, Jacksonville, Florida
  • | 10 j Department of Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida
  • | 11 k Peace River Manasota Regional Water Supply Authority, Lakewood Ranch, Florida
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Abstract

We present here the analysis of 20 years of high-resolution experimental winter seasonal climate reforecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10-km grid spacing from a global model run at T62 spectral resolution (~210-km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage the public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida. CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November–December–January (NDJ) and one-month lead for December–January–February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Bhardwaj’s additional affiliation: India Meteorological Department, Ministry of Earth Sciences, New Delhi, India.

Corresponding author: Amit Bhardwaj, abhardwaj@fsu.edu

Abstract

We present here the analysis of 20 years of high-resolution experimental winter seasonal climate reforecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10-km grid spacing from a global model run at T62 spectral resolution (~210-km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage the public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida. CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November–December–January (NDJ) and one-month lead for December–January–February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Bhardwaj’s additional affiliation: India Meteorological Department, Ministry of Earth Sciences, New Delhi, India.

Corresponding author: Amit Bhardwaj, abhardwaj@fsu.edu

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