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The Critical Need for Hindcast Infrastructure in Climate Science and Sectoral Applications

  • Weston Anderson
  • , Marybeth C. Arcodia
  • , Dillon Amaya
  • , Emily Becker
  • , John A. Callahan
  • , Jason C. Furtado
  • , Benjamin Kirtman
  • , Sanjiv Kumar
  • , Michelle L. L’heureux
  • , Sarah M. Larson
  • , Dan Li
  • , Maria J. Molina
  • , Matthew Newman
  • , Kathleen Pegion
  • , Andrew Robertson
  • , Erin Towler
  • , Baoqiang Xiang
  • University of Maryland, College Park
  • Colorado State University
  • National Oceanic and Atmospheric Administration
  • University of Miami
  • Inc
  • University of Oklahoma
  • Auburn University
  • North Carolina State University
  • Boston University
  • Columbia University
  • University Corporation For Atmospheric Res

Research output: Contribution to journalArticlepeer-review

Abstract

Forecasting the impacts of climate extremes is challenging but critical to a range of sectors, including agriculture, water management, public health and safety, infrastructure, energy, national defense, and ecology. Foundational to these forecasts are hindcast model archives, which are routinely used for applications produced for the private sector and agencies, including the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Department of State, and the U.S. Department of Defense. Forecast and hindcast archives underpin scientific inquiry funded by these agencies, particularly in relation to forecasting weather and climate extremes, as well as by the U.S. National Science Foundation (NSF). In this article, we catalog the sector-specific decision support systems that depend upon hindcast archives and survey the current state of hindcast archive infrastructure. We find that despite the tremendous amount of investment and the dependent decision support systems, the U.S. hindcast archive is relatively fragile and underfunded, especially when compared with the Copernicus system in Europe. We conclude with recommendations for improving hind-cast archive infrastructure to support routine sector-specific applications and improve resilience to climate extremes.

Original languageEnglish
Pages (from-to)E615-E627
JournalBulletin of the American Meteorological Society
Volume107
Issue number3
DOIs
StatePublished - Mar 2026
Externally publishedYes

Keywords

  • Decision making
  • Decision support
  • Forecasting
  • Hindcasts
  • Policy

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