Predictability, Predictions, and Applications Interface Panel

The Predictability, Predictions, and Applications Interface (PPAI) Panel's mission is to foster improved practices in the provision, validation and uses of climate information and forecasts through coordinated participation within the US and international climate science and applications communities.


Predictability, Predictions, and Applications Interface (PPAI) Panel
Member name Institution Term through
Gregg Garfin, Co-chair University of Arizona/School of Natural Resources & the Environment Dec. 2015
Kathy Pegion, Co-chair George Mason University Dec. 2016
Bruce Anderson Boston University Dec. 2015
Christopher Castro University of Arizona Dec. 2018
Enrique Curchitser Rutgers University Dec. 2017
Hyemi Kim Stony Brook University/School of Marine & Atmospheric Sciences Dec. 2015
Xin-Zhong Liang University of Maryland Dec. 2017
Andrea Ray NOAA Earth System Research Laboratory Dec. 2015
Shih-Yu (Simon) Wang Utah State University Dec. 2018
Muyin Wang University of Washington Dec. 2018
Scott Weaver Environmental Defense Fund Dec. 2017


Terms of Reference

  • Review, prioritize, and coordinate US research plans to understand predictability of the oceans and climate on sub-seasonal, seasonal-to-interannual, decade-to-century and longer time scales.
  • Advise US CLIVAR on research priorities, gaps, and milestones to advance ocean and climate predictions and projections through improved evaluation, and better quantification and communication of skill and uncertainty.
  • Advocate for new funding opportunities and national and international activities to advance in prediction and predictability research, understand user needs, and develop decision support capabilities.
  • Coordinate US CLIVAR efforts to communicate with operational and decision-making communities on improved understanding of ocean and climate phenomena and predictability and on implementation of this understanding in their activities.
  • Liaise with other US CLIVAR panels and Working Groups to ensure predictability, prediction, and applications are part of their efforts.