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Initialized Earth system prediction from subseasonal to decadal timescales

Journal Article · · Nature Reviews Earth and Environment
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  1. National Center for Atmospheric Research
  2. University Corporation for Atmospheric Research
  3. BATTELLE (PACIFIC NW LAB)
  4. University of Colorado at Boulder
  5. Georgia Institute of Technology
  6. Barcelona Supercomputing Center and ICREA, Barcelona, Spain
  7. University of New South Wales
  8. Canadian Centre for Climate Modelling & Analysis
  9. Stony Brook University, NY
  10. University of Miami
  11. Columbia University
  12. Pennsylvania State University
  13. (Canadian Centre for Climate Modelling an Analysis
  14. Universidad Politecnica de Valencia
  15. George Mason Universitiy
  16. NCAR
  17. University of Washington
  18. University of Exeter
  19. Scripps Institution of Oceanography
  20. University of Arizona
  21. Woods Hole Oceanographic Institution
  22. University of California, San Diego
+ Show Author Affiliations
Initialized Earth system predictions are made by starting a numerical prediction model in a state as consistent as possible to observations, and running it forward in time for up to ten years. Skillful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, from agriculture to water resource management, and human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skillful predictions can be made: at S2S timescales, there has been some skill in predicting the Madden-Julian Oscillation and North Atlantic Oscillation; at S2I in predicting the El Niño-Southern Oscillation; and at S2D, in predicting variability in North Atlantic sea surface temperatures. However, challenges remain, and future work must prioritise reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could increase predictive skill and, in turn, confidence. As numerical models progress towards Earth system models, initialized predictions are expanding to include prediction of sea-ice, air pollution, terrestrial and ocean biochemistry which can bring clear benefit to society and various stakeholders.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-76RL01830
OSTI ID:
1817682
Report Number(s):
PNNL-SA-160319
Journal Information:
Nature Reviews Earth and Environment, Journal Name: Nature Reviews Earth and Environment Journal Issue: 5 Vol. 2
Country of Publication:
United States
Language:
English

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