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Initialized Earth System prediction from subseasonal to decadal timescales

Journal Article · · Nature Reviews Earth & Environment
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  1. National Center for Atmospheric Research, Boulder, CO (United States); National Center for Atmospheric Research
  2. National Center for Atmospheric Research, Boulder, CO (United States)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  4. Univ. of Colorado, Boulder, CO (United States); NOAA Physical Sciences Laboratory, Boulder, CO (United States)
  5. Georgia Inst. of Technology, Atlanta, GA (United States)
  6. Barcelona Supercomputing Center-Centro Nacional de Supercomputación (Spain); Inst. Catalana de Recerca i Estudis Avançats, Barcelona (Spain)
  7. Barcelona Supercomputing Center-Centro Nacional de Supercomputación (Spain)
  8. Univ. of New South Wales, Sydney, NSW (Australia)
  9. Environment and Climate Change Canada, Victoria, BC (Canada)
  10. Univ. of Colorado, Boulder, CO (United States)
  11. Stony Brook Univ., NY (United States)
  12. Univ. of Miami, FL (United States)
  13. Columbia Univ., Palisades, NY (United States)
  14. Pennsylvania State Univ., State College, PA (United States)
  15. Univ. of Valencia (Spain)
  16. George Mason Univ., Fairfax, VA (United States)
  17. Univ. of Washington, Seattle, WA (United States)
  18. Hadley Centre, Exeter (United Kingdom); Univ. of Exeter (United Kingdom)
  19. Hadley Centre, Exeter (United Kingdom)
  20. Colorado State Univ., Ft. Collins, CO (United States)
  21. Univ. of Arizona, Tucson, AZ (United States)
  22. Woods Hole Oceanographic Inst., MA (United States); ARC Centre of Excellence for Climate Extremes, Sydney, NSW (Australia)
  23. Scripps Inst. of Oceanography, La Jolla, CA (United States)
+ 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 10 years. Skilful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, ranging from agriculture to water resource management to 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 skilful predictions can be made: on S2S timescales, there has been some skill in predicting the Madden–Julian Oscillation and North Atlantic Oscillation; on S2I, in predicting the El Niño–Southern Oscillation; and on S2D, in predicting ocean and atmosphere variability in the North Atlantic region. However, challenges remain, and future work must prioritize reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could enhance predictive skill and, in turn, confidence. Here, as numerical models progress towards Earth System models, initialized predictions are expanding to include prediction of sea ice, air pollution, and terrestrial and ocean biochemistry that can bring clear benefit to society and various stakeholders.
Research Organization:
Univ. Corporation for Atmospheric Research, Boulder, CO (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER); Australian Research Council
Grant/Contract Number:
89243018SSC000007
OSTI ID:
1785982
Alternate ID(s):
OSTI ID: 1817682
Journal Information:
Nature Reviews Earth & Environment, Journal Name: Nature Reviews Earth & Environment Journal Issue: 5 Vol. 2; ISSN 2662-138X
Publisher:
Springer NatureCopyright Statement
Country of Publication:
United States
Language:
English

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