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Earth System Reanalysis in Support of Climate Model Improvements

Journal Article · · Bulletin of the American Meteorological Society
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  1. University of Hamburg (Germany)
  2. NSF National Center for Atmospheric Research, Boulder, CO (United States)
  3. European Centre for Medium Range Weather Forecast, Reading (United Kingdom)
  4. Nansen Environmental and Remote Sensing Center (NERSC), Bergen (Norway)
  5. World Meteorological Organization (WMO), Geneva (Switzerland)
  6. California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL)
  7. Massachusetts Institute of Technology (MIT), Cambridge, MA (United States)
  8. Japan Meteorological Agency, Tokyo (Japan)
  9. University of Texas, Austin, TX (United States)
  10. Columbia University, New York, NY (United States)
  11. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  12. University of California, San Diego, CA (United States)
  13. Japan Meteorological Agency, Tsukuba (Japan)
  14. University of Toronto, ON (Canada)
  15. University of Tokyo (Japan)
  16. Sofar Ocean, San Francisco, CA (United States)
  17. National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)
  18. University of Saskatchewan, Saskatoon, SK (Canada)
  19. New York University (NYU), NY (United States)
+ Show Author Affiliations
Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
2566150
Report Number(s):
PNNL-SA--198975
Journal Information:
Bulletin of the American Meteorological Society, Journal Name: Bulletin of the American Meteorological Society Journal Issue: 8 Vol. 105; ISSN 0003-0007
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Data assimilation
Model errors
Model evaluation/performance
Model initialization
Numerical weather prediction/forecasting