contributed by Clara Deser and Adam Phillips (December, 2020)

A major challenge for climate researchers is to construct comprehensive numerical models of the Earth’s climate system that include realistic representations of both internally-generated and externally-driven sources of climate variability and change. Evaluation of such models is of key importance, but presents its own set of challenges due to the brevity of the observational record as well as its sparse and inhomogeneous data coverage. Best practices for evaluating models’ simulated characteristics of climate variability are discussed in the User’s Guide to the Climate Variability Diagnostics Package for Large Ensembles (Phillips et al., 2020).  A key concept in such evaluation is to contextualize the observational record within the ensemble spread from so-called “initial-condition Large Ensembles” (Deser et al. 2020). Such contextualization is crucial in order to avoid confounding model structural uncertainty with uncertainty due to limited temporal sampling, and is thus essential for properly assessing model fidelity and model inter-comparisons.

References

Deser, C., F. Lehner, K. B. Rodgers, T. Ault, T. L. Delworth, P. N. DiNezio, A. Fiore, C. Frankignoul, J. C. Fyfe, D. E. Horton, J. E. Kay, R. Knutti, N. S. Lovenduski, J. Marotzke, K. A. McKinnon, S. Minobe, J. Randerson, J. A. Screen, I. R. Simpson and M. Ting, 2020: Insights from earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change, doi: 10.1038/s41558-020-0731-2.

Phillips, A. S., Deser, C., Fasullo, J., Schneider, D. P., & Simpson, I. R. (2020). Assessing Climate Variability and Change in Model Large Ensembles: A User's Guide to the "Climate Variability Diagnostics Package for Large Ensembles" Version 1.0. doi:10.5065/h7c7-f961