SMYLE initialization

The Seasonal-to-Multiyear Large Ensemble (SMYLE) seeks to use a CESM B Compset on the ~1.0° f09_g17 finite-volume grid in order to make multi-year predictions.

It’s important to note that the peer-reviewed papers used to justify the SMYLE effort use anomaly correlations to support the notion that extented forecasts have meaningful predictive value: Luo et al. (2008), 1 Dunstone et al. (2016), 2 DiNezio et al. (2017), 3 Lovenduski et al. (2019), 4 Dunstone et al. (2020), 5 and Esit et al. (2021). 6

Anomaly correlations are distinct from the skill scores used historically in studies of prediction skill. The skill scores that have been used by the numerical prediction community exhibit two features:

  1. they make an explicit prediction

  2. they estimate an error in that prediction.

For example, the \(S_1\) score compares the error in the forecasted 500 hPa pressure surface against the magnitude of the horizontal pressure gradient (Teweles and Wobus, 1954 7 ). The height of the 500 hPa pressure is an explicit prediction of the future state of the atmosphere and the horizontal gradient “normalizes” in a sense, the magnitude of the error, since the error should be larger in areas where gradients are large.

Skill scores of this type are meaningful and useful – the National Centers for Environmental Prediction have tracked the operational \(S_1\) score throughout the history of the center since it effectively tracks the improvement predictive skill through several scientific generations.

Before devoting considerable time to the SMYLE effort, note that:

  1. anomaly correlations aren’t predictions,

  2. strong, spatially coherent correlations on interannual timescales can be observed even when the “signal” that is being correlated is synthetic noise (Livezey and Chen, 1983 8 ), and

  3. anomaly correlations aren’t predictions (this bears repeating).

References

1

Luo et al., 2008: Extended ENSO Predictions Using a Fully Coupled Ocean–Atmosphere Model, J Clim, 21(1), 84–93, https://doi.org/10.1175/2007JCLI1412.1.

2

Dunstone et al., 2016: Skilful predictions of the winter North Atlantic Oscillation one year ahead, Nat Geosci, 9, 809–814, https://doi.org/10.1038/NGEO2824.

3

DiNezio et al., 2017: A 2 Year Forecast for a 60–80% Chance of La Niña in 2017–2018, GRL, 44(22) 11,624-11,635, https://doi.org/10.1002/2017GL074904.

4

Lovenduski et al., 2019: Predicting near-term variability in ocean carbon uptake, Earth Syst Dynam, 10, 45–57, https://doi.org/10.5194/esd-10-45-2019.

5

Dunstone et al., 2020: Skilful interannual climate prediction from two large initialized model ensembles, ERL, 15(9), https://doi.org/10.1088/1748-9326/ab9f7d.

6

Esit, M., S. Kumar, A. Pandey, D. M. Lawrence, I. Rangwala, and S. Yeager, 2021: Seasonal to multi-year soil moisture drought forecasting. npj Clim Atmos Sci, 4, 1–8, https://doi.org/10.1038/s41612-021-00172-z.

7

Teweles, S., and H. B. Wobus, 1954: Verification of Prognostic Charts. Bul Am Meteor Soc, 35, 455–463, https://doi.org/10.1175/1520-0477-35.10.455.

8

Livezey, R. E., and W. Y. Chen, 1983: Statistical Field Significance and its Determination by Monte Carlo Techniques. Mon Wea Rev, 111, 46–59, doi:10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2.