climpred.stats.dpp¶

climpred.stats.dpp(ds, dim='time', m=10, chunk=True)[source]¶

Calculates the Diagnostic Potential Predictability (dpp)

$DPP_{\mathrm{unbiased}}(m) = \frac{\sigma^{2}_{m} - \frac{1}{m}\cdot\sigma^{2}}{\sigma^{2}}$

Note

Resplandy et al. 2015 and Seferian et al. 2018 calculate unbiased DPP in a slightly different way: chunk=False.

Parameters:	ds (xr.DataArray) – control simulation with time dimension as years. dim (str) – dimension to apply DPP on. Default: time. m (optional int) – separation time scale in years between predictable low-freq component and high-freq noise. chunk (optional boolean) – Whether chunking is applied. Default: True. If False, then uses Resplandy 2015 / Seferian 2018 method.
Returns:	ds without time dimension.
Return type:	dpp (xr.DataArray)

References

Boer, G. J. “Long Time-Scale Potential Predictability in an Ensemble of Coupled Climate Models.” Climate Dynamics 23, no. 1 (August 1, 2004): 29–44. https://doi.org/10/csjjbh.
Resplandy, L., R. Séférian, and L. Bopp. “Natural Variability of CO2 and O2 Fluxes: What Can We Learn from Centuries-Long Climate Models Simulations?” Journal of Geophysical Research: Oceans 120, no. 1 (January 2015): 384–404. https://doi.org/10/f63c3h.
Séférian, Roland, Sarah Berthet, and Matthieu Chevallier. “Assessing the Decadal Predictability of Land and Ocean Carbon Uptake.” Geophysical Research Letters, March 15, 2018. https://doi.org/10/gdb424.