Prediction Terminology

Terminology is often confusing and highly variable amongst those that make predictions in the geoscience community. Here we define some common terms in climate prediction and how we use them in climpred.

Simulation Design

Initialized Ensemble

Perfect Model Experiment: m ensemble members are initialized from a control simulation at n randomly chosen initialization dates and integrated for l lead years [Griffies1997] (PerfectModelEnsemble).

Hindcast Ensemble: m ensemble members are initialized from a reference simulation (generally a reconstruction from reanalysis) at n initialization dates and integrated for l lead years [Boer2016] (HindcastEnsemble).

Uninitialized Ensemble

In this framework, an uninitialized ensemble is one that is generated by perturbing initial conditions only at one point in the historical run. These are generated via micro (round-off error perturbations) or macro (starting from completely different restart files) methods. Uninitialized ensembles are used to approximate the magnitude of internal climate variability and to confidently extract the forced response (ensemble mean) in the climate system.

In climpred, we use uninitialized ensembles as a baseline for how important (reoccurring) initializations are for lending predictability to the system. Some modeling centers (such as NCAR) provide a dynamical uninitialized ensemble (the CESM Large Ensemble) along with their initialized prediction system (the CESM Decadal Prediction Large Ensemble). If this isn’t available, one can approximate the unintiailized response by bootstrapping a control simulation.

Reconstruction:

Reconstruction/Assimilation: A “reconstruction” is a model solution that uses observations in some capacity to approximate historical conditions. This could be done via a forced simulation, such as an OMIP run that uses a dynamical ocean/sea ice core with reanalysis forcing from atmospheric winds. This could also be a fully data assimilative model, which assimilates observations into the model solution.

Predictability vs. Prediction skill

(Potential) Predictability: This characterizes the “ability to be predicted” rather than the current “ability to predict.” One acquires this by computing a metric (like the anomaly correlation coefficient (ACC)) between the prediction ensemble and a verification member (in a perfect-model setup) or the reconstruction that initialized it (in a hindcast setup) [Meehl2013].

(Prediction) Skill: This characterizes the current ability of the ensemble forecasting system to predict the real world. This is derived by computing the ACC between the prediction ensemble and observations of the real world [Meehl2013].

Forecasting

Hindcast: Retrospective forecasts of the past initialized from a reconstruction integrated under external forcing [Boer2016].

Prediction: Forecasts initialized from a reconstruction integrated into the future with external forcing [Boer2016].

Projection An estimate of the future climate that is dependent on the externally forced climate response, such as anthropogenic greenhouse gases, aerosols, and volcanic eruptions [Meehl2013].

References

[Griffies1997]

Griffies, S. M., and K. Bryan. “A Predictability Study of Simulated North Atlantic Multidecadal Variability.” Climate Dynamics 13, no. 7–8 (August 1, 1997): 459–87. https://doi.org/10/ch4kc4

[Boer2016]

(1, 2, 3) Boer, G. J., Smith, D. M., Cassou, C., Doblas-Reyes, F., Danabasoglu, G., Kirtman, B., Kushnir, Y., Kimoto, M., Meehl, G. A., Msadek, R., Mueller, W. A., Taylor, K. E., Zwiers, F., Rixen, M., Ruprich-Robert, Y., and Eade, R.: The Decadal Climate Prediction Project (DCPP) contribution to CMIP6, Geosci. Model Dev., 9, 3751-3777, https://doi.org/10.5194/gmd-9-3751-2016, 2016.

[Meehl2013]

(1, 2, 3) Meehl, G. A., Goddard, L., Boer, G., Burgman, R., Branstator, G., Cassou, C., … & Karspeck, A. (2014). Decadal climate prediction: an update from the trenches. Bulletin of the American Meteorological Society, 95(2), 243-267. https://doi.org/10.1175/BAMS-D-12-00241.1.