Abstract

A modified approach to the assessment of the prediction skill of “modes of variability” is proposed and applied to a decadal prediction experiment. In particular, the skill of predicting the Pacific Decadal Oscillation (PDO) is investigated. The approach depends on separately calculating the EOFs of the observations, the ensemble of forecasts, and an ensemble of simulations made with the same model and external forcing. The skill of predicting and simulating the spatial structure of the modes is captured by comparing forecast and simulated EOFs with the observation-based EOFs. This is in contrast to the case where forecasts and simulations are expanded in observation-based EOFs, or other structure functions, which gives no direct information about the model-based EOF structures themselves. The skill of predicting the temporal evolution of EOFs is separately captured by comparing the associated expansion functions. Finally, the contribution of the modes to the overall prediction skill is obtained by weighting the spatial and temporal skills with the variances involved. The behaviour of the first mode, identified as the PDO, is given particular attention. Perhaps not unexpectedly, the EOF structure of the forecasts more closely resembles that of the simulations than that of the observations, but both reproduce the structure of the observed PDO quite well with spatial correlations near 0.8. The temporal correlation of the expansion functions is near 0.7 for year 1 forecasts and declines toward zero subsequently. The overall correlation skill for the North Pacific is dominated by the PDO with a small contribution from the second mode and none from the third mode.

Highlights

  • Many climate studies investigate “modes” of variability which are typically identified in terms of statistical constructs

  • While the Pacific Decadal Oscillation (PDO) is defined for the North Pacific, it is viewed more widely by means of the PDO “index” which is the principal component (PC) associated with the leading empirical orthogonal functions (EOFs) pattern

  • It is the dominant contribution to annual mean sea surface temperature (SST) variance, accounting for some 40% of the total based on the ERSSTv5 data set

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Summary

Introduction

Many climate studies investigate “modes” of variability which are typically identified in terms of statistical constructs. The spatial structures and temporal evolution of the modes of variability of the observations, the ensemble of predictions, and an ensemble of simulations of sea surface temperature (SST) are separately calculated, compared, and assessed with the first mode identified as the PDO. This approach avoids the expansion of all three data sources in terms of. Sospedra‐Alfonso a single set of spatial basis functions and thereby retains their own “natural” modes of variability so to say In this approach, the skill of predicting North Pacific SSTs has contributions depending on the agreement between the spatial structures and temporal evolutions of the modes and their contribution to the overall SST variability

The PDO
Predicting the PDO
Data and statistics
Approach
Forecast‐based EOFs
EOFs and expansion functions
Modal statistics and skill measures
Variances and covariances
Skill measures
EOF pattern skill
Temporal skill
X 2 YA
Correlation skill of the PDO
Modal contributions to North Pacific correlation skill
Findings
Summary and conclusions
Full Text
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