Skillful prediction of winter Arctic Oscillation from previous summer in a linear empirical model

Hong-Li Ren,Yu Nie

doi:10.1007/s11430-020-9665-3

Abstract

The winter Arctic Oscillation (WAO), as a primary atmospheric variability mode in the Northern Hemisphere, plays a key role in influencing mid-high-latitude climate variations. However, current dynamical seasonal forecasting systems have limited skills in predicting WAO with lead time longer than two months. In this study, we design a linear empirical model using two effective precursors from anomalies of the Arctic sea ice concentration (SIC) and the tropical sea surface temperature (SST) initiated in preceding late summer (August) which are both significantly correlated with WAO in recent four decades. This model can provide a skillful prediction of WAO at about half-year lead started from previous summer and perform much better than the dynamical models. Such a significantly prolonged lead time is owed to the stable precursor signals extracted from the SIC and SST anomalies over specific areas, which can persist from previous August and be further enhanced through autumn months. Validation results show that this model can produce a 20-year independent-validated prediction skill of 0.45 for 1999–2018 and a 39-year cross-validated skill of 0.67 for 1980–2018, providing a potentially effective tool for earlier predictions of winter climate variations at mid-high latitudes.

Highlights

The winter Arctic Oscillation has been well known as a primary atmospheric variability mode in the Northern Hemisphere (NH, Thompson and Wallace, 1998, 2000; Wallace, 2000) and plays a key role in affecting weather events and climate variations at mid-high latitudes (e.g., Thompson and Wallace, 2001; Zuo et al, 2015 and their review)
The winter Arctic Oscillation (WAO) prediction is at the heart of the mid-high-latitude climate prediction but usually loses its skills quickly in the dynamical seasonal forecasting systems with lead time longer than two months
We show that the summer Arctic sea ice concentration (SIC) anomalies contribute primarily to the predictability of WAO through sea ice-atmosphere interactions

Summary

Introduction

The winter Arctic Oscillation (denoted as WAO) has been well known as a primary atmospheric variability mode in the Northern Hemisphere (NH, Thompson and Wallace, 1998, 2000; Wallace, 2000) and plays a key role in affecting weather events and climate variations at mid-high latitudes (e.g., Thompson and Wallace, 2001; Zuo et al, 2015 and their review). Previous studies showed statistically significant skill for WAO prediction based on atmospheric and coupled climate models, with a useful skill (superior to persistence forecast skill or with temporal correlation coefficient exceeding specific thresholds) at a lead up to 2 months (Derome et al, 2005; Riddle et al, 2013; Kang et al, 2014; Sun and Ahn, 2015; Kim and Ahn, 2015; Zuo et al, 2016) and slightly longer lead time by multi-model ensemble (MME) mean (L’Heureux et al, 2017). The model predicted WAO index is obtained by projecting the predicted monthly SLP anomalies onto the observed WAO patterns and normalized by the ensemble mean standard deviation for each model. This ensures the index represents the canonical AO. Both the take-one-year-out cross-validation and the 20-year forwardrolling independent validation (Ren et al, 2017) are used to validate the newly constructed LEM

Data and methods

Validations of the empirical prediction model

Findings

Summary and discussions