Work

Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method

Year: 2024

Type: article

Abstract: Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice concentration (SIC) for up to one year based on the season-reliant empirical orthogonal functions (SEOFs) technique. Its prediction skill was compared with that of a dynamical prediction model. The spatiotemporal pattern of sea ice anomalies, which exhibit strong seasonality and are maintained for a significant period above the seasonal time scale by atmosphere-ocean interactions, was extracted using SEOFs. A prediction model was constructed by extrapolating from the recent anomalous state of sea ice to predict the future. Experimental retrospective predictions with monthly time resolution for 1982–2021 were performed to validate the prediction skill of Arctic SIC and areal extent. Statistically significant prediction skills were achieved over several months, even up to six months, exceeding the skill of the dynamical model.

Source: Environmental Research Letters

DOI: 10.1088/1748-9326/ad7d1f

Authors: Gyu-Ri Lee, Sung-Ho Woo, Eun-Hyuk Baek, Joo-Hong Kim, Baek-Min Kim, Jee-Hoon Jeong

Institutions: Sejong University, Chonnam National University, Korea Polar Research Institute, Pukyong National University

Language: English

Topic: Arctic and Antarctic ice dynamics

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,820