{"title":"Can seasonal prediction models capture the Arctic mid-latitude teleconnection on monthly time scales?","authors":"Gaeun Kim, Woo-Seop Lee, Baek-Min Kim","doi":"10.1002/asl.1235","DOIUrl":null,"url":null,"abstract":"<p>This study explores Arctic warming's effect on Eurasia's temperature variability, notably the warm Arctic–cold Eurasia (WACE) pattern, and assesses seasonal prediction models' accuracy in capturing this phenomenon and its monthly variation. Arctic warming events are categorized into deep Arctic warming (DAW), shallow Arctic warming (SAW), warming aloft (WA), and no Arctic warming (NOAW), based on the temperatures at 2 m and 500 hPa in the Barents–Kara Sea. It is revealed that DAW events are significantly correlated with monthly cold temperature anomalies in East Asia, predominantly occurring in January–February, excluding December. This study evaluates two primary capabilities of seasonal prediction models: their proficiency in forecasting these Arctic warming events, particularly DAW, and their ability to replicate the spatial patterns associated with DAW. Some models demonstrated notable predictive skill for DAW events, with enhanced performance in January and February. Regarding spatial pattern reproduction, models showed limited alignment with the reference dataset over the Northern Hemisphere (above 25° N) in December, whereas a higher degree of concordance was observed in January–February. This indicates their capability in capturing the atmospheric circulation patterns associated with DAW, pointing to areas where model performance can be enhanced.</p>","PeriodicalId":50734,"journal":{"name":"Atmospheric Science Letters","volume":"25 8","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asl.1235","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Science Letters","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asl.1235","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This study explores Arctic warming's effect on Eurasia's temperature variability, notably the warm Arctic–cold Eurasia (WACE) pattern, and assesses seasonal prediction models' accuracy in capturing this phenomenon and its monthly variation. Arctic warming events are categorized into deep Arctic warming (DAW), shallow Arctic warming (SAW), warming aloft (WA), and no Arctic warming (NOAW), based on the temperatures at 2 m and 500 hPa in the Barents–Kara Sea. It is revealed that DAW events are significantly correlated with monthly cold temperature anomalies in East Asia, predominantly occurring in January–February, excluding December. This study evaluates two primary capabilities of seasonal prediction models: their proficiency in forecasting these Arctic warming events, particularly DAW, and their ability to replicate the spatial patterns associated with DAW. Some models demonstrated notable predictive skill for DAW events, with enhanced performance in January and February. Regarding spatial pattern reproduction, models showed limited alignment with the reference dataset over the Northern Hemisphere (above 25° N) in December, whereas a higher degree of concordance was observed in January–February. This indicates their capability in capturing the atmospheric circulation patterns associated with DAW, pointing to areas where model performance can be enhanced.
期刊介绍:
Atmospheric Science Letters (ASL) is a wholly Open Access electronic journal. Its aim is to provide a fully peer reviewed publication route for new shorter contributions in the field of atmospheric and closely related sciences. Through its ability to publish shorter contributions more rapidly than conventional journals, ASL offers a framework that promotes new understanding and creates scientific debate - providing a platform for discussing scientific issues and techniques.
We encourage the presentation of multi-disciplinary work and contributions that utilise ideas and techniques from parallel areas. We particularly welcome contributions that maximise the visualisation capabilities offered by a purely on-line journal. ASL welcomes papers in the fields of: Dynamical meteorology; Ocean-atmosphere systems; Climate change, variability and impacts; New or improved observations from instrumentation; Hydrometeorology; Numerical weather prediction; Data assimilation and ensemble forecasting; Physical processes of the atmosphere; Land surface-atmosphere systems.