Seasonal Prediction of Northern European Winter Air Temperatures From SST Anomalies Based on Sensitivity Estimates. Issue 11 (7th June 2019)

• Record Type: Journal Article
• Authors: Köhl, Armin; Vlasenko, Andrey
• Is Part Of: Geophysical research letters. Volume 46:Issue 11(2019)
• Page Start: 6109
• Page End: 6117
• Publication Date: 2019-06-07
• Subjects: air temperatures -- sensitivity analysis -- sea surface temperature -- statistical models; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2018GL081800
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: The climate over Europe is significantly affected by the state of the atmospheric circulation over the North Atlantic, which in turn is influenced by various slowly varying components of the climate system. Linear models are employed to investigate how much of the European winter air temperature variability can directly be predicted from the sea surface temperature. By comparing relations from a statistical model to those from an adjoint to a linearized climate model we evaluate the ability of the lagged maximum covariance analysis to reconstruct the dynamical relation between sea surface temperature (SST) and atmosphere temperature. Applied to specific SST conditions, both approaches allow prediction of a similar and significant fraction of the air temperature variability from SST. For a 1‐month lag, extreme air temperature responses are s ignificant and found to be associated with an omega‐blocking pattern over Scandinavia. Plain Language Summary: A connection between sea surface temperature (SST) and atmospheric circulation anomalies has been established for decades. Certain SST anomalies force changes in the atmospheric circulation and cause over Europe typical weather patterns (dry and cold or wet and warm) after some lag. A relationship between the SST pattern and the associated response can be derived from numerical models or statistical relations and used for forecasting the atmospheric response to SST anomalies. In this paper, we validate this forecasting method with observational data. Key Points: Linear models show that sea surface temperature anomalies may provide significant skill for predicting northern European air temperatures Statistical methods can provide sufficiently precise estimates of the lagged relation between sea surface and air temperature