Evolution of observed and modelled temperatures in Finland in 1901-2018 and potential dynamical reasons for the differences

• Published in: International Journal of Climatology
• Main Authors: Ruosteenoja, Kimmo, Räisänen, Jouni
• Other Authors: Department of Physics
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: atmospheric circulation; climate change; CMIP5 models; internal variability; observed temperature trends; regression; 114 Physical sciences; Northeast Atlantic
• Online Access: http://hdl.handle.net/10138/329101

Observed monthly and annual mean temperatures in Finland in 1901-2018 were compared with simulations performed with 28 global climate models (GCMs), and dynamical factors behind the emerging differences were studied by regression analysis. Observational temperatures were extracted from high-quality kriging analyses specifically tailored for Finland. Considering the entire time interval, the increase in the annual multi-GCM mean temperature agrees well with the observed warming, even though observations exhibit substantial inter-decadal fluctuations. After 2000, the mean temperatures have been higher than during any period in the 20th century. In the baseline regression model, the 10 leading EOFs of the European-Northeast Atlantic sea-level pressure (SLP) field were used to explain differences between the GCM-mean and observed evolution of temperature. The regression model is able to reduce the mean squared difference of the temporally-smoothed temperature by 58%. The performance is highest in winter and summer and lowest in April. For a sensitivity assessment, multiple alternative regression models were tested, for example, one using the local SLP, geostrophic wind and vorticity as predictors. These models mostly showed somewhat inferior performance. We specifically explored the trends of monthly temperatures during 1961-2018, a period considerably affected by anthropogenic emissions. Compared with the multi-GCM mean, warming proved to be negligible in June, fairly slow in October and quite rapid in December. All these features were explained rather nicely by dynamical factors. Accordingly, the deviations of the observed regional temperature trends from the multi-GCM mean largely appear to be related to internal variability. Peer reviewed