Abstract
AbstractObserved 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.
Highlights
During the industrial era, mean temperatures have increased nearly everywhere in the world (Hartmann et al, 2013, fig. 2.21)
The main findings of the study are presented in Section 3: a comparison of the temporal evolution of observed temperatures with the global climate models (GCMs) output; explaining the emerging differences by dynamical factors, especially by the spatial distribution of sea-level pressure (SLP); and the monthly trends of temperature during a sub-period substantially affected by anthropogenic greenhouse gas emissions
As an alternative for regression models based on the Empirical Orthogonal Functions (EOFs) expansions, we studied a model in which the predictors represent local circulation conditions in the subregion considered
Summary
Mean temperatures have increased nearly everywhere in the world (Hartmann et al, 2013, fig. 2.21). In the regression model to be developed, we shall use EOF coefficients 1–10 of SLP to explain temporal variations in the observed temperature (represented as a deviation from the multi-GCM mean). This choice is consistent with the well-known 1 in 10 rule, according to which a regression model should not use more than one predictor per 10 data points to keep the risk of overfitting (b) low
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