Abstract
The effect of atmospheric circulation on temperature variability and trends in Finland in 1979–2018 is studied using a trajectory-based method. On the average 81% of the detrended interannual variance of monthly mean temperatures is explained by the start points of the three-dimensional trajectories, with the best performance in autumn and winter. Atmospheric circulation change is only found to have had a small impact on the observed annual mean temperature trends, but it has considerably modified the trends in individual months. In particular, changes in circulation explain the lack of observed warming in June, the very modest warming in October in southern Finland, and about a half of the very large warming in December. The residual trends obtained by subtracting the circulation-related change from observations are robustly positive in all months of the year, exhibit a smoother seasonal cycle, and agree better with the multi-model mean temperature trends from models in the 5th Coupled Model Intercomparison Project (CMIP5). Nevertheless, some differences between the residual trends and the average CMIP5 trends are also found.
Highlights
The observed evolution of climate results from the interplay of two factors
Temperature trends in the two study areas are calculated for the same set of 42 CMIP5 models as used in the Intergovernmental Panel on Climate Change 5th assessment report (Collins et al 2013), concatenating the historical simulations for 1979-2005 with RCP4.5 simulations for 2006-2018
A trajectory-based method was used to diagnose the effect of atmospheric circulation changes on interannual variability and trends of surface air temperature in Finland in the years 1979–2018
Summary
The observed evolution of climate results from the interplay of two factors. On one hand, climate is affected by changes in external forcing on different time-scales (e.g., anthropogenic increases in greenhouse gas concentrations and variations in stratospheric aerosol load due to volcanic eruptions). Since atmospheric greenhouse gas concentrations have increased gradually over a period of several decades, the resulting forced climate change should be a smooth function of time, assuming that this forced response is approximately linear (Tebaldi and Arblaster 2014). On local to regional scales, internal climate variability may substantially affect even multi-decadal trends in climate (Deser et al 2012, 2014) This holds even for surface air temperature, which generally has a higher signal-to-noise ratio between the greenhouse gas induced change and internal variability than, for example, precipitation and sea level pressure (Räisänen 2001; Deser et al 2012)
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