Projected climate change in Fennoscandia – and its relation to ensemble spread and global trends

Abstract. The need for information about climate change is great. This information is usually based on climate model data, which often have systematic biases. Furthermore, climate information is based on ensembles of climate models, which raises the question about how such ensembles are affected by the choice of models and emission scenarios. Here, we aim to describe climate change in Sweden and neighbouring countries and discuss how local changes relate to global warming. We present climate change projections based on bias adjusted Euro-CORDEX (Coordinated Regional Downscaling Experiment) regional climate model data centred over Sweden. Global warming results in higher temperature, more warm days, and fewer cold days in Sweden. The regional climate models replicate the signal of the driving global models. Yet, the model spread is smaller than in the full CMIP5 ensemble, which means that the RCMs do not fully represent the potential model spread. The choice of emission scenario has minimal effect on the calculation of mean climate change at a global warming level of 2 degrees. This implies that it would be safe to mix emission scenarios in calculations of global warming levels, at least up to +2 °C, and as long as mean values are concerned. Moreover, the differences in local and global warming rates seem to decrease with time, suggesting that climate change in Sweden may currently be at its fastest.

Authors

Institutions

Publication Details

Journal
Weather and Climate Dynamics
Published
2026-01-22
DOI
https://doi.org/10.5194/wcd-7-185-2026
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
36.62

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Projected climate change in Fennoscandia – and its relation to ensemble spread and global trends

Lars Bärring, R. Wilcke, Gustav Strandberg, August Thomasson et al.
1 citations
Weather and Climate Dynamics
Climate variability and models
36.62
article

Projected climate change in Fennoscandia – and its relation to ensemble spread and global trends

Lars Bärring, R. Wilcke, Gustav Strandberg, August Thomasson, Erik Kjellström, Michael Sahlin, Grigory Nikulin
article en
1 citations

Abstract

Abstract. The need for information about climate change is great. This information is usually based on climate model data, which often have systematic biases. Furthermore, climate information is based on ensembles of climate models, which raises the question about how such ensembles are affected by the choice of models and emission scenarios. Here, we aim to describe climate change in Sweden and neighbouring countries and discuss how local changes relate to global warming. We present climate change projections based on bias adjusted Euro-CORDEX (Coordinated Regional Downscaling Experiment) regional climate model data centred over Sweden. Global warming results in higher temperature, more warm days, and fewer cold days in Sweden. The regional climate models replicate the signal of the driving global models. Yet, the model spread is smaller than in the full CMIP5 ensemble, which means that the RCMs do not fully represent the potential model spread. The choice of emission scenario has minimal effect on the calculation of mean climate change at a global warming level of 2 degrees. This implies that it would be safe to mix emission scenarios in calculations of global warming levels, at least up to +2 °C, and as long as mean values are concerned. Moreover, the differences in local and global warming rates seem to decrease with time, suggesting that climate change in Sweden may currently be at its fastest.

Weather and Climate DynamicsVol. 7(1)
Linköping University (SE), Swedish Meteorological and Hydrological Institute (SE), Lund University (SE)
VINNOVA, Svenska Forskningsrådet Formas, National Science Council, Linköpings Universitet, Vetenskapsrådet, National Supercomputing Centre Singapore, Horizon 2020 Framework Programme, H2020 European Institute of Innovation and Technology
Climate action
Openalex Percentile: Top 1%
Climate variability and models
36.62
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.