Imprint of global climate transitions on Swedish climate
Title: Imprint of global climate transitions on Swedish climate
SNIC Project: SNIC 2013/1-231
Project Type: SNAC Medium
Principal Investigator: Jenny Brandefelt <>
Affiliation: KTH Royal Institute of Technology
Duration: 2013-09-17 – 2014-10-01
Classification: 10501 10508 10504


The aim of this project is to enhance knowledge on Scandinavian climate evolution and variability during the global climate transition from glacial to inter-glacial climate. The project is initiated and funded by the Swedish nuclear fuel and waste management company (SKB) with the goal of improved quantitative climate data for south-central Sweden. The last deglaciation, from the end of the Last Glacial Maximum (LGM; 21-18 ka BP; ka: 1000 years) to the early part of the Holocene (ca 10 ka BP) will be studied with focus on (south-central) Sweden. The project involves global climate modelling performed by a post-doc at KTH Mechanics, paleo-proxy data collection and evaluation performed by a PhD student at the Department of Geological Sciences (IGV) at Stockholm University and model – paleo proxy data comparison performed by the post-doc and PhD student together. Liu et al (Nature, 2009) performed a coupled atmosphere-ocean-sea ice-land surface climate simulation of the transition from the last glacial maximum (LGM; 21,000 ka BP) to the late Holocene (2 ka BP) with the Community Climate System Model version 3 (CCSM3). In the current project, this low-resolution (c. 3.8 degree in latitude and longitude) simulation will be used to provide ocean surface boundary conditions to higher resolution (c. 1 degree in latitude and longitude) atmosphere-land surface simulations with the newest version of the same climate model (Community Earth System Model version 1; CESM1). The purpose of this dynamical downscaling of the results of Liu et al.'s study is to resolve the climate variability at a regional scale and to facilitate comparison to proxy data which generally represent a local or regional spatial scale. The modelling will focus on periods with significant variability and fast transitions identified from the low-resolution simulation of Liu et al.