CL1.2.2 | Climate of the last two millennia
EDI
Climate of the last two millennia
Co-sponsored by PAGES 2k
Convener: Andrea Seim | Co-conveners: Daniel Boateng, Jun Hu, Hugo Beltrami, Stefan Bronnimann

This session aims to place recently observed climate change in a long-term perspective by highlighting the importance of paleoclimate research spanning the past 2000 years. We invite presentations that provide insights into past climate variability, over decadal to millennial timescales, from different paleoclimate archives (ice cores, marine sediments, terrestrial records, historical archives and more). In particular, we are focussing on quantitative temperature and hydroclimate reconstructions, and reconstructions of large-scale modes of climate variability from local to global scales. This session also encourages presentations on the attribution of past climate variability to external drivers or internal climate processes, data syntheses, model-data comparison exercises, proxy system modelling, and novel approaches to producing multi-proxy climate field reconstructions such as data assimilation or machine learning.

This session aims to place recently observed climate change in a long-term perspective by highlighting the importance of paleoclimate research spanning the past 2000 years. We invite presentations that provide insights into past climate variability, over decadal to millennial timescales, from different paleoclimate archives (ice cores, marine sediments, terrestrial records, historical archives and more). In particular, we are focussing on quantitative temperature and hydroclimate reconstructions, and reconstructions of large-scale modes of climate variability from local to global scales. This session also encourages presentations on the attribution of past climate variability to external drivers or internal climate processes, data syntheses, model-data comparison exercises, proxy system modelling, and novel approaches to producing multi-proxy climate field reconstructions such as data assimilation or machine learning.