Can we reconstruct Arctic sea ice back to 1900 with a hybrid approach?

• Published in: SOIL
• Main Authors: Brönnimann, Stefan, Lehmann, T., Griesser, Thomas, Ewen, Tracy, id_orcid:0 000-0002-7751-5235, Grant, A.N., Bleisch, R.
• Format: Report
• Language: English
• Published: Copernicus 2008
• Subjects: Arctic; Sea ice
• Online Access: https://hdl.handle.net/20.500.11850/12098; https://doi.org/10.3929/ethz-b-000012098

The variability and trend of Arctic sea ice since the mid 1970s is well documented and linked to rising temperatures. However, much less is known for the first half of the 20th century, when the Arctic also underwent a period of strong warming. For studying this period in atmospheric models, gridded sea ice data are needed as boundary conditions. Current data sets (e.g., HadISST) provide a historical climatology, but may not be suitable when interannual-to-decadal variability is important, as they are interpolated and relaxed towards a (historical) climatology to fill in gaps, particularly in winter. Regional historical sea ice information exhibits considerable variability on interannnual-to-decadal scales, but is only available for summer and not in gridded form. Combining the advantages of both types of information could be used to constrain model simulations in a more realistic way. Here we discuss the feasibility of reconstructing year-round gridded Arctic sea ice from 1900 to 1953 from historical information and a coupled climate model. We decompose sea ice variability into centennial (due to climate forcings), decadal (coupled processes in the ocean-sea ice system) and interannual time scales (atmospheric circulation). The three time scales are represented by a historical climatology from HadISST (centennial), a closest analogue approach using the coupled control run of the CCSM-3.0 model (decadal), and a statistical reconstruction based on high-pass filtered data (interannual variability), respectively. Results show that differences in the model climatology, the length of the control run, and inconsistent historical data strongly limit the quality of the product. However, with more realistic and longer simulations becoming available in the future as well as with improved historical data, useful reconstructions are possible. We suggest that hybrid approaches, using both statistical reconstruction methods and numerical models, may find wider applications in the future. ISSN:1814-9340 ISSN:1814-9359