Ice Algae Model Intercomparison Project phase 2 (IAMIP2)

• Published in: Geoscientific Model Development
• Main Authors: Hayashida, H, Jin, M, Steiner, NS, Swart, NC, Watanabe, E, Fiedler, R, Hogg, AMcC, Kiss, AE, Matear, RJ, Strutton, PG
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus GmbH 2021
• Subjects: polar marine ecosystem; arctic; modelling; sea ice; antarctic; biogeochemistry; Arctic; Antarctic; Antarc*; ice algae; Sea ice
• Online Access: https://eprints.utas.edu.au/45757/; https://eprints.utas.edu.au/45757/1/147735%20-%20Ice%20Algae%20Model%20Intercomparison%20Project%20phase%202%20%28IAMIP2%29.pdf

Ice algae play a fundamental role in shaping sea-ice-associated ecosystems and biogeochemistry. This role can be investigated by field observations; however the influence of ice algae at the regional and global scales remains unclear due to limited spatial and temporal coverage of observations and because ice algae are typically not included in current Earth system models. To address this knowledge gap, we introduce a new model intercomparison project (MIP), referred to here as the Ice Algae Model Intercomparison Project phase 2 (IAMIP2). IAMIP2 is built upon the experience from its previous phase and expands its scope to global coverage (both Arctic and Antarctic) and centennial timescales (spanning the mid-20th century to the end of the 21st century). Participating models are three-dimensional regional and global coupled sea-ice–ocean models that incorporate sea-ice ecosystem components. These models are driven by the same initial conditions and atmospheric forcing datasets by incorporating and expanding the protocols of the Ocean Model Intercomparison Project, an endorsed MIP of the Coupled Model Intercomparison Project phase 6 (CMIP6). Doing so provides more robust estimates of model bias and uncertainty and consequently advances the science of polar marine ecosystems and biogeochemistry. A diagnostic protocol is designed to enhance the reusability of the model data products of IAMIP2. Lastly, the limitations and strengths of IAMIP2 are discussed in the context of prospective research outcomes.