Speed Presentation 2024 Australian Marine Sciences Association Annual Meeting combined with NZMSS

Can a high-resolution earth system models improve the accuracy of past fish biomass estimates in the Southern Ocean? (#70)

Denisse Fierro Arcos 1 2 , Stuart Corney 2 3 , Julia Blanchard 2 , Hakase Hayashida 2 4 , Petra Heil 3 5 , Andrew Kiss 1 2 6 , Amelie Meyer 1 2
  1. Australian Research Council Centre of Excellence for Climate Extremes, Hobart, TAS, Australia
  2. University of Tasmania, Battery Point, TAS, Australia
  3. Australian Antarctic Partnership Program, Hobart, Tasmania, Australia
  4. Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa , Japan
  5. Australian Antarctic Division, Hobart, TAS, Australia
  6. Research School of Earth Sciences, Australian National University, Canberra, ACT, Australia

The effects of climate change have been reported in ecosystems worldwide, but areas like the Southern Ocean that are warming at a rate four times faster than the global average are especially at risk. Ecosystems in the Southern Ocean are uniquely adapted to high seasonal fluctuations in the environment conditions. As the ocean continues to warm and sea ice continues to decrease, it is expected that the distribution and abundance of marine organisms will be affected via direct effects linked to their physiology, and indirectly through disruption of inter-species interactions. These effects could be compounded by an increase in human activities (e.g., fishing, tourism) as sea ice loss opens up previously inaccessible areas. Marine ecosystem models (MEM) forced by earth system models (ESM) can provide estimates of biological and ecosystem change under a changing climate. However, the choice of ESM has an impact on the accuracy of MEM predictions. We forced a MEM with a high-resolution ESM capable of reproducing the observed seasonal cycle and broad baseline climatological conditions of the Southern Ocean (ACCESS-OM2-01) and evaluated the past biomass estimates against observations. We expect this will improve the accuracy of predictions and reduce the uncertainty from earth system models.