Time of Emergence of Ocean Interior Acidification and De-oxygenation in a Water Mass Framework

Maricela Coronado, Princeton University, Department of Geosciences, Princeton, NJ, United States, Ivy Frenger, ETH Swiss Federal Institute of Technology Zurich, Zurich, Switzerland, Thomas L Froelicher, Universtity of Bern, Climate and Environmental Physics, Bern, Switzerland, Keith B Rodgers, IBS Center for Climate Physics, Busan, South Korea, Sarah Schlunegger, Princeton University, Princeton, NJ, United States, Daisuke Sasano, Meteorological Research Institute, Oceanography and Geochemistry Research Department, Tsukuba, Japan and Masao Ishii, Japan Meteorological Agency, Meteorological Research Institute, Tsukuba, Japan
Abstract:
Potential marine ecosystem stressors, such as acidification and de-oxygenation, are expected to impact biology over the course of the 21st century. Detection of these changes in ocean biogeochemistry is made complicated by the background natural variability of the climate system (Frölicher et al., 2007 and Rodgers et al., 2015).

Here we present a novel method for the interpretation of ocean interior measurement for environmental change. We use a water mass framework to compare a high-frequency repeat hydrographic section at 165E in the Pacific (Sasano et al., 2015) with initial condition ensemble experiments ran with GFDL’s Earth System Model (ESM2M). In this study, “emergence” for a trend occurs when an anthropogenic signal (either modeled or observed) exceeds the noise (envelope of spread amongst ensemble members, generated by internal variability). By using a water mass as opposed to the standard depth framework, we remove the effects of anthropogenic trends and internal variability of deepening isopycnals, allowing for greater emergence of bio-geochemical signals. We find that emergence of anthropogenic trends in acidification and omega aragonite emerge sooner and with greater confidence than do trends in ocean interior oxygen concentrations. More broadly, this study demonstrates the utility of applying initial condition ensembles to interpret ocean interior variability and trends, rather than the traditional practice of using observations to validate models.

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