Earth Big Data: Today’s Ocean Models can only Simulate Less Than 5% of the Currents at 1,000 m Depth
Ocean motion plays a key role in the Earth’s energy and climate system. In recent decades, ocean science has made great progress, achieving general estimates of large-scale ocean motion. However, there are still many dynamic mechanisms that are not fully understood or resolved.
Prof. SU Fenzhen’s team at the Institute of Geographic Sciences and Natural Resources Research (IGSNRR) of the Chinese Academy of Sciences (CAS) and their collaborators found humans know less than 5% of the ocean currents at depths of 1,000 meters below the sea surface, with important implications for modelled predictions of climate change and carbon sequestration. Their findings were published in Nature Communications on April 12.
The researchers adopted a displacement dataset of 842,421 observations produced by Argo floats from 2001-2020. Lagrangian currents velocities at 1,000?m depth were computed and multiple evaluation indicators were used to compare Argo float velocities with simulated velocities from ocean global circulation models. The study results showed only 3.8% of the mid-depth oceans can be regarded as accurately modelled.
“An important finding is that circulation energy in almost all of the global ocean is underestimated. This is probably because high-frequency dynamics are poorly resolved in ocean circulation models and current parameterizations solutions of sub-grid processes are inadequate,” said Prof. SU.
“In the future, we expect to work out ocean circulation models that could more faithfully represent observed ocean currents through more intensive observations, more productive parameterizations, more efficient computing technologies and more in-depth theoretical analyses,” he said.
The study revealed the nature and scale of the disparity between the scientific knowledge and the actual ocean circulation. It can help guide recommendations for more targeted observation and more accurate predictions in order to decrease the significant biases between models and observations.
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