“...this paper presents a well known and important variable in oceanography that we compute on the global scale with a new methodology.”

One of the main purposes of the paper is the presentation of a new oceanographic dataset (and related findings), that can be very useful not only in oceanography for dynamics but also in biogeochemistry, marine biology, or greenhouse gases emissions. It therefore has several fields of application. More generally, this variable, namely the thickness of the upper ocean layer—called Mixed Layer Depth or MLD—is a key parameter and essential in understanding the climate system. Another important point that may have played a role in the fact that people know the paper is that we have made a point of good communication on this subject as we thought it should be useful for many scientists. Lastly, the dataset was immediately available on the web and ready to be used.

Exactly, this paper presents a well known and important variable in oceanography that we compute on the global scale with a new methodology. Basically, this methodology has been used before, but only on regional scales, or the treatment of the data was not precisely or fully done. Here we have set up the complete treatment of data, from the original oceanographic profiles data to the final field of MLD on a global scale. We have also described the process precisely and made the data ready to use as-is.

The upper ocean layer is vertically homogeneous due to strong turbulence at the ocean-atmosphere interface (winds, heat fluxes). This oceanic boundary layer, also called the "mixed layer," is of primary importance for studying the climate system as all exchanges of mass and energy between ocean and atmosphere occur through this layer. Those exchanges provide the source of almost all oceanic motions. The thickness of this layer, namely the MLD, determines the heat content and mechanical inertia of the layer that directly interacts with the atmosphere. Emissions of CO₂ by the oceans, or climate variability of phenomenon such as El Niño are for example directly dependent on the evolution and variations of such an oceanic variable. Oceanic general circulation numerical models also need to be validated against observations to ensure they simulate a proper MLD and eventually realistic modes of climate variability.

I was primarily involved in oceanic numerical models. I investigated the upper ocean heat budget in the northern Indian Ocean to understand the regulation of sea surface temperature (SST) in that region and its potential impact on regional climate such as the Asian Monsoon. Therefore, I needed a reliable validation of the simulated MLD and realized that previous climatologies may present biases especially in my area due to the way they were constructed. Discussing with my colleagues and co-authors, it turned out that they also needed such a dataset in other regions of the world ocean. As a real need for this dataset appeared quite obvious, we decided to compute climatology after discussing an appropriate and innovative methodology.

There may be no direct social or political implications of this research. However, it results in a better understanding of our climate system. In particular, further studies may help in quantifying and demonstrating climate change such as global warming, which hopefully can have important impacts on our social behavior and political decisions.