Energy does not vanish
The energy of a closed system is steady. It is not lost but rather converted into other forms, such as when kinetic energy is transferred into thermal energy or vice versa heat results in a force.
However, this fundamental principle of natural science is often still a problem for climate research. For example, in case of the calculation of ocean currents, where small-scale vortices as well as mixing processes they induce need to be considered, without fully understanding where the energy for their creation originates from. This is similar in the atmosphere, the only difference being that air is moving instead of water. Again, local turbulences can drive larger movements or vice versa waves on a larger scale can disintegrate into small structures.
All these processes are important for the Earth’s climate and determine how temperatures will rise in the future.
Being Part of the Team: What TRR 181 PhDs say
Existing climate models show energetic and mathematical inconsistencies which may lead to fundamental errors in climate forecasts. Now is the right time to combine recent efforts in Meteorology, Oceanography and applied Mathematics and to go new ways.
Our newsletter comes out every three months and includes information about the work done in our project and more.
Expedition M180 SONETT (Synoptic Observations - a Nested approach to study Energy Transfer & Turbulence in the ocean) is a key part of the ocean observations in the second phase of TRR181 'Energy transfers in Atmosphere and Ocean'. The expedition will take scientists to an ocean region where they can observe many processes that affect energy fluxes in the ocean and the ocean's exchange with the atmosphere.
From Monday to Wednesday, January 31 to February 2, the first part of the RTG spring school took place online. The second part is planned to take place in person in Plön in May.
Pollmann, F. (2022): Global characterization of the ocean's internal gravity wave vertical wavenumber spectrum from Argo float profiles [Data set]. Zenodo, doi: https://doi.org/10.5281/zenodo.6966416.
Franzke, C. L., Gugole, F., & Juricke, S. (2022): Systematic multi-scale decomposition of ocean variability using machine learning. Chaos: An Interdisciplinary Journal of Nonlinear Science 32(7), 073122, doi: https://doi.org/10.1063/5.0090064.
Chouksey, M., Eden, C. & Olbers, D. (2022): Gravity Wave Generation in Balanced Sheared Flow Revisited. J. Phys. Oceanogr. 52, 1351–1362, doi: https://doi.org/10.1175/JPO-D-21-0115.1.