Jupyter notebooks to calculate and analyze eddy kinetic energy in AWI-CM-1-1-MR's CMIP6 simulations.
The model data used in this analysis is available from the WDCC Long-term Archive:
Semmler, Tido; Danilov, Sergey; Rackow, Thomas; Sidorenko, Dmitry; Barbi, Dirk; Hegewald, Jan; Sein, Dmitri; Wang, Qiang; Jung, Thomas (2022). CMIP6_supplemental CMIP AWI AWI-CM-1-1-MR. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/C6sCMAWAWM
Semmler, Tido; Danilov, Sergey; Rackow, Thomas; Sidorenko, Dmitry; Barbi, Dirk; Hegewald, Jan; Sein, Dmitri; Wang, Qiang; Jung, Thomas (2022). CMIP6_supplemental ScenarioMIP AWI AWI-CM-1-1-MR. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/C6sSPAWAWM
Mesh information for the analysis of these datasets is available in the associated Zenodo repository:
Beech, N. (2022). n-beech/awicm-cmip6-eke: Inititial (v1.0): Jupyter notebooks to calculate and analyze eddy kinetic energy in AWI-CM-1-1-MR's CMIP6 simulations. Zenodo. https://doi.org/10.5281/zenodo.7050573
Observational data used in this analysis is available from E.U. Copernicus Marine Service Information:
https://doi.org/10.48670/moi-00148
Some notebooks can run independently of others, some must be run in a certain order. An example order: geostrophy_calculations, merge_velocity_datasets, running_mean_anomalies, EKE_calcualtions, observational_period_modeled_EKE_calculations, observed_EKE_calcualtions, area_weighting, area-integrated_ocean_basins, any/all visualization notbooks.
In this notebook geostrophic velocities are computed from modeled daily sea surface height data on an unstructure ocean grid.
Geostrophic velocity calculations break down around the equator due to a negligible Coriolis parameter. In this notebook the geostrophic velocities between 3S and 3N calculated in 'geostrophy_calculations' are replaced with interpolated monthly data available directly as model output.
Anomaly calculations relative to a reference period mean are not representative of variability when the mean state shifts to a point at which the reference period mean is no longer representative of typical conditions. To account for shifting patterns of ocean circulation, ocean surface velocity anomalies are calculated relative to a running mean.
Ocean surface velocities are used to calculate eddy kinetic energy. A running mean is also applied to the data to mask seasonality in time series analysis. FInally the metadata of each file is changed to reflect the calculations.
For comparison with observed EKE, modeled EKE over the diration of the satelite altimetry period (1993-2020) is calculated using the same reference period employed for the observational dataset (1993-2012).
EKE is calculated using geostrophic velocity anomalies from satelite altimetry observations. The EKE data is then remapped to the FESOM grid for a consistent comparison with the modeled data.
Cell area information from the FESOM mesh is used to area-weight the EKE data.
Time series of area-integrated EKE are computed for each of the selected ocean basins.
The spatial patterns of simulated and observed EKE during the observational period (1993-2020) are plotted. The spatial patterns of simulated EKE change between the early historical period (1860-1949) and the end of the 21st century (2061-2090) are plotted.
The area-integrated time series of each ocean basin are standardized and plotted. Includes both model ensembles and observations.
The relationship between EKE and mean global surface temperature (MGST) rise is plotted. The rate of change of EKE in terms of MGST anomaly is calculated.