Types of Major Baltic Inflows: A Machine Learning Approach
Creators: name: Dietze, Heiner
name: Löptien, Ulrike
Publisher: name: Zenodo
Year: 2025
Subjects: subject: Research
subject: German Marine Research Alliance (DAM)
subject: Baltic Sea
subject: Modelling
subject: Machine learning
subject: Unsupervised Machine Learning
subject: Machine Learning
subject: High-resolution
subject: Oxygen
subject: Oceanography
subject: FOS: Earth and related environmental sciences

description: This data set supplements to the publication “Major Baltic Inflows come in different flavours” published in Communications Earth & Environment, 2025. 

Overfertilization and oxygen depletion are infamous problems in the Baltic Sea that may be may be aggravated by a warming climate. In this context, strong saline inflows from the North Sea (dubbed Major Baltic Inflows or MBI) are of particular interest, as the are currently the major mechanism that supplies the deep Baltic Sea with fresh oxygen.  In the publication we investigate the link between atmospheric drivers and MBIs by combining machine learning (ML) techniques with numerical modeling. Here we present respective numerical results: 

(1) The clustering of 12-hourly atmospheric sea level pressure anomlies from reanalysis data 1991-2024 (ERA5 [1,2]) related to Major Baltic Inflows (from 20 days prior the identified events until 10 days after) has been performed in Matlab - accordingly all clustering results are stored in the Matlab-file upload.mat.

Respecitve variables are:kopti: standard k-means clustering resultskopti3: k-means with dynamic timewarping clustering resultsclust_dyn_mean1: cluster centre STORMYclust_dyn_mean2: cluster centre CLASSICydim: numbers of y-pointsxdim: numbers of x-pointstimelag: considered time range (12-houerly)distances_clust1:mean distances to cluster centre STORMYdistances_clust2:mean distances to cluster centre STORMYdate_MBIs: Dates where MBIs were detectdCL1: indices of MBIs associated to cluster STORMYCl2: indices of MBIs associated to cluster CLASSICcost1i: mean distances of cluster 1 to cluster center STORMYcost2i: mean distances of cluster2 to cluster center STORMYcost1a: mean distances of cluster 1 to cluster center CLASSICcost2a: mean distances of cluster 2 to cluster center CLASSIC

=> These variables serve as input for the the Matlab-Program plot_final.m(producing Fig.2 as well as the Supplementary Figures 2-3)

 

(2) Numerical Model Results (idealized experiemnts with the Baltic Sea model MOMBA [3]). The model output is stored in NetCDF-format (here in the sub-directory “ncFiles”).

Sea level elevations from the sensitivity experiments according to Fig.4: ocean_eta_1988_JanFeb_ideal1.ncocean_eta_1988_FebMar_ideal1.ncocean_eta_1988_MarApr_ideal1.ncocean_eta_1994_JanFeb_ideal1.ncocean_eta_1988_JanFeb_ideal2.ncocean_eta_1988_FebMar_ideal2.ncocean_eta_1988_MarApr_ideal2.ncocean_eta_1994_JanFeb_ideal2.nc

Trnsports from the sensitivity experiments according to Fig.4: ocean_trans_1988_JanFeb_ideal1.ncocean_trans_1988_FebMar_ideal1.ncocean_trans_1988_MarApr_ideal1.ncocean_trans_1994_JanFeb_ideal1.ncocean_trans_1988_JanFeb_ideal2.ncocean_trans_1988_FebMar_ideal2.ncocean_trans_1988_MarApr_ideal2.ncocean_trans_1994_JanFeb_ideal2.nc

s_by2.nc,s_by5.nc and s_by15.nc contain the simulated salinity profiles at the respective Swedish observing stations (used in the Supplementary Figs.7 & 9) t_by2.nc,t_by5.nc and t_by15.nc contain the simulated salinity profiles at the respective Swedish observing stations (used in the Supplementary Fig.10)

temp_Bal.nc contains simulated surface temperatures from 21.Sept.1997 (Supplementary Fig.8)

(3) Numerical Model Code (MOMBA-model) (a) The file input.nml replaces the old input.nml file in the existing MOMBA-repositoryhttps://data.geomar.de/downloads/20.500.12085/900c10df-7b34-4565-b2be-1fff9e801f02/

(b) Atmospheric boudary conditions refer to Era5 Resanlysis data [1,2] in combination with the idealized forcing provided in the subdirectory “Idealized”. All these data are stored in NetCDF-format.

(4) The data can be visualised with the following scripts(a) ferret-scripts plot_section_kattegat.jnl (for Fig.3; cluster STORMY)plot_section_kattegat2.jnl (for Fig.3; cluster CLASSIC) plot_vgl_eta_ideal1.jnl (sea level elevations in Fig.4; cluster STORMY) plot_vgl_eta_ideal1.jnl (sea level elevations in Fig.4; cluster CLASSIC) plot_vgl_trans_ideal1.jnl (transports in Fig.4; cluster STORMY)plot_vgl_trans_ideal2.jnl (transports in Fig.4; cluster CLASSIC)plot_sst.jnl (exemplary simulted sea surface temperatures in Supplementary Fig.8)plot_bottomsalinity.jnl (bottom salinities from Supplementary Fig.7)

(b) python-scripts for profiles in Figs.9 and 10:plot_model_depth_percentiles.py