A marine physical biogeochemical model simulation was performed for the year 2012 covering the North Sea and Baltic Sea. Only data for the western Baltic Sea are provided here. The model output has been validated in Neumann et al. (2018a, doi: 10.5194/os-2018-71). The work was funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI, FKZ 50EW1601, https://www.io-warnemuende.de/meramo-en.html). The simulation was performed at the North-German Supercomputing Alliance (HLRN, project id: mvk00054, zulassung.hlrn.de/kurzbeschreibungen/mvk00054.pdf).
The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A).
The model simulation was forced by operational meteorological data of the German Weather Service (DWD). Atmospheric nitrogen deposition data of high spatial resolution of 4x4 km2 were provided by the Helmholtz-Zentrum Geesthacht within the EU BONUS SHEBA Project (Karl et al., 2019, doi: 10.5194/acp-2018-1317). Information on the riverine inputs, boundary conditions, and the model itself are provided in detail in Neumann et al. (2018b, doi: 10.5194/bg-2018-364).
Nitrogen from atmospheric deposition of shipping-related nitrogen has been tagged in the model simulation according to a method by Menésguen et al. (2006, 10.4319/lo.2006.51.1_part_2.0591). Therefore, all nitrogen-containing model variables exist twice in the output: once as regular variables and once as nitrogen content from shipping-related activities.
The concentrations of all prognostic biogeochemical model variables are given in nitrogen units according to the Redfield ratio.
Neumann, Daniel; Karl, Matthias; Radtke, Hagen; Neumann, Thomas (2019). HBM-ERGOM western Baltic Sea simulations with tagging of high resolution atmospheric nitrogen deposition by CMAQ. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/MeRamo_exp1
There are not gaps in the spatial and temporal coverage. Land grid cells are indicated by missing values (float, -999.0).
...
Description
There are not gaps in the spatial and temporal coverage. Land grid cells are indicated by missing values (float, -999.0).
Not all prognostic biogeochemical model variables but just amm, nit, ldon, phos, sil, oxy, dia, flag, cyano, zoo, protzoo, detn and dets of the water column and sed_n and sed_sil of the sediment are provided here (please see the readme for the meaning of these abbreviations). The diagnostic variable chl and the grid information variables cell_thinkness, depth, and bathymetry are provided as well. Each nitrogen-containing prognostic and diagnostic variable has a duplicate with the suffix "_ship" indicating the shipping-related nitrogen in this variable.
FAIR
F-UJI result: total 66 %
Description
Summary: Findable: 6 of 7 level; Accessible: 2 of 3 level; Interoperable: 3 of 4 level; Reusable: 5 of 10 level
SQA - Scientific Quality Assurance 'approved by author'
Result Date
2019-02-06
Technical Quality Assurance (TQA)
TQA - Technical Quality Assurance 'approved by WDCC'
Description
1. Number of data sets is correct and > 0: passed; 2. Size of every data set is > 0: passed; 3. The data sets and corresponding metadata are accessible: passed; 4. The data sizes are controlled and correct: passed; 5. The temporal coverage description (metadata) is consistent to the data: passed; 6. The format is correct: passed; 7. Variable description and data are consistent: passed
Method
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
[2] DOINeumann, Thomas; Fennel, Wolfgang; Kremp, Christine. (2002). Experimental simulations with an ecosystem model of the Baltic Sea: A nutrient load reduction experiment. doi:10.1029/2001gb001450
Cites
[1] DOINeumann, Daniel; Friedland, René; Karl, Matthias; Radtke, Hagen; Matthias, Volker; Neumann, Thomas. (2018). Importance of high resolution nitrogen deposition data for biogeochemical modeling in the western Baltic Sea and the contribution of the shipping sector . doi:10.5194/os-2018-71
[2] DOIKarl, Matthias; Jonson, J. E.; Uppstu, A.; Aulinger, Armin; Prank, M.; Jalkanen, Jukka-Pekka; Johansson, L.; Quante, Markus; Matthias, Volker. (2019). Effects of ship emissions on air quality in the Baltic Sea region simulated with three different chemistry transport models. doi:10.5194/acp-19-7019-2019
[2] DOINeumann, Thomas; Schernewski, Gerald. (2005). An ecological model evaluation of two nutrient abatement strategies for the Baltic Sea. doi:10.1016/j.jmarsys.2004.10.002