A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the years 2003 to 2014 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1999 and, then, from 2000 to 2002 with tagging (see below). The model output has been validated with measurement data of the "IOW Baltic Monitoring and long-term data program" (https://www.io-warnemuende.de/iowdb.html IOW: Leibniz Institute for Baltic Sea Research Warnemünde) and from the HELCOM database (http://ocean.ices.dk/helcom/Helcom.aspx HELCOM: Helsinki Commission). A publication is in preparation.
The model simulation was forced by coastDat2 COSMO-CLM data (doi:10.1594/WDCC/coastDat-2_COSMO-CLM). Atmospheric nitrogen deposition data of 0.1° x 0.1° spatial resolution were taken from the 2018 reporting of the European Measurement and Evaluation Programme (EMEP) as presented in EMEP (2018, url: http://emep.int/publ/reports/2018/EMEP_Status_Report_1_2018.pdf) and available from the Norwegian Meteorological Institute (2018, url: http://thredds.met.no/thredds/catalog/data/EMEP/2018_Reporting/catalog.html).
Nitrogen from atmospheric deposition of nitrogen from livestock/agricultural emissions (estimated, see documentation) and from all emission sectors has been tagged in the model simulation according to a method by Menésguen et al. (2006, doi:10.4319/lo.2006.51.1_part_2.0591). Therefore, all nitrogen-containing model variables exist three times in the output: once as regular variables and once per tagged nitrogen source (total atmospheric and agriculturally-related).
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).
Technical details:
model run on MPP1 Cluster of the HLRN-III Konrad Comlpex
each simulation performed on 527 cores distributed on 22 nodes (24 cores per node; one core on one node not used) configuration of one node:
Processor: 2x Intel Xeon E5-2695v2 CPUs (12 cores per CPU), R_peak: 230 GFlop/s
RAM: 64 GiB DDR3-1866
OS: SuSE Linux Enterprise Server (SLES) version 11
Interconnect: Cray Aries
Neumann, Daniel; Radtke, Hagen; Neumann, Thomas (2019). MOM-ERGOM western Baltic Sea simulations with tagging of atmospheric nitrogen deposition by EMEP. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/MOMERGOMBSEMEP
There are not gaps in the spatial and temporal coverage. Land grid cells are indicated by missing values (-1.e+20f).
...
Description
There are not gaps in the spatial and temporal coverage. Land grid cells are indicated by missing values (-1.e+20f).
All prognostic biogeochemical model variables and a few relevant physical model variables are provided. Each nitrogen-containing prognostic variable has two counterparts with the suffixes "_atmos_tot_N" and "_atmos_agri_N" indicating the total atmosheric and livestock/agricultural-related nitrogen, respectively, in this variable.
Consistency report
In the used ERGOM version, the biogeochemical system is represented by 31 state variables of which 26 are in the water column and 5 in the surface sed...
Description
In the used ERGOM version, the biogeochemical system is represented by 31 state variables of which 26 are in the water column and 5 in the surface sediment. Basic nutrients - e.g. nitrate or phosphate- enter the system via river input, atmospheric deposition, or recycling of organic matter. They are consumed by phytoplankton including cyanobacteria. Cyanobacteria do not consume nitrate or ammonium but fixate molecular nitrogen to obtain needed nitrogen. Phytoplankton including cyanobacteria is grazed by zooplankton. Plankton respirates and dies. Dead plankton becomes detritus that sinks to the sediment. The sediment is represented by an one-layer sediment only but contains relevant sediment processes such as phosphate release under anoxic conditions or denitrification.
FAIR
result: total 66 %
Description
Findable: 6 of 7 level Accessible: 2 of 3 level Interoperable: 3 of 4 level Reusable: 5 of 10 level
Consistency/Data Organisation and Data Object: 4 of 5 level; Consistency/Versioning and Controlled Vocabularies (CVs): level 4 of 5 level; Consistency/Data-Metadata Consistency: 4 of 5 level; Completeness/Existence of Metadata: 4 of 5 level; Completeness/Existence of Data (Completeness and Persistence): 5 of 5 level; Accessibility/Metadata Access by Identifier: 5 of 5 level; Accessibility/Data Access by Identifier: 5 of 5 level; Accuracy/Plausibility: 5 of 5 level; Accuracy/Statistical Anomalies: 4 of 5 level https://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=QMM_MOMERGOMBSEMEP
Method
QMM checklist manual
Method Description
Checks performed by WDCC. The criteria are documented in the 'Fitness for Use of Data Objects Described with Quality Maturity Matrix at Different Phases of Data Production'
SQA - Scientific Quality Assurance 'approved by author'
Result Date
2019-11-06
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 spatial-temporal coverage description (metadata) is consistent to the data, time steps are correct and the time coordinate is continuous: 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
[3] 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
[5] DOIRadtke, Hagen; Neumann, Thomas; Fennel, Wolfgang. (2013). A Eulerian nutrient to fish model of the Baltic Sea — A feasibility-study. doi:10.1016/j.jmarsys.2012.07.010
[4] DOIMénesguen, Alain; Cugier, Philippe; Leblond, Isabelle. (2006). A new numerical technique for tracking chemical species in a multi-source, coastal ecosystem, applied to nitrogen causing Ulva blooms in the Bay of Brest (France). doi:10.4319/lo.2006.51.1_part_2.0591
[7] DOIRadtke, H.; Neumann, T.; Voss, M.; Fennel, W. (2012). Modeling pathways of riverine nitrogen and phosphorus in the Baltic Sea. doi:10.1029/2012jc008119
[8] DOISchernewski, Gerald; Friedland, Rene; Carstens, Marina; Hirt, Ulrike; Leujak, Wera; Nausch, Günther; Neumann, Thomas; Petenati, Thorkild; Sagert, Sigrid; Wasmund, Norbert; Weber, Mario von. (2015). Implementation of European marine policy: New water quality targets for German Baltic waters. doi:10.1016/j.marpol.2014.09.002
[9] 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