MOM-ERGOM western Baltic Sea simulations with tagging of atmospheric nitrogen deposition by CMAQ

doi:10.26050/WDCC/MOMERGOMBSCMAQ

Neumann, Daniel et al.

ExperimentDOI
Biogeochemical CycleERGOMMOMOcean-atmosphere interaction
Summary
A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the year 2012 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1999 and, then, from 2000 to 2011 with tagging (see below; three years would have been sufficient). 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 which, now, is part of the ICES Oceanography Data Portal (https://www.ices.dk/data/data-portals/Pages/ocean.aspx; HELCOM: Helsinki Commissionm; ICES: International Council for the Exploration of the Seas). 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 16x16 km2 horizontal resolution were provided by the Helmholtz-Zentrum Geesthacht within the EU BONUS SHEBA Project (Karl et al., 2019, doi:10.5194/acp-19-7019-2019).
Nitrogen from atmospheric deposition of nitrogen from shipping emissions 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 shipping-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
Project
MeRamo (HBM-ERGOM western Baltic Sea simulations with tagging of atmospheric nitrogen deposition within the MeRamo project)
Contact
Dr. Thomas Neumann (
 thomas.neumann@nullio-warnemuende.de
 0000-0002-5653-906X)
Spatial Coverage
Longitude 8.23 to 30.63 Latitude 53.83 to 65.93 Altitude: 0 m to -268 m
Temporal Coverage
2012-01-01 to 2012-12-31 (julian)
Use constraints
Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)
Data Catalog
World Data Center for Climate
Size
1.30 GiB (1392574873 Byte)
Format
NetCDF
Status
completely archived
Creation Date
Future Review Date
2029-07-25
Find data Export dataset acronyms
Cite as
Neumann, Daniel; Karl, Matthias; Radtke, Hagen; Neumann, Thomas (2019). MOM-ERGOM western Baltic Sea simulations with tagging of atmospheric nitrogen deposition by CMAQ. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/MOMERGOMBSCMAQ

BibTeX RIS
Description
will be provided as extra document (references, summary and add_infos)
Model: The ocean physics were simulated with the Modular Ocean Model(MOM) version 5.1 (Griffies, Fundamentals of Ocean Climate Models, 2014, https://press.princeton.edu/titles/7797.html). The whole Baltic Sea was modeled with a horizontal resolution of 3 n.m. x 3 n.m. and 134 vertical layers. A dynamic ice model simulates ice cover thickness and extent. MOM has been used and validated in several Baltic Sea studies (Neumann et al., 2015, doi: https://doi.org/10.1016/j.jmarsys.2015.08.001 Radtke et al., 2012, doi: https://doi.org/10.1029/2012JC008119 Schernewski et al., 2015, doi: https://doi.org/10.1016/j.marpol.2014.09.002).
The marine biogeochemical processes are simulated with the Ecological ReGional Ocean Model (ERGOM). It is coupled to MOM and shared the same model domain. ERGOM has been developed at the Leibniz Institute for Baltic Sea Research Warnemünde and is still under active development (Neumann, 2000, doi: https://doi.org/10.1016/S0924-7963(00)00030-0 Neumann et al., 2002, doi: https://doi.org/10.1029/2001GB001450 Kuznetsov and Neumann, 2013, doi: https://doi.org/10.1016/j.jmarsys.2012.10.011 Radtke et al., 2013, doi: https://doi.org/10.1016/j.jmarsys.2012.07.010 Neumann et al., 2015).
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_ship_N" indicating the total atmosheric and shipping-related nitrogen, respectively, in this variable.
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.
Description
Summary:
Findable: 6 of 7 level;
Accessible: 2 of 3 level;
Interoperable: 3 of 4 level;
Reusable: 5 of 10 level
Method
F-UJI online v2.2.1 automated
Method Description
Checks performed by WDCC. Metrics documentation: https://doi.org/10.5281/zenodo.4081213 Metric Version: metrics_v0.5
Method Url
https://www.f-uji.net
Result Date
2022-12-06
Result Date
2019-11-06
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
Method Url
https://www.wdc-climate.de/docs/WDCC_User_Guide_Data_v1.pdf
Result Date
2019-11-06
Contact typePersonORCIDOrganization
Contact
Dr. Thomas Neumann
0000-0002-5653-906X
Leibniz-Institut für Ostseeforschung Warnemünde
Author
Dr. Daniel Neumann
0000-0001-8574-9093
Leibniz-Institut für Ostseeforschung Warnemünde
Author
Dr. Matthias Karl
0000-0002-0821-018X
Helmholtz-Zentrum Geesthacht, Zentrum für Material- und Küstenforschung GmbH
Author
Dr. Hagen Radtke
-
Leibniz-Institut für Ostseeforschung Warnemünde
Author
Dr. Thomas Neumann
0000-0002-5653-906X
Leibniz-Institut für Ostseeforschung Warnemünde
Investigator
Dr. Daniel Neumann
0000-0001-8574-9093
Leibniz-Institut für Ostseeforschung Warnemünde
Metadata
Dr. Daniel Neumann
0000-0001-8574-9093
Leibniz-Institut für Ostseeforschung Warnemünde

Is documented by

[1] DOI Karl, 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] DOI Geyer, Beate. (2013). coastDat-2 COSMO-CLM. doi:10.1594/WDCC/coastDat-2_COSMO-CLM
[3] DOI Ménesguen, 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
[4] DOI Neumann, Thomas; Siegel, Herbert; Gerth, Monika. (2016). A new radiation model for Baltic Sea ecosystem modelling. doi:10.1016/j.jmarsys.2015.08.001
[5] DOI Radtke, H.; Neumann, T.; Voss, M.; Fennel, W. (2012). Modeling pathways of riverine nitrogen and phosphorus in the Baltic Sea. doi:10.1029/2012jc008119
[6] DOI Schernewski, 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
[7] DOI Neumann, 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

Is compiled by

[1] Griffies, Stephen M. (2005). Fundamentals of Ocean Climate Models. https://press.princeton.edu/titles/7797.html
[2] DOI Neumann, Thomas. (2000). Towards a 3D-ecosystem model of the Baltic Sea. doi:10.1016/S0924-7963(00)00030-0
[3] DOI Neumann, 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
[4] DOI Kuznetsov, Ivan; Neumann, Thomas. (2013). Simulation of carbon dynamics in the Baltic Sea with a 3D model. doi:10.1016/j.jmarsys.2012.10.011
[5] DOI Radtke, 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

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HBM-ERGOM western Baltic Sea simulations with tagging of atmospheric nitrogen deposition within the MeRamo project

[Entry acronym: MOMERGOMBSCMAQ] [Entry id: 3862865]