MOM-ERGOM western Baltic Sea simulations with tagging of phosphorus inputs of the Warnow River, base scenario, v05 Unterwarnow turnover

doi:10.26050/WDCC/MOMERGOMBSWRU_3873382

Neumann, Daniel et al.

ExperimentDOI
Biogeochemical CycleERGOMMOMocean simulation
Summary
A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the years 1995 to 2014 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1984 and, then, from 1985 to 1994 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). The model simulation was forced by coastDat2 COSMO-CLM data (doi:10.1594/WDCC/coastDat-2_COSMO-CLM). Riverine phosphorus input of the Warnow River was calculated with the Soil & Water Assessment Tool (SWAT; Bauwe et al., 2019, doi:10.1016/j.ecohyd.2019.03.003). Phosphorus from the Warnow River 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 phosphorus-containing model variables exist twice in the output: once as regular variables and once as tagged variable.

The default phosphorus input by the Warnow River based on real phosphorus release patterns and real atmospheric conditions was used (PhosWaM SWAT case "ist"). The turnover of phosphorus compounds in the Unterwarnow was calculated based on the "Unterwarnow turnover estimation v05" (see final project report of PhosWaM for details).

The simulation was performed at the North-German Supercomputing Alliance (HLRN). 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
PhosWaM (Phosphor von der Quelle bis ins Meer - Integriertes Phosphor- und Wasserressourcenmanagement für nachhaltigen Gewässerschutz)
Contact
Dr. Thomas Neumann (
 thomas.neumann@nullio-warnemuende.de
 0000-0002-5653-906X)
Spatial Coverage
Longitude 8.2333 to 30.6333 Latitude 53.8333 to 65.9333 Altitude: 0 m to -268 m
Temporal Coverage
1995-01-01 to 2014-12-31 (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
21.47 GiB (23049968051 Byte)
Format
NetCDF
Status
completely archived
Creation Date
Future Review Date
2029-07-27
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Cite as
Neumann, Daniel; Bauwe, Andreas; Radtke, Hagen; Neumann, Thomas (2019). MOM-ERGOM western Baltic Sea simulations with tagging of phosphorus inputs of the Warnow River, base scenario, v05 Unterwarnow turnover. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/MOMERGOMBSWRU_3873382

BibTeX RIS
[Entry acronym: MOMERGOMBSWRU_3873382] [Entry id: 3873382]
Description
will be provided as extra document (see references additional infos and summary)
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 phosphorus-containing prognostic variable has a counterpart with the suffix "_warnow_P" indicating phosphorus from Warnow River input 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. The sediment is represented by an one-layer sediment only but contains relevant sediment processes such as phosphate release under anoxic conditions or denitrification.
Result Date
2019-11-20
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
2019-11-20
Contact typePersonORCIDInstitute
Contact Dr. Thomas Neumann 0000-0002-5653-906XLeibniz-Institut für Ostseeforschung Warnemünde
Author Dr. Daniel Neumann 0000-0001-8574-9093Leibniz-Institut für Ostseeforschung Warnemünde
Author Dr. agr. Andreas Bauwe-University of Rostock
Author Dr. Hagen Radtke-Leibniz-Institut für Ostseeforschung Warnemünde
Author Dr. Thomas Neumann 0000-0002-5653-906XLeibniz-Institut für Ostseeforschung Warnemünde
Investigator Dr. Daniel Neumann 0000-0001-8574-9093Leibniz-Institut für Ostseeforschung Warnemünde
Metadata Dr. Daniel Neumann 0000-0001-8574-9093Leibniz-Institut für Ostseeforschung Warnemünde

Cites

[1] DOI Neumann, Daniel. (2019). Abschätzung der biogeochemischen Umsetzung, Sedimentation und Resuspension von Phosphorverbindungen in der Unterwarnow. doi:10.12754/misc-2019-0002

Is documented by

[1] DOI Bauwe, Andreas; Eckhardt, Kai-Uwe; Lennartz, Bernd. (2019). Predicting dissolved reactive phosphorus in tile-drained catchments using a modified SWAT model. doi:10.1016/j.ecohyd.2019.03.003
[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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