ERSEM model domains
ERSEM is used in a variety of geographical regions, coupled to different physical (hydrodynamic) models. The table bellow outlines several of these setups.
Area |
Spatial Scale |
Quality (data used) |
||
Domain |
Res.(km) |
|||
Water column (GOTM-ERSEM) |
Various locations |
N/A |
4,6,8,14,16,22 |
|
Global (NEMO) |
Global |
111 |
8,11,19,20,21 |
|
North Atlantic (NEMO) |
20N-8ON |
28 |
8,11,19,20,21 |
|
North Atlantic (NEMO shelf) |
20N-80N |
7 |
8,11,19,20,21 |
|
Irish and Celtic Seas (FVCOM) |
41.8-56.8N, 9.6-2.5W |
3.5 |
7,23,24,25 |
|
NW European shelf (NEMO) |
40N-65N, 2OW-13E |
7 |
3,8,11,12,13,14,19 |
|
NW European shelf (POLCOMS) |
40N-65N, 20W-13E |
10 |
3,8,9,10,11,12, 13,14,15,16,17,19 |
|
NW European shelf (POLCOMS + data assimilation) |
40N-65N, 20w-13E |
10 |
2,3,8,9,10,12,13, 14,15,17 |
|
AMM7NW European-Baltic-GCOMS |
46.4-63N, 17.5W-13E |
10 |
3,8,11,14,17,18 |
|
Western European Channel (WEC) |
48.5-50.9N, 7.6-1.25W |
1.9 |
2,4,8,12,13,14 |
|
WEC (data assimilation) |
48.5-50.9N, 7.6-1.25W |
7 |
1,2,4,8,12,13,14 |
Key
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Note
The table of regional setups is not yet complete, please stay tuned for updates.
Existing uses:
Natural resources: understanding biogeochemical cycles, biodiversity and valuation of ecosystem services.
Resilience to environmental hazards: eutrophication, microplastics, fishing pressure, invasive species and harmful algal blooms. Impacts of offshore renewable energy and ecological risks of carbon capture and storage.
Environmental change: climate change impacts, ocean acidification, multiple stressors, and sustainable fisheries.
Run operationally by the UK Met Office to predict water quality.
Estimation of the carbon and nutrient budgets of the UK shelf.
Potential new uses:
Understanding shelf seas carbon (“blue carbon”) and nutrient budgets (past and future climate).
Expansion to represent biodiversity-relevant processes over a range of spatial and temporal scales, and simulate changes in function in the context of ecosystem services.
Implementation and testing scalable models of differing complexity.
Key modelling issues:
Setting inputs (parameterisation} and testing outputs against real data (calibration) is an essential, but resource-intensive and on-going process to ensure quality and improve predictions. Understanding the impact of changing inputs on the outputs from the models (sensitivity) and the effect of uncertainty in model parameters on robustness of model predictions.
Challenge to assess model capability with respect to seasonal variability, long-term changes, regime shift and tipping points due to limitations of the data available.
Complexity of model leads to a need for significant interpretation and explanation for stakeholders.
Potential mismatch between scales of model output and data sets.
Significant expertise needed to operate system and high performance parallel computing facility required for three-dimensional full scale simulations.