Last modified on Nov 08, 2023 12:43

Contributors: A. Hall (Telespazio Vega UK), J. Marsh (Telespazio Vega UK), J. Clark (Plymouth Marine Laboratory), S. Kay (Plymouth Marine Laboratory), J. A. Fernandes (AZTI)

Issued by: Jenny Marsh (TVUK)

Issued Date: 31/07/2019

Ref:  C3S_D422Lot2.PML.3.1_201907_Product_User_Guide_ERSEM_v1.1

Official reference number service contract: 2018/C3S_422_Lot2_PML/SC2

Introduction

Climate change is likely to have a significant impact upon the seas and oceans, affecting oceanic ecosystems and marine and coastal resources, such as natural fisheries, aquaculture; and services, such as tourism and recreational activities. In general, global climate models do not give the level of spatial and temporal detail needed to understand the ecological, social and economic impacts of climate change on regional shelf seas. To address this problem, and to allow for spatially detailed assessments of regional seas to be produced, an approach known as regional climate downscaling is often used. Regional downscaling involves running relatively high resolution models of limited areal extent that are driven at the boundaries by the outputs of coarser resolution models that are often global in extent.

This data set contains the outputs of two, regionally downscaled projections for European seas that have been generated using coupled hydrodynamic-biogeochemical models. In both cases, the European Regional Seas Ecosystem Model (ERSEM) is used to simulate marine biogeochemical processes. Two different hydrodynamic models are used: The Nucleus for European Modelling of the Ocean (NEMO) model, and the Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS).

ERSEM is an established ecosystem model for the lower trophic levels of the marine food web in the scientific literature, widely used in European waters. Since its original development in the early nineties (RD.7), it has evolved significantly from a coastal ecosystem model for the North Sea to a generic tool for ecosystem simulations from shelf seas to the global ocean (RD.8). The ERSEM model is composed of a set of modules that compute the rates of change of its state variables given the environmental conditions of the surrounding water body, physiological processes, and the result of predator–prey interactions.

This user guide provides an overview of the NEMO-ERSEM and POLCOMS-ERSEM models and configurations used; and the climate variables which they produce. Quality assurance information regarding both models is also provided. The user guide additionally contains information on how to access and download the data.

Reference Documents

The following is a list of documents that are referenced in this user guide. Where referenced in the text, these are identified as RD-n, where 'n' is the number in the list below:

RD.1. C3S-MFC model validation and known issues for the pan-European domain v1.

RD.2.  Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y., Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin, G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley, S., Stephens, N., and Torres, R. (2016). ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels, Geoscientific Model Development, 9, 1293-1339, https://doi.org/10.5194/gmd-9-1293-2016.

RD.3.  Butenschön, M. and Kay, S. (2016). Projections of change in key ecosystem indicators for planning and management of Marine Protected Areas: An example study for European seas, Estuarine Coastal and Shelf Science. DOI: 10.1016/j.ecss.2016.03.003

RD.4. Chantal Donnelly, Jafet C.M. Andersson & Berit Arheimer (2016). Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe, Hydrological Sciences Journal, 61:2, 255-273, DOI: 10.1080/02626667.2015.1027710

RD.5. OSPAR. (2017). 'Third Integrated Report on the Eutrophication Status of the OSPAR
Maritime Area'. OSPAR Publication 694.

RD.6.NEMO-ERSEM model validation

RD.7. Baretta, J. W., Ebenhöh, W., and Ruardij, P.: The European regional seas ecosystem model, a complex marine ecosystem model, Neth. J. Sea Res., 33, 233–246, doi:10.1016/00777579(95)90047-0, 1995.

RD.8. Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y., Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin, G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley, S., Stephens, N., and Torres, R. (2016). ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels, Geoscientific Model Development, 9, 1293-1339, https://doi.org/10.5194/gmd-9-1293-2016.

RD.9. Bruggeman, J., Bolding, K., 2014. A general framework for aquatic biogeochemical models. Environmental Modelling & Software 61: 249–265. DOI: 10.1016/j.envsoft.2014.04.002"

RD.10. Madec, G.: NEMO reference manual 3_6_STABLE: "NEMO ocean engine" Note du Pôle de modélisation, Institut Pierre Simon Laplace (IPSL), France, No 27, ISSN No 1288- 1619, 2016.

NEMO-ERSEM and POLCOMS-ERSEM Dataset

The dataset contains modelled projections of changes in marine physics and biogeochemical variables used to infer climate change indicators, as well as changes in the lower trophic levels of the marine food web. It covers the northwest European shelf, Mediterranean Sea and part of the North East Atlantic from 2006 up to 2049 or 2099, depending on the model output set used.
The ERSEM model is coupled to two hydrodynamic biogeochemical models configured for two different study areas: NEMO and POLCOMS. Hydrodynamic models calculate mixing and transport coefficients for each tracer in the biogeochemical model, which makes it possible to include the impact of transport and mixing processes on tracer concentrations. They also pass spatio-temporal information (e.g. temperature) about the physical environment to the biogeochemical model, which is then used to calculate rates of change for key processes (e.g. respiration, which is a temperature dependent process).
POLCOMS is coupled to ERSEM using a bespoke coupler, whereas NEMO is coupled to ERSEM using the Framework for Aquatic Biogeochemical Models coupler (RD.9). Both the NEMO and POLCOMS models require various types of input data to simulate the future climate, including data describing model initial conditions, atmospheric conditions, open ocean boundary conditions and land contributions of fresh water and nutrients. The hydrodynamic biogeochemical models are forced at the surface boundary using the outputs of atmospheric models, the details of which can be found in section 2.4.

The ERSEM Model

ERSEM is an ecosystem model of marine biogeochemistry and the lower trophic levels of the marine food web. It simulates the cycles of carbon and the major nutrient elements nitrogen, phosphorous, and silicon within the marine environment. Organisms at the bottom of the marine food web (phytoplankton) play an integral role in these cycles and are represented in the model. The four types of phytoplankton included in ERSEM are diatoms, which use silicon to build their outer cell walls; and three groups that are primarily distinguished by their size: the pico-, nano- and micro- phytoplankton. Zooplankton are heterotrophic organisms, meaning that they cannot produce their own food and instead rely on nutrition from plant and animal matter, mainly phytoplankton. This provides a vital link to commercially exploited species higher up the marine food web. Three types of zooplankton are represented in the model, which are again grouped according to their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. A schematic of ERSEM is shown in Figure 1.

Figure 1: Schematic of ERSEM, the European Regional Seas Ecosystem Model (RD.2)

NEMO-ERSEM

NEMO is a physical ocean circulation model that has been adapted to run in either regional or global configurations (RD.10). The NEMO-ERSEM dataset is based on the Atlantic Margin Model 7 km NEMO configuration. It extends over the northwest European shelf and northeast Atlantic Ocean, as shown in Figure 2. Although the domain extends beyond the shelf to include some of the adjacent Northeast Atlantic, the focus of this system is on the shelf itself and the deep water is primarily included to ensure there is appropriate cross-shelf exchange. Grid-points near to the model boundaries are strongly affected by the model boundary conditions and so products are provided for the interior of the domain only. The outermost 10 grid-points and points East of 10°E on the Baltic boundary are masked. Table 1 provides the dataset description for NEMO- ERSEM.
Due to the nature of NEMO's computational grid, horizontal velocities are evaluated at coordinate positions offset from those at which scalar variables (e.g. salinity) are evaluated. In order to make the dataset easier to use, all outputs have been interpolated onto the same grid as that on which the scalar variables are defined.

Figure 2: The northeast Atlantic domain.

Table 1: NEMO-ERSEM dataset description. Conventions are used in NetCDF files, and are required to ensure conforming datasets follow metadata standards e.g. each variable must have an associated description of what it represents, including physical units if appropriate.

POLCOMS-ERSEM

POLCOMS is a physical ocean circulation model which is tailored for the simulation of shelf-sea and coastal areas. To create the POLCOMS-ERSEM dataset POLCOMS has been coupled to ERSEM and run on a pan-European domain. As for the NEMO-ERSEM dataset, all variables have been interpolated on to the same vertical and horizontal grid in order to make analysis and visualization easier for users. The domain extends over the northwest European shelf and the Mediterranean Sea. Grid-points near to the model boundaries are strongly affected by the model boundary

conditions and so points on the Atlantic boundary have been masked. Figure 3 shows the extent of the pan-European domain, not including these masked points. Table 2 provides the dataset description for POLCOMS-ERSEM

Figure 3: The pan-European domain.

Table 2: POLCOMS-ERSEM dataset description

Dataset Production

Indicators from NEMO-ERSEM and POLCOMS-ERSEM are provided on the basis of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report scenarios with two Representative Concentration Pathways (RCPs), RCP 8.5 and RCP 4.5. An RCP is a greenhouse gas concentration (not emission) trajectory used by the IPCC to describe different climate scenarios, all

of which are considered possible depending on how much greenhouse gas is emitted in the future. RCP 8.5 is a 'business as usual' scenario, with high concentrations of greenhouse gases in the atmosphere, associated with unmitigated emissions and concentrations continuing to rise throughout the century. RCP 4.5 is a more moderate scenario that envisions peak concentrations at 2040 before declining. The products are available under both RCPs for 2006 onwards to provide continuity over the whole modelling period, but in practice differences between the RCPs do not emerge until the 2030s.
The POLCOMS-ERSEM and NEMO-ERSEM models were driven by global climate model projections generated for the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Global model outputs, interpolated onto the respective regional grids, were used at the open ocean boundary for both physical and biogeochemical conditions. Initial conditions for physical variables were taken from the driving global models. Initial conditions for biogeochemical variables were taken from a combination of global datasets and hindcast simulations. For POLCOMS-ERSEM, river discharge and N and P loadings were taken from E-HYPE model outputs (RD.4), using the same global climate model and a business-as-usual nutrient scenario. E-HYPE is a hydrological model from the Swedish Meteorological and Hydrological Institute (hypeweb.smhi.se). Climatological water and nutrient flows were used at the Baltic boundary, and these were kept constant through the modelled period. For NEMO-ERSEM, climatological river inputs were used.
The two model configurations used downscaled atmospheric forcing data generated using the Swedish Meteorological and Hydrological Institute (SMHI) Rossby Centre Regional Atmospheric Model (RCA4). These regional atmospheric model outputs were taken from the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative; for further information see www.cordex.org. Below is a short summary of the surface atmospheric forcing data used with each model:

• The Northeast Atlantic domain (NEMO model)
  • CMIP5 driving model: Met Office Hadley Centre (MOHC) Hadley Global Environment Model 2 – Earth System (HadGEM2-ES). Further information regarding the model is available here: https://portal.enes.org/models/earthsystem-models/metoffice-hadley-centre/hadgem2-es
  • Regional climate model: SMHI RCA4, as for the pan-European domain
  • Greenhouse gas concentration scenarios: RCP 4.5 and RCP 8.5
  • Variables: 6 hourly surface forcing 10 m wind components, sea level pressure, 2 m air temperature and relative humidity, daily precipitation, shortwave and longwave radiation flux and cloud cover
• The pan-European domain (POLCOMS model)
  • CMIP5 driving model: Max Planck Institute-Earth System Model-Low Resolution (MPI-ESM-LR). Further information regarding the model is available here: https://www.mpimet.mpg.de/en/science/models/mpi-esm/
  • Regional climate model: Swedish Meteorological and Hydrological Institute Rossby Centre Regional Atmospheric Model. Further information regarding the model is available here: https://www.smhi.se/en/research/research-departments/climate-research-at-the-rossby-centre/rossby-centre-regional-atmospheric-model-rca4-1.16562
  • Greenhouse gas concentration scenarios: RCP 4.5 and RCP 8.5
  • Variables: 6 hourly surface forcing 10 m wind components, sea level pressure, 2 m air temperature and relative humidity, daily precipitation, shortwave and longwave radiation flux and cloud cover

The dataset production workflow is available in Figure 4.

Figure 4: Production workflow

The dataset is provided through the C3S Climate Data Store (CDS). Details on how to access the data are provided in section CDS Catalogue Download5.1.

Climate Variables

The following variables are available to download as part of the NEMO-ERSEM and POLCOMS- ERSEM dataset.

Apparent Oxygen Utilisation

Apparent oxygen utilisation (AOU) is the difference between the dissolved oxygen concentration and the measured oxygen concentration in water with the same physical and chemical properties.

Such differences typically occur when biological activity acts to change the ambient concentration of oxygen, for example, during photosynthesis. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, depth, latitude, longitude) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible.

• Temporal resolution: Daily
• Units: mmol m^-3

Concentration of Nitrate and Nitrite

The concentration of nitrate and nitrite in sea water, expressed as moles per unit volume. Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, depth, latitude, longitude) and is calculated by ERSEM. Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, including inputs from rivers, atmospheric deposition, and various biological processes involved in the breakdown of organic material by marine bacteria.

• Temporal resolution: Monthly
• Units: mmol m^-3

Concentration of Phosphate

The concentration of phosphate in sea water, expressed as moles per unit volume. Phosphate is taken up from sea water by marine phytoplankton, which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, depth, latitude, longitude) and is calculated by ERSEM. Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton reduce concentration as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. The concentration of phosphate is also influenced by inputs from rivers and various biological processes involved in the breakdown of organic material by marine bacteria.

• Temporal resolution: Monthly
• Units: mmol m^-3

Concentration of Silicate

The concentration of silicate in sea water, expressed as moles per unit volume. Silicate is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are photosynthesizing algae (phytoplankton) that utilise silicic acid (the primary form of silicate in sea water) to build their cell

wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export, the process by which carbon is transferred to the deep ocean from productive near-surface waters, which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, depth, latitude, longitude), and is calculated by ERSEM.

• Temporal resolution: Monthly
• Units: mmol m^-3

Dissolved Oxygen

The concentration of dissolved molecular oxygen in sea water, expressed as moles per unit volume. The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shellfish. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration. Oxygen concentrations above 190 mmol m^-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m^-3 are considered to be a source of serious concern (RD.5). The concentration of dissolved molecular oxygen is a 4D field (time, depth, latitude, longitude), and is calculated by ERSEM.

• Temporal resolution: Daily, monthly
• Units: mmol m^-3

Euphotic Depth

The radiation from the sun impinging on the ocean is a major source of energy, driving life in the ocean and impacting other processes. Light is reduced in the water column due to its absorption by in-water constituents. The euphotic depth is the depth at which downwelling irradiance has been reduced to 1% of that at the surface, with irradiance defined as the density of radiation incident on a given surface. Downwelling irradiance includes both direct and diffuse contributions. The part of the water column above the euphotic depth is the productive part of the water column: below the euphotic depth there is not enough light to support photosynthesis. The euphotic depth is a 3D field (time, latitude, longitude) and is calculated by ERSEM.

• Temporal resolution: Daily
• Units: m

Net Primary Production

Net primary production is the biomass produced by organisms at the bottom of the food chain, once their own energy requirements have been taken into account. These organisms are called primary producers and in this case are phytoplankton. Phytoplankton produce biomass by photosynthesis but also use consume it for respiration: net primary production is the difference between production and consumption, expressed as carbon produced per unit volume. Primary production varies with depth in the water column, because more light is available for photosynthesis nearer the surface. The dataset includes net primary production as a 4D field (time, depth, latitude, longitude) that is made up of contributions from the four different phytoplankton

groups included in ERSEM (diatoms, pico-, nano-, and micro-phytoplankton). Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The region covered by the model domains is characterised by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients).

• Temporal resolution: Monthly
• Units: kg C m^-3 s^-1

Organic Carbon in the Water Column

The mass concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. The mass concentration of total organic carbon is a 4D field (time, depth, latitude, longitude).

• Temporal resolution: Monthly
• Units: kg C m^-3

Phytoplankton Carbon

The mass concentration of phytoplankton carbon in sea water. Phytoplankton are primary producers that are either prokaryotic (single celled organisms without a nucleus) or eukaryotic (organisms with cells that contain a nucleus). They live near the water surface where there is sufficient light to support photosynthesis. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups. The mass concentration of phytoplankton carbon is a 4D field (time, depth, latitude, longitude), which exhibits large spatial and temporal variations across the model domain.

• Temporal resolution: Monthly
• Units: kg C m^-3

Potential Energy Anomaly

Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, latitude, longitude). The higher the potential energy anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn.

• Temporal resolution: Monthly
• Units: J m^-3

Saturation State of Aragonite

Aragonite is a carbonate mineral, and is one of the more soluble forms of calcium carbonate (CaCO3). It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton (self-feeding plankton) to build physical structures. The saturation state of aragonite

is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is represented as a floating point number. When it is greater than one, marine organisms can generate physical structures made from aragonite. When the saturation state is less than one, meaning the aragonite mineral tends to dissolve in seawater.
When carbon dioxide is added to the sea from the atmosphere, the pH of the water tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite, causing aragonite to dissolve. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, depth, latitude, longitude) and is calculated by ERSEM.

• Temporal resolution: Monthly
• Units: None

Sea Water pH

The pH is a measure of how acidic or alkaline the water is. It is calculated by the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic the solution. The pH is measured on a scale of 0-14. A pH of 0 indicates strong acidity, whereas a pH of 14 indicates strong alkalinity. A pH of 7 is neutral. Today, ocean water is normally slightly alkaline, or basic, with a surface water pH of about 8.1. The pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Sea water pH is a 4D field (time, depth, latitude, longitude) and is calculated by ERSEM. The pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton.

• Temporal resolution: Monthly
• Units: None

Sea Water Potential Temperature

This is the temperature a parcel of sea water would have if moved, without loss or gain of heat, to sea surface pressure. It is widely used in oceanography to represent the temperature of the water. The potential temperature field is 4D (time, depth, latitude, longitude), and is calculated by the physical circulation model (NEMO or POLCOMS). The area covered by the model domain is characterised by large latitudinal and seasonal variations in surface temperature. The lowest surface temperatures are found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 °C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable and much lower. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and fish species are generally adapted to a specific range of temperatures.

• Temporal resolution: Daily, monthly
• Units: degrees Celsius (°C)

Sea Water Salinity

Salinity is the amount of salt dissolved in a body of water, where 1 Practical Salinity Unit (PSU) = ~1 gram of salt per kilogram of water. It is a 4D field (time, depth, latitude, longitude), and is calculated by the physical circulation model (NEMO or POLCOMS). Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterised by relatively high salinity values, which may approach 40 PSU. The salinity of sea water influences ocean currents and mixing.

• Temporal resolution: Daily, monthly
• Units: PSU

Secondary Production

Secondary production is the difference between the organic carbon consumed by zooplankton and the inorganic carbon produced by zooplankton respiration. It is a 4D field (time, depth, latitude, longitude) and is made up of contributions from the three different zooplankton groups included in ERSEM (heterotrophic nanoflagellates, microzooplankton and mesozooplankton). Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species higher up the food chain. The area covered by the model domain is characterised by large spatial and seasonal variations in net secondary production, which reflect differences in food availability and environmental conditions.

• Temporal resolution: Monthly
• Units: kg C m^-3 s^-1

Total Chlorophyll-A

The concentration of chlorophyll-a in the water column, due to all phytoplankton types. Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. Chlorophyll-a is the most commonly occurring form of chlorophyll. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. The chlorophyll field is 4D (time, depth, latitude, longitude), and is calculated by ERSEM.

• Temporal resolution: Daily, monthly
• Units: mg m^-3

U-Component of Water Velocity

The horizontal velocity of water is broken up into Northward and Eastward components. The U- component refers to the Eastward component. "Eastward" indicates a vector component which is positive when directed eastward and negative when westward. Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in excess of 1 m s^-1. In POLCOMS-ERSEM, the velocity components are computed as 25 hour mean values to remove the major tidal effects. In the NEMO-ERSEM dataset, velocities are supplied as 24 hour mean values. The horizontal velocity of a water parcel is a 4D field (time, depth, latitude, longitude) and is calculated by the physical circulation model (NEMO or POLCOMS).

• Temporal resolution: Daily
• Units: m s^-1

V-Component of Water Velocity

The V-component of horizontal water velocity refers to the Northward component. "Northward" indicates a vector component which is positive when directed northward and negative when southward. It is influenced by the same factors as the U-component and supplied at the same mean

values. The horizontal velocity of a water parcel is a 4D field (time, depth, latitude, longitude) and is calculated by the physical circulation model (NEMO or POLCOMS).

• Temporal resolution: Daily
• Units: m s^-1

Zooplankton Carbon

The mass concentration of zooplankton carbon in sea water. Zooplankton, split into heterotrophic nanoflagellates, micro-zooplankton and meso-zooplankton, are heterotrophic organisms (meaning they cannot produce their own food) that feed on both living and non-living organic matter within the water column. The mass concentration of zooplankton carbon corresponds to the mass of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from the three different zooplankton groups. The mass concentration of zooplankton carbon is a 4D field (time, depth, latitude, longitude), which exhibits large spatial and temporal variations across the model domain.

• Temporal resolution: Monthly
• Units: kg C m^-3

Quality Assurance

POLCOMS-ERSEM

The POLCOMS-ERSEM model has been validated through comparison to satellite data, using monthly values for years 1998-2015. Satellite chlorophyll data came from the ESA Climate Change Initiative (CCI) Ocean Colour project. Sea surface temperature is from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset. The model broadly reproduces the temporal and spatial patterns of chlorophyll concentration across the region. However, model estimates tend to be higher than those from satellite, especially in regions of high chlorophyll. Sea surface temperature also produced a good spatial and temporal match between the model and the satellite data. The POLCOMS-ERSEM model has been extensively used in studies of the Northwest European Continental Shelf, and has been applied successfully in the Mediterranean Sea (RD.2, RD.3).
The POLCOMS-ERSEM model contains the following known issues in model outputs:

• Mediterranean phosphate values are too high. The effect of this is that the modelled ecosystem is limited by nitrogen instead of phosphorus. However, tests suggest that the resulting productivity is similar.
• Nutrients and pH near river mouths are inaccurate. River inputs of nutrients and dissolved inorganic carbon were inaccurate affecting biogeochemical model outputs around river mouths. The pH in the western Mediterranean, influenced by the Rhône output, is very affected and should not be used. Other biogeochemical variables should be treated with caution near large river mouths. There is also excess nitrate input from the Baltic Sea leading to over-high production in the Norwegian Trench. River discharge volume and physical model outputs such as temperature and salinity are not affected.

Further details regarding model validation and known issues for the POLCOMS-ERSEM model covering the pan-European domain is available in RD.1.

NEMO-ERSEM

Quality assurance information regarding the model validation process and known issues is available in RD.6.

Dataset User Guide

CDS Catalogue Search

The dataset is called 'Marine Properties and Food Web Monthly and Daily Data for the European and Mediterranean Sea Shelf from 2006 to 2100 Derived from Climate Projections' in the Climate Data Store (CDS). It can be easily discovered in the CDS Catalogue by searching for 'Marine' or the full dataset name. To search the CDS, select the search tab and enter any keywords into the search box. There are filters to refine the search if necessary.

CDS Catalogue Download

The following headings describe each section of the download tab for this dataset.

Model

There are two models available to select, POLCOMS-ERSEM and NEMO-ERSEM. A description of these models can be found in sections 2.2 and 0. In short, the NEMO-ERSEM model has higher resolution but only covers the northwest European shelf and the years up to 2049. The POLCOMS- ERSEM model has a lower resolution but covers the full pan-European domain up to the year 2099.

Experiment

There are two future greenhouse gas concentration scenarios available to download, RCP 4.5 and RCP 8.5. RCP 4.5 is a moderate scenario, envisioning peak concentrations at 2040 before declining. RCP 8.5 is higher emissions scenario, envisioning concentrations continuing to rise throughout the century. Both RCPs are available for each model option.

Variable

A full description of the climate variables available under the 'Variable' heading can be found in this user guide in section 3. Climate variables are available to download as part of the NEMO-ERSEM and POLCOMS-ERSEM dataset, either individually or multiple at once.

Vertical Levels

The dataset can be downloaded at either surface level only, the entire water column, or both, depending on the selection of variables.

Time Frequency

Time frequency refers to both the duration for the time averaging and frequency at which averages are stored. Options of 'Day' or 'Month' are available based on the variable selection. Variables is the dataset are available as monthly averages and in some cases also as daily averages.

Year

The dataset is available to download by year. It is also possible to download all years at once. Some years may not be available depending on which model is selected.

Month

The dataset is available to download by month. It is also possible to download all months at once, or just the month or months of interest.

Format

The dataset can either be downloaded as a zip file (.zip) or a compressed tar file (.tar.gz). The file format is NetCDF.

Dataset Applications

The following are provided to give indications of how the NEMO-ERSEM and POLCOMS-ERSEM datasets have been used within the C3S Marine, Coastal, and Fisheries project.

Eutrophication

Coastal areas of Europe are commercially important for fishing and tourism, yet are subject to the increasingly adverse effects of harmful algal blooms and eutrophication. Eutrophication is the anthropogenic enrichment of water by nutrients that causes an accelerated growth of algae and higher forms of plant life, which produce undesirable disturbances to the balance of organisms in the water and to the quality of the water.
A range of biological variables respond to increases in nutrients. In turn, this can lead to disturbances in the organisms present in the water and its associated quality. The NEMO-ERSEM and POLCOMS-ERSEM dataset simulates the cycles of nutrient elements nitrogen, phosphorous, and silicon, and can therefore be used to predict and monitor eutrophication. Phytoplankton is affected by eutrophication and is also included in the dataset. The NEMO-ERSEM and POLCOMS-ERSEM dataset also includes a total chlorophyll-a climate variable, which can be used to monitor and predict eutrophication rates.

Fisheries and Aquaculture

The European Union (EU) requires fishing to be environmentally friendly, economically viable and socially sustainable to provide long-term European food security given prevailing and future climatic conditions. EU Policies also aim to boost aquaculture, and Strategic Guidelines have been published outlining the common priorities and general objectives.
Climate change may create new opportunities for commercial exploitation, in terms of the availability of new species to fisheries (range shifts) and conditions more suitable to the growth of new farmed products (e.g. warm water fishes and shellfish in temperate coastal and offshore areas). However, climate change is also expected to affect our overall capacity to achieve these ambitions. Consequently, a greater understanding is urgently required to ensure that management measures remain appropriate and achievable. The NEMO-ERSEM and POLCOMS-ERSEM dataset can aid this understanding by providing data on the physical and biogeochemical properties of the water, which may affect fisheries and aquaculture.

Marine Spatial Planning

Marine spatial planning is one the most important activities for marine-focussed policy-makers and regulators. Marine spatial planning, including the designation of conservation areas, is usually undertaken based on the current uses of territorial waters, advising exclusive use or co-location of multiple uses based on trade-offs across economic sectors, as well as maximising environmental sustainability.
This approach may be informed by stock assessment processes and other scientific advice that considers the progression of an ecosystem and its resources to the present time. However, based on this information alone, it is difficult to consider within planning mechanisms how pervasive and wide-scaled processes linked to climate change are and will continue to modify the distribution and availability of marine living resources, as well as the distribution of habitats suitable for species of conservation value. This is because climate change imposes combinations of environmental stressors and ecosystem conditions on marine species that may be markedly different from those historically observed within a region.
Marine spatial planning commitments can often take no account of how resources could change in time. This is important since changes happening in the ocean, driven by pressures associated with climate change, will modify the future distribution of marine resources underlying the conservation, fisheries and aquaculture sectors.
The NEMO-ERSEM and POLCOMS-ERSEM dataset can be used to support marine spatial planning by providing a sound scientific basis with which to assess dependencies between the status of environmental conditions that may affect fisheries returns and marine conservation effectiveness, including present and future climate change. Models can also offer the possibility to explore management scenarios and anticipate 'surprises', such as regime shifts, trophic cascades and bottlenecks in human responses resulting from the pressures of climate change on marine species.

Natural Capital Accounting

Natural capital refers to the world's stocks of natural assets, which provide goods and services fundamental to supporting economic development and human well-being. Natural capital accounting provides a structured approach to recording and monitoring the extent and condition of natural resources and ecosystems over time.
In the past, measures of human interactions with natural capital in the marine domain have been restricted to measurements of the income generated for the use of the natural resource, such as income from the sale of wild caught fish. It has long been recognised, though, that focussing solely on measuring income omits changes in the stocks of natural assets, often leading to their mismanagement. This is most clearly seen in the instances of overexploitation of fisheries in the pursuit of income growth. More recently, the physical assessments of marine natural capital accounts are based on the ecosystem condition (compiled from key characteristics) and extent. The NEMO-ERSEM and POLCOMS-ERSEM dataset contains data on lower levels of the marine food web and information concerning marine physics and biogeochemistry, therefore would be useful when considering natural capital accounting.

Related articles

• Page:
  NEMO-ERSEM and POLCOMS-ERSEM: User Guides for Products (v1.21)
• Page:
  NEMO-ERSEM and POLCOMS-ERSEM: User Guides for Products (v1.1)
• Page:
  Summary indicators (Tier 1): Product User Guide
• Page:
  Risk indicators (Tier 3): Product User Guide
• Page:
  Loss indicators (Tier 3): Product User Guide