Last modified on Jun 20, 2024 14:55

Contributors: A. Hall (Telespazio Vega UK), J. Marsh (Telespazio Vega UK), G. Tilstone (Plymouth Marine Laboratory), P. Land (Plymouth Marine Laboratory), J. Fernandes (AZTI) 

Issued by: Jenny Marsh (TVUK)

Issued Date: 31/07/2019 

Ref:  C3S_D422Lot2.PML.3.1_201907_Product_User_Guide_Eutrophication_v1.2

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. The dataset includes a set of Climate Impact Indicators (CIIs) to download from 2006 up to 2100 under different emission concentration scenarios, depending on the origin selected. It is available to download from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
This dataset includes eutrophication indices based on chlorophyll-a anomalies, calculated using the 90th percentile (P90). The P90 is a measure of statistical distribution used to find the value for which 90% of data points are smaller and 10% are greater. The P90 of chlorophyll-a is used as a measure of eutrophication in coastal waters and is a robust ecological indicator of trophic ecological status, to detect abnormal levels of chlorophyll-a concentration in an ecosystem.
The dataset has two main origins: Copernicus Marine Environment Monitoring Service (CMEMS) Ocean Colour satellite product, and the European Regional Seas Ecosystem Model (ERSEM).
CMEMS provides oceanographic products and services for maritime safety, coastal and marine environment, climate and weather forecasting and marine resources users. A combination of ocean observations (measured in the sea), satellite remotely sensed images and ocean forecast models comprise the CMEMS catalogue.
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 nutrient elements nitrogen, phosphorous, and silicon within the marine environment. To produce the eutrophication indicators, the ERSEM model is coupled to the Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS) and Nucleus for European Modelling of the Ocean (NEMO) models. Both POLCOMS and NEMO are physical models that can simulate regions in both the deep ocean and continental shelf, as well as modelling water, temperature, salinity, and currents.

Reference Documents

The following is a list of reference documents with a direct bearing on the content of 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. User Guide for Products: NEMO-ERSEM and POLCOMS-ERSEM Dataset v1.1
RD.2. CMEMS Quality Information Document: Ocean Colour Production Centre for the Atlantic and Arctic Observation Products. Issue 1.6. Document reference: CMEMS-OC-QUID- 009-066-067-068-069-088-091.
Available at: http://resources.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-066-067-068-069-088-091.pdf
RD.3. CMEMS Ocean Colour Satellite Products. Available at: {+}http://marine.copernicus.eu/tutorials/cmems-ocean-colour-satellite/

RD.4.Ocean Colour Climate Change Initiative (OC_CCI) – Interim Phase: Product User Guide. Issue 4.1.1. D3.4 PUG. Available at: https://esa-oceancolour-cci.org/?q=documents

Eutrophication Dataset

This dataset includes eutrophication indices based on the P90 of chlorophyll-a. The measure of eutrophication used here is the proportion of days in a time series month exceeding that day's climatological P90. P90 is usually observed to be 0.1 in steady state conditions. The anomaly is the difference between the calculated value and the expected 0.1, ranging from -0.1 to 0.9. The dataset also provides the cumulative anomaly and the rate of change, which is the mean annual change in proportion from the start of the time series to the given year and month.
The dataset has two main origins: CMEMS Ocean Colour satellite product, and ERSEM. Full dataset details can be found in Table 1.
Table 1: Dataset description

The dataset is provided through the C3S CDS. Details on how to access the data are provided in section 3.

CMEMS

CMEMS provides environmental data, oceanographic products, and services for maritime safety, coastal and marine environment, climate and weather forecasting, and marine resources users. A combination of ocean observations (measured in the sea), satellite remotely sensed images and ocean forecast models comprise the CMEMS catalogue.
The eutrophication dataset uses the CMEMS Ocean Colour data for the the Northeast Atlantic and Western Mediterranean Sea. Further information regarding the Ocean Colour data is available in RD.3. The input dataset for the production is from the ESA Ocean Colour Climate Change Initiative (OC_CCI) project. The OC_CCI produces daily global chlorophyll-a concentration at 4 km resolution derived from the NASA Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution

Imaging Spectrometer (MODIS) and ESA Medium Resolution Imaging Spectrometer (MERIS) satellite sensors. Full information regarding the production of the CMEMS OC_CCI dataset is available in RD.4.
CMEMS satellite data produces eutrophication indices based on the 90^th percentile of chlorophyll-a. Here a climatological P90 value in one map grid cell at a given time of year is calculated by combining data at that time of year from all years in the climatology. Increases in the frequency with which chlorophyll-a exceeds the climatological P90 value potentially indicate eutrophication. The chlorophyll-a 90^th percentile is computed using long time series data from September 1997 to December 2005 as a baseline period to calculate steady values. The measure of eutrophication used is the proportion of days in a time series month exceeding that day's climatological P90 at that location. It is has dimensionless units and its expected value in the absence of changes in eutrophication state is 0.1.

ERSEM

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 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, therefore, are represented in the model. The four types of phytoplankton included in ERSEM are diatoms, which use silicon to build their outer walls, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro- phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. Organisms that feed upon phytoplankton (zooplankton) are also represented as they provide a vital link to commercially exploited fish and shellfish higher up the food web. The zooplankton pool is divided into hetero nanoflagellates, microzooplankton and mesozooplankton. The classification is based on their size, and reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. As ERSEM is a model, it can provide future projections.
The ERSEM model is coupled to the POLCOMS and NEMO models. Both POLCOMS and NEMO are physical models, covering different areas of all European seas, that can simulate regions in both the deep ocean and continental shelf, as well as modelling water, temperature, salinity, and currents. POLCOMS covers a pan-European domain at a resolution of 11 km and up to the end of 2099. NEMO offers a higher resolution of 7 km but offers a smaller coverage of the northwest European shelf and northeast Atlantic up to the end of 2049. These coverages are displayed in Figure 1. Further information on POLCOMS, NEMO, and ERSEM is available in RD.1.

Figure 1: Pan-European (left) and northwest European shelf and northeast Atlantic (right) domains

Similarly to the CMEMS origin, here a climatological P90 value in one model grid cell at a given time of year is calculated by combining data at that time of year from all years in the climatology. The chlorophyll-a 90^th percentile is computed using long time series data from 1986 to 2005 as a baseline period to calculate steady values. The measure of eutrophication used for this origin is the same as for CMEMS.
The CIIs from ERSEM that are required to be able to monitor eutrophication are:

• Nitrate concentrations in sea water
• Phosphate concentrations in sea water

Under ERSEM, there are two future greenhouse gas scenarios based on Representative Concentration Pathways (RCPs) to allow different eutrophication predictions. An RCP is a greenhouse gas concentration (not emission) trajectory used by the Intergovernmental Panel on Climate Change to describe different climate scenarios, all of which are considered possible depending on how much greenhouse gases are emitted in the future. RCP 8.5 represents unmitigated and extreme climate changes, with emissions continuing to rise throughout the 21^st century. RCP 4.5 represents modest climate change, with emissions peaking at 2040 before declining. The projections are driven by regional dynamically down-scaled projections of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) simulations.

Dataset Production Workflow

A full production workflow is available in Figure 2. Both CMEMS and ERSEM produce the same indicators, which are described in section 2.4.

Figure 2: Production workflow

Climate Impact Indicators

The following CIIs are available to download as part of the eutrophication dataset.

Anomaly

The difference between the proportion of days in a time series month exceeding that day's climatological P90 at that location and the expected value of 0.1.

• Units: Dimensionless

Anomaly Gradient

Annual rate of change of proportion from the start of the time series to the given year and month.

• Units: Dimensionless

Cumulative Anomaly

The cumulative sum of the monthly anomaly from the start of the time series to the given year and month, a measure of consistent trends in the anomaly.

• Units: Cumulative difference in proportion (hence may exceed 1)

Anomaly P Value

The two-tailed P value of the anomaly gradient relative to a null hypothesis of no change, a measure of whether the slope found is statistically significant.

• Units: Dimensionless

Cumulative Anomaly Standard Deviation

The standard deviation of the cumulative anomaly. Cumulative anomalies large compared with their standard deviations are statistically significant

• Units: Cumulative difference in proportion

Quality Assurance

As metrics of uncertainty in the data, the standard deviation of the cumulative anomaly and the anomaly P value are provided.

CMEMS

The input dataset for the production is from the ESA OC_CCI project. The OC_CCI project developed and implemented a method of band-shifting, bias correcting, and merging data from multiple ocean colour sensors: SeaWiFs global area coverage, MODIS Aqua, MERIS, SeaWiFS local area coverage, and VIIRS. The performance of the available atmospheric correction and in-water algorithms were assessed in an open process and the best performing algorithms were selected according to published criteria. No major or data errors are known. Full quality assurance information for the CMEMS Ocean Colour products is available in RD.2 and RD.4.

ERSEM

ERSEM uses CMIP5 data as an input. The POLCOMS-ERSEM model has been validated through comparison to satellite values from the ESA Climate Change Initiative (CCI) Ocean Colour project and the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset, using monthly values for years 1998-2015. The model broadly reproduces the temporal and spatial patterns of chlorophyll concentration and sea surface temperatures across the region.
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 discharge volume and physical model outputs such as temperature and salinity are not affected.

Further quality assurance information regarding the NEMO-ERSEM and POLCOMS-ERSEM can be found in RD.1.

Dataset User Guide

CDS Catalogue Search

The full name of the dataset within the CDS is 'Eutrophication indicators for the Northwest European Shelf and Mediterranean Sea from 2006 up to 2100'. The dataset can be found in the CDS Catalogue by searching for 'Eutrophication' or for 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

Origin

The origin options are CMEMS satellite product, NEMO-ERSEM, and POLCOMS-ERSEM. Both POLCOMS and NEMO models are physical models that can simulate regions in both the deep ocean and continental shelf, as well as modelling water, temperature, salinity, and currents. POLCOMS is a pan-European model with a resolution of 11 km, whereas NEMO covers the northwest European shelf and northeast Atlantic with a resolution of 7 km. Further information on the NEMO and POLCOMS models is available in RD.1. As they are models, they can be used to predict future conditions. The CMEMS satellite product, on the other hand, is based on satellite data, therefore, is only available up to the present.

Experiment

There are two future greenhouse gas concentration scenarios available to download, based on two Representative Concentration Pathways (RCPs). RCP 4.5 is a moderate scenario, envisioning peak emissions at 2040 before declining. RCP 8.5 is an extreme scenario, envisioning emissions continuing to rise throughout the century. RCP scenarios are only available if the origin selected is NEMO or POLCOMS as they provide future projections.

Variable

A full description of the CIIs available under the 'Variable' heading can be found in this user guide in section 2.4. CIIs are available to download as part of the eutrophication dataset.

Time Aggregation

Time aggregation refers to how the data is served. Data may be selected by individual years to provide a 12-month time series over the annual cycle or, alternatively, selected by individual months for all available years. This allows the user to easily extract a particular month and investigate how it has changed over multiple years.

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 origin is selected. NEMO and POLCOMS are models that can be used to predict future eutrophication, whereas CMEMS uses current satellite data, therefore, cannot be used for future years.

Month

The dataset is available to download by month. It is also possible to download all months at once.

Format

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

Dataset Applications

Coastal 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.

Chlorophyll-a is the photosynthetically active pigment of phytoplankton, which can increase in concentration under eutrophication conditions. Increases in chlorophyll-a above a certain threshold are often associated with the growth of Phaeocystis globosa, an indicator species of water disturbance resulting from high anthropogenic nutrient loads. The Oslo/Paris Convention (OSPAR) Strategy to combat eutrophication recommended that the abundance of P. globosa be reduced to similar levels as in non-problem areas with good ecological status. Therefore, information on the marine environmental parameter chlorophyll-a, as provided in the Eutrophication dataset, is fundamental for monitoring water quality, eutrophication and climate change.

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 (NCA) 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. Eutrophication affects the ecosystem condition by disturbing the balance of organisms and affecting the quality of water. Therefore, it is useful to have a dataset for current and future eutrophication levels as NCA is affected by this.

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