Contributors: A. Hall (TVUK), J. Marsh (TVUK), P. Miller (PML)

Issued by: Jenny Marsh (TVUK)

Issued Date: 31/07/2019

Ref: C3S_D422Lot2.PML.3.1_201907_Product_User_Guide_Ocean_Fronts_v1.1

Official reference number service contract: 2018/C3S_422_Lot2_PML/SC2

Introduction

This dataset provides monthly projections of changes in the spatial and temporal distribution of ocean fronts. Ocean fronts are the interfaces between water masses, and are recognised to enhance productivity and promote the aggregation of commercial pelagic fish species and top predators, such as sharks, marine mammals, and seabirds, as prey availability increases. Moreover, primary productivity associated with oceanographic fronts strongly influence species richness in deep-sea fish communities (RD.7), as well as the functioning of deep-sea fish communities (RD.8).

Many ocean front locations depend upon bathymetry, though future currents and weather have the potential to increase the severity of changes in the strength and persistence of these features. This dataset will be used to study the potential for climate change to modify productive fishing zones and related marine conservation measures (e.g. boundaries of protected areas).

The ocean front module uses algorithms developed on Earth observation data, applied to European Regional Seas Ecosystem Model (ERSEM) projections and the European Space Agency (ESA) Climate Change Initiative (CCI) data. Ocean fronts are automatically detected using daily data of sea surface temperature and chlorophyll-a, producing thermal and chlorophyll-a ocean fronts, respectively. The daily data originate from the following sources:

• ERSEM projections
  • Northwest European shelf
  • Northwest European shelf and Mediterranean Sea
• ESA CCI satellite data
  • Satellite observations from the ESA CCI Sea Surface Temperature and Ocean Colour chlorophyll-a datasets.

The daily fronts are combined into monthly Climate Impact Indicators (CIIs) for both sea surface temperature and chlorophyll-a ocean front properties, being front gradient strength, persistence, and distance. CIIs are observed or projected measures that indicate an environmental, human, or economic impact that can be linked to changes in the climate. The CIIs are also provided as monthly maps indicating changes in front strength, persistence and distance, relative to baseline climatological monthly maps derived from observations or historical model runs. The dataset is available to download from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) either as a zip file (.zip) or as a compressed tar file (.tar.gz). The default file format is NetCDF.

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. Miller, P.I. (2009) Composite front maps for improved visibility of dynamic sea-surface features on cloudy SeaWiFS and AVHRR data. Journal of Marine Systems, 78(3), 327-336, doi: 10.1016/j.jmarsys.2008.11.019
RD.3. Miller, P.I., Scales, K.L., Ingram, S.N., Southall, E.J. & Sims, D.W. (2015) Basking sharks and oceanographic fronts: quantifying associations in the north-east Atlantic. Functional Ecology, 29(8), 1099-1109, doi: 10.1111/1365-2435.12423
RD.4. SST CCI Product User Guide – SST-CII Phase-II. Issue 1. Document reference: SST_CCI-PUG- UKMO-001. Available at: https://climate.esa.int/media/documents/SST_CCI-PUG-UKMO-201-Issue_1-signed.pdf
RD.5. Climate Assessment Report: Ocean Colour Climate Change Initiative (OC_CCI) – Phase Two. Issue 3.0. Document reference: AO-1/6207/09/I-LG.
RD.6. Miller, P.I. & Christodoulou, S. (2014) Frequent locations of ocean fronts as an indicator of pelagic diversity: application to marine protected areas and renewables. Marine Policy.
45, 318–329. doi: 10.1016/j.marpol.2013.09.009
RD.7. Leathwick, J.R., Elith, J., Francis, M.P., Hastie, T., Taylor, P., 2006. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Mar. Ecol. Prog. Ser. 321, 267–281.
RD.8. Tecchio, S., van Oevelen, D., Soetaert, K., Navarro, J., Ramírez-Llodra, E., 2013. Trophic dynamics of deep-sea megabenthos are mediated by surface productivity. PLoS One 8, e63796. http://dx.doi.org/10.1371/journal.pone.0063796.

Ocean Front Properties Dataset

This dataset provides monthly projections of changes in the spatial and temporal distribution of ocean fronts. Ocean fronts are the boundaries between distinct water masses of different properties. The water masses are defined by moving in different directions and may have different temperatures, salinities, and densities. They are recognised to enhance productivity and promote the aggregation of pelagic species (those that live in the pelagic zone, i.e. open ocean) from lower (zooplankton) to upper trophic levels, such as commercial fish species and top predators e.g. sharks and marine mammals, as prey availability increases.

Generally, as the strength of an ocean front increases, so does the productivity associated with it. Many ocean front locations depend upon bathymetry, though future currents and weather affect the strength and persistence of these features. Most ocean fronts form and dissipate relatively quickly; however, some fronts can persist for longer periods of time. This dataset will be used to study the potential for climate change to modify productive fishing zones and related marine conservation measures. Full dataset details are available in Table 1.
Table 1: Dataset description

Because ocean fronts act as boundaries between different water masses which have different temperature and chlorophyll properties, fronts can be detected using sea surface temperature and chlorophyll data. For this dataset, ocean fronts are automatically detected using daily data of sea surface temperature and chlorophyll-a, producing thermal and chlorophyll-a ocean fronts, respectively. Further information on the automatic detection of fronts is available in RD.2. The daily data originate from two main origins: European Regional Seas Ecosystem Model (ERSEM) projections and the European Space Agency (ESA) Climate Change Initiative (CCI) historical data.

European Regional Seas Ecosystem Model Projection

The ocean front module uses algorithms developed on Earth observation data, applied to European Regional Seas Ecosystem Model (ERSEM) projections. The inputs to this module produced by the ERSEM model are:

• Total Chlorophyll-a
• Sea Surface Temperature

The ERSEM model used for this dataset is coupled to two separate models to produce two different models:

• Northwest European shelf model. This is a model of the physical and biogeochemical processes active within the shelf seas off the northwest coast of Europe, at 7 km resolution. Figure 1 displays the northwest European domain. It is based on the Nucleus for European Modelling of the Ocean (NEMO) modelling framework, combined with 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 nutrient elements nitrogen, phosphorous, and silicon within the marine environment. The domain is also called Atlantic Margin Model (AMM7). The model produces outputs under two Representative Concentration Pathway (RCP) scenarios: RCP 4.5 and RCP 8.5. Further information regarding these scenarios is available in section 3.2.4.

Figure 1: Northwest European domain

• Northwest European shelf and Mediterranean Sea model. The physical model is provided by the Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS), coupled with ERSEM for the biogeochemistry. The model is run on a domain covering the northwest European shelf and Mediterranean Sea at 11 km resolution, displayed in Figure 2. The model produces outputs under two Representative Concentration Pathway (RCP) scenarios: RCP 4.5 and RCP 8.5.

Figure 2: Northwest European shelf and Mediterranean domain

ESA Climate Change Initiative Satellite Data

The ocean front module uses algorithms developed on Earth observation data, also applied to the European Space Agency (ESA) Climate Change Initiative (CCI) historical data. The inputs to this module are:

• Chlorophyll-a Concentration
• Sea Surface Temperature

Fronts are automatically detected using a robust algorithm based on local window histogram analysis, applied to daily fields of sea-surface temperature from the ESA Sea Surface Temperature Climate Change Initiative (SST_CCI) (http://esa-sst-cci.org/) and chlorophyll-a from the ESA Ocean Colour Climate Change Initiative (OC_CCI) (https://www.oceancolour.org/). The OC_CCI project focuses on the Ocean Colour Essential Climate Variable (ECV) encompassing water-leaving radiance in the visible domain, derived chlorophyll and inherent optical properties. It uses data archives from Medium Resolution Imaging Spectrometer (MERIS), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectrometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). The Sea Surface Temperature CCI uses data from Along Track Scanning Radiometer (ATSR) and Advanced Very High Resolution Radiometer (AVHRR). The CIIs derived from the satellite data are offered for the same domains as the ERSEM projections (Figure 1 and Figure 2).

Dataset Production

The daily fronts are combined into monthly CIIs of ocean front strength, persistence, and distance. Front strength is the mean gradient magnitude of detected fronts; persistence is the fraction of cloud- free observations of a pixel for which a front was detected; distance is calculated to the closest major front determined using a simplified version of the frontal map; and all metrics are spatially smoothed to reduce differences due to daily variability. The CIIs are also provided as monthly maps indicating changes in front strength, persistence and location, relative to baseline climatological monthly maps derived from observations or historical model runs. The baseline is from a historical run of the same ERSEM model from 1990-2005, or in the case of the satellite datasets, from the available CCI data up to 2005. A workflow showing the production stages of the CIIs is shown in Figure 3.

Figure 3: CII production workflow. The following acronyms are used: CMIP5 (Coupled Model Inter-comparison Project Phase 5).

The dataset is provided through the C3S Climate Data Store (CDS) and is available to download as a zip file (.zip) or compressed tar file (.tar.gz). Instructions on how to access the dataset are available in section 3.1.

Climate Impact Indicators

The following CIIs are available to download as part of the ocean front dataset under the 'Variable' heading. Each CII is available for both sea surface temperature and chlorophyll-a. These CIIs are important as ocean fronts are the interfaces between water masses, and are recognised to enhance primary and secondary production and promote the aggregation of upper trophic level species (including commercial pelagic fish species). These CIIs will be able to address the end-user concern that future changes in the distribution of ocean fronts will modify productive fishing zones. Further information regarding uses of the dataset and CIIs is available in section 3.3.

Distance to nearest major front

Monthly mean distance to nearest major front. Distance is calculated to the closest major front determined using a simplified version of the frontal map

• Units: km

Change in distance to nearest major front

Change in ocean front location, defined as the monthly mean change in the distance to the nearest major front relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available CCI data up to 2005.

• Units: km

Frontal gradient magnitude

Monthly mean thermal/chlorophyll-a frontal gradient magnitude. Frontal gradient magnitude can also be referred to as ocean front strength. Generally, as the strength of an ocean front increases, so does the productivity associated with it.

• Units: Thermal frontal gradient magnitude: °C km^-1
• Units: Chlorophyll-a frontal gradient magnitude: log Chl mg m^-3

Change in frontal gradient magnitude

Monthly mean change in thermal/chlorophyll-a frontal gradient magnitude relative to the baseline. Frontal gradient magnitude can also be referred to as ocean front strength. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available CCI data up to 2005. Generally, as the strength of an ocean front increases, so does the productivity associated with it.

• Units: Thermal frontal gradient magnitude: °C km^-1
• Units: Chlorophyll-a frontal gradient magnitude: log Chl mg m^-3

Frontal persistence

Monthly mean ocean frontal persistence. Persistence is the fraction of cloud-free observations of a pixel for which a front was detected. Most ocean fronts form and dissipate relatively quickly; however, some fronts can persist for longer periods of time.

• Units: relative frequency % (i.e. the proportion of time that a front was present in the given month)

Change in frontal persistence

Monthly mean change in ocean front persistence relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available CCI data up to 2005. Most ocean fronts form and dissipate relatively quickly; however, some fronts can persist for longer periods of time.

• Units: relative frequency % (i.e. the proportion of time that a front was present in the given month)

Quality Assurance

Methods

Information regarding the method and validation used for front detection is available in RD.2 and RD.3. The method used here requires ocean fronts to be detected on each individual satellite image before detecting them on the composite image. Automated front detection on single scenes and composite front maps was validated against expert manual annotation of fronts on satellite data from the Advanced Very High Resolution Radiometer (AVHRR) and the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS). When validating front detection on individual scenes, 93% of the thermal fronts were detected, with a mean offset of approximately two pixels (2.2 km) from the true front location and 1.5 false alarms (n=209 segments). Similarly, 94% of the chlorophyll-a fronts were detected, with a mean offset of 2.3 pixels (2.5 km) and 2 false alarms (n=247 segments). These results indicate accurate and robust detection of the genuine fronts using both sea surface temperature and chlorophyll-a data.

When validating composite front maps, 94% of the thermal fronts were detected, with a mean offset of approximately 1.2 km from the true front and 6.3 false alarms. (n=235 segments). Chlorophyll-a maps proved slightly better, with 96% of fronts detected, with a mean offset of approximately 1.8 km and 4.7 false alarms (n=675 segments). All input data were first manually quality-checked for accurate cloud masking and geolocation. Full results are available in RD.2.

Input Data

ESA Climate Change Initiative Satellite Data

For the sea surface temperature CCI input data, quality assurance information is provided in data arrays. Bad quality or missing data is highlighted on a scale of 0-5 (0 = no data, 5 = excellent data). Further information on the sea surface temperature CCI can be found in RD.4. For the Ocean Colour CCI, quality assurance information is available in RD.5.

ERSEM

ERSEM uses Coupled Model Inter-comparison Project Phase 5 (CMIP5) data as an input. CMIP5 is phase 5 of a standard experimental framework for studying the output of coupled atmosphere-ocean general circulation models. The POLCOMS-ERSEM model has been validated through comparison to satellite values from the ESA CCI Ocean Colour project and the Operational Sea Surface Temperature and Sea Ice Analysis 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.

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 is 'Ocean Fronts' Properties for the European and Mediterranean Sea Shelf from 2006 to 2100 Derived from CMIP5 Projections'. It can be easily discovered in the CDS Catalogue by searching for 'Ocean Fronts' 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.

Origin

The four origin options are NEMO-CCI, NEMO-ERSEM, POLCOMS-ERSEM, and POLCOMS-CCI.

• NEMO-CCI: This is the ESA CCI satellite data mapped onto the NEMO grid for the northwest European shelf;
• NEMO-ERSEM: This is the NEMO model grid for the northwest European shelf coupled to the ERSEM model;
• POLCOMS-CCI: This is the ESA CCI satellite data mapped onto the POLCOMS grid for the northwest European shelf and Mediterranean Sea;
• POLCOMS-ERSEM: This is the POLCOMS model grid for the northwest European shelf and Mediterranean Sea coupled to the ERSEM model.

All of the origins have separate versions for thermal fronts and chlorophyll fronts.

Variable

'Variable' is used to refer to CIIs. A full description of the CIIs available can be found in section 2.1. CIIs are available to download as part of the ocean front dataset, either individually or multiple at once.

Indicator

There are two climate variables used as front indicators available: 'sea surface temperature' and 'chlorophyll-a density'. The climate variables are used to automatically detect fronts from model forecasts and satellite observations. Information regarding the performance of the climate variables for front detection is available in section 2.3.

Experiment

Experiment refers to future greenhouse gas concentration scenarios, expressed as Representative Concentration Pathways (RCPs). 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 gases are emitted in the future. For this dataset, two scenarios are available: RCP 4.5, which envisions emissions peaking in 2040 before declining due to mitigating measures; and RCP 8.5, which envisions business as usual with little mitigation. These scenarios are not available for the NEMO-CCI and POLCOMS-CCI origins.

Year

The dataset is available to download between 1991 and 2099, depending on the origin selection. NEMO-CCI and POLCOMS-CCI are only available from 1991 to 2016, since this is the coverage of the ESA CCI input datasets. NEMO-ERSEM is available from 2006-2049, whereas POLCOMS-ERSEM is available from 2006-2099.

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 either as a zip file (.zip) or as a compressed tar file (.tar.gz). The default download format is NetCDF.

Dataset Applications

Marine Spatial Planning

Marine spatial planning enables the regulation, management, and protection of the marine environment, and addresses the multiple and sometimes conflicting uses of the seas, including fisheries, aquaculture, recreation, and conservation. Marine spatial planning is one the most important activities for marine-focused policy-makers and regulators. Marine spatial planning, including the designation of conservation areas, is usually undertaken with a base 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 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 to climate change, will modify the future distribution of marine resources underlying the conservation, fisheries and aquaculture sectors.

This dataset provides monthly projections of changes in the spatial and temporal distribution of ocean fronts, which are recognised to enhance productivity and promote the aggregation of commercial pelagic fish species and top predators, such as sharks and marine mammals. Many ocean front locations depend upon bathymetry, though future currents and weather have the potential to affect the strength and persistence of these features, causing climate change to impact productive fishing zones and related marine conservation measures, such as marine spatial planning.

Ocean front datasets have previously influenced boundaries and evidence for proposed marine protected areas within marine spatial planning in RD.6, as many pelagic biodiversity hotspots are related to fronts for example. The created front maps were among the most widely used datasets in the recommendation of UK Marine Protected Areas, and would be applicable to other geographic regions and to other policy drivers such as facilitating the deployment of offshore renewable energy devices with minimal environmental impact.

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. Ocean fronts can be affected by currents and weather and are known to enhance productivity and promote the aggregation of commercial pelagic fish species. Therefore, this dataset can be used to observe (from satellite data) and predict (from model fields) the change in ocean fronts and can inform decisions related to fisheries and aquaculture.

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 from the use of the natural resource, such as income from the sale of wild caught fish. It has long been recognised, though, that focusing 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. Ocean fronts influence the ecosystem condition by enhancing productivity and the aggregation of commercial fish species. Front locations often depend upon bathymetry, as well as future currents and weather conditions. Therefore, the ocean front dataset can inform natural capital accounting.

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