diff --git a/data/layers-config.json b/data/layers-config.json
index 86861087d..173340437 100644
--- a/data/layers-config.json
+++ b/data/layers-config.json
@@ -176,7 +176,8 @@
"year": "numeric",
"month": "long",
"day": "numeric"
- }
+ },
+ "legendValues": ["3.0 ppm", "2.0", "1.0", "0.0"]
},
"ozone.total_ozone_column": {
"colorMap": "custom",
diff --git a/storage/layers/layers-de.json b/storage/layers/layers-de.json
index 6c36323df..0bae5904d 100644
--- a/storage/layers/layers-de.json
+++ b/storage/layers/layers-de.json
@@ -4,14 +4,14 @@
"type": "Biosphere",
"name": "Fire Burned Area*",
"shortName": "Fire*",
- "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](http://www.esa-fire-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
+ "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](https://climate.esa.int/projects/fire/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
},
{
"id": "soil_moisture.sm_mean",
"type": "Hydrosphere",
"name": "Soil Moisture",
"shortName": "Soil Moisture",
- "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** 1978-11 – 2019-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://www.esa-soilmoisture-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
+ "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** November 1978 – December 2019 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://climate.esa.int/projects/soil-moisture/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
},
{
"id": "soil_moisture.Anomaly",
@@ -25,56 +25,56 @@
"type": "Surface Ocean Physics",
"name": "Sea Surface Temperature",
"shortName": "Sea Surface Temperature",
- "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](http://www.esa-sst-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
+ "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](https://climate.esa.int/projects/sea-surface-temperature/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
},
{
"id": "oc.chlor_a",
"type": "Ocean Biogeochemistry",
"name": "Ocean Colour – Chlorophyll-a Concentration",
"shortName": "Ocean Colour",
- "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://esa-oceancolour-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
+ "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://climate.esa.int/projects/ocean-colour/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
},
{
"id": "aerosol.AOD550_mean",
"type": "Atmospheric Composition",
"name": "Aerosol Optical Depth",
"shortName": "Aerosols",
- "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](http://cci.esa.int/aerosol) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
+ "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](https://climate.esa.int/projects/aerosol/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
},
{
"id": "cloud.cfc",
"type": "Upper-air Atmosphere",
"name": "Cloud Fraction*",
"shortName": "Clouds*",
- "description": "**Variable Shown:** Cloud Fraction \n**Time Span:** 1982-01 – 2016-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 3.0 \n\n[ESA CCI Cloud ECV Project website](http://www.esa-cloud-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
+ "description": "**Variable shown:** Cloud Fraction \n**Time span:** January 1982 – December 2016 \n**Temporal resolution:** monthly \n**Geographic extent:** global \n**Geographic resolution:** 1km x 1km \n**Version:** 3.0 \n**DOI:** , [10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003](https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003) \n\n[ESA CCI Cloud ECV Project website](https://climate.esa.int/projects/cloud/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
},
{
"id": "sea_level.sla",
"type": "Surface Ocean Physics",
"name": "Sea Level",
"shortName": "Sea Level",
- "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](http://www.esa-sealevel-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
+ "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](https://climate.esa.int/projects/sea-level/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
},
{
"id": "sea_state.swh_mean",
"type": "Surface Ocean Physics",
"name": "Sea State – Mean Significant Wave Height",
"shortName": "Sea State",
- "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](http://cci.esa.int/seastate) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
+ "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
},
{
"id": "sea_ice_sh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Southern Hemisphere",
"shortName": "Sea Ice – Southern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "sea_ice_nh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Northern Hemisphere",
"shortName": "Sea Ice – Northern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "greenland_ice.sec",
@@ -88,40 +88,40 @@
"type": "Cryosphere",
"name": "Permafrost",
"shortName": "Permafrost",
- "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](http://cci.esa.int/Permafrost) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
+ "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](https://climate.esa.int/projects/permafrost/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
},
{
"id": "land_cover.lccs_class",
"type": "Biosphere",
"name": "Land Cover*",
"shortName": "Land Cover*",
- "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://www.esa-landcover-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
\n"
+ "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://climate.esa.int/projects/land-cover/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
"
},
{
"id": "sea_surface_salinity.sss",
"type": "Surface Ocean Physics",
"name": "Sea Surface Salinity",
"shortName": "Sea Surface Salinity",
- "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](http://cci.esa.int/salinity) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
+ "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
},
{
"id": "snow.swe",
"type": "Cryosphere",
"name": "Snow*",
"shortName": "Snow*",
- "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** 1979-01 – 2018-05 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n\n[ESA CCI Snow ECV Project website](http://cci.esa.int/snow) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
+ "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** January 1979 – May 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n**DOI:** [10.5285/fa20aaa2060e40cabf5fedce7a9716d0](http://dx.doi.org/10.5285/fa20aaa2060e40cabf5fedce7a9716d0) \n\n[ESA CCI Snow ECV Project website](https://climate.esa.int/projects/snow/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
},
{
"id": "biomass.agb",
"type": "Biosphere",
"name": "Above Ground Biomass",
"shortName": "Biomass",
- "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](http://cci.esa.int/biomass) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
+ "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](https://climate.esa.int/projects/biomass/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
},
{
"id": "ozone.ozone_profile",
"type": "Atmospheric Composition",
- "name": "Ozone Profile*",
+ "name": "Ozone Profile – at 50° West*",
"shortName": "Ozone Profile*",
"description": ""
},
@@ -142,8 +142,8 @@
{
"id": "ozone.total_ozone_column",
"type": "Atmospheric Composition",
- "name": "Ozone*",
- "shortName": "Ozone*",
- "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson ts \n**Time Span:** 1996-03 – 2011-06 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](http://cci.esa.int/ozone) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
+ "name": "Ozone",
+ "shortName": "Ozone",
+ "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson Units \n**Time Span:** March 1996 – June 2011 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Geographic Resolution:** \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](https://climate.esa.int/projects/ozone/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
}
]
diff --git a/storage/layers/layers-en.json b/storage/layers/layers-en.json
index 6c36323df..0bae5904d 100644
--- a/storage/layers/layers-en.json
+++ b/storage/layers/layers-en.json
@@ -4,14 +4,14 @@
"type": "Biosphere",
"name": "Fire Burned Area*",
"shortName": "Fire*",
- "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](http://www.esa-fire-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
+ "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](https://climate.esa.int/projects/fire/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
},
{
"id": "soil_moisture.sm_mean",
"type": "Hydrosphere",
"name": "Soil Moisture",
"shortName": "Soil Moisture",
- "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** 1978-11 – 2019-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://www.esa-soilmoisture-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
+ "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** November 1978 – December 2019 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://climate.esa.int/projects/soil-moisture/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
},
{
"id": "soil_moisture.Anomaly",
@@ -25,56 +25,56 @@
"type": "Surface Ocean Physics",
"name": "Sea Surface Temperature",
"shortName": "Sea Surface Temperature",
- "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](http://www.esa-sst-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
+ "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](https://climate.esa.int/projects/sea-surface-temperature/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
},
{
"id": "oc.chlor_a",
"type": "Ocean Biogeochemistry",
"name": "Ocean Colour – Chlorophyll-a Concentration",
"shortName": "Ocean Colour",
- "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://esa-oceancolour-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
+ "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://climate.esa.int/projects/ocean-colour/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
},
{
"id": "aerosol.AOD550_mean",
"type": "Atmospheric Composition",
"name": "Aerosol Optical Depth",
"shortName": "Aerosols",
- "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](http://cci.esa.int/aerosol) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
+ "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](https://climate.esa.int/projects/aerosol/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
},
{
"id": "cloud.cfc",
"type": "Upper-air Atmosphere",
"name": "Cloud Fraction*",
"shortName": "Clouds*",
- "description": "**Variable Shown:** Cloud Fraction \n**Time Span:** 1982-01 – 2016-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 3.0 \n\n[ESA CCI Cloud ECV Project website](http://www.esa-cloud-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
+ "description": "**Variable shown:** Cloud Fraction \n**Time span:** January 1982 – December 2016 \n**Temporal resolution:** monthly \n**Geographic extent:** global \n**Geographic resolution:** 1km x 1km \n**Version:** 3.0 \n**DOI:** , [10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003](https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003) \n\n[ESA CCI Cloud ECV Project website](https://climate.esa.int/projects/cloud/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
},
{
"id": "sea_level.sla",
"type": "Surface Ocean Physics",
"name": "Sea Level",
"shortName": "Sea Level",
- "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](http://www.esa-sealevel-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
+ "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](https://climate.esa.int/projects/sea-level/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
},
{
"id": "sea_state.swh_mean",
"type": "Surface Ocean Physics",
"name": "Sea State – Mean Significant Wave Height",
"shortName": "Sea State",
- "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](http://cci.esa.int/seastate) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
+ "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
},
{
"id": "sea_ice_sh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Southern Hemisphere",
"shortName": "Sea Ice – Southern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "sea_ice_nh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Northern Hemisphere",
"shortName": "Sea Ice – Northern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "greenland_ice.sec",
@@ -88,40 +88,40 @@
"type": "Cryosphere",
"name": "Permafrost",
"shortName": "Permafrost",
- "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](http://cci.esa.int/Permafrost) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
+ "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](https://climate.esa.int/projects/permafrost/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
},
{
"id": "land_cover.lccs_class",
"type": "Biosphere",
"name": "Land Cover*",
"shortName": "Land Cover*",
- "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://www.esa-landcover-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
\n"
+ "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://climate.esa.int/projects/land-cover/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
"
},
{
"id": "sea_surface_salinity.sss",
"type": "Surface Ocean Physics",
"name": "Sea Surface Salinity",
"shortName": "Sea Surface Salinity",
- "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](http://cci.esa.int/salinity) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
+ "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
},
{
"id": "snow.swe",
"type": "Cryosphere",
"name": "Snow*",
"shortName": "Snow*",
- "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** 1979-01 – 2018-05 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n\n[ESA CCI Snow ECV Project website](http://cci.esa.int/snow) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
+ "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** January 1979 – May 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n**DOI:** [10.5285/fa20aaa2060e40cabf5fedce7a9716d0](http://dx.doi.org/10.5285/fa20aaa2060e40cabf5fedce7a9716d0) \n\n[ESA CCI Snow ECV Project website](https://climate.esa.int/projects/snow/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
},
{
"id": "biomass.agb",
"type": "Biosphere",
"name": "Above Ground Biomass",
"shortName": "Biomass",
- "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](http://cci.esa.int/biomass) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
+ "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](https://climate.esa.int/projects/biomass/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
},
{
"id": "ozone.ozone_profile",
"type": "Atmospheric Composition",
- "name": "Ozone Profile*",
+ "name": "Ozone Profile – at 50° West*",
"shortName": "Ozone Profile*",
"description": ""
},
@@ -142,8 +142,8 @@
{
"id": "ozone.total_ozone_column",
"type": "Atmospheric Composition",
- "name": "Ozone*",
- "shortName": "Ozone*",
- "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson ts \n**Time Span:** 1996-03 – 2011-06 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](http://cci.esa.int/ozone) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
+ "name": "Ozone",
+ "shortName": "Ozone",
+ "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson Units \n**Time Span:** March 1996 – June 2011 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Geographic Resolution:** \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](https://climate.esa.int/projects/ozone/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
}
]
diff --git a/storage/layers/layers-es.json b/storage/layers/layers-es.json
index 6c36323df..0bae5904d 100644
--- a/storage/layers/layers-es.json
+++ b/storage/layers/layers-es.json
@@ -4,14 +4,14 @@
"type": "Biosphere",
"name": "Fire Burned Area*",
"shortName": "Fire*",
- "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](http://www.esa-fire-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
+ "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](https://climate.esa.int/projects/fire/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
},
{
"id": "soil_moisture.sm_mean",
"type": "Hydrosphere",
"name": "Soil Moisture",
"shortName": "Soil Moisture",
- "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** 1978-11 – 2019-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://www.esa-soilmoisture-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
+ "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** November 1978 – December 2019 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://climate.esa.int/projects/soil-moisture/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
},
{
"id": "soil_moisture.Anomaly",
@@ -25,56 +25,56 @@
"type": "Surface Ocean Physics",
"name": "Sea Surface Temperature",
"shortName": "Sea Surface Temperature",
- "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](http://www.esa-sst-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
+ "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](https://climate.esa.int/projects/sea-surface-temperature/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
},
{
"id": "oc.chlor_a",
"type": "Ocean Biogeochemistry",
"name": "Ocean Colour – Chlorophyll-a Concentration",
"shortName": "Ocean Colour",
- "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://esa-oceancolour-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
+ "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://climate.esa.int/projects/ocean-colour/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
},
{
"id": "aerosol.AOD550_mean",
"type": "Atmospheric Composition",
"name": "Aerosol Optical Depth",
"shortName": "Aerosols",
- "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](http://cci.esa.int/aerosol) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
+ "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](https://climate.esa.int/projects/aerosol/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
},
{
"id": "cloud.cfc",
"type": "Upper-air Atmosphere",
"name": "Cloud Fraction*",
"shortName": "Clouds*",
- "description": "**Variable Shown:** Cloud Fraction \n**Time Span:** 1982-01 – 2016-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 3.0 \n\n[ESA CCI Cloud ECV Project website](http://www.esa-cloud-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
+ "description": "**Variable shown:** Cloud Fraction \n**Time span:** January 1982 – December 2016 \n**Temporal resolution:** monthly \n**Geographic extent:** global \n**Geographic resolution:** 1km x 1km \n**Version:** 3.0 \n**DOI:** , [10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003](https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003) \n\n[ESA CCI Cloud ECV Project website](https://climate.esa.int/projects/cloud/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
},
{
"id": "sea_level.sla",
"type": "Surface Ocean Physics",
"name": "Sea Level",
"shortName": "Sea Level",
- "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](http://www.esa-sealevel-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
+ "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](https://climate.esa.int/projects/sea-level/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
},
{
"id": "sea_state.swh_mean",
"type": "Surface Ocean Physics",
"name": "Sea State – Mean Significant Wave Height",
"shortName": "Sea State",
- "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](http://cci.esa.int/seastate) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
+ "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
},
{
"id": "sea_ice_sh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Southern Hemisphere",
"shortName": "Sea Ice – Southern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "sea_ice_nh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Northern Hemisphere",
"shortName": "Sea Ice – Northern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "greenland_ice.sec",
@@ -88,40 +88,40 @@
"type": "Cryosphere",
"name": "Permafrost",
"shortName": "Permafrost",
- "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](http://cci.esa.int/Permafrost) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
+ "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](https://climate.esa.int/projects/permafrost/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
},
{
"id": "land_cover.lccs_class",
"type": "Biosphere",
"name": "Land Cover*",
"shortName": "Land Cover*",
- "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://www.esa-landcover-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
\n"
+ "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://climate.esa.int/projects/land-cover/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
"
},
{
"id": "sea_surface_salinity.sss",
"type": "Surface Ocean Physics",
"name": "Sea Surface Salinity",
"shortName": "Sea Surface Salinity",
- "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](http://cci.esa.int/salinity) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
+ "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
},
{
"id": "snow.swe",
"type": "Cryosphere",
"name": "Snow*",
"shortName": "Snow*",
- "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** 1979-01 – 2018-05 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n\n[ESA CCI Snow ECV Project website](http://cci.esa.int/snow) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
+ "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** January 1979 – May 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n**DOI:** [10.5285/fa20aaa2060e40cabf5fedce7a9716d0](http://dx.doi.org/10.5285/fa20aaa2060e40cabf5fedce7a9716d0) \n\n[ESA CCI Snow ECV Project website](https://climate.esa.int/projects/snow/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
},
{
"id": "biomass.agb",
"type": "Biosphere",
"name": "Above Ground Biomass",
"shortName": "Biomass",
- "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](http://cci.esa.int/biomass) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
+ "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](https://climate.esa.int/projects/biomass/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
},
{
"id": "ozone.ozone_profile",
"type": "Atmospheric Composition",
- "name": "Ozone Profile*",
+ "name": "Ozone Profile – at 50° West*",
"shortName": "Ozone Profile*",
"description": ""
},
@@ -142,8 +142,8 @@
{
"id": "ozone.total_ozone_column",
"type": "Atmospheric Composition",
- "name": "Ozone*",
- "shortName": "Ozone*",
- "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson ts \n**Time Span:** 1996-03 – 2011-06 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](http://cci.esa.int/ozone) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
+ "name": "Ozone",
+ "shortName": "Ozone",
+ "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson Units \n**Time Span:** March 1996 – June 2011 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Geographic Resolution:** \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](https://climate.esa.int/projects/ozone/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
}
]
diff --git a/storage/layers/layers-fr.json b/storage/layers/layers-fr.json
index 6c36323df..0bae5904d 100644
--- a/storage/layers/layers-fr.json
+++ b/storage/layers/layers-fr.json
@@ -4,14 +4,14 @@
"type": "Biosphere",
"name": "Fire Burned Area*",
"shortName": "Fire*",
- "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](http://www.esa-fire-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
+ "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](https://climate.esa.int/projects/fire/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
},
{
"id": "soil_moisture.sm_mean",
"type": "Hydrosphere",
"name": "Soil Moisture",
"shortName": "Soil Moisture",
- "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** 1978-11 – 2019-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://www.esa-soilmoisture-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
+ "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** November 1978 – December 2019 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://climate.esa.int/projects/soil-moisture/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
},
{
"id": "soil_moisture.Anomaly",
@@ -25,56 +25,56 @@
"type": "Surface Ocean Physics",
"name": "Sea Surface Temperature",
"shortName": "Sea Surface Temperature",
- "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](http://www.esa-sst-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
+ "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](https://climate.esa.int/projects/sea-surface-temperature/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
},
{
"id": "oc.chlor_a",
"type": "Ocean Biogeochemistry",
"name": "Ocean Colour – Chlorophyll-a Concentration",
"shortName": "Ocean Colour",
- "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://esa-oceancolour-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
+ "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://climate.esa.int/projects/ocean-colour/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
},
{
"id": "aerosol.AOD550_mean",
"type": "Atmospheric Composition",
"name": "Aerosol Optical Depth",
"shortName": "Aerosols",
- "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](http://cci.esa.int/aerosol) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
+ "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](https://climate.esa.int/projects/aerosol/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
},
{
"id": "cloud.cfc",
"type": "Upper-air Atmosphere",
"name": "Cloud Fraction*",
"shortName": "Clouds*",
- "description": "**Variable Shown:** Cloud Fraction \n**Time Span:** 1982-01 – 2016-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 3.0 \n\n[ESA CCI Cloud ECV Project website](http://www.esa-cloud-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
+ "description": "**Variable shown:** Cloud Fraction \n**Time span:** January 1982 – December 2016 \n**Temporal resolution:** monthly \n**Geographic extent:** global \n**Geographic resolution:** 1km x 1km \n**Version:** 3.0 \n**DOI:** , [10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003](https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003) \n\n[ESA CCI Cloud ECV Project website](https://climate.esa.int/projects/cloud/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
},
{
"id": "sea_level.sla",
"type": "Surface Ocean Physics",
"name": "Sea Level",
"shortName": "Sea Level",
- "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](http://www.esa-sealevel-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
+ "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](https://climate.esa.int/projects/sea-level/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
},
{
"id": "sea_state.swh_mean",
"type": "Surface Ocean Physics",
"name": "Sea State – Mean Significant Wave Height",
"shortName": "Sea State",
- "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](http://cci.esa.int/seastate) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
+ "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
},
{
"id": "sea_ice_sh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Southern Hemisphere",
"shortName": "Sea Ice – Southern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "sea_ice_nh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Northern Hemisphere",
"shortName": "Sea Ice – Northern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "greenland_ice.sec",
@@ -88,40 +88,40 @@
"type": "Cryosphere",
"name": "Permafrost",
"shortName": "Permafrost",
- "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](http://cci.esa.int/Permafrost) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
+ "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](https://climate.esa.int/projects/permafrost/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
},
{
"id": "land_cover.lccs_class",
"type": "Biosphere",
"name": "Land Cover*",
"shortName": "Land Cover*",
- "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://www.esa-landcover-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
\n"
+ "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://climate.esa.int/projects/land-cover/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
"
},
{
"id": "sea_surface_salinity.sss",
"type": "Surface Ocean Physics",
"name": "Sea Surface Salinity",
"shortName": "Sea Surface Salinity",
- "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](http://cci.esa.int/salinity) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
+ "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
},
{
"id": "snow.swe",
"type": "Cryosphere",
"name": "Snow*",
"shortName": "Snow*",
- "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** 1979-01 – 2018-05 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n\n[ESA CCI Snow ECV Project website](http://cci.esa.int/snow) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
+ "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** January 1979 – May 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n**DOI:** [10.5285/fa20aaa2060e40cabf5fedce7a9716d0](http://dx.doi.org/10.5285/fa20aaa2060e40cabf5fedce7a9716d0) \n\n[ESA CCI Snow ECV Project website](https://climate.esa.int/projects/snow/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
},
{
"id": "biomass.agb",
"type": "Biosphere",
"name": "Above Ground Biomass",
"shortName": "Biomass",
- "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](http://cci.esa.int/biomass) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
+ "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](https://climate.esa.int/projects/biomass/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
},
{
"id": "ozone.ozone_profile",
"type": "Atmospheric Composition",
- "name": "Ozone Profile*",
+ "name": "Ozone Profile – at 50° West*",
"shortName": "Ozone Profile*",
"description": ""
},
@@ -142,8 +142,8 @@
{
"id": "ozone.total_ozone_column",
"type": "Atmospheric Composition",
- "name": "Ozone*",
- "shortName": "Ozone*",
- "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson ts \n**Time Span:** 1996-03 – 2011-06 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](http://cci.esa.int/ozone) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
+ "name": "Ozone",
+ "shortName": "Ozone",
+ "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson Units \n**Time Span:** March 1996 – June 2011 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Geographic Resolution:** \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](https://climate.esa.int/projects/ozone/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
}
]
diff --git a/storage/layers/layers-nl.json b/storage/layers/layers-nl.json
index 6c36323df..0bae5904d 100644
--- a/storage/layers/layers-nl.json
+++ b/storage/layers/layers-nl.json
@@ -4,14 +4,14 @@
"type": "Biosphere",
"name": "Fire Burned Area*",
"shortName": "Fire*",
- "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](http://www.esa-fire-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
+ "description": "Throughout history, humans have used fire as an environmental management tool. Vegetation characteristics, mainly biomass loads and moisture, determine fire behaviour, but fire also modifies vegetation structure and evolution. Climate affects fire occurrence through the thermal and precipitation cycles, but it is also affected by fire, particularly by gaseous and PM emissions. This mutual influences among vegetation, climate and fire highlight the importance of having global long-term burned area (BA) information that serves as input for climate and vegetation modellers.\nCurrently, the FireCCI51 BA product includes 19 years of global BA data. The product is offered at 250m resolution with date of detection and land cover burned, and at 0.25 degree cells with total BA per grid cell, total BA per land cover, and observed area. In both cases, uncertainty layers are also incorporated.\n\n**Variable Shown:** Total Burned Area in Square Meters \n**Time Span:** January 2001 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 x 0.25 degree \n**Version:** 5.1 \n**DOI:** [10.5285/3628cb2fdba443588155e15dee8e5352](http://dx.doi.org/10.5285/3628cb2fdba443588155e15dee8e5352) \n\n[ESA CCI Fire ECV Project website](https://climate.esa.int/projects/fire/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352)"
},
{
"id": "soil_moisture.sm_mean",
"type": "Hydrosphere",
"name": "Soil Moisture",
"shortName": "Soil Moisture",
- "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** 1978-11 – 2019-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://www.esa-soilmoisture-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
+ "description": "**Variable Shown:** Volumetric Soil Moisture in m3/m3 \n**Time Span:** November 1978 – December 2019 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 4.7 \n\n[ESA CCI Soil Moisture ECV Project website](https://climate.esa.int/projects/soil-moisture/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/2d4a50f390064820a9dcc2fcf7ac4b18)"
},
{
"id": "soil_moisture.Anomaly",
@@ -25,56 +25,56 @@
"type": "Surface Ocean Physics",
"name": "Sea Surface Temperature",
"shortName": "Sea Surface Temperature",
- "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](http://www.esa-sst-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
+ "description": "Sea surface temperature is the temperature the ocean surface presents to the atmosphere and influences weather and climate around the globe. The wind, warmth and water content of the atmosphere are all strongly determined by the distribution of sea surface temperature as the ocean interacts with the air above it. As well as the cycle of warming and cooling with the seasons, ocean currents and circulations can be seen, and even on occasion the cooling effect of passing hurricanes.\n\nThis CCI record covers 1981 to recent years, exploiting four trillion observations from many satellites, all brought together to make a valuable detailed picture of how this influential climate variable has evolved over almost 40 years.\n\n**Variable Shown:** Analysed Sea Surface Temperature in kelvin \n**Time Span:** September 1981 – December 2016 \n**Temporal Resolution (Dataset):** daily (visualisation is monthly) \n**Geographic Extent:** global \n**Spatial Resolution:** 0.05 degrees \n**Version:** 2.1 \n**DOI:** [10.5285/62c0f97b1eac4e0197a674870afe1ee6](http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) \n\n[ESA CCI Sea Surface Temperature ECV Project website](https://climate.esa.int/projects/sea-surface-temperature/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/62c0f97b1eac4e0197a674870afe1ee6)"
},
{
"id": "oc.chlor_a",
"type": "Ocean Biogeochemistry",
"name": "Ocean Colour – Chlorophyll-a Concentration",
"shortName": "Ocean Colour",
- "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://esa-oceancolour-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
+ "description": "Chlorophyll-a is the primary pigment in many plants, including the phytoplankton in the ocean. It absorbs light, allowing plants to photosynthesise and so generate energy to grow. Ocean colour remote sensing uses this light absorption to quantify the amount of Chlorophyll-a in the surface ocean, the depth to which the light penetrates, and so quantify the amount of phytoplankton. Phytoplankton are essential because they are the foundation of the marine food chain, playing a role in carbon fixation that potentially reduces human-induced carbon dioxide in the atmosphere, but can also increase ocean acidification.\n\nThe Ocean Colour CCI project has created a consistent time-series by merging data from multiple ocean colour satellites, including ESA’s MERIS dataset and NASA’s SeaWiFS, MODIS-Aqua and VIIRS datasets. The project is also in the process of adding the Copernicus/ESA OLCI dataset. Further details can be found online at [climate.esa.int/projects/ocean-colour](https://climate.esa.int/en/projects/ocean-colour/)\n\n**Variable Shown:** Chlorophyll-a concentration in seawater in Milligram/m3 \n**Time Span:** September 1997 – December 2019 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 4 km \n**Version:** 4.2 \n**DOI:** \n\n[ESA CCI Ocean Color ECV Project website](https://climate.esa.int/projects/ocean-colour/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5400de38636d43de9808bfc0b500e863)"
},
{
"id": "aerosol.AOD550_mean",
"type": "Atmospheric Composition",
"name": "Aerosol Optical Depth",
"shortName": "Aerosols",
- "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](http://cci.esa.int/aerosol) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
+ "description": "Aerosol Optical Depth (AOD) quantifies the total aerosol extinction integrated over the entire vertical column of the atmosphere. Maps of AOD show the dominant global aerosol sources (e.g. Sahara desert, tropical biomass burning, Industrial emissions over for example China, the Po valley, North-Western Europe) as well as the large spatial and temporal variations due to the particle lifetime in the atmosphere of a few days to a week.\n\nIn CCI a global record of AOD covering 1995 to 2012 has been processed together with further data records which contain additional aerosol parameters (e.g. Fine Mode AOD of small particles, Mineral Dust AOD, aerosol absorption, aerosol layer height). The animation in the App shows monthly AOD maps from dual-view radiometers ATSR-2 / AATSR (Swansea algorithm, version 4.21).\n\n**Variable Shown:** Aerosol Optical Depth at 550 nm \n**Time Span:** May 2002 – April 2012 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 4.21 \n\n[ESA CCI Aerosol ECV Project website](https://climate.esa.int/projects/aerosol/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/5dd3881e89c8419bbdba4ab06aa91112)"
},
{
"id": "cloud.cfc",
"type": "Upper-air Atmosphere",
"name": "Cloud Fraction*",
"shortName": "Clouds*",
- "description": "**Variable Shown:** Cloud Fraction \n**Time Span:** 1982-01 – 2016-12 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 3.0 \n\n[ESA CCI Cloud ECV Project website](http://www.esa-cloud-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
+ "description": "**Variable shown:** Cloud Fraction \n**Time span:** January 1982 – December 2016 \n**Temporal resolution:** monthly \n**Geographic extent:** global \n**Geographic resolution:** 1km x 1km \n**Version:** 3.0 \n**DOI:** , [10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003](https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003) \n\n[ESA CCI Cloud ECV Project website](https://climate.esa.int/projects/cloud/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/004fd44ff5124174ad3c03dd2c67d548)"
},
{
"id": "sea_level.sla",
"type": "Surface Ocean Physics",
"name": "Sea Level",
"shortName": "Sea Level",
- "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](http://www.esa-sealevel-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
+ "description": "The global mean level of the oceans is an indicator of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans is vitally important for understanding not just the climate but also the socioeconomic consequences of any rise in sea level. The ESA Sea Level Climate Change Initiative project has produced a stable and homogeneous sea level Essential Climate Variable (ECV) product. This is a multi-mission altimeter time series of monthly maps of sea level anomalies over the global ocean from 1993 to 2015.\n\n**Variable Shown:** Monthly Sea Level Anomalies in Meters \n**Time Span:** January 1993 – December 2015 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 0.25 degrees \n**Version:** 2.0 \n**DOI:** [10.5194/os-11-67-2015](https://dx.doi.org/10.5194/os-11-67-2015) \n\n[ESA CCI Sea Level ECV Project website](https://climate.esa.int/projects/sea-level/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/142052b9dc754f6da47a631e35ec4609)"
},
{
"id": "sea_state.swh_mean",
"type": "Surface Ocean Physics",
"name": "Sea State – Mean Significant Wave Height",
"shortName": "Sea State",
- "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](http://cci.esa.int/seastate) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
+ "description": "Sea states are relevant for all activities at sea and on the coast, and their climatology is the main driver for the design and maintenance of marine structures. Sea states also modify air-sea exchanges of heat and momentum, and contribute to coastal sea level and sea ice properties. The Sea State CCI is processing altimeter and Synthetic Aperture Radar data from 2002 onward in order to consistently calibrate and validate these datasets and investigate the variability of sea states in our changing climate. One of the most noticeable change occurs in the Arctic region, where receding sea ice gives more open water over which wind generates waves. Extreme sea states also have a profound impact on the coasts, as they may increase substantially the water level and cause flooding in low lying lands, or severely erode sandy beaches.\n\n**Variable Shown:** Mean of Median Significant Wave Height Values in Meters \n**Time Span:** August 1991 – December 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Spatial Resolution:** 1 degree \n**Version:** 1.1 \n**DOI:** [10.5285/47140d618dcc40309e1edbca7e773478](http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478) \n\n[ESA CCI Sea State ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/47140d618dcc40309e1edbca7e773478)"
},
{
"id": "sea_ice_sh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Southern Hemisphere",
"shortName": "Sea Ice – Southern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "sea_ice_nh.ice_conc",
"type": "Surface Ocean Physics",
"name": "Sea Ice – Northern Hemisphere",
"shortName": "Sea Ice – Northern Hemisphere",
- "description": "Sea ice is frozen sea water and floats at the surface of the polar oceans. Sea ice concentration (SIC) quantifies the area of ocean surface covered by sea ice. Sea ice has a strong seasonal cycle. It freezes over large areas every winter with a maximum in March in the Arctic (September in the Antarctic). It melts every summer with a minimum in September in the Arctic (February in the Antarctic).\n\nDue to climate change, sea ice declines in all seasons in the Arctic. In the Antarctic, there is more year-to-year variability and no clear trends.\n\nIn CCI, a global record of SIC at medium resolution was processed from passive microwave\nmissions AMSR-E (2002-2010) and AMSR2 (2012-2017). Data records of sea-ice thickness\nwere also prepared in CCI. The animation in the App shows SIC maps from this CCI record,\nand from the EUMETSAT OSI SAF in the period Oct 2010 - July 2012.\n\n**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** daily (1st, 8th, of each month) \n**Geographic Extent:** global (but split in two hemispheres in the App) \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](http://esa-cci.nersc.no/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
+ "description": "**Variable Shown:** Sea Ice Area Fraction in Percent \n**Time Span:** June 2002 – May 2017 (with a gap from October 2011 – July 2012) \n**Temporal Resolution:** monthly \n**Geographic Extent:** Arctic \n**Spatial Resolution:** 25km \n**Version:** 2.1 \n**DOI:** [10.5285/f17f146a31b14dfd960cde0874236ee5](http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5) \n\n[ESA CCI Sea Ice ECV Project website](https://climate.esa.int/projects/sea-ice/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/f17f146a31b14dfd960cde0874236ee5)"
},
{
"id": "greenland_ice.sec",
@@ -88,40 +88,40 @@
"type": "Cryosphere",
"name": "Permafrost",
"shortName": "Permafrost",
- "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](http://cci.esa.int/Permafrost) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
+ "description": "Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature and landcover to drive the transient permafrost model CryoGrid CCI, which yields thaw depth and ground temperature at various depths, while ground temperature at 2m forms the basis for permafrost fraction.\n\n**Variable Shown:** Permafrost Fraction in Percent \n**Time Span:** 2003 – 2017 \n**Temporal Resolution:** yearly \n**Geographic Extent:** 90° N – 30° N \n**Spatial Resolution:** 2km \n**Version:** 1.0 \n**DOI:** [10.5285/c7590fe40d8e44169d511c70a60ccbcc](http://dx.doi.org/10.5285/c7590fe40d8e44169d511c70a60ccbcc) \n\n[ESA CCI Permafrost ECV Project website](https://climate.esa.int/projects/permafrost/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/c7590fe40d8e44169d511c70a60ccbcc)"
},
{
"id": "land_cover.lccs_class",
"type": "Biosphere",
"name": "Land Cover*",
"shortName": "Land Cover*",
- "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://www.esa-landcover-cci.org/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
\n"
+ "description": "**Variable Shown:** Land cover class defined in LCCS \n**Time Span:** 1992-01 – 2015-12 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Spatial Resolution:** 300m \n**Version:** 2.0.7 \n\n[ESA CCI Landcover ECV Project website](https://climate.esa.int/projects/land-cover/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c)\n\n**Detailed Legend:** \n\nNo data
\nCropland, rainfed
\nHerbaceous cover
\nTree or shrub cover
\nCropland, irrigated or post-flooding
\nMosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)
\nMosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)
\nTree cover, broadleaved, evergreen, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed to open (>15%)
\nTree cover, broadleaved, deciduous, closed (>40%)
\nTree cover, broadleaved, deciduous, open (15-40%)
\nTree cover, needleleaved, evergreen, closed to open (>15%)
\nTree cover, needleleaved, evergreen, closed (>40%)
\nTree cover, needleleaved, evergreen, open (15-40%)
\nTree cover, needleleaved, deciduous, closed to open (>15%)
\nTree cover, needleleaved, deciduous, closed (>40%)
\nTree cover, needleleaved, deciduous, open (15-40%)
\nTree cover, mixed leaf type (broadleaved and needleleaved)
\nMosaic tree and shrub (>50%) / herbaceous cover (<50%)
\nMosaic herbaceous cover (>50%) / tree and shrub (<50%)
\nShrubland
\nShrubland evergreen
\nShrubland deciduous
\nGrassland
\nLichens and mosses
\nSparse vegetation (tree, shrub, herbaceous cover) (<15%)
\nSparse tree (<15%)
\nSparse shrub (<15%)
\nSparse herbaceous cover (<15%)
\nTree cover, flooded, fresh or brakish water
\nTree cover, flooded, saline water
\nShrub or herbaceous cover, flooded, fresh/saline/brakish water
\nUrban areas
\nBare areas
\nConsolidated bare areas
\nUnconsolidated bare areas
\nWater bodies
\nPermanent snow and ice
"
},
{
"id": "sea_surface_salinity.sss",
"type": "Surface Ocean Physics",
"name": "Sea Surface Salinity",
"shortName": "Sea Surface Salinity",
- "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](http://cci.esa.int/salinity) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
+ "description": "Sea Surface Salinity (SSS) quantifies the amount of salt in surface waters. Unusual salinity levels may indicate the onset of extreme climate events, such as El Niño. Global maps of sea-surface salinity are particularly helpful for studying the water cycle, ocean–atmosphere exchanges and ocean circulation, which are all vital components of the climate system transporting heat, momentum, carbon and nutrients around the globe.\n\nIn CCI, a global record of SSS covering the period 2010-2018 has been processed from SMOS, Aquarius and SMAP SSS. In addition to SSS, indicators for SSS uncertainties are provided. This new data set allows to monitor 2 El Niño and La Niña events of very different intensities.\n\n**Variable shown:** Sea Surface Salinity \n**Time Span:** 2010-2018 \n**Temporal resolution:** monthly (temporal sampling: 2 weeks) \n**Spatial resolution:** 50km (spatial sampling: 25km) \n**Geographical extent:** global \n**Version:** 1.8 \n**DOI:** [doi:10.5285/4ce685bff631459fb2a30faa699f3fc5](https://dx.doi.org/10.5285/4ce685bff631459fb2a30faa699f3fc5)\n\n[ESA CCI Sea Surface Salinity ECV Project website](https://climate.esa.int/projects/sea-state/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/aea368301bff448bbbea7e90e66819e5)"
},
{
"id": "snow.swe",
"type": "Cryosphere",
"name": "Snow*",
"shortName": "Snow*",
- "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** 1979-01 – 2018-05 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n\n[ESA CCI Snow ECV Project website](http://cci.esa.int/snow) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
+ "description": "**Variable Shown:** Snow Water Equivalent (mm) \n**Time Span:** January 1979 – May 2018 \n**Temporal resolution:** monthly \n**Geographic Extent:** global\n**Spatial Resolution:** 15km - 69km \n**Version:** 1.0 \n**DOI:** [10.5285/fa20aaa2060e40cabf5fedce7a9716d0](http://dx.doi.org/10.5285/fa20aaa2060e40cabf5fedce7a9716d0) \n\n[ESA CCI Snow ECV Project website](https://climate.esa.int/projects/snow/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/fa20aaa2060e40cabf5fedce7a9716d0)"
},
{
"id": "biomass.agb",
"type": "Biosphere",
"name": "Above Ground Biomass",
"shortName": "Biomass",
- "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](http://cci.esa.int/biomass) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
+ "description": "Biomass is the weight of living material in vegetation, which for forests is mainly contained in the\nwoody parts of the trees. Only above-ground biomass (AGB) can be estimated from space (and in\npractice also for most on-ground estimates). Quantitative information on forest AGB is crucial\nfor understanding climate change, since loss of AGB caused by deforestation and forest degradation\nis second only to fossil fuel burning as a source of greenhouse gases, while CO₂ taken from the\natmosphere by forest growth and stored in woody biomass helps to slow climate warming.\n\nThe map shows estimated forest AGB in 2017 derived mainly from the combination of the ALOS\nPALSAR-2 L-band radar and the Sentinel-1 C-band radars, with support from a range of other sensors\nand environmental datasets.\n\n**Variable Shown:** Above-ground biomass in Mg/ha \n**Time Span:** 2017 \n**Temporal resolution:** yearly \n**Geographic Extent:** global \n**Grid Spacing:** 100m \n**Version:** 1 \n**DOI:** [doi:10.5285/bedc59f37c9545c981a839eb552e4084](http://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084)\n\n[ESA CCI Biomass ECV Project website](https://climate.esa.int/projects/biomass/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084)"
},
{
"id": "ozone.ozone_profile",
"type": "Atmospheric Composition",
- "name": "Ozone Profile*",
+ "name": "Ozone Profile – at 50° West*",
"shortName": "Ozone Profile*",
"description": ""
},
@@ -142,8 +142,8 @@
{
"id": "ozone.total_ozone_column",
"type": "Atmospheric Composition",
- "name": "Ozone*",
- "shortName": "Ozone*",
- "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson ts \n**Time Span:** 1996-03 – 2011-06 \n**Temporal resolution:** monthly \n**Geographic Extent:** global \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](http://cci.esa.int/ozone) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
+ "name": "Ozone",
+ "shortName": "Ozone",
+ "description": "Ozone is an important trace gas in the atmosphere and protects life on Earth by absorbing most of the biologically harmful ultraviolet sunlight. In the framework of ESA’s Climate Change Initiative a high-quality global total ozone data set has been generated from space-based observations that cover the period from 1995 to present. The data record can be used to investigate the long-term evolution of ozone in the atmosphere, which is intimately coupled to climate change. Of particular interest is the search for signs of recovery from the severe thinning and damage of the ozone layer, that was caused by human activities.\n\n\n**Variable Shown:** Mean Total Ozone Column in Dobson Units \n**Time Span:** March 1996 – June 2011 \n**Temporal Resolution:** monthly \n**Geographic Extent:** global \n**Geographic Resolution:** \n**Version:** 1 \n\n[ESA CCI Ozone ECV Project website](https://climate.esa.int/projects/ozone/) \n[Data in the Open Data Portal](https://catalogue.ceda.ac.uk/uuid/0d2260ad4e2c42b6b14fe5b3308f5eaa)"
}
]