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  • C3S EQC quality assessments

➡️ Satellite Observations

  • 1. Quality assessments for Satellite Observations
    • 1.1. Atmosphere Physics
      • 1.1.1. Water vapor amplification of Earth’s Greenhouse Effect
    • 1.2. Atmospheric Composition
      • 1.2.1. Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)
      • 1.2.2. Consistency of carbon dioxide satellite observations for the evaluation of Earth system models.
      • 1.2.3. Carbon dioxide satellite observations completeness assessment for greenhouse gas monitoring
      • 1.2.4. Spatial and temporal completeness and uncertainties of carbon dioxide satellite observations for quantifying atmospheric greenhouse gas variability
      • 1.2.5. Methane satellite observations completeness assessment for greenhouse gas monitoring
      • 1.2.6. Methane satellite observations completeness assessment for greenhouse gas monitoring
      • 1.2.7. Spatial and temporal completeness of methane satellite observations for the analysis of extreme events related to large episodic releases
      • 1.2.8. Methane satellite observations uncertainty and completeness assessment for carbon cycle
      • 1.2.9. Temporal and spatial completeness of the satellite-derived Ozone for policy making and variability studies
    • 1.3. Cryosphere
      • 1.3.1. Glacier mass change data from satellite and in-situ observations: uncertainty analysis for glaciological and climate change monitoring
      • 1.3.2. Glacier mass change data from satellite and in-situ observations: resolution and coverage for trend analysis
      • 1.3.3. Utility of the Randolph Glacier Inventory (RGI) for regional and global glacier volume estimates
      • 1.3.4. Ice sheet velocity data from satellite observations: temporal and spatial coverage and data completeness for trend analysis in glaciological applications
      • 1.3.5. Ice sheet velocity data from satellite observations: temporal and spatial variability of velocity-related uncertainties for glaciological applications
      • 1.3.6. Spatio-temporal data completeness and consistency of remote sensing-derived ice sheet surface elevation changes over Antarctica
      • 1.3.7. Uncertainty of ice sheet surface elevation change data over Greenland for volume and mass change trend assessments
      • 1.3.8. Gravimetric mass balance data from satellite observations: utility analysis for mass change monitoring of the ice sheets
      • 1.3.9. Gravimetric mass changes from satellite data: assessment of resolution and uncertainty of Greenland ice sheet mass changes for glaciological applications
    • 1.4. Land Biosphere
      • 1.4.1. Satellite fire burned area completeness for seasonal climatology maps
      • 1.4.2. Satellite fire burned area trends assessment for climate monitoring
    • 1.5. Land Hydrology
      • 1.5.1. Lake Victoria’s 2020 Flood Event Analysis
      • 1.5.2. Assessing Lake Titicaca’s water levels in support of water management
    • 1.6. Ocean
      • 1.6.1. Completeness of ocean colour observations for biogeochemical models
      • 1.6.2. Impact of using successive satellites on Ocean Colour time series
      • 1.6.3. Intercomparison of satellite multi-year sea ice extent estimates
      • 1.6.4. Suitability of satellite sea ice thickness data for studying climate change
      • 1.6.5. Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale
      • 1.6.6. Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring
      • 1.6.7. Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends

➡️ Insitu Observations

  • 2. Quality assessments for Insitu Observations

➡️ Reanalysis

  • 3. Quality assessments for Reanalysis

➡️ Seasonal Forecasts

  • 4. Quality assessments for Seasonal Forecasts
    • 4.1. Seasonal forecasts bias assessment for impact models
    • 4.2. Evaluation of the hit-rate of seasonal forecasts for tercile categories of monthly anomalies
    • 4.3. Assessing the impact of spatial scale and temporal trends on seasonal forecast quality
    • 4.4. Assessing possible outcomes of seasonal temperature forecast

➡️ Climate Projections

  • 5. Quality assessments for Climate Projections
    • 5.1. CMIP6
      • 5.1.1. Bias in extreme temperature indices for the reinsurance sector
      • 5.1.2. Uncertainty in projected changes in extreme temperature indices for the reinsurance sector
      • 5.1.3. Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector
      • 5.1.4. Biases in energy-consumption-related indices in Europe
      • 5.1.5. Uncertainty in projected changes of energy consumption in Europe
      • 5.1.6. Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level
      • 5.1.7. Projections of future ice-free periods for the Arctic and Antarctic
      • 5.1.8. Historical accuracy of sea ice extent in the CMIP6 experiments
      • 5.1.9. Evaluation of the Arctic sea ice thickness in the CMIP6 historical experiments
      • 5.1.10. Testing the capability of CMIP6 GCMs to represent precipitation inter-annual variability
      • 5.1.11. Testing the capability of CMIP6 GCMs to represent precipitation intra-seasonal variability
      • 5.1.12. Biases in temperature trends at different pressure levels using CMIP6 across latitudinal bands
      • 5.1.13. CMIP6 biases in the SPEI6 drought index over the Mediterranean region
    • 5.2. CORDEX
      • 5.2.1. Bias in extreme temperature indices for the reinsurance sector
      • 5.2.2. Uncertainty in projected changes in extreme temperature indices for the reinsurance sector
      • 5.2.3. Bias in precipitation-based indices for impact models
      • 5.2.4. Projected changes in precipitation-based indices for impact models
      • 5.2.5. CORDEX temperature biases over the Alpine region for the ski and hydrological sectors.
      • 5.2.6. CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA

➡️ Climate Indicators

  • 6. Quality assessments for Climate Indicators

➡️ Derived Datasets

  • 7. Quality assessments for Derived Datasets
    • 7.1. Consistency between the C3S Atlas dataset and its origins: Case study
    • 7.2. Consistency between the C3S Atlas dataset and its origins: Multiple indicators
    • 7.3. Consistency between the C3S Atlas dataset and its origins: Multiple origin datasets

➡️ Applications

  • 8. Quality assessments for C3S Applications
    • 8.1. Assessing global warming with the C3S Global Temperature Trend Monitor
    • 8.2. Visualisation of temperature during extreme weather events with Climate Pulse

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Index

By the Copernicus Climate Change Service (C3S), entrusted to ECMWF (European Centre for Medium-Range Weather Forecasts)

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