Contents
1
Disclaimer
2
Introduction to Copernicus Climate Change Service
2.1
Available tools & setup
3
Data Resources Introduction
3.1
What are essential Climate Variables?
3.2
Types of climate data resources
3.2.1
Observations
3.2.2
Models
3.3
Pro’s and con’s of different data sources
3.3.1
Advantages and disadvantages of different measurement types
3.3.2
Advantages and disadvantages of different types of data
3.4
References
4
Climate data from models
4.1
What is a climate model?
4.1.1
Complexity of climate models
4.1.2
Earth System Models
4.1.3
Spatial and Temporal Resolution
4.2
Differences between climate projections, predictions and scenarios
4.2.1
Climate scenarios
4.2.2
Emission scenarios and RCPs
4.3
How is the quality of models evaluated? (what is a climate model bias?)
4.3.1
Climate model bias and model skill
4.3.2
Types of climate ensembles
4.3.3
Climate Model Intercomparison Project (CMIP)
4.4
The use of climate model data
4.4.1
What climate model data are available?
4.4.2
Names of climate model data files
4.4.3
Names of climate variables
4.4.4
Assumption on bias
4.4.5
Use one or more projections?
4.4.6
Importance of bias correction
4.4.7
Spatial resolution and downscaling
4.4.8
Required spatial resolution
4.5
Developments in climate model research
4.6
References and Links
5
Climate Data Discovery
5.1
Difference between weather and climate
5.1.1
Weather and climate vs. decision making
5.2
Different sources of climate data
5.2.1
Relations between the different data sources
5.2.2
Sources available on C3S
5.3
Strategies to find the data you need: “Be specific”
5.3.1
Time horizon and region/location
5.3.2
Source (observation or model-based)
5.3.3
Variable of interest
5.3.4
Model selection
5.3.5
Scenario selection
5.3.6
Model selection for seasonal forecast
5.3.7
References
5.4
Strategies to find the data you need: Climate data processing chain (Advanced)
5.4.1
Overview of climate processing chain
5.4.2
Variable Selection
5.4.3
Domain selection
5.4.4
RCPs and SSPs
5.4.5
Climate model selection: how to distinguish? (climate sensitivity, transient response, (dis)similarity, spatial scale, …)
5.4.6
Dynamical and statistical downscaling
5.4.7
Skill assessment, model weighting and bias correction
5.4.8
Indices calculation (temporal statistics) for different variables
6
Climate Change Uncertainties
6.1
Definition of uncertainty
6.2
Why taking uncertainty into account?
6.3
Types of uncertainty and how to deal with them
6.3.1
Natural variability
6.3.2
Model Bias
6.3.3
Inhomogenieties
6.3.4
Uncertainties in statistics due to limited data
6.3.5
Model Uncertainty
6.3.6
Scenario uncertainty
6.4
Levels of uncertainty
6.5
Communicating uncertainties
6.5.1
Perception
6.5.2
Framing
6.5.3
Do’s for effective communication
6.5.4
The Uncertainty Handbook
6.5.5
Storytelling
6.5.6
Confidence and uncertainty
6.5.7
Standardized lexicons of uncertainty
6.5.8
Suggestions to improve the IPCC likelihood statements
6.6
Example of likelihood statement IPCC:
6.7
Visulalisation of uncertainties
6.7.1
Focus on the main message
6.7.2
Distortion of the message
6.7.3
Show long time series, talking about long term trends
6.7.4
Quantify natural variability
6.7.5
Choose the period for describing natural variability
6.7.6
Probability density function
6.7.7
Effect of uneven number of scenarios
6.7.8
Combine weather and climate information
6.7.9
Don’t present just one scenario
7
Climate data store and toolbox
8
Climate Observations
8.1
Differentiation between types of measurements (direct/indirect, remote, in-situ)
8.1.1
Direct vs. Indirect
8.1.2
In-situ vs remote
8.2
Identification of different types of meteorological observing systems and their of observational representativeness including temporal and spatial (microscale, local scale, mesoscale, large scale, planetary scale) scales of phenomena, and measurement capability
8.2.1
Weather stations on land
8.2.2
Observations at sea
8.2.3
Radio- and dropsondes
8.2.4
Aircraft measurements
8.2.5
Radar
8.2.6
Lidar
8.2.7
Satellites
8.2.8
Historical data records
8.2.9
Global Networks
8.3
Describe instrument and measurement uncertainty and the factors that are used to assess systematic and random errors, and the propagation of errors
8.3.1
Measurement guidelines
8.3.2
Data homogenisation
9
Bias correction and Downscaling
9.1
Terminology
9.2
Why are models biased and do we need bias correction?
9.3
Examples of model bias
9.3.1
Geographical differences
9.3.2
Differences in amount of bias
9.4
Impact of model bias on climate assessments
9.5
Theory of bias correction and downscaling
9.6
Bias correction example - tutorial
10
Regional reanalysis (UERRA-HARMONIE) & Surface Reanalysis (MESCAN-SURFEX)
10.1
Importance of Regional Reanalysis
10.2
Copernicus Regional Reanalysis for Europe service
10.3
Available data
10.4
Methodology
10.4.1
Global Reanalysis (ERA40/ERA-Interim) —> Regional Reanalysis (UERRA-HARMONIE)
10.4.2
Regional Reanalysis (UERRA-HARMONIE) —> Surface Reanalysis (MESCAN-SURFEX)
10.5
Guidelines on using UERRA-HARMONIE & MESCAN-SURFEX data
10.5.1
Spatial Resolution
10.5.2
Temporal Resolution
10.6
Limitations of reanalysis
10.7
Model specific issues
10.7.1
UERRA-HARMONIE spin-up issues (and effects on wind, temperature and precipitation data)
10.7.2
MESCAN-SURFEX precipitation observations
10.8
Download and processing of UERRA-HARMONIE or MESCAN-SURFEX data
10.8.1
Copernicus Climate Data Store Website
10.8.2
Copernicus Climate Data Store API
11
Using climate models for climate scenarios
11.1
Definition climate scenario
11.1.1
Cascade of scenarios
11.2
Why do we use scenarios?
11.3
Constrution of IPCC scenarios
11.3.1
Types of scenarios
11.3.2
Steps in producing scenarios
11.4
National climate scenarios: why constructing them? and why are there differences?
11.4.1
Overview of national scenarios (January 2019)
11.4.2
Reasons for differences between national scenarios
11.4.3
Examples of differences between national scenarios
11.4.4
RCPs used in national scenarios
11.4.5
Selection of reference period
11.4.6
Selection of time horizon
11.4.7
Selection of global climate models (GCMs)
11.4.8
Selection of RCMs for downscaling
11.4.9
Approaches to use ensembles of climate models
11.4.10
Level of detail in climate scenarios
11.4.11
Additional information delivered with the climate scenarios
11.4.12
Consistency between climate variables
11.4.13
Representation of uncertainties
11.5
References
References
Published with bookdown
Copernicus Climate Change Programme: User Learning Service Content
Chapter 7
Climate data store and toolbox
—> see online lesson, or visit the Climate Data Store.