Documentation and videos
In this section you can find the following documentation:
Instructions & Guidelines
- How to use the impact indicator toolkit
- How to use the ClimateClimate
Climate in a narrow sense is usually defined as the average weather, or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization. The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system. Viewer
- MyCLIPC data processing wizard (in development)
- Check also videos below!
- Flyer: "CLIPC - Helping Europe respond to the impact of climate change" (download in pdf format)
- First CLIPC policy brief - introducing the CLIPC portal (download in pdf format)
- Second CLIPC policy brief - introducing the CLIPC portal (download in pdf format)
- CLIPC posters at ECCA 2015 (news article)
- CLIPC Climate Dataset Inventory
- CLIPC Project Resources
- Project Deliverables and Milestones, you will find here
Instruction videos - Overview
A plausible description of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technological change, prices) and relationships. Note that scenarios are neither predictions nor forecasts, but are useful to provide a view of the implications of developments and actions. See also Climate scenario, Emission scenario, Representative Concentration Pathways and SRES scenarios. Viewer
The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts including sensitivity or susceptibility to harm and lack of capacity to cope and adapt.
|Simple indicator processing
Lack of precision or unpredictability of the exact value at a given moment in time. It does not usually imply lack of knowledge. Often, the future state of a process may not be predictable, such as a roll with dice, but the probability of finding it in a certain state may be well known (the probability of rolling a six is 1/6, and flipping tails with a coin is 1/2). In climate science, the dice may be loaded, and we may refer to uncertainties even with perfect knowledge of the odds. Uncertainties can be modelled statistically in terms of pdfs, extreme value theory and stochastic time series models.