D7.3 Toolkit indicator module
The objectives of this deliverable are to describe our workflow from the production of a climate impactclimate impact
See Impact Assessment indicator to its publication in the CLIPC portal and use by the toolkit. In particular 1) coordinating and providing data to the CLIPC toolkit following the metadata-standards established in WP5, and 2) providing the expert-based confidenceconfidence
The validity of a finding based on the type, amount, quality, and consistency of evidence (e.g., mechanistic understanding, theory, data, models, expert judgment) and on the degree of agreement. Confidence is expressed qualitatively (Mastrandrea et al., 2010). assessment for a sub-set of indicators following the template provided by WP8.
The provision of impact indicators with a harmonized meta-data underpins the adequate functioning of CLIPC indicator toolkit and functions within. In total the 71 climate impact indicators (including 3 Tiers and also socio-economic sensitivitysensitivity
The degree to which a system or species is affected, either adversely or beneficially, by climate variability or change. The effect may be direct (e.g., a change in crop yield in response to a change in the mean, range or variability of temperature) or indirect (e.g., damages caused by an increase in the frequency of coastal flooding due to sea-level rise). data) are made available for the CLIPC portal and toolkit and compliant with the meta-data standards established in WP5 and WP6 (see distribution in Figure 1). Also, the expert-based information about uncertaintyuncertainty
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. and confidence is available for a subset of the indicators. This uncertainty assessment is created after the specifications described in WP8. Specifications include the enumeration of predefined sources of uncertainty (e.g., external uncertainty or external natural forcing), their nature (e.g., unpredictabilityunpredictability
Unpredictability is caused by the variable behaviour of human beings or social processes. It differs from 'incomplete knowledge' because it concerns what ‘we cannot know’ and therefore cannot be reduced or changed by further research. ‘Unpredictatility’ is therefore non-reducible., stochasticity), and a final expert statement on the overall degree of confidencedegree of confidence
The degree of confidence defines the degree to which we trust an outcome - no matter if this outcome is a climate impact indicator derived from surface observations, re-analysis, simulations or projections describing the bio-physical or socio-economic impact of climate impact. The degree of confidence results from ‘evidence and agreement’ of the datasets used for a selected climate impact indicator and what ‘type of method’ is used for the calculation of it. of the dataset. These assessments are now integral part of the CLIPC portal and can be consulted in the CLIPC toolkit. The documentation database to trace the progress of indicators across the established workflow was established and is available on line: https://goo.gl/8X0xT9
It is expected that tracking the progress of indicators from production to portal publication might help forthcoming projects on the optimization of their own workflow. Moreover, the challenges and barriers described when attempting to follow the CLIPC workflow warn future similar initiatives of potential pitfalls to consider and suggest potential solutions.
Download the deliverable here.
Download the spreadsheet of indicators here.