The demand is growing for practical information on climate projections and the impacts expected in different geographical regions and different sectors. It is a challenge to transform the vast amount of data produced in climate models into relevant information for climate changeclimate change
Climate change refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings such as modulations of the solar cycles, volcanic eruptions and persistent anthropogenic changes in the composition of the atmosphere or in land use. Note that the United Nations Framework Convention on Climate Change (UNFCCC), in its Article 1, defines climate change as: 'a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods'. The UNFCCC thus makes a distinction between climate change attributable to human activities altering the atmospheric composition, and climate variability attributa impact studies. Climate indices based on climate model data can be used as means to communicate climate change - impact relations.
Climate indices are developed as a simplified way to communicate more complex climate change - impact relations. Mean temperature and precipitation sums can be seen as (simple) climate indices, and the same applies for various measuresmeasures
In climate policy, measures are technologies, processes, and practices that contribute to mitigation, for example renewable energy technologies, waste minimization processes and public transport commuting practices. of climate extremes. The power of the climate index concept is strikingly illustrated with the more complex climate indices that incorporate information on the 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). of a specific system, such as exposureexposure
The presence of people, livelihoods, species or ecosystems, environmental services and resources, infrastructure, or economic, social, or cultural assets in places that could be adversely affected. See also Vulnerability. time or threshold levels of event intensity.
The climate is usually described in terms of basic variables such as temperature and precipitation. Furthermore, usually mean values and seasonal variations are given. Typical climate indices are based on annual, seasonal or monthly values. However, the climate is not only represented by mean values and seasonal variations. Rare extreme events are also an integral part of the climate. Extremes of short-lived nature (e.g. windstorms, heavy downpours, etc.) often have a rather local extent but extremes of persistent nature (e.g. heat and cold spells) typically cover larger regions. Each day in such an event is not necessarily extreme in itself. Rather, it is the accumulated effect over a long period that becomes noticeable. Climate extremes can be defined as climatologically rare events (infrequent) but also based on how they affect society and the environment. Examples of different kinds of climate extremes are:
- Maximum and minimum values (e.g. lowest temperature during the day in January)
- Number of times a special threshold value is exceeded (e.g. number of days when it rains more than 25 mm)
- longest period when a threshold values is exceeded (e.g. longest summer drought)
- The first or last occurrence of a certain weather condition (e.g. last frost in spring)
Indices based on threshold values are sensitive to the precision of the climate models. If a model produces slightly colder conditions it can strongly influence the amount of hot days. This does not have to be a problem regarding future conditions since the most interesting point is how the climate index changes.
Apart from statistical complexity of indicators, it is also useful to distinguish different levels of direct relevance, denoted ‘Tier’ levels numbered 1 to 3. For example, an indicator that combines temperature extremes with sensitivity of ecosystems or people to temperature (‘Tier 2’) is more directly useful than just the temperature extremes index (‘Tier 1’). And an index that combines this impact with a socio-economic model to indicate the effect on society (‘Tier 3’) is again of a higher level of relevance.
The figure shows an example of percentage change in maximum 7-day precipitation for the period 1961-1990 (observed) and two future scenariosscenarios
Scenarios can be thought of as stories of possible futures. They allow the description of factors that are difficult to quantify. In the context of climate change scenarios are used for the future development of factors such as governance, social structures, future population growth, technical development and agriculture. These descriptions are essential to model the future climate..