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CLIPC: Constructing Europe's Climate Information Portal

CLIPC provides access to Europe's climate data and information.

Use case informing EU decision-makers on future heat-stress

Starting point

Use case in development!

In the perspective of delineating a new 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.
The process of adjustment to actual or expected climate and its effects. In human systems, adaptation seeks to moderate harm or exploit beneficial opportunities. In natural systems, human intervention may facilitate adjustment to expected climate and its effects.
strategy, a group of EU decision makers ask an impact researcher to identify the regions within the Iberian Peninsula where the combined effect of temperature change and urbanization is expected to contribute to an increase in heat-stress of the population. Furthermore the impact researcher is asked to provide a more in-depth assessment of future heat-stress riskrisk
often taken to be the product of the probability of an event and the severity of its consequences. In statistical terms, this can be expressed as Risk(Y)=Pr(X) C(Y|X), where Pr is the probability, C is the cost, X is a variable describing the magnitude of the event, and Y is a sector or region.
for the city of Lisbon where a pilot early warning system is to be tested.

Basic approach 

The researcher breaks down her task into three main steps:

  • Identify regions where current capacity of the population to sustain heat is likely be overcome due to changes in future temperatures.
  • Identify the areas where urban land is expected to decrease in the near future in comparison with the current situation.
  • Combine the results of steps 1 and 2 to produce a heat vulnerabilityvulnerability
    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.
    index map for the Iberian Peninsula.

Basic indicators 

  • Minimum mortality temperature (current)
  • Average daily temperature (current)
  • Urban land (current and future projection)

Extended approach

After the initial scoping the user conducts a more detailed risk analysis for the city of Lisbon by:

  • determining the mean Urban Heat Island (UHI) effect for the city of Lisbon (using the histogram functionality)
  • and calculating (using the CLIPC processing tool) a tailor-made indicator of future days above a user-defined threshold

Extended indicators

  • Mean heat island potential for the city of Lisbon (observations)
  • Maximum daily temperature (projections)

CLIP-C tools featured 

Basic version: Map viewer, Compare tool, Combine tool
Extended version: Histogram, Processing tool

Contact person

Analytical steps in detail
Basic approach

  1. Calculate a new climate impactclimate impact
    See Impact Assessment

    Load indicator 'Average daily temperature' in the left side of the Combine Tool and select the 2020-2050 time period. Add also the indicator 'Minimum mortality temperature' (mmt) to the right side of the combining tool. Subtract the values of the 'Average daily temperature' (2020-2050) dataset from the 'Minimum mortality temperature' (current) dataset. Save the result as new indicator 'degrees difference to mmt'.

  2. Calculate a urban change indicator
    Load 'urban land use' indicator twice into the Combine tool: on the left side select the 2020 time period and on the right side the 2050 time period. Then subtract the values of the 2020 dataset from the 2050 dataset and save the result as new indicator 'urban land change'.

  3. Produce a simple heat vulnerability index for the Iberian Peninsula
    Load the two new indicators into the Combine tool. Because they have different units (degrees vs. km²), both indicators need to first be normalised (select the min-max option) so that they both have values ranging from 0 to 1.

Combine the two indicators by using the 'add' function with a weight of 0.5 for each indicator. Interpret the map of the new combined indicator and show on the map which areas of the Iberian Peninsula are expected to be more vulnerable. Save the result as new indicator 'Iberian Peninsula heat vulnerability'.

Extended approach

  1. Review of existing literature on minimum mortality temperatures for the city of Lisbon
    (the 'ground work' of the more advanced user)

    User consulted literature on the association between temperature and mortality for the city of Lisbon and found that there is a triggering effect on mortality when maximum daily temperature exceeds a given threshold 34 degrees (Garcia-Herrera et al 2010). Note this number as 'mortality threshold'. The impact researcher wants now to investigate how likely (e.g. how many days in a year) this literature-informed temperature is expected to be overcome in the future for the region around the city of Lisbon.
    The researcher knows in addition that climate models do not resolve well urban processes taking place at smaller scales, in particular the UHI effect; which makes temperatures within the city in average higher than those observed in the rural surroundings. Therefore, temperature estimates from model projections are not entirely representative of the temperatures within the city, but an average value that is expected across a large geographic area. In order to conduct a realistic assessment of heat-stress at the city scale, the UHI effect has to be somehow integrated in the analysis.

  2. Estimating the UHI effect for the city of Lisbon
    In the 'Map viewer' load the UHI indicator for the city of Lisbon and chose the summer of 2006 as time step. Using the histogram functionality determine the most frequent value of UHI in that summer and note it. Repeat it for the subsequent years (2006-2013) in order to have a rough idea of the variability in UHI potential in the city of Lisbon. If you like you can do an average of the most frequent value found across all year or you can just pick one year and use it. Note the value as 'UHI potential'.

  3. Calculate a tailor-made temperature indicator
    Get to the 'Processing tool' and chose the functionality 'process simple index'. Proceed with selecting '2020-01-01/2050-12-31' as time range. Name your output as 'heat-mortality risk days'.
    In the next steps select 'tasmax' (maximum air temperature) as the variable. For the purposes of this demonstration select as input the ‘maximum daily temperature’ dataset corresponding to the RCP 4.5. Chose 'year' as the slice mode so you get days a certain threshold in a year.
    The final step is to choose the threshold value. In our case our threshold will be the resulting value of 'mortality threshold'- 'UHI potential'- this is done in order to correct for the fact that the temperatures given by models do not include the UHI phenomena.

  4. Assess risk-days frequencies
    Load the in the 'Map viewer' the newly created 'heat-mortality risk days'. Find Lisbon and zoom a bit out. Then use the histogram functionality in order to estimate how many days per year are expected to be above the corrected mortality threshold for the region of Lisbon. Note that the histogram tool provides you the histogram for the visible extent in your screen. Zooming in and out changes the extent and hence the shape of the histogram.