Case Study: Human Health

Case study description:

Hypothetical “Region C” is in the outback. It currently experiences many hot days and the population and industries are fairly well adapted to heat. But if there is a large increase in the frequency of very hot days and nights, this may affect mortality of older people and the ability of agricultural and mining workers to perform their work. Health managers and industry operators in the region have three broad policy frameworks to deal with a potential increase in hot days, depending on the severity of change: no change, incremental change or transformational change.

The range of incremental changes include adding cooling systems, changes to architecture and construction, greening of public spaces and changes to behavioural guidelines. Transformational change includes a complete change in the way areas are used, who uses them or whether some areas are inhabited at all. Measures range in cost and impact from relatively cheap and easy to very expensive and difficult. Managers would like to know the likelihood of needing incremental or transformational change. They would like to gauge the potential financial cost and social impacts. Impacts involve many non-climate factors, but projections of hot days and heat risk form one crucial part of the analysis. The frequency of days over 40 °C, and increase in heat indices that include temperature and humidity are the preferred measures of climate. The three policy frameworks are summarised below:

Policy

Health Managers

Industry

No change

Current settings remain - health risk from heat remains within current management bounds

No changes

Incremental change

Infrastructure

Architecture

Behavioural

Greening

Technology - cooling

Architectural - shelter

Behaviour guidelines - working hours

Transformational change

Communities move or change their demographics

Fundamentally changing or ceasing work in the current style

Set time-frame and risk:

The health plan has a timeframe to the end of the century, to allow planning of major infrastructure such as hospitals and clinics. Health managers want to take a precautionary approach, so are primarily interested in the "worst case" scenario , with the context of lower projections given as qualitative statements. Therefore, the hot end of projections for 2090 under a high emissions scenario (RCP8.5) are used, but adding the context of the "maximum consensus" and cooler projections as well as other emissions scenarios.

Industry managers are going through major change. For this reason they feel they can’t confidently engage in planning beyond 15 years at the moment, or can only consider changes beyond this only very vaguely. They are interested in the 'most likely’ projection for a high emissions scenario, as they feel this will be useful to manage risk in the current environment. Therefore, the maximum consensus projection for 2030 is used, with qualitative statements putting this in context.

Back of the envelope:

In general, projections of change in extreme temperature roughly scale with change in mean temperature. There is generally no large change in the shape of the temperature distribution, just a shift towards higher temperatures. The projections for Rangelands are most relevant to this region:

For the health managers (RCP8.5, 2090):

  1. Mean temperature projection is up to 5.9 °C (median is 4.4 °C, range is 2.6 to 5.9 °C)
  2. Number of days over 40 °C per year from 17 currently to as much as 114 (median 83, range 58 to 114)
  3. Little change or small decrease in relative humidity, however a small number of models show a small increase
  4. Suggests at least incremental adaptation will be needed and transformational adaptation may be needed

For industry (RCP8.5, 2030):

  1. Median projection of mean temperature is 1.0 °C (range is 0.8 to 1.4 °C)
  2. Number of days over 40 °C per year from 17 currently to around 31 (range of results 24 to 40)
  3. Little change in mean relative humidity
  4. Suggests incremental adaptation will be needed

In both cases more detailed analysis of the joint probability of humidity and temperature could illustrate the projected changes with more sophistication, but this is perhaps not even needed.

Key context for choosing datasets:

  1. Sub-setting global climate models is important to assess – range of results from Climate Futures assessment
  2. For days over 40 °C, scaling by the mean is ok – avoids problem of bias and difficulty of complex scaling
  3. Downscaling doesn’t offer a lot of added value for this region and this question
  4. Further analysis of heat indices, joint probabilities of temperature and humidity may be useful

Choice of data:

For temperature threshold work, simple analysis available on this website may be adequate to decide an appropriate policy setting. If not, then a more detailed mean scaling of temperature from more models can be done.

For analysis of comfort indices of temperature and humidity, material on this website may be adequate. However, if new work is needed, use internally-consistent quantile-scaling of temperature and humidity.

Page updated: 1st August 2016