The Near Future The Universal Thermal Climate Index UTCI

Although each of the published heat budget models is, in principle, appropriate for use in any kind of assessment of the thermal environment, none of the models is accepted as a fundamental standard, neither by researchers nor by end-users.

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Fig. 2.4 Acclimatisation related thresholds for example Lisbon 2003 based on the HeRATE approach (Koppe and Jendritzky 2005)

Fig. 2.4 Acclimatisation related thresholds for example Lisbon 2003 based on the HeRATE approach (Koppe and Jendritzky 2005)

On the other hand, it is surprising that after 40 years experience with heat budget modelling and easy access both to computational power and meteorological data, the oversimplified and thus unreliable indices are still widely used.

Some years ago the International Society on Biometeorology ISB recognised the issue presented above and established a Commission "On the development of a Universal Thermal Climate Index UTCI" (Jendritzky et al. 2002) (www.utci. org). Since 2005 these efforts have been reinforced by the COST Action 730 (Cooperation in Science and Technical Development) of the European Science Foundation ESF that provides the basis that at least the European researchers plus experts from abroad can join together on a regular basis in order to achieve significant progress in deriving such an index (COST UTCI 2004). Aim is an international standard based on scientific progress in human response related thermo physiological modelling of the last 4 decades (Fiala et al. 2001, 2003) including the acclimatisation issue.

This work is performed under the umbrella of WMO's Commission on Climatology CCl, and will finally be made available in a WMO "Guideline on the Thermal Environment", probably by 2009, so that everybody dealing with biometeorological assessments, in particular NMSs (National Meteorological and Hydrological Services), but also universities, public health agencies, epidemiologists, environmental agencies, city authorities, planners etc. can then easily apply the state-of-the-art procedure for their specific purposes. The guideline will provide numerous examples for applications and solutions for handling meteorological input data.

The Universal Thermal Climate Index UTCI (working title) must meet the following requirements:

1. Thermo physiologically significant across the entire range of heat exchange

2. Applicable for whole-body calculations but also for local skin cooling (frost bite)

3. Valid in all climates, seasons, and scales

4. Useful for key applications in human biometeorology

The following fields of applications are considered as particularly significant for users:

1. Public weather service PWS. The issue is how to inform and advice the public on thermal conditions at a short time scale (weather forecast) for outdoor activities, appropriate behavior, and climate-therapy.

2. Public health system PHS. In order to mitigate adverse health effects by extreme weather events (here heat waves and cold spells) it is necessary to implement appropriate disaster preparedness plans. This requires warnings about extreme thermal stress so that interventions can be released in order to save lives and reduce health impacts.

3. Precautionary planning. UTCI assessments provide the basis for a wide range of applications in public and individual precautionary planning such as urban and regional planning, and in the tourism industry. This is true for all applications where climate is related to human beings. The increasing reliability of monthly or seasonal forecasts will be considered to help develop appropriate operational UTCI products.

4. Climate impact research in the health sector. The increasing awareness of climate change and therewith related health impacts requires epidemiological studies based on cause-effect related approaches. UTCI will be the appropriate impact assessment tool. So also do scenario based calculations and down-scaling methods in the climate change and human health field need appropriate UTCI based procedures.

Mathematical modeling of the human thermal system goes back 70 years. In the past four decades more detailed, multi-node models of human thermoregulation have been developed, e.g. Stolwijk (1971), Konz et al. (1977), Wissler (1985), Fiala et al. (1999, 2001), Huizenga et al. (2001) and Tanabe et al. (2002). These models simulate phenomena of the human heat transfer inside the body and at its surface taking into account the anatomical, thermal and physiological properties of the human body (see Fig. 2.1). Environmental heat losses from body parts are modeled considering the inhomogeneous distribution of temperature and thermoregulatory responses over the body surface. Besides overall thermo physiological variables, multi-segmental models are thus capable of predicting 'local' characteristics such as skin temperatures of individual body parts. Validation studies have shown that recent multi-node models reproduce the human dynamic thermal behaviour over a wide range of thermal circumstances (Fiala et al. 2001, 2003; Havenith 2001; Huizenga et al. 2001). Many of these models have been valuable research tools contributing to a deeper understanding of the principles of human thermoregulation (Fiala et al. 2001). However, there is still a need for better understanding of adaptive responses and their physiological implications.

The passive system of the Fiala model (Fiala et al. 1999, 2001) is a multi-segmental, multi-layered representation of the human body with spatial subdivisions. Each tissue node is assigned appropriate thermo physical and thermo physiological properties. The overall data replicates an average person with respect to body weight, body fat content, and Dubois-area. The physiological data aggregates to a basal whole body heat output and basal cardiac output, which are appropriate for a reclining adult in a thermo-neutral environment of 30°C. In these conditions, where no thermoregulation occurs, the model predicts a basal skin wettedness of 6%; a mean skin temperature of 34.4°C; and body core temperatures of 37.0°C in the head core (hypothalamus) and 36.9°C in the abdomen core (rectum) (Fiala et al. 1999). Verification and validation work using independent experiments from air exposures to cold stress, cold, moderate, warm and hot stress conditions, and a wide range of exercise intensities revealed good agreement with measured data for regulatory responses, mean and local skin temperatures, and internal temperatures for the whole spectrum of boundary conditions considered (Richards and Havenith 2007).

The experts of the COST Action 730 WG on Thermo Physiological Modeling have agreed to base the UTCI model on the Fiala approach which will be substantially advanced by including as yet unused data from other research groups. The UTCI model must meet all the above listed requirements in application. From practical considerations the advanced Fiala multi-segmental model cannot be applied explicitly on a routine basis. Thus the future UTCI computations will make use of a statistical approach derived from simulations with the Fiala model that covers all conceivable combinations of air temperature, wind, humidity, and mean radiant temperature plus clothing.

In an operational procedure the non-meteorological variables metabolic rate MET and thermal resistance of clothing are of great importance. The UTCI Commission has defined a representative activity to be that of a person walking with a speed of 4 km/h. This provides a metabolic rate of 2.3 MET (135 W/m2). Clothing isolation Icl will be considered as an intrinsic clo-value in the range of Icl = 0.4-1.7 clo (1 clo = 0.155 km2/W) determined by air temperature. This should cover the kinds of clothing worn by people who are adapted to their local climate. The need to address specific characteristics of clothing, such as significant ventilation between body surface and inner surface of clothing is still subject of discussion.

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