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Thermal Stress Index

The Thermal Stress Analysis endpoint offers a comprehensive examination of human-perceived temperature extremes influenced by climate change scenarios. It utilizes recognized thermal indices, such as Humidex and Windchill, to measure the combined effects of heat and humidity or wind and temperature, respectively.

This endpoint is designed to retrieve return periods for specific thermal stress scenarios based on historical data and predictive models for future conditions. These metrics are pivotal for sectors like public health, urban planning, and environmental monitoring, which require accurate forecasts and likelihood analysis of thermal stress events for risk assessment and strategic planning.

Understanding Humidex and Windchill

The Humidex and Windchill indices are essential tools for evaluating the human-perceived effects of temperature. They take into account more than just the thermometer readings, providing a more comprehensive understanding of thermal stress.

  • Humidex: This index combines air temperature with humidity to reflect the perceived temperature. High humidity levels can significantly increase discomfort, as they hinder the body’s ability to cool itself through perspiration. The Humidex is particularly useful in hot, humid climates where it can help to identify potentially dangerous conditions for heat stress.

  • Windchill: In contrast, Windchill is an index that combines air temperature with wind speed to estimate the chilling effect of the wind on the human body during cold weather. It reflects how cold the weather feels on exposed skin, which can be significantly colder than the actual air temperature. Windchill is crucial for understanding the potential for frostbite and hypothermia in cold, windy conditions.

Index Creation Methodology​

For an in-depth understanding of our calculation process, refer to the detailed breakdown below:

🌡️ Detailed Breakdown: Unpacking Thermal Stress Calculations

Data Collection and Index Calculation​

 

  • Annual Extremes: Our models consider the highest and lowest index (e.g., humidex or windchill) values annually to determine thermal stress, focusing on human-perceived temperature extremes. These extremes are calculated from an initial monthly field, representing long term trends.

 

Statistical Analysis and Thresholds for Extremes​

  • Return Periods: By determining the index values for specific return periods, we identify how often extreme thermal conditions are likely to occur. These periods are statistical estimates indicating that an event of a certain intensity is expected to happen on average once within a given number of years.

  • Gumbel Distribution: To model the occurrence of extreme index values, a Gumbel distribution is fitted to the annual maximums. This approach is suitable for assessing the risk of rare and significant events.

 

Thermal Stress Types​

 

  • The Humidex metric combines temperature and humidity to represent how hot the weather feels to the average person.
  • The Windchill index measures how cold it feels based on the wind speed and actual air temperature.

 

Interpretation of Thermal Stress Levels​

 

Humidex:​
  • Low Risk: Below 20°C.
  • Increased Risk: Above 30°C.

If your extreme values never rise above 20°C, you should disregard humidex as your thermal stress metric, indicating a minimal chance of high-temperature stress.

 

Windchill:​
  • Low Risk: Between 0°C and -10°C.
  • Increased Risk: Below -10°C.

If your extreme values never fall below 0°C, you should disregard windchill as your thermal stress metric, indicating a minimal chance of low-temperature stress.

 

Considerations:

Interpolation between data points for return periods can be performed, but caution is advised when interpreting values outside the standard range of 1 in 2 to 1 in 100 years to maintain accuracy.

Understand the specific thermal indices and choose the appropriate one for your climate analysis needs. Misinterpretation can lead to incorrect assessments of thermal stress risks.

Usage Instructions​

This API provides insights into thermal stress changes over time, analyzing heat and cold events, their frequency, and intensity based on various climate models and scenarios.

Endpoint 1: Extreme Thermal Stress Return Period​

 

Retrieve 1 in 2, 5, 10, 20, 50, and 100 years for both historical (1950 to 2010) and future (based on entered period) on thermal stress distribution. This can allow for understanding potential future deviations and impacts on perceived temperature.

 

Inputs​

  • thermalstress_type: The type of thermal stress, such as humidex or windchill.
  • latitude: Geographical coordinate (decimal degrees) specifying north–south position.
  • longitude: Geographical coordinate (decimal degrees) specifying east-west position.
  • time_period: The range of years for the analysis, e.g., 2020_2050.
  • model: The climate model to be used for the analysis. Example: cmip6_ec_earth3_cc

 

Use Case: Change in Humidex return periods by 2070-2100 over Zurich, Switzerland​

API SPI Case Study Image

In this example, we're examining the values associated with specific return periods, such as 1 in 2, 5, 10, 20, 50, or 100 years. Historically, the annual extreme humidex value for a 1 in 100 year return period has been 35.8266 C. However, projections indicate a significant increase in this extreme by the years 2070-2100. Under the SSP2-4.5 scenario, the extreme is expected to rise to 39.9455 C, and under the SSP5-8.5 scenario, it could reach a staggering 47.8647 C.

 

Example API Output:
{
  "metadata": {
    "generated_at": "2024-02-16T15:09:38.115543+00:00",
    "nearest_location": {
      "latitude": 43.85954,
      "longitude": 4.921875
    },
    "time_period_historical": "1950_2010",
    "time_period_projection": "2070_2100",
    "units": "degree_celsius"
  },
  "return_periods": {
    "historical": {
      "2_year": 26.7157,
      "5_year": 29.1548,
      "10_year": 30.7698,
      "20_year": 32.3189,
      "50_year": 34.324,
      "100_year": 35.8266
    },
    "ssp2_4_5": {
      "2_year": 31.8518,
      "5_year": 34.0186,
      "10_year": 35.4533,
      "20_year": 36.8294,
      "50_year": 38.6107,
      "100_year": 39.9455
    },
    "ssp5_8_5": {
      "2_year": 36.9033,
      "5_year": 39.8379,
      "10_year": 41.7808,
      "20_year": 43.6445,
      "50_year": 46.0569,
      "100_year": 47.8647
    }
  }
}

Interactive Endpoint​

You can access and interact with the API endpoints using our API Endpoint Directory.

References and Further Reading:​

Gumbel, Emil Julius. Statistics of extremes. Columbia university press, 1958. Access the textbook.