Heat waves

Microclimatev0

Responsible:Niccolò Calandri

To calculate extreme Heat waves Risk, we use the variable [Copernicus Climate Data Store (CDS)]:

Warm spell duration index (WSDI): annual count of days when, for at least 6 consecutive days, the daily maximum temperature exceeds the 90th percentile calculated for that calendar day over a 5-day moving window during the reference period. This indicator informs about how often and how long temperatures are extremely high. To define risk levels, we consider the return period RP (see Cold Wave Risk) for this indicator.

The methodology described for Heat Wave Risk is parallel to that for cold waves and based on the same scientific foundations.

The method combines two consolidated scientific pillars:

The use of standardized extreme event indices, in this case the Warm Spell Duration Index (WSDI).

The definition of the WSDI index we use comes directly from the Expert Team on Climate Change Detection and Indices (ETCCDI). This group, sponsored by the World Meteorological Organization (WMO), has standardized a series of 27 climate extreme indices to ensure that climate change studies are consistent and comparable globally.

Indices for monitoring changes in extremes based on daily temperature and precipitation data This paper is one of the reference documents summarizing ETCCDI indices, providing precise definitions. It explains the logic behind creating percentile-based indices (like WSDI), which measure extremes relative to local climate, making them applicable to any region worldwide.
ETCCFI Climate Change Indices Definition of all indices by ETCCDI.
Observed coherent changes in climatic extremes during the second half of the twentieth century This is one of the seminal studies that introduced and analyzed a broad suite of extreme indices, including an early version of WSDI, demonstrating their utility for tracking global-scale climate changes.
The application of Extreme Value Statistical Analysis (Extreme Value Theory), using Return Period to classify rarity and therefore associated risk of an event.

The use of return period to classify risk of rare events is standard practice in hydrology, civil engineering and, increasingly, in climatology. It is based on Extreme Value Theory (EVT).

An Introduction to Statistical Modeling of Extreme Values This book is the academic reference text on extreme value statistics. It explains the mathematical foundations for calculating probability of rare events and how the concept of 'return period' is derived from this.
Guidelines on Analysis of Extremes in a Changing Climate in Support of Adaptation to Climate Change This WMO document provides practical guidance to scientists and risk managers on how to analyze extreme events. It explicitly discusses the importance of calculating return periods and how these can be used to inform adaptation strategies and infrastructure planning. It supports the approach you described: when an event is rarer (higher RP), the associated risk is greater because society is less prepared to face it.

In conclusion, our methodology is fully validated by the international scientific community. The use of the WSDI index ensures measurement of a relevant aspect of heat waves in a standardized way, while the use of Return Period provides an objective, robust and universally understandable risk classification system.

Data Sources & References

Sources

WRD_CDSEI_99

Methodology

The methodology described for Heat Wave Risk is parallel to that for cold waves and based on the same scientific foundations.The method combines two consolidated scientific pillars:• The use of standardized extreme event indices, in this case the Warm Spell Duration Index (WSDI).The definition of the WSDI index we use comes directly from the Expert Team on Climate Change Detection and Indices (ETCCDI). This group, sponsored by the World Meteorological Organization (WMO), has standardized a series of 27 climate extreme indices to ensure that climate change studies are consistent and comparable globally.- Indices for monitoring changes in extremes based on daily temperature and precipitation dataThis paper is one of the reference documents summarizing ETCCDI indices, providing precise definitions. It explains the logic behind creating percentile-based indices (like WSDI), which measure extremes relative to local climate, making them applicable to any region worldwide.- ETCCFI Climate Change IndicesDefinition of all indices by ETCCDI.- Observed coherent changes in climatic extremes during the second half of the twentieth centuryThis is one of the seminal studies that introduced and analyzed a broad suite of extreme indices, including an early version of WSDI, demonstrating their utility for tracking global-scale climate changes.• The application of Extreme Value Statistical Analysis (Extreme Value Theory), using Return Period to classify rarity and therefore associated risk of an event.The use of return period to classify risk of rare events is standard practice in hydrology, civil engineering and, increasingly, in climatology. It is based on Extreme Value Theory (EVT).- An Introduction to Statistical Modeling of Extreme ValuesThis book is the academic reference text on extreme value statistics. It explains the mathematical foundations for calculating probability of rare events and how the concept of 'return period' is derived from this.- Guidelines on Analysis of Extremes in a Changing Climate in Support of Adaptation to Climate ChangeThis WMO document provides practical guidance to scientists and risk managers on how to analyze extreme events. It explicitly discusses the importance of calculating return periods and how these can be used to inform adaptation strategies and infrastructure planning. It supports the approach you described: when an event is rarer (higher RP), the associated risk is greater because society is less prepared to face it.In conclusion, our methodology is fully validated by the international scientific community. The use of the WSDI index ensures measurement of a relevant aspect of heat waves in a standardized way, while the use of Return Period provides an objective, robust and universally understandable risk classification system.