Cold waves

Microclimatev0

Responsible:Niccolò Calandri

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

Cold spell duration index (CSDI): annual count of days when, for at least 6 consecutive days, the daily minimum temperature is below the 10th 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 low. For this parameter (as for others described later), it is not possible to define absolute value thresholds valid globally, as the parameter is closely related to the particular geographical region. Thus, a risk level division criterion valid globally must be based on statistical rarity of events, i.e., return period RP. For details on return period calculation, a technical note can be found at the end of the document.

The method described for assessing Cold Wave Risk is based on two consolidated scientific pillars:

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

The definition of the CSDI 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 CSDI), which measure extremes relative to local climate, making them applicable to any region worldwide.
ETCCFI Climate Change Indices Definition of all indices by ETCCDI.
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 summary, our methodology combines a standardized physical impact indicator (CSDI) with a probability-based risk classification method (Return Period). This two-level approach is fully supported by scientific literature and guidelines from major world meteorological organizations.

Data Sources & References

Sources

WRD_CDSEI_99

Methodology

The method described for assessing Cold Wave Risk is based on two consolidated scientific pillars:• The use of standardized extreme event indices, in this case the Cold Spell Duration Index (CSDI).The definition of the CSDI 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 CSDI), which measure extremes relative to local climate, making them applicable to any region worldwide.- ETCCFI Climate Change IndicesDefinition of all indices by ETCCDI.• 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 summary, our methodology combines a standardized physical impact indicator (CSDI) with a probability-based risk classification method (Return Period). This two-level approach is fully supported by scientific literature and guidelines from major world meteorological organizations.