EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > Analysis and results > 1. Extreme events > Example: Northwestern Europe January 1963 Cold snap
---
The winter of 1963 was one of the coldest in European history,
setting records in
Germany (6.3 °C colder than the 1981–2010 average),
the UK (coldest winter since 1740 in central England),
and much of the rest of Europe
[[Sippel+24](https://doi.org/10.5194/wcd-5-943-2024), [Twardosz+16](https://doi.org/10.1515/acgeo-2016-0083), [Prior+11](https://doi.org/10.1002/wea.735)].
In the Netherlands, the national meteorological office KNMI defines a cold snap as a sequence of at least five days with a maximum temperature below 0.0 °C, of which at least three have a minimum temperature colder than –10.0 °C.
Four such events were recorded in January 1963 alone [[KNMI](https://www.knmi.nl/nederland-nu/klimatologie/lijsten/koudegolven)].
The 1963 edition of the _Elfstedentocht_,
a nearly 200 km speed skating competition held irregularly in the Dutch province of Fryslân,
has been nicknamed "The Hell of '63" due to the extremely harsh conditions faced by competitors.
Only 127 out of 9 862 participants finished the race,
with many dropping out due to frozen eyes and frostbite before the race was cancelled for safety concerns [[De Friesche Elf Steden](https://elfstedentocht.frl/en/tour/the-tour-of-1963/), [Wielinga+24](https://www.groningentoen.nl/elfstedentocht/medische-gevolgen-elfstedentocht-1963/)].

In Thermal Trace,
the severe cold faced by Elfstedentocht participants on 18 January 1963 is clearly visible,
with strong to extreme cold stress across much of northwestern Europe
and
UTCI values around 20 °C colder than usual in the entire Netherlands
(Figure {numref}`{number} <application_thermaltrace_cold_maps_daily>`).
Over the 1962–63 winter season,
most of this part of Europe experienced strong or extreme cold stress at least once,
with seasonal minimum UTCI values down to –45 °C,
which is 10 to 15 °C colder than the typical seasonal minimum,
and ≥70 days with strong cold stress
(Figure {numref}`{number} <application_thermaltrace_cold_maps_season>`).

The hourly time series illustrates the difference between UTCI and physical temperature very well (Figure {numref}`{number} <application_thermaltrace_cold_maps_timeseries>`).
The air temperature at 2 m increased from 17 to 18 January
but the UTCI fell sharply,
corresponding to known conditions (severe wind chill and lack of sunlight) that day [[De Friesche Elf Steden](https://elfstedentocht.frl/en/tour/the-tour-of-1963/)].
Since UTCI is calculated for an average human performing standard tasks,
not someone speed skating 200 km,
there may be some difference between UTCI and the participants' actual experiences.
For example, exercise creates more body heat and thus reduces cold stress, but high speed increases wind chill which worsens cold stress.
Lastly, the seasonal time series in the same figure clearly shows the cold snap of 16–22 January as well as an earlier one around the turn of the year.


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > Analysis and results > 1. Extreme events > Example: Northwestern Europe January 1963 Cold snap
---
cold stress.
Lastly, the seasonal time series in the same figure clearly shows the cold snap of 16–22 January as well as an earlier one around the turn of the year.

Uncertainty is particularly important to consider
for case studies early in the ERA5 data record.
Due to the limited number of observational data available for assimilation in the early years,
particularly the absence of satellite data prior to 1979,
the uncertainty in individual ERA5 data can be markedly larger than in later years.
The accuracy may also be lower for the same reason.
Both accuracy and uncertainty are strongly tied to data availability and hence vary strongly with time and location.
For example, ERA5 is trustworthy over much of Europe and the United States as early as the 1940s,
but not until several decades later in the Southern Hemisphere [[Soci+24](https://doi.org/10.1002/qj.4803)].
Since ERA5-HEAT, the dataset underpinning Thermal Trace, combines multiple ERA5 variables to determine UTCI,
covariance between these variables may increase or decrease the resulting uncertainties.
For a discussion on uncertainty in ERA5 and in UTCI, the reader is referred to
[[Soci+24](https://doi.org/10.1002/qj.4803), [Di Napoli+21b](https://doi.org/10.1007/978-3-030-76716-7_10), [Hersbach+20](https://doi.org/10.1002/qj.3803), [Schreier+13](https://doi.org/10.1007/s00484-012-0525-y)].
A general rule of thumb for using Thermal Trace would be to investigate the uncertainty in ERA5 for the preferred location and time,
and to cross-reference with alternative data sources or descriptions of known events
(such as heatwaves or cold snaps)
for increased confidence.


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > Analysis and results > 1. Extreme events > Example: Northwestern Europe January 1963 Cold snap
---
of known events
(such as heatwaves or cold snaps)
for increased confidence.

::::{tab-set}
:::{tab-item} Daily maps
:sync: heat_dailymaps
 attachment:application_thermaltrace_cold_maps_daily.png
---
name: application_thermaltrace_cold_maps_daily
---
Screenshots of Thermal Trace showing:
a) Anomaly in daily minimum UTCI (top; app view).
b) Daily peak cold stress category (bottom; app view).
in Northwestern Europe on 18 January 1963, the day of the 1963 Elfstedentocht. The marker indicates Leeuwarden, where the race started and finished.
Note that this figure uses the LAEA (EPSG:3035) projection and that the colour scale is different from Figure {numref}`{number} <application_thermaltrace_heatwave_maps_over_time>`.
```
:::
:::{tab-item} Seasonal maps
:sync: heat_monthlymaps
 attachment:application_thermaltrace_cold_maps_season.png
---
name: application_thermaltrace_cold_maps_season
---
Screenshots of Thermal Trace showing:
a) Seasonal peak cold stress (top; <a href="https://thermaltrace.climate.copernicus.eu/?agg=seasonal&anomaly=false&date=1962-12-01&heat=false&lat=53.20&lng=5.79&variable=utci_seasonal_min_cat" target="_blank">app view</a>).
b) Seasonal minimum UTCI (centre left; <a href="https://thermaltrace.climate.copernicus.eu/?agg=seasonal&anomaly=false&date=1962-12-01&heat=false&lat=53.20&lng=5.79&variable=utci_seasonal_min" target="_blank">app view</a>).
c) Anomaly in seasonal minimum UTCI (centre right; <a href="https://thermaltrace.climate.copernicus.eu/?agg=seasonal&anomaly=true&date=1962-12-01&heat=false&lat=53.20&lng=5.79&variable=utci_seasonal_min" target="_blank">app view</a>).
d) Number of strong cold stress days (bottom left; <a href="https://thermaltrace.climate.copernicus.eu/?agg=seasonal&anomaly=false&date=1962-12-01&heat=false&lat=53.20&lng=5.79&variable=utci_days_below_-13_daily_min" target="_blank">app view</a>).
e) Anomaly in strong cold stress days (bottom right; <a href="https://thermaltrace.climate.copernicus.eu/?agg=seasonal&anomaly=true&date=1962-12-01&heat=false&lat=53.20&lng=5.79&variable=utci_days_below_-13_daily_min" target="_blank">app view</a>).
over the December 1962–February 1963 winter season.
Note that this figure uses the LAEA (EPSG:3035) projection.
```
:::
:::{tab-item} Time series
:sync: heat_timeseries
 attachment:application_thermaltrace_cold_maps_timeseries.png
---
name: application_thermaltrace_cold_maps_timeseries
---
Time series, from top to bottom:
a) Hourly temperature (dashed) and UTCI (solid) around the 18 January 1963 Elfstedentocht (app view).
b) Daily minimum/maximum (blue/red) UTCI over the 1962–63 winter season (December–February; solid) and average climatology (1991–2020; transparent) (<a href="https://thermaltrace.climate.copernicus.eu/?agg=seasonal&anomaly=false&date=1962-12-01


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > Analysis and results > 1. Extreme events > Example: Northwestern Europe January 1963 Cold snap
---
February; solid) and average climatology (1991–2020; transparent) (app view).
in Leeuwarden, Netherlands.
```
:::
::::


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > Analysis and results > 2. Long-term trends > Introduction
---
Climate change is causing a general
increase in temperature across most (but not all) of the world,
which naturally comes with increased heat stress and generally decreased cold stress [[Vargas Zeppetello+22](https://doi.org/10.1038/s43247-022-00524-4), [Van Oldenborgh+19](https://doi.org/10.1088/1748-9326/ab4867)].
For example,
in the Caribbean Netherlands, heat stress is increasingly becoming "unbearable",
and a recent report by health and climate experts urged the government to address this issue
– for both Caribbean and European parts of the country –
as a matter of priority for public health and the economy
[[Gezondheidsraad+26](https://www.healthcouncil.nl/documents/2026/05/21/climate-change-and-health-directions-for-policy)].
This general trend towards increased heat stress is compounded by an increase in the frequency and severity extreme events,
both heatwaves and (in some locations) cold snaps
[[Thompson+22](https://doi.org/10.1126/sciadv.abm6860), [Ma+19](https://doi.org/10.1175/JCLI-D-18-0234.1), [Kundzewicz+16](https://doi.org/10.1515/igbp-2016-0005)].

In popular media,
the increase in heat stress has been a particularly common discussion topic relating to climate change.
Explainer pieces on climate change have become a mainstay in journalism,
with many published every summer and during heatwaves
[[Hassan+26](https://www.theguardian.com/world/2026/may/26/high-temperatures-millions-workers-impacted-by-heat-india-asia), [Overton+25](https://www.mainepublic.org/climate/2025-08-07/heat-danger-maines-most-vulnerable-at-risk-from-rising-summer-temps), [Rowlatt+23](https://www.bbc.co.uk/news/science-environment-66143682)].
The same is true for heat health alerts,
issued and reported on when temperatures (physical or feels-like) hit certain thresholds [[Young+26](https://www.bbc.co.uk/news/articles/cp8ppdk5zp2o)].
The press also frequently report on projected outcomes of climate change,
such as migration patterns likely to emerge from increased heat stress [[Hall+26](https://www.smh.com.au/environment/climate-change/how-lethal-humidity-threatens-to-displace-millions-in-our-region-20260525-p600j0.html)].

Thermal Trace does not provide future projections of UTCI,
but can be used to explore historical trends up to the present.
Many studies have looked into historical trends for different variables and locations and based on different datasets,
e.g.
UTCI across the Arabian Peninsula [[Ullah+24](https://doi.org/10.1038/s41598-024-54766-7)]
and
tropical nights in Ukraine [[Klok+23](https://doi.org/10.1088/1755-1315/1126/1/012023)] and Spain [[Correa+24](https://doi.org/10.1002/joc.8510)].
In one study, Thermal Trace was used to assess the long-term (1940–2024) trend of UTCI and days with very strong/extreme heat stress in Oman
[[Hereher+26](https://doi.org/10.3390/su18041800)].
For future projections of temperature including daily thresholds, but not UTCI, the user is referred to the [_Copernicus Interactive Climate Atlas_](https://apps.climate.copernicus.eu/overview?app=climate-atlas).


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > Analysis and results > 2. Long-term trends > Assessment of Thermal Trace
---
Thermal Trace is determined to be suitable for visualising long-term trends in heat and cold stress for the following reasons:
* **Variables:** Thermal Trace displays multiple variables that address common questions in the scientific and popular literature, as well as in policymaking, particularly the number of heat/cold stress days per year and the number of tropical nights per year. For similar information based on physical (rather than feels-like) temperature, users may be interested in the [ERA Explorer](https://apps.climate.copernicus.eu/overview?app=era-explorer) application.
* **Consistency:** Temporal consistency is key for (long-term) trend analysis, as one needs to be confident that apparent trends are real, not artefacts of the data production. As such, this was a key goal in the production of ERA5 and its extension back to 1940, but the ability to ensure consistency is inherently limited by changes in availability of observations over time [[Soci+24](https://doi.org/10.1002/qj.4803)]. There are some known biases and patterns in ERA5, such as a cold bias over land before 1967 [[Simmons+21](https://doi.org/10.21957/ly5vbtbfd)], differences between the pre- and post-satellite (1979) eras [[Liu+24](https://doi.org/10.1016/j.ecolind.2024.112481), [Lussana+24](https://doi.org/10.1002/asl.1239)], and spatial differences in temporal consistency [[Wang+25](https://doi.org/10.3390/rs17071317)]. Keeping these caveats in mind, ERA5 and its derivative ERA5-HEAT can be reasonably used for long-term trend analysis, and hence Thermal Trace is suitable for visualisation of these trends.
* **Spatial Coverage and Resolution:** Thermal Trace provides global coverage at a resolution (0.25° × 0.25°) suitable for investigating specific locations on the scale of counties. The ability to draw custom areas also enables larger-scale analysis, e.g. for a small country or wider region. The spatial resolution may be a limiting factor in locations with varied terrain, such as coastlines and cities (ERA5 does not account for the [urban heat island](https://stories.ecmwf.int/urban-heat-islands-and-heat-mortality/index.html) effect). For example, the grid cell containing Bonaire, Netherlands (see example below) is mixed land-sea and its data therefore may not fully represent conditions on the island itself.
* **Temporal Coverage and Resolution:** Time series are available at hourly, daily, and yearly (summary) resolution, allowing for trend analysis at different temporal scales. For long-term trend analysis, the yearly summary statistics are particularly useful.
* **Uncertainty:** Thermal Trace does not show uncertainty estimates, because these are not available for the underpinning dataset (ERA5-HEAT). A general estimate of uncertainty in the data cannot be provided here, but users can refer to the relevant [documentation](https://confluence.ecmwf.int/x/ZSOgBg) and scientific literature for ERA5 [[Soci+24](https://doi.org/10.1002/qj.4803), [Simmons+21](https://doi.org/10.21957/ly5vbtbfd), [Hersbach+20](https://doi.org/10.1002/qj.3803)] to determine the confidence in specific times and locations. Overall, the data are most trustworthy after 1979 (the satellite era), and in locations with more observations (Europe and United States) before then.


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > Analysis and results > 2. Long-term trends > Examples
---
Thermal Trace allows the user to generate and export time series for a given grid cell,
e.g. by searching for a particular town or clicking a location on the map,
or a custom area drawn directly on the map.
For long-term trends,
this creates a stacked bar chart of yearly statistics,
i.e. the number of days with _x_ level of heat or cold stress per year.
This chart can be used to visually identify trends.
The displayed data can be exported in CSV format for quantitative analysis or custom visualisation.
Here, we assess only Thermal Trace's suitability for visualising trends within the application;
the quality of the underpinning ERA5-HEAT dataset is assessed separately
(see [](../Indicators/indicator)).

Visually comparing time series exported from Thermal Trace
(Figure {numref}`{number} <application_thermaltrace_trends_heat>`)
to the literature shows general agreement.
For example,
the time series show
an increase in very strong heat stress days in Bonaire, Caribbean Netherlands [[Gezondheidsraad+26](https://www.healthcouncil.nl/documents/2026/05/21/climate-change-and-health-directions-for-policy)]
and
an increase in the number of tropical nights,
as well as a potential increase in the number of heat stress days,
in Madrid, Spain [[Correa+24](https://doi.org/10.1002/joc.8510)].
Kherson, Ukraine, also appears to show an increase in tropical nights and heat stress days [[Klok+23](https://doi.org/10.1088/1755-1315/1126/1/012023)],
but this is more ambiguous in the time series.
In Hyderabad, India, the number of tropical nights seems to have increased, but the number of heat stress days appears stable [[Hassan+26](https://www.theguardian.com/world/2026/may/26/high-temperatures-millions-workers-impacted-by-heat-india-asia)].
Similarly,
Thermal Trace shows a slight decline in cold stress days in recent years
(Figure {numref}`{number} <application_thermaltrace_trends_cold>`)
in the Midwest region of the United States [[Van Oldenborgh+19](https://doi.org/10.1088/1748-9326/ab4867)].
In each of these cases,
Thermal Trace is a very useful tool for a first glance at the data,
e.g. in a new location or timeframe,
prior to a quantitative analysis.


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > Analysis and results > 2. Long-term trends > Examples
---
useful tool for a first glance at the data,
e.g. in a new location or timeframe,
prior to a quantitative analysis.

::::{tab-set}
:::{tab-item} Heat stress
:sync: trends_heat
 attachment:application_thermaltrace_trends_heat.png
---
name: application_thermaltrace_trends_heat
---
Time series exports showing yearly number of days with a given heat stress level, and yearly number of tropical nights, in (from top to bottom):
a) Bonaire, Caribbean Netherlands (app view). Note that this requires the ocean mask to be toggled off.
b) Hyderabad, India (app view).
c) Kherson, Ukraine (app view).
d) Madrid, Spain (app view).
```
:::
:::{tab-item} Cold stress
:sync: trends_cold
 attachment:application_thermaltrace_trends_cold.png
---
name: application_thermaltrace_trends_cold
---
Time series exports showing yearly number of days with a given cold stress level in a custom drawn region stretching from Charleston, West Virginia to Cleveland, Ohio (north) and Omaha, Nebraska (east), all in the United States. From top to bottom:
a) Time series.
b) Region shown on the map, zoomed in.
c) Region shown on the map, in the wider American context.
These figures can be reproduced by navigating to the same location
(<a href="https://thermaltrace.climate.copernicus.eu/?agg=yearly&anomaly=false&date=2025-12-31&heat=false&lat=38.34&lng=-81.63&variable=utci_yearly_min_cat" target="_blank">app view</a>)
and manually re-drawing the region.
```
:::
::::


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > ℹ If you want to know more > Key resources
---
More about the ERA5 reanalysis and ERA5-HEAT dataset:
* [The ERA5 global reanalysis](https://doi.org/10.1002/qj.3803)
* [The ERA5 global reanalysis from 1940 to 2022](https://doi.org/10.1002/qj.4803)
* [ERA5-HEAT: A global gridded historical dataset of human thermal comfort indices from climate reanalysis](https://doi.org/10.1002/gdj3.102)
* [Thermal comfort indices derived from ERA5 reanalysis (ERA5-HEAT)](https://doi.org/10.24381/cds.553b7518) on the CDS
    * [Time series optimised version of the dataset](https://cds.climate.copernicus.eu/datasets/derived-utci-historical-timeseries)

Related datasets on the CDS:
* [Climate extreme indices and heat stress indicators derived from CMIP6 global climate projections](https://doi.org/10.24381/cds.776e08bd)
* [Heat waves and cold spells in Europe derived from climate projections](https://doi.org/10.24381/cds.9e7ca677)

More about feels-like temperature and UTCI:
* [Literature Review on UTCI Applications](https://doi.org/10.1007/978-3-030-76716-7_3)
* [Principles of the New Universal Thermal Climate Index (UTCI) and its Application to Bioclimatic Research in European Scale](https://doi.org/10.2478/mgrsd-2010-0009)
* [Thermofeel: A python thermal comfort indices library](https://doi.org/10.1016/j.softx.2022.101005)
* [UTCI—Why another thermal index?](https://doi.org/10.1007/s00484-011-0513-7)

More about cold snaps:
* [Could an extremely cold central European winter such as 1963 happen again despite climate change?](https://doi.org/10.5194/wcd-5-943-2024)
* [The UK winter of 2009/2010 compared with severe winters of the last 100 years](https://doi.org/10.1002/wea.735)
* [Where Do Cold Air Outbreaks Occur, and How Have They Changed Over Time?](https://doi.org/10.1029/2020GL086983)

More about heatwaves:
* [Heatwaves – a brief introduction](https://climate.copernicus.eu/heatwaves-brief-introduction)
* [Heat Waves: Physical Understanding and Scientific Challenges](https://doi.org/10.1029/2022RG000780)
* [World Health Organization: "Heat and health"](https://www.who.int/news-room/fact-sheets/detail/climate-change-heat-and-health)

More about long-term trends in heat and cold stress:
* [Climate change and health: Directions for policy](https://www.healthcouncil.nl/documents/2026/05/21/climate-change-and-health-directions-for-policy)
* [Cold waves are getting milder in the northern midlatitudes](https://doi.org/10.1088/1748-9326/ab4867)
* [Extreme Cold Wave over East Asia in January 2016: A Possible Response to the Larger Internal Atmospheric Variability Induced by Arctic Warming](https://doi.org/10.1175/JCLI-D-18-0234.1)
* [Probabilistic projections of increased heat stress driven by climate change](https://doi.org/10.1038/s43247-022-00524-4)

More applications for visualising temperature and thermal comfort:
* [Climate Pulse](https://apps.climate.copernicus.eu/overview?app=climate-pulse)
    * [](./application_climate-pulse_extreme-events_q01)
* [ECMWF's experimental UTCI forecast](https://charts.ecmwf.int/products/medium-thermofeel)
* [ERA Explorer](https://apps.climate.copernicus.eu/overview?app=era-explorer)
* [Weather Replay](https://apps.climate.copernicus.eu/overview?app=weather-replay)


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > ℹ If you want to know more > References
---
[[Bell+18](https://doi.org/10.1080/10962247.2017.1401017)] J. E. Bell et al., ‘Changes in extreme events and the potential impacts on human health’, Journal of the Air & Waste Management Association, vol. 68, no. 4, pp. 265–287, Apr. 2018, doi: 10.1080/10962247.2017.1401017.

[[Błażejczyk+10](https://doi.org/10.2478/mgrsd-2010-0009)] K. Błażejczyk et al., ‘Principles of the New Universal Thermal Climate Index (UTCI) and its Application to Bioclimatic Research in European Scale’, Miscellanea Geographica, vol. 14, no. 1, pp. 91–102, Dec. 2010, doi: 10.2478/mgrsd-2010-0009.

[[Correa+24](https://doi.org/10.1002/joc.8510)] J. Correa, P. Dorta, A. López-Díez, and J. Díaz-Pacheco, ‘Analysis of tropical nights in Spain (1970–2023): Minimum temperatures as an indicator of climate change’, International Journal of Climatology, vol. 44, no. 9, pp. 3006–3027, Jun. 2024, doi: 10.1002/joc.8510.

[[De Friesche Elf Steden](https://elfstedentocht.frl/en/tour/the-tour-of-1963/)] De Friesche Elf Steden, ‘Tour of 1963: The Toughest Tour in the History of the Frisian Elfstedentocht’, Koninklijke Vereniging De Friesche Elf Steden. Accessed: May 18, 2026. [Online]. Available: https://elfstedentocht.frl/en/tour/the-tour-of-1963/

[[Di Napoli+19](https://doi.org/10.1175/JAMC-D-18-0246.1)] C. Di Napoli, F. Pappenberger, and H. L. Cloke, ‘Verification of Heat Stress Thresholds for a Health-Based Heat-Wave Definition’, Journal of Applied Meteorology and Climatology, vol. 58, no. 6, pp. 1177–1194, Jun. 2019, doi: 10.1175/JAMC-D-18-0246.1.

[[Di Napoli+21a](https://doi.org/10.1002/gdj3.102)] C. Di Napoli, C. Barnard, C. Prudhomme, H. L. Cloke, and F. Pappenberger, ‘ERA5-HEAT: A global gridded historical dataset of human thermal comfort indices from climate reanalysis’, Geoscience Data Journal, vol. 8, no. 1, pp. 2–10, 2021, doi: 10.1002/gdj3.102.

[[Di Napoli+21b](https://doi.org/10.1007/978-3-030-76716-7_10)] C. Di Napoli et al., ‘The Universal Thermal Climate Index as an Operational Forecasting Tool of Human Biometeorological Conditions in Europe’, in Applications of the Universal Thermal Climate Index UTCI in Biometeorology: Latest Developments and Case Studies, E. L. Krüger, Ed., Cham: Springer International Publishing, 2021, pp. 193–208. doi: 10.1007/978-3-030-76716-7_10.

[[Fiala+12](https://doi.org/10.1007/s00484-011-0424-7)] G. Havenith et al., ‘The UTCI-clothing model’, International Journal of Biometeorology, vol. 56, no. 3, pp. 461–470, 2012, doi: 10.1007/s00484-011-0451-4.


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > ℹ If you want to know more > References
---
Biometeorology, vol. 56, no. 3, pp. 461–470, 2012, doi: 10.1007/s00484-011-0451-4.

[[Gezondheidsraad+26](https://www.healthcouncil.nl/documents/2026/05/21/climate-change-and-health-directions-for-policy)] Gezondheidsraad and Wetenschappelijke Klimaatraad, ‘Climate change and health: Directions for policy’, Den Haag, Netherlands, 2026/05, WKR-advies 009, May 2026. [Online]. Available: https://www.healthcouncil.nl/documents/2026/05/21/climate-change-and-health-directions-for-policy

[[Hall+26](https://www.smh.com.au/environment/climate-change/how-lethal-humidity-threatens-to-displace-millions-in-our-region-20260525-p600j0.html)] B. Hall, ‘How lethal humidity threatens to displace millions in our region’, The Sydney Morning Herald, May 27, 2026. Accessed: May 27, 2026. [Online]. Available: https://www.smh.com.au/environment/climate-change/how-lethal-humidity-threatens-to-displace-millions-in-our-region-20260525-p600j0.html

[[Hassan+26](https://www.theguardian.com/world/2026/may/26/high-temperatures-millions-workers-impacted-by-heat-india-asia)] A. Hassan, ‘“My head spins with the heat”: India’s gig workers battle exhaustion amid soaring temperatures’, The Guardian, May 26, 2026. Accessed: May 27, 2026. [Online]. Available: https://www.theguardian.com/world/2026/may/26/high-temperatures-millions-workers-impacted-by-heat-india-asia

[[Havenith+12](https://doi.org/10.1007/s00484-011-0451-4)] D. Fiala, G. Havenith, P. Bröde, B. Kampmann, and G. Jendritzky, ‘UTCI-Fiala multi-node model of human heat transfer and temperature regulation’, International Journal of Biometeorology, vol. 56, no. 3, pp. 429–441, 2012, doi: 10.1007/s00484-011-0424-7.

[[Hereher+26](https://doi.org/10.3390/su18041800)] M. E. Hereher, ‘Assessment of Seasonal Patterns of Apparent Heat Stress in Oman Using ERA5 Climatic Data’, Sustainability, vol. 18, no. 4, p. 1800, Feb. 2026, doi: 10.3390/su18041800.

[[Hersbach+20](https://doi.org/10.1002/qj.3803)] H. Hersbach et al., ‘The ERA5 global reanalysis’, Quarterly Journal of the Royal Meteorological Society, vol. 146, no. 730, pp. 1999–2049, Jul. 2020, doi: 10.1002/qj.3803.

[[Jendritzky+12](https://doi.org/10.1007/s00484-011-0513-7)] G. Jendritzky, R. de Dear, and G. Havenith, ‘UTCI—Why another thermal index?’, International Journal of Biometeorology, vol. 56, no. 3, pp. 421–428, 2012, doi: 10.1007/s00484-011-0513-7.

[[Klok+23](https://doi.org/10.1088/1755-1315/1126/1/012023)] S. Klok, A. Kornus, O. Kornus, O. Danylchenko, and O. Skyba, ‘Tropical nights (1976–2019) as an indicator of climate change in Ukraine’, in IOP Conference Series: Earth and Environmental Science, Riga, Latvia: IOP Publishing, Jan. 2023, p. 012023. doi: 10.1088/1755-1315/1126/1/012023.

[[KNMI](https://www.knmi.nl/nederland-nu/klimatologie/lijsten/koudegolven)] KNMI, ‘Koudegolven’, KNMI – Koninklijk Nederlands Meteorologisch Instituut. Accessed: May 22, 2026. [Online]. Available: https://www.knmi.nl/nederland-nu/klimatologie/lijsten/koudegolven


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > ℹ If you want to know more > References
---
lijk Nederlands Meteorologisch Instituut. Accessed: May 22, 2026. [Online]. Available: https://www.knmi.nl/nederland-nu/klimatologie/lijsten/koudegolven

[[Kundzewicz+16](https://doi.org/10.1515/igbp-2016-0005)] Z. W. Kundzewicz, ‘Extreme Weather Events and their Consequences’, Papers on Global Change IGBP, vol. 23, no. 1, pp. 59–69, Jan. 2016, doi: 10.1515/igbp-2016-0005.

[[Liu+24](https://doi.org/10.1016/j.ecolind.2024.112481)] R. Liu et al., ‘Global-scale ERA5 product precipitation and temperature evaluation’, Ecological Indicators, vol. 166, p. 112481, Sep. 2024, doi: 10.1016/j.ecolind.2024.112481.

[[Lussana+24](https://doi.org/10.1002/asl.1239)] C. Lussana, F. Cavalleri, M. Brunetti, V. Manara, and M. Maugeri, ‘Evaluating long-term trends in annual precipitation: A temporal consistency analysis of ERA5 data in the Alps and Italy’, Atmospheric Science Letters, vol. 25, no. 9, p. e1239, Apr. 2024, doi: 10.1002/asl.1239.

[[Ma+19](https://doi.org/10.1175/JCLI-D-18-0234.1)] S. Ma and C. Zhu, ‘Extreme Cold Wave over East Asia in January 2016: A Possible Response to the Larger Internal Atmospheric Variability Induced by Arctic Warming’, Journal of Climate, vol. 32, no. 4, pp. 1203–1216, Feb. 2019, doi: 10.1175/JCLI-D-18-0234.1.

[[Menary+25](https://www.ecmwf.int/en/newsletter/185/news/thermal-trace-health-related-weather-and-climate-monitoring)] M. Menary, R. Emerton, C. Barnard, A. Lombardi, J. Varndell, and C. Cagnazzo, ‘Thermal Trace: health-related weather and climate monitoring’, ECMWF Newsletter, vol. 185, pp. 5–6, Oct. 2025.

[[Morris+18](https://www.theguardian.com/uk-news/2018/mar/02/death-toll-reaches-10-as-destructive-weather-batters-uk)] S. Morris, M. Weaver, and J. Halliday, ‘UK storm death toll reaches 10 as ice warnings follow snow chaos’, The Guardian, Mar. 02, 2018. Accessed: May 22, 2026. [Online]. Available: https://www.theguardian.com/uk-news/2018/mar/02/death-toll-reaches-10-as-destructive-weather-batters-uk

[[Overton+25](https://www.mainepublic.org/climate/2025-08-07/heat-danger-maines-most-vulnerable-at-risk-from-rising-summer-temps)] P. Overton, ‘Heat danger: Maine’s most vulnerable at risk from rising summer temps’, Maine Public, Aug. 07, 2025. Accessed: May 27, 2026. [Online]. Available: https://www.mainepublic.org/climate/2025-08-07/heat-danger-maines-most-vulnerable-at-risk-from-rising-summer-temps

[[Prior+11](https://doi.org/10.1002/wea.735)] J. Prior and M. Kendon, ‘The UK winter of 2009/2010 compared with severe winters of the last 100 years’, Weather, vol. 66, no. 1, pp. 4–10, 2011, doi: 10.1002/wea.735.

[[Rowlatt+23](https://www.bbc.co.uk/news/science-environment-66143682)] J. Rowlatt, ‘Excessive heat: Why this summer has been so hot’, BBC, Jul. 13, 2023. Accessed: Jun. 16, 2025. [Online]. Available: https://www.bbc.co.uk/news/science-environment-66143682


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > ℹ If you want to know more > References
---
so hot’, BBC, Jul. 13, 2023. Accessed: Jun. 16, 2025. [Online]. Available: https://www.bbc.co.uk/news/science-environment-66143682

[[Royé+21](https://doi.org/10.1097/EDE.0000000000001359)] D. Royé et al., ‘Effects of Hot Nights on Mortality in Southern Europe’, Epidemiology, vol. 32, no. 4, pp. 487–498, Jul. 2021, doi: 10.1097/EDE.0000000000001359.

[[Schreier+13](https://doi.org/10.1007/s00484-012-0525-y)] S. F. Schreier et al., ‘The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from numerical weather prediction and regional climate model simulations’, International Journal of Biometeorology, vol. 57, no. 2, pp. 207–223, Mar. 2013, doi: 10.1007/s00484-012-0525-y.

[[Simmons+21](https://doi.org/10.21957/ly5vbtbfd)] A. Simmons et al., ‘Low frequency variability and trends in surface air temperature and humidity from ERA5 and other datasets’, European Centre for Medium-Range Weather Forecasts, Reading, UK, Technical Memo 881, Feb. 2021. doi: 10.21957/ly5vbtbfd.

[[Sippel+24](https://doi.org/10.5194/wcd-5-943-2024)] S. Sippel et al., ‘Could an extremely cold central European winter such as 1963 happen again despite climate change?’, Weather and Climate Dynamics, vol. 5, no. 3, pp. 943–957, Jul. 2024, doi: 10.5194/wcd-5-943-2024.

[[Soci+24](https://doi.org/10.1002/qj.4803)] C. Soci et al., ‘The ERA5 global reanalysis from 1940 to 2022’, Quarterly Journal of the Royal Meteorological Society, vol. 150, no. 764, pp. 4014–4048, Jul. 2024, doi: 10.1002/qj.4803.

[[Steadman+84](https://doi.org/10.1175/1520-0450%281984%29023%3C1674:AUSOAT%3E2.0.CO;2)] R. G. Steadman, ‘A Universal Scale of Apparent Temperature’, Journal of Applied Meteorology and Climatology, vol. 23, no. 12, pp. 1674–1687, Dec. 1984, doi: 10.1175/1520-0450(1984)023<1674:AUSOAT>2.0.CO;2.

[[Thompson+22](https://doi.org/10.1126/sciadv.abm6860)] V. Thompson et al., ‘The 2021 western North America heat wave among the most extreme events ever recorded globally’, Science Advances, vol. 8, no. 18, p. eabm6860, May 2022, doi: 10.1126/sciadv.abm6860.

[[Tousi+24](https://doi.org/10.3390/urbansci8040193)] E. Tousi, A. Mela, and A. Tseliou, ‘Thermal Stress in Outdoor Spaces During Mediterranean Heatwaves: A PET and UTCI Analysis of Different Demographics’, Urban Science, vol. 8, no. 4, p. 193, Oct. 2024, doi: 10.3390/urbansci8040193.

[[Twardosz+16](https://doi.org/10.1515/acgeo-2016-0083)] R. Twardosz, U. Kossowska-Cezak, and S. Pełech, ‘Extremely Cold Winter Months in Europe (1951–2010)’, Acta Geophys., vol. 64, no. 6, pp. 2609–2629, Dec. 2016, doi: 10.1515/acgeo-2016-0083.


EQC Quality Assessment: "Visualisation of heat and cold stress with Thermal Trace"
Dataset: thermaltrace [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of heat and cold stress with Thermal Trace > ℹ If you want to know more > References
---
a Geophys., vol. 64, no. 6, pp. 2609–2629, Dec. 2016, doi: 10.1515/acgeo-2016-0083.

[[Ullah+24](https://doi.org/10.1038/s41598-024-54766-7)] S. Ullah, A. Aldossary, W. Ullah, and S. G. Al-Ghamdi, ‘Augmented human thermal discomfort in urban centers of the Arabian Peninsula’, Scientific Reports, vol. 14, no. 1, p. 3974, Feb. 2024, doi: 10.1038/s41598-024-54766-7.

[[Van Oldenborgh+19](https://doi.org/10.1088/1748-9326/ab4867)] G. J. van Oldenborgh, E. Mitchell-Larson, G. A. Vecchi, H. de Vries, R. Vautard, and F. Otto, ‘Cold waves are getting milder in the northern midlatitudes’, Environmental Research Letters, vol. 14, no. 11, p. 114004, Oct. 2019, doi: 10.1088/1748-9326/ab4867.

[[Vargas Zeppetello+22](https://doi.org/10.1038/s43247-022-00524-4)] L. R. Vargas Zeppetello, A. E. Raftery, and D. S. Battisti, ‘Probabilistic projections of increased heat stress driven by climate change’, Communications Earth & Environment, vol. 3, p. 183, Aug. 2022, doi: 10.1038/s43247-022-00524-4.

[[Wang+25](https://doi.org/10.3390/rs17071317)] H. Wang and Y. Wang, ‘Evaluation of the Accuracy and Trend Consistency of Hourly Surface Solar Radiation Datasets of ERA5, MERRA-2, SARAH-E, CERES, and Solcast over China’, Remote Sensing, vol. 17, no. 7, p. 1317, Apr. 2025, doi: 10.3390/rs17071317.

[[Wielinga+24](https://www.groningentoen.nl/elfstedentocht/medische-gevolgen-elfstedentocht-1963/)] M. Wielinga, ‘Medische aspecten Elfstedentocht 1963’, Groningen Toen. Accessed: May 22, 2026. [Online]. Available: https://www.groningentoen.nl/elfstedentocht/medische-gevolgen-elfstedentocht-1963/

[[Young+26](https://www.bbc.co.uk/news/articles/cp8ppdk5zp2o)] L. Young, ‘Amber heat health alert issued for the South West by UKHSA’, BBC, May 26, 2026. Accessed: May 27, 2026. [Online]. Available: https://www.bbc.co.uk/news/articles/cp8ppdk5zp2o

[[Zhao+21](https://doi.org/10.1016/S2542-5196%2821%2900081-4)] Q. Zhao et al., ‘Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study’, The Lancet Planetary Health, vol. 5, no. 7, pp. e415–e425, Jul. 2021, doi: 10.1016/S2542-5196(21)00081-4.


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector
---
Production date: 23-05-2024

Produced by: CMCC foundation - Euro-Mediterranean Center on Climate Change. Albert Martinez Boti.


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Quality assessment question
---
* **How well do CMIP6 projections represent climatology and trends of air temperature extremes in Europe?**

Climate change has a major impact on the reinsurance market [[1]](https://doi.org/10.3390/atmos11020146)[[2]](https://doi.org/10.5194/nhess-22-659-2022). In the third assessment report of the IPCC, hot temperature extremes were already presented as relevant to insurance and related services [[3]](https://www.ipcc.ch/site/assets/uploads/2018/03/wg2TARchap8.pdf). Consequently, the need for reliable regional and global climate projections has become paramount, offering valuable insights for optimising reinsurance strategies in the face of a changing climate landscape. Nonetheless, despite their pivotal role, uncertainties inherent in these projections can potentially lead to misuse [[4]](https://doi.org/10.1002/wcc.71)[[5]](https://doi.org/10.1002/wcc.579). This underscores the importance of accurately calculating and accounting for uncertainties to ensure their appropriate consideration. This notebook utilises data from a subset of models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview) Global Climate Models (GCMs) and compares them with [ERA5](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-complete?tab=doc) reanalysis, serving as the reference product. Two maximum-temperature-based indices from [ECA&D](https://www.ecad.eu/indicesextremes/) indices (one of physical nature and the other of statistical nature) are computed using the [icclim](https://icclim.readthedocs.io/en/stable/) Python package. The first index, identified by the ETCCDI short name 'SU', quantifies the occurrence of summer days (i.e., with daily maximum temperatures exceeding 25°C) within a year or a season (JJA in this notebook). The second index, labeled 'TX90p', describes the number of days with daily maximum temperatures exceeding the daily 90th percentile of maximum temperature for a 5-day moving window. Within this notebook, these calculations are performed over the historical period spanning from 1971 to 2000. It is important to mention that the results presented here pertain to a specific subset of the CMIP6 ensemble and may not be generalisable to the entire dataset. Also note that a separate assessment examines the representation of trends of these indices for the same models during a fixed future period (2015-2099), while another assessment looks at the projected climate signal of these indices for the same models at a 2°C Global Warming Level.


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Quality assessment statement
---
These are the key outcomes of this assessment

* CMIP6 projections based on maximum temperature indices can be valuable for designing strategies to optimise reinsurance protections. However, it is crucial for users to acknowledge that all climate projections inherently contain certain biases. This is especially significant when using projections in regions with complex orography or extensive continental areas, where biases are more pronounced and impact the accuracy of the projections. Therefore, users should exercise caution and consider these limitations when applying the projections to their specific use cases, understanding that biases in the mean climate and trends affect indices differently and that the choice of bias correction method should be tailored accordingly [[6]](https://doi.org/10.1002/asl.1072)[[7]](https://ibicus.readthedocs.io/en/latest/).

* GCMs exhibit biases in capturing mean values of extreme temperature indices like the number of summer days ('SU') and days exceeding the 90th percentile threshold ('TX90p'), with underestimation of index magnitudes, especially in continental areas and regions with complex terrain.

* ERA5 shows that the magnitude of the historical trend varies spatially. The trend bias assessment suggests that, overall, the trend derived from the CMIP6 projections is underestimated with slight overestimations observed in certain regions. Hence, users need to consider this regional variation in both the trend and its bias.

* Despite limitations and biases, the considered subset of CMIP6 models generally reproduces historical trends reasonably well for the summer season (JJA), providing a foundation for understanding past climate behavior. These findings also increase confidence (though they do not ensure accuracy) when analysing future trends using these models.
```

attachment:1c0e8cf0-01f8-4c79-a63c-395662058d5e.png
---
width: 900px
alt: Mean Bias SU
---
Number of summer days ('SU') for the temporal aggregation of 'JJA'. Mean bias for the historical period (1971 - 2000) of each individual CMIP6 model.
```


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Methodology
---
This notebook provides an assessment of the systematic errors (trend and climate mean) in a subset of 16 models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview). It achieves this by comparing the model predictions with the ERA5 reanalysis for the maximum-temperature-based indices of 'SU' and 'TX90p', calculated over the temporal aggregation of JJA and for the historical period spanning from 1971 to 2000 (chosen to allow comparison to [CORDEX](https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels?tab=overview) models in another assessment). In particular, spatial patterns of climate mean and trend, along with biases, are examined and displayed for each model and the ensemble median (calculated for each grid cell). Additionally, spatially-averaged trend values are analysed and presented using box plots to provide an overview of trend behavior across the distribution of the chosen subset of models when averaged across Europe.

The analysis and results follow the next outline:

**[](section-1)**
 * [](section-1.1)
 * [](section-1.2)
 * [](section-1.3)
 * [](section-1.4)
 * [](section-1.5)
 * [](section-1.6)

**[](section-2)**
 * [](section-2.1)
 * [](section-2.2)
 * [](section-2.3)

**[](section-3)**
 * [](section-3.1)
 * [](section-3.2)
 * [](section-3.3)
 * [](section-3.4)
 * [](section-3.5)
 * [](section-3.6)


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.2. Define Parameters
---
In the "Define Parameters" section, various customisable options for the notebook are specified:

- The initial and ending year used for the historical period can be specified by changing the parameters `year_start` and `year_stop` (1971-2000 is chosen for consistency between CMIP6 and CORDEX).
- The `timeseries` set the temporal aggregation. For instance, selecting "JJA" implies considering only the JJA season.
- `collection_id` provides the choice between Global Climate Models CMIP6 or Regional Climate Models CORDEX. Although the code allows choosing between CMIP6 or CORDEX, the example provided in this notebook deals with CMIP6.
- `area` allows specifying the geographical domain of interest.
- The `interpolation_method` parameter allows selecting the interpolation method when regridding is performed over the indices.
- The `chunk` selection allows the user to define if dividing into chunks when downloading the data on their local machine. Although it does not significantly affect the analysis, it is recommended to keep the default value for optimal performance.

Choose annual or seasonal timeseries
Choose CORDEX or CMIP6
Interpolation method
Area to show
Chunks for download

(section-1.3)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.3. Define models
---
The following climate analyses are performed considering a subset of GCMs from CMIP6. Models names are listed in the parameters below. Some variable-dependent parameters are also selected, as the `index_names` parameter, which specifies the temperature-based indices ('SU' and 'TX90p' in our case) from the [icclim](https://icclim.readthedocs.io/en/stable/) Python package.

The selected CMIP6 models have available both the historical and SSP5-8.5 experiments.

Define dictionaries to use in titles and caption
Define dictionaries to use in titles and caption

(section-1.4)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.4. Define ERA5 request
---
Within this notebook, ERA5 serves as the reference product. In this section, we set the required parameters for the cds-api data-request of ERA5.

(section-1.5)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.5. Define model requests
---
In this section we set the required parameters for the cds-api data-request.

When `weights = True`, spatial weighting is applied for calculations requiring spatial data aggregation. This is particularly relevant for CMIP6 GCMs with regular lon-lat grids that do not consider varying surface extensions at different latitudes. In contrast, CORDEX RCMs, using rotated grids, inherently account for different cell surfaces based on latitude, eliminating the need for a latitude cosine multiplicative factor (`weights = False`).

(section-1.6)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.6. Functions to cache
---
In this section, functions that will be executed in the caching phase are defined. Caching is the process of storing copies of files in a temporary storage location, so that they can be accessed more quickly. This process also checks if the user has already downloaded a file, avoiding redundant downloads.

Functions description:

- The `select_timeseries` function subsets the dataset based on the chosen `timeseries` parameter.

- The `compute_indices` function utilises the icclim package to calculate the selected maximum-temperature-based indices.

- The `compute_trends` function employs the Mann-Kendall test for trend calculation.

- Finally, the `compute_indices_and_trends` function selects the temporal aggregation using the `select_timeseries` function. It then computes daily maximum temperature (only if we are dealing with ERA5), calculates maximum-temperature-based indices via the `compute_indices` function, determines the indices mean over the historical period (1971-2000), obtain the trends using the `compute_trends` function, and offers an option for regridding to ERA5 if required.

Original bounds for conservative interpolation

(section-2)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.1. Download and transform ERA5
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download ERA5 reference data, select the temporal aggregation ("JJA" in our example), compute daily maximum temperature from hourly data, compute the maximum-temperature-based indices, calculate the mean and trend for the historical period (1971-2000) and cache the result (to avoid redundant downloads and processing).

(section-2.2)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.2. Download and transform models
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download daily data from the CMIP6 models, compute the maximum-temperature-based indices for the selected temporal aggregation, calculate the mean and trend over the historical period (1971-2000), interpolate to ERA5's grid (only for the cases in which it is specified, in the other cases, the original model's grid is mantained), and cache the result (to avoid redundant downloads and processing).

Original model
Interpolated model

```text
model='access_cm2'
model='awi_cm_1_1_mr'
model='cmcc_esm2'
model='cnrm_cm6_1_hr'
model='cnrm_esm2_1'
model='ec_earth3_cc'
model='gfdl_esm4'
model='inm_cm4_8'
model='inm_cm5_0'
model='kiost_esm'
model='mpi_esm1_2_lr'
model='miroc6'
model='miroc_es2l'
model='mri_esm2_0'
model='noresm2_mm'
model='nesm3'
```

(section-2.3)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.3. Apply land-sea mask, change attributes and cut the region to show
---
This section performs the following tasks:

1. Cut the region of interest.
2. Downloads the sea mask for ERA5.
3. Applies the sea mask to both ERA5 data and the model data, which were previously regridded to ERA5's grid (i.e., it applies the ERA5 sea mask to `ds_interpolated`).
4. Regrids the ERA5 land-sea mask to the model's grid and applies it to them.
5. Change some variable attributes for plotting purposes.

**Note:** `ds_interpolated` contains data from the models (mean and trend over the historical period, p-value of the trends...) regridded to ERA5. `model_datasets` contain the same data but in the original grid of each model.

Edit attributes

(section-3)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results
---
This section will display the following results:

- Maps representing the spatial distribution of the **historical mean values** (1971-2000) of the 'SU' index for ERA5, each model individually, the ensemble median (understood as the median of the mean values of the chosen subset of models calculated for each grid cell), and the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).
- Maps representing the spatial distribution of the **historical trends** (1971-2000) of the indices 'SU' and 'TX90p'. Similar to the first analysis, this includes ERA5, each model individually, the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell), and the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).
- **Bias maps of the historical mean values**.
- **Trend bias maps**.
- **Boxplots** representing statistical distributions (PDF) built on the spatially-averaged historical trends from each considered model, displayed together with ERA5.

(section-3.1)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.1. Define plotting functions
---
The functions presented here are used to plot the mean values and trends calculated over the historical period (1971-2000) for each of the indices ('SU' and 'TX90p').

For a selected index, three layout types can be displayed, depending on the chosen function:

1. Layout including the reference ERA5 product, the ensemble median, the bias of the ensemble median, and the ensemble spread: `plot_ensemble()` is used.
2. Layout including every model (for the trend and mean values): `plot_models()` is employed.
3. Layout including the bias of every model (for the trend and mean values): `plot_models()` is used again.

`trend==True` argument allows displaying trend values over the historical period, while `trend==False` will show mean values. When the `trend` argument is set to `True`, regions with no significance are hatched. For individual models and ERA5, a grid point is considered to have a statistically significant trend when the p-value is lower than 0.05 (in such cases, no hatching is shown). However, for determining trend significance for the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell), reliance is placed on agreement categories, following the advanced approach proposed in AR6 [IPCC](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Atlas.pdf) on pages 1945-1950. The `hatch_p_value_ensemble()` function is used to distinguish, for each grid point, between three possible cases:

1. If more than 66% of the models are statistically significant (p-value < 0.05) and more than 80% of the models share the same sign, we consider the ensemble median trend to be statistically significant, and there is agreement on the sign. To represent this, no hatching is used.
2. If less than 66% of the models are statistically significant, regardless of agreement on the sign of the trend, hatching is applied (indicating that the ensemble median trend is not statistically significant).
3. If more than 66% of the models are statistically significant but less than 80% of the models share the same sign, we consider the ensemble median trend to be statistically significant, but there is no agreement on the sign of the trend. This is represented using crosses.

Define function to plot the caption of the figures (for the ensemble case)
Add caption to the figure
Add each line to the figure
end captioning
Define function to plot the caption of the figures (for the individual models case)
Add caption to the figure
Add each line to the figure

(section-3.2)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.2. Plot ensemble maps
---
In this section, we invoke the `plot_ensemble()` function to visualise the mean values and trends calculated over the historical period (1971-2000) for the model ensemble and ERA5 reference product across Europe. Note that the model data used in this section has previously been interpolated to the ERA5 grid.

Specifically, for each of the indices ('SU' and 'TX90p'), this section presents two layouts:

1. Only for the 'SU' index: mean values of the historical period (1971-2000) for: (a) the reference ERA5 product, (b) the ensemble median (understood as the median of the mean values of the chosen subset of models calculated for each grid cell), (c) the bias of the ensemble median, and (d) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

2. Trend values of the historical period (1971-2000) for: (a) the reference ERA5 product, (b) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell), (c) the bias of the ensemble median, and (d) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

Change default colorbars
Fig number counter
Common title

(section-3.3)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.3. Plot model maps
---
In this section, we invoke the `plot_models()` function to visualise the mean values and trends calculated over the historical period (1971-2000) for every model individually across Europe. Note that the model data used in this section maintains its original grid.

Specifically, for each of the indices ('SU' and 'TX90p'), this section presents two layouts:

1. A layout including the historical mean (1971-2000) of every model (only for the 'SU' index).

2. A layout including the historical trend (1971-2000) of every model.

(section-3.4)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.4. Plot bias maps
---
In this section, we invoke the `plot_models()` function to visualise the bias for the mean values and trends calculated over the historical period (1971-2000) for every model individually across Europe. Note that the model data used in this section has previously been interpolated to the ERA5 grid.

Specifically, for each of the indices ('SU' and 'TX90p'), this section presents two layouts:

1. A layout including the bias for the historical mean (1971-2000) of every model (only for the 'SU' index).

2. A layout including the bias for the historical trend (1971-2000) of every model.

(section-3.5)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.5. Boxplots of the historical trend
---
In this last section, we compare the trends of the climate models with the reference trend from ERA5.

Dots represent the spatially-averaged historical trend over the selected region (change of the number of days per decade) for each model (grey), the ensemble mean (blue), and the reference product (orange). The ensemble median is shown as a green line. Note that the spatially averaged values are calculated for each model from its original grid (i.e., no interpolated data has been used here).

The boxplot visually illustrates the distribution of trends among the climate models, with the box covering the first quartile (Q1 = 25th percentile) to the third quartile (Q3 = 75th percentile), and a green line indicating the ensemble median (Q2 = 50th percentile). Whiskers extend from the edges of the box to show the full data range.

Ensemble mean

Fig 10. Boxplots illustrating the historical trends of the distribution of the chosen subset of models and ERA5 for: (a) the 'SU' index and (b) the 'TX90p' index. The distribution is created by considering spatially averaged trends across Europe. The ensemble mean and the ensemble median trends are both included. Outliers in the distribution are denoted by a grey circle with a black contour.

(section-3.6)=


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.6. Results summary and discussion
---
- The selected subset of CMIP6 Global Climate Models (GCMs) exhibit biases in capturing both mean values and trends for the 'number of summer days' (SU) and 'the number of days with daily maximum temperature exceeding the daily 90th percentile' (TX90p) indices. These indices are calculated over the temporal aggregation of JJA and for the historical period spanning from 1971 to 2000.

- GCMs struggle to accurately represent the climatology of the 'SU' index in regions with complex orography. Furthermore, underestimating the magnitude of this index is common, particularly in continental areas.

- While the mean climate bias is mainly affected by orography, the regional variation in the trend bias is broader. ERA5 shows that the magnitude of the historical trend varies spatially. The trend bias assessment suggests that, overall, the trend is underestimated, with slight overestimations observed in certain regions. Hence, users need to consider this regional variation in both the trend and its bias.

- Boxplots reveal a consistent positive spatial-averaged historical trend characterising the distribution of the chosen subset of models. Magnitudes, however, are slightly underestimated when compared to ERA5. For the 'SU' index, the CMIP6 ensemble median displays approximately 1.0 days/10 years, contrasting with ERA5's ~2 days/10 years. The 'TX90p' index reveals around 1.4 days/10 years for the CMIP6 ensemble median in contrast to the ~2 days/10 years observed in ERA5. The interquantile range of the ensemble ranges from 0.9 to 1.2 days per decade for the 'SU' index (with outliers closer to 1.9 days per decade) and from 1.1 to more than 1.7 days per decade for the 'TX90p' index.

- What do the results mean for users?  Are the biases relevant?

- These results indicate that CMIP6 projections of maximum temperature indices can be useful in applications like designing strategies to optimise reinsurance protections. The findings increase confidence (though they do not ensure accuracy) when analysing future trends using these models.

- However, users must recognise that all climate projections inherently include certain biases. This is particularly important when using projections of maximum temperature indices in areas with complex orography or large continental regions. In such cases, the biases may be more pronounced and could impact the accuracy of the projections. Therefore, users should exercise caution and consider these limitations when applying the projections to their specific use cases, recognising that biases in the mean climate and trends affect indices differently and that the choice of bias correction method should be tailored accordingly [[6]](https://doi.org/10.1002/asl.1072)[[7]](https://ibicus.readthedocs.io/en/latest/).

RESULTS NOTE:

It is important to note that the results presented are specific to the 16 models chosen, and users should aim to assess as wide a range of models as possible before making a sub-selection.


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

The CDS catalogue entries for the data used were:
* CMIP6 climate projections (Daily - Daily maximum near-surface air temperature): https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview
* ERA5 hourly data on single levels from 1940 to present (2m temperature): https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)
* [icclim](https://icclim.readthedocs.io/en/stable/) Python package


EQC Quality Assessment: "Bias in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Climate_Projections
Section: Bias in extreme temperature indices for the reinsurance sector > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.3390/atmos11020146) Tesselaar, M., Botzen, W.J.W., Aerts, J.C.J.H. (2020). Impacts of Climate Change and Remote Natural Catastrophes on EU Flood Insurance Markets: An Analysis of Soft and Hard Reinsurance Markets for Flood Coverage. Atmosphere 2020, 11, 146. https://doi.org/10.3390/atmos11020146

[[2]](https://doi.org/10.5194/nhess-22-659-2022) Rädler, A. T. (2022). Invited perspectives: how does climate change affect the risk of natural hazards? Challenges and step changes from the reinsurance perspective. Nat. Hazards Earth Syst. Sci., 22, 659–664. https://doi.org/10.5194/nhess-22-659-2022

[[3]](https://www.ipcc.ch/site/assets/uploads/2018/03/wg2TARchap8.pdf) Vellinga, P., Mills, E., Bowers, L., Berz, G.A., Huq, S., Kozak, L.M., Paultikof, J., Schanzenbacker, B., Shida, S., Soler, G., Benson, C., Bidan, P., Bruce, J.W., Huyck, P.M., Lemcke, G., Peara, A., Radevsky, R., Schoubroeck, C.V., Dlugolecki, A.F. (2001). Insurance and other financial services. In J. J. McCarthy, O. F. Canziani, N. A. Leary, D. J. Dokken, & K. S. White (Eds.), Climate change 2001: impacts, adaptation, and vulnerability. Contribution of working group 2 to the third assessment report of the intergovernmental panel on climate change. (pp. 417-450). Cambridge University Press.

[[4]](https://doi.org/10.1002/wcc.71) Lemos, M.C. and Rood, R.B. (2010). Climate projections and their impact on policy and practice. WIREs Clim Chg, 1: 670-682. https://doi.org/10.1002/wcc.71

[[5]](https://doi.org/10.1002/wcc.579) Nissan, H., Goddard, L., de Perez, E.C., et al. (2019). On the use and misuse of climate change projections in international development. WIREs Clim Change, 10:e579. https://doi.org/10.1002/wcc.579

[[6]](https://doi.org/10.1002/asl.1072) Iturbide, M., Casanueva, A., Bedia, J., Herrera, S., Milovac, J., Gutiérrez, J.M. (2021). On the need of bias adjustment for more plausible climate change projections of extreme heat. Atmos. Sci. Lett., 23, e1072. https://doi.org/10.1002/asl.1072

[[7]](https://ibicus.readthedocs.io/en/latest/) https://ibicus.readthedocs.io/en/latest/


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector
---
Production date: 24-05-2024

Produced by: CMCC foundation - Euro-Mediterranean Center on Climate Change. Albert Martinez Boti.


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Quality assessment question
---
* **What are the projected future changes and associated uncertainties in air temperature extremes in Europe?**

Climate change has a major impact on the reinsurance market [[1]](https://doi.org/10.3390/atmos11020146)[[2]](https://doi.org/10.5194/nhess-22-659-2022). In the third assessment report of the IPCC, hot temperature extremes were already presented as relevant to insurance and related services [[3]](https://www.ipcc.ch/site/assets/uploads/2018/03/wg2TARchap8.pdf). Consequently, the need for reliable regional and global climate projections has become paramount, offering valuable insights for optimising reinsurance strategies in the face of a changing climate landscape. Nonetheless, despite their pivotal role, uncertainties inherent in these projections can potentially lead to misuse [[4]](https://doi.org/10.1002/wcc.71)[[5]](https://doi.org/10.1002/wcc.579). This underscores the importance of accurately calculating and accounting for uncertainties to ensure their appropriate consideration. This notebook utilises data from a subset of models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview) Global Climate Models (GCMs) and explores the uncertainty in future projections of maximum temperature-based extreme indices by considering the ensemble inter-model spread of projected changes. Two maximum temperature-based indices from [ECA&D](https://www.ecad.eu/indicesextremes/) indices (one of physical nature and the other of statistical nature) are computed using the [icclim](https://icclim.readthedocs.io/en/stable/) Python package. The first index, identified by the ETCCDI short name 'SU', quantifies the occurrence of summer days (i.e., with daily maximum temperatures exceeding 25°C) within a year or a season (JJA in this notebook). The second index, labeled 'TX90p', describes the number of days with daily maximum temperatures exceeding the daily 90th percentile of maximum temperature for a 5-day moving window. For this notebook, the daily 90th percentile threshold is calculated for the historical period spanning from 1971 to 2000. The index calculations, though, are performed over the future period from 2015 to 2099, following the Shared Socioeconomic Pathways SSP5-8.5. It is important to mention that the results presented here pertain to a specific subset of the CMIP6 ensemble and may not be generalisable to the entire dataset. Also note that a separate assessment examines the representation of climatology and trends of these indices for the same models during the historical period (1971-2000), while another assessment looks at the projected climate signal of these indices for the same models at a 2°C Global Warming Level.


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Quality assessment statement
---
These are the key outcomes of this assessment

* Looking ahead to future projections (2015-2099), all models within the subset agree on projecting general positive trends for both indices across Europe during the temporal aggregation of JJA, with a particularly notable positive projected trend in the Mediterranean Basin for 'TX90p'. This finding is consistent with the results of Josep Cos et al. (2022) [[6]](https://doi.org/10.5194/esd-13-321-2022), who evaluated the Mediterranean climate change hotspot using CMIP6 projections.

* While certain regions exhibit near-zero trends for the 'SU' index, possibly due to threshold temperature constraints, others show higher values, highlighting the importance of considering both statistically and physically based extreme indices for comprehensive assessments.

* Utilising CMIP6 projections presents a valuable opportunity to anticipate future trends in air temperature extremes across Europe, enabling the insurance industry to refine risk management strategies. While all considered models show a positive trend for these indices, the magnitude of these trends and their uncertainty (quantified by the inter-model spread) vary spatially and need to be considered.

* A separate assessment evaluates the biases in climatology and trends of these indices for the historical period from 1971 to 2000 ("CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector"). The results of that assessment show an overall underestimation of the trends for both indices and the climatology of 'SU', as well as difficulty in correctly representing the spatial distribution, particularly over regions with complex orography. These biases may affect future projections and should be taken into account before using them.
```

attachment:00e7ccee-ce24-4448-a32d-d37b6cce9162.png
---
alt: trend_future_TX90p
width: 850px
---
Number of days with daily maximum temperatures exceeding the daily 90th percentile of maximum temperature for a 5-day moving window ('TX90p') for the temporal aggregation of 'JJA'. Trend for the future period (2015-2099). For this index, the reference daily 90th percentile threshold is calculated based on the historical period (1971-2000). The layout includes data corresponding to: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).
```


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Methodology
---
This notebook offers an assessment of the projected changes and their associated uncertainties using a subset of 16 models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview). The uncertainty is examined by analysing the ensemble inter-model spread of projected changes for the maximum-temperature-based indices 'SU' and 'TX90p,' calculated over the temporal aggregation of JJA for the future period spanning from 2015 to 2099. In particular, spatial patterns of climate projected trends are examined and displayed for each model individually and for the ensemble median (calculated for each grid cell), alongside the ensemble inter-model spread to account for projected uncertainty. Additionally, spatially-averaged trend values are analysed and presented using box plots to provide an overview of trend behavior across the distribution of the chosen subset of models when averaged across Europe.

The analysis and results follow the next outline:

**[](section-1)**
 * [](section-1.1)
 * [](section-1.2)
 * [](section-1.3)
 * [](section-1.4)
 * [](section-1.5)
 * [](section-1.6)

**[](section-2)**
 * [](section-2.1)
 * [](section-2.2)
 * [](section-2.3)

**[](section-3)**
 * [](section-3.1)
 * [](section-3.2)
 * [](section-3.3)
 * [](section-3.4)
 * [](section-3.5)


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.2. Define Parameters
---
In the "Define Parameters" section, various customisable options for the notebook are specified. Most of the parameters chosen are the same as those used in other assessments ("CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector"), being them:
- The initial and ending year used for the future projections period can be specified by changing the parametes `future_slice` (2015-2099 is chosen for consistency between CMIP6 and CORDEX).
- `historical_slice` determines the historical period used (1971 to 2000 is choosen to allow comparison to CORDEX models in other assessments).
- The `timeseries` set the temporal aggregation. For instance, selecting "JJA" implies considering only the JJA season.
- `collection_id` provides the choice between Global Climate Models CMIP6 or Regional Climate Models CORDEX. Although the code allows choosing between CMIP6 or CORDEX, the example provided in this notebook deals with CMIP6.
- `area` allows specifying the geographical domain of interest.
- The `interpolation_method` parameter allows selecting the interpolation method when regridding is performed over the indices.
- The `chunk` selection allows the user to define if dividing into chunks when downloading the data on their local machine. Although it does not significantly affect the analysis, it is recommended to keep the default value for optimal performance.

Choose annual or seasonal timeseries
Choose CORDEX or CMIP6
Interpolation method
Area to show
Chunks for download

(section-1.3)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.3. Define models
---
The following climate analyses are performed considering a subset of GCMs from CMIP6. Models names are listed in the parameters below. Some variable-dependent parameters are also selected, as the `index_names` parameter, which specifies the maximum-temperature-based indices ('SU' and 'TX90p' in our case) from the [icclim](https://icclim.readthedocs.io/en/stable/) Python package.

The selected CMIP6 models have available both the historical and SSP8.5 experiments, and they are the same as those used in other assessments ("CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector").

Define dictionaries to use in titles and caption
Define dictionaries to use in titles and caption

(section-1.4)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.4. Define land-sea mask request
---
Within this notebook, ERA5 will be used to download the land-sea mask when plotting. In this section, we set the required parameters for the cds-api data-request of ERA5 land-sea mask.

(section-1.5)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.5. Define model requests
---
In this section we set the required parameters for the cds-api data-request.

When `weights = True`, spatial weighting is applied for calculations requiring spatial data aggregation. This is particularly relevant for CMIP6 GCMs with regular lon-lat grids that do not consider varying surface extensions at different latitudes. In contrast, CORDEX RCMs, using rotated grids, inherently account for different cell surfaces based on latitude, eliminating the need for a latitude cosine multiplicative factor (`weights = False`).

(section-1.6)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.6. Functions to cache
---
In this section, functions that will be executed in the caching phase are defined. Caching is the process of storing copies of files in a temporary storage location, so that they can be accessed more quickly. This process also checks if the user has already downloaded a file, avoiding redundant downloads.

Functions description:

- The `select_timeseries` function subsets the dataset based on the chosen `timeseries` parameter.

- The `compute_indices` function utilises the icclim package to calculate the maximum-temperature-based indices.

- The `compute_trends` function employs the Mann-Kendall test for trend calculation.

- Finally, the `compute_indices_and_trends` function calculates the maximum-temperature-based indices for the corresponding temporal aggregation using the `compute_indices` function, determines the indices mean for the future period (2015-2099), obtain the trends using the `compute_trends` function, and offers an option for regridding to `model_regrid`.

Original bounds for conservative interpolation

(section-2)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.1. Download and transform the regridding model
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download daily data from the selected CMIP6 regridding model, compute the maximum-temperature-based indices for the selected temporal aggregation, calculate the mean and trend over the future projections period (2015-2099), and cache the result (to avoid redundant downloads and processing).

The regridding model is intended here as the model whose grid will be used to interpolate the others. This ensures all models share a common grid, facilitating the calculation of median values for each cell point. The regridding model within this notebook is "gfdl_esm4" but a different one can be selected by just modifying the `model_regrid` parameter at [](section-1.3). It is key to highlight the importance of the chosen target grid depending on the specific application.

(section-2.2)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.2. Download and transform models
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download daily data from the CMIP6 models, compute the maximum-temperature-based indices for the selected temporal aggregation, calculate the mean and trend over the future period (2015-2099), interpolate to the regridding model's grid (only for the cases in which it is specified, in the other cases, the original model's grid is mantained), and cache the result (to avoid redundant downloads and processing).

Original model
Interpolated model

```text
model='access_cm2'
model='awi_cm_1_1_mr'
model='cmcc_esm2'
model='cnrm_cm6_1_hr'
model='cnrm_esm2_1'
model='ec_earth3_cc'
model='gfdl_esm4'
model='inm_cm4_8'
model='inm_cm5_0'
model='kiost_esm'
model='mpi_esm1_2_lr'
model='miroc6'
model='miroc_es2l'
model='mri_esm2_0'
model='noresm2_mm'
model='nesm3'
```

(section-2.3)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.3. Apply land-sea mask, change attributes and cut the region to show
---
This section performs the following tasks:

1. Cut the region of interest.
2. Downloads the sea mask for ERA5.
3. Regrids ERA5's mask to the `model_regrid` grid and applies it to the regridded data
4. Regrids the ERA5 land-sea mask to the model's original grid and applies it to them.
5. Change some variable attributes for plotting purposes.

**Note:** `ds_interpolated` contains data from the models regridded to the regridding model's grid. `model_datasets` contain the same data but in the original grid of each model.

Edit attributes

```text
100%|██████████| 1/1 [00:00<00:00, 16.00it/s]
```

(section-3)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results
---
This section will display the following results:

- Maps representing the spatial distribution of the **future trends** (2015-2099) of the indices 'SU' and 'TX90p' for each model individually, the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell), and the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).
- **Boxplots** which represent statistical distributions (PDF) built on the the spatially-averaged future trend from each considered model.

(section-3.1)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.1. Define plotting functions
---
The functions presented here are used to plot the trends calculated over the future period (2015-2099) for each of the indices ('SU' and 'TX90p').

For a selected index, two layout types will be displayed, depending on the chosen function:

1. Layout including the ensemble median and the ensemble spread for the trend: `plot_ensemble()` is used.
2. Layout including every model trend: `plot_models()` is employed.

`trend==True` allows displaying trend values over the future period, while `trend==False` show mean values. In this notebook, which focuses on the future period, only trend values will be shown, and, consequently, `trend==True`. When the `trend` argument is set to True, regions with no significance are hatched. For individual models, a grid point is considered to have a statistically significant trend when the p-value is lower than 0.05 (in such cases, no hatching is shown). However, for determining trend significance for the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell), reliance is placed on agreement categories, following the advanced approach proposed in AR6 [IPCC](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Atlas.pdf) on pages 1945-1950. The `hatch_p_value_ensemble()` function is used to distinguish, for each grid point, between three possible cases:

1. If more than 66% of the models are statistically significant (p-value < 0.05) and more than 80% of the models share the same sign, we consider the ensemble median trend to be statistically significant, and there is agreement on the sign. To represent this, no hatching is used.
2. If less than 66% of the models are statistically significant, regardless of agreement on the sign of the trend, hatching is applied (indicating that the ensemble median trend is not statistically significant).
3. If more than 66% of the models are statistically significant but less than 80% of the models share the same sign, we consider the ensemble median trend to be statistically significant, but there is no agreement on the sign of the trend. This is represented using crosses.

Define function to plot the caption of the figures (for the ensemble case)
Add caption to the figure
Add each line to the figure
end captioning
Define function to plot the caption of the figures (for the individual models case)
Add caption to the figure
Add each line to the figure

(section-3.2)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.2. Plot ensemble maps
---
In this section, we invoke the `plot_ensemble()` function to visualise the trend calculated over the future period (2015-2099) for the model ensemble across Europe. Note that the model data used in this section has previously been interpolated to the "regridding model" grid (`"gfdl_esm4"` for this notebook).

Specifically, for each of the indices ('SU' and 'TX90p'), this section presents a single layout including trend values of the future period (2015-2099) for: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

Change default colorbars
Fig number counter
Common title

(section-3.3)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.3. Plot model maps
---
In this section, we invoke the `plot_models()` function to visualise the trend over the future period (2015-2099) for every model individually. Note that the model data used in this section maintains its original grid.

Specifically, for each of the indices ('SU' and 'TX90p'), this section presents a single layout including the trend for the future period (2015-2099) of every model.

(section-3.4)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.4. Boxplots of the future trend
---
Finally, we present boxplots representing the ensemble distribution of each climate model trend calculated over the future period (2015-2099) across Europe.

Dots represent the spatially-averaged future trend over the selected region (change of the number of days per decade) for each model (grey) and the ensemble mean (blue). The ensemble median is shown as a green line. Note that the spatially averaged values are calculated for each model from its original grid (i.e., no interpolated data has been used here).

The boxplot visually illustrates the distribution of trends among the climate models, with the box covering the first quartile (Q1 = 25th percentile) to the third quartile (Q3 = 75th percentile), and a green line indicating the ensemble median (Q2 = 50th percentile). Whiskers extend from the edges of the box to show the full data range.

Ensemble mean

Fig 5. Boxplots illustrating the future trends of the distribution of the chosen subset of models for: (a) the 'SU' index and (b) the 'TX90p' index. The distribution is created by considering spatially averaged trends across Europe. The ensemble mean and the ensemble median trends are both included. Outliers in the distribution are denoted by a grey circle with a black contour.

(section-3.5)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.5. Results summary and discussion
---
- What do the results mean for users?  Are the biases from the historical assessment ("CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector") relevant?

- The projected increase in summer days and days above the 90th percentile offers valuable insights for some applications like developing strategies to optimise reinsurance protections from 2015-2099. While all considered models show a positive trend for these indices, the magnitude of these trends and their uncertainty (quantified by the inter-model spread) vary spatially, emphasising the need for careful interpretation.

- Additionally, biases identified in these models during the historical period (1971-2000), as reviewed in "CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector", should be accounted for before using these results. This evaluation highlighted an overall underestimation of trends and climatology for 'SU', along with challenges in accurately representing spatial distribution, particularly over regions with complex orography.

- For the selected subset of models, the 'SU' index indicates near-zero trends in the southern Mediterranean Basin and northernmost Europe. This disparity may stem from northern Europe rarely reaching the 25°C threshold required for summer days, while the southern Mediterranean consistently exceeds this threshold throughout the JJA season. This underscores the need for a comprehensive assessment using both statistically ('SU' index) and physically-based extreme indices ('TX90p' index).

- Regional differences in 'TX90p' reveal higher values in the southern half of Europe. This regional differences are better appreciated in another assessment using CORDEX Regional Climate Models data ("CORDEX Climate projections: evaluating uncertainty in projected changes in extreme temperature indices for the reinsurance sector.").

- Boxplots display spatially averaged positive trends across Europe. The ensemble median trend for the 'SU' index is approximately 2 days per decade, similar to ERA5 data from 1971 to 2000. For the 'TX90p' index, the ensemble median trend is around 6.5 days per decade. The interquantile range spans from 1.7 to 2.25 days per decade for 'SU' and from 6 to 7 days per decade for 'TX90p'.

RESULTS NOTE:

It is important to note that the results presented are specific to the 16 models chosen, and users should aim to assess as wide a range of models as possible before making a sub-selection.


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

The CDS catalogue entries for the data used were:
* CMIP6 climate projections (Daily - Daily maximum near-surface air temperature): https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)
* [icclim](https://icclim.readthedocs.io/en/stable/) Python package