EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse
---
Production date: 2025-06-30 (updated 2026-01-09).

Application version: 1.0.

Produced by: Nicole Reynolds, Olivier Burggraaff (National Physical Laboratory).


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Quality assessment question
---
* **Is the Climate Pulse application an appropriate tool for broad audiences to visualise temperature data relating to extreme weather and climate events, including El Niño & La Niña, heatwaves, and volcanic eruptions?**

Extreme weather events – such as El Niño & La Niña, heatwaves, and volcanic eruptions – can have widespread impacts on human health, society, and the economy [[Bell+18](https://doi.org/10.1080/10962247.2017.1401017)]. The frequency and intensity of extreme weather events are increasing under climate change [[Kundzewicz+16](https://doi.org/10.1515/igbp-2016-0005)], making timely and accurate communication of these events and their impacts more critical than ever.

The [*Climate Pulse*](https://pulse.climate.copernicus.eu/) application provides near real-time visualisations of global surface air and sea surface temperature, along with an archive of past daily, monthly, and annual maps derived from the global ERA5 climate reanalysis dataset [[Hersbach+20](https://doi.org/10.1002/qj.3803)]. Climate Pulse is designed to help a broad audience clearly and effectively communicate global temperatures.

This assessment examines the suitability of the Climate Pulse application for visualising the temperature effects of extreme weather events.


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Quality assessment statement
---
These are the key outcomes of this assessment

The Climate Pulse application:
* Is suitable for visualising current and historical (from 1979) **El Niño and La Niña** events. The monthly maps are at a suitable spatial and temporal scale for the visualisation of global surface air and sea surface temperature anomalies. The global average temperature anomaly time series allows for long-term comparison and inspection of the impacts of ENSO events.
* Is suitable for visualising **heatwaves** from January 2023. This is due to the availability of daily surface air temperature anomaly maps, which can be zoomed in on affected regions, thereby ensuring both spatial and temporal suitability. Prior to 2023, only monthly maps are available, which lack the temporal resolution needed to capture these typically short-term events.
* Is not suitable for visualising the temperature impacts of **volcanic eruptions** due to it only displaying near-surface (2 m) air temperature data that do not capture the stratospheric effects of these events, along with its use of a 1991–2020 baseline, which makes cooling from historical eruptions like Pinatubo less visible.
```


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Methodology
---
Climate Pulse is an interactive application designed for a broad audience to visualise global surface air temperature (at 2 m above the surface) and sea surface temperature (at 10 m below the surface).
It displays daily, monthly, and annual maps along with a global average time series (Figure {numref}`{number} <application_climate-pulse_example_exports>`).
These are based on near real-time data (typical delay of about two days) from the ERA5 climate reanalysis [[Hersbach+20](https://doi.org/10.1002/qj.3803)],
including preliminary data from ERA5T,
regridded onto a regular 0.25° × 0.25° grid, approximately 30 km × 30 km.
Table {numref}`{number} <application_climate-pulse_table_availability>` outlines the temporal availability of surface air temperature and sea surface temperature data.

Climate Pulse displays both absolute temperature values and anomalies.
The anomalies are calculated as the difference between the current temperature and the 1991–2020 average,
the standard reference period used by the World Meteorological Organization (WMO).
For daily anomalies, this baseline is smoothed with a low-pass filter to remove short-term variability.

:::{table} Temporal availability of each visualisation format for surface air temperature and sea surface temperature (start dates of available data).
:widths: auto
:align: left
:name: application_climate-pulse_table_availability
|               | Surface Air Temperature | Sea Surface Temperature |
|---------------|-------------------------| ------------------------|
| Time Series   | 1 January 1940          | 1 January 1979          |
| Daily Map     | 1 January 2023          | 1 January 2023          |
| Monthly Map   | January 1979            | January 1979            |
| Annual Map    | 1979                    | 1979                    |
:::

Outputs from Climate Pulse can be exported in three ways:
1. Data download
2. Time series and global image export (Figure {numref}`{number} <application_climate-pulse_example_exports>`)
3. Screenshot of the dashboard (Figure {numref}`{number} <application_climate-pulse_example_screenshots>`)

attachment:application_climate-pulse_example_exports.png
---
name: application_climate-pulse_example_exports
---
Example image exports from Climate Pulse. a) Time series of daily global surface air temperature average from 1940, b) Daily map of surface air temperature anomaly (7 January 2026).
```

attachment:application_climate-pulse_example_screenshots.png
---
name: application_climate-pulse_example_screenshots
---
Screenshots of the Climate Pulse dashboard. a) Air temperature absolute values, b) Air temperature anomalies, c) Sea temperature absolute values, d) Sea temperature anomalies.
```

Applications are quality assessed separately from their underpinning datasets. For information on quality attributes of the underlying datasets, the reader is referred to the quality assessment of the datasets, which can be found in their respective CDS catalogue entries or [on the EQC hub](../intro).

The analysis and results are organised in the following use cases, which are detailed in the sections below:

**[](section-enso)**

**[](section-heatwaves)**

**[](section-volcanoes)**


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results
---
This quality assessment evaluates the suitability of Climate Pulse for visualising extreme weather events for use by broad audiences.
Three types of events were selected as test cases: El Niño & La Niña, heatwaves, and volcanic eruptions.
Climate Pulse was assessed based on the availability of relevant data variables, granularity of the data, spatial and temporal coverage and resolution, and the timeliness of data updates.
The assessment is illustrated with practical examples of how these extreme weather events appear in Climate Pulse in real-world scenarios.

(section-enso)=


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 1. El Niño–Southern Oscillation (ENSO) > Introduction
---
El Niño (warm phase) and La Niña (cold phase) are opposite phases of the ENSO (El Niño–Southern Oscillation),
a recurring climate phenomenon in the equatorial Pacific Ocean.
They typically recur every 2 to 7 years and last 9 to 12 months
[[NOAA+24](https://oceanservice.noaa.gov/facts/ninonina.html), [Wang+16](https://doi.org/10.1007/978-94-017-7499-4_4), [McPhaden+06](https://doi.org/10.1126/science.1132588)].
ENSO events are declared when regions in the central and eastern equatorial Pacific,
so-called *Niño regions*,
meet certain temperature anomaly thresholds.
Commonly used thresholds are +0.5 °C for an El Niño and –0.5 °C for a La Niña event,
observed for 3 months in a 5-month consecutive period
[[Shen+23](https://doi.org/10.1007/s44195-023-00051-5), [McPhaden+06](https://doi.org/10.1126/science.1132588), [Philander+85](https://doi.org/10.1175/1520-0469%281985%29042%3C2652:ENALN%3E2.0.CO;2)].

The ENSO has widespread effects on global weather patterns (Figure {numref}`{number} <application_climate-pulse_noaa_enso>`).
Warming due to El Niño,
along with ongoing climate change,
has led recent events to break many temperature records.
The 2023–2024 El Niño event was widely reported in the media as driving nine straight months of record-breaking high temperatures [[AFP+24](https://timesofmalta.com/article/february-marks-9th-straight-month-recordsmashing-global-heat.1087990), [Rowlatt+23](https://www.bbc.co.uk/news/science-environment-66143682)].
he media also focused on the severely negative impact of this event on human health,
such as higher temperatures causing the spread of malaria into traditionally cooler regions of East Africa,
and on the negative economic impacts historically associated with El Niño
[[Mefo Newuh+24](https://www.dw.com/en/el-nino-climate-pattern-intensifies-in-east-africa/a-68932184), [Gerretsen+23](https://www.bbc.co.uk/future/article/20230525-what-will-an-el-nino-in-2023-mean-for-you)].
The 2020–2021 La Niña prompted the media to publish warnings of increased flood risk and increased storm power and frequency in North America;
however,
some outlets focused on potential positive impacts,
such as a reduction in Australia’s summer bushfires [[Johnson+20](https://www.dailymail.co.uk/news/article-8784073/How-La-Nina-weather-event-decade-bring-wet-summer.html), [McGrath+20](https://www.bbc.co.uk/news/science-environment-54725970)].

attachment:application_climate-pulse_noaa_enso.png
---
name: application_climate-pulse_noaa_enso
---
Global distribution of climate impacts of El Niño and La Niña.
Source: NOAA (National Oceanic and Atmospheric Administration).
Available at: https://www.climate.gov/news-features/featured-images/global-impacts-el-ni%C3%B1o-and-la-ni%C3%B1a
```


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 1. El Niño–Southern Oscillation (ENSO) > Assessment of Climate Pulse
---
Climate Pulse is determined to be suitable for visualising the temperature impacts of El Niño and La Niña events for the following reasons:
* **Variables:** Climate Pulse allows for the visualisation of temperature anomalies against WMO’s standard reference period, which is a key indicator of ENSO events. The application is suitable for visualising both increases (El Niño) and decreases (La Niña) in temperature anomalies.
* **Data Granularity:** Climate Pulse’s monthly maps show anomalies on a scale from –7 °C to +7 °C, in increments of 0.5 to 1 °C (nonlinear colour scale) for surface air temperature and –3 °C to +3 °C, in increments of 0.1 to 0.5 °C for sea surface temperature. This scale is fine and wide enough to display the effects of ENSO events.
* **Spatial Coverage and Resolution:** The spatial coverage (global) and resolution (0.25° × 0.25°) of the temperature anomaly maps are appropriate for visualising the effects of the ENSO on air and sea temperatures, notably including the central and eastern Pacific regions where ENSO patterns are most pronounced.
* **Temporal Coverage and Resolution:** Climate Pulse shows near real-time and historical temperature data at daily, monthly, and annual time scales. For ENSO events, which typically develop and persist over several months, the monthly data are particularly suitable. The availability of monthly data from January 1979 allows users to look back at previous events, supporting comparisons across multiple ENSO cycles, and helping to contextualise current events within long-term trends.
* **Timeliness:** Climate Pulse is typically updated with data for the latest month within three days, making it well-suited for timely reporting on current events and climate monitoring.
* **Uncertainty:** Climate Pulse does not show uncertainty estimates in the time series or maps. [Uncertainties in ERA5 temperatures](https://confluence.ecmwf.int/x/ZSOgBg) are generally on the order of 0.5 °C. This is suitable for the use case of visualising ENSO events, which cause significantly larger temperature anomalies.


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 1. El Niño–Southern Oscillation (ENSO) > Example: 2023–2024 El Niño event and 2020–2021 La Niña event
---
The 2023–2024 El Niño event was officially declared by WMO on 4 July 2023 and dissipated in April 2024 [[WMO+24](https://wmo.int/news/media-centre/el-nino-forecast-swing-la-nina-later-year), [WMO+23](https://wmo.int/news/media-centre/world-meteorological-organization-declares-onset-of-el-nino-conditions)].
The 2020–2021 La Niña event emerged in August 2020 and dissipated in May 2021 [[Li+22](https://doi.org/10.1029/2021JD035546)].
Figures {numref}`{number} <application_climate-pulse_enso_air>` and {numref}`{number} <application_climate-pulse_enso_sst>` show global surface air and sea surface temperature anomalies during these periods,
along with a typical year without ENSO activity,
visualised using Climate Pulse.
The similarities with Figure {numref}`{number} <application_climate-pulse_noaa_enso>` are evident, particularly the warming (El Niño) and cooling (La Niña) over and in the central and eastern Pacific, and broader global temperature increases.

Climate Pulse is shown to visualise ENSO events effectively by clearly depicting the associated temperature patterns.
Some variations from the expected patterns exist,
but these are characteristic of the event.

Figure {numref}`{number} <application_climate-pulse_timeseries_elnino>` shows the global temperatures for both surface air and sea surface as clearly higher for the El Niño period.
The general increase in global temperatures relative to the baseline period (1991–2020) may cause the amplitude of El Niño anomalies to appear exaggerated.
The opposite is true for La Niña, which may appear weaker than in reality.
This issue can be mitigated by using year-on-year comparisons.

Figure {numref}`{number} <application_climate-pulse_timeseries_lanina>` shows the global average temperatures for 2019 (no ENSO activity), 2020, and 2021.
The 2020–2021 La Niña was part of a rare triple-dip La Niña,
which occurred over three consecutive years from 2020;
hence, comparison with succeeding years is not useful [[Shi+23](https://doi.org/10.1016/j.atmosres.2023.106937)].
Instead, Figure {numref}`{number} <application_climate-pulse_timeseries_lanina>` compares temperature anomalies with 2019,
showing generally lower values in late 2020 and the first half of 2021,
corresponding with La Niña.

In conclusion, Climate Pulse is suitable for visualising El Niño and La Niña events through their temperature anomalies,
making it a valuable tool for a broad audience,
including journalists and communicators,
seeking to illustrate the climate impacts of such events.

attachment:application_climate-pulse_enso_air.png
---
name: application_climate-pulse_enso_air
---
Image exports of monthly surface air temperature anomaly for the 2020–2021 La Niña event (left), a typical year without ENSO activity (2019–2020; centre), and the 2023–2024 El Niño event (right).
```

attachment:application_climate-pulse_enso_sst.png
---
name: application_climate-pulse_enso_sst
---
Image exports of monthly sea surface temperature anomaly for the 2020–2021 La Niña event (left), a typical year without ENSO activity (2019–2020; centre), and the 2023–2024 El Niño event (right).
```

attachment:application_climate-pulse_timeseries_elnino.png
---
name: application_climate-pulse_timeseries_elnino
---
Screenshot of the Climate Pulse dashboard showing global surface air and sea surface temperature anomalies for 2023 and 2024.
The thick coloured lines marked El Niño have been added in this assessment to show when the events occurred; colours correspond to the year.
```

attachment:application_climate-pulse_timeseries_lanina.png
---
name: application_climate-pulse_timeseries_lanina
---
Screenshot of the Climate Pulse dashboard showing global surface air and sea surface temperature anomalies for 2019, 2020, and 2021.
The thick coloured lines marked La Niña have been added in this assessment to show when the events occurred; colours correspond to the year.
```

(section-heatwaves)=


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 2. Heatwaves > Introduction
---
There is no universal definition of a [heatwave](https://climate.copernicus.eu/heatwaves-brief-introduction),
with different organisations and countries having established their own criteria.
However, we can generally define them as a period of time, from a few days up to a few weeks,
of temperatures significantly higher than the normal range for that location at that time of year
[[Lhotka+22](https://doi.org/10.1029/2022EA002567), [Stefanon+12](https://doi.org/10.1088/1748-9326/7/1/014023)].
For example, the [European State of the Climate 2023](https://climate.copernicus.eu/heatwaves-brief-introduction) used the definition
"a period of at least three consecutive days when both the daily surface air temperature minima and maxima are higher than the highest 5% of values for the day in question during the 1991–2020 reference period".

One of the main impacts of heatwaves is the danger they pose to human health.
Roughly 489 000 heat-related deaths occur each year [[Zhao+21](https://doi.org/10.1016/S2542-5196%2821%2900081-4)],
and high-intensity heatwave events are known to increase mortality rates.
For example,
in Europe alone, in the summer of 2022,
an estimated 61 672 heat-related excess deaths occurred [[Ballester+23](https://doi.org/10.1038/s41591-023-02419-z)].
Due to these impacts, heatwaves often generate significant news coverage.
The June 2024 Eastern Mediterranean heatwave led to real-time reporting of its increasing death toll,
as well as journalistic investigations into the overall impact and the drivers of this extreme event
[[Bali+24](https://www.dw.com/en/fatal-heat-wave-sweeps-greece-claims-more-lives/a-69485803), [World Weather Attribution+24](https://www.worldweatherattribution.org/deadly-mediterranean-heatwave-would-not-have-occurred-without-human-induced-climate-change/)].

Users interested in how humans experience heat and cold waves,
including feels-like temperatures and thermal stress indicators,
may want to use the [_Thermal Trace_ application](https://apps.climate.copernicus.eu/overview?app=thermal-trace),
for which there is an equivalent [quality assessment](./application_thermaltrace_extreme-events_q01) investigating its suitability for visualising extreme weather events.


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 2. Heatwaves > Assessment of Climate Pulse
---
Climate Pulse is determined to be moderately suitable for visualising the temperature impacts of heatwaves, depending on the date and extent of the heatwave, for the following reasons:
* **Variables:** Climate Pulse allows for the visualisation of temperature anomalies against WMO’s standard reference period, and significant regional warming anomalies are the key indicator of heatwaves.
* **Data Granularity:** Climate Pulse’s daily maps have anomalies on a scale from –12 °C to +12 °C, with approximately 1 °C increments for surface air temperature. This is in line with the temperature anomalies experienced during a heatwave, with regional anomalies usually being around 3 to 8 °C  of warming ([for Europe](https://www.ecmwf.int/en/about/media-centre/news/2023/european-climate-marked-heat-and-drought-2022-report)) [[Ma+20](https://doi.org/10.1029/2020GL087809)].
* **Spatial Coverage and Resolution:** Climate Pulse uses a 0.25° × 0.25° grid, which is suitable for showing regional, national, and multi-country heatwave events. While exported maps are global by default, one can manually zoom in on the globe and take screenshots at different scales. The global time series is unsuitable for visualising heatwaves as they are regional, not global events.
* **Temporal Coverage and Resolution:** Daily air temperature anomaly maps, matching the typical duration of heatwaves, are available in Climate Pulse from 1 January 2023 onwards. Before this date, only monthly and annual maps are available, limiting the usefulness of the application for visualising short historical heatwaves, which may be smoothed out and hence not visible over these time frames.
* **Timeliness:** The near real-time updates of Climate Pulse, typically 2 days behind, allow for timely visualisation of the heatwaves currently taking place, supporting near-real-time weather monitoring and communication.
* **Uncertainty:** Climate Pulse does not show uncertainty estimates in the time series or maps. The [ERA5 dataset has an uncertainty](https://confluence.ecmwf.int/x/ZSOgBg) of around 0.5 °C. This is suitable for the use case of visualising heatwave events, which cause significantly larger temperature anomalies.


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 2. Heatwaves > Example: Southeastern Europe June 2024 Heatwave
---
Between 3 and 27 June 2024,
several countries in Southeastern Europe and part of the Middle East were affected by a heatwave,
with the most extreme temperatures occurring between 11 and 14 June [[Dafis+24](https://www.climameter.org/20240611-14-june-eastern-mediterranean-heatwave)].
Figure {numref}`{number} <application_climate-pulse_heatwave_global>` shows the image exports from Climate Pulse;
these are global maps that are useful to show where large-scale events are occurring,
but less useful for interpreting regional and national effects.
Figure {numref}`{number} <application_climate-pulse_heatwave_global>` also displays the June 2024 average,
where the heatwave event is visible due to its extended duration.
This visibility typically occurs only with longer heatwaves, not shorter ones.
In Figure {numref}`{number} <application_climate-pulse_heatwave_regional>`,
the region of interest has been magnified,
making details in the location of the temperature anomalies more visible.
These magnified anomaly maps are more useful for visualising heatwave events with the Climate Pulse application,
but the image exports are still suitable.

attachment:application_climate-pulse_heatwave_global.png
---
name: application_climate-pulse_heatwave_global
---
Image exports of the global surface air temperature anomaly over the 11 to 14 June 2024 peak heatwave period (top and middle) and averaged over June 2024 (bottom).
```

attachment:application_climate-pulse_heatwave_regional.png
---
name: application_climate-pulse_heatwave_regional
---
Magnified regions of interest of the global surface air temperature anomaly over the 11 to 14 June 2024 peak heatwave period. Note: these are screenshots of the Climate Pulse dashboard.
```

(section-volcanoes)=


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 3. Volcanic Eruptions > Introduction
---
A [volcanic eruption](https://www.nps.gov/subjects/volcanoes/volcanic-eruptions.htm) is a sudden release of gas, ash, rock fragments, and/or molten lava through a vent onto the Earth's surface and/or into the atmosphere.
Beyond their dramatic local impacts,
large volcanic eruptions can influence global climate through the injection of sulfur dioxide (SO2) into the stratosphere,
the second major layer of Earth's atmosphere
– above the troposphere –
extending from approximately 11 to 50 km in altitude.
Once in the stratosphere,
sulfur gases oxidise into sulfate aerosols, which reflect incoming solar radiation [[Cole-Dai+10](https://doi.org/10.1002/wcc.76)].

In the short term, volcanic eruptions can disrupt local tropospheric conditions, typically over a few days.
These effects include a reduction in the diurnal temperature cycle,
with cooling in the day due to reduced solar radiation and warming in the night from trapped infrared radiation.
The magnitude of this effect depends on the size and location of the eruption,
but can be around 15 °C [[Robock+00](https://doi.org/10.1029/1998RG000054)].
On a global scale, the long-term impact of stratospheric volcanic eruptions is tropospheric cooling,
usually up to around 0.5 °C,
lasting from a few months up to two years,
again dependent on the nature and scale of the eruption [[Robock+00](https://doi.org/10.1029/1998RG000054)].

Conversely,
the immediate stratospheric response to a volcanic eruption is localised heating.
This results from the absorption of infrared solar radiation at the top of the stratospheric layer and terrestrial radiation at its base,
following the injection of aerosols.
However,
this effect only lasts a few weeks,
depending on the eruption magnitude and atmospheric circulation patterns,
as the aerosols will mix through the stratosphere globally [[Robock+13](https://doi.org/10.1002/2013EO350001)].
Large eruptions may cause stratospheric temperature increases of a few degrees [[Boretti+24](https://doi.org/10.1016/j.jastp.2024.106187), [Robock+00](https://doi.org/10.1029/1998RG000054)].
While the global signal is one of net surface cooling and net stratospheric heating,
the regional climate response to volcanic eruptions can be more complex.

Volcanic eruptions are widely reported in the media due to their immediate and often severe impacts on local populations.
However, media coverage also frequently highlights the broader implications of such events,
including their effects on global surface temperatures and the insights they offer into climate processes and what can be learnt from volcanic eruptions for potential geoengineering strategies [[King+24](https://www.bbc.co.uk/news/articles/c98qp79gj4no)].


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 3. Volcanic Eruptions > Assessment of Climate Pulse
---
Climate Pulse is determined not to be suitable for visualising the temperature impacts of volcanic eruptions, for the following reasons:
* **Vertical resolution:** Climate Pulse only displays air temperature 2 m above the surface. Many of the effects of major volcanic eruptions take place in the stratosphere; these are not captured by near-surface temperature data.
* **Confounding factors:** Climate Pulse uses a 1991–2020 baseline for the calculation of temperature anomalies, a period that includes warming from climate change. When analysing historical volcanic eruptions like Pinatubo (1991), this reference period dampens or distorts the apparent magnitude of cooling following the eruption. A baseline more representative of conditions at the time would more accurately reflect the cooling effect of an eruption, although it may be difficult to disentangle from other year-on-year differences such as ENSO events.
* **Temporal resolution:** The annual and monthly maps in Climate Pulse cannot visualise the immediate, local surface air temperature impact of an eruption effectively, since the local effects only last a few days and are thus washed out in longer-term maps. However, with Climate Pulse now providing daily maps from January 2023, any major future volcanic eruptions should prompt an assessment of its suitability for visualising the associated localised, short-term surface cooling effects. The same is true for the sensitivity of the underpinning ERA5 dataset to the near-surface temperature impacts of sudden events like volcanic eruptions.


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 3. Volcanic Eruptions > Example: Mount Pinatubo (June 1991)
---
The volcanic eruption of Mount Pinatubo in the Philippines occurred on 15 June 1991,
releasing approximately 15 million tons of SO2 into the stratosphere [[Boretti+24](https://doi.org/10.1016/j.jastp.2024.106187)],
forming a global layer of aerosols in weeks.
These aerosols reflected incoming solar radiation,
reducing global surface temperatures by an average of about 0.4 °C during 1992–1993 [[Boretti+24](https://doi.org/10.1016/j.jastp.2024.106187)],
making it one of the most notable short-term climate impacts of a volcanic eruption in the 20th century.
The aerosol layer gradually dissipated over the following three years [[Minnis+93](https://doi.org/10.1126/science.259.5100.1411)].
There is no information available on the localised short-term effects of the eruption;
however, due to the temporal availability of the data (monthly), the short-term effects would not be visible in Climate Pulse.

Figure {numref}`{number} <application_climate-pulse_pinatubo_timeseries>` shows lower global average temperatures for 1992 and 1993 compared with 1991.
These years are known to have experienced cooling influenced by the volcanic eruption.
However,
further analysis is needed to distinguish the impact of the eruption from other contributing factors,
such as ENSO events.
Therefore, it is not possible to directly identify the temperature impact of the volcanic eruption based solely on Figure {numref}`{number} <application_climate-pulse_pinatubo_timeseries>`.

attachment:application_climate-pulse_pinatubo_timeseries.png
---
name: application_climate-pulse_pinatubo_timeseries
---
Screenshot of the Climate Pulse dashboard showing global average surface air temperature anomalies for 1990–1993. Mount Pinatubo erupted in June 1991.
```


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > Analysis and results > 3. Volcanic Eruptions > Example: 2022 Hunga Tonga–Hunga Haʻapai eruption
---
On 15 January 2022,
the [Hunga Tonga–Hunga Haʻapai volcano](https://www.ecmwf.int/en/about/media-centre/news/2022/hunga-tonga-eruption-seen-ecmwf) erupted explosively after weeks of activity,
injecting volcanic gases and materials into the stratosphere and generating atmospheric shock waves and tsunami waves across multiple ocean basins.
Unlike the Mount Pinatubo eruption,
the Hunga eruption led to a cooling of global stratospheric temperatures by 0.5 to 1.0 °C from 2022 to mid-2023 due to the injection of large amounts of water vapour and minimal sulfur dioxide [[Randel+24](https://doi.org/10.1029/2024GL111500)].
While this excess water vapour is believed to have contributed to global warming through enhanced radiative forcing,
its precise impact remains uncertain due to the influence of other factors like ongoing climate change and ENSO events [[Gupta+25](https://doi.org/10.1038/s43247-025-02181-9)].
Whether the eruption caused surface temperature warming or cooling is still being debated in the scientific literature [[Gupta+25](https://doi.org/10.1038/s43247-025-02181-9), [Jenkins+23](https://doi.org/10.1038/s41558-022-01568-2)].

Localised monthly maps (Figure {numref}`{number} <application_climate-pulse_tonga_map>`) do not show any surface air temperature effect of the eruption,
as the local effects are very short-term;
daily or higher spatial resolution maps would be required to visualise such an effect.
Figure {numref}`{number} <application_climate-pulse_tonga_timeseries>` shows the global average surface air temperature time series.
Any potential warming or cooling effects of the eruption cannot be disentangled from the other contributing factors to global temperatures,
such as ENSO events.
Hence, overall Climate Pulse is not suitable for visualising the air temperature impacts of this event.

attachment:application_climate-pulse_tonga_map.png
---
name: application_climate-pulse_tonga_map
---
Magnified region of interest of the global surface air temperature anomaly for the months before (December 2021), during (January 2022), and after (February and March 2022) the eruption. Note: these are screenshots of the Climate Pulse dashboard.
```

attachment:application_climate-pulse_tonga_timeseries.png
---
name: application_climate-pulse_tonga_timeseries
---
Screenshot of the Climate Pulse dashboard of average surface air temperature anomaly, for 2021: the year before the eruption for comparison, 2022: the year of the eruption, 2023: the year after the eruption.
```


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > ℹ If you want to know more > Key resources
---
More about the ERA5 reanalysis:
* [The ERA5 global reanalysis](https://doi.org/10.1002/qj.3803)

More about temperature anomalies:
* [WMO Climatological Normals](https://community.wmo.int/en/activity-areas/climate-services/climate-products-and-initiatives/wmo-climatological-normals)

More about ENSO:
* [C3S NINO3.4 SST Forecast](https://climate.copernicus.eu/charts/packages/c3s_seasonal/products/c3s_seasonal_plume_mm?area=nino34&base_time=202507010000&type=plume)
* [ENSO as an Integrating Concept in Earth Science](https://doi.org/10.1126/science.1132588)
* [NOAA: "What are El Niño and La Niña?"](https://oceanservice.noaa.gov/facts/ninonina.html)

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)
* [Thermal Trace application](https://apps.climate.copernicus.eu/overview?app=thermal-trace) for visualising feels-like temperatures
    * [](./application_thermaltrace_extreme-events_q01)

More about volcanic eruptions:
* [The Hunga Tonga eruption as seen by ECMWF](https://www.ecmwf.int/en/about/media-centre/news/2022/hunga-tonga-eruption-seen-ecmwf)
* [Volcanic eruptions and climate](https://doi.org/10.1029/1998RG000054)


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > ℹ If you want to know more > References
---
[[AFP+24](https://timesofmalta.com/article/february-marks-9th-straight-month-recordsmashing-global-heat.1087990)] AFP, ‘February marks 9th straight month of record-smashing global heat’, Times of Malta, Mar. 07, 2024. Accessed: May 14, 2026. [Online]. Available: https://timesofmalta.com/article/february-marks-9th-straight-month-recordsmashing-global-heat.1087990

[[Bali+24](https://www.dw.com/en/fatal-heat-wave-sweeps-greece-claims-more-lives/a-69485803)] K. Bali, ‘Heat wave in Greece claims more tourist lives’, DW, Jun. 26, 2024. Accessed: May 14, 2026. [Online]. Available: https://www.dw.com/en/fatal-heat-wave-sweeps-greece-claims-more-lives/a-69485803

[[Ballester+23](https://doi.org/10.1038/s41591-023-02419-z)] J. Ballester et al., ‘Heat-related mortality in Europe during the summer of 2022’, Nature Medicine, vol. 29, no. 7, pp. 1857–1866, Jul. 2023, doi: 10.1038/s41591-023-02419-z.

[[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.

[[Boretti+24](https://doi.org/10.1016/j.jastp.2024.106187)] A. Boretti, ‘Reassessing the cooling that followed the 1991 volcanic eruption of Mt. Pinatubo’, Journal of Atmospheric and Solar-Terrestrial Physics, vol. 256, p. 106187, Mar. 2024, doi: 10.1016/j.jastp.2024.106187.

[[Cole-Dai+10](https://doi.org/10.1002/wcc.76)] J. Cole‐Dai, ‘Volcanoes and climate’, WIREs Climate Change, vol. 1, no. 6, pp. 824–839, Nov. 2010, doi: 10.1002/wcc.76.

[[Dafis+24](https://www.climameter.org/20240611-14-june-eastern-mediterranean-heatwave)] S. Dafis and D. Faranda, ‘The June 2024 Eastern Mediterranean Heatwave likely exacerbated by human-driven Climate Change and Natural Variability’, 2024, doi: 10.5281/zenodo.14103941.

[[Gerretsen+23](https://www.bbc.co.uk/future/article/20230525-what-will-an-el-nino-in-2023-mean-for-you)] I. Gerretsen, ‘The high cost of an El Niño in 2023’, BBC, Jun. 08, 2023. Accessed: May 14, 2026. [Online]. Available: https://www.bbc.co.uk/future/article/20230525-what-will-an-el-nino-in-2023-mean-for-you

[[Gupta+25](https://doi.org/10.1038/s43247-025-02181-9)] A. K. Gupta, T. Mittal, K. E. Fauria, R. Bennartz, and J. F. Kok, ‘The January 2022 Hunga eruption cooled the southern hemisphere in 2022 and 2023’, Communications Earth & Environment, vol. 6, no. 1, p. 240, Mar. 2025, doi: 10.1038/s43247-025-02181-9.

[[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.


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > ℹ If you want to know more > References
---
analysis’, Quarterly Journal of the Royal Meteorological Society, vol. 146, no. 730, pp. 1999–2049, Jul. 2020, doi: 10.1002/qj.3803.

[[Jenkins+23](https://doi.org/10.1038/s41558-022-01568-2)] S. Jenkins, C. Smith, M. Allen, and R. Grainger, ‘Tonga eruption increases chance of temporary surface temperature anomaly above 1.5 °C’, Nature Climate Change, vol. 13, no. 2, pp. 127–129, Feb. 2023, doi: 10.1038/s41558-022-01568-2.

[[Johnson+20](https://www.dailymail.co.uk/news/article-8784073/How-La-Nina-weather-event-decade-bring-wet-summer.html)] S. Johnson, ‘La Niña forms off Australia’s coast sparking warnings of dangerous weather and cyclones - here’s how the weather pattern will affect your city’, Mail Online, Sep. 29, 2020. Accessed: Jun. 16, 2025. [Online]. Available: https://www.dailymail.co.uk/news/article-8784073/How-La-Nina-weather-event-decade-bring-wet-summer.html

[[King+24](https://www.bbc.co.uk/news/articles/c98qp79gj4no)] S. King, ‘Conspiracy theories swirl about geo-engineering, but could it help save the planet?’, BBC, Jul. 21, 2024. Accessed: Jun. 16, 2025. [Online]. Available: https://www.bbc.co.uk/news/articles/c98qp79gj4no

[[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.

[[Li+22](https://doi.org/10.1029/2021JD035546)] X. Li, Z. Hu, Y. Tseng, Y. Liu, and P. Liang, ‘A Historical Perspective of the La Niña Event in 2020/2021’, JGR Atmospheres, vol. 127, no. 7, p. e2021JD035546, Apr. 2022, doi: 10.1029/2021JD035546.

[[Lhotka+22](https://doi.org/10.1029/2022EA002567)] O. Lhotka and J. Kyselý, ‘The 2021 European Heat Wave in the Context of Past Major Heat Waves’, Earth and Space Science, vol. 9, no. 11, p. e2022EA002567, Nov. 2022, doi: 10.1029/2022EA002567.

[[Ma+20](https://doi.org/10.1029/2020GL087809)] F. Ma, X. Yuan, Y. Jiao, and P. Ji, ‘Unprecedented Europe Heat in June–July 2019: Risk in the Historical and Future Context’, Geophysical Research Letters, vol. 47, no. 11, p. e2020GL087809, Jun. 2020, doi: 10.1029/2020GL087809.

[[McGrath+20](https://www.bbc.co.uk/news/science-environment-54725970)] M. McGrath, ‘“Moderate to strong” La Niña weather event develops in the Pacific’, Oct. 29, 2020. [Online]. Available: https://www.bbc.co.uk/news/science-environment-54725970

[[McPhaden+06](https://doi.org/10.1126/science.1132588)] M. J. McPhaden, S. E. Zebiak, and M. H. Glantz, ‘ENSO as an Integrating Concept in Earth Science’, Science, vol. 314, no. 5806, pp. 1740–1745, Dec. 2006, doi: 10.1126/science.1132588.


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > ℹ If you want to know more > References
---
Earth Science’, Science, vol. 314, no. 5806, pp. 1740–1745, Dec. 2006, doi: 10.1126/science.1132588.

[[Mefo Newuh+24](https://www.dw.com/en/el-nino-climate-pattern-intensifies-in-east-africa/a-68932184)] M. Mefo Newuh, ‘El Nino climate pattern intensifies in East Africa’, DW, Apr. 26, 2024. Accessed: Jul. 07, 2025. [Online]. Available: https://www.dw.com/en/el-nino-climate-pattern-intensifies-in-east-africa/a-68932184

[[Minnis+93](https://doi.org/10.1126/science.259.5100.1411)] P. Minnis et al., ‘Radiative Climate Forcing by the Mount Pinatubo Eruption’, Science, vol. 259, no. 5100, pp. 1411–1415, Mar. 1993, doi: 10.1126/science.259.5100.1411.

[[NOAA+24](https://oceanservice.noaa.gov/facts/ninonina.html)] NOAA, ‘What are El Niño and La Niña?’, National Ocean Service. Accessed: Jun. 11, 2025. [Online]. Available: https://oceanservice.noaa.gov/facts/ninonina.html

[[Philander+85](https://doi.org/10.1175/1520-0469%281985%29042%3C2652:ENALN%3E2.0.CO;2)] S. G. H. Philander, ‘El Niño and La Niña’, Journal of the Atmospheric Sciences, vol. 42, no. 23, pp. 2652–2662, Dec. 1985, doi: 10.1175/1520-0469(1985)042<2652:ENALN>2.0.CO;2.

[[Randel+24](https://doi.org/10.1029/2024GL111500)] W. J. Randel, X. Wang, J. Starr, R. R. Garcia, and D. Kinnison, ‘Long‐Term Temperature Impacts of the Hunga Volcanic Eruption in the Stratosphere and Above’, Geophysical Research Letters, vol. 51, no. 21, p. e2024GL111500, Nov. 2024, doi: 10.1029/2024GL111500.

[[Robock+00](https://doi.org/10.1029/1998RG000054)] A. Robock, ‘Volcanic eruptions and climate’, Reviews of Geophysics, vol. 38, no. 2, pp. 191–219, May 2000, doi: 10.1029/1998RG000054.

[[Robock+13](https://doi.org/10.1002/2013EO350001)] A. Robock, ‘The Latest on Volcanic Eruptions and Climate’, EoS Transactions, vol. 94, no. 35, pp. 305–306, Aug. 2013, doi: 10.1002/2013EO350001.

[[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

[[Shen+23](https://doi.org/10.1007/s44195-023-00051-5)] M.-H. Shen and J.-Y. Yu, ‘Changes in El Niño characteristics and air–sea feedback mechanisms under progressive global warming’, Terrestrial, Atmospheric and Oceanic Sciences, vol. 34, no. 1, p. 19, Dec. 2023, doi: 10.1007/s44195-023-00051-5.

[[Shi+23](https://doi.org/10.1016/j.atmosres.2023.106937)] L. Shi, R. Ding, S. Hu, X. Li, and J. Li, ‘Extratropical impacts on the 2020–2023 Triple-Dip La Niña event’, Atmospheric Research, vol. 294, p. 106937, Oct. 2023, doi: 10.1016/j.atmosres.2023.106937.


EQC Quality Assessment: "Visualisation of temperature during extreme weather events with Climate Pulse"
Dataset: climate-pulse [CDS]
Aspect: extreme-events_q01  |  Category: Applications
Section: Visualisation of temperature during extreme weather events with Climate Pulse > ℹ If you want to know more > References
---
–2023 Triple-Dip La Niña event’, Atmospheric Research, vol. 294, p. 106937, Oct. 2023, doi: 10.1016/j.atmosres.2023.106937.

[[Stefanon+12](https://doi.org/10.1088/1748-9326/7/1/014023)] M. Stefanon, F. D’Andrea, and P. Drobinski, ‘Heatwave classification over Europe and the Mediterranean region’, Environmental Research Letters, vol. 7, no. 1, p. 014023, Mar. 2012, doi: 10.1088/1748-9326/7/1/014023.

[[Wang+16](https://doi.org/10.1007/978-94-017-7499-4_4)] C. Wang, C. Deser, J.-Y. Yu, P. DiNezio, and A. Clement, ‘El Niño and Southern Oscillation (ENSO): A Review’, in Coral Reefs of the Eastern Tropical Pacific, vol. 8, P. W. Glynn, D. P. Manzello, and I. C. Enochs, Eds., in Coral Reefs of the World, vol. 8., Dordrecht: Springer Netherlands, 2017, pp. 85–106. doi: 10.1007/978-94-017-7499-4_4.

[[WMO+23](https://wmo.int/news/media-centre/world-meteorological-organization-declares-onset-of-el-nino-conditions)] World Meteorological Organization (WMO), ‘World Meteorological Organization declares onset of El Niño conditions’, World Meteorological Organization. Accessed: Jun. 16, 2025. [Online]. Available: https://wmo.int/news/media-centre/world-meteorological-organization-declares-onset-of-el-nino-conditions

[[WMO+24](https://wmo.int/news/media-centre/el-nino-forecast-swing-la-nina-later-year)] World Meteorological Organization (WMO), ‘El Niño is forecast to swing to La Niña later this year’, World Meteorological Organization. Accessed: Jun. 16, 2025. [Online]. Available: https://wmo.int/news/media-centre/el-nino-forecast-swing-la-nina-later-year

[[World Weather Attribution+24](https://www.worldweatherattribution.org/deadly-mediterranean-heatwave-would-not-have-occurred-without-human-induced-climate-change/)] World Weather Attribution, ‘Deadly Mediterranean heatwave would not have occurred without human induced climate change’, World Weather Attribution, Jul. 31, 2024. Accessed: Jun. 16, 2025. [Online]. Available: https://www.worldweatherattribution.org/deadly-mediterranean-heatwave-would-not-have-occurred-without-human-induced-climate-change/

[[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: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > Quality assessment question
---
* **Is the Global Temperature Trend Monitor application a suitable tool for calculating and visualising temperature trends?**

Global warming, the steady increase in global average temperature over the past few decades [[Shen+22](https://doi.org/10.1007/s11442-022-1937-1), [Mudelsee+19](https://doi.org/10.1016/j.earscirev.2018.12.005)], is one of the most prominent aspects of climate change.
More hot, dry days lead to a stronger personal perception of climate change among the general public [[Marlon+21](https://doi.org/10.1016/j.gloenvcha.2021.102247)].
Accordingly, changes in global and regional temperature are widely reported on in both scientific [[Copernicus Climate Change Service+24](https://doi.org/10.24381/bs9v-8c66), [IPCC+23](https://doi.org/10.59327/IPCC/AR6-9789291691647)] and popular media [[Frijters+25](https://www.volkskrant.nl/wetenschap/~b301f81f2/), [Poynting+25](https://www.bbc.com/news/articles/cd7575x8yq5o), [Horton+24](https://www.theguardian.com/environment/2024/nov/20/the-climate-crisis-in-charts-how-2024-has-set-unwanted-new-records)].
The Paris Agreement [[United Nations+15](https://treaties.un.org/doc/Treaties/2016/02/20160215%2006-03%20PM/Ch_XXVII-7-d.pdf)] set a goal of limiting average global warming to well below 2 °C, ideally 1.5 °C.
The actual rate of warming compared to this threshold is a crucial indicator of the progress of efforts to combat climate change.

In this assessment, the [*C3S Global Temperature Trend Monitor*](https://apps.climate.copernicus.eu/global-temperature-trend-monitor/) web application (Figure {numref}`{number} <application_gttm_may2025>`) is examined for its ability to calculate and display trends in global temperature.
The uncertainty in the data and methods, and the ability of the application to display its uncertainty, are assessed.

attachment:application_gttm_may2025.png
---
height: 600px
name: application_gttm_may2025
---
The C3S Global Temperature Trend Monitor (GTTM) in May 2025. Orange dots show monthly global average temperature anomalies within (bright) and outside (faded) a user-specified 30-year window. A linear trend (red solid line) is fitted through the data and indicates the current average anomaly (orange dotted line; top text) and predicted time to reach 1.5 °C (red vertical line; top text), in this case April 2029. The orange envelope shows the IPCC climate projection uncertainty estimate.
```


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > Quality assessment statement
---
These are the key outcomes of this assessment

* The Global Temperature Trend Monitor (GTTM) is a scientifically sound tool for monitoring global temperature trends over decade-long time scales, and yearly changes therein.
* The uncertainty associated with the methods used in GTTM (anomaly baselines, least squares fitting) is sufficiently small compared to the uncertainty in the underpinning dataset and inherent to the climate, and the application provides suitable methods for visualising the expected level of uncertainty.
* The provided source code makes it possible to adapt GTTM to different user needs, e.g. regional rather than global trends, albeit with some additional considerations such as the calculation of anomalies relative to a local baseline.
```


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > Methodology
---
The [*C3S Global Temperature Trend Monitor*](https://apps.climate.copernicus.eu/global-temperature-trend-monitor/) (*GTTM*) is a C3S web application displaying monthly global average temperature anomalies.
Temperatures from the ERA5 reanalysis [[Hersbach+20](https://doi.org/10.1002/qj.3803)] are compared to the pre-industrial (1850–1900) average to calculate temperature anomalies.
A 30-year linear trend is fitted to these anomalies and extrapolated to estimate when global warming will reach 1.5 °C.
The start and end of the 30-year range can be modified by the user to explore how the predicted date of reaching 1.5 °C has changed over time.

GTTM is based on two datasets:
* [_ERA5 monthly averaged data on single levels from 1940 to present_](https://doi.org/10.24381/cds.f17050d7) [[ERA5 monthly dataset](https://doi.org/10.24381/cds.f17050d7)].
* [_Essential climate variables for assessment of climate variability from 1979 to present_](https://doi.org/10.24381/7470b643) [[ECVs for climate variability dataset](https://doi.org/10.24381/7470b643)].

Applications are quality assessed separately from their underpinning datasets. For information on quality attributes of the underlying datasets, the reader is referred to the quality assessment of the datasets, which can be found in their respective CDS catalogue entries or [on the EQC hub](../intro).

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](section-data)**

**[](section-anomalies)**

**[](section-trends)**

**[](section-visualisation)**


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > Analysis and results > 1. Underpinning dataset
---
GTTM is based on ERA5 data, both directly through its monthly averaged data [[ERA5 monthly dataset](https://doi.org/10.24381/cds.f17050d7)] and indirectly through a dataset of essential climate variables (ECVs) derived from ERA5, ERA5-Land, and ERA-Interim [[ECVs for climate variability dataset](https://doi.org/10.24381/7470b643)].
Numerous validation studies have shown that ERA5 provides accurate and reliable estimates of surface air temperature, with strong agreement against in-situ measurements and other datasets, albeit with regional variations [[Cavalleri+24](https://doi.org/10.1002/joc.8475), [Liu+24](https://doi.org/10.1016/j.ecolind.2024.112481), [Yilmaz+23](https://doi.org/10.1016/j.scitotenv.2022.159182)].

Uncertainties on individual ERA5 temperature data are typically 0.1–0.5 °C [[Copernicus Climate Change Service+25a](https://confluence.ecmwf.int/display/CKB/ERA5%3A+uncertainty+estimation)].
Some level of spatial and temporal covariance is to be expected, both physically and due to the methods of data production.
While an exact value is difficult to provide, it can be fairly assumed that the uncertainty in global averages is smaller than that in individual data.

(section-anomalies)=


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > Analysis and results > 2. Temperature anomalies
---
GTTM calculates monthly temperature anomalies by subtracting the 1991–2020 climatology (ECV dataset) from ERA5 monthly temperatures.
In general, ERA5 temperature anomalies agree well with those from other sources [[Hersbach+20](https://doi.org/10.1002/qj.3803)].

The 1991–2020 baseline anomalies are converted to anomalies against the 1850–1900 pre-industrial average using a set of monthly offsets based on three datasets (Berkeley Earth, HadCRUT5, NOAAGlobalTemp; see table below) spanning 1850–2020.
These monthly offsets were calculated as global averages: averaged first by grid square, then by hemisphere, then overall.
This method is also used in the C3S Climate Bulletin and is explained in detail in [[Copernicus Climate Change Service+25b](https://climate.copernicus.eu/climate-bulletin-about-data-and-analysis)].

| Month      | Berkeley Earth | HadCRUT5 | NOAAGlobalTemp | Average | Applied Offset |
|:-----------|---------------:|---------:|---------------:|--------:|---------------:|
|Jan         | 0.99           | 0.97     | 0.83           | 0.93    | 0.96           |
|Feb         | 1.03           | 1.00     | 0.84           | 0.96    | 0.96           |
|Mar         | 1.04           | 1.03     | 0.87           | 0.98    | 0.95           |
|Apr         | 0.98           | 0.97     | 0.84           | 0.93    | 0.91           |
|May         | 0.90           | 0.88     | 0.76           | 0.85    | 0.87           |
|Jun         | 0.86           | 0.83     | 0.76           | 0.82    | 0.83           |
|Jul         | 0.81           | 0.77     | 0.71           | 0.77    | 0.80           |
|Aug         | 0.88           | 0.80     | 0.73           | 0.80    | 0.80           |
|Sep         | 0.93           | 0.80     | 0.76           | 0.83    | 0.81           |
|Oct         | 0.92           | 0.87     | 0.81           | 0.87    | 0.85           |
|Nov         | 0.95           | 0.97     | 0.82           | 0.92    | 0.89           |
|Dec         | 0.97           | 0.93     | 0.80           | 0.90    | 0.93           |
|            |                |          |                |         |                |
|Annual Mean | 0.94           | 0.90     | 0.80           | 0.88    | 0.88           |

The applied offset differs from the three source datasets by –0.13 to +0.12 °C or –14% to +15% (Figure {numref}`{number} <application_gttm_offsets>`).
This uncertainty in the baseline correction propagates into the anomalies used in GTTM on two scales:
1. Yearly: Since the same offset is applied to every year, there is an uncertainty in the overall offset of the data but not in year-on-year differences. This uncertainty affects the offset of the fitted line but not its slope.
2. Monthly: Seasonal differences in the offsets and in the uncertainties therein cause an uncertainty in the month-on-month differences within each year. Over multiple years, this averages out and should not significantly affect the fitted line.

attachment:application_gttm_offsets.png
---
height: 387px
name: application_gttm_offsets
---
Offsets applied to adjust temperature anomalies from a 1991–2020 baseline to an 1850–1900 (pre-industrial) baseline.
```


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > Analysis and results > 2. Temperature anomalies
---
: 387px
name: application_gttm_offsets
---
Offsets applied to adjust temperature anomalies from a 1991–2020 baseline to an 1850–1900 (pre-industrial) baseline.
```

The uncertainty on the global average temperature anomaly offset resulting from this variability can be roughly estimated as the standard deviation of the three annual means, 0.06 °C.
This propagates into an uncertainty of 2–4 years on the estimated date of reaching 1.5 °C, depending on the slope and date range.
Since the slope of the fitted line is not affected, there is no additional uncertainty on comparisons between 30-year fitting ranges.
For early starting dates, an uncertainty of 2–4 years is small compared to the year-on-year variations and to the total duration.
It is significant for recent years – for example, in January 2025, the estimated time left until 1.5 °C was <5 years.

Users intending to modify the application for a specific region should be aware that the global average offset might not be directly applicable, especially in regions with particularly strong (e.g. the Arctic [[Rantanen+22](https://doi.org/10.1038/s43247-022-00498-3)]) or weak (e.g. the North Atlantic [[Sgubin+17](https://doi.org/10.1038/ncomms14375)]) warming.
In these cases, it can be worthwhile to derive region-specific offsets from the source datasets.

(section-trends)=


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > Analysis and results > 3. Trend estimation
---
GTTM uses a least squares linear fit (implemented through [`numpy.polyfit`](https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html)) to estimate and extrapolate a 30-year trend.
While past and projected temperature curves are typically more complicated than a straight line [[O’Neill+16](https://doi.org/10.5194/gmd-9-3461-2016)], a simple linear fit can adequately describe trends since the 1970s [[Mudelsee+19](https://doi.org/10.1016/j.earscirev.2018.12.005)], especially for illustrative rather than predictive purposes.
The 30-year range corresponds to the standard length of time used by the World Meteorological Organization (WMO) for climatology [[WMO](https://community.wmo.int/en/activity-areas/climate-services/climate-products-and-initiatives/wmo-climatological-normals)].

As an example, for the period of January 1995 – January 2025, the best-fitting parameters are a slope $m$ = 0.00214 °C/month (0.0257 °C/year) and an offset $c$ = 0.604 °C, meaning the 1.5 °C threshold would be reached $(1.5\,\text{°C} \, – \, c)/m$ = 418.6 months from January 1995, or November–December 2029. The GTTM interface rounds this result down, in this example to November 2029.

The `numpy.polyfit` routine can provide an uncertainty estimate in its outputs, representing the fit uncertainty due to the spread in the data but not the uncertainty in the data themselves. The corresponding estimated uncertainties are $\sigma_m$ = 8.2 × 10<sup>–5</sup> °C/month (3.8%) and $\sigma_c$ = 0.017 °C (2.8%), with a correlation coefficient of $r_{m,c}$ = –0.87 (i.e. strongly negatively correlated). The corresponding uncertainty $\sigma_t$ in the month of reaching 1.5 °C $t$ can be estimated using a Taylor expansion, the correlated equivalent of the sum-of-squares method:

$$ \sigma_t^2 = \mathbf{J} \mathbf{\Sigma} \mathbf{J}^\top $$

Where $\mathbf{\Sigma}$ is the covariance matrix and $\mathbf{J}$ is the Jacobian matrix $\mathbf{J} = \left[\begin{matrix}\frac{\partial t}{\partial m}&\frac{\partial t}{\partial c}\\\end{matrix}\right]$.

The resulting uncertainty is 10 months, or February 2029 – October 2030 in this example.

The choice of a least squares fit for the regression line, rather than an alternative, does not significantly affect the result.
For example, a Theil–Sen (median-of-slopes) estimator, implemented using [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.TheilSenRegressor.html), provides an estimated slope $m$ = 0.00208 °C/month and offset $c$ = 0.594 °C for the same data.
The equivalent date of reaching 1.5 °C would be April 2031.
The scikit-learn method does not provide an uncertainty estimate on these parameters, and a manual calculation is outside the scope of this assessment.
However, comparing the difference between the two linear estimators (16 months) to the uncertainty in one (10 months), the unknown uncertainty in the other, the uncertainty resulting from the anomaly calculation (see above), as well as the uncertainty in ERA5 data in general, it can be concluded that the difference is not significant and that the least squares fit is suitable.

(section-visualisation)=


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > Analysis and results > 4. Visualisation of uncertainty
---
While GTTM does not explicitly provide uncertainty estimates on the underpinning data, its fitted trend line, or the estimated date of reaching 1.5 °C, it does provide users with some tools to intuit these uncertainties.
First, sliding between different 30-year ranges and seeing the change in estimated date indicates the level of uncertainty due to natural variability (e.g. particularly hot or cold months) and actual changes in the rate of global warming.
Second, there is an overlay showing the typical uncertainty in IPCC projections of climate projections.

The documentation extensively explains that the purpose of GTTM is to *illustrate* climate change to a broad audience, not to *predict* it.
Comparing the uncertainty in different aspects of the application (anomaly calculation, trend estimation) to the uncertainty in the underpinning dataset and the physical variability in global temperature (natural and man-made), we can conclude that GTTM is fit for this purpose.


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > ℹ If you want to know more > Key resources
---
More about the ERA5 reanalysis:
* [The ERA5 global reanalysis](https://doi.org/10.1002/qj.3803)
* [ERA5: uncertainty estimation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+uncertainty+estimation)

More about temperature anomalies:
* [Climate Bulletin – About the data and analysis](https://climate.copernicus.eu/climate-bulletin-about-data-and-analysis)

More about climate trend estimation:
* [Trend analysis of climate time series: A review of methods](https://doi.org/10.1016/j.earscirev.2018.12.005)
* [Estimating trends and the current climate mean in a changing climate](https://doi.org/10.1016/j.cliser.2023.100428)

More about the 1.5 °C and 2 °C targets:
* [Paris Agreement](https://treaties.un.org/doc/Treaties/2016/02/20160215%2006-03%20PM/Ch_XXVII-7-d.pdf)
* [A history of the 1.5 °C target](https://doi.org/10.1002/wcc.824)
* [Realizing the impacts of a 1.5 °C warmer world](https://doi.org/10.1038/nclimate3055)

Code libraries used:
* [NumPy polyfit](https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html)
* [scikit-learn Theil–Sen estimator](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.TheilSenRegressor.html)


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > ℹ If you want to know more > References
---
[[Cavalleri+24](https://doi.org/10.1002/joc.8475)] F. Cavalleri et al., ‘Inter-comparison and validation of high-resolution surface air temperature reanalysis fields over Italy’, International Journal of Climatology, vol. 44, no. 8, pp. 2681–2700, 2024, doi: 10.1002/joc.8475.

[[Cointe+23](https://doi.org/10.1002/wcc.824)] B. Cointe and H. Guillemot, ‘A history of the 1.5°C target’, WIREs Climate Change, vol. 14, no. 3, p. e824, 2023, doi: 10.1002/wcc.824.

[[Copernicus Climate Change Service+24](https://doi.org/10.24381/bs9v-8c66)] Copernicus Climate Change Service (C3S), ‘European State of the Climate 2023’, Copernicus Climate Change Service (C3S), Apr. 2024. doi: 10.24381/bs9v-8c66.

[[Copernicus Climate Change Service+25a](https://confluence.ecmwf.int/display/CKB/ERA5%3A+uncertainty+estimation)] Copernicus Climate Change Service (C3S), ‘ERA5: uncertainty estimation’, Copernicus Knowledge Base. Accessed: May 21, 2025. [Online]. Available: https://confluence.ecmwf.int/display/CKB/ERA5%3A+uncertainty+estimation

[[Copernicus Climate Change Service+25b](https://climate.copernicus.eu/climate-bulletin-about-data-and-analysis)] Copernicus Climate Change Service (C3S), ‘Climate Bulletin – About the data and analysis’, Copernicus Climate Change Service (C3S). Accessed: May 13, 2025. [Online]. Available: https://climate.copernicus.eu/climate-bulletin-about-data-and-analysis

[[ECVs for climate variability dataset](https://doi.org/10.24381/7470b643)] H. Hersbach et al., ‘Essential climate variables for assessment of climate variability from 1979 to present’. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), Apr. 18, 2019. doi: 10.24381/7470b643.

[[ERA5 monthly dataset](https://doi.org/10.24381/cds.f17050d7)] H. Hersbach et al., ‘ERA5 monthly averaged data on single levels from 1940 to present’. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), Apr. 18, 2019. doi: 10.24381/cds.f17050d7.

[[Frijters+25](https://www.volkskrant.nl/wetenschap/~b301f81f2/)] S. Frijters, ‘366 warme dagen: het temperatuurrecord van 2024 in cijfers’, De Volkskrant, Jan. 10, 2025. Accessed: May 09, 2025. [Online]. Available: https://www.volkskrant.nl/wetenschap/~b301f81f2/

[[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, May 2020, doi: 10.1002/qj.3803.

[[Horton+24](https://www.theguardian.com/environment/2024/nov/20/the-climate-crisis-in-charts-how-2024-has-set-unwanted-new-records)] H. Horton, L. Swan, A. L. González Paz, and H. Symons, ‘The climate crisis in charts: how 2024 has set unwanted new records’, The Guardian, Nov. 20, 2024. Accessed: May 09, 2025. [Online]. Available: https://www.theguardian.com/environment/2024/nov/20/the-climate-crisis-in-charts-how-2024-has-set-unwanted-new-records


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > ℹ If you want to know more > References
---
09, 2025. [Online]. Available: https://www.theguardian.com/environment/2024/nov/20/the-climate-crisis-in-charts-how-2024-has-set-unwanted-new-records

[[IPCC+23](https://doi.org/10.59327/IPCC/AR6-9789291691647)] IPCC, ‘Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change’, Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, Jul. 2023. doi: 10.59327/IPCC/AR6-9789291691647.

[[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.

[[Marlon+21](https://doi.org/10.1016/j.gloenvcha.2021.102247)] J. R. Marlon et al., ‘Hot dry days increase perceived experience with global warming’, Global Environmental Change, vol. 68, p. 102247, May 2021, doi: 10.1016/j.gloenvcha.2021.102247.

[[Mitchell+16](https://doi.org/10.1038/nclimate3055)] D. Mitchell, R. James, P. M. Forster, R. A. Betts, H. Shiogama, and M. Allen, ‘Realizing the impacts of a 1.5 °C warmer world’, Nature Climate Change, vol. 6, no. 8, pp. 735–737, Aug. 2016, doi: 10.1038/nclimate3055.

[[Mudelsee+19](https://doi.org/10.1016/j.earscirev.2018.12.005)] M. Mudelsee, ‘Trend analysis of climate time series: A review of methods’, Earth-Science Reviews, vol. 190, pp. 310–322, Mar. 2019, doi: 10.1016/j.earscirev.2018.12.005.

[[O’Neill+16](https://doi.org/10.5194/gmd-9-3461-2016)] B. C. O’Neill et al., ‘The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6’, Geoscientific Model Development, vol. 9, pp. 3461–3482, Sep. 2016, doi: 10.5194/gmd-9-3461-2016.

[[Poynting+25](https://www.bbc.com/news/articles/cd7575x8yq5o)] M. Poynting, E. Rivault, and B. Dale, ‘World’s hottest year: 2024 first to pass 1.5C warming limit’, BBC News, Jan. 10, 2025. Accessed: May 09, 2025. [Online]. Available: https://www.bbc.com/news/articles/cd7575x8yq5o

[[Rantanen+22](https://doi.org/10.1038/s43247-022-00498-3)] M. Rantanen et al., ‘The Arctic has warmed nearly four times faster than the globe since 1979’, Communications Earth & Environment, vol. 3, p. 168, Aug. 2022, doi: 10.1038/s43247-022-00498-3.

[[Scherrer+24](https://doi.org/10.1016/j.cliser.2023.100428)] S. C. Scherrer, C. de Valk, M. Begert, S. Gubler, S. Kotlarski, and M. Croci-Maspoli, ‘Estimating trends and the current climate mean in a changing climate’, Climate Services, vol. 33, p. 100428, Jan. 2024, doi: 10.1016/j.cliser.2023.100428.

[[Sgubin+17](https://doi.org/10.1038/ncomms14375)] G. Sgubin, D. Swingedouw, S. Drijfhout, Y. Mary, and A. Bennabi, ‘Abrupt cooling over the North Atlantic in modern climate models’, Nature Communications, vol. 8, p. 14375, Feb. 2017, doi: 10.1038/ncomms14375.


EQC Quality Assessment: "Assessing global warming with the C3S Global Temperature Trend Monitor"
Dataset: gttm [CDS]
Aspect: uncertainty_q01  |  Category: Applications
Section: Assessing global warming with the C3S Global Temperature Trend Monitor > ℹ If you want to know more > References
---
Bennabi, ‘Abrupt cooling over the North Atlantic in modern climate models’, Nature Communications, vol. 8, p. 14375, Feb. 2017, doi: 10.1038/ncomms14375.

[[Shen+22](https://doi.org/10.1007/s11442-022-1937-1)] B. Shen, S. Song, L. Zhang, Z. Wang, C. Ren, and Y. Li, ‘Temperature trends in some major countries from the 1980s to 2019’, Journal of Geographical Sciences, vol. 32, no. 1, pp. 79–100, Jan. 2022, doi: 10.1007/s11442-022-1937-1.

[[United Nations+15](https://treaties.un.org/doc/Treaties/2016/02/20160215%2006-03%20PM/Ch_XXVII-7-d.pdf)] United Nations, ‘Paris Agreement’, United Nations, Paris, France, Dec. 2015. Accessed: May 12, 2025. [Online]. Available: https://treaties.un.org/doc/Treaties/2016/02/20160215%2006-03%20PM/Ch_XXVII-7-d.pdf

[[Yilmaz+23](https://doi.org/10.1016/j.scitotenv.2022.159182)] M. Yilmaz, ‘Accuracy assessment of temperature trends from ERA5 and ERA5-Land’, Science of The Total Environment, vol. 856, p. 159182, Jan. 2023, doi: 10.1016/j.scitotenv.2022.159182.

[[WMO](https://community.wmo.int/en/activity-areas/climate-services/climate-products-and-initiatives/wmo-climatological-normals)] World Meteorological Organization, ‘WMO Climatological Normals’, World Meteorological Organization. Accessed: Jun. 20, 2025. [Online]. Available: https://community.wmo.int/en/activity-areas/climate-services/climate-products-and-initiatives/wmo-climatological-normals


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
---
Production date: 2026-06-15.
<!--
Application version: xx.
-->
Produced by: Olivier Burggraaff (National Physical Laboratory).


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 > Quality assessment question
---
* **Is the Thermal Trace application an appropriate tool for broad audiences to visualise heat and cold stress relating to extreme temperature events, including heatwaves and cold snaps?**
* **Is the Thermal Trace application an appropriate tool for broad audiences to visualise long-term trends in heat and cold stress, such as those relating to climate change?**

Heat stress and cold stress occur when the human body faces extreme temperatures outside its comfort zone.
These thermal extremes cause widespread, severe impacts on human health, society, and regional economies [[Bell+18](https://doi.org/10.1080/10962247.2017.1401017)].
Specifically, global data show that cold temperatures cause about 4 594 000 deaths annually,
while extreme heat contributes to another 489 000 deaths [[Zhao+21](https://doi.org/10.1016/S2542-5196%2821%2900081-4)].
As climate change accelerates,
these dangerous meteorological events are growing increasingly frequent and intense.
Consequently, there is greater desire for decision-makers, urban planners, and the general public to be able to visualise and track heat and cold stress-health trends over time,
to understand and plan for their widespread impacts on human health, society, and the economy.

The [_Thermal Trace_](https://apps.climate.copernicus.eu/overview?app=thermal-trace) application provides near real-time visualisations of heat and cold stress indicators as well as daily minimum/maximum feels-like temperature based on the ERA5 reanalysis [[Menary+25](https://www.ecmwf.int/en/newsletter/185/news/thermal-trace-health-related-weather-and-climate-monitoring)].
The application is focused on the human experience of temperature and weather rather than solely the physical air temperature.
For physical surface air and sea surface temperatures,
users may want to use the [_Climate Pulse_ application](https://apps.climate.copernicus.eu/overview?app=climate-pulse),
for which there is an equivalent [quality assessment](./application_climate-pulse_extreme-events_q01) investigating its suitability for visualising extreme weather events.

_Feels-like temperature_
(or _apparent temperature_)
expresses how humans _experience_ temperature by including additional variables relating to vapour pressure, wind speed, and irradiation [[Steadman+84](https://doi.org/10.1175/1520-0450%281984%29023%3C1674:AUSOAT%3E2.0.CO;2)].

The _Universal Thermal Climate Index_ (_UTCI_) is an international standard feels-like temperature indicator
that quantifies the thermal stress experienced by a representative human in given meteorological circumstances.
UTCI is expressed quantitatively as an equivalent temperature (in K or °C) which acts as a feels-like temperature
– e.g. a UTCI of 30 °C under any circumstances causes stress equivalent to an air temperature of 30 °C under standard circumstances [[Jendritzky+12](https://doi.org/10.1007/s00484-011-0513-7), [Błażejczyk+10](https://doi.org/10.2478/mgrsd-2010-0009)].
It is important to note that UTCI assumes an "average person" [[Fiala+12](https://doi.org/10.1007/s00484-011-0424-7)]
wearing clothing typical for given weather conditions [[Havenith+12](https://doi.org/10.1007/s00484-011-0451-4)]
and may be less representative especially for vulnerable groups, such as children, the elderly, and people with reduced thermoregulation [[Tousi+24](https://doi.org/10.3390/urbansci8040193)].

This assessment examines the suitability of the Thermal Trace application for visualising heat and cold stress,
both during extreme events like heatwaves and cold snaps and over longer periods of time,
for a broad (scientific and non-scientific) audience.


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 > Quality assessment statement
---
These are the key outcomes of this assessment

* The Thermal Trace application is a scientifically sound and convenient tool for visualising feels-like temperature (UTCI) and thermal stress indicators. Its visuals can be used directly (via screenshot or image export) or it can be used as a jumping-off point for data download and quantitative analysis or custom visualisation.
* Thermal Trace is suitable for visualising extreme events, like heatwaves and cold snaps, both recently and decades in the past. Its daily to yearly maps and hourly to yearly time series provide sufficient coverage and resolution to see the spatial extent and temporal evolution of an event. When using the application, it is important to note the known biases and uncertainties in the underpinning ERA5 data, particularly pre-1979 and in areas where ERA5 has fewer input observations.
* Thermal Trace is suitable for visualising long-term trends in thermal stress, reproducing known trends in different locations. Its yearly summary statistics time series are particularly useful for visualising the number of days with certain heat/cold stress levels and tropical nights, important variables for health and climate policy. Users should be aware of known biases and temporal inconsistencies in ERA5.
```


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 > Methodology
---
Thermal Trace is an interactive application designed for a broad audience to visualise global feels-like temperature and UTCI.
It displays daily, monthly, seasonal, and yearly maps along with time series, yearly statistics, and climate stripes
(Figures {numref}`{number} <application_thermaltrace_example_map>`–{numref}`{number} <application_thermaltrace_example_timeseries>`).

Thermal Trace is based on the [_Thermal comfort indices derived from ERA5 reanalysis_](https://doi.org/10.24381/cds.553b7518) ([derived-utci-historical](https://doi.org/10.24381/cds.553b7518); ERA5-HEAT) dataset [[Di Napoli+21a](https://doi.org/10.1002/gdj3.102)],
which is derived from the ERA5 reanalysis [[Hersbach+20](https://doi.org/10.1002/qj.3803)].
Data are available from 1940 up to near real-time (typical delay of about two days),
including preliminary data from ERA5T,
on a regular 0.25° × 0.25° grid, approximately 30 km × 30 km.
For some variables,
data can be displayed not only as absolute values but also as anomalies relative to the 1991–2020 average,
the standard reference period used by the World Meteorological Organization (WMO).

Within the map and time series views,
UTCI values are displayed both quantitatively and categorically.
The stress categories and colours used in Thermal Trace are as follows [[Di Napoli+19](https://doi.org/10.1175/JAMC-D-18-0246.1)]:

      Stress category
      UTCI range [°C]

      Extreme cold stress
      Lower than –40

      Very strong cold stress
      –40 to –27

      Strong cold stress
      –27 to –13

      Moderate cold stress
      –13 to 0

      Slight cold stress
      0 to 9

      None
      9 to 26

      Moderate heat stress
      26 to 32

      Strong heat stress
      32 to 38

      Very strong heat stress
      38 to 46

      Extreme heat stress
      Higher than 46


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 > Methodology
---
="background-color:#993333; color:white;">
      Extreme heat stress
      Higher than 46

Outputs from Thermal Trace can be exported in four ways:
1. Screenshot of the map view (Figure {numref}`{number} <application_thermaltrace_example_map>`)
2. Time series exports (Figure {numref}`{number} <application_thermaltrace_example_timeseries>`)
3. Time series data download
4. Linking directly to a specific view in the application by copying the current URL, e.g. [daily peak heat stress in Reading, UK, on 20 August 2025](https://thermaltrace.climate.copernicus.eu/?agg=daily&anomaly=false&date=2025-08-20&heat=true&lat=51.46&lng=-0.97&variable=utci_daily_max_cat).

It is not possible to export the map view or download the corresponding data
(global for one time stamp)
directly from Thermal Trace.
Instead, this can be done from the [ERA5-HEAT CDS catalogue entry](https://doi.org/10.24381/cds.553b7518).

::::{tab-set}
:::{tab-item} Map view screenshots
:sync: example_map
 attachment:application_thermaltrace_example_map.png
---
name: application_thermaltrace_example_map
---
Example screenshots of the Thermal Trace map view, from top to bottom:
a) Daily peak heat stress on 20 August 2025, centered on Reading, UK (app view).
b) Number of days with at least strong cold stress in December 2025–February 2026, centered on Reading, UK (app view).
```
:::
:::{tab-item} Time series exports
:sync: example_timeseries
 attachment:application_thermaltrace_example_timeseries.png
---
name: application_thermaltrace_example_timeseries
---
Example time series exports from the Thermal Trace, from top to bottom:
a) Hourly temperature and UTCI (feels-like temperature) with heat stress categories on 18–22 August 2025, in Reading, UK (<a href="https://thermaltrace.climate.copernicus.eu/?agg=daily&anomaly=false&date=2025-08-20&heat=true&lat=51.46&lng=-0.97&variable=utci_daily_max_cat" target="_blank">app view</a>).
b) Yearly number of days with a given heat stress level, and yearly number of tropical nights, in Reading, UK (<a href="https://thermaltrace.climate.copernicus.eu/?agg=yearly&anomaly=false&date=2025-08-20&heat=true&lat=51.46&lng=-0.97&variable=utci_yearly_max_cat" target="_blank">app view</a>).
```
:::
::::

Applications are quality assessed separately from their underpinning datasets. For information on quality attributes of the underlying datasets, the reader is referred to the quality assessment of the datasets, which can be found in their respective CDS catalogue entries or [on the EQC hub](../intro).

The analysis and results are organised in the following use cases, which are detailed in the sections below:

**[](section-events)**
* Introduction
* Assessment of Thermal Trace
* Example: Southeastern Europe June 2024 Heatwave
* Example: Northwestern Europe January 1963 Cold snap

**[](section-trends)**
* Introduction
* Assessment of Thermal Trace
* Examples


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
---
This quality assessment evaluates the suitability of Thermal Trace for use by broad audiences to visualise heat and cold stress,
both long-term trends and specific extreme events (heatwaves and cold snaps).
Thermal Trace was assessed based on the availability of relevant data variables, granularity of the data, spatial and temporal coverage and resolution, and the timeliness of data updates.
The assessment is illustrated with practical examples of how these events and trends appear in Thermal Trace in real-world scenarios.

(section-events)=


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 > Introduction
---
There is no universal definition of a [heatwave](https://climate.copernicus.eu/heatwaves-brief-introduction),
with different organisations and countries having established their own criteria.
However, we can generally define them as a period of time, from a few days up to a few weeks,
of temperatures significantly higher than the normal range for that location at that time of year
[[Lhotka+22](https://doi.org/10.1029/2022EA002567), [Stefanon+12](https://doi.org/10.1088/1748-9326/7/1/014023)].
For example, the [European State of the Climate 2023](https://climate.copernicus.eu/heatwaves-brief-introduction) used the definition
"a period of at least three consecutive days when both the daily surface air temperature minima and maxima are higher than the highest 5% of values for the day in question during the 1991–2020 reference period".
Analogously,
we can define cold snaps
(also known as cold waves, cold spells, and cold air outbreaks)
as a period of time with temperatures significantly lower than typical.

Due to their significant economic and health impacts, as described above, these extreme events often generate significant news coverage.
For example,
the February–March 2018 cold snap in the UK and Ireland was widely reported on in the popular press under the nickname "Beast from the East",
including real-time reporting of its death toll and economic damages [[Morris+18](https://www.theguardian.com/uk-news/2018/mar/02/death-toll-reaches-10-as-destructive-weather-batters-uk)].
The June 2024 Eastern Mediterranean heatwave led to similar real-time reporting of its increasing death toll,
as well as journalistic investigations into the overall impact and the drivers of this extreme event
[[Bali+24](https://www.dw.com/en/fatal-heat-wave-sweeps-greece-claims-more-lives/a-69485803), [World Weather Attribution+24](https://www.worldweatherattribution.org/deadly-mediterranean-heatwave-would-not-have-occurred-without-human-induced-climate-change/)].


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 > Assessment of Thermal Trace
---
Thermal Trace is determined to be suitable for visualising the impacts of heatwaves and cold snaps, depending on the date and location of the event, for the following reasons:
* **Variables:** Thermal Trace displays multiple useful variables for visualising and characterising extreme heat and cold events, most importantly hourly (time series) or daily (map) UTCI and the derived heat/cold stress categories, as well as the number of days with _x_ stress category and tropical nights per month or season. Using the "display extra variables" option also provides stress indicators based on the opposite UTCI limit (i.e. minimum UTCI for heat, maximum for cold), which is useful for determining the amount of relief available, e.g. during a heatwave, are the nights cold enough for the body to rest? Note that Thermal Trace does not provide physical surface air temperatures, but users can view these in the [Climate Pulse application](https://apps.climate.copernicus.eu/overview?app=climate-pulse), for which there is [an equivalent quality assessment](./application_climate-pulse_extreme-events_q01).
* **Spatial Coverage and Resolution:** Thermal Trace provides global coverage (coverage over the oceans can be toggled in the options) at a resolution (0.25° × 0.25°) suitable for showing regional, national, and multi-country events. Time series are available at the individual grid cell level. The spatial grid is very clearly indicated within the application, e.g. when a specific location is selected. 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).
* **Temporal Coverage and Resolution:** Data are available on an hourly to yearly basis, making it possible to characterise temperatures over the course of a heatwave or cold snap including daily variations (e.g. tropical nights), as well as comparisons to the rest of the year and the overall climatology of a site. Coverage extends back to 1940, providing a long record for comparison and enabling visualisation of historical events such as the 1963 European cold snap. Note that the accuracy and uncertainty of ERA5, the data underpinning this application, vary over time [[Soci+24](https://doi.org/10.1002/qj.4803), [Hersbach+20](https://doi.org/10.1002/qj.3803)].
* **Timeliness:** Data become available in Thermal Trace on a delay of a few days (typically ~5), based on the update schedule of the ERA5-HEAT dataset. This enables monitoring of an ongoing event in near real-time, but not true real-time. For real-time and near-future UTCI, users may be interested in [ECMWF's experimental UTCI forecast](https://charts.ecmwf.int/products/medium-thermofeel).
* **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 > 1. Extreme events > Example: Southeastern Europe June 2024 Heatwave
---
Between 3 and 27 June 2024,
several countries in Southeastern Europe and part of the Middle East were affected by a heatwave,
with the most extreme temperatures occurring between 11 and 14 June [[Dafis+24](https://www.climameter.org/20240611-14-june-eastern-mediterranean-heatwave)].

In Thermal Trace,
this heatwave is clearly visible in the daily maximum UTCI anomaly,
with UTCI values over 10 °C warmer than usual,
and peak heat stress
(Figure {numref}`{number} <application_thermaltrace_heatwave_maps_over_time>`).
These daily figures also show a clear progression from west to east over time,
particularly in North Africa.
Over the whole month of June 2024,
most of the region experienced strong or very strong (Southeastern Europe and Türkiye) or extreme (North Africa and Middle East) heat stress at least once
(Figure {numref}`{number} <application_thermaltrace_heatwave_maps_june_summary>`).
The number of days with at least strong heat stress (max UTCI over 32 °C) and tropical nights (min UTCI over 20 °C) in June 2024 was
much (>15 days) higher than normal.
Tropical nights are particularly impactful to human health as they prevent the body from recovering after a hot day [[Royé+21](https://doi.org/10.1097/EDE.0000000000001359)].

In addition to the map view,
the time series exports can be used to monitor the progression of the heatwave in a particular location or a custom area.
For example,
the exported time series in Figure {numref}`{number} <application_thermaltrace_heatwave_timeseries>`
clearly display the daily cycle of temperature with 12 hours of strong to very strong heat stress in Athens, Greece during the peak of the heatwave.
The seasonal timeseries in Figure {numref}`{number} <application_thermaltrace_heatwave_timeseries>`
show that June 2024 was exceptionally warm in this region compared to the typical climatology,
particularly in Cyprus where the rest of the summer season followed the normal patterns more closely.
While humans can adapt physically and behaviourally to a gradual seasonal rise in temperature,
an intense heatwave like in June 2024 can increase heat stress levels much faster than usual
(e.g. very strong heat stress in early June when one would expect only moderate),
overwhelming this ability and posing serious health risks [[Di Napoli+21b](https://doi.org/10.1007/978-3-030-76716-7_10)].


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: Southeastern Europe June 2024 Heatwave
---
+21b](https://doi.org/10.1007/978-3-030-76716-7_10)].

::::{tab-set}
:::{tab-item} Daily maps
:sync: heat_dailymaps
 attachment:application_thermaltrace_heatwave_maps_over_time.png
---
name: application_thermaltrace_heatwave_maps_over_time
---
Screenshots of Thermal Trace showing:
a) Anomaly in daily maximum UTCI (top; app view).
b) Daily peak heat stress category (bottom; app view).
over the 11 to 14 (left to right) June 2024 peak heatwave period.
This figure is comparable to Figure {numref}`{number} <application_climate-pulse_heatwave_regional>` in the quality assessment for Climate Pulse.
```
:::
:::{tab-item} Monthly maps
:sync: heat_monthlymaps
 attachment:application_thermaltrace_heatwave_maps_june_summary.png
---
name: application_thermaltrace_heatwave_maps_june_summary
---
Screenshots of Thermal Trace showing:
a) Monthly peak heat stress category (top left; <a href="https://thermaltrace.climate.copernicus.eu/?agg=monthly&anomaly=false&date=2024-06-11&heat=true&lat=35.30&lng=25.16&variable=utci_monthly_max_cat" target="_blank">app view</a>).
b) Anomaly in number of tropical nights (top right; <a href="https://thermaltrace.climate.copernicus.eu/?agg=monthly&anomaly=true&date=2024-06-11&heat=true&lat=35.30&lng=25.16&variable=count_tropical_nights" target="_blank">app view</a>).
c) Number of days with at least strong heat stress (bottom left; <a href="https://thermaltrace.climate.copernicus.eu/?agg=monthly&anomaly=false&date=2024-06-11&heat=true&lat=35.30&lng=25.16&variable=utci_days_above_32_daily_max" target="_blank">app view</a>).
d) Anomaly in days with at least strong heat stress (bottom right; <a href="https://thermaltrace.climate.copernicus.eu/?agg=monthly&anomaly=true&date=2024-06-11&heat=true&lat=35.30&lng=25.16&variable=utci_days_above_32_daily_max" target="_blank">app view</a>).
over all of June 2024.
```
:::
:::{tab-item} Time series
:sync: heat_timeseries
 attachment:application_thermaltrace_heatwave_timeseries.png
---
name: application_thermaltrace_heatwave_timeseries
---
Time series, from top to bottom:
a) Hourly temperature (dashed) and UTCI (solid) in Athens, Greece during the peak of the 2024 heatwave (app view).
b) Daily minimum/maximum (blue/red) UTCI over the 2024 summer season (June–August; solid) and average climatology (1991–2020; transparent)
in Athens, Greece (app view).
c) Same as b), in Nicosia, Cyprus (app view).
```
:::
::::