EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices for the reinsurance sector > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.3390/atmos11020146) Tesselaar, M., Botzen, W.J.W., Aerts, J.C.J.H. (2020). Impacts of Climate Change and Remote Natural Catastrophes on EU Flood Insurance Markets: An Analysis of Soft and Hard Reinsurance Markets for Flood Coverage. Atmosphere 2020, 11, 146. https://doi.org/10.3390/atmos11020146

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

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

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

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

[[6]](https://doi.org/10.5194/esd-13-321-2022) Cos, J., Doblas-Reyes, F., Jury, M., Marcos, R., Bretonnière, P.-A., and Samsó, M. (2022). The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections, Earth Syst. Dynam., 13, 321–340. https://doi.org/10.5194/esd-13-321-2022


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector
---
Production date: 10-07-2024

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


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Quality assessment question
---
* **What are the projected changes for a global warming level of 2°C and associated uncertainties in air temperature extremes in Europe?**

Climate change has a major impact on the reinsurance market [[1]](https://doi.org/10.3390/atmos11020146)[[2]](https://doi.org/10.5194/nhess-22-659-2022). In the third assessment report of the IPCC, hot temperature extremes were already presented as relevant to insurance and related services [[3]](https://www.ipcc.ch/site/assets/uploads/2018/03/wg2TARchap8.pdf). Consequently, the need for reliable regional and global climate projections has become paramount, offering valuable insights for optimising reinsurance strategies in the face of a changing climate landscape. Nonetheless, despite their pivotal role, uncertainties inherent in these projections can potentially lead to misuse [[4]](https://doi.org/10.1002/wcc.71)[[5]](https://doi.org/10.1002/wcc.579). This underscores the importance of accurately calculating and accounting for uncertainties to ensure their appropriate consideration. This notebook utilises data from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview) Global Climate Models (GCM) and explores the projected changes and uncertainties in future projections of maximum temperature-based extreme indices by considering the ensemble inter-model spread of a subset of CMIP6 models at a global mean warming level of 2°C. Two maximum temperature-based indices from [ECA&D](https://www.ecad.eu/indicesextremes/) indices (one of physical nature and the other of statistical nature) are computed using the [icclim](https://icclim.readthedocs.io/en/stable/) Python package. The first index, identified by the ETCCDI short name 'SU', quantifies the occurrence of summer days (i.e., with daily maximum temperatures exceeding 25°C) within a year or a season (JJA in this notebook). The second index, labeled 'TX90p', describes the number of days with daily maximum temperatures exceeding the daily 90th percentile of maximum temperature for a 5-day moving window. In this notebook, these calculations are performed over the historical period from 1971 to 2000 and compared to the global warming level of 2°C. The global warming level of 2°C can be defined as the first time the 30-year moving average (centre year) of global temperature is above 2°C compared to pre-industrial (Grigory Nikulin et al. (2018) [[6]](https://doi.org/10.1088/1748-9326/aab1b1)). The preindustrial period is defined here as the period spanning from 1861 to 1890 and the index calculations are performed for the Shared Socioeconomic Pathways SSP5-8.5. It is important to mention that the results presented here pertain to a specific subset of the CMIP6 ensemble and may not be generalisable to the entire dataset. Also note that a separate assessment examines the representation of climatology and trends of these indices for the same models during the historical period (1971-2000), while another assessment looks at the projected trends of these indices for the same models during a fixed future period (2015-2099).


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Quality assessment statement
---
These are the key outcomes of this assessment

* For the CMIP6 models analysed, there is clear agreement on the sign of the climate signal for the selected indices during the JJA season, underscoring the need for proactive measures to optimise reinsurance protections in the face of projected increases in air temperature extremes for a world 2°C warmer. The advantage of using Global Warming levels (GWL of 2°C for this notebook), as opposed to considering the trend for a fixed future period, is that it eliminates the systematic biases of the models by focusing on differences rather than absolute values. However, it is important to note that the timing of the thirty-year period when the global mean temperature reaches 2°C above the preindustrial baseline varies depending on the model, making it challenging to determine.

* The considered subset of CMIP6 models shows that, in a world 2°C warmer than the preindustrial baseline, the average number of days with maximum daily temperatures surpassing the daily 90th percentile (calculated for the historical period) during the JJA season exhibits spatial differences across Europe. Notably, values are higher for the Mediterranean Basin, consistent with the findings of Josep Cos et al. (2022) [[7]](https://doi.org/10.5194/esd-13-321-2022), who assessed the Mediterranean climate change hotspot using CMIP6 projections. The boxplot analysis for this index displays an ensemble median spatially-averaged value over Europe larger than 32 days for the JJA season, indicating that under a 2°C global warming level, one out of every three days is projected to have a maximum daily temperature above the daily 90th percentile calculated for the historical period.

* The frequency of summer days occurring during the JJA season, is projected to generally increase in a world 2°C warmer than the preindustrial baseline (1861-1890), compared to the average occurrences during the control period (1971-2000) across Europe, albeit with regional variations. Minor changes are expected in the northernmost and southern regions. In the north, where temperatures historically remain below the 25°C threshold even during the JJA season, the increase in the number of summer days is minimal (the threshold temperature of 25°C may be too high to be reached even in a world 2°C warmer than the preindustrial). In the southern Mediterranean Basin, the threshold is usually surpassed throughout the entire JJA season in the historical period, and thus, little change is observed (specially in northern Africa). This arise limitations of this index, indicating the potential need to select a higher threshold to capture the changes in these regions. Such regions, where the changes may be just as impactful or even more so than areas with a significant increase in days above 25 degrees, require careful consideration.

* These findings emphasise the importance of integrating both statistical and physical extreme indices for a comprehensive assessment of climate impacts, particularly when developing adaptive strategies such as reinsurance protections. While percentile-based indices may better capture changes across all regions, they may pose challenges for users who are more used to fixed thresholds.
```

attachment:ffd3cf41-b222-4773-9815-fb507773368c.png
---
alt: GWL_TX90p
width: 700px
---
Boxplot illustrating the climate signal (i.e., the mean values for the warming level of 2°C compared to the historical period from 1971 to 2000) for the ensemble distribution of the 'TX90p' index. For this index, the daily 90th percentile threshold is calculated based on the historical period (1971-2000). The distribution is created by considering spatially averaged values across Europe. The ensemble mean and the ensemble median are both included. Outliers in the distribution are denoted by a grey circle with a black contour.
```


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Methodology
---
This notebook provides an assessment of the projected changes and their associated uncertainties, utilising a subset of 16 models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview) under a global warming level of 2°C. The uncertainty is explored by analysing the ensemble inter-model spread of projected changes for the maximum-temperature-based indices 'SU' and 'TX90p,' calculated over the temporal aggregation of JJA for the specific global warming level of 2°C. In particular, spatial patterns of the climate signal (i.e., mean values of the indices for the warming level of 2°C compared to the historical period from 1971 to 2000) are examined and displayed for each model individually and for the ensemble median (calculated for each grid cell), alongside the ensemble inter-model spread to account for projected uncertainty. Additionally, spatially-averaged values are analysed and presented using box plots to provide an overview of climate signal behavior across the distribution of the chosen subset of models when averaged across Europe.

The analysis and results follow the next outline:

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

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

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


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.2. Define Parameters
---
In the "Define Parameters" section, various customisable options for the notebook are specified. Most of the parameters chosen are the same as those used in other assessments ("CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector"), being them:

- `historical_slice` determines the historical (or control) period used (1971 to 2000 to be consistent with other user question of the same use case)
- The `timeseries` set the temporal aggregation. For instance, selecting "JJA" implies considering only the JJA season.
- The `index_names` parameter enables the selection of maximum-temperature-based indices ('SU' and 'TX90p' in our case) from the [icclim](https://icclim.readthedocs.io/en/stable/) Python package.
- `collection_id` sets the family of models. Only CMIP6 is implemented for this notebook.
- `area` allows specifying the geographical domain of interest.
- The `interpolation_method` parameter allows selecting the interpolation method when regridding is performed over the indices.
- The `chunk` selection allows the user to define if dividing into chunks when downloading the data on their local machine. Although it does not significantly affect the analysis, it is recommended to keep the default value for optimal performance.

Choose annual or seasonal timeseries
Select the family of models
Define region for analysis
Define region for request
Define index names
Interpolation method
Chunks for download

(section-1.3)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.3. Define models
---
The following climate analyses are performed considering a subset of GCMs from CMIP6. Models names are listed in the parameters below. The selected CMIP6 models have available both the historical and SSP8.5 experiments, and are the same as those used in another assessment ("CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector"). Additionally, for each model, the thirty-year period corresponding to the Global Warming Level of 2°C above the preindustrial period is specified.

The Global Warming Level of 2°C can be defined as the earliest point at which a 30-year moving average of global temperature exceeds by two degrees compared to the 1861–1890 baseline [[6]](https://doi.org/10.1088/1748-9326/aab1b1). This thirty-year period varies depending on the Global Climate Model being analysed. To determine this timeframe, the global mean surface temperature for the preindustrial baseline is first calculated for each model. Then, a rolling mean over 30 years is applied to the future period from 2015 to 2099, following the SSP5-8.5 scenario. Finally, the earliest 30-year period at which each model reaches the global warming level of 2°C above the preindustrial period is identified.

NOTE on the 30-year slice used for Global Warming Level calculations:

Given the 30-year duration of the historical/control period, the period of mean global surface temperature reaching 2 degrees above preindustrial levels was defined as a 30-year interval (while some studies use 20-year slices for this purpose). The pre-industrial period was also calculated as a 30-year slice (as done in  [6]), despite the standard practice of considering the period from 1850 to 1900. This approach not only saves computational time but also ensures consistency in working with 30-year slices.

NOTE on the Global Warming Level calculation:

To streamline the current notebook and avoid excessive length and complexity, the calculation of the Global Warming Level of 2°C above the preindustrial period for each model has been conducted externally.

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

(section-1.4)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.4. Define land-sea mask request
---
Within this notebook, ERA5 will be used to download the land-sea mask when plotting. In this section, we set the required parameters for the cds-api data-request of ERA5 land-sea mask.

(section-1.5)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.5. Define model requests
---
In this section we set the required parameters for the cds-api data-request.

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

(section-1.6)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 1. Parameters, requests and functions definition > 1.6. Functions to cache
---
In this section, functions that will be executed in the caching phase are defined. Caching is the process of storing copies of files in a temporary storage location, so that they can be accessed more quickly. This process also checks if the user has already downloaded a file, avoiding redundant downloads.

Functions description:

- The `select_timeseries` function subsets the dataset based on the chosen `timeseries` parameter, which could be a specific season (e.g., "JJA") or "annual." It tailors the dataset according to the specified criteria, providing either annual or seasonal data as the outcome.

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

- Finally, the `compute_indices_and_trends` function selects the temporal aggregation using the `select_timeseries` function. It then calculates maximum-temperature-based indices via the `compute_indices` function and determines the indices mean over the period of interest.

Original bounds for conservative interpolation

(section-2)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.1. Download and transform the regridding model
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download daily data from the selected CMIP6 regridding model, compute the indices for the selected temporal aggregation ("JJA" in our example), calculate the mean for the historical period (1971-2000) and for the thirty-year period corresponding to the 2°C Global Warming Level, and cache the result (to avoid redundant downloads and processing).

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

NOTE on the Global Warming Level calculation:

The thirty year-period corresponding to the 2°C Global Warming Level is different for each model. Its calculation has been conducted externally.

(section-2.2)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.2. Download and transform models
---
In this section, we utilise the `download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package to download daily data from the CMIP6 models for the historical period (1971-2000) and for the thirty-year period corresponding to the 2°C warming level of each model. Subsequently, temporal aggregation is selected (in our example, 'JJA'). The next step involves calculating the indices and the climate signal. For the 'SU' index, as we are dealing with an index calculated using a physical threshold, the climate signal is obtained as the difference between the mean values for the thirty-year period corresponding to the 2°C Global Warming Level and the mean values calculated over the historical period (1971-2000). The climate signal for the 'TX90p' index, as we are dealing with an index based on a statistical threshold, is obtained by calculating - for the thirty-year period corresponding to the 2°C warming level - the mean number of days with daily maximum temperatures exceeding the daily 90th percentile. The daily 90th percentile threshold is calculated based on the historical period of 1971-2000.

NOTE on the Global Warming Level calculation:

The thirty year-period corresponding to the 2°C Global Warming Level is different for each model. Its calculation has been conducted externally.

Original model
Interpolated model

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

(section-2.3)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 2. Downloading and processing > 2.3. Apply land-sea mask, change attributes and cut the region to show
---
This section performs the following tasks:

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

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

Edit attributes

(section-3)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 3. Plot and describe results
---
This section will display the following results:

- Maps representing the spatial distribution of the climate change signal (i.e., mean values for the warming level of 2°C compared to the historical period 1971-2000) of the indices 'SU' and 'TX90p' for each model individually, the ensemble median (understood as the median of the climate signal values of the chosen subset of models calculated for each grid cell), and the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

- Boxplots representing statistical distributions (PDFs) constructed from the spatially-averaged climate signal of each considered model.

(section-3.1)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.1. Define plotting functions
---
The functions presented here are used to plot the climate signal (i.e., the mean values for the warming level of 2°C compared to the historical period) for each of the indices ('SU' and 'TX90p').

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

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

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

(section-3.2)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.2. Plot ensemble maps
---
In this section, we invoke the `plot_ensemble()` function to visualise the climate signal (i.e., the mean values for the warming level of 2°C compared to the historical period) across Europe for each of the indices ('SU' and 'TX90p'). The layout includes: (a) the ensemble median (understood as the median of the climate signal values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

Note that the model data used in this section has previously been interpolated to the "regridding model" grid (`"gfdl_esm4"` for this notebook).

Fig number counter
Common title

(section-3.3)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.3. Plot model maps
---
In this section, we invoke the `plot_models()` function to visualise the climate signal for every model individually across Europe. Note that the model data used in this section maintains its original grid.

Specifically, for each of the indices ('SU' and 'TX90p'), this section presents a single layout including the climate signal of every model.

(section-3.4)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.4. Boxplots of the climate change signal
---
Finally, we present boxplots representing the ensemble distribution of each climate model signal for the 2°C global warming level.

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

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

Ensemble mean

Fig 5. Boxplots illustrating the climate signal (i.e., mean values for the warming level of 2°C compared to the historical period from 1971 to 2000) of the distribution of the chosen subset of models for: (a) the 'SU' index, and (b) the 'TX90p' index. The distribution is created by considering spatially averaged values across Europe. The ensemble mean and the ensemble median are both included. Outliers in the distribution are denoted by a grey circle with a black contour.

(section-3.5)=


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > Analysis and results > 3. Plot and describe results > 3.5. Results summary and discussion
---
- The frequency of mean summer days occurring during the JJA season, defined as those with a daily maximum temperature exceeding 25°C, is projected to generally increase in a world 2°C warmer than the preindustrial baseline (1861-1890), compared to the average occurrences during the control period (1971-2000) across Europe, albeit with regional variations. Minor changes are expected in the northernmost and southern regions. In the north, where temperatures historically remain below the 25°C threshold even during the JJA season, the increase in the number of summer days is minimal (the threshold temperature of 25°C may be too high to be reached even in a world 2°C warmer than the preindustrial). In the southern Mediterranean Basin, the threshold is already surpassed throughout the entire JJA season in the historical period, and thus, no change is observed. This underscores the importance of incorporating both statistical and physical extreme indices for a comprehensive assessment of climate impacts.

- The ensemble median spatially-averaged value shown through the boxplot indicates that with a 2°C global warming, the count of summer days ('SU') is projected to rise by approximately 10 days compared to the historical average for the JJA season. The interquartile range for the 'SU' index extends from around 6 to almost 11 days.

- In a world 2°C warmer than the preindustrial baseline, the average number of days with maximum daily temperatures surpassing the reference 90th percentile (calculated for the historical period) during the JJA season exhibits spatial differences across Europe. Notably, values are higher for the Mediterranean Basin, consistent with the findings of Josep Cos et al. (2022) [[7]](https://doi.org/10.5194/esd-13-321-2022), who assessed the Mediterranean climate change hotspot using CMIP6 projections. The boxplot analysis for this index displays an ensemble median spatially-averaged value over Europe larger than 32 days for the JJA season, indicating that under a 2°C global warming level, one out of every three days will have a maximum daily temperature above the 90th percentile reference calculated for the historical period. The interquartile range spans approximately 28 to 33 days for the 'TX90p' index.

- It is important to highlight that the boxplots reflect spatially-averaged values, and their outputs should be interpreted with caution, as the results may vary substantially when considering different regions across Europe.

- What do the results mean for users?  Are the biases from the historical notebook ("CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector") relevant? What are the differences compared to looking at the trend for a fixed future period?
    - For the CMIP6 models analysed, there is clear agreement on the sign of the projected climate signal, defined as the differences between the mean values of the indices calculated for a centered thirty-year period when the global mean temperature is 2°C higher than the preindustrial baseline and the mean values for the historical period from 1971 to 2000. This agreement suggests that it may be safe to include this information when designing strategies to optimise reinsurance protections in a world 2°C warmer than the preindustrial baseline.

-  The advantage of using this approach, instead of considering the trend for a fixed future period, is that some of the systematic biases of the models may be removed [[7]](https://doi.org/10.5194/esd-13-321-2022), as we are dealing with differences rather than absolute values. However, it is important to note that the centered thirty-year period when the global mean temperature reaches 2°C above the preindustrial baseline varies depending on the model considered, and determining this timing can be challenging.

RESULTS NOTE:

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


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

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

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


EQC Quality Assessment: "Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector"
Dataset: projections-cmip6 [CDS]
Aspect: climate-and-weather-extremes_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes in extreme temperature indices at a 2°C Global Warming Level for the reinsurance sector > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.3390/atmos11020146) Tesselaar, M., Botzen, W.J.W., Aerts, J.C.J.H. (2020). Impacts of Climate Change and Remote Natural Catastrophes on EU Flood Insurance Markets: An Analysis of Soft and Hard Reinsurance Markets for Flood Coverage. Atmosphere 2020, 11, 146. https://doi.org/10.3390/atmos11020146

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

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

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

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

[[6]](https://doi.org/10.1088/1748-9326/aab1b1) Nikulin, G., Lennard, C., Dosio, A., Kjellström, E., Chen, Y., Hänsler, A., Kupiainen, M., Laprise, R., Laura Mariotti, L., Maule C.F., et al. (2018). The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble. Environ. Res. Lett. 13 065003. https://doi.org/10.1088/1748-9326/aab1b1

[[7]](https://doi.org/10.5194/esd-13-321-2022) Cos, J., Doblas-Reyes, F., Jury, M., Marcos, R., Bretonnière, P.-A., and Samsó, M. (2022). The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections, Earth Syst. Dynam., 13, 321–340. https://doi.org/10.5194/esd-13-321-2022


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe
---
Production date: 22-05-2024

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


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Quality assessment question
---
* **How well do the CMIP6 projections represent Energy Degree Days climatology and trend over a historic period?**

Sectors affected by climate change are varied including agriculture [[1]](https://doi.org/10.1007/s10113-010-0173-x), forest ecosystems [[2]](https://doi.org/10.1016/j.foreco.2009.09.023), and energy consumption [[3]](https://doi.org/10.1016/j.enbuild.2014.09.052). Under projected future global warming over Europe [[4]](https://doi.org/10.1007/s10113-013-0499-2)[[5]](http://hdl.handle.net/10013/epic.45156.d001), the current increase in energy demand is expected to persist until the end of this century and beyond [[6]](https://doi.org/10.1002/joc.5362). Identifying which climate-change-related impacts are likely to increase, by how much, and inherent regional patterns, is important for any effective strategy for managing future climate risks. This notebook utilises data from a subset of models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview) Global Climate Models (GCMs) and compares them with [ERA5](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-complete?tab=doc) reanalysis, serving as the reference product. Two energy-consumption-related indices are calculated from daily mean temperatures using the [icclim](https://icclim.readthedocs.io/en/stable/) Python package: Cooling Degree Days (CDDs) and Heating Degree Days (HDDs). Degree days measure how much warmer or colder it is compared to standard temperatures (usually 15.5°C for heating and 22°C for cooling). Higher degree day numbers indicate more extreme temperatures, which typically lead to increased energy use for heating or cooling buildings. In the presented code, CDD calculations use summer aggregation (CDD22), while HDD calculations focus on winter (HDD15.5), presenting results as daily averages rather than cumulative values. Within this notebook, these calculations are performed over the historical period spanning from 1971 to 2000. It is important to note that the results presented here pertain to a specific subset of the CMIP6 ensemble and may not be generalisable to the entire dataset. Also note that a separate assessment examines the representation of trends of these indices for the same models during a fixed future period (2015-2099), while another assessment looks at the projected climate signal of these indices for the same models at a 2°C Global Warming Level.


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Quality assessment statement
---
These are the key outcomes of this assessment

* CMIP6 projections provide valuable insights into trends and climatology of Energy Degree Days across Europe, though with inherent uncertainties, and differences compared to the reference dataset (ERA5). During the historical period (1971-2000), GCMs exhibited biases in capturing both mean values and trends for energy-consumption-related indices.

* For the 16 models considered, biases were observed in mean values of Heating Degree Days ('HDD15.5') over DJF and Cooling Degree Days ('CDD22') over JJA. Specifically, for most of the models, there is an overestimation of HDD15.5 mean values for DJF (except in regions with complex orography where there was underestimation) and CDD22 mean values for JJA across most of the Mediterranean Basin.

* The subset of CMIP6 models show a decrease in the energy needed for heating during winter within the historical period across Europe, especially in the eastern and northern regions. This aligns with results obtained using the ERA5 reference product, except in the Balkans where there is an increase in required heating energy during this period. The ERA5 reference product also shows an increase of the amount of energy needed for cooling buildings in summer during this period, particularly across the Mediterranean Basin. This trend is well captured by the subset of CMIP6 models. Depending on the region, these results may enhance confidence in using these models for analysing future trends, although they do not guarantee accuracy.

* Despite the biases and high inter-model spread in some regions, the outcomes of this notebook offer valuable insights for decisions sensitive to future energy demand. These results may enhance confidence in using these models to analyse future trends, although their accuracy is not assured. To improve the accuracy of these insights, biases should be considered and corrected [[6]](https://doi.org/10.1002/joc.5362)[[7]](https://doi.org/10.1038/s41467-021-25504-8).
```

attachment:83f3529b-f7ec-456a-aab1-9dee68ba895b.png
---
alt: Mean Bias HDD
width: 900px
---
Heating Degree Days daily average calculated using the winter comfort threshold of 15.5°C ('HDD15.5') for the temporal aggreggtion of 'DJF'. Mean bias for the historical period (1971 - 2000) of each individual CMIP6 model. The colorbar chosen for representing biases for the HDD15.5 index ranges from red to blue. In this color scheme, negative bias values (red colors) indicate that the Heating Degree Days displayed by the models are lower than those shown by ERA5. Instead, blueish colors denote more Heating Degree Days, indicating a positive bias compared to ERA5. This selection is based on the rationale that more Heating Degree Days are associated with colder conditions, typically represented by blueish colors, while fewer Heating Degree Days imply warmer conditions, depicted by reddish colors.
```


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Methodology
---
The reference methodology used here for the indices calculation is similar to the one followed by Scoccimarro et al., (2023) [[8]](https://doi.org/10.1038/s43247-023-00878-3). However the thermal comfort thresholds used in this notebook are slightly different. A winter comfort temperature of 15.5°C and a summer comfort temperature of 22.0°C are used here (as in the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf)). In the presented code, the CDD calculations are based on the JJA aggregation, with a comfort temperature of 22°C (CDD22), while HDD calculations focus on winter (DJF) with a comfort temperature of 15.5°C (HDD15.5). More specifically, to calculate CDD22, the sum of the differences between the daily mean temperature and the thermal comfort temperature of 22°C is computed. This calculation occurs only when the mean temperature is above the thermal comfort level; otherwise, the CDD22 for that day is set to 0. For example, a day with a mean temperature of 28°C would result in 6°C. Two consecutive hot days like this would total 12°C over the two-day period. Similarly, to calculate HDD15.5, the sum of the differences between the thermal comfort temperature of 15.5°C and the daily mean temperature is determined. This happens only when the mean temperature is below the thermal comfort level; otherwise, the HDD15.5 for that day is set to 0. Finally, to obtain more intuitive values, the sum is averaged over the number of days in the season to produce daily average values. This approach differs from the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf), where both the sum over a period and the daily average values can be displayed.  In Spinoni et al., (2018) [[6]](https://doi.org/10.1002/joc.5362), as well as in the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf), more advanced methods for calculating CDD and HDD involve considering maximum, minimum, and mean temperatures. However, to prevent overloading the notebook and maintain simplicity while ensuring compatibility with the [icclim](https://icclim.readthedocs.io/en/stable/) Python package, we opted to utilise a single variable (2m mean temperature).

This notebook provides an assessment of the systematic errors (trend and climate mean) in a subset of 16 models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview). It achieves this by comparing the model predictions with the ERA5 reanalysis for the energy-consumption-related indices 'HDD15.5' and 'CDD22' for the historical period spanning from 1971 to 2000. In particular, spatial patterns of climate mean and trend, along with biases, are examined and displayed for each model and the ensemble median (calculated for each grid cell). Additionally, spatially-averaged trend values are analysed and presented using box plots to provide an overview of trend behavior across the distribution of the chosen subset of models when averaged across Europe.

The analysis and results follow the next outline:

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

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

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


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.2. Define Parameters
---
In the "Define Parameters" section, various customisable options for the notebook are specified:

- The initial and ending year used for the historical period can be specified by changing the parameters `year_start` and `year_stop` (1971-2000 is chosen for consistency between CORDEX and CMIP6).
- `index_timeseries` is a dictionary that set the temporal aggregation for every index considered within this notebook ('HDD15.5' and 'CDD22'). In the presented code, the CDD calculations are always based on the JJA aggregation, with a comfort temperature of 22°C (CDD22), while HDD calculations focus on winter (DJF) with a comfort temperature of 15.5°C (HDD15.5).
- `collection_id` sets the family of models. Only CMIP6 is implemented for this notebook.
- `area` allows specifying the geographical domain of interest.
- The `interpolation_method` parameter allows selecting the interpolation method when regridding is performed over the indices.
- The `chunk` selection allows the user to define if dividing into chunks when downloading the data on their local machine. Although it does not significantly affect the analysis, it is recommended to keep the default value for optimal performance.

Choose annual or seasonal timeseries
Interpolation method
Select the family of models
Area to show
Chunks for download

(section-1.3)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.3. Define models
---
The following climate analyses are performed considering a subset of GCMs from CMIP6. Models names are listed in the parameters below. Some variable-dependent parameters are also selected.

The selected CMIP6 models have available both experiments the historical and the SSP8.5.

Define models
Define dictionaries to use in titles and caption

(section-1.4)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.4. Define ERA5 request
---
Within this notebook, ERA5 serves as the reference product. In this section, we set the required parameters for the cds-api data-request of ERA5.

(section-1.5)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.5. Define model requests
---
In this section we set the required parameters for the cds-api data-request.

When `weights = True`, spatial weighting is applied for calculations requiring spatial data aggregation. This is particularly relevant for CMIP6 GCMs with regular lon-lat grids that do not consider varying surface extensions at different latitudes.

(section-1.6)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.6. Functions to cache
---
In this section, functions that will be executed in the caching phase are defined. Caching is the process of storing copies of files in a temporary storage location, so that they can be accessed more quickly. This process also checks if the user has already downloaded a file, avoiding redundant downloads.

Functions description:

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

- The `compute_indices` function utilises the icclim package to calculate the energy-consumption-related indices.

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

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

Original bounds for conservative interpolation

(section-2)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 2. Downloading and processing > 2.1. Download and transform ERA5
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download ERA5 reference data, obtain the daily mean temperature from hourly data, compute the energy-consumption-related indices for the selected temporal aggregation ("DJF" for HDD and "JJA" for CDD), calculate the mean and trend over the historical period (1971-2000) and cache the result (to avoid redundant downloads and processing).

(section-2.2)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 2. Downloading and processing > 2.2. Download and transform models
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download daily data from the CMIP6 models, compute the energy-consumption-related indices for the selected temporal aggregation ("DJF" for HDD and "JJA" for CDD), calculate the mean and trend over the historical period (1971-2000), interpolate to ERA5's grid (only for the cases in which it is specified, in the other cases, the original model's grid is mantained), and cache the result (to avoid redundant downloads and processing).

Original model
Interpolated model

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

(section-2.3)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 2. Downloading and processing > 2.3. Apply land-sea mask, change attributes and cut the region to show
---
This section performs the following tasks:

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

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

Edit attributes

(section-3)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 3. Plot and describe results
---
This section will display the following results:

- Maps representing the spatial distribution of the **historical mean values** (1971-2000) of the indices 'HDD15.5' and 'CDD22' for ERA5, each model individually, the ensemble median (understood as the median of the mean values of the chosen subset of models calculated for each grid cell), and the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).
- Maps representing the spatial distribution of the **historical trends** (1971-2000) of the considered indices. Similar to the first analysis, this includes ERA5, each model individually, the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell), and the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).
- **Bias maps of the historical mean values**.
- **Trend bias maps**.
- **Boxplots** representing statistical distributions (PDF) built on the spatially-averaged historical trends from each considered model, displayed together with ERA5.

(section-3.1)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 3. Plot and describe results > 3.1. Define plotting functions
---
The functions presented here are used to plot the mean values and trends calculated over the historical period (1971-2000) for each of the indices ('HDD15.5' and 'CDD22').

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

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

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

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

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

COLORBAR NOTE:

The colorbar chosen for representing biases and trends for the HDD15.5 index ranges from red to blue. In this color scheme, negative bias values (red colors) indicate that the Heating Degree Days displayed by the models are lower than those shown by ERA5. Similarly, negative trend values (red colors) indicate a decrease in Heating Degree Days over time. Instead, blueish colors denote more Heating Degree Days, either indicating a positive bias compared to ERA5 or an increase in Heating Degree Days over time. This selection is based on the rationale that more Heating Degree Days are associated with colder conditions, typically represented by blueish colors, while fewer Heating Degree Days imply warmer conditions, depicted by reddish colors.

(section-3.2)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 3. Plot and describe results > 3.2. Plot ensemble maps
---
In this section, we invoke the `plot_ensemble()` function to visualise the mean values and trends calculated over the historical period (1971-2000) for the model ensemble and ERA5 reference product across Europe. Note that the model data used in this section has previously been interpolated to the ERA5 grid.

Specifically, for each of the indices ('HDD15.5' and 'CDD22'), this section presents two layouts:

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

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

Fig number counter
Common title

(section-3.3)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 3. Plot and describe results > 3.3. Plot model maps
---
In this section, we invoke the `plot_models()` function to visualise the mean values and trends calculated over the historical period (1971-2000) for every model individually across Europe. Note that the model data used in this section maintains its original grid.

Specifically, for each of the indices ('HDD15.5' and 'CDD22'), this section presents two layouts:

1. A layout including the historical mean (1971-2000) of every model.

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

(section-3.4)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 3. Plot and describe results > 3.4. Plot bias maps
---
In this section, we invoke the `plot_models()` function to visualise the bias for the mean values and trends calculated over the historical period (1971-2000) for every model individually across Europe. Note that the model data used in this section has previously been interpolated to the ERA5 grid.

Specifically, for each of the indices ('HDD15.5' and 'CDD22'), this section presents two layouts:

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

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

(section-3.5)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 3. Plot and describe results > 3.5. Boxplots of the historical trend
---
In this last section, we compare the trends of the climate models with the reference trend from ERA5.

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

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

Ensemble mean

Fig 13. Boxplots illustrating the historical trends of the distribution of the chosen subset of models and ERA5 for the Energy Degree Days indices daily averaged: (a) 'CDD22', and (b) 'HDD15.5'. The distribution is created by considering spatially averaged trends across Europe. The ensemble mean and the ensemble median trends are both included. Outliers in the distribution are denoted by a grey circle with a black contour.

(section-3.6)=


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > Analysis and results > 3. Plot and describe results > 3.6. Results summary and discussion
---
- GCMs struggle to accurately represent the climatology of these indices in regions with complex orography, specially for the mean values of the HDD15.5 index. There is a common overestimation of HDD15.5 mean values for DJF, especially in continental and northern areas. Conversely, underestimation is evident in mountainous regions and in some western zones. For the climatological values of CDD22, there is overestimation for JJA across most of the Mediterranean Basin (except some Saharan regions).

- The ERA5 historical data shows a reduction in Heating Degree Days (HDD) for DJF across most of Europe. However, while the ensemble median of GCMs (calculated for each grid cell) captures the trend behavior for most regions, it does not accurately reflect their magnitude. Indeed, a significant inter-model spread is evident, with differences in trend direction among individual models observed in some regions. This variability is particularly pronounced in the northern and northeastern parts of Europe.

- The ERA5 historical trend shows an increase in Cooling Degree Days during the summer (JJA) across Europe, especially in the Mediterranean Basin. GCMs agree on the direction of the CDD2 trend for summer and replicate the historical trends (1971-2000) of the selected index, although they underestimate its magnitude.

- Boxplots reveal different signs for the spatial-averaged historical trend characterising the distribution of the chosen subset of models for the HDD15.5 and DJF. The CMIP6 ensemble median trend displays a daily average value around -0.15 °C/10 years, similar to ERA5's -0.15 °C/10 years. The interquantile range of the ensemble ranges from below -0.2 to near -0.05 °C per decade. When assessing the biases of the trend, it can be seen that the interquantile range of the ensemble for the trend bias goes approximately from -0.1 to 0.1 °C per decade.

- Boxplots exhibit a consistent positive spatial-averaged historical trend characterising the different models for the CDD22 for JJA. Magnitudes, however, are slightly underestimated when compared to ERA5. The CMIP6 ensemble median displays near 0.08°C/10 years, contrasting with ERA5's ~0.11 °C/10 years. The interquantile range of the ensemble ranges from 0.06 to 0.11 °C per decade. The interquantile range of the ensemble for the trend bias goes approximately from -0.05 to slightly above 0°C per decade.

- What do the results mean for users? Are the biases relevant?
    - Despite the biases and high inter-model spread in some regions, the outcomes of this notebook may offer valuable insights for decisions sensitive to future energy demand. These results, particularly for CDD22 calculated for summer, may increase confidence in using these models to analyse future trends, although their accuracy is not guaranteed. To improve the accuracy of these insights, biases should be considered and corrected [[6]](https://doi.org/10.1002/joc.5362)[[7]](https://doi.org/10.1038/s41467-021-25504-8).

RESULTS NOTE:

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


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

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

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


EQC Quality Assessment: "Biases in energy-consumption-related indices in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q01  |  Category: Climate_Projections
Section: Biases in energy-consumption-related indices in Europe > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1007/s10113-010-0173-x) Bindi, M., Olesen, J.E. The responses of agriculture in Europe to climate change (2011). Reg Environ Change 11 (Suppl 1), 151–158. https://doi.org/10.1007/s10113-010-0173-x

[[2]](https://doi.org/10.1016/j.foreco.2009.09.023) Lindner, M., Maroschek, M., Netherer, S., Kremer, A., Barbati, A., Garcia-Gonzalo, J., Seidi, R., Delzon, S., Corona, P., Kolstrom, M., Lexer, M.J., Marchetti, M. (2010). Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For. Ecol. Manage. 259(4): 698–709. https://doi.org/10.1016/j.foreco.2009.09.023

[[3]](https://doi.org/10.1016/j.enbuild.2014.09.052) Santamouris, M., Cartalis, C., Synnefa, A., Kolokotsa, D. (2015). On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings – a review. Energy Build. 98: 119–124. https://doi.org/10.1016/j.enbuild.2014.09.052

[[4]](https://doi.org/10.1007/s10113-013-0499-2) Jacob, D., Petersen, J., Eggert, B. et al. (2014). EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14, 563–578. https://doi.org/10.1007/s10113-013-0499-2

[[5]](http://hdl.handle.net/10013/epic.45156.d001) IPCC. 2014. In Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, RK Pachauri, LA Meyer (eds). IPCC: Geneva, Switzerland 151 pp.

[[6]](https://doi.org/10.1002/joc.5362) Spinoni, J., Vogt, J.V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A. and Füssel, H.-M. (2018). Changes of heating and cooling degree-days in Europe from 1981 to 2100. Int. J. Climatol, 38: e191-e208. https://doi.org/10.1002/joc.5362

[[7]](https://doi.org/10.1038/s41467-021-25504-8) Deroubaix, A., Labuhn, I., Camredon, M. et al. (2021). Large uncertainties in trends of energy demand for heating and cooling under climate change. Nat Commun 12, 5197. https://doi.org/10.1038/s41467-021-25504-8

[[8]](https://doi.org/10.1038/s43247-023-00878-3) Scoccimarro, E., Cattaneo, O., Gualdi, S. et al. (2023). Country-level energy demand for cooling has increased over the past two decades. Commun Earth Environ 4, 208. https://doi.org/10.1038/s43247-023-00878-3


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe
---
Production date: 23-05-2024

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Quality assessment question
---
* **What are the projected future changes and associated uncertainties of Energy Degree Days in Europe?**

Sectors affected by climate change are varied including agriculture [[1]](https://doi.org/10.1007/s10113-010-0173-x), forest ecosystems [[2]](https://doi.org/10.1016/j.foreco.2009.09.023), and energy consumption [[3]](https://doi.org/10.1016/j.enbuild.2014.09.052). Under projected future global warming over Europe [[4]](https://doi.org/10.1007/s10113-013-0499-2)[[5]](http://hdl.handle.net/10013/epic.45156.d001), the current increase in energy demand is expected to persist until the end of this century and beyond [[6]](https://doi.org/10.1002/joc.5362). Identifying which climate-change-related impacts are likely to increase, by how much, and inherent regional patterns, is important for any effective strategy for managing future climate risks. This notebook utilises data from a subset of models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview) Global Climate Models (GCMs) and explores the uncertainty in future projections of energy-consumption-related indices by considering the ensemble inter-model spread of projected changes. Two energy-consumption-related indices are calculated from daily mean temperatures using the [icclim](https://icclim.readthedocs.io/en/stable/) Python package: Cooling Degree Days (CDDs) and Heating Degree Days (HDDs). Degree days measure how much warmer or colder it is compared to standard temperatures (usually 15.5°C for heating and 22°C for cooling). Higher degree day numbers indicate more extreme temperatures, which typically lead to increased energy use for heating or cooling buildings. In the presented code, CDD calculations use summer aggregation (CDD22), while HDD calculations focus on winter (HDD15.5), presenting results as daily averages rather than cumulative values. Within this notebook, these calculations are performed over the future period from 2016 to 2099, following the Shared Socioeconomic Pathways SSP5-8.5. It is important to note that the results presented here pertain to a specific subset of the CMIP6 ensemble and may not be generalisable to the entire dataset. Also note that a separate assessment examines the representation of climatology and trends of these indices for the same models during the historical period (1971-2000), while another assessment looks at the projected climate signal of these indices for the same models at a 2°C Global Warming Level.


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Quality assessment statement
---
These are the key outcomes of this assessment

* The subset of considered CMIP6 models agree on a general decrease in future trends (2016-2099) for HDD15.5 across Europe during DJF and an increase in CDD22 during JJA. However, regional variations exist.

* The northern and eastern parts of Europe are projected to experience the largest decrease in HDD15.5, accompanied by higher inter-model variability. Some areas show no trend for CDD2 due to threshold temperatures not being reached. The Mediterranean Basin will see the greatest increase in CDD22, with higher inter-model variability.

* The findings of this notebook could support decisions sensitive to future energy demand. Despite regional variations and some inter-model spread (calculated to account for projected uncertainty), the subset of 16 models from CMIP6 agree on a significant decrease in the energy required for heating spaces during winter. This decrease is particularly notable in regions with high HDD (northern and eastern regions that experience substantial heating energy consumption in winter). Conversely, more energy will be needed in the future to cool buildings during summer, especially in the Mediterranean Basin.
```

attachment:7245f9d8-4fce-41a6-9b6b-a4a3519f59e0.png
---
alt: trend_future_CDD
width: 850px
---
Cooling Degree Days daily average calculated using the summer comfort threshold of 22°C ('CDD22) for the temporal aggregation of 'JA'. Trend for the future period (2016-2099). The layout includes data corresponding to: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).
```


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Methodology
---
The reference methodology used here for the indices calculation is similar to the one followed by Scoccimarro et al., (2023) [[7]](https://doi.org/10.1038/s43247-023-00878-3). However the thermal comfort thresholds used in this notebook are slightly different. A winter comfort temperature of 15.5°C and a summer comfort temperature of 22.0°C are used here (as in the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf)). In the presented code, the CDD calculations are based on the JJA aggregation, with a comfort temperature of 22°C (CDD22), while HDD calculations focus on winter (DJF) with a comfort temperature of 15.5°C (HDD15.5). More specifically, to calculate CDD22, the sum of the differences between the daily mean temperature and the thermal comfort temperature of 22°C is computed. This calculation occurs only when the mean temperature is above the thermal comfort level; otherwise, the CDD22 for that day is set to 0. For example, a day with a mean temperature of 28°C would result in 6°C. Two consecutive hot days like this would total 12°C over the two-day period. Similarly, to calculate HDD15.5, the sum of the differences between the thermal comfort temperature of 15.5°C and the daily mean temperature is determined. This happens only when the mean temperature is below the thermal comfort level; otherwise, the HDD15.5 for that day is set to 0. Finally, to obtain more intuitive values, the sum is averaged over the number of days in the season to produce daily average values. This approach differs from the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf), where both the sum over a period and the daily average values can be displayed.  In Spinoni et al., (2018) [[6]](https://doi.org/10.1002/joc.5362), as well as in the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf), more advanced methods for calculating CDD and HDD involve considering maximum, minimum, and mean temperatures. However, to prevent overloading the notebook and maintain simplicity while ensuring compatibility with the [icclim](https://icclim.readthedocs.io/en/stable/) Python package, we opted to utilise a single variable (2m mean temperature).

This notebook offers an assessment of the projected changes and their associated uncertainties using a subset of 16 models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview). The uncertainty is examined by analysing the ensemble inter-model spread of projected changes for the energy-consumption-related indices 'HDD15.5' and 'CDD22', calculated for the future period spanning from 2016 to 2099. In particular, spatial patterns of climate projected trends are examined and displayed for each model individually and for the ensemble median (calculated for each grid cell), alongside the ensemble inter-model spread to account for projected uncertainty. Additionally, spatially-averaged trend values are analysed and presented using box plots to provide an overview of trend behavior across the distribution of the chosen subset of models when averaged across Europe.

The analysis and results follow the next outline:

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

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

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.2. Define Parameters
---
In the "Define Parameters" section, various customisable options for the notebook are specified. Most of the parameters chosen are the same as those used in another assessment ("CMIP6 Climate Projections: evaluating biases in energy-consumption-related indices in Europe"), being them:

- The initial and ending year used for the future projections period can be specified by changing the parameters `year_start` and `year_stop` (2016-2099 is chosen).
- `index_timeseries` is a dictionary that set the temporal aggregation for every index considered within this notebook ('HDD15.5' and 'CDD22'). In the presented code, the CDD calculations are always based on the JJA aggregation, with a comfort temperature of 22°C (CDD22), while HDD calculations focus on winter (DJF) with a comfort temperature of 15.5°C (HDD15.5).
- `collection_id` set the family of models. Only CMIP6 is implemented for this sub-notebook.
- `area` allows specifying the geographical domain of interest.
- The `interpolation_method` parameter allows selecting the interpolation method when regridding is performed over the indices.
- The `chunk` selection allows the user to define if dividing into chunks when downloading the data on their local machine. Although it does not significantly affect the analysis, it is recommended to keep the default value for optimal performance.

Choose annual or seasonal timeseries
Select the family of models
Interpolation method
Area to show
Chunks for download

(section-1.3)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.3. Define models
---
The following climate analyses are performed considering a subset of GCMs from CMIP6. Models names are listed in the parameters below. Some variable-dependent parameters are also selected.

The selected CMIP6 models have available both the historical and SSP8.5 experiments, and they are the same as those used in other assessments ("CMIP6 Climate Projections: evaluating biases in energy-consumption-related indices in Europe").

Define models
Define dictionaries to use in titles and caption

(section-1.4)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.4. Define land-sea mask request
---
Within this sub-notebook, ERA5 will be used to download the land-sea mask when plotting. In this section, we set the required parameters for the cds-api data-request of ERA5 land-sea mask.

(section-1.5)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.5. Define model requests
---
In this section we set the required parameters for the cds-api data-request.

When `weights = True`, spatial weighting is applied for calculations requiring spatial data aggregation. This is particularly relevant for CMIP6 GCMs with regular lon-lat grids that do not consider varying surface extensions at different latitudes.

(section-1.6)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 1. Parameters, requests and functions definition > 1.6. Functions to cache
---
In this section, functions that will be executed in the caching phase are defined. Caching is the process of storing copies of files in a temporary storage location, so that they can be accessed more quickly. This process also checks if the user has already downloaded a file, avoiding redundant downloads.

Functions description:

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

- The `compute_indices` function utilises the icclim package to calculate the energy-consumption-related indices.

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

- Finally, the `compute_indices_and_trends` function calculates the energy consumption-related indices for the corresponding temporal aggregation using the `compute_indices` function, determines the indices mean for the future period (2016-2099), obtain the trends using the `compute_trends` function, and offers an option for regridding to `model_regrid`.

Original bounds for conservative interpolation

(section-2)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 2. Downloading and processing > 2.1. Download and transform the regridding model
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download daily data from the selected CMIP6 regridding model, compute the energy-consumption-related indices for the selected temporal aggregation ("DJF" for HDD and "JJA" for CDD), calculate the mean and trend over the future projections period (2016-2099), and cache the result (to avoid redundant downloads and processing).

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

(section-2.2)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 2. Downloading and processing > 2.2. Download and transform models
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download daily data from the CMIP6 models, compute the energy-consumption-related indices for the selected temporal aggregation ("DJF" for HDD and "JJA" for CDD), calculate the mean and trend over the future period (2016-2099), interpolate to the regridding model's grid (only for the cases in which it is specified, in the other cases, the original model's grid is mantained), and cache the result (to avoid redundant downloads and processing).

Original model
Interpolated model

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

(section-2.3)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 2. Downloading and processing > 2.3. Apply land-sea mask, change attributes and cut the region to show
---
This section performs the following tasks:

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

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

Edit attributes

(section-3)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 3. Plot and describe results
---
This section will display the following results:

- Maps representing the spatial distribution of the **future trends** (2016-2099) of the indices 'HDD15.5' and 'CDD22' for each model individually, the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell), and the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).
- **Boxplots** which represent statistical distributions (PDF) built on the spatially-averaged future trend from each considered model.

(section-3.1)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 3. Plot and describe results > 3.1. Define plotting functions
---
The functions presented here are used to plot the trends calculated over the future period (2016-2099) for each of the indices ('HDD15.5' and 'CDD22').

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

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

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

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

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

COLORBAR NOTE:

The colorbar chosen to represent trends for the HDD15.5 index spans from red to blue. In this color scheme, negative trend values (shown in red) indicate a decrease in Heating Degree Days over time. Conversely, blueish colors indicate an increase in Heating Degree Days over time. This selection is based on the rationale that more Heating Degree Days are associated with colder conditions, typically represented by blueish colors, while fewer Heating Degree Days indicate warmer conditions, depicted by reddish colors.

(section-3.2)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 3. Plot and describe results > 3.2. Plot ensemble maps
---
In this section, we invoke the `plot_ensemble()` function to visualise the trend calculated over the future period (2016-2099) for the model ensemble across Europe. Note that the model data used in this section has previously been interpolated to the "regridding model" grid (`"gfdl_esm4"` for this notebook).

Specifically, for each of the indices ('HDD15.5' and 'CDD22'), this section presents a single layout including trend values of the future period (2016-2099) for: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

Fig number counter
Common title

(section-3.3)=