EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Quality assessment statement
---
These are the key outcomes of this assessment

* For the selected subset of models, future projections suggest diversity on the sign of the trend of precipitation-based indices for the DJF aggregation depending on the index selection and the region under consideration.

* The spatially-averaged values displayed by boxplots illustrate the diversity of outcomes, emphasising the importance of regional analyses. There is a significant inter-model variability, suggesting uncertainties in future projections. It is essential to note that the interpretation of boxplot results should be approached cautiously, considering the potential distortion of outcomes due to the influence of regions with trends that behave differently.

* A larger GCM-RCM matrix should be considered when addressing specific cases to enhance the robustness of the analysis and account for uncertainties [[3]](https://doi.org/10.1002/wcc.8)[[4]](https://doi.org/10.1088/1748-9326/aacc77).

* The outcomes of this notebook are highly dependent on the index and region. Projected changes for RR1, RX1day, and RX5day show similar spatial patterns among the subset of considered models. In contrast, CWD and R20mm exhibit more scattered spatial patterns, and the regions with significant trend signals appear to have large uncertainties (inter-model spread). Results obtained from another assessment of the same indices and temporal aggregation (DJF) for the historical period 1971-2000, which evaluated biases ("CORDEX Climate Projections: evaluating bias in precipitation-based indices for impact models"), should also be taken into consideration. The high uncertainties in both assessments encourage users to bias-correct precipitation-based indices before using them for specific applications [[5]](https://doi.org/10.1016/j.jhydrol.2012.05.052).

* Model agreement on precipitation-based indices projections is lower than on maximum temperature indices. This difference is evident when comparing the results of this assessment with those obtained in "RCM uncertainty in projected changes in extreme temperature indices for the reinsurance sector"
```

attachment:13536fdf-aabd-48d2-b1a4-3c84b6fb58ac.png
---
alt: trend_future_RX5day
width: 900px
---
Maximum 5-day total precipitation ('RX5day') for the temporal aggregation of 'DJF'. Trend for the future period (2015-2099). 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).
```

NOTE on the DJF selection:

It is important to note that using seasonal temporal aggregations offers only partial insights into the dynamics. For more comprehensive results, it is advisable to also consider other seasons and annual aggregations. However, for the sake of efficiency and to avoid making the notebook too heavy, we have opted to prioritise seasonal aggregations.


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Methodology
---
This notebook offers an assessment of the projected changes and their associated uncertainties using a subset of 9 models from [CORDEX](https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels?tab=overview). The analysis involves evaluating the ensemble inter-model spread of projected changes using several precipitation-based indices. These indices, calculated over the DJF period for the future period spanning from 2015 to 2099, include:

- Spell length of days with precipitation greater than 1 mm (also known as "Maximum consecutive wet days" - "CWD")
- Number of heavy precipitation days (Precip >= 20mm - "R20mm")
- Number of wet days (Precip >= 1mm - "RR1")
- Maximum 1-day total precipitation ("RX1day")
- Maximum 5-day total precipitation ("RX5day").

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: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > 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 ("CORDEX Climate Projections: evaluating bias in precipitation-based indices for impact models"), being them:

- The initial and ending year used for the future projections period can be specified by changing the parametes `future_slice` (2015-2099 is chosen for consistency between CORDEX and CMIP6).
- The `timeseries` set the temporal aggregation. For instance, selecting "DJF" implies considering only the winter season.
- `collection_id` provides the choice between Global Climate Models CMIP6 or Regional Climate Models CORDEX. Although the code allows choosing between CMIP6 or CORDEX, the example provided in this notebook deals with CORDEX RCMs.
- `area` allows specifying the geographical domain of interest.
- The `interpolation_method` parameter allows selecting the interpolation method when regridding is performed over the indices.
- The `chunk` selection allows the user to define if dividing into chunks when downloading the data on their local machine. Although it does not significantly affect the analysis, it is recommended to keep the default value for optimal performance.

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

(section-1.3)=


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Analysis and results > 1. Parameters, requests and functions definition > 1.3. Define models
---
he following climate analyses are performed considering a subset of GCMs from CMIP6. Models names are listed in the parameters below. Some variable-dependent parameters are also selected, as the `index_names` parameter, which specifies the precipitation-based indices ('CWD', 'R20mm', 'RR1', 'RX1day' and 'RX5day' in our case) from the [icclim](https://icclim.readthedocs.io/en/stable/) Python package.

When choosing Cordex models, it is crucial to consider the availability of RCMs for the selected GCM and the specified region. The listed RCMs, for instance, are accessible for the GCM “mpi_m_mpi_esm_lr” in the “europe” cordex_domain, and they are the same as those used in another assessment  ("CORDEX Climate Projections: evaluating bias in precipitation-based indices for impact models"). To confirm the available combinations, refer to the [CORDEX](https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels?tab=overview) CDS catalogue entry.

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

(section-1.4)=


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > 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: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > 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.

The `get_cordex_years` function is employed to choose suitable data chunks for CORDEX data requests.

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: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > 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 precipitation-based indices.

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

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

Original bounds for conservative interpolation

(section-2)=


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > 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 CORDEX regridding model, compute the precipitation-based indices for the selected temporal aggregation, calculate the mean and trend over the future projections period (2015-2099), and cache the result (to avoid redundant downloads and processing).

The regridding model is intended here as the model whose grid will be used to interpolate the others. This ensures all models share a common grid, facilitating the calculation of median values for each cell point. The regridding model within this notebook is "clmcom_eth_cosmo_crclim" 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: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > 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 CORDDEX models, compute the precipitation-based indices for the selected temporal aggregation, calculate the mean and trend over the future period (2015-2099), interpolate to the regridding model's grid (only for the cases in which it is specified, in the other cases, the original model's grid is mantained), and cache the result (to avoid redundant downloads and processing).

Original model
Interpolated model

```text
model='clmcom_clm_cclm4_8_17'
model='clmcom_eth_cosmo_crclim'
model='cnrm_aladin63'
model='dmi_hirham5'
model='knmi_racmo22e'
model='mohc_hadrem3_ga7_05'
model='mpi_csc_remo2009'
model='smhi_rca4'
model='uhoh_wrf361h'
```

(section-2.3)=


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > 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: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Analysis and results > 3. Plot and describe results
---
This section will display the following results:

- Maps representing the spatial distribution of the **future trends** (2015-2099) of the indices ('CWD', 'R20mm', 'RR1', 'RX1day' and 'RX5day') 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: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Analysis and results > 3. Plot and describe results > 3.1. Define plotting functions
---
The functions presented here are used to plot the trends calculated over the future period (2015-2099) for each of the indices ('CWD', 'R20mm', 'RR1', 'RX1day' and 'RX5day').

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

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

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

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

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

(section-3.2)=


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Analysis and results > 3. Plot and describe results > 3.2. Plot ensemble maps
---
In this section, we invoke the `plot_ensemble()` function to visualise the trend calculated over the future period (2015-2099) for the model ensemble across Europe. Note that the model data used in this section has previously been interpolated to the "regridding model" grid (`"clmcom_eth_cosmo_crclim"` for this notebook).

Specifically, for each of the indices ('CWD', 'R20mm', 'RR1', 'RX1day' and 'RX5day'), this section presents a single layout including trend values of the future period (2015-2099) for: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

Fig number counter
Common title

(section-3.3)=


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Analysis and results > 3. Plot and describe results > 3.3. Plot model maps
---
In this section, we invoke the `plot_models()` function to visualise the trend calculated over the future period (2015-2099) 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 ('CWD', 'R20mm', 'RR1', 'RX1day' and 'RX5day'), this section presents a single layout including the trend for the future period (2015-2099) of every model.

(section-3.4)=


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Analysis and results > 3. Plot and describe results > 3.4. Boxplots of the future trend
---
Finally, we present boxplots representing the ensemble distribution of each climate model trend calculated over the future period (2015-2099) across Europe.

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

The boxplot visually illustrates the distribution of trends (or bias 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 11. Boxplots illustrating the future trends (2015-2099) of the ensemble distribution for the precipitation-based indices: (a) 'CWD', (b) R20mm, (c) RR1, (d) RX1day and (e) RX5day. The distribution is created by considering spatially averaged trends across Europe. The ensemble mean and the ensemble median trends are both included. Outliers in the distribution are denoted by a grey circle with a black contour.

(section-3.5)=


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > Analysis and results > 3. Plot and describe results > 3.5. Results summary and discussion
---
- The level of agreement among models for precipitation-based indices projections is lower than for maximum temperature indices. Additionally, the trends are clearly less robust compared to maximum temperature. This is evident when comparing the findings of this assessment with those presented in "CORDEX Climate Projections: Evaluating Uncertainty in Projected Changes in Extreme Temperature Indices for the Reinsurance Sector."

- The future trend (calculated over the 2015-2099 period) in precipitation-based indices for the temporal aggregation of DJF largely depends on the considered index.

- The maximum Consecutive Wet Days (CWD) are projected to increase on the Atlantic coasts of the western part of the continent (except in the central and northwest of Scandinavia and the west of Iceland, where it is expected to decrease). Decreases are also expected on the southernmost and eastern coasts of the Mediterranean basin. No significant trend is detected in the rest of the regions.

- The number of heavy precipitation days (R20mm) is expected to increase in the west of the Iberian Peninsula, southwest of France, west of the United Kingdom, south of Norway, Balkan coasts, and some parts of the northern part of Italy and southeast of France. Decreases are expected in the Mediterranean coastal areas of Turkey. No significant trend is present in the model output for the rest of the regions.

- Increases in the number of wet days (RR1) are projected to increase in the Scandinavian Peninsula (in contrast to the decrease expected in the western coastal regions), United Kingdom, center, center-east, and northeast of Europe. Conversely, decreases are expected in the Mediterranean regions of Africa and the eastern part of the Mediterranean basin.

- The future trend of the maximum 1-day total precipitation (RX1day) and maximum 5-day total precipitation (RX5day) have a similar spatial pattern. An increase is projected for most of Europe (indicating more extreme precipitation events), with the exception of the Mediterranean regions of Africa and the southeastern part of the Mediterranean basin, and the center-west and northwestern Atlantic coastal regions of Scandinavia where a decrease is expected.

- The boxplots displaying spatially-averaged values of the future trend for the temporal aggregation of winter (DJF) reveal diverse outcomes depending on the index under consideration. Notably, significant inter-model variability is evident for certain indices, whose interquartile range encompasses trends of varying directions. These are the cases of CWD and RR1. The other indices show a positive trend with outliers in some cases (RX5day).

- It is important to emphasise that the boxplots present spatially-averaged values, and their interpretation should be approached with caution, as the outcomes can vary significantly when considering different regions across Europe. For regional analyses, it is essential to focus solely on the region of interest, as the influence of other European regions may distort the results, leading to conclusions that do not accurately reflect the specific area under study.

- What do the results mean for users? Are the biases relevant?
  - The results of this notebook are strongly influenced by the index and region. Projected changes for RR1, RX1day, and RX5day display similar spatial patterns across the subset of models considered. In contrast, CWD and R20mm show more scattered spatial patterns, with regions that have significant trend signals exhibiting high uncertainty (inter-model spread).

- Findings from another assessment of the same indices and seasonal aggregation (DJF) for the historical period 1971-2000, which evaluated biases ("CORDEX Climate Projections: evaluating bias in precipitation-based indices for impact models"), should also be taken into account. The high uncertainties in both assessments suggest that users should bias-correct precipitation-based indices before using them for specific applications [[5]](https://doi.org/10.1016/j.jhydrol.2012.05.052).

RESULTS NOTE:

It is important to note that the results presented are specific to the 9 models chosen, and users should aim to assess as wide a range of models as possible before making a sub-selection.


EQC Quality Assessment: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > 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:
* CORDEX regional climate model data on single levels (Daily mean - Mean precipitation flux): https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-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: "Projected changes in precipitation-based indices for impact models"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: climate-and-weather-extremes_q04  |  Category: Climate_Projections
Section: Projected changes in precipitation-based indices for impact models > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1016/j.envsci.2015.08.012) Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K., Montanarella, L., Alewell, C. (2015). The new assessment of soil loss by water erosion in Europe. Environ. Sci. Policy, 54, pp. 438-447. https://doi.org/10.1016/j.envsci.2015.08.012

[[2]](https://doi.org/10.1016/j.landusepol.2012.11.007) Salvati, L., Carlucci, M. (2013). The impact of mediterranean land degradation on agricultural income: a short-term scenario. Land Use Policy, 32, pp. 302-308. https://doi.org/10.1016/j.landusepol.2012.11.007

[[3]](https://doi.org/10.1002/wcc.8) Rummukainen, M. (2010). State-of-the-art with regional climate models. WIREs Clim Change, 1: 82-96. https://doi.org/10.1002/wcc.8

[[4]](https://doi.org/10.1088/1748-9326/aacc77) Silje Lund Sørland et al. (2018). Bias patterns and climate change signals in GCM-RCM model chains. Environ. Res. Lett. 13 074017. https://doi.org/10.1088/1748-9326/aacc77

[[5]](https://doi.org/10.1016/j.jhydrol.2012.05.052) Teutschbein, C., Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology,
Volumes 456–457, pp. 12-29. https://doi.org/10.1016/j.jhydrol.2012.05.052


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors.
---
Production date: 31-03-2025

Produced by: CMCC foundation - Euro-Mediterranean Center on Climate Change. Daniele Peano, Lorenzo Sangelantoni and Albert Martinez.


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > Quality assessment question
---
* **Are atmospheric models too cold in the mountain regions?**

Despite mountain areas covering a limited amount of land, they play a paramount role in the world’s water tower by supplying water for both natural and anthropogenic demands (Immerzeel et al., 2020 [[1]](https://doi.org/10.1038/s41586-019-1822-y)). In many mountain regions, various water-related processes (such as the fraction of rain versus snow, snowmelt, and streamflow drought, e.g., Rudisill et al., 2024 [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1)) are linked to temperature conditions, making the mountain areas an indicator of the ongoing climate change.

Mountain terrains complicate the proper representation of temperature by models in those areas due to complex topography, slopes, aspect, terrain shadowing, snow-albedo feedbacks, and vegetation variability (*Figure .1*, reproduced from Rudisill et al., 2024 [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1)).

Thanks to their higher resolution, Regional Climate Models (RCMs) are expected to represent the dynamical and thermodynamic processes underlying temperature variability over complex orography more accurately than Global Climate Models (GCMs). However, RCMs still present biases over mountain regions. Among these, winter cold biases—especially across peaks and ridges—are especially pronounced (Kotlarski et al., 2014 [[3]](https://doi.org/10.5194/gmd-7-1297-2014), Winter et al., 2017 [[4]](https://doi.org/10.1007/s00382-016-3130-7), Matiu et al., 2019 [[5]](https://doi.org/10.3390/atmos11010046), Vautard et al., 2020 [[6]](https://doi.org/10.1029/2019JD032344), Sy et al., 2024 [[7]](https://doi.org/10.1002/joc.8331)). Warm biases in summer have also been reported mainly in valley areas, but only in studies using convection-permitting RCMs (Rudisill et al., 2024 [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1), Napoli et al., 2023 [[8]](https://doi.org/10.1002/joc.8222), Liu et al., 2017 [[9]](https://doi.org/10.1007/s00382-016-3327-9)); such fine-scale features are not captured by RCMs with coarser resolution. These temperature biases, consequently, impact their ability to properly represent snow-related processes in mountain areas (Matiu et al., 2019 [[5]](https://doi.org/10.3390/atmos11010046)). In fact, biases in temperature and snowpack, have a crucial knock-on effect on surface runoff, undermining the evaluation of the water-supplying capability of mountain ranges in Europe (Immerzeel et al., 2020 [[1]](https://doi.org/10.1038/s41586-019-1822-y)). Those cascading effects are crucial for lowlands water availability (van Tiel et al., 2023 [[10]](https://doi.org/10.1029/2022EF003408)) and inherent economic sectors such as tourism, hydropower, and agriculture (Beniston et al., 2011 [[11]](https://doi.org/10.1016/j.envsci.2010.12.009)).


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > Quality assessment statement
---
These are the key outcomes of this assessment

* State-of-the-art regional climate models exhibit cold biases in the winter season compared to both station/point-based and gridded observations.

* EURO-CORDEX RCMs exhibit systematic winter cold biases over the Alpine region at elevations above 1000 m, with even the "hottest" models underestimating temperatures in mountain areas.

* Winter cold biases introduce additional uncertainties in snow estimates, reducing confidence in the use of RCM temperature projections for applications in the ski industry.

* Temperature and mountain snowpack biases affect estimates of water availability, influencing the ability to assess changes in various hydrological and economic sectors, such as tourism, hydropower, and agriculture.

* The outcomes of this assessment, focused on the Alpine region, advocate caution when using RCM future temperature projections. A key concern is the potential propagation of present-climate biases into a warmer future climate, representing an aspect (the propagation) still largely unexplored.

```

attachment:6360ba57-d679-4630-8214-a5a2ec7098a7.jpg
---
width: 900px
alt: Abstract
---
Depiction of processes influencing mountain T2m (temperature at 2 metres above ground level). (a) Incident solar radiation at the ground surface depends on terrain aspect and shadowing. Mountain slopes can also reflect SW and emit LW radiation; (b) snow reflects solar radiation, cooling surface temperatures through the snow-albedo feedback; (c) vegetation’s high surface roughness and lower albedos warm surrounding air relative to non-vegetated snow surfaces; (d) moderate synoptic forcing leads to upper-air and boundary layer temperature mixing, with an un-inverted vs elevation profile; (e) katabatic flows develop valley temperature inversions during clear-sky, cloud-free conditions when surface LW cooling is strong. Image reproduced from Figure 1 in Rudisill et al. (2024) [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1), © American Meteorological Society. Used with permission.

```


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > Methodology
---
A review of the relevant scientific literature documenting state-of-the-art RCMs cold biases over mountainous regions, with a focus on the Alpine region, is conducted. Methods and key findings from a series of studies on this topic are presented and discussed.

The analysis and results follow the next outline:

**[](climate_projections-cordex-domains-single-levels_validation_q05:section-1)**

**[](climate_projections-cordex-domains-single-levels_validation_q05:section-2)**

**[](climate_projections-cordex-domains-single-levels_validation_q05:section-3)**


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > Analysis and results > 1.  Main methods within the literature
---
The modelled temperatures are typically evaluated against gridded meteorological observations, reanalysis datasets, or point-based observations (i.e. data from individual weather stations). Usually, the assessments focus on the daily average, minimum (occurring at night), and maximum (occurring during the day) values (e.g., Rudisill et al., 2024 [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1)).

Vautard et al. (2021) [[6]](https://doi.org/10.1029/2019JD032344) use the E-OBS17 0.22° dataset (Haylock et al., 2008 [[12]](https://doi.org/10.1029/2008JD010201)) as reference to conduct a comprehensive evaluation of a set of 55 EURO-CORDEX regional climate models. The E-OBS datasets are interpolated onto a common 0.11° resolution grid using a bilinear interpolation to perform their evaluation. Matiu et al. (2019) [[5]](https://doi.org/10.3390/atmos11010046) use a similar approach to evaluate temperature biases in EURO-CORDEX models, based on the E-OBSv20 dataset (Cornes et al., 2018 [[13]](https://doi.org/10.1029/2017JD028200)). Differently from Vautard et al. (2021) [[6]](https://doi.org/10.1029/2019JD032344), their study also focuses on snow biases over the European Alps, linking them to orography, temperature, and precipitation mismatches. The analysis combines gridded observations with station data from the province of Bolzano, Germany, and Switzerland (*Figure .2*), which are also used to validate temperature estimates.

Matiu et al. (2024) [[14]](https://doi.org/10.1007/s00382-024-07376-y) analysed EURO-CORDEX temperature and precipitation data for the Alpine region (*Figure .3*) across different elevations, comparing them with gridded observations from E-OBS and the Alpine Precipitation Grid Dataset (APGD; Isotta et al., 2014 [[15]](https://doi.org/10.1002/joc.3794)). They also conducted an observational intercomparison using MESAN (Landelius et al., 2016 [[16]](https://doi.org/10.1002/qj.2813)), WFDEI (Weedon et al., 2014 [[17]](https://doi.org/10.1002/2014WR015638)), and high-resolution national datasets.

attachment:add9873d-30b4-46ad-a83c-ba72f293890f.png
---
width: 900px
alt: figure 2
---
Map of European Alps. Terrain map with boundaries of the Alpine Convention (Alpconv), marking the core Alpine region. Points indicate the station locations separated by their characteristic and application based on analysis performed in Matiu et al. (2019) (+: show stations used for evaluating reanalysis-driven RCMs, x: show stations used for evaluating GCM-driven RCMs; stars (+ and x overlaid): show stations used for evaluating both). (Map tiles by Stamen Design, under CC BY 3.0. Data by Open Street Map, under ODbL). Image reproduced from Figure 1 in Matiu et al. (2019) [[5]](https://doi.org/10.3390/atmos11010046), under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

```

attachment:209c2943-7ef2-4da6-8246-c8473fd6239c.png
---
width: 900px
alt: figure 3
---
Elevation map of the study area. The source is the European Digital Elevation Model (EU-DEM), version 1.1, which has been aggregated to 1 km. The black rectangle is the area for which the regional climate model data was cropped. Image reproduced from *Figure 1* in Matiu et al. (2024) [[14]](https://doi.org/10.1007/s00382-024-07376-y), under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

```

(climate_projections-cordex-domains-single-levels_validation_q05:section-2)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > Analysis and results > 2. RCMs temperature biases over Alps
---
Vautard et al. (2021) [[6]](https://doi.org/10.1029/2019JD032344) assessed the performance of 55 combinations of Regional Climate Models (RCMs) driven by Global Climate Models (GCMs), representing the full CORDEX ensemble available for the European domain as of 2019, which, to our knowledge, remains unchanged. *Figure .4* (adapted from Vautard et al., 2021 [[6]](https://doi.org/10.1029/2019JD032344)) presents the distribution of mean temperature biases for the winter and summer seasons. Specifically, it shows the median, 5th, and 95th percentiles of the mean temperature bias distribution derived from the 55 RCM simulations for each season. Unlike the summer season, during winter, even the 95th percentile of the mean temperature bias among the 55 RCM simulations shows cold biases over high mountain regions, such as Scandinavia and the Alps. This indicates that even the warmest models within the Euro-CORDEX ensemble still exhibit negative biases in mountain regions. Similar biases were also identified by other studies. For example, Kotlarski et al. (2014) [[3]](https://doi.org/10.5194/gmd-7-1297-2014) evaluated EURO-CORDEX regional climate models driven by ERA-Interim over the Alps for the period 1989–2008, identifying similar biases. Blázquez et al. (2023) [[18]](https://doi.org/10.1007/s00382-023-06727-5) analysed temperature and precipitation biases in CORDEX RCM simulations over the Andes, while Sy et al. (2024) [[7]](https://doi.org/10.1002/joc.8331) assessed NA-CORDEX models, reanalysis products, and observational datasets against the U.S. Climate Reference Network in North America. These studies all report cold biases over mountainous regions, emphasizing common RCMs’ issues in representing temperature in complex orography regions.

attachment:238d2b8d-0ca9-4d2b-b9a4-8e0277793201.png
---
width: 900px
alt: figure 4
---
Distribution of temperature biases (°C) for the winter season (top row) and the summer season (bottom row). Top row, left: median of the mean temperature bias among the 55 RCM simulations, middle: 5th percentile of the RCM temperature bias distribution, and right: 95th percentile of the RCM temperature bias distribution; bottom row: same as top row but for the summer season. Image adapted from *Figure 1* in Vautard et al. (2021) [[6]](https://doi.org/10.1029/2019JD032344), under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
```

Various potential sources have been suggested to explain the large cold temperature biases across major mountain regions. Rudisill et al. (2024) [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1) present a comprehensive list of possible mechanisms, including the representation of snow thermodynamics, elevation and orographic discrepancies due to grid spacing, and biases in radiation and wind.

Other studies have explored specific factors contributing to these biases. For example, García-Díez et al. (2015) [[19]](https://doi.org/10.1007/s00382-015-2529-x) remark a link between winter cold biases and snow-covered regions, highlighting a poor representation of snow-atmosphere interaction amplified by albedo feedback as a possible driver of the winter cold biases in RCMs. Moreover, the underestimation of cloud cover and consequent changes in incoming radiative fluxes (shortwave and longwave) may also influence temperature biases in Alpine areas (Vionnet et al., 2016 [[20]](https://doi.org/10.1175/JHM-D-15-0241.1)).


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > Analysis and results > 2. RCMs temperature biases over Alps
---
wave) may also influence temperature biases in Alpine areas (Vionnet et al., 2016 [[20]](https://doi.org/10.1175/JHM-D-15-0241.1)).

Relevant studies have further explored elevation-dependent biases in regional climate models across mountain regions. Notably, Matiu et al. (2024) [[14]](https://doi.org/10.1007/s00382-024-07376-y) evaluated data from the EURO-CORDEX ensemble for the Alpine region and the domain specified in *Figure .3*, comparing it with multiple gridded observational datasets, including E-OBS, APGD, and high-resolution national datasets. Their study indicates that biases in seasonal temperature increase with elevation, while also highlighting that observational datasets themselves are not immune to biases, making the choice of dataset for evaluation critical (also see Shrestha et al., 2023 [[21]](https://doi.org/10.3390/cli11070154)). *Figure .5c* (reproduced from  Matiu et al., 2024 [[14]](https://doi.org/10.1007/s00382-024-07376-y)) presents the differences in temperature indices between EURO-CORDEX GCM-RCM combinations and E-OBS for various elevations, comparing summer and winter, with values based on the 1971–2000 period. The cold bias became more pronounced with lower temperatures, particularly in winter and with increasing elevation. Smaller biases were observed below 1000 m, while above this threshold and during winter, all considered RCMs exhibited a strong cold bias. This denotes mountain peaks and ridges as the areas characterised by the most substantial cold biases, as highlighted by Rudisill et al. (2024) [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1).

attachment:f1bf2739-4225-4015-bace-595255b2ae1a.png
---
width: 900px
alt: figure 5
---
(a) Precipitation indices in APGD and GCM-RCMs. (b) Differences between each GCM-RCM and APGD. (c) Differences in temperature indices between each GCM-RCM combination and E-OBS. Each line is one model combination. Values refer to the 1971–2000 climatology. Image reproduced from *Figure 5* in Matiu et al. (2024) [[14]](https://doi.org/10.1007/s00382-024-07376-y), under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

```

Even though local-scale forcing misrepresentation plays a paramount role in driving the above-mentioned cold biases, large-scale-forcing-related biases can also exacerbate or mitigate temperature biases in mountainous areas (e.g., Lhotka et al., 2018 [[22]](https://doi.org/10.1007/s00704-016-2031-3) and Maraun et al., 2021 [[23]](https://doi.org/10.1029/2020JD032824)), especially during the winter season. In RCMs, these biases may stem from inherited large-scale misrepresentations introduced by the driving global climate model, or from processes internal to the RCM formulation. Importantly, the presence of such biases does not imply that RCM data offer no added value over GCM outputs. On the contrary, RCMs — particularly in regions with complex orography — can provide more realistic representations of local climate processes, and their added value should not be overlooked, even when biases persist. An example of this can be found in Bilbao-Barrenetxea et al., 2024 [[24]](https://doi.org/10.1007/s00382-024-07318-8), which assessed the added value of using EURO-CORDEX over the complex orography of the Pyrenees compared to GCMs.


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > Analysis and results > 2. RCMs temperature biases over Alps
---
1007/s00382-024-07318-8), which assessed the added value of using EURO-CORDEX over the complex orography of the Pyrenees compared to GCMs.

In recent years, several promising high-resolution modelling initiatives (including both regional and global climate models) are paving the path toward a better understanding and eventual reduction of this long-standing biases in climate models (Demory et al., 2020 [[25]](https://doi.org/10.5194/gmd-13-5485-2020), Moreno-Chamarro et al., 2022 [[26]](https://doi.org/10.5194/gmd-15-269-2022), Roberts et al., 2025 [[27]](https://doi.org/10.5194/gmd-18-1307-2025), Sangelantoni et al., 2024 [[28]](https://doi.org/10.1007/s00382-023-06769-9), Sangelantoni et al., 2025 [[29]](https://doi.org/10.1029/2024GL111147) and Soares et al., 2022 [[30]](https://doi.org/10.1007/s00382-022-06593-7)).

(climate_projections-cordex-domains-single-levels_validation_q05:section-3)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > Analysis and results > 3. Implications for the users
---
The state-of-the-art regional climate models represent the principal tool to be implemented in estimating past, present, and future conditions of mountain areas, such as the Alps.

Based on the results of this assessment, users need to be cautious when using the temperature projections provided by these tools. Notably, cold biases over the Alps are systematic only in winter across all EURO-CORDEX models above 1000 m, with the magnitude of the bias increasing with elevation. Contrary to what might be expected, these winter biases in mountainous regions cannot be solely attributed to altitude underestimation by models (which would typically lead to warmer biases). Instead, as discussed throughout this notebook, these cold biases arise from complex interactions within the models’ numerical schemes, particularly in the treatment of snow and ice processes, which result in the underestimation of surface temperatures.

Bias-adjustment techniques can partially relieve the assessed cold temperature biases. In particular, it can reduce the present-day discrepancies between modelled and observed temperature fields (Matiu et al., 2024 [[14]](https://doi.org/10.1007/s00382-024-07376-y), *Figure .6b*). However, bias-adjustment methods do not guarantee that the adjusted projected results will better match future reality, since the bias may not be stationary in a changing climate (e.g., Schmith et al., 2021 [[31]](https://doi.org/10.5194/hess-25-273-2021)). Special consideration should also be given to the representation of physical relationships among variables subjected to bias adjustment (Gennaretti et al., 2015 [[32]](https://doi.org/10.1002/2015JD023890)), as well as to unphysical artefacts such as the damping or amplification of original simulation trends, particularly in regions with complex orography (Sangelantoni et al., 2019 [[33]](https://doi.org/10.1007/s00704-018-2406-8)). Furthermore, different bias-adjustment methods do not always improve downstream products, as no single technique works reliably across all locations, variables, and applications.

The assessed temperature and mountain snowpack biases influence the current regional climate models' ability to quantify water availability in mountain areas and, consequently, the capability to estimate changes in resources for local and downstream hydrology and economic sectors, such as tourism, hydropower, and agriculture.

attachment:013623ec-4a51-4ab5-bf67-483d3b6e405a.png
---
width: 900px
alt: figure 6
---
Impact of different bias adjustment methods on (a) precipitation indices and (b) temperature indices over 1971–2000. Each line is a GCM-RCM combination. Grey dashed lines are raw (uncorrected) models, while solid colored lines are bias-adjusted using different methods (see legend). Image reproduced from *Figure 8* in Matiu et al. (2024) [[14]](https://doi.org/10.1007/s00382-024-07376-y), under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

```


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

* Regional climate models - CORDEX: https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels?tab=overview

* Gridded observation dataset for Europe, E-OBS:
https://cds.climate.copernicus.eu/datasets/insitu-gridded-observations-europe?tab=overview

* High-resolution global climate models:
https://highresmip.org/

* Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections: https://cds.climate.copernicus.eu/datasets/sis-tourism-snow-indicators?tab=overview

Special mention to:
* [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/)


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1038/s41586-019-1822-y) Immerzeel, W.W., Lutz, A.F., Andrade, M. et al., 2020. Importance and vulnerability of the world’s water towers. Nature 577, 364–369. https://doi.org/10.1038/s41586-019-1822-y

[[2]](https://doi.org/10.1175/BAMS-D-23-0082.1) Rudisill, W., Rhoades, A., Xu, Z., and Feldman, D. R.,2024. Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign. Bull. Amer. Meteor. Soc., 105, E1237–E1264, https://doi.org/10.1175/BAMS-D-23-0082.1

[[3]](https://doi.org/10.5194/gmd-7-1297-2014) Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V., 2014. Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014

[[4]](https://doi.org/10.1007/s00382-016-3130-7) Winter, K.J.P.M., Kotlarski, S., Scherrer, S.C. et al., 2017. The Alpine snow-albedo feedback in regional climate models. Clim Dyn 48, 1109–1124. https://doi.org/10.1007/s00382-016-3130-7

[[5]](https://doi.org/10.3390/atmos11010046) Matiu, M., Petitta, M., Notarnicola, C., and Zebisch, M., 2019. Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches. Atmosphere, 11(1), 46. https://doi.org/10.3390/atmos11010046

[[6]](https://doi.org/10.1029/2019JD032344) Vautard, R., Kadygrov, N., Iles, C., Boberg, F., Buonomo, E., Bülow, K., et al., 2021. Evaluation of the large EURO-CORDEX regional climate model ensemble. Journal of Geophysical Research: Atmospheres, 126, e2019JD032344. https://doi.org/10.1029/2019JD032344

[[7]](https://doi.org/10.1002/joc.8331) Sy, S., Madonna, F., Serva, F., Diallo, I., and Quesada, B., 2024. Assessment of NA-CORDEX regional climate models, reanalysis and in situ gridded-observational data sets against the U.S. Climate Reference Network. International Journal of Climatology, 44(1), 305–327. https://doi.org/10.1002/joc.8331

[[8]](https://doi.org/10.1002/joc.8222) Napoli, A., Parodi, A., von Hardenberg, J., and Pasquero, C. ,2023. Altitudinal dependence of projected changes in occurrence of extreme events in the Great Alpine Region. International Journal of Climatology, 43(12), 5813–5829. https://doi.org/10.1002/joc.8222

[[9]](https://doi.org/10.1007/s00382-016-3327-9) Liu, C., Ikeda, K., Rasmussen, R. et al., 2017. Continental-scale convection-permitting modeling of the current and future climate of North America. Clim Dyn 49, 71–95. https://doi.org/10.1007/s00382-016-3327-9


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > ℹ If you want to know more > References
---
7. Continental-scale convection-permitting modeling of the current and future climate of North America. Clim Dyn 49, 71–95. https://doi.org/10.1007/s00382-016-3327-9

[[10]](https://doi.org/10.1029/2022EF003408) van Tiel, M., Weiler, M., Freudiger, D., Moretti, G., Kohn, I., Gerlinger, K., and Stahl, K., 2023. Melting alpine water towers aggravate downstream low flows: A stress-test storyline approach. Earth's Future, 11, e2022EF003408.  https://doi.org/10.1029/2022EF003408

[[11]](https://doi.org/10.1016/j.envsci.2010.12.009) Beniston, M., Stoffel, M., and Hill, M., 2011. Impacts of climatic change on water and natural hazards in the Alps: Can current water governance cope with future challenges? Examples from the European “ACQWA” project, Environ. Sci. Policy, 14, 734–743. https://doi.org/10.1016/j.envsci.2010.12.009

[[12]](https://doi.org/10.1029/2008JD010201) Haylock, M. R., Hofstra, N., Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M., 2008. A European daily high‐resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research, 113, D20119. https://doi.org/10.1029/2008JD010201

[[13]](https://doi.org/10.1029/2017JD028200) Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D., 2018. An ensemble version of the E-OBS temperature and precipitation data sets. Journal of Geophysical Research: Atmospheres, 123, 9391–9409. https://doi.org/10.1029/2017JD028200

[[14]](https://doi.org/10.1007/s00382-024-07376-y) Matiu, M., Napoli, A., Kotlarski, S. et al., 2024. Elevation-dependent biases of raw and bias-adjusted EURO-CORDEX regional climate models in the European Alps. Clim Dyn 62, 9013–9030. https://doi.org/10.1007/s00382-024-07376-y

[[15]](https://doi.org/10.1002/joc.3794) Isotta, F.A., Frei, C., Weilguni, V., Tadic Melita, P., Lassegues, P., Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G., Ratto, S.M., Munari, M., Micheletti, S., Bonati, V., Lussana, C., Ronchi, C., Panettieri, E., Marigo, G., Vertacnik, G.. 2014. The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data, International Journal of Climatology, 34, 1657-1675,  https://doi.org/10.1002/joc.3794

[[16]](https://doi.org/10.1002/qj.2813) Landelius, T., Dahlgren, P., Gollvik, S. et al., 2016. A high-resolution regional reanalysis for Europe. Part 2: 2D analysis of surface temperature, precipitation and wind. Q J R Meteorol Soc 142(698):2132–2142. https://doi.org/10.1002/qj.2813

[[17]](https://doi.org/10.1002/2014WR015638) Weedon, G.P., Balsamo, G., Bellouin, N. et al., 2014. The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data. Water Resour Res 50(9):7505–7514. https://doi.org/10.1002/2014WR015638


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > ℹ If you want to know more > References
---
set: WATCH forcing data methodology applied to ERA-Interim reanalysis data. Water Resour Res 50(9):7505–7514. https://doi.org/10.1002/2014WR015638

[[18]](https://doi.org/10.1007/s00382-023-06727-5) Blázquez, J., and Solman, S. A., 2023. Temperature and precipitation biases in CORDEX RCM simulations over South America: Possible origin and impacts on the regional climate change signal. Climate Dyn., 61, 2907–2920, https://doi.org/10.1007/s00382-023-06727-5

[[19]](https://doi.org/10.1007/s00382-015-2529-x) García-Díez, M., Fernández, J., and Vautard, R., 2015. An RCM multi-physics ensemble over Europe: multi-variable evaluation to avoid error compensation. Clim Dyn 45, 3141–3156. https://doi.org/10.1007/s00382-015-2529-x

[[20]](https://doi.org/10.1175/JHM-D-15-0241.1) Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Quéno, L., Seity, Y., and Bazile, E., 2016. Numerical weather forecasts at kilometer scale in the French Alps: evaluation and application for snowpack modeling, J. Hydrometeorol., 17, 2591–2614, https://doi.org/10.1175/JHM-D-15-0241.1

[[21]](https://doi.org/10.3390/cli11070154) Shrestha, S., Zaramella, M., Callegari, M., Greifeneder, F., Borga, M., 2023. Scale Dependence of Errors in Snow Water Equivalent Simulations Using ERA5 Reanalysis over Alpine Basins. Climate, 11, 154. https://doi.org/10.3390/cli11070154

[[22]](https://doi.org/10.1007/s00704-016-2031-3) Lhotka, O., Kyselý, J. and Farda, A., 2018. Climate change scenarios of heat waves in Central Europe and their uncertainties. Theor Appl Climatol 131, 1043–1054. https://doi.org/10.1007/s00704-016-2031-3

[[23]](https://doi.org/10.1029/2020JD032824) Maraun, D., Truhetz, H., and Schaffer, A., 2021. Regional climate modelbiases, their dependence on synopticcirculation biases and the potentialfor bias adjustment: A process-oriented evaluation of the Austrianregional climate projections. Journalof Geophysical Research: Atmospheres,126, e2020JD032824. https://doi.org/10.1029/2020JD032824

[[24]](https://doi.org/10.1007/s00382-024-07318-8) Bilbao-Barrenetxea, N., Santolaria-Otín, M., Teichmann, C. et al., 2024. Added value of EURO-CORDEX downscaling over the complex orography region of the Pyrenees. Clim Dyn 62, 7981–7996. https://doi.org/10.1007/s00382-024-07318-8

[[25]](https://doi.org/10.5194/gmd-13-5485-2020) Demory, M.-E., Berthou, S., Fernández, J., Sørland, S. L., Brogli, R., Roberts, M. J., Beyerle, U., Seddon, J., Haarsma, R., Schär, C., Buonomo, E., Christensen, O. B., Ciarlo, J. M., Fealy, R., Nikulin, G., Peano, D., Putrasahan, D., Roberts, C. D., Senan, R., Steger, C., Teichmann, C., and Vautard, R., 2020. European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 13, 5485–5506, https://doi.org/10.5194/gmd-13-5485-2020


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region for the ski and hydrological sectors."
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q05  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region for the ski and hydrological sectors. > ℹ If you want to know more > References
---
High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 13, 5485–5506, https://doi.org/10.5194/gmd-13-5485-2020

[[26]](https://doi.org/10.5194/gmd-15-269-2022) Moreno-Chamarro, E., Caron, L. P., Loosveldt Tomas, S., Vegas-Regidor, J., Gutjahr, O., Moine, M. P., et al., 2022. Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models. Geoscientific Model Development, 15(1), 269–289. https://doi.org/10.5194/gmd-15-269-2022

[[27]](https://doi.org/10.5194/gmd-18-1307-2025) Roberts, M. J., Reed, K. A., Bao, Q., Barsugli, J. J., Camargo, S. J., Caron, L. P., et al., 2025. High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7. Geoscientific Model Development, 18(4), 1307–1332. https://doi.org/10.5194/gmd-18-1307-2025

[[28]](https://doi.org/10.1007/s00382-023-06769-9) Sangelantoni, L., Sobolowski, S., Lorenz, T., Hodnebrog, Cardoso, R. M., Soares, P. M. M., et al., 2024. Investigating the representation of heatwaves from an ensemble of km-scale regional climate simulations within CORDEX-FPS convection. Climate Dynamics. https://doi.org/10.1007/s00382-023-06769-9

[[29]](https://doi.org/10.1029/2024GL111147) Sangelantoni, L., Sobolowski, S. P., Soares, P. M. M., Goergen, K., Cardoso, R. M., Adinolfi, M., et al., 2025. Heatwave future changes from an ensemble of km‐scale regional climate simulations within CORDEX‐FPS convection. Geophysical Research Letters, 52, e2024GL111147. https://doi.org/10.1029/2024GL111147

[[30]](https://doi.org/10.1007/s00382-022-06593-7) Soares, P. M. M., Careto, J. A. M., Cardoso, R. M., Goergen, K., Katragkou, E., Sobolowski, S., et al., 2022. The added value of km-scale simulations to describe temperature over complex orography: the CORDEX FPS-Convection multi-model ensemble runs over the Alps. Climate Dynamics, (0123456789). https://doi.org/10.1007/s00382-022-06593-7

[[31]](https://doi.org/10.5194/hess-25-273-2021) Schmith, T., Thejll, P., Berg, P., Boberg, F., Christensen, O. B., Christiansen, B., Christensen, J. H., Madsen, M. S., and Steger, C., 2021. Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting, Hydrol. Earth Syst. Sci., 25, 273–290, https://doi.org/10.5194/hess-25-273-2021

[[32]](https://doi.org/10.1002/2015JD023890) Gennaretti, F., L. Sangelantoni, and P. Grenier, 2015. Toward daily climate scenarios for Canadian Arctic coastal zones with more realistic temperature-precipitation interdependence, J. Geophys. Res. Atmos., 120, 11,862–11,877, https://doi.org/10.1002/2015JD023890

[[33]](https://doi.org/10.1007/s00704-018-2406-8) Sangelantoni, L., Russo, A. and Gennaretti, F., 2019. Impact of bias correction and downscaling through quantile mapping on simulated climate change signal: a case study over Central Italy. Theor Appl Climatol 135, 725–740. https://doi.org/10.1007/s00704-018-2406-8


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA
---
Production date: 30-06-2025

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


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Quality assessment question
---
* **Are the cold biases commonly found in Regional Climate Models over the Alps apparent when evaluated against CERRA?**
* **Does applying a lapse-rate correction reduce these biases?**

Mountains, although covering a limited land area, are vital to the global water cycle, acting as key sources of freshwater for both ecological and human needs [[1]](https://doi.org/10.1038/s41586-019-1822-y). In many mountainous regions, temperature is a critical driver of hydrometeorological processes such as the ratio of rain to snow, the timing of snowmelt, and the frequency of low-flow conditions [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1). These sensitivities make mountain regions key indicators of climate change. However, accurately representing temperature in complex terrain remains challenging for models due to factors like topographic variability, slope orientation, terrain shading, snow–albedo feedbacks, and vegetation heterogeneity [[2]](https://doi.org/10.1175/BAMS-D-23-0082.1)).

A recent literature-based quality assessment, [CORDEX temperature biases over the Alpine region for the ski and hydrological sectors](https://ecmwf-projects.github.io/c3s2-eqc-quality-assessment/Climate_Projections/CORDEX/climate_projections-cordex-domains-single-levels_validation_q05.html), synthesised multiple studies and confirmed that Regional Climate Models (RCMs) exhibit persistent cold biases over the Alps despite their relatively high resolution and improved representation of physical processes in mountainous terrain. Given that bias estimates are sensitive to the choice of reference dataset -as highlighted in the quality assessment [[3]](https://doi.org/10.1007/s00382-024-07376-y)[[4]](https://doi.org/10.3390/cli11070154)- here we evaluate RCM biases against E-OBS and include CERRA, a high-resolution (5.5 km) regional reanalysis produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system. The analysis includes a broad ensemble of EURO-CORDEX models and examines the altitude dependency of biases by evaluating results across different elevation bands. Finally, we apply a lapse-rate correction to place all datasets at a common elevation and investigate whether this influences the estimated biases.


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Quality assessment statement
---
These are the key outcomes of this assessment

* CORDEX simulations exhibit biases relative to both reference datasets (E-OBS and CERRA).

* RCMs’ systematic cold biases above 1000–1500 m, reported in the literature, are evident when evaluated against E-OBS.

* CORDEX biases relative to CERRA are weaker and show no systematic increase with altitude, unlike the case with E-OBS.

* Large inter-model spread—especially for winter cold extremes above 1500 m—highlights significant uncertainty in representing conditions critical for alpine risk assessments.

* Lapse-rate correction might slightly reduce biases by ensuring no elevation differences exist between datasets, but it does not fundamentally change the shape of vertical bias profiles or address the underlying model or observational issues driving the biases.

* The findings of this Alpine-focused assessment highlight the need for caution when interpreting future temperature projections from RCMs. A central concern is the potential for present-climate biases to persist or propagate into future warming scenarios—an aspect that remains largely underexplored.

```

attachment:0eeb016f-678f-44d4-b821-4d782590b4f2.png
---
width: 900px
alt: Visual abstract
---
Elevation profiles of biases relative to E-OBS. Differences compared to the E-OBS vertical profile are shown for each EURO-CORDEX model individually, the ensemble median, the spread (calculated as the standard deviation), and CERRA. Profiles are plotted separately for winter (DJF) and summer (JJA), and for the 5th, 50th, and 95th percentiles. The ensemble median is represented by a solid black line, while individual EURO-CORDEX models appear as grey dashed lines. The model spread, calculated as the standard deviation across models, is shown as a grey shaded area. CERRA is depicted in orange. No lapse-rate correction is applied in this case, and data are grouped by the elevation of each model or reference dataset.
```


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Methodology
---
A subset of 39 models from [EURO-CORDEX](https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels?tab=quality_assurance_tab) is compared to [E-OBSv30.0e](https://cds.climate.copernicus.eu/datasets/insitu-gridded-observations-europe?tab=overview) to assess the elevation dependency of temperature biases. Following the approach of [[3]](https://doi.org/10.1007/s00382-024-07376-y), we evaluate differences between the RCMs and E-OBS in representing the 5th, 50th, and 95th percentiles of temperature at various altitude ranges, for both summer and winter, across the Greater Alpine Region (GAR; 4–19°E, 43–49°N, 0–3500 m a.s.l.; [[5]](https://doi.org/10.1007/s00704-018-2506-5)[[6]](https://doi.org/10.1002/joc.1377)). The extent of the GAR domain can be seen in *Figure .2 - GAR* reproduced from Haslinger et al., 2019 [[5]](https://doi.org/10.1007/s00704-018-2506-5) under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

To assess consistency across reference datasets, CERRA—a high-resolution (5.5 km) regional reanalysis produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system—is also compared to E-OBS and the RCMs.

All datasets are regridded to a 0.11° grid corresponding to a selected RCM (user-defined). There is no clear consensus in the literature on the optimal regridding approach: regridding to a finer resolution can be useful for certain applications but risks introducing artificial spatial detail not supported by the underlying data, whereas regridding to a coarser grid preserves the original physical consistency. In this work, we have chosen to regrid to the coarser resolution, following the approach in [[7]](https://doi.org/10.1002/joc.5249).

While regridding enables consistent spatial comparison, it does not inherently align the elevations of different datasets, which may still differ due to variations in underlying orography. To address this, a lapse-rate correction is applied to adjust all datasets to the same target elevation. CERRA's elevation (regridded to 0.11°) is used as the target, as CERRA originally has the highest spatial resolution. A spatially and temporally uniform lapse rate of 0.0065 °C m⁻¹ is applied to all regridded datasets—including E-OBS and the RCMs—following a procedure similar to [[7]](https://doi.org/10.1002/joc.5249). Differences in temperatures before and after this correction are evaluated to determine whether the adjustment produces substantial changes.

The analysis and results follow the next outline:

**[](climate_projections-cordex-domains-single-levels_validation_q06:section-1)**
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-1.1)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-1.2)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-1.3)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-1.4)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-1.5)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-1.6)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-1.7)

**[](climate_projections-cordex-domains-single-levels_validation_q06:section-2)**
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-2.1)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-2.2)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-2.3)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-2.4)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-2.5)


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Methodology
---
.3)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-2.4)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-2.5)

**[](climate_projections-cordex-domains-single-levels_validation_q06:section-3)**
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-3.1)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-3.2)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-3.3)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-3.4)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-3.5)
 * [](climate_projections-cordex-domains-single-levels_validation_q06:section-3.6)

attachment:5fd0ab4f-7024-4560-8446-80e6664efd46.png
---
width: 900px
alt: figure 2
---
Map of Central and Southern Europe. The broken line indicates the boundaries of the Greater Alpine Region; the solid line represents a generalized outline of the 1000 m a.s.l. isoline of the Alps which should help in locating the mountainous areas of the domain in the following figures. Image reproduced from *Figure 1* in Haslinger et al., 2019 [[5]](https://doi.org/10.1007/s00704-018-2506-5), under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

```


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > 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 start and end years of the historical period can be specified using the `year_start` and `year_stop` parameters. The default period, 1986–2005, is selected to maximise dataset availability while avoiding the need to include future projection data.
- 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.
- `variable` and `collection_id` are not customisable for this assessment and are set to 'temperture' and 'CORDEX'. Expert users can use this notebook as a guide to create similar analyses for other variables or model sets.
- `area` defines the geographical domain over which the analysis will be performed. Note that this notebook is designed for the EURO-CORDEX domain, so the selected area must lie within its boundaries. The Greater Alpine Region (GAR; 4–19°E, 43–49°N, 0–3500 m a.s.l.; [[5]](https://doi.org/10.1007/s00704-018-2506-5)[[6]](https://doi.org/10.1002/joc.1377)) is choosen for this assessment.
- `rcm_model_regrid` and `gcm_driven_model_regrid` specify the EURO-CORDEX regional climate model and the driving global climate model, respectively, whose grid will be used as the target for remapping. Note that both models must be included in the matrix of RCM-GCM combinations defined by the `models_cordex` dictionary in Section 1.3.

Choose CORDEX or CMIP6
Area to show (Alpine Area)

(climate_projections-cordex-domains-single-levels_validation_q06:section-1.3)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 1. Parameters, requests and functions definition > 1.3. Define models
---
The following climate analyses are conducted using the EURO-CORDEX models available at the time of download, limited to those that include the orography variable in the Climate Data Store ([CDS](https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels?tab=overview)).

(climate_projections-cordex-domains-single-levels_validation_q06:section-1.4)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 1. Parameters, requests and functions definition > 1.4. Define CERRA request
---
Within this notebook, CERRA is also used to stress the sensitivity of results to the choice of reference dataset. In this section, we define the necessary parameters for the cds-api data request for CERRA.

(climate_projections-cordex-domains-single-levels_validation_q06:section-1.5)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 1. Parameters, requests and functions definition > 1.5. Define E-OBS request
---
Within this notebook, E-OBS serves as the reference product. In this section, we set the required parameters for the cds-api data-request of E-OBS.

(climate_projections-cordex-domains-single-levels_validation_q06:section-1.6)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 1. Parameters, requests and functions definition > 1.6. Define model requests
---
In this section we set the required parameters for the cds-api data-request of EURO-CORDEX models.

(climate_projections-cordex-domains-single-levels_validation_q06:section-1.7)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 1. Parameters, requests and functions definition > 1.7. 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.

Description of the main functions:

- **`compute_percentiles`**: This function resamples data to daily frequency if needed (e.g., for hourly CERRA data), performs regridding to the target grid, selects the subregion of interest, and calculates the 5th, 50th, and 95th percentiles for summer and winter.
- **`orog_regrid`**: This function regrids the orography data from the models, E-OBS and CERRA onto the selected RCM grid (defined by the `model_regrid` parameter), enabling a subsequent point-by-point lapse-rate correction.

Ensure time is datetime type
Filter by years
Filter by season
Select a subdomain
Original bounds for conservative interpolation
Compute percentiles
Select a subdomain
Original bounds for conservative interpolation

(climate_projections-cordex-domains-single-levels_validation_q06:section-2)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > 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 EURO-CORDEX regridding model, select the subregion of interest, and calculate the 5th, 50th, and 95th percentiles for summer and winter over the period 1986–2005, caching the results to avoid redundant downloads and processing.

The regridding model here refers to the model whose grid is used as the target grid for remapping the other models, as well as CERRA5 and E-OBS. This ensures all datasets share a common grid, facilitating direct comparison at each grid cell. Within this notebook, the regridding model is set to `"ichec_ec_earth_smhi_rca4"` but it can be changed by modifying the `gcm_driven_model_regrid` and `rcm_model_regrid` parameters in Section 1.2. Note that both models must be included in the matrix of RCM-GCM combinations defined by the `models_cordex` dictionary in Section 1.3. It is important to highlight that the choice of the target grid can impact the analysis depending on the specific application.

(climate_projections-cordex-domains-single-levels_validation_q06:section-2.2)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 2. Downloading and processing > 2.2. Download and transform CERRA
---
In this section, the `download.download_and_transform` function from the `c3s_eqc_automatic_quality_control` package is used to download sub-daily data from CERRA, resample it to daily frequency, regrid to the target grid, select the subregion of interest, and compute the 5th, 50th, and 95th percentiles for summer and winter over the period 1986–2005. The results are cached to avoid redundant downloads and processing. Elevation data is also retrieved and regridded to the target grid (defined by the model_regrid parameter), as it is required to perform the lapse-rate correction.

(climate_projections-cordex-domains-single-levels_validation_q06:section-2.3)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 2. Downloading and processing > 2.3. Download and transform E-OBS
---
In this section, the `download.download_and_transform` function from the `c3s_eqc_automatic_quality_control` package is used to download daily data from E-OBS, regrid to the target grid, select the subregion of interest, and compute the 5th, 50th, and 95th percentiles for summer and winter over the period 1986–2005. The results are cached to avoid redundant downloads and processing. Elevation data is also retrieved and regridded to the target grid (defined by the model_regrid parameter), as it is required to perform the lapse-rate correction.

(climate_projections-cordex-domains-single-levels_validation_q06:section-2.4)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 2. Downloading and processing > 2.4. Download and transform models
---
In this section, the `download.download_and_transform` function from the `c3s_eqc_automatic_quality_control` package is used to download daily data from the selected EURO-CORDEX models, regrid to the target grid, select the subregion of interest, and compute the 5th, 50th, and 95th percentiles for summer and winter over the period 1986–2005. The results are cached to avoid redundant downloads and processing. Elevation data is also retrieved and regridded to the target grid (defined by the model_regrid parameter), as it is required to perform the lapse-rate correction.

```text
model='cccma_canesm2_gerics_remo2015'
model='cnrm_cerfacs_cm5_clmcom_eth_cosmo_crclim'
model='cnrm_cerfacs_cm5_cnrm_aladin63'
model='cnrm_cerfacs_cm5_dmi_hirham5'
model='cnrm_cerfacs_cm5_gerics_remo2015'
model='cnrm_cerfacs_cm5_knmi_racmo22e'
model='cnrm_cerfacs_cm5_mohc_hadrem3_ga7_05'
model='ichec_ec_earth_clmcom_eth_cosmo_crclim'
model='ichec_ec_earth_dmi_hirham5'
model='ichec_ec_earth_knmi_racmo22e'
model='ichec_ec_earth_smhi_rca4'
model='ipsl_cm5a_mr_dmi_hirham5'
model='ipsl_cm5a_mr_gerics_remo2015'
model='ipsl_cm5a_mr_knmi_racmo22e'
model='ipsl_cm5a_mr_smhi_rca4'
model='miroc_miroc5_clmcom_clm_cclm4_8_17'
model='miroc_miroc5_gerics_remo2015'
model='mohc_hadgem2_es_clmcom_clm_cclm4_8_17'
model='mohc_hadgem2_es_cnrm_aladin63'
model='mohc_hadgem2_es_dmi_hirham5'
model='mohc_hadgem2_es_gerics_remo2015'
model='mohc_hadgem2_es_knmi_racmo22e'
model='mohc_hadgem2_es_mohc_hadrem3_ga7_05'
model='mohc_hadgem2_es_smhi_rca4'
model='mpi_m_mpi_esm_lr_clmcom_clm_cclm4_8_17'
model='mpi_m_mpi_esm_lr_clmcom_eth_cosmo_crclim'
model='mpi_m_mpi_esm_lr_cnrm_aladin63'
model='mpi_m_mpi_esm_lr_dmi_hirham5'
model='mpi_m_mpi_esm_lr_knmi_racmo22e'
model='mpi_m_mpi_esm_lr_mohc_hadrem3_ga7_05'
model='mpi_m_mpi_esm_lr_mpi_csc_remo2009'
model='mpi_m_mpi_esm_lr_smhi_rca4'
model='mpi_m_mpi_esm_lr_uhoh_wrf361h'
model='ncc_noresm1_m_cnrm_aladin63'
model='ncc_noresm1_m_dmi_hirham5'
model='ncc_noresm1_m_gerics_remo2015'
model='ncc_noresm1_m_knmi_racmo22e'
model='ncc_noresm1_m_mohc_hadrem3_ga7_05'
model='ncc_noresm1_m_smhi_rca4'
```

(climate_projections-cordex-domains-single-levels_validation_q06:section-2.5)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 2. Downloading and processing > 2.5. Apply land-sea mask
---
This section applies a land–sea mask to all models, as well as to CERRA and E-OBS, to ensure consistency in the land-only analysis.

Rename elevation variable in ds_orog_eobs

```text
100%|██████████| 1/1 [00:00<00:00, 20.27it/s]
```

(climate_projections-cordex-domains-single-levels_validation_q06:section-3)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 3. Plot and describe results
---
This section will display the following results:

- **Elevation profiles of biases relative to E-OBS**: Differences compared to the E-OBS vertical profile are shown for each EURO-CORDEX model individually, the ensemble median, the spread (calculated as the standard deviation), and CERRA. Profiles are plotted separately for winter (DJF) and summer (JJA), and for the 5th, 50th, and 95th percentiles.

- **Elevation profiles of biases after applying lapse-rate correction**: As above, this includes each EURO-CORDEX model, the ensemble median, the spread, and CERRA, for both seasons and selected percentiles, but with all datasets corrected to CERRA’s elevation (regridded to 0.11°) using a uniform lapse rate.

- **Elevation profiles of the impact of lapse-rate correction**: This shows the difference between the corrected and uncorrected profiles for E-OBS, individual EURO-CORDEX models, the ensemble median, and the spread.

(climate_projections-cordex-domains-single-levels_validation_q06:section-3.1)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 3. Plot and describe results > 3.1. Define plotting functions
---
The functions **`compute_elevation_profile`**, **`plot_differences_vs_eobs`**, **`plot_lapse_rate_impact`** and **`apply_lapse_rate_correction`** are used to generate and visualise the elevation-dependent biases of temperature. Specifically:

- **`compute_elevation_profile`** calculates the vertical (elevation-based) profiles of the 5th, 50th, and 95th percentiles for each dataset (EURO-CORDEX models, CERRA, and E-OBS), separately for winter (DJF) and summer (JJA).

- **`apply_lapse_rate_correction`** adjusts temperatures to account for differences in elevation by applying a uniform lapse rate (−0.0065 °C m⁻¹), bringing the regridded models and E-OBS to the same elevation as CERRA (regridded to 0.11°).

- **`plot_differences_vs_eobs`** plots the differences (biases) between the elevation profiles of the EURO-CORDEX models and CERRA, relative to that of E-OBS, for each percentile and season.

- **`plot_lapse_rate_impact`** highlights the change introduced by the lapse-rate correction, showing the difference between corrected and uncorrected profiles for each dataset.

In the plots, the ensemble median is represented by a solid black line, while individual EURO-CORDEX models appear as grey dashed lines. The model spread, calculated as the standard deviation across models, is shown as a grey shaded area. CERRA is depicted in orange. Finally, E-OBS is shown in blue, but only in the plots that assess the impact of the lapse-rate correction.

Compute E-OBS and CERRA profiles
Store model differences
Convert to DataFrame for ensemble stats
Plot ensemble median
Plot ensemble spread
Add gridlines
Shared legend
--- Compute difference: corrected - original ---
Plot individual model differences
Ensemble statistics
Plot ensemble median + spread

(climate_projections-cordex-domains-single-levels_validation_q06:section-3.2)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 3. Plot and describe results > 3.2.Elevation profiles of biases relative to E-OBS
---
Differences compared to the E-OBS vertical profile are shown for each EURO-CORDEX model individually, the ensemble median, the spread (calculated as the standard deviation), and CERRA. Profiles are plotted separately for winter (DJF) and summer (JJA), and for the 5th, 50th, and 95th percentiles.

Note that no lapse-rate correction is applied in this case, and data are grouped by the elevation of each model or reference dataset.

Fig 3.
        Elevation profiles of biases relative to E-OBS. Differences compared to the E-OBS vertical profile are shown for each EURO-CORDEX model individually, the ensemble median, the spread (calculated as the standard deviation), and CERRA. Profiles are plotted separately for winter (DJF) and summer (JJA), and for the 5th, 50th, and 95th percentiles. The ensemble median is represented by a solid black line, while individual EURO-CORDEX models appear as grey dashed lines. The model spread, calculated as the standard deviation across models, is shown as a grey shaded area. CERRA is depicted in orange. No lapse-rate correction is applied in this case, and data are grouped by the elevation of each model or reference dataset.

(climate_projections-cordex-domains-single-levels_validation_q06:section-3.3)=


EQC Quality Assessment: "CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA"
Dataset: projections-cordex-domains-single-levels [CDS]
Aspect: validation_q06  |  Category: Climate_Projections
Section: CORDEX temperature biases over the Alpine region — assessment using E-OBS and CERRA > Analysis and results > 3. Plot and describe results > 3.3. Elevation profiles of biases after applying lapse-rate correction
---
Differences compared to the E-OBS vertical profile are shown for each EURO-CORDEX model individually, the ensemble median, the spread (calculated as the standard deviation), and CERRA. Profiles are plotted separately for winter (DJF) and summer (JJA), and for the 5th, 50th, and 95th percentiles.

Note that in this case, each dataset is adjusted to a common target elevation—the CERRA orography regridded to 0.11°—using a uniform lapse-rate correction (−0.0065 °C m⁻¹) before computing the differences. For example, if a grid point in a model is 200 m lower than the corresponding point in the target CERRA, the temperature at that point is corrected by 200 m × −0.0065 °C m⁻¹ = −1.3 °C.

Applying lapse-rate correction to align model elevations with CERRA's elevation
Applying lapse-rate correction to align E-OBS elevation with CERRA's elevation

Fig 4.
        Elevation profiles of biases after applying lapse-rate correction. Differences compared to the E-OBS vertical profile are shown for each EURO-CORDEX model individually, the ensemble median, the spread (calculated as the standard deviation), and CERRA. Profiles are plotted separately for winter (DJF) and summer (JJA), and for the 5th, 50th, and 95th percentiles. The ensemble median is represented by a solid black line, while individual EURO-CORDEX models appear as grey dashed lines. The model spread, calculated as the standard deviation across models, is shown as a grey shaded area. CERRA is depicted in orange. Each dataset is adjusted to a common target elevation—the CERRA orography regridded to 0.11°—using a uniform lapse-rate correction (−0.0065 °C m⁻¹) before computing the differences. For example, if a grid point in a model is 200 m lower than the corresponding point in the target CERRA, the temperature at that point is corrected by 200 m × −0.0065 °C m⁻¹ = −1.3 °C.

(climate_projections-cordex-domains-single-levels_validation_q06:section-3.4)=