EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 3. Plot and describe results > 3.3. Plot model maps
---
In this section, we invoke the `plot_models()` function to visualise the trend calculated over the future period (2016-2099) 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 ('HDD15.5' and 'CDD22'), this section presents a single layout including the trend for the future period (2016-2099) of every model.

(section-3.4)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 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 (2016-2099) across Europe.

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

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

Ensemble mean

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

(section-3.5)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > Analysis and results > 3. Plot and describe results > 3.5. Results summary and discussion
---
- Trends calculated for the future period (2016-2099) exhibit a general decrease in Heating Degree Days (HDD15.5) across Europe for DJF, especially in the northern and central-eastern regions where winter Heating Degree Days are typically higher. For Cooling Degree Days (CDD22) trends calculated for summer in Europe, an increase can be observed, particularly in the southern half and central regions. However, no trend is observed for some northern regions and certain mountain areas, possibly due to the threshold temperature of 22°C being too high to be reached across these regions.

- The boxplots illustrate a decrease in Heating Degree Days (HDD15.5) across Europe for DJF during the future period from 2016 to 2099. The ensemble median trend reaches a daily average value near -0.23 °C per decade, which is a larger decrease than that captured by ERA5 for the historical period (around -0.15 °C per decade). The interquantile range of the ensemble ranges from -0.275 to around -0.225 °C per decade.

- The boxplot analysis reveals an increase in Cooling Degree Days (CDD22) across Europe for JJA, with an ensemble median trend value near 0.25 °C per decade and an interquantile range that spans approximately from 0.20 to 0.27 °C per decade. This increase is again larger than that reflected by the ERA5 calculations over the historical period (which is around 0.11°C/decade).

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

- The projected decrease in Heating Degree Days during winter and the projected increase in Cooling Degree Days during summer provide valuable information for decisions sensitive to future energy demand. However, it is crucial to consider the biases identified during the historical period (1971-2000) in the assessment "CMIP6 Climate Projections: evaluating biases in energy-consumption-related indices in Europe". Depending on the region, these biases can either enhance or diminish confidence in interpreting the assessment results. The inter-model spread, which is used to account for projected uncertainties, should also be taken into account, as these uncertainties can lead to misinterpretations regarding the magnitude of projected increases or decreases.

RESULTS NOTE:

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

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

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q02  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1007/s10113-010-0173-x) Bindi, M., Olesen, J.E. The responses of agriculture in Europe to climate change (2011). Reg Environ Change 11 (Suppl 1), 151–158. https://doi.org/10.1007/s10113-010-0173-x

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

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

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

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

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

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level
---
Production date: 23-05-2024

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Quality assessment question
---
* **What are the projected changes for a global warming level of 2°C and associated uncertainties of Energy Degree Days in Europe?**

Sectors affected by climate change are varied including agriculture [[1]](https://doi.org/10.1007/s10113-010-0173-x), forest ecosystems [[2]](https://doi.org/10.1016/j.foreco.2009.09.023), and energy consumption [[3]](https://doi.org/10.1016/j.enbuild.2014.09.052). Under projected future global warming over Europe [[4]](https://doi.org/10.1007/s10113-013-0499-2)[[5]](http://hdl.handle.net/10013/epic.45156.d001), the current increase in energy demand is expected to persist until the end of this century and beyond [[6]](https://doi.org/10.1002/joc.5362). Identifying which climate-change-related impacts are likely to increase, by how much, and inherent regional patterns, is important for any effective strategy for managing future climate risks. This notebook utilises data from a subset of models from CMIP6 Global Climate Models (GCMs) and explores the projected changes and uncertainties in future projections of energy-consumption-related indices by considering the ensemble inter-model spread of a subset of CMIP6 models at a global mean warming level of 2°C. Two energy-consumption-related indices are calculated from daily mean temperatures using the [icclim](https://icclim.readthedocs.io/en/stable/) Python package: Cooling Degree Days (CDDs) and Heating Degree Days (HDDs). Degree days measure how much warmer or colder it is compared to standard temperatures (usually 15.5°C for heating and 22°C for cooling). Higher degree day numbers indicate more extreme temperatures, which typically lead to increased energy use for heating or cooling buildings. In the presented code, CDD calculations use summer aggregation (CDD22), while HDD calculations focus on winter (HDD15.5), presenting results as daily averages rather than cumulative values. In this notebook, these calculations are performed over the historical period from 1971 to 2000 and compared to the global warming level of 2°C. The global warming level of 2°C can be defined as the first time the 30-year moving average (centre year) of global temperature is above 2°C compared to pre-industrial (Grigory Nikulin et al. (2018) [[7]](https://doi.org/10.1088/1748-9326/aab1b1)). The preindustrial period is defined here as the period spanning from 1861 to 1890 and the index calculations are performed for the Shared Socioeconomic Pathways SSP5-8.5. It is important to note that the results presented here pertain to a specific subset of the CMIP6 ensemble and may not be generalisable to the entire dataset. Also note that a separate assessment examines the representation of climatology and trends of these indices for the same models during the historical period (1971-2000), while another assessment looks at the projected trends of these indices for the same models during a fixed future period (2015-2099).


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Quality assessment statement
---
These are the key outcomes of this assessment

* Under a 2°C global warming scenario, projections indicate further changes in Energy Degree Days for the selected subset of models.

* HDD15.5 is projected to decrease across Europe for DJF, with the largest decreases expected in northern and continental Europe. Conversely, CDD22 is expected to increase, with the Mediterranean Basin experiencing the most significant rise.

* Despite a clear agreement on the sign of the climate signal for the selected models, differences on the magnitude of the climate signal between models is high and larger ensemble should be considered when addressing specific cases to enhance the robustness of the analysis and account for uncertainties.

* The projected decrease in Heating Degree Days during winter and the projected increase in Cooling Degree Days during summer in a world 2°C warmer than the preindustrial baseline provide valuable information for decisions sensitive to future energy demand. Using climate global warming levels instead of fixed future periods helps mitigate the systematic biases of the models [[8]](https://doi.org/10.5194/esd-13-321-2022). However, it is important to note that the centered thirty-year period when the global mean temperature reaches 2°C above the preindustrial baseline varies depending on the model considered, making it challenging to determine this timing. Users must identify whether their interest lies in projected values for fixed periods or in working with global warming levels. A combined approach may provide a more comprehensive assessment.
```

attachment:1b204289-a2d7-4cee-bad9-d5f270df7baf.png
---
alt: GWL_CDD22.png
width: 550px
---
Boxplot illustrating the climate signal (i.e., the mean values for the warming level of 2°C compared to the historical period from 1971 to 2000) for the ensemble distribution of the 'CDD22' index daily averaged. The distribution is created by considering spatially averaged values across Europe. The ensemble mean and the ensemble median are both included. Outliers in the distribution are denoted by a grey circle with a black contour.
```


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Methodology
---
The reference methodology used here for the indices calculation is similar to the one followed by Scoccimarro et al., (2023) [[9]](https://doi.org/10.1038/s43247-023-00878-3). However the thermal comfort thresholds used in this notebook are slightly different. A winter comfort temperature of 15.5°C and a summer comfort temperature of 22.0°C are used here (as in the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf)). In the presented code, the CDD calculations are based on the JJA aggregation, with a comfort temperature of 22°C (CDD22), while HDD calculations focus on winter (DJF) with a comfort temperature of 15.5°C (HDD15.5). More specifically, to calculate CDD22, the sum of the differences between the daily mean temperature and the thermal comfort temperature of 22°C is computed. This calculation occurs only when the mean temperature is above the thermal comfort level; otherwise, the CDD22 for that day is set to 0. For example, a day with a mean temperature of 28°C would result in 6°C. Two consecutive hot days like this would total 12°C over the two-day period. Similarly, to calculate HDD15.5, the sum of the differences between the thermal comfort temperature of 15.5°C and the daily mean temperature is determined. This happens only when the mean temperature is below the thermal comfort level; otherwise, the HDD15.5 for that day is set to 0. Finally, to obtain more intuitive values, the sum is averaged over the number of days in the season to produce daily average values. This approach differs from the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf), where both the sum over a period and the daily average values can be displayed.  In Spinoni et al., (2018) [[6]](https://doi.org/10.1002/joc.5362), as well as in the [CDS application](https://dast.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf), more advanced methods for calculating CDD and HDD involve considering maximum, minimum, and mean temperatures. However, to prevent overloading the notebook and maintain simplicity while ensuring compatibility with the [icclim](https://icclim.readthedocs.io/en/stable/) Python package, we opted to utilise a single variable (2m mean temperature).

This notebook provides an assessment of the projected changes and their associated uncertainties, utilising a subset of 16 models from [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview) under a global warming level of 2°C. The uncertainty is explored by analysing the ensemble inter-model spread of projected changes the energy-consumption-related indices 'HDD15.5' and 'CDD22', calculated for the specific global warming level of 2°C. In particular, spatial patterns of the climate signal (i.e., mean values for the warming level of 2°C compared to the historical period from 1971 to 2000) are examined and displayed for each model individually and for the ensemble median (calculated for each grid cell), alongside the ensemble inter-model spread to account for projected uncertainty. Additionally, spatially-averaged values are analysed and presented using box plots to provide an overview of climate signal behavior across the distribution of the chosen subset of models when averaged across Europe.

The analysis and results follow the next outline:

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

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

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 1. Parameters, requests and functions definition > 1.2. Define Parameters
---
In the "Define Parameters" section, various customisable options for the notebook are specified. Most of the parameters chosen are the same as those used in other assessments ([](./climate_projections-cmip6_climate-impact-indicators_q01)), being them:

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

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

(section-1.3)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 1. Parameters, requests and functions definition > 1.3. Define models
---
The following climate analyses are performed considering a subset of GCMs from CMIP6. Models names are listed in the parameters below. The selected CMIP6 models have available both the historical and SSP8.5 experiments, and are the same as those used in other assessments ([](./climate_projections-cmip6_climate-impact-indicators_q01)). Additionally, for each model, the thirty-year period corresponding to the Global Warming Level of 2°C above the preindustrial period is specified.

The Global Warming Level of 2°C can be defined as the earliest point at which a 30-year moving average of global temperature exceeds by two degrees compared to the 1861–1890 baseline [[7]](https://doi.org/10.1088/1748-9326/aab1b1). This thirty-year period varies depending on the Global Climate Model being analysed. To determine this timeframe, the global mean surface temperature for the preindustrial baseline is first calculated for each model. Then, a rolling mean over 30 years is applied to the future period from 2015 to 2099, following the SSP5-8.5 scenario. Finally, the earliest 30-year period at which each model reaches the global warming level of 2°C above the preindustrial period is identified.

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

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

NOTE on the Global Warming Level calculation:

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

Define models
Define dictionaries to use in titles and caption

(section-1.4)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 1. Parameters, requests and functions definition > 1.4. Define land-sea mask request
---
Within this sub-notebook, ERA5 will be used to download the land-sea mask when plotting. In this section, we set the required parameters for the cds-api data-request of ERA5 land-sea mask.

(section-1.5)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 1. Parameters, requests and functions definition > 1.5. Define model requests
---
In this section we set the required parameters for the cds-api data-request.

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

(section-1.6)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 1. Parameters, requests and functions definition > 1.6. Functions to cache
---
In this section, functions that will be executed in the caching phase are defined. Caching is the process of storing copies of files in a temporary storage location, so that they can be accessed more quickly. This process also checks if the user has already downloaded a file, avoiding redundant downloads.

Functions description:

- The `select_timeseries` function subsets the dataset based on the chosen `timeseries` parameter, which could be a specific season (e.g., "JJA") or "annual.

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

- Finally, the `compute_indices_and_trends` function calculates the energy consumption-related indices for the corresponding temporal aggregation using the  `compute_indices` function and determines the indices mean over the period of interest.

Original bounds for conservative interpolation

(section-2)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 2. Downloading and processing > 2.1. Download and transform the regridding model
---
In this section, the `download.download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package is employed to download daily data from the selected CMIP6 regridding model, compute the energy-consumption-related indices for the selected temporal aggregation ("DJF" for HDD and "JJA" for CDD), calculate the mean over the historical period (1971-2000) and for the thirty-year period corresponding to the 2°C warming level, and cache the result (to avoid redundant downloads and processing).

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

NOTE on the Global Warming Level calculation:

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

(section-2.2)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 2. Downloading and processing > 2.2. Download and transform models
---
In this section, we utilise the `download_and_transform` function from the 'c3s_eqc_automatic_quality_control' package to download daily data from the CMIP6 models for the historical period (1971-2000) and for the thirty-year period corresponding to the 2°C warming level of each model, compute the energy-consumption-related indices for the selected temporal aggregation ("DJF" for HDD and "JJA" for CDD), calculate the mean over the historical period (1971-2000) and for the thirty-year period corresponding to the 2°C warming level, 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).

NOTE on the Global Warming Level calculation:

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

Original model
Interpolated model

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

(section-2.3)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 2. Downloading and processing > 2.3. Apply land-sea mask, change attributes and cut the region to show
---
This section performs the following tasks:

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

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

Edit attributes

(section-3)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 3. Plot and describe results
---
This section will display the following results:

- Maps representing the spatial distribution of the climate change signal (i.e., mean values for the warming level of 2°C compared to the historical period 1971-2000) of the indices 'HDD15.5' and 'CDD22' for each model individually, the ensemble median (understood as the median of the climate signal values of the chosen subset of models calculated for each grid cell), and the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

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

(section-3.1)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 3. Plot and describe results > 3.1. Define plotting functions
---
The functions presented here are used to plot the climate signal (i.e., mean values for the warming level of 2°C compared to the historical period) for each of the indices ('HDD15.5' and 'CDD22').

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

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

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

COLORBAR NOTE:

The colorbar chosen to represent the climate signal for the HDD15.5 index spans from intense to light red. In this color scheme, large negative climate signal values (shown in more intense reds) indicate a significant decrease in Heating Degree Days compared to the historical period. Conversely, lighter red colors indicate a less pronounced decrease in Heating Degree Days compared to the historical period values. This selection is based on the rationale that more Heating Degree Days are associated with colder conditions, typically represented by blueish colors, while fewer Heating Degree Days are associated with warmer conditions, depicted by reddish colors.

(section-3.2)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 3. Plot and describe results > 3.2. Plot ensemble maps
---
In this section, we invoke the `plot_ensemble()` function to visualise the climate signal (i.e., mean values for the warming level of 2°C compared to the historical period) for each of the indices ('HDD15.5' and 'CDD22') across Europe.  The layout includes: (a) the ensemble median (understood as the median of the climate signal values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).

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

Fig number counter
Common title

(section-3.3)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 3. Plot and describe results > 3.3. Plot model maps
---
In this section, we invoke the `plot_models()` function to visualise the climate signal for every model individually across Europe. Note that the model data used in this section maintains its original grid.

Specifically, for each of the indices ('HDD15.5' and CDD22'), this section presents a single layout including the climate signal of every model.

(section-3.4)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 3. Plot and describe results > 3.4. Boxplots of the climate change signal
---
Finally, we present boxplots representing the ensemble distribution of each climate model signal for the 2°C global warming level.

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

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

Ensemble mean

Fig 5. Boxplots illustrating the climate signal (i.e., mean values for the warming level of 2°C compared to the historical period from 1971 to 2000) of the distribution of the chosen subset of models for the Energy Degree Days indices daily averaged: (a) 'CDD22', and (b) 'HDD15.5'. The distribution is created by considering spatially averaged values across Europe. The ensemble mean and the ensemble median are both included. Outliers in the distribution are denoted by a grey circle with a black contour.

(section-3.5)=


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > Analysis and results > 3. Plot and describe results > 3.5. Results summary and discussion
---
- The Heating Degree Days (HDD15.5) during the DJF season (i.e., the sum of the differences between the thermal comfort temperature of 15.5°C and the daily mean temperature, only when the mean temperature is below the thermal comfort temperature) are projected to generally decrease in a world 2°C warmer than the preindustrial baseline (1861-1890), compared to the average daily values typical of the control period (1971-2000) across Europe. The largest decrease is expected in the northern parts of Europe and continental Europe.

- The Cooling Degree Days (CDD22) during the JJA season (i.e., the sum of the differences between the daily mean temperature and the thermal comfort temperature of 22°C, only when the mean temperature is above the thermal comfort temperature) are projected to generally increase in a world 2°C warmer than the preindustrial baseline (1861-1890), compared to the average daily values typical of the control period (1971-2000) across Europe. The largest increase is expected in the Mediterranean Basin, while northern Europe will experience a lower increase, possibly because exceeding a mean temperature of 22°C in those regions may not be too frequent even in a world 2°C warmer than the preindustrial era.

- Boxplots indicate that, across the considered region, the Heating Degree Days for winter (HDD15.5) will have daily average values (if the ensemble median is considered) 2.2°C lower compared to the historical period's DJF daily average values. If the focus is on the Cooling Degree Days in JJA (CDD22), an increase can be observed, reaching a daily average value for the ensemble median which is almost 1.5°C higher than that calculated for the historical period.

- What do the results mean for users? Are the biases relevant?
    - The projected decrease in Heating Degree Days during winter and the projected increase in Cooling Degree Days during summer in a world 2°C warmer than the preindustrial baseline provide useful information for decisions related to future energy demand. Utilising climate global warming levels rather than fixed future periods helps reduce the systematic biases present in models [[8]](https://doi.org/10.5194/esd-13-321-2022). However, it is important to recognise that the centered thirty-year period when the global mean temperature reaches 2°C above the preindustrial baseline varies depending on the model used, making this timing difficult to determine. Users need to determine whether they are more interested in projected values for specific periods or in working with global warming levels. A combined approach could yield a more comprehensive assessment.

RESULTS NOTE:

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

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

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


EQC Quality Assessment: "Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q03  |  Category: Climate_Projections
Section: Uncertainty in projected changes of energy consumption in Europe at a 2°C Global Warming Level > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1007/s10113-010-0173-x) Bindi, M., Olesen, J.E. The responses of agriculture in Europe to climate change (2011). Reg Environ Change 11 (Suppl 1), 151–158. https://doi.org/10.1007/s10113-010-0173-x

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

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

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

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

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

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

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

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


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic
---
Production date: 2025-04-23

Produced by: Timothy Williams, Nansen Environmental and Remote Sensing Center (NERSC)


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Quality assessment question
---
**How likely are sea-ice-free conditions in the Arctic and Antarctic, and along the Arctic shipping routes, and when under which warming scenarios would they become more likely?**


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Quality assessment statement
---
These are the key outcomes of this assessment

- Depending on the warming scenario, the Arctic itself has varying chances of being ice-free in the summer. While some authors project the first ice-free year as being before 2050 (regardless of warming scenario) (eg. [SIMIP community, 2020](https://doi.org/10.1029/2019GL086749); [X. Zhao et al., 2022](https://doi.org/10.1029/2022EF002708)), we find (considering the full set of models proving sea ice concentration) more dependence on the warming scenario, with only 40% of models projecting an ice-free Arctic in 2050 under `ssp1_2_6` while 60% of them project it under `ssp5_8_5`. In 2070 this range has increased to between 50% and 85%. The period with high probabilities also lengthens according to the warming scenario, from just September in `ssp1_2_6` to the 6-month period from June to December in `ssp5_8_5`.

- The maximum Arctic area is projected to drop by between $4\times 10^6$ km$^2$ and $8\times 10^6$ km$^2$ from the pre-1960 levels of about $17\times 10^6$ km$^2$.

- Around Antarctica, the climate models predict too little ice by quite a large margin, as seen by [Roach et al. (2023)](https://doi.org/10.1029/2019GL086729) and the [CDS assessment of historical sea ice extent](https://ecmwf-projects.github.io/c3s2-eqc-quality-assessment/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q02.html), and this limits the conclusions that can be made about accessibility in this region. However with a definition of ice-free conditions consistent with the Arctic, we also see a similar pattern of increased overall probability and length of the period of high probabilities with increased warming.

- We also see this behaviour for the two Arctic shipping routes, the Northern Sea Route (NSR) and the Transpolar Sea Route (TSR). There is higher probability of the TSR being ice-free in December than for the NSR. This is consistent with the results of other authors ([Chen et al, 2023](https://doi.org/10.1016/j.accre.2023.11.011); [Min et al., 2022](https://doi.org/10.1029/2022GL099157)).
```


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Methodology
---
Related to this assessment are the assessments of [sea ice extent](https://ecmwf-projects.github.io/c3s2-eqc-quality-assessment/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q02.html) and [Arctic sea ice thickness](https://ecmwf-projects.github.io/c3s2-eqc-quality-assessment/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q03.html) in the CMIP6 historical experiment. The sea ice thickness bias is not directly relevant to this one, but it could have been used in subsetting the models.

For each experiment, we take all the models that provide sea ice concentration (SIC) outputs for our ensemble - i.e. we don't do any subsetting of models in the results shown. We did do some sensitivity testing for the accessibility results however, and found no change to these results when using different model subsets. Specifically, noting that [Pan et al. (2023)](https://doi.org/10.1029/2022GL102077) found that climate models whose ocean model component was NEMO (Nucleus for European Modelling of the Ocean) had noticeably reduced Arctic sea ice in projections compared to other climate models, we tried either excluding or only using such models. Users should still bear in mind that using a different subset could still effect results. For example, the [SIMIP community (2020)](https://doi.org/10.1029/2019GL086749) selected models whose ensemble spread of sea ice area in the historical experiment included the satellite estimate. Alternatively, subsetting with "emergent constraints" (physically explainable empirical relationships) has been used to reduce the uncertainty in predictions. In examples of this approach, [X. Zhao et al. (2022)](https://doi.org/10.1029/2022EF002708) used the accuracy of the mean April SIT and the response of the minimum sea ice area to the mean April SIT (compared to the value from Pan-Arctic Ice-Ocean Assimlation System, or PIOMAS) to select models, while [Wang et al. (2021)](https://doi.org/10.1088/1748-9326/ac0b17) used the accuracy of the surface air temperature and the sensitivity of the ice area to the surface air temperature. Another approach is to use weighted statistics (as done by [J. Zhou et al., 2022](https://doi.org/10.1029/2022EF002708)), where weights are determined by model skill and interdependance.

We also do not consider the individual ensemble members available for each model, as the [SIMIP community (2020)](https://doi.org/10.1029/2019GL086749) did, but only the ensemble mean for each model. This is done to save computational cost, but also avoids biasing the mean towards models with larger ensembles. Also note that we only use the monthly mean - considering daily means as done by [Heuze & Jahn (2024)](https://doi.org/10.1038/s41467-024-54508-3) can lead to projected estimates of a much earlier first ice-free year (before 2030) than estimated using monthly means.

As well as the historical experiment, which has 33 models that output SIC, we use the following projection experiments, chosen because they provide a sufficiently large ensemble:
- `ssp1_2_6` (22 models outputting SIC)
- `ssp2_4_5` (22 models outputting SIC)
- `ssp3_7_0` (21 models outputting SIC)
- `ssp5_8_5` (21 models outputting SIC)


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Methodology
---
5` (22 models outputting SIC)
- `ssp3_7_0` (21 models outputting SIC)
- `ssp5_8_5` (21 models outputting SIC)

The experiments are ordered by expected warming with `ssp_1_2_6` giving the least warming (reaching 2.6 W/m$^2$ in 2100, which is a kind of "best case" warming scenario) and `ssp5_8_5` giving high warming (8.5 W/m$^2$ in 2100).
The first number refers to which of the five [Shared Socioeconomic Pathways](https://www.dkrz.de/en/communication/climate-simulations/cmip6-en/the-ssp-scenarios) (SSP) that the experiment corresponds to.

We calculate the minimum and maximum sea ice areas for the Arctic and Antarctica, show the historical and projected results as well as results derived from satellite observations, and show some climatological maps to show projected changes in the spatial distribution of SIC.
To calculate the area we remap the model SIC onto the equal-area grids used by the observations. The ice-covered area in each grid cell can be determined from the SIC and the grid cell area, and then summed up to get the total ice-covered area.
For each experiment, we then have an ensemble of areal time series (one from each model) and we plot the median and IQL (inter-quartile limits) of this ensemble.
For the climatological maps we follow a similar procedure but average over time instead of space before plotting on the same grids that the observations use.

We also calculate the probability of them being "sea-ice-free" in the future as the fraction of models predicting the sea ice area dropping below a given region-dependent threshold (discussed below). We also do the same calculations for two Arctic shipping routes - the Transpolar Sea Route (TSR) and the Northern |Sea Route (NSR).

One point to note here is that we need to choose an area threshold for when a region is ice-free. A consequence of this is that the probabilities we calculate are a little arbitrary and so we should pay more attention to how they are changing with time than the absolute values.
The thresholds we use to decide when the regions are ice-free are as follows. The ice-free threshold for the Arctic is usually taken as $10^6$ km$^2$ (e.g. [Jahn et al., 2016](https://doi.org/10.1002/2016GL070067)). For the Antarctic this is too high since nearly all of the CMIP6 models significantly underestimate the Antarctic area and accordingly the minimum area for these models is less than $10^6$ km$^2$ even in the historical period ([Roach et al., 2023](https://doi.org/10.1029/2019GL086729); [CDS assessment of historical sea ice extent](https://ecmwf-projects.github.io/c3s2-eqc-quality-assessment/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q02.html)). Therefore we choose the threshold to be $0.25 \times 10^6$ km$^2$, which is about one seventh of the pre-1970 Antarctic minima (according to the models) of about $1.8 \times 10^6$ km$^2$. This relationship is the approximate relationship in the Arctic, since the pre-1970 Arctic minima are about $7 \times 10^6$ km$^2$. For the two shipping routes, we also divide the pre-1970 Antarctic minima (again according to the models) by seven, giving thresholds of $0.14 \times 10^6$ km$^2$ for the NSR and $0.21 \times 10^6$ km$^2$ for the TSR.


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Methodology
---
$0.14 \times 10^6$ km$^2$ for the NSR and $0.21 \times 10^6$ km$^2$ for the TSR.

Other authors have also studied accessibility of these and other shipping routes (eg. [Chen et al, 2023](https://doi.org/10.1016/j.accre.2023.11.011);[X. Zhao et al., 2022](https://doi.org/10.1029/2022EF002708); [Wang et al, 2021](https://doi.org/10.1088/1748-9326/ac0b17); [Min et al., 2022](https://doi.org/10.1029/2022GL099157); [Li et al., 2021](https://doi.org/10.1007/s10584-021-03172-3)). For these authors, accessibility is determined by the polar class of the ship (how much ice strengthening the ship has) (if any) and the sea ice concentration and thickness. These variables are generally used as inputs into numerical ship-routing software like the Arctic Transport Accessibility Model ([Smith & Stephenson, 2013](https://doi.org/10.1017/jog.2020.73)) to create an ensemble of routes (created by using different start and end points for the journey) which are then analysed. Our approach, which would be applicable to open water vessels without ice strengthening, is comparatively simpler but is enough to give an overview of how likely the routes are to be clear and the effect of increased warming.

The "Analysis and results" section is organised as follows:

**[](section-1)**

**[](section-2)**

**[](section-3)**

        **[](section-3.1)**

        **[](section-3.2)**

        **[](section-3.3)**

        **[](section-3.4)**

        **[](section-3.5)**

                **[](section-3.5.1)**

                **[](section-3.5.2)**


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 1. Import libraries, set parameters and definitions of functions > 1.2 Set parameters
---
- Set the time period to be analysed with `year_start` and `year_stop`.
- Set the regions to be analysed with the list `sea_masks`.
- Set the area thresholds for determining when each region is ice-free with `area_thresholds`.
- Set the concentration threshold `sic_threshold` for determining sea ice extent (we use 30% to be consistent with the ice edge product).
- Set the months we want to determine climatologies for with `clim_months`.
- Set the averaging window (in years) for the climatologies for with `clim_length`.
- Set the starting years for the climatologies for with `clim_start_years`.
- Set the map projections for plotting climatologies with `projections`.
- Set the map extents for plotting climatologies with `map_slices`.
- Set the experiments to be considered with the list `experiments`.
- Set the models to be evaluated with the dictionary `models_dict`.

Select masks
Extent thresholds for being considered ice-free (units 10^6 km^2)
- we take the Antarctic threshold to be about one seventh of the pre-1970 minima,
since this is the relationship in the Arctic
Sea Ice Concentration Threshold
Months to get climatologies for
Lengths of climatology windows
Start years of the climatologies
Map projections for plotting climatologies
Slices in x and y directions (on observation grid) for zooming in on climatologies


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 1. Import libraries, set parameters and definitions of functions > 1.4 Define functions to compute time series of extent and area
---
- `apply_sea_mask` chooses the region to analyse.
- `compute_extent_and_area_from_sic` computes the extent and area from a sea ice concentration field.
- `interpolate_to_satellite_grid` interpolates the model sea ice concentration to the reference grid (chosen to be the grid of the satellite sea ice concentration dataset).
- `compute_interpolated_sea_ice_extent_and_area` calls `interpolate_to_satellite_grid` to interpolate the model sea ice concentration to the reference grid and then calls `compute_extent_and_area_from_sic` to calculate the extent and area.

Convert longitude
Compute diagnostics
Merge and add attributes
Remove nan columns
Monthly resample


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 1. Import libraries, set parameters and definitions of functions > 1.5 Functions to post-process and plot time series
---
- `postprocess_dataset` makes the combined dataset easier to work with by making sure all each individual dataset uses the same calendar, and by renaming some variables and attributes.
- `full_year_only_resample` resamples a time series to yearly, by either taking the minimum or maximum for the year.
- `plot_timeseries`loops over each experiment and plots the median and the interquartile range (IQR) of the sea ice area of the ensemble of models. It also plots the area given by the satellite sea ice concentration products. The time series are reduced to yearly frequency according to the argument `reduction` which is a string eg `"min"` or `"max"`.
- `plot_time_series_regional_reduction` loops over two different time periods, the full CMIP6 period and a "zoomed-in" period (1980-2080 or 2020-2100), and calls `plot_time_series` for each.

Define colors
Get dataarrays
Plot satellites
plot the ice-free level if it is passed in
set xtick label format


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 1. Import libraries, set parameters and definitions of functions > 1.6 Functions to create the monthly climatologies
---
- `compute_monthly_climatology` is the function that is used by `download.download_and_transform`.  It takes in an `xarray.Dataset` and takes the temporal mean for each month, before calling `interpolate_to_satellite_grid` to interpolate the result to the satellite grid.
- `postprocess_climatology` makes the dataset easier to use by renaming some variables and ensuring the sea ice concentration has units `%`.
- `get_monthly_climatology_model` finalises the request to be passed to `download.download_and_transform`, calls `download.download_and_transform`, and then calls `postprocess_climatology` and returns the post-processed dataset.
- `get_monthly_climatologies_cmip6` creates the ensemble mean of monthly climatologies of the sea ice concentration, for a given experiment and time period. It does this by looping over the models in a given experiment and and calling `get_monthly_climatology_model` for each.

rename month
rename SIC and convert to %
some models produce extra variables so drop any that are not needed


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 1. Import libraries, set parameters and definitions of functions > 1.7 Functions to plot climatology maps
---
- `get_datasets_eumetsat_clim` downloads satellite data for each hemisphere for one day. These datasets will be used to add a mask to CMIP6 projection data to improve their visual appearance.
- `make_sic_maps` plots the climatology maps for each experiment (in columns) and time period (in rows).
- `compare_sic_maps` is a wrapper that organises the inputs to `make_sic_maps` before calling it.


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 1. Import libraries, set parameters and definitions of functions > 1.8 Functions to plot the access to a given area
---
- `plot_sea_masks` makes maps illustrating where we define the transpolar shipping route and the northern sea shipping route.
- `get_access_table` sorts the time series of sea ice area to make a dataset with dimensions `year` and `month` containing the probability that a region will be clear for that year and month. The probability is calculated by considering each model in an experiment.
- `get_access_tables` loops over each CMIP6 projection experiment and calls `get_access_table`.
- `plot_access_tables` then plots the probability of a region being clear for each experiment by year and month.
- `plot_access_vs_month_one_year` plots `Probability clear` against `month` for all the experiments and for a given year. That is, it combines vertical slices of the access tables for all the different experiments.
- `plot_access_vs_month` loops over a number of years and calls `plot_access_vs_month_one_year`.

initialise mask from SIC land mask
apply sea mask and zoom in on it
add area to title
Create a boolean mask where area > area_threshold
Convert boolean values to integers (True -> 1, False -> 0)
Calculate the fraction of models where area > area_threshold along the 'model' dimension
Get the time values and extract year and month
Find unique years and months
Create an empty 2D array with dimensions ('month', 'year')
Fill the 2D array with the corresponding probability_clear values

(section-2)=


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 2. Download and transform data > 2.1 Set some downloading parameters
---
- `io_kwargs` are some options to speed-up the downloading.
- `common_kwargs` adds the `transform_func` option to make `download.download_and_transform` transform the data using `compute_interpolated_sea_ice_extent_and_area`.
- `transform_func_kwargs` are options to be passed to `compute_interpolated_sea_ice_extent_and_area`.
- `interpolation_kwargs` are options for interpolation.
- `region_name_mapper` makes the region names more user friendly.

Parameters to speed up IO


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 2. Download and transform data > 2.2 Download and transform satellite data for time series
---
Loops over the two satellites and the four masks to get time series of sea ice extent and area for the satellite sea ice concentration products. `download.download_and_transform` downloads the data and transforms it with `compute_interpolated_sea_ice_extent_and_area` before saving it to disk. The dataset is then post-processed with `postprocess_dataset`.  The time series are stored in the dictionary `datasets_satellite`.

download SIC for one date to get land mask for grid to illustrate the different sea masks


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 2. Download and transform data > 2.3 Download and transform CMIP6 data for time series
---
Loops over each model in each experiment, and the four masks, to get time series of sea ice extent and area for the CMIP6 models. For each combination, `download.download_and_transform` downloads the data and transforms it with `compute_interpolated_sea_ice_extent_and_area` before saving it to disk. The dataset is then post-processed with `postprocess_dataset`. The time series are stored in the dictionary `datasets_cmip6`.


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 2. Download and transform data > 2.4 Download and process climatologies for CMIP6 projections
---
This step loops over each hemisphere, each model in each experiment, and each time period, and calls `get_monthly_climatologies_cmip6` to get the ensemble mean (average over all models in an experiment) for each hemisphere, experiment and time period. The climatologies are stored in the dictionary `datasets_cmip6_clim`.

options for plotting climatologies

(section-3)=


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 3. Results
---
In this section we plot yearly minima and maxima for sea ice area, for the Arctic and Antarctic. We also estimate the probability that a region is ice-free,
by taking the ensemble of models and using the percentage of models predicting it as having low enough area as the probability. We chart this by month-by-month to also see how much of the year periods with a high chance of being ice-free are happening.

(section-3.1)=


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 3. Results > 3.1 Arctic sea ice minima and the projected probabilities of an ice-free Arctic
---
In the Arctic region, the yearly minimum in sea ice area shows a clear effect of increased warming. Before about 1990 the historical CMIP6 is relatively constant. After this time however it drops rapidly. This agrees quite well with the satellite data when they are available. As we come to the start of the projection period (2015-2100), the yearly minima continues to drop. The minimum area is highest for the `ssp1_2_6` experiment and its median plateaus around the ice-free level, so about half the models predict the Arctic will be ice-free in the projection period under this scenario. The medians for the `ssp2_4_5` and `ssp3_7_0` minimum extents are similar, both reaching the ice-free level about 2050, and even dropping close to zero by the end of the century. The `ssp5_8_5` median is consistently the lowest and reaches the ice-free level around 2040. The median drops to near zero around 2070.

The yearly minimum in sea ice area follows mostly the same pattern, although the time when the median and lower quartile reach zero is slightly later since there is no concentration threshold used in the calculation (a grid cell is classed as ice-free if the concentration is below 30%).

The maps below show the spatial pattern of the decrease in sea ice cover by plotting the ensemble mean of the sea ice concentration for a series of 20-year climatologies for the month of September. The magenta lines plot the ice edge (15% concentration contour). Going from left to right (increasing the amount of warming in the projection), there is quite a big difference, especially from 2035. From 2055 only the `ssp_1_2_6` experiment has a significant amount of ice.

The heat maps below show the proportion of models having a minimum Arctic sea ice area less than $10^6$ km$^2$. For the `ssp1_2_6` experiment, the chances  of being ice-free in September reach 20% around 2040, 40% around 2050 and 50% around 2070. In August there are consistently 40-50% probabilities after 2080, while in October the chances are about 20-30% from this time. From 2090, about 15% of the models predict ice-free conditions in July under this warming scenario.

We can see however that the chances are being overestimated (perhaps indicating a bias of about 10%) since some years in the past (at time of writing i.e. 2015-2024) have 10% chance of the Arctic being ice-free  (1 in 10 models predict this) while it has never been observed yet. Choosing a different subset of models could also avoid this bias.

In the `ssp2_4_5` experiment, the pattern is similar but the probabilities have increased somewhat. In September they are above 80% from about 2085, with it being 95-100% for three of the later years. In August and October the chances reach about 70% (in 2085 and 2093 respectively), and 20% of the models are predicting ice-free conditions in July and November.

In the `ssp3_7_0` experiment, there are now 85%-90% chances of being ice-free in August-October after about 2085. The chances in July and November have also increased (above 40% after about 2083, and sometimes reaching 60%), and there is also a 20%-30% chance of it being ice-free in June and December from about 2095.

In the `ssp5_8_5` experiment, there are now chances of the Arctic being ice-free from August to October of around 70-80% from about 2070. In September, the chances of being ice-free are consistently over 80% from around 2065. July and November have high chances (70%-85%) of an ice-free Arctic in the last decade or two under this scenario, and the chances of June and December start to become significant earlier than under `ssp3_7_0`. In this experiment (`ssp5_8_5`), even May and January have 20% probability in the last 5 years or so.


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 3. Results > 3.1 Arctic sea ice minima and the projected probabilities of an ice-free Arctic
---
start to become significant earlier than under `ssp3_7_0`. In this experiment (`ssp5_8_5`), even May and January have 20% probability in the last 5 years or so.

In summary, the heat maps above show that as the warming increases, we can clearly see that the overall probability of being ice-free is increasing, and the length of the season where there is high probability is also increasing from just September (`ssp1_2_6`) to the 6-month period from June to December (`ssp5_8_5`).
This can also be seen in the plots below, where we compare the experiments more directly by plotting the monthly probabilities for a selection of years.

In the years before 2060, the probabilities are not ordered by warming strength, but around 2060 `ssp3_7_0` reaches the `ssp2_4_5` level, and after that year the probabilities' order does correspond to warming strength.
In 2040, the maximum probability ranges from about 20% to 40%; in 2050 and 2060 it ranges from about 40% to 60%; in 2070, 2080 and 2090 it ranges from about 45% to about 85%. In the latter three years the main effect is the widening of the season with the highest probabilities.

Comparing to other authors estimates, the [SIMIP community (2020)](https://doi.org/10.1029/2019GL086749) found that nearly all of their selected models projected ice-free conditions by 2050, regardless of warming scenario. [X. Zhao et al. (2022)](https://doi.org/10.1088/1748-9326/ac9d4d) agree with this - they projected the first ice-free year to be $2049\pm 12$ under `ssp2_4_5` and $2043\pm 9$ under `ssp5_8_5`, giving a similar range to the [SIMIP community (2020)](https://doi.org/10.1029/2019GL086749). While our 2050 probability of just under 60% for `ssp5_8_5` is quite high, 40% for `ssp1_2_6` is quite a lot lower. This difference possibly reflects the effect both of using a different subset of models and of using the full ensemble set provided for each model, as [SIMIP community (2020)](https://doi.org/10.1029/2019GL086749) did (instead of just using the ensemble mean for each model).

(section-3.2)=


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 3. Results > 3.2 Arctic sea ice maxima
---
Like the Arctic minimum in sea ice area, the maximum sea ice area is constant until about 1990 the historical CMIP6 is relatively constant, before starting to drop rapidly. However, unlike with the minimum area, there is a clear bias in this variable, with both the ERA5 reanalysis and the CMIP6 ensembles overestimating it by about $1.5\times10^6$km$^2$. As we come to the start of the projection period (2015-2100), the yearly maxima continues to drop. Unlike with the minima, the maximum sea ice area shows a clear response to increased warming - as the warming increases the median Arctic maximum area drops. The experiment with the most warming (`ssp5_8_5`) also shows a very large spread in this variable by 2100.

Looking at the maps below, we can see that there is not too much difference between the maps in March before about 2055. After this however, the `ssp5_8_5` experiment especially starts to have reduced concentration in the central Arctic, and also in the Hudson Bay. The `ssp3_7_0` experiment also shows this pattern to some extent. The drop in maximum sea ice area with time can also be seen, e.g. in the Bering and Labrador Seas.

(section-3.3)=


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 3. Results > 3.3 Antarctic sea ice minima and the projected probabilities of being ice-free around Antarctica
---
The CMIP6 models show a large bias in the Antarctic minimum sea ice area compared to the satellite data, underestimating it by about 1-$2\times10^6$km$^2$. The satellites seem to show a relatively constant minimum, while there is a steady drop predicted by the CMIP6 models in this time period when observations are available (1979-2017).

Keeping in mind the large bias in the CMIP6 models in this area ([Roach et al., 2023](https://doi.org/10.1029/2019GL086729); [CDS assessment of historical sea ice extent](https://ecmwf-projects.github.io/c3s2-eqc-quality-assessment/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q02.html)), the modelled Antarctic minimum in sea ice area shows a clear effect of increased warming. After about 1960 the CMIP6 ensemble median starts to slowly drop, and then after 2015 the rate depends on the warming scenario. The median only reaches our ice-free limit of $0.25\times10^6$ km$^2$ under the `ssp3_7_0` and `ssp5_8_5` scenarios, but the `ssp2_4_5` median is close to it. Under the `ssp1_2_6` scenario the area stays about $0.4\times 10^6$ km$^2$ from about 2040. The spread of the models is quite high.

A clear effect of climate change can be see in the maps below of the Antarctic sea ice concentration for March. Apart from the `ssp1_2_6` experiment, there is very little ice outside the Weddell and Ross Sea, and the `ssp5_8_5` has mostly lost its ice in the Ross Sea by 2075.

The heat maps below show the proportion of models dropping below $0.25\times10^6$ km$^2$ for a given month and year, and below them we compare the monthly probabilities more directly for all the experiments for a selection of years.

As with the Arctic, increased warming increases the overall probability and also the length of the period when there is high probability. However, unlike the Arctic, the Antarctic region has lower chance of being ice-free, only getting consistently over 70% only in the warmest scenario `ssp5_8_5`. The chances in February reach 50% around 2040 and 80% around 2088 in that experiment.
In March and January the probability also reaches 60% (in 2070 and 2095 respectively), and even 70% (from about 2080 in March).

The `ssp_2_4_5` and `ssp_3_7_0` experiments show similar probabilities, consistently reaching 60% in February in the last two decades or so. In March they get to 50% from about 2080 (a few years earlier under `ssp_2_4_5`).

The `ssp1_2_6` experiment has probabilities of being ice-free oscillating between 20%-40% in February, and between 20%-35% in March.

(section-3.4)=


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 3. Results > 3.4 Antarctic sea ice maxima
---
The projected Antarctic sea ice maxima behave in a similar way to the Arctic maxima with time and warming, with a similar relative drop (30%-50%). The CMIP6 models again show a large bias in the Antarctic sea ice area compared to the satellite data, although the difference is less at the start of the start of the satellite period (1979). However, since the satellites show a slight increase in minimum area, while there is a steady drop predicted by the CMIP6 models, the difference increases with time, reaching about $2\times10^6$km$^2$ by 2015.

Less effect can be seen in the maps below of the ensemble mean Antarctic sea ice concentration in September than in March. For the `ssp1_2_6` experiment, there is little difference between the last time period and the first, but there small decreases in area, which are relatively even around the continent, visible for the other experiments. In the last two time periods, there is also a slight decrease in area as the warming increases.

(section-3.5)=


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 3. Results > 3.5 Projected access to Arctic shipping routes
---
We consider two Arctic shipping routes, the Transpolar Shipping Route (TSR) and the Northern Sea Route (NSR). We do not consider the Northwest Passage (NWP) since the climate models generally don't have enough resolution to represent the Canadian archipelago. The figure below shows our definition of these two routes.

(section-3.5.1)=
##### 3.5.1 Northern sea route
In the plots below we can see the minimum sea ice area in the NSR as stable at about $10^6 $km$^2$ until about 1970 when it starts dropping. The historical area is fairly close to the observations in this area, athough they are initially a bit low and a bit high after about 2005. Most experiments' median have dropped below the ice free level by 2040.

The heat maps below show the proportion of models dropping below $0.14\times10^6$ km$^2$ for a given month and year, and below them we compare the monthly probabilities more directly for all the experiments for a selection of years.

We can see even in the experiment with the least warming, there is a high chance of the Northern Sea Route (NSR) being clear in September, especially after about 2040, when the probability reaches about 70%. After this the probabilty fluctuates between about 70% and 80%. August has quite high probabilities too, getting to around 70% around 2060. The probability in October reaches about 50%, while it reaches about 20% in July. The chances increase as the experiments change to produce more warming, and the chances of having the NSR clear in November and July start to increase as well. In November and July `ssp3_7_0` has about 60-70% (sometimes higher) chance after 2082 or 2083. `ssp5_8_5` has 80% chance in November and 70-80% chance in July, both from about 2075. In July `ssp3_7_0` has about 60-70% chance after 2080, while `ssp5_8_5` has 80% chance from about 2075. The experiment `ssp5_8_5` even has 40-50% chance of it being clear in December in later years, and 80-100% of the models are projecting the NSR to be free in August, September, and October from about 2055 under this scenario.

The probabilities shown below for September in 2040 and 2050 are only slightly lower than the results of [Chen et al. (2023)](https://doi.org/10.1016/j.accre.2023.11.011) (see Figures 6a and 6b), who estimated the probability (averaged over 2045-2055) of the Sannikov Strait being passable by open water vessels (without ice strengthening) of 60% under `ssp1_2_6` and 100% under `ssp5_8_5`. The size of their navigability window (about July-November) is also similar to ours. This strait (along with the Dmitry Laptev Strait which had slightly lower passability than the Sannikov Strait) is key for the overall passability of the NSR overall. This consistency is reassuring given the quite different methods of calculating the probabilities and the different subsets of the climate models used.

(section-3.5.2)=
##### 3.5.2 Transpolar shipping route
In the plots below we can see the minimum sea ice area in the TSR as stable at about $1.5 \times 10^6 $km$^2$ until about 1970 when it starts dropping. The area from historical experiment is fairly close to the observations in this area. Most experiments' median have dropped below the ice free level by 2050, with the exception of the `ssp1_2_6` experiment, which is not consistently beneath it until about 2070. This experiment has quite a large spread compared to the others.

The heat maps below show the proportion of models dropping below $0.21\times10^6$ km$^2$ for a given month and year, and below them we compare the monthly probabilities more directly for all the experiments for a selection of years.


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > Analysis and results > 3. Results > 3.5 Projected access to Arctic shipping routes
---
of models dropping below $0.21\times10^6$ km$^2$ for a given month and year, and below them we compare the monthly probabilities more directly for all the experiments for a selection of years.

The Transpolar shipping route (TSR) has slightly lower chances of being ice-free in the months August-October than the NSR but somewhat surprisingly it is a bit higher in other months, with it having some chance of it being clear in winter months in the experiments with higher warming.
For example, under `ssp5_8_5` there is a 35% chance of the TSR being clear from February to May after 2089 (after 2085 for February), and there is more than 60% chance of it being clear in December from about 2088, and the same chance in January from 2095.
Under `ssp3_7_0` there is more than 20% chance of the TSR being ice-free in December, and also in January from 2090.

The maps below plot the ensemble mean sea ice concentration for December for a series of time intervals between 2015 and 2095. In the 2075-2095 period, the `ssp5_8_5` has a distinct gap where we have defined our TSR, while there is still ice off the Russian coast blocking the NSR. The effect is also present to a lesser extent for the `ssp2_4_5` and `ssp3_7_0` xperiments, so perhaps ice-strengthened vessels could still traverse the TSR under these scenarios also. This is consistent with the results of [Min et al (2022)](https://doi.org/10.1029/2022GL099157) (see Figure 3), who found that from 2080 to 2100, the TSR would be a more robust option than the NSR or the North-West Passage for open water vessels.


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > ℹ If you want to know more > Key resources
---
Introductory sea ice materials:
- [Role of sea ice in the climate](https://marine.copernicus.eu/explainers/why-ocean-important/sea-ice)
- [Sea ice as an indicator of climate change](https://climate.copernicus.eu/climate-indicators/sea-ice#)
- [Observing sea ice with satellites](https://www.metoffice.gov.uk/research/climate/cryosphere-oceans/sea-ice/measure)

Introductory CMIP6 materials:
- [A short introduction to CMIP and CMIP6](https://www.wcrp-climate.org/wgcm-cmip/cmip-video)
- [CMIP6: the next generation of climate models explained](https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained/)

Code libraries used:
- [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control) developed by [B-Open](https://www.bopen.eu/)


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > ℹ If you want to know more > References
---
1. Chen, J. L., Kang, S. C., Wu, A. D., Chen, L. H., & Li, Y. W. (2023). Accessibility in key areas of the Arctic in the 21st mid-century. Advances in Climate Change Research, 14(6), 896-903, [ https://doi.org/10.1016/j.accre.2023.11.011 ](https://doi.org/10.1016/j.accre.2023.11.011).

1. Chen, J., Kang, S., You, Q., Zhang, Y., & Du, W. (2022). Projected changes in sea ice and the navigability of the Arctic Passages under global warming of 2℃ and 3℃. Anthropocene, 40, 100349, [ https://doi.org/10.1016/j.ancene.2022.100349 ](https://doi.org/10.1016/j.ancene.2022.100349).

1. Chen, J. L., Kang, S. C., Guo, J. M., Xu, M., & Zhang, Z. M. (2021). Variation of sea ice and perspectives of the Northwest Passage in the Arctic Ocean. Advances in Climate Change Research, 12(4), 447-455, [ https://doi.org/10.1016/j.accre.2021.02.002 ](https://doi.org/10.1016/j.accre.2021.02.002).

1. Davy, R., and S. Outten (2020). The Arctic Surface Climate in CMIP6: Status and Developments since CMIP5. J. Climate, 33, 8047–8068, [ https://doi.org/10.1175/JCLI-D-19-0990.1 ](https://doi.org/10.1175/JCLI-D-19-0990.1).

1. Heuzé, C. & Jahn, A. (2024). The first ice-free day in the Arctic Ocean could occur before 2030. Nat. Commun. 15, 10101, [ https://doi.org/10.1038/s41467-024-54508-3 ](https://doi.org/10.1038/s41467-024-54508-3).

1. Jahn, A., Kay, J. E., Holland, M. M., & Hall, D. M. (2016). How predictable is the timing of a summer ice‐free Arctic? Geophysical Research Letters, 43(17), 9113-9120 , [ https://doi.org/10.1002/2016GL070067 ](https://doi.org/10.1002/2016GL070067).

1. Jahn, A., Holland, M.M. & Kay, J.E. (2024). Projections of an ice-free Arctic Ocean. Nat. Rev. Earth. Environ. 5, 164–176, [ https://doi.org/10.1038/s43017-023-00515-9 ](https://doi.org/10.1038/s43017-023-00515-9).

1. Li, X., Stephenson, S. R., Lynch, A. H., Goldstein, M. A., Bailey, D. A., & Veland, S. (2021). Arctic shipping guidance from the CMIP6 ensemble on operational and infrastructural timescales. Climatic Change, 167, 1-19, [ https://doi.org/10.1007/s10584-021-03172-3 ](https://doi.org/10.1007/s10584-021-03172-3).

1. Melia, N., Haines, K., and Hawkins, E. (2016). Sea ice decline and 21st century trans-Arctic shipping routes, Geophys. Res. Lett., 43, 9720–9728, [ https://doi.org/10.1002/2016GL069315 ](https://doi.org/10.1002/2016GL069315).


EQC Quality Assessment: "Projections of future ice-free periods for the Arctic and Antarctic"
Dataset: projections-cmip6 [CDS]
Aspect: climate-impact-indicators_q04  |  Category: Climate_Projections
Section: Projections of future ice-free periods for the Arctic and Antarctic > ℹ If you want to know more > References
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0–9728, [ https://doi.org/10.1002/2016GL069315 ](https://doi.org/10.1002/2016GL069315).

1. Min, C., Yang, Q., Chen, D., Yang, Y., Zhou, X., Shu, Q., & Liu, J. (2022). The emerging Arctic shipping corridors. Geophysical Research Letters, 49(10), e2022GL099157. [ https://doi.org/10.1029/2022GL099157 ](https://doi.org/10.1029/2022GL099157).

1. Pan, R., Shu, Q., Wang, Q., Wang, S., Song, Z., He, Y., & Qiao, F. (2023). Future Arctic climate change in CMIP6 strikingly intensified by NEMO‐family climate models. Geophysical Research Letters, 50(4), e2022GL102077, [ https://doi.org/10.1029/2022GL102077 ](https://doi.org/10.1029/2022GL102077).

1. Roach, L. A., J. Dörr, C. R. Holmes, F. Massonnet, E. W. Blockley, D. Notz, T. Rackow, M. N. Raphael, S. P. O'Farrell, D. A. Bailey, and C. M. Bitz (2020). Antarctic sea ice area in CMIP6. Geophysical Research Letters, 47, e2019GL086729, [ https://doi.org/10.1029/2019GL086729 ](https://doi.org/10.1029/2019GL086729).

1. SIMIP Community (2020). Arctic sea ice in CMIP6. Geophysical Research Letters, 47, e2019GL086749, [ https://doi.org/10.1029/2019GL086749 ](https://doi.org/10.1029/2019GL086749).

1. Smith, L. C., & Stephenson, S. R. (2013). New Trans-Arctic shipping routes navigable by midcentury. Proceedings of the National Academy of Sciences, 110(13), E1191-E1195, [ https://doi.org/10.1073/pnas.1214212110 ](https://doi.org/10.1073/pnas.1214212110).

1. Wei, T., Yan, Q., Qi, W., Ding, M., & Wang, C. (2020). Projections of Arctic sea ice conditions and shipping routes in the twenty-first century using CMIP6 forcing scenarios. Environmental Research Letters, 15(10), 104079, [ https://doi.org/10.1175/JCLI-D-19-0990.1 ](https://doi.org/10.1175/JCLI-D-19-0990.1).

1. Wang, B., Zhou, X., Ding, Q., & Liu, J. (2021). Increasing confidence in projecting the Arctic ice-free year with emergent constraints. Environmental Research Letters, 16(9), 094016, [ https://doi.org/10.1088/1748-9326/ac0b17 ](https://doi.org/10.1088/1748-9326/ac0b17).

1. Zhao, J., He, S., Wang, H., & Li, F. (2022). Constraining CMIP6 Projections of an ice‐free Arctic Using a weighting scheme. Earth's Future, 10(10), e2022EF002708, [ https://doi.org/10.1029/2022EF002708 ](https://doi.org/10.1029/2022EF002708).

1. Zhou, X., Wang, B., & Huang, F. (2022). Evaluating sea ice thickness simulation is critical for projecting a summer ice-free Arctic Ocean. Environmental Research Letters, 17(11), 114033, [ https://doi.org/10.1088/1748-9326/ac9d4d ](https://doi.org/10.1088/1748-9326/ac9d4d).


EQC Quality Assessment: "Historical accuracy of sea ice extent in the CMIP6 experiments"
Dataset: projections-cmip6 [CDS]
Aspect: model-performance_q02  |  Category: Climate_Projections
Section: Historical accuracy of sea ice extent in the CMIP6 experiments
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Production date: 2024-05-31

Produced by: Timothy Williams, Nansen Environmental and Remote Sensing Center (NERSC)


EQC Quality Assessment: "Historical accuracy of sea ice extent in the CMIP6 experiments"
Dataset: projections-cmip6 [CDS]
Aspect: model-performance_q02  |  Category: Climate_Projections
Section: Historical accuracy of sea ice extent in the CMIP6 experiments > Quality assessment statement
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These are the key outcomes of this assessment

- Bearing in mind that sea ice concentration estimates from passive microwave observations themselves are quite uncertain, we find that the errors between them and the CMIP6 models in the historical experiment can have significant biases and also have quite a large spread, so should be treated with some caution.
- We find that the CMIP6 models generally underestimate the sea ice extent and area. Errors for the Antarctic are approximately double those in the Arctic. While the Arctic sea ice minima are generally quite accurate, the Arctic maxima are consistently underestimated, as are the Antarctic extrema.
- At the time of the Arctic minimum, there is a general underestimation in the pack ice, but an overestimation in the marginal ice zone (MIZ) and near the coasts.
- At the time of the Arctic maximum, the concentration in the pack is relatively unbiased, but there are some areas where there is strong underestimation (Bering Sea, Sea of Okhotsk)and overestimation (Greenland Sea, Labrador Sea).
- Arctic December: one of the other CMIP6 quality assessments looks at projections of accessibility of Arctic shipping routes, where sea ice in December is particularly interesting. Therefore we also check the reliability of the December concentrations in the CMIP6 models. We find that the concentration in the pack is quite similar to the observations, although there is significant underestimation in Hudson Bay, and less pronounced underestimation in the Bering Sea. In later years, there is also slight underestimation off the Russian coast. The ice extent in the Greenland and Barents Sea is consistently overestimated.
- At the time of the Antarctic minimum, there is strong underestimation in the Weddell, Bellingshausen and Amundsen Seas, while south of the Pacific and Indian Oceans, there is less bias. However, there there is too little ice at the coast and too much away from it.
- At the time of the Antarctic maximum, there is in general too little ice everywhere, with the region away from the coast at longitude about 140W south of the Atlantic Ocean, and in the region south of the Indian Ocean having the most pronounced underestimation. The underestimation on those areas is also increasing with time, while the concentration from satellite is staying relatively constant in this month as well.
- These results are generally consistent with analyses from other authors - for the Arctic by the [SIMIP community (2020)](https://doi.org/10.1029/2019GL086749), [Davy and Outten (2020)](https://doi.org/10.1175/JCLI-D-19-0990.1), [Shu et al (2020)](https://doi.org/10.1029/2020GL087965), [Watts et al (2021)](https://doi.org/10.1175/JCLI-D-20-0491.1), [Henke et al (2023)](https://doi.org/10.1080/15230430.2023.2271592) and [Frankignoul et al (2024)](https://doi.org/10.1175/JCLI-D-23-0452.1); for the Antarctic by [Roach et al (2020)](https://doi.org/10.1029/2019GL086729), [Shu et al (2020)](https://doi.org/10.1029/2020GL087965), [Nie et al (2023)](https://doi.org/10.1029/2023GL105265) and [Li et al (2023)](https://doi.org/10.3390/rs15082048).
- We ourselves don't consider time series of the sea ice minima themselves, but only plot climatologies of the multi-model mean to see how the differences between the models and the observations are distributed spatially. However, this has been done by a few others, e.g. [Shu et al (2020)](https://doi.org/10.1029/2020GL087965), who found the observed Arctic September sea ice extent (SIE) declining trend between 1979 and 2014 is slightly underestimated in CMIP6 models, while the observed weak but significant upward trend of the Antarctic SIE is not captured.
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