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Climate_Projections/CMIP6/climate_projections-cmip6_climate-and-weather-extremes_q01.html

@@ -521,11 +521,13 @@ <h2>📢 Quality assessment statement<a class="headerlink" href="#quality-assess
 <li><p>Despite limitations and biases, the considered subset of CMIP6 models generally reproduces historical trends reasonably well for the summer season (JJA), providing a foundation for understanding past climate behavior. These findings also increase confidence (though they do not ensure accuracy) when analysing future trends using these models.</p></li>
 </ul>
 </div>
+<figure class="align-default" id="id1">
 <a class="reference internal image-reference" href="../../_images/1c0e8cf0-01f8-4c79-a63c-395662058d5e.png"><img alt="Mean Bias SU" src="../../_images/1c0e8cf0-01f8-4c79-a63c-395662058d5e.png" style="width: 900px;" /></a>
-<div>
-    <div style="max-width: 950px;">
-    <p><strong>Fig A.</strong> Number of summer days ('SU') for the temporal aggregation of 'JJA'. Mean bias for the historical period (1971 - 2000) of each individual CMIP6 model.</p>
-</div></section>
+<figcaption>
+<p><span class="caption-number">Fig. 5.1.1.1 </span><span class="caption-text">Number of summer days (‘SU’) for the temporal aggregation of ‘JJA’. Mean bias for the historical period (1971 - 2000) of each individual CMIP6 model.</span><a class="headerlink" href="#id1" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
+</section>
 <section id="methodology">
 <h2>📋 Methodology<a class="headerlink" href="#methodology" title="Link to this heading">#</a></h2>
 <p>This notebook provides an assessment of the systematic errors (trend and climate mean) in a subset of 16 models from <a class="reference external" href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=overview">CMIP6</a>. It achieves this by comparing the model predictions with the ERA5 reanalysis for the maximum-temperature-based indices of ‘SU’ and ‘TX90p’, calculated over the temporal aggregation of JJA and for the historical period spanning from 1971 to 2000 (chosen to allow comparison to <a class="reference external" href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">CORDEX</a> models in another assessment). In particular, spatial patterns of climate mean and trend, along with biases, are examined and displayed for each model and the ensemble median (calculated for each grid cell). Additionally, spatially-averaged trend values are analysed and presented using box plots to provide an overview of trend behavior across the distribution of the chosen subset of models when averaged across Europe.</p>

Climate_Projections/CMIP6/climate_projections-cmip6_climate-and-weather-extremes_q02.html

@@ -520,11 +520,13 @@ <h2>📢 Quality assessment statement<a class="headerlink" href="#quality-assess
 <li><p>A separate assessment evaluates the biases in climatology and trends of these indices for the historical period from 1971 to 2000 (“CMIP6 Climate Projections: evaluating bias in extreme temperature indices for the reinsurance sector”). The results of that assessment show an overall underestimation of the trends for both indices and the climatology of ‘SU’, as well as difficulty in correctly representing the spatial distribution, particularly over regions with complex orography. These biases may affect future projections and should be taken into account before using them.</p></li>
 </ul>
 </div>
+<figure class="align-default" id="id1">
 <a class="reference internal image-reference" href="../../_images/00e7ccee-ce24-4448-a32d-d37b6cce9162.png"><img alt="trend_future_TX90p" src="../../_images/00e7ccee-ce24-4448-a32d-d37b6cce9162.png" style="width: 850px;" /></a>
-<div>
-    <div style="max-width: 900px;">
-    <p><strong>Fig A.</strong> Number of days with daily maximum temperatures exceeding the daily 90th percentile of maximum temperature for a 5-day moving window ('TX90p') for the temporal aggregation of 'JJA'. Trend for the future period (2015-2099). For this index, the reference daily 90th percentile threshold is calculated based on the historical period (1971-2000). The layout includes data corresponding to: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).</p>
-</div></section>
+<figcaption>
+<p><span class="caption-number">Fig. 5.1.2.1 </span><span class="caption-text">Number of days with daily maximum temperatures exceeding the daily 90th percentile of maximum temperature for a 5-day moving window (‘TX90p’) for the temporal aggregation of ‘JJA’. Trend for the future period (2015-2099). For this index, the reference daily 90th percentile threshold is calculated based on the historical period (1971-2000). The layout includes data corresponding to: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).</span><a class="headerlink" href="#id1" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
+</section>
 <section id="methodology">
 <h2>📋 Methodology<a class="headerlink" href="#methodology" title="Link to this heading">#</a></h2>
 <p>This notebook offers an assessment of the projected changes and their associated uncertainties using a subset of 16 models from <a class="reference external" href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=overview">CMIP6</a>. The uncertainty is examined by analysing the ensemble inter-model spread of projected changes for the maximum-temperature-based indices ‘SU’ and ‘TX90p,’ calculated over the temporal aggregation of JJA for the future period spanning from 2015 to 2099. In particular, spatial patterns of climate projected trends are examined and displayed for each model individually and for the ensemble median (calculated for each grid cell), alongside the ensemble inter-model spread to account for projected uncertainty. Additionally, spatially-averaged trend values are analysed and presented using box plots to provide an overview of trend behavior across the distribution of the chosen subset of models when averaged across Europe.</p>

Climate_Projections/CMIP6/climate_projections-cmip6_climate-and-weather-extremes_q03.html

@@ -520,11 +520,13 @@ <h2>📢 Quality assessment statement<a class="headerlink" href="#quality-assess
 <li><p>These findings emphasise the importance of integrating both statistical and physical extreme indices for a comprehensive assessment of climate impacts, particularly when developing adaptive strategies such as reinsurance protections. While percentile-based indices may better capture changes across all regions, they may pose challenges for users who are more used to fixed thresholds.</p></li>
 </ul>
 </div>
+<figure class="align-default" id="id1">
 <a class="reference internal image-reference" href="../../_images/ffd3cf41-b222-4773-9815-fb507773368c.png"><img alt="GWL_TX90p" src="../../_images/ffd3cf41-b222-4773-9815-fb507773368c.png" style="width: 700px;" /></a>
-<div>
-    <div style="max-width: 750px;">
-    <p><strong>Fig A.</strong> Boxplot illustrating the climate signal (i.e., the mean values for the warming level of 2°C compared to the historical period from 1971 to 2000) for the ensemble distribution of the 'TX90p' index. For this index, the daily 90th percentile threshold is calculated based on the historical period (1971-2000). The distribution is created by considering spatially averaged values across Europe. The ensemble mean and the ensemble median are both included. Outliers in the distribution are denoted by a grey circle with a black contour.</p>
-</div></section>
+<figcaption>
+<p><span class="caption-number">Fig. 5.1.3.1 </span><span class="caption-text">Boxplot illustrating the climate signal (i.e., the mean values for the warming level of 2°C compared to the historical period from 1971 to 2000) for the ensemble distribution of the ‘TX90p’ index. For this index, the daily 90th percentile threshold is calculated based on the historical period (1971-2000). The distribution is created by considering spatially averaged values across Europe. The ensemble mean and the ensemble median are both included. Outliers in the distribution are denoted by a grey circle with a black contour.</span><a class="headerlink" href="#id1" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
+</section>
 <section id="methodology">
 <h2>📋 Methodology<a class="headerlink" href="#methodology" title="Link to this heading">#</a></h2>
 <p>This notebook provides an assessment of the projected changes and their associated uncertainties, utilising a subset of 16 models from <a class="reference external" href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=overview">CMIP6</a> under a global warming level of 2°C. The uncertainty is explored by analysing the ensemble inter-model spread of projected changes for the maximum-temperature-based indices ‘SU’ and ‘TX90p,’ calculated over the temporal aggregation of JJA for the specific global warming level of 2°C. In particular, spatial patterns of the climate signal (i.e., mean values of the indices for the warming level of 2°C compared to the historical period from 1971 to 2000) are examined and displayed for each model individually and for the ensemble median (calculated for each grid cell), alongside the ensemble inter-model spread to account for projected uncertainty. Additionally, spatially-averaged values are analysed and presented using box plots to provide an overview of climate signal behavior across the distribution of the chosen subset of models when averaged across Europe.</p>

Climate_Projections/CMIP6/climate_projections-cmip6_climate-impact-indicators_q01.html

@@ -521,11 +521,13 @@ <h2>📢 Quality assessment statement<a class="headerlink" href="#quality-assess
 <li><p>Despite the biases and high inter-model spread in some regions, the outcomes of this notebook offer valuable insights for decisions sensitive to future energy demand. These results may enhance confidence in using these models to analyse future trends, although their accuracy is not assured. To improve the accuracy of these insights, biases should be considered and corrected <a class="reference external" href="https://doi.org/10.1002/joc.5362">[6]</a><a class="reference external" href="https://doi.org/10.1038/s41467-021-25504-8">[7]</a>.</p></li>
 </ul>
 </div>
+<figure class="align-default" id="id1">
 <a class="reference internal image-reference" href="../../_images/83f3529b-f7ec-456a-aab1-9dee68ba895b.png"><img alt="Mean Bias HDD" src="../../_images/83f3529b-f7ec-456a-aab1-9dee68ba895b.png" style="width: 900px;" /></a>
-<div>
-    <div style="max-width: 900px;">
-    <p><strong>Fig A.</strong> Heating Degree Days daily average calculated using the winter comfort threshold of 15.5°C ('HDD15.5') for the temporal aggreggtion of 'DJF'. Mean bias for the historical period (1971 - 2000) of each individual CMIP6 model. The colorbar chosen for representing biases for the HDD15.5 index ranges from red to blue. In this color scheme, negative bias values (red colors) indicate that the Heating Degree Days displayed by the models are lower than those shown by ERA5. Instead, blueish colors denote more Heating Degree Days, indicating a positive bias compared to ERA5. This selection is based on the rationale that more Heating Degree Days are associated with colder conditions, typically represented by blueish colors, while fewer Heating Degree Days imply warmer conditions, depicted by reddish colors.</p>
-</div></section>
+<figcaption>
+<p><span class="caption-number">Fig. 5.1.4.1 </span><span class="caption-text">Heating Degree Days daily average calculated using the winter comfort threshold of 15.5°C (‘HDD15.5’) for the temporal aggreggtion of ‘DJF’. Mean bias for the historical period (1971 - 2000) of each individual CMIP6 model. The colorbar chosen for representing biases for the HDD15.5 index ranges from red to blue. In this color scheme, negative bias values (red colors) indicate that the Heating Degree Days displayed by the models are lower than those shown by ERA5. Instead, blueish colors denote more Heating Degree Days, indicating a positive bias compared to ERA5. This selection is based on the rationale that more Heating Degree Days are associated with colder conditions, typically represented by blueish colors, while fewer Heating Degree Days imply warmer conditions, depicted by reddish colors.</span><a class="headerlink" href="#id1" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
+</section>
 <section id="methodology">
 <h2>📋 Methodology<a class="headerlink" href="#methodology" title="Link to this heading">#</a></h2>
 <p>The reference methodology used here for the indices calculation is similar to the one followed by Scoccimarro et al., (2023) <a class="reference external" href="https://doi.org/10.1038/s43247-023-00878-3">[8]</a>. 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 <a class="reference external" href="https://datastore.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf">CDS application</a>). 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 <a class="reference external" href="https://datastore.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf">CDS application</a>, where both the sum over a period and the daily average values can be displayed.  In Spinoni et al., (2018) <a class="reference external" href="https://doi.org/10.1002/joc.5362">[6]</a>, as well as in the <a class="reference external" href="https://datastore.copernicus-climate.eu/documents/app-heating-cooling-degree-days/C3S_EEA_HDD_CDD_application_user_guide_v0.9.pdf">CDS application</a>, 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 <a class="reference external" href="https://icclim.readthedocs.io/en/stable/">icclim</a> Python package, we opted to utilise a single variable (2m mean temperature).</p>

Climate_Projections/CMIP6/climate_projections-cmip6_climate-impact-indicators_q02.html

@@ -519,7 +519,12 @@ <h2>📢 Quality assessment statement<a class="headerlink" href="#quality-assess
 <li><p>The findings of this notebook could support decisions sensitive to future energy demand. Despite regional variations and some inter-model spread (calculated to account for projected uncertainty), the subset of 16 models from CMIP6 agree on a significant decrease in the energy required for heating spaces during winter. This decrease is particularly notable in regions with high HDD (northern and eastern regions that experience substantial heating energy consumption in winter). Conversely, more energy will be needed in the future to cool buildings during summer, especially in the Mediterranean Basin.</p></li>
 </ul>
 </div>
+<figure class="align-default" id="id1">
 <a class="reference internal image-reference" href="../../_images/7245f9d8-4fce-41a6-9b6b-a4a3519f59e0.png"><img alt="trend_future_CDD" src="../../_images/7245f9d8-4fce-41a6-9b6b-a4a3519f59e0.png" style="width: 850px;" /></a>
+<figcaption>
+<p><span class="caption-number">Fig. 5.1.5.1 </span><span class="caption-text">Cooling Degree Days daily average calculated using the summer comfort threshold of 22°C (‘CDD22) for the temporal aggregation of ‘JA’. Trend for the future period (2016-2099). The layout includes data corresponding to: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).</span><a class="headerlink" href="#id1" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
 <div>
     <div style="max-width: 850px;">
     <p><strong>Fig A.</strong> Cooling Degree Days daily average calculated using the summer comfort threshold of 22°C ('CDD22) for the temporal aggregation of 'JA'. Trend for the future period (2016-2099). The layout includes data corresponding to: (a) the ensemble median (understood as the median of the trend values of the chosen subset of models calculated for each grid cell) and (b) the ensemble spread (derived as the standard deviation of the distribution of the chosen subset of models).</p>