fix assessment statement box for all projections nbs (#179)

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

@@ -38,14 +38,16 @@
     "\n",
     "## 📢 Quality assessment statement\n",
     "\n",
+    "```{admonition} These are the key outcomes of this assessment\n",
+    ":class: note\n",
     "* CMIP6 projections based on maximum temperature indices can be valuable for designing strategies to optimise reinsurance protections. However, it is crucial for users to acknowledge that all climate projections inherently contain certain biases. This is especially significant when using projections in regions with complex orography or extensive continental areas, where biases are more pronounced and impact the accuracy of the projections. Therefore, users should exercise caution and consider these limitations when applying the projections to their specific use cases, understanding that biases in the mean climate and trends affect indices differently and that the choice of bias correction method should be tailored accordingly [[6]](https://doi.org/10.1002/asl.1072)[[7]](https://ibicus.readthedocs.io/en/latest/).\n",
     "\n",
     "* GCMs exhibit biases in capturing mean values of extreme temperature indices like the number of summer days ('SU') and days exceeding the 90th percentile threshold ('TX90p'), with underestimation of index magnitudes, especially in continental areas and regions with complex terrain.\n",
     "\n",
     "* ERA5 shows that the magnitude of the historical trend varies spatially. The trend bias assessment suggests that, overall, the trend derived from the CMIP6 projections is underestimated with slight overestimations observed in certain regions. Hence, users need to consider this regional variation in both the trend and its bias.\n",
     "\n",
     "* 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.\n",
-    "\n"
+    "```"
    ]
   },
   {

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

@@ -38,14 +38,16 @@
     "\n",
     "\n",
     "## 📢 Quality assessment statement\n",
-    "\n",
+    "```{admonition} These are the key outcomes of this assessment\n",
+    ":class: note\n",
     "* Looking ahead to future projections (2015-2099), all models within the subset agree on projecting general positive trends for both indices across Europe during the temporal aggregation of JJA, with a particularly notable positive projected trend in the Mediterranean Basin for 'TX90p'. This finding is consistent with the results of Josep Cos et al. (2022) [[6]](https://doi.org/10.5194/esd-13-321-2022), who evaluated the Mediterranean climate change hotspot using CMIP6 projections.\n",
     "\n",
     "* While certain regions exhibit near-zero trends for the 'SU' index, possibly due to threshold temperature constraints, others show higher values, highlighting the importance of considering both statistically and physically based extreme indices for comprehensive assessments.\n",
     "\n",
     "* Utilising CMIP6 projections presents a valuable opportunity to anticipate future trends in air temperature extremes across Europe, enabling the insurance industry to refine risk management strategies. While all considered models show a positive trend for these indices, the magnitude of these trends and their uncertainty (quantified by the inter-model spread) vary spatially and need to be considered.\n",
     "\n",
-    "* 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.\n"
+    "* 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.\n",
+    "```"
    ]
   },
   {

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

@@ -43,15 +43,16 @@
    "metadata": {},
    "source": [
     "## 📢 Quality assessment statement\n",
-    "\n",
+    "```{admonition} These are the key outcomes of this assessment\n",
+    ":class: note\n",
     "* For the CMIP6 models analysed, there is clear agreement on the sign of the climate signal for the selected indices during the JJA season, underscoring the need for proactive measures to optimise reinsurance protections in the face of projected increases in air temperature extremes for a world 2°C warmer. The advantage of using Global Warming levels (GWL of 2°C for this notebook), as opposed to considering the trend for a fixed future period, is that it eliminates the systematic biases of the models by focusing on differences rather than absolute values. However, it is important to note that the timing of the thirty-year period when the global mean temperature reaches 2°C above the preindustrial baseline varies depending on the model, making it challenging to determine.\n",
     "\n",
     "* The considered subset of CMIP6 models shows that, in a world 2°C warmer than the preindustrial baseline, the average number of days with maximum daily temperatures surpassing the daily 90th percentile (calculated for the historical period) during the JJA season exhibits spatial differences across Europe. Notably, values are higher for the Mediterranean Basin, consistent with the findings of Josep Cos et al. (2022) [[7]](https://doi.org/10.5194/esd-13-321-2022), who assessed the Mediterranean climate change hotspot using CMIP6 projections. The boxplot analysis for this index displays an ensemble median spatially-averaged value over Europe larger than 32 days for the JJA season, indicating that under a 2°C global warming level, one out of every three days is projected to have a maximum daily temperature above the daily 90th percentile calculated for the historical period. \n",
     "\n",
     "* The frequency of summer days occurring during the JJA season, is projected to generally increase in a world 2°C warmer than the preindustrial baseline (1861-1890), compared to the average occurrences during the control period (1971-2000) across Europe, albeit with regional variations. Minor changes are expected in the northernmost and southern regions. In the north, where temperatures historically remain below the 25°C threshold even during the JJA season, the increase in the number of summer days is minimal (the threshold temperature of 25°C may be too high to be reached even in a world 2°C warmer than the preindustrial). In the southern Mediterranean Basin, the threshold is usually surpassed throughout the entire JJA season in the historical period, and thus, little change is observed (specially in northern Africa). This arise limitations of this index, indicating the potential need to select a higher threshold to capture the changes in these regions. Such regions, where the changes may be just as impactful or even more so than areas with a significant increase in days above 25 degrees, require careful consideration.\n",
     "\n",
     "* 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.\n",
-    "\n"
+    "```"
    ]
   },
   {

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

@@ -38,14 +38,16 @@
     "\n",
     "\n",
     "## 📢 Quality assessment statement\n",
-    "\n",
+    "```{admonition} These are the key outcomes of this assessment\n",
+    ":class: note\n",
     "* CMIP6 projections provide valuable insights into trends and climatology of Energy Degree Days across Europe, though with inherent uncertainties, and differences compared to the reference dataset (ERA5). During the historical period (1971-2000), GCMs exhibited biases in capturing both mean values and trends for energy-consumption-related indices.\n",
     "\n",
     "* For the 16 models considered, biases were observed in mean values of Heating Degree Days ('HDD15.5') over DJF and Cooling Degree Days ('CDD22') over JJA. Specifically, for most of the models, there is an overestimation of HDD15.5 mean values for DJF (except in regions with complex orography where there was underestimation) and CDD22 mean values for JJA across most of the Mediterranean Basin.\n",
     "\n",
     "* The subset of CMIP6 models show a decrease in the energy needed for heating during winter within the historical period across Europe, especially in the eastern and northern regions. This aligns with results obtained using the ERA5 reference product, except in the Balkans where there is an increase in required heating energy during this period. The ERA5 reference product also shows an increase of the amount of energy needed for cooling buildings in summer during this period, particularly across the Mediterranean Basin. This trend is well captured by the subset of CMIP6 models. Depending on the region, these results may enhance confidence in using these models for analysing future trends, although they do not guarantee accuracy.\n",
     "\n",
-    "* Despite the biases and high inter-model spread in some regions, the outcomes of this notebook offer valuable insights for decisions sensitive to future energy demand. These results may enhance confidence in using these models to analyse future trends, although their accuracy is not assured. To improve the accuracy of these insights, biases should be considered and corrected [[6]](https://doi.org/10.1002/joc.5362)[[7]](https://doi.org/10.1038/s41467-021-25504-8). "
+    "* Despite the biases and high inter-model spread in some regions, the outcomes of this notebook offer valuable insights for decisions sensitive to future energy demand. These results may enhance confidence in using these models to analyse future trends, although their accuracy is not assured. To improve the accuracy of these insights, biases should be considered and corrected [[6]](https://doi.org/10.1002/joc.5362)[[7]](https://doi.org/10.1038/s41467-021-25504-8). \n",
+    "```"
    ]
   },
   {

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

@@ -36,12 +36,14 @@
     "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]](https://epic.awi.de/id/eprint/37530/), the current increase in energy demand is expected to persist until the end of this century and beyond [[6]](https://doi.org/10.1002/joc.5362). Identifying which climate-change-related impacts are likely to increase, by how much, and inherent regional patterns, is important for any effective strategy for managing future climate risks. This notebook utilises data from a subset of models from [CMIP6](https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=overview) Global Climate Models (GCMs) and explores the uncertainty in future projections of energy-consumption-related indices by considering the ensemble inter-model spread of projected changes. Two energy-consumption-related indices are calculated from daily mean temperatures using the [icclim](https://icclim.readthedocs.io/en/stable/) Python package: Cooling Degree Days (CDDs) and Heating Degree Days (HDDs). Degree days measure how much warmer or colder it is compared to standard temperatures (usually 15.5°C for heating and 22°C for cooling). Higher degree day numbers indicate more extreme temperatures, which typically lead to increased energy use for heating or cooling buildings. In the presented code, CDD calculations use summer aggregation (CDD22), while HDD calculations focus on winter (HDD15.5), presenting results as daily averages rather than cumulative values. Within this notebook, these calculations are performed over the future period from 2016 to 2099, following the Shared Socioeconomic Pathways SSP5-8.5. It is important to note that the results presented here pertain to a specific subset of the CMIP6 ensemble and may not be generalisable to the entire dataset. Also note that a separate assessment examines the representation of climatology and trends of these indices for the same models during the historical period (1971-2000), while another assessment looks at the projected climate signal of these indices for the same models at a 2°C Global Warming Level.\n",
     "\n",
     "## 📢 Quality assessment statement\n",
-    "\n",
+    "```{admonition} These are the key outcomes of this assessment\n",
+    ":class: note\n",
     "* The subset of considered CMIP6 models agree on a general decrease in future trends (2016-2099) for HDD15.5 across Europe during DJF and an increase in CDD22 during JJA. However, regional variations exist. \n",
     "\n",
     "* The northern and eastern parts of Europe are projected to experience the largest decrease in HDD15.5, accompanied by higher inter-model variability. Some areas show no trend for CDD2 due to threshold temperatures not being reached. The Mediterranean Basin will see the greatest increase in CDD22, with higher inter-model variability.\n",
     "\n",
-    "* 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.\n"
+    "* 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.\n",
+    "```"
    ]
   },
   {