diff --git a/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q02.ipynb b/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q02.ipynb
index b4acdab2..9fb637e0 100644
--- a/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q02.ipynb
+++ b/Climate_Projections/CMIP6/climate_projections-cmip6_model-performance_q02.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "## 📢 Quality assessment statement\n",
     "\n",
-    "{admonition}\n",
+    "```{admonition} These are the key outcomes of this assessment\n",
     ":class: note\n",
     "\n",
     "- 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.\n",
@@ -29,6 +29,7 @@
     "- 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.\n",
     "- 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).\n",
     "- 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.\n",
+    "```\n",
     "\n",
     "## 📋 Methodology\n",
     "We compare sea ice concentrations from the [CMIP6](https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=overview) historical experiment with that obtained from [passive microwave satellite products from EUMETSAT OSI- SAF and ESA CCI](https://cds.climate.copernicus.eu/datasets/satellite-sea-ice-concentration?tab=overview).  Time series of the evaluation metrics Integrated Ice Edge Error (IIEE) ([Goessling et al, 2016](https://doi.org/10.1002/2015GL067232); [Henke et al, 2023)](https://doi.org/10.1080/15230430), bias in extent and area, and the root mean square error (RMSE) in sea ice concentration are produced and plotted. All of these statistics are area-weighted averages. We consider all the CMIP6 models that output sea ice concentration in the historical experiment, but remove outliers by only plotting the interquartile limits (IQLs). Other authors have chosen different subsetting approaches - e.g. by scoring the models and only retaining the top-performing ones. We recommend users test as many models as possible, before deciding if a subset adequately represents the uncertainty for their application.\n",