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The Annual Count of Icing Days is the number of days per year where the maximum daily temperature is below 0°C. Note the Annual Count of Icing Days is more severe than the Annual Count of Frost Days as icing days refer to the daily maximum temperature whereas the frost days refer to the daily minimum temperature. The Annual Count of Icing Days measures how many times the threshold is exceeded (not by how much) in a year. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.

The Annual Count of Icing Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of icing days to previous values. 

The Annual Count of Icing Days indicates increased cold weather disruption due to a higher than normal chance of ice and snow. It is based on the maximum daily temperature being below 0°C, the temperature does not rise above 0°C for the entire day. Impacts include:

The Annual Count of Frost Days, is a similar metric measuring impacts from cold temperatures, it indicates less severe cold weather impacts.

The Annual Count of Icing Days is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.   

The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Count of Icing Days, an average is taken across the 21 year period. Therefore, the Annual Count of Icing Days show the number of icing days that could occur each year, for each given level of warming. 

We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.

This data contains a field for each global warming level and two baselines. They are named ‘Icing Days’, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Icing Days 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Icing Days 2.5 median’ is ‘IcingDays_25_median’. 

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘Icing Days 2.0°C median’ values.

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. 

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Icing Days was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. 

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.