How our data is built

The climate risk dashboard includes time series plots showing the median evolution of each indicator over time for each country and for each scenario. To obtain these time series, results from each simulation obtained with each model configuration were first spatially averaged over the country areas. Data were then extracted for each timestep visualised in the tool (from 2020 till 2100 or 2300 depending on the scenario), as well as each reference period (2011-2020 and 1850-1900), from which the changes between each timestep and each reference period were calculated.

For the indicators quantifying changes in soil moisture, fire weather or Annual maximum temperature (obtained with MESMER-X) and Mean temperature (obtained with MESMER), the median projections (visualised with a thick line) were calculated by identifying the 50th percentile across all obtained realisations (1000 times the number of model configurations for each scenario), while the confidence interval was calculated by identifying the 5th and 95th percentiles across those. Changes in indicators quantifying changes in soil moisture are expressed in % relative to values simulated over the selected reference period.

For the Extremely cold year and Extremely hot year indicators, in order to calculate the results for their 1-in-10-year, 1-in-20-year and 1-in-50-year events their 2nd, 5th, 10th, 90th, 95th, and 98th percentiles were first identified from the ensemble of 1000 MESMER realisations of variability, for each of the 25 model configurations. These percentiles were then combined with the 100 MESMER forced responses corresponding to each model configuration. 25*100 values are thus obtained for each of the 6 indicators (1-in-10-year, 1-in-20-year and 1-in-50-year Extremely cold and hot years). The mean projections (visualised with a thick line) were calculated by identifying the 50th percentile across this ensemble, while the confidence interval was calculated by identifying the 5th and 95th percentiles across those.

The climate risk dashboard also includes maps showing projected median changes in each indicator for each country and for each scenario. These are obtained by following a similar procedure as for the time series plots, except that results are not averaged over the country area but that percentiles are identified for each model grid cell individually.

For most indicators of the Terrestrial Climate sector, the climate risk dashboard includes graphs describing the evolution of the probability (risk) that the indicator of interest averaged over a country exceeds specific levels of increase or decrease between today and 2030, 2050, 2100, as well as 2200 and 2300 if applicable.

For the Annual maximum temperature and Mean temperature indicators, these levels can be selected by users and correspond to predefined increases in that indicator (for example +0.5°C or +1°C), compared to the levels of the present-day (2011-2020) or pre-industrial (1850-1900) reference period. For each country, the indicator values over the reference periods were first identified by calculating the 50th percentiles of the values simulated for those reference periods of across all MESMER-X realisations or the representative MESMER ensemble members (100x10 times the number of model configurations), using the same procedure as for the time series plots. Then, the risk that these specific values are exceeded in each scenario and year of interest is calculated by looking at the share of MESMER-X realisations or representative MESMER ensemble members exceeding those values in the given year.

For the Extremely cold year and Extremely hot year indicators, the reference values are defined by the event frequency of interest (1-in-10 year, 1-in 20-year, 1-in-50-year for both indicators). For example, the reference value for the 1-in-20-year Extremely hot year in 2011-2020 over a specific country corresponds to the 95th percentile across the country average values derived from the representative MESMER ensemble members. For each frequency, the corresponding percentile is identified for each reference period, scenario and country. Then, the probability that these percentiles are exceeded at the timesteps of interest (2030, 2050, 2100, as well as 2200 and 2300 if applicable) is calculated by looking at the share of the country average values derived from the representative MESMER ensemble members that exceeds them.

The hourly output from UrbClim is averaged over periods of 10 years to even out the effect of natural variability. On the climate risk dashboard, maps of projected changes in some indicators between the 2021-2030, 2041-2050 and 2091-2100 time periods compared to present-day are shown when the years 2030, 2050 or 2100 are selected, respectively. These changes correspond to those obtained by forcing UrbClim with the 50th percentile of the FaIR-MESMER ensemble. The resolution of the maps is 100 meters. For each city, the shapes shown on the maps correspond to mostly contiguous built-up areas (i.e., represent the urban fabric).

The time series plots shown above the maps visualise the spatially averaged results displayed on the maps, with the confidence interval obtained from UrbClim results obtained when using forcing data representative of the 5th and 95th percentiles of the FaIR-MESMER ensemble.

The indicator ‘Population exposed to heatwaves’ was calculated by, for each location, multiplying the population living in each location by the mean annual number of heatwave days occurring in this location. Population data is taken from Worldpop (constrained UN adjusted 2020), and it is here assumed that the size and distribution of the population stays constant, so that only changes in climate and not those in socio-economic factors are accounted for.

Time series and individual indicator maps can be downloaded individually by clicking on the corresponding buttons underneath. A bulk downloading option is available via ftp upon request (niels.souverijns@vito.be).

Apart from 100m, for the iconic cities, also 1m-scale heat stress simulations are executed using the HiREx model (Souverijns et al., 2023). This requires a detailed representation of individual features within the city quarters that are modelled, such as trees, buildings (and their height), roads, etc. These simulations are limited to one typical day in summer, due to their high computational costs. By considering the solar zenith and azimuth angles, shaded areas cast by buildings or trees were calculated for each hour of the day, as also the sky view factor (the fraction of the sky hemisphere visible from the ground). Combining the meteorological data with the detailed land surface properties, the HiREx module can iteratively calculate the Wet Bulb Globe Temperature at a 1m resolution, considering shade and solar zenith angles in each model time step. As this approach is nested in the UrbClim simulations, one can do calculations easily for both present and any future scenario calculated by UrbClim.

For the dashboard, we analyse the glacier projections for various geographies, including the country level. For the aggregation, each glacier is assigned to a geography based on the glacier’s terminus position. For each geography and each climate change scenario, we processed the corresponding data to generate three distinct plots, as described below.

The uncertainty in the projections originates from the global climate system response and climate variability as well as the emulated local climate. The uncertainty range is computed from the full ensemble for each scenario, taking the respective weight of each quantile into account. Glacier model uncertainty is not taken into account here.

The glacier variables shown in the dashboard consist of volume and area as percentages relative to the geographies total value in 2020 and the thinning rate in metres of water equivalent (w. e.) per year. A thinning rate of 1 means that, on average, the glaciers lost what corresponds to 1 metre of water across the entire glacierized area in that year. Thinning rate is calculated by dividing the annual volume change by the average area and adjusting for the density of ice and water.

For more details on our data aggregation methods, please refer to our GitHub repository at https://github.com/OGGM/provide, where you can find the actual code used for the creation of the glacier data displayed on the dashboard.

The Climate Change Impact on GDP timeseries in the dashboard shows GDP in a given year under the influence of climate change compared to what GDP would have been in the same year without climate change. The baseline growth rate follows SSP2. The median, 5th and 95th percentiles are derived from ther 100 GDP estimates.