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Climate Data and Indices

Input Data

Historical and projected climate variables are the main data sources that are exploited for generating the climate indicators. They are complimented by Earth Observation (EO) data and in-situ observation data. CRII uses the following datasets for generating the climate indices:

  • The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis datasets version 5 (ERA5) which provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data covers the Earth on a 0.25° (~30 km) grid and resolve the atmosphere using 137 levels from the surface up to a height of 80 km. The ERA5 dataset from 1950 to present is now available on the Climate Data Store (CDS). Telespazio UK obtained daily 2 m air temperature, total precipitation and u- and v- components of wind to compute wind power (1980-present) within CRII.
  • The climate projection dataset is retrieved from NASA’s NEX-GDDP-CMIP6 (NASA Earth Exchange Global Daily Downscaled Projections). “The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across all four ‘Tier 1’ greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions". This dataset includes downscaled projections from ScenarioMIP model for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate condition.

For CRII, outputs from GFDL-ESM4 and UKESM1-0-LL climate models have been used under the following scenarios:

  1. SSP1/RCP 2.6 (multi-model mean of less than 2°C warming by 2100)
  2. SSP2/RCP 4.5 (multi-model mean of 2.7°C warming by 2100)
  3. SSP3/RCP 7.0 (multi-model mean of between 3°C and 4°C warming by 2100)
  4. SSP5/RCP 8.5 (multi-model mean of above 5°C warming by 2100)

Indices

Extreme Temperature Index

CRII extreme temperature is defined as the frequency of temperatures above the 90th percentile (T90) and below the 10th percentile (T10), relative to the reference period of 1981 to 2010. To compute these indices, first the 10th and 90th percentile of daily maximum and minimum temperature for each calendar day is calculated for a 5-day window centred on each calendar day in the 1981–2010 period. Then, the monthly frequency of daily maximum and minimum temperatures lying below the 10th and above the 90th percentiles are calculated. They are expressed as percentages (percentages of days in a month above/ below the percentile threshold). Finally, the mean values of these monthly percentile differences over the reference period 1981-2010 is calculated, giving 12 monthly values of mean and standard deviation for the reference period. The differences (e.g. anomalies) in exceedance frequency are then computed for each month in the entire period (1981-2100).

Extreme Rainfall Index

The Precipitation (P) component is defined as the maximum five-day precipitation in a given month (rx5day). The anomaly of rx5day relative to the reference period value for month of year is calculated. Positive values of anomalies (∆P) express an increase in multiple day, heavy-precipitation events compared to the reference period.

Draught Index

Consecutive Dry Days (CDD), defined as the maximum number of consecutive days in a year with less than 1 mm of daily precipitation, were calculated for the entire period. Monthly values were obtained by the linear interpolation of annual values. The conversion of consecutive dry days to a percentage anomaly (denoted by CDD) was done in the same manner as for the other components and then converted to standardized anomalies.

High Wind Index

Daily wind speed and direction measurements from ERA5 and ISIMIP projections are converted to Wind Power (WP). Similar to extreme temperature, the 90th percentile of WP over the reference period is computed. The change in the frequency of winds above 90th percentile (WP90) is then calculated for each month in the entire period, expressed as a percent anomaly and then normalised using standard deviation over the reference time period.

Actuaries Climate Index

For any individual index, the standardized anomaly corresponds to how unusual that month’s value is, compared to the reference period mean and standard deviation for that month. Hence, each component is in units of the standard deviation of that quantity. The use of standardized anomalies allows us to combine these indicators in a straightforward and meaningful manner. Using standardized anomalies allows such inherently different quantities to be combined in a single index, while preserving the accuracy of each component.

For all indices, a positive variation reflects an increase in climate-related extremes and thus increases the value of the Index. The only exception is T10, because cold temperatures have been declining by about the same magnitude as warm temperatures have been increasing.