Property Climate Risk Report - Residential
Historical data sources
Climate Insights applied the best available data sources for each country, and we will keep updating our data. The following table is an example of the USA and European data. More details can be found on the Climate Insights website.
| Variable name | Original Data Sources for Baseline data development |
|---|---|
| Tsunami |
World Bank Global Tsunami Hazard: https://datacatalog.worldbank.org/dataset/global-tsunami-hazard |
| Monthly precipitation, mean temperature, and relative humidity |
National and International Agencies Europe: ECAD: https://www.ecad.eu/dailydata/ USA: PRISM: https://prism.oregonstate.edu/normals/ Australia: BOM: http://www.bom.gov.au/ WorldCLIM2: http://www.worldclim.com/version2 |
| Daily extreme precipitation |
National Agencies NOAA ATLAS14: https://hdsc.nws.noaa.gov/hdsc/pfds/pfds_gis.html Daymet: https://daymet.ornl.gov/ |
| Daily Extreme Wind Speed |
MERRA2 hourly wind data: https://disc.gsfc.nasa.gov/datasets/M2T1NXRAD_5.12.4/summary?keywords=%22MERRA-2%22%20%20hourly Cyclone wind speed: (https://risk.preventionweb.net/capraviewer/main.jsp?tab=3 or ) |
| Aridity Index |
CRU TS4.04: Climatic Research Unit (CRU) Time-Series (TS) version 4.04: https://catalogue.ceda.ac.uk/uuid/89e1e34ec3554dc98594a5732622bce9 |
| No. of days Max temperature >35 °C |
Global Meteorological Forcing Dataset for land surface modelling https://hydrology.princeton.edu/data.pgf.php Europe: ECAD: https://www.ecad.eu/dailydata/ Australia: BOM: http://www.bom.gov.au/ |
| No. of days Min Temperature <2 °C |
Europe: ECAD: https://www.ecad.eu/dailydata/ Global Meteorological Forcing Dataset for land surface modelling https://hydrology.princeton.edu/data.pgf.php |
| Heatwave Days |
Global Meteorological Forcing Dataset for land surface modelling https://hydrology.princeton.edu/data.pgf.php |
| Heating Degree days |
Global Meteorological Forcing Dataset for land surface modelling https://hydrology.princeton.edu/data.pgf.php E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations: |
| Cooling Degree Days |
Global Meteorological Forcing Dataset for land surface modelling https://hydrology.princeton.edu/data.pgf.php E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations: https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview |
| Wildfire |
Global Meteorological Forcing Dataset for land surface modelling https://hydrology.princeton.edu/data.pgf.php |
| Mean Sea Level Rise | Historical Land Movement (VLM) was estimated from the direct observations (SONEL) and observed tides archived by the permanent service for mean sea level (PSMSL); The future projections are created based on the Fifth Assessment Report (AR5) of the IPCC, applying an ensemble of 28 general circulation models (GCMs) |
| Coastal Extreme Water Level |
Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C., & Ward, P. J. (2016). A global reanalysis of storm surges and extreme sea levels. Nature communications, 7, 11969. A global reanalysis of storm surges and extreme sea levels https://data.4tu.nl/repository/uuid:29614991-345e-4ffd-be22-2930912a2798 |
GCM/RCM data sources:
CMIP5 GCM data: https://esgf-node.llnl.gov/search/cmip5/
CORDEX RCM data: https://esg-dn1.nsc.liu.se/search/cordex/
GCM and RCM data list could be found: (find the file of GCM/RCMs)
For more details: Climate Insights(https://climateinsights.global/); SimCLIM documentation (https://www.climsystems.com/simclim/downloads)
Acknowledgement: We greatly appreciate the hard work and dedication shown by all the data collectors, processors, and providers worldwide.
Brief Methodology Descriptions
| Variable | Methodology Description |
|---|---|
| Tsunami | The tropical cyclone intensity (based on wind speed) and frequency risk levels have been calculated from IBrACS (International Best Track Archive for Climate Stewardship) data from 1980 to 2020. |
| Monthly precipitation, mean temperature, and relative humidity | Monthly mean precipitation, temperature changes are calculated based on a pattern scaling approach. Pattern scaling techniques have been widely used to provide climate change projections for periods and emission scenarios that GCMs and RCMs have not simulated. The assumption underlying these methods is that a climate variable's local response is linearly related to the global mean temperature change, with the geographical pattern of change independent of the forcing (Mitchell et al., 1999; Mitchell, 2003). Change factors are applied to the historical monthly values obtained from the data sources' data, using GCM and RCM ensemble approaches. |
| Extreme precipitation |
Future extreme precipitation change factors were derived using the daily GCM precipitation outputs at their original spatial resolution (i.e., not downscaled). A GEV distribution is built for the baseline period associated with the relevant GCM analysis period (1986-2005 for Assessment Report 5) using daily extremes for selected return periods. Another GEV distribution is constructed for a 20-year window centred on future target years. The percentage difference in the extreme precipitation value between the baseline and future target years are calculated. The annual global average mean temperature change is calculated between the baseline and future target years for the GCM. The extreme precipitation changes are normalized by using the linear least square regression method for the individual grid cell and percentage change in global mean temperature. A bi-linear interpolation method is then used to obtain finer scale resolution based on local precipitation patterns using GIS layers such as PRISM. The resulting percentage change is applied to the historical daily climate observations for the station located in the local grid square. The change factors are applied to the historical extreme precipitation values obtained by GEV analysis using the data listed in the data sources table. |
| Daily Extreme Wind Speed | A change factor approach is applied for extreme wind speed change analysis. For each GCM, the wind speed change values were derived directly from the GCM data between the future and historical period. The 75th percentile of the GCM ensemble for both wind speed changes was applied. |
| Aridity Index | Aridity is usually expressed as a generalized function of precipitation, temperature, and/or potential evapotranspiration (PET). An Aridity Index (UNEP, 1997) can quantify precipitation availability over atmospheric water demand. The Aridity Index is expressed as the ratio of the annual precipitation to the PET. Change factors of temperature and precipitation are applied for future Aridity Index calculations. |
| No. of days Max temperature >35 °C | For the historical period, it is the average number of days in a year when the daily maximum temperature is above 35 °C. For future years, monthly maximum temperature change factors were applied to the daily time series of the historical maximum temperature data, then count the number of days above 35 °C. |
| No. of days Min Temperature <2 °C | For the historical period, it is the average number of days in a year when the daily minimum temperature is below 2°C. For future years, monthly minimum temperature change factors were applied to daily time series of the historical minimum temperature data, then count the number of days below 2 °C. |
| Heating and Cooling Degree days | The base temperature for heating degree days (HDD) and cooling degree days (CDD) in this assessment is 18.0 °C. Daily mean temperature from gridded data was applied for HDD and CDD analysis. Monthly mean temperature change factors are applied for daily mean temperature change in different scenarios. |
| Wildfire |
The McArthur's Forest Fire Danger Index (FFDI) was applied to assess the wildfire potential that refers to meteorological conditions and vegetative fuel sources conducive to wildfires. KBDI is applied as Drought Factor . Daily precipitation and temperature are the only variables needed to calculate KBDI. Future FFDI was calculated by applying monthly precipitation and temperature change factors to the historical daily time series. |
| Mean Sea Level Rise | Historical Land Movement (VLM) was estimated from the direct observations (SONEL) and observed tides archived by the permanent service for mean sea level (PSMSL); The future projections are created based on the Fifth Assessment Report (AR5) of the IPCC, applying an ensemble of 28 general circulation models (GCMs) |
| Coastal Extreme Water Level | Change factors were applied to the historical extreme water level as future extreme water level projection, including the extreme wind speed, extreme sea level pressure, and mean sea level rise, derived from GCM outputs. |
Risk Score Methodology
Risk is defined as the potential for consequences where value is at stake and where the outcome is uncertain, recognizing the diversity of values. Risk is often represented as the probability of occurrence of hazardous events or trends multiplied by the impacts of these events or trends occurring—risk results from vulnerability and hazard interaction.
In Climate Insights: Property Climate Risk Report - Residential, each variable's risk score is calculated based on the global dataset, excluding desert and ice areas, and K-mean methods are applied to define the risk score thresholds. It means the risk score applied for a property is generated within a global context, and it may differ from the perception of the risk level on the local scale. One represents a low risk, while 5 is high risk. The colour code is from green (low risk) to red (high risk).