EMS Annual Meeting Abstracts
Vol. 20, EMS2023-377, 2023, updated on 06 Jul 2023
EMS Annual Meeting 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Resolving the interannual variability in climate prediction data for statistical climate risk assessments

Sebastian Schlögl1, Gereon Klein2, Simona Trefalt1, and Karl Gutbrod1
Sebastian Schlögl et al.
  • 1meteoblue AG, Meteorology, Basel, Switzerland (sebastian.schloegl@meteoblue.com)
  • 2sustainable AG, München, Germany

Climate prediction data from e.g., CMIP6 become more important in the future as companies, cities and municipalities must mitigate and adapt their processes and infrastructure to a changing climate. Regulatorily frameworks already exist for large companies (e.g., regulations from the Corporate Sustainability Reporting Directive (CSRD) or the EU taxonomy) and will be also affecting small and medium-sized enterprises in the future.   

One limitation of these climate prediction data is the lack of properly resolving the interannual variability and hence a loss of information regarding the uncertainty of climate data.  

Therefore, climate prediction data have been combined with data from the reanalysis model ERA5 from the ECMWF. This dataset provides a realistic interannual variability from 1940 until now with a horizontal resolution of 30 km as ERA5 is driven by measurements and satellite imagery.   

The combination of climate prediction data and the reanalysis data from ERA5 are the basis to calculate location-specific climate risks of individual variables and apply the uncertainty of the interannual variability. In this study, the climate change signal is added to the hourly time series of the ERA5 dataset allowing the calculation of climate indices such as e.g., number of tropical nights, number of hot days, or cooling/heating degree days within one time period in the future. Furthermore, this approach allows to estimate the probability that a certain climate index reaches a critical threshold. For example, the probability that the yearly number of tropical nights is higher than 5 in the time period 2070 – 2099 for the RCP8.5 emission scenario is estimated with 25 % for the location London, UK.  

Climate prediction data can be further downscaled to 10 m horizontal resolution including heat maps from cities to resolve the urban heat island effect. This downscaling approach is of high relevance for decision makers in cities as e.g., the number of tropical nights (a proxy for heat related mortality during heat waves) strongly varies in the city.  

The change of climate indices, precipitation sums and events, wind speed and storms in a future climate for different emission scenarios and time periods create a reliable information basis for city planners and companies, which are obligated to report their climate risks according to the EU taxonomy.    

How to cite: Schlögl, S., Klein, G., Trefalt, S., and Gutbrod, K.: Resolving the interannual variability in climate prediction data for statistical climate risk assessments, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-377, https://doi.org/10.5194/ems2023-377, 2023.