No abstract

The climate is changing, and not for the better – this is old news and will surprise nobody. Immediate action to mitigate and adapt to climate change is necessary on all scales from single households to the continental level, including companies.  In recent years, governments worldwide have been expanding their policies to evaluate the environmental sustainability of economic activities, particularly by asking big companies to assess both their effect on the climate and the climate’s effect on them. This has, in some cases, been formalised under official regulations, such as the ”Corporate Sustainability Reporting Directive (CSRD)” or the “EU taxonomy regulation (2020/852)” of the European Union. Advances in climate prediction have been helping generate increasingly detailed and confident information on the climate means, variability, and extremes in the future, providing a basis for the assessment of climate hazards at company locations. However, stakeholders outside the atmospheric sciences will need assistance in interpreting the data and reducing it to key information related to physical hazards. The process from climate model prediction output to actionable information, used in support of decision-making, is a new climate service provided by meteoblue AG.  While the topics for which the risk should be evaluated, for instance for the EU taxonomy, are clear, several issues remain. Firstly, climate projections at a local scale are not straightforward to obtain, and they are generally not suitable for products that need to be inter-comparable worldwide. Secondly, regulations require the assessment of single topics for which even the present hazard is unknown or the uncertainty in the future evolution remains high. Thirdly, the topics go beyond what is explicitly covered by climate models. All these issues need to be appropriately addresses and resolved in standardised processes to provide a product that is valid for any possible company location worldwide. In this presentation we will focus on how we tailor climate data to meet the clients’ requirements to be able to assess the climate risks at their locations, plan accordingly and pass mandatory company audits related to climate change.  

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.