Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany

Carney, Meagan; Kantz, Holger

doi:https://doi.org/10.5194/ascmo-6-61-2020

Articles | Volume 6, issue 1

https://doi.org/10.5194/ascmo-6-61-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/ascmo-6-61-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 6, issue 1

08 Jun 2020

| 08 Jun 2020

Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany

Meagan Carney and Holger Kantz

Data sets

Historical hourly staton observations of 2m air temperature and humidity for Germany, version v006 DWD Climate Data Center (CDC) https://opendata.dwd.de/climate_environment/CDC/observations_ germany/climate/hourly/air_temperature/historical/

Historical hourly station observations of precipitation for Germany, versionv006 DWD Climate Data Center (CDC) https://opendata.dwd.de/climate_environment/CDC/ observations_germany/climate/hourly/precipitation/historical/

Historical daily precip- itation observations for Germany, version v007 DWD Climate Data Center (CDC) https://opendata.dwd.de/climate_environment/CDC/ observations_germany/climate/daily/more_precip/historical/

Short summary

Extremes in weather can have lasting effects on human health and resource consumption. Studying the recurrence of these events on a regional scale can improve response times and provide insight into a changing climate. We introduce a set of clustering tools that allow for regional clustering of weather recordings from stations across Germany. We use these clusters to form regional models of summer temperature extremes and find an increase in the mean from 1960 to 2018.