Gridded datasets of climate indices: comparison of two approaches

Gridded data is highly appreciated for many climate related applications due to their spatial information compared to single station locations. The quality and additional value of a gridded dataset is depending on the method itself and on the amount of additional information, e.g. topographical data, used for the interpolation. Furthermore, there is an ongoing discussion on the implications of using gridded datasets of primary climate parameters like temperature or precipitation to derive gridded datasets of climate indices. Within the project SDGHUB (https://www.sdghub.at/), we are exploring the influence of different approaches on the final gridded climate index dataset.  The focus is on the frequently used climate index of hot days (days with a maximum air temperature above or equal to 30 °C). In the first approach the hot days were computed from the gridded climate dataset SPARTACUS (Hiebl and Frei, 2016), while in the second approach the hot days were directly interpolated considering station values. SPARTACUS is a national gridded dataset of Austria, available on a daily basis with a 1 km-spatial resolution via the DataHub of GeoSphere Austria (https://data.hub.geosphere.at/). It covers the period from 1961 onwards and includes the parameters of maximum, minimum and mean air temperature and rain amount.  For the second approach the efficacy of several geostatistical interpolation methods, including Kriging with External Drift (KED), Ordinary Kriging (OK), and Regression Kriging (RK), with a particular emphasis on their ability to represent the spatial variability of hot days was explored. The comparison showed that KED provides the best results, as the complex terrain in Austria can be considered best. Therefore, this method was used for further analysis. To further improve the interpolation results of KED, different data transformations were tested, with the square root transformation emerging as the most effective one. As for SPARTACUS, the direct interpolation was done on a 1 km-scale for Austria.To evaluate the performance of the two respective approaches a cross-validation is applied.The presentation will include information on the interpolation methods and provide insights into the evaluation and differences of the two datasets as well as the effect of station density. The final dataset of hot days will be available via the DataHub of GeoSphere Austria with a monthly, seasonal and annual resolution. ReferencesHiebl J, Frei C (2016) Daily temperature grids for Austria since 1961 – concept, creation and applicability. Theor Appl Climatol 124:161–178. https://doi.org/10.1007/s00704-015-1411-4 AcknowledgementThe project SDGHUB is funded by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility and Technology (BMK) via the ICT of the Future Program - FFG No 892212.