Regional dynamical downscaling and analysis of water balance across different regions and time scales

Employing the regional dynamical downscaling (RDD) technique, we can enhance the spatial and temporal resolution of global reanalysis and general circulation model (GCM) data, providing improved data for studying ecological processes. This method ensures physical validity of the data, albeit at high computational costs.This thesis highlights the application and potential added value of RDD in addressing precipitation-related research questions across three distinct regions: the Galápagos Archipelago (GA), the Qaidam Basin (QB), and the Berlin-Brandenburg region in Germany (BB). The studies in these regions, characterised by unique climatic and geographic challenges, demonstrate the ability of RDD to resolve regional- and meso-scale processes and improve data availability in data-scarce region and enhance data-quality over complex terrains. The findings underline the critical role of optimised downscaling approaches in advancing process-based understanding and supporting ecological and climatic research. For the GA, a long-term high-resolution data set Galápagos Refined Analysis (GAR) was developed, by downscaling ERA5 reanalysis data to a grid-spacing of 2 km. The GAR dataset enables detailed analysis of meso-scale atmospheric processes in the GA, validated against in-situ measurements from a network of strategically installed weather stations. The data set is able to reproduce seasonal variability and annual measurement values for precipitation, air temperature at 2 m a.g.l., and mixing ratio at 2 m a.g.l. Further analysis of the GAR data set reveals its ability to reproduce the effects of quasi-periodic El Niño-Southern Oscillation and its extreme states, La Niña and El Niño. We present a spatially explicit analysis of these variables in the GAR dataset, describing their spatial distribution and the impacts of topography and the general wind field. From the GAR, we derive and examine long-term climatic trends in the GA, concluding that over the course of the study period from 1980 to 2023, the GA experienced does not exhibit any significant trends in these variables, but that its climate is dominated by interannual variability. The 3-dimensional data set also allows analysis of trends in the troposphere, where we find increase in layer thickness, induced by a warming trend in the lower and upper troposphere. Regarding the QB, the basin's water balance sensitivity to present-day and mid-Pliocene climate regimes, were examined, demonstrating the potential of RDD in paleoclimatic research. Downscaling of GCM data, to a resolution of 30 km, enabled us to investigate the regional drivers of the basin's water balance, including the influence of the mid-latitude westerlies and the effects of the East Asian Summer Monsoon. We find that the basin's water balance in the mid-Pliocene was influenced by the strengthened mid-latitude westerlies and by the East Asian Summer Monsoon reaching farther west into the QB. For the BB area, we present the Central Europe Refined analysis dataset version 2 (CER v2), building on the first version of the dataset (CER v1) by incorporating higher resolution, improved parameterisations and substituting the ERA-Interim forcing data with ERA5. Through rigorous sensitivity studies, an improved model setup was found, and the resulting data was validated with in-situ measurements from the measurement network of the German Weather Service. Comparison to the CER v1, global gridded data sets and the radar-derived RADOLAN data set, shows the improved performance of the CER v2 and its ability to reproduce monthly, seasonal, and annual precipitation patterns and amounts. This thesis substantiates the ability of RDD to provide deeper insights into regional and meso-scale climatic processes, supporting its application in diverse research contexts across regions, time periods and time-scales.

Author dc:creator

• Schmidt, Benjamin Rasmus Leander

Advisor dc:contributor.advisor

• Scherer, Dieter

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc/4.0/

Language dc:language.iso

en

Identifier URI

https://doi.org/10.14279/depositonce-24373

OAI identifier oai:identifier

oai:depositonce.tu-berlin.de:11303/25550