Luca Glawion

and 4 more

Climate models face limitations in their ability to accurately represent highly variable atmospheric phenomena. To resolve fine-scale physical processes, allowing for local impact assessments, downscaling techniques are essential. We propose spateGAN, a novel approach for spatio-temporal downscaling of precipitation data using conditional generative adversarial networks. Our method is based on a video super-resolution approach and trained on ten years of country wide radar observations for Germany. It simultaneously increases the spatial and temporal resolution of coarsened precipitation observations from 32 km to 2 km and from 1 hour to 10 minutes. Our experiments indicate that the ensembles of generated temporally consistent rainfall fields are in high agreement with the observational data. Spatial structures with plausible advection were accurately generated. Compared to trilinear interpolation and a classical convolutional neural network, the generative model reconstructs the resolution-dependent extreme value distribution with high skill. It showed a high Fractions Skill Score of 0.73 for rainfall intensities over 15mmh-1 and a low BIAS of 3.55%. A power spectrum analysis confirmed that the probabilistic downscaling ability of our model further increased its skill. We observed that neural network predictions may be interspersed by recurrent structures not related to rainfall climatology, which should be a known issue for future studies. We were able to mitigate them by using an appropriate model architecture and model selection process. Our findings suggest that spateGAN offers the potential to complement and further advance the development of climate model downscaling techniques, due to its performance and computational efficiency.

Steffen Zacharias

and 35 more

The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long-term research programme to investigate the effects of global environmental change on terrestrial ecosystems and their socio-economic consequences. State-of-the-art methods from the field of environmental monitoring, geophysics, remote sensing, and modelling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long-term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modelling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large-scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO’s key services and functions, presents the main lessons learned from this 15-year effort, and emphasises the need to continue long-term integrated environmental monitoring programmes in the future.

Jianhui Wei

and 17 more

Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting WRF model, namely WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times accordingly. Moreover, we have implemented the three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the MPI-ESM Representative Concentration Pathway 8.5 scenario (RCP8.5) simulation exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged moisture source area), for two 10-year slices of historical (1980-1989) and future (2040-2049) times. In comparison to the historical simulation, the future 2-meter temperature rises by +1.4 °C, evaporation increases by +6%, and precipitation decreases by -38% under RCP8.5 on average. In this context, global warming leads to regionally decreased residence times for the tagged water vapor by 8 hours and the tagged condensed moisture by 12 hours in the atmosphere, but increased transit times for the tagged precipitation by 4 hours over the land surface that is partly attributed to a slower fallout of precipitating moisture components in the atmosphere under global warming.