Understanding variations in isotopic composition of precipitation from monsoon regions is crucial for its utilization in paleoclimate studies. This study explores the relationship between precipitation δ18O data for the East Asian monsoon (EAM) region archived in Global Network for Isotopes in Precipitation (GNIP) and the cloud data archived in ISCCP and their linkage with large-scale atmospheric circulation patterns. Results show that precipitation δ18O are significantly and positively correlated with cloud-top pressure (CTP) on both local and regional scales. Mechanically speaking, the stronger the monsoon convection precipitation, the higher the cloud and the lower the condensation temperature and thus the lower the precipitation δ18O. This result implies that the sharp drop in precipitation δ18O in the early summer in monsoonal Asia is related to the atmospheric circulation pattern rather than the different moisture sources, as was previously assumed. This result helps explain the processes leading to the observed “amount effect.” A comparison of atmospheric circulation patterns with precipitation δ18O on an interannual scale shows that the positive CTP anomalies in the central Indo-Pacific within the weak Walker circulation (El Niño) can be associated with positive δ18O anomalies, while negative CTP anomalies in the central Indo-Pacific within the strong Walker circulation (La Niña) can be linked to negative δ18O anomalies. This result further confirms the aforementioned conclusion. This is important for understanding paleoclimatic change in monsoonal Asia, as interannual variations in stable isotopes in that region have received less attention in the past.

Lagrangian models are fundamental tools to study atmospheric transport processes and for practical applications such as dispersion modeling for anthropogenic and natural emission sources. However, conducting large-scale Lagrangian transport simulations with millions of air parcels or more can become rather numerically costly. In this study, we assessed the potential of exploiting graphics processing units (GPUs) to accelerate Lagrangian transport simulations. We ported the Massive-Parallel Trajectory Calculations (MPTRAC) model to GPUs using the open accelerator (OpenACC) programming model. The trajectory calculations conducted within the MPTRAC model were fully ported to GPUs, i.e., except for feeding in the meteorological input data and for extracting the particle output data, the code operates entirely on the GPU devices without frequent data transfers between CPU and GPU memory. Model verification, performance analyses, and scaling tests of the Message Passing Interface (MPI) – Open Multi-Processing (OpenMP) – OpenACC hybrid parallelization of MPTRAC were conducted on the Jülich Wizard for European Leadership Science (JUWELS) Booster supercomputer operated by the Jülich Supercomputing Centre, Germany. The JUWELS Booster comprises 3744 NVIDIA A100 Tensor Core GPUs, providing a peak performance of 71.0 PFlop s⁻¹. As of June 2021, it is the most powerful supercomputer in Europe and listed among the most energy-efficient systems internationally. For large-scale simulations comprising 10⁸ particles driven by the European Centre for Medium-Range Weather Forecasts' fifth-generation reanalysis (ERA5), the performance evaluation showed a maximum speed-up of a factor of 16 due to the utilization of GPUs compared to CPU-only runs on the JUWELS Booster. In the large-scale GPU run, about 67 % of the runtime is spent on the physics calculations, conducted on the GPUs. Another 15 % of the runtime is required for file I/O, mostly to read the large ERA5 data set from disk. Meteorological data preprocessing on the CPUs also requires about 15 % of the runtime. Although this study identified potential for further improvements of the GPU code, we consider the MPTRAC model ready for production runs on the JUWELS Booster in its present form. The GPU code provides a much faster time to solution than the CPU code, which is particularly relevant for near-real-time applications of a Lagrangian transport model.