Rolf Müller

Locating the boundary of the Asian summer monsoon anticyclone (ASMA) in the upper troposphere-lower stratosphere (UTLS) (350 K–410 K potential temperature) is important for analyzing the transport of pollutants by the ASMA into the stratosphere. The definition of the ASMA boundary highly impacts the information about the anticyclone's behavior and affects the results when studying its spatio-temporal variability in particular regarding inferred trends over the last decades. In this work, we quantify the differences between methodologies based on potential vorticity (PV) and the Montgomery streamfunction (MSF) used to determine the location of the ASMA boundary. In addition, we apply the unsupervised machine learning spectral clustering method which combines both the PV and MSF fields to derive the location of the boundary. By analyzing the centroid position and boundary geometry for July and August 2023, we demonstrate that at 380 K, the PV-based, MSF-based, and spectral clustering methods exhibit good agreement during the Asian summer monsoon season. We find that the MSF-based method tends to incorporate the eastward-shed eddy over the Pacific around 140° E into the main anticyclone, whereas the PV-based method tends to separate it from the main body. Spectral clustering, however, can exhibit either behavior during the season. Finally, we compare the derived boundaries with satellite measurements of carbon monoxide, showing that the PV-based and MSF-based methodologies successfully enclose the core of elevated carbon monoxide levels as effectively as the spectral clustering method.

The level of deep convective transport (LCT) is a critical quantity for determining the impact of deep convection on the composition of the upper troposphere and lower stratosphere (UTLS) within the Asian summer monsoon anticyclone (ASMA). Here, the LCT is estimated using high resolution balloon-borne profile measurements of water vapor obtained over Lhasa and Kunming during eleven ASM seasons. The signature of deep convection is revealed by a modal profile of near 100% saturation over ice (RHi) derived from frost-point temperature and air temperature. Analysis shows that the LCT is ~16 km (359–376 K) over Lhasa and reaches the tropopause (~17 km, 372–391 K) over Kunming. Deep convection frequently reaches above the center of the ASM anticyclone (150 hPa or 14 km), allowing upward transport into the stratosphere, whereas earlier satellite studies suggested a much lower top altitude for deep ASM convection.

Reliable vertical profiles of temperature, humidity, and wind over the Indian Summer Monsoon (ISM) region are essential for diagnosing monsoon dynamics and for downstream forecasting and climate‐service applications, yet remain uncertain over data‐sparse and orographically complex regions. In this study, we evaluate three modern reanalyses—ERA5, JRA‐3Q, and MERRA‐2—against collocated RS41 radiosonde profiles from 11 Indian stations during the 2016–2019 monsoon seasons. The analysis covers temperature and wind from the lower troposphere to 100 hPa and relative humidity to 300 hPa. Beyond mean bias and normalized root‐mean‐square error (NRMSE), we decompose total error into systematic bias and non‐systematic pattern error components and examine lower‐ and upper‐quintile observed regimes. All three reanalyses show near‐surface cold biases (−0.2 to −2 K). Relative humidity has the largest total errors (NRMSE = 0.4–1.2), mainly due to pattern errors associated with variability and phase/timing mismatches. The quintile analysis shows that reanalyses weaken observed temperature, humidity, and wind speed extremes by overestimating lower‐quintile values and underestimating upper‐quintile values. Pattern error is the larger component in about 88% of valid variable‐, level‐, station‐, and regime‐dependent comparisons and commonly contributes around 80% of the total squared error, although its contribution varies among individual cases. ERA5 shows the most robust overall performance, but errors remain variable‐, level‐, station‐, and regime‐dependent. All reanalyses underestimate low‐level vertical shear and misrepresent stability. These uncertainties indicate that reanalyses should be used cautiously for humidity variability, wind‐shear diagnostics, and convection‐related applications over the Indian monsoon region.

Locating the boundary of the Asian summer monsoon anticyclone (ASMA) in the upper troposphere-lower stratosphere (UTLS) (350 K–410 K potential temperature) is important for analyzing the transport of pollutants by the ASMA into the stratosphere. The definition of the ASMA boundary highly impacts the information about the anticyclone's behavior and affects the results when studying its spatio-temporal variability in particular regarding inferred trends over the last decades. In this work, we quantify the differences between methodologies based on potential vorticity (PV) and the Montgomery streamfunction (MSF) used to determine the location of the ASMA boundary. In addition, we apply the unsupervised machine learning spectral clustering method which combines both the PV and MSF fields to derive the location of the boundary. By analyzing the centroid position and boundary geometry for July and August 2023, we demonstrate that at 380 K, the PV-based, MSF-based, and spectral clustering methods exhibit good agreement during the Asian summer monsoon season. We find that the MSF-based method tends to incorporate the eastward-shed eddy over the Pacific around 140° E into the main anticyclone, whereas the PV-based method tends to separate it from the main body. Spectral clustering, however, can exhibit either behavior during the season. Finally, we compare the derived boundaries with satellite measurements of carbon monoxide, showing that the PV-based and MSF-based methodologies successfully enclose the core of elevated carbon monoxide levels as effectively as the spectral clustering method.

Tropospheric ozone pollution in South Asia is mainly blamed on anthropogenic emissions. However, based on ERA5 reanalysis data, this study highlights the contribution of stratospheric ozone intrusions into the Upper Troposphere and Lower Stratosphere (UTLS) associated with Sudden Stratospheric Warming (SSW) events in enhancing upper tropospheric ozone over the South Asian region. We report an enhancement in ozone in the UTLS by more than 80 % for 2018 and ∼ 30 % within ±6 d of the onset during SSW events concurrent with the westerly phase of Quasi-biennial oscillation (WQBO-SSW) compared to non-SSW years. The equatorward shift (south of 30° N) of the subtropical jet during WQBO-SSW causes lowering of the tropopause and more Rossby-wave breaking in the upper troposphere. This results in higher stratospheric ozone intrusions over the South Asian region. The ozone enhancement during WQBO-SSW events produces an instantaneous radiative forcing at the top of the atmosphere of 0.09±0.05 W m−2 due to UTLS ozone changes and 0.17±0.05 W m−2 from total-column ozone changes over South Asia.

During the PHILEAS aircraft campaign, which probed the export of air from the Asian summer monsoon anticyclone (ASMA), observations were carried out from Germany and Alaska during August and September 2023. The flights from Germany sampled the western part of the ASMA and its westward outflow. The flights from Alaska probed filaments that had separated from the ASMA at its eastern flank and were transported across the Pacific. This work integrates PHILEAS aircraft in situ measurements with results from Lagrangian transport simulations. Our results show that air masses within the ASMA and its outflow are characterised by a mixture of different continental and marine sources. In the western part of the ASMA and its westward outflow, enhanced pollutants and greenhouse gases such as CH₂Cl₂, aerosols, CH₄, and H₂O were detected, indicating sources in South Asia. Additional marine air masses from the western Pacific, characterised by enhanced CH₂Br₂ and low O₃, were found in filaments separated at the ASMA's eastern flank. Thus, air masses from two different surface regions and with different chemical compositions are mixed in filaments separated at the ASMA's eastern flank. Our findings show that the chemical composition of the ASMA's outflow is highly variable and depends on altitude, surface emissions, mixing of air masses from different source regions, and the interplay between the ASMA and tropical cyclones. Overall, the results provide strong support for the Asian summer monsoon is an important pathway for transporting short-lived ozone-depleting and radiatively active substances into the stratosphere.

Tropospheric and stratospheric airmasses are separated by the tropopause. Here we investigate the lapse rate tropopause and the cold point tropopause in the Asian summer monsoon anticyclone (ASMA) based on high-altitude airborne measurements in summer 2017. We find that the lapse rate tropopause, and not the cold point, constitutes a good estimate of the upper boundary of the well mixed tropospheric air for many species. There is slow, diabatic, upward transport in the vicinity of the lapse rate tropopause and above. The cold point is located on average about 1 km above the lapse rate tropopause and is about 3 K colder (pressure lower by about 12 hPa). The cold point is in particular important for water vapour. Above the cold point in the ASMA molar water vapour mixing ratios (including hydration patches) range between ∼3 and 10 ppm. In the observations, no indication of substantial dehydration above the cold point was found. Ozone mixing ratios increase substantially with altitude; between the lapse rate and the cold point tropopause molar ozone mixing ratios are in the range of 50–200 ppb. For strong convection (flight on 10 August 2017) there is substantial dehydration at the cold point tropopause (indicated by high values of total water, ice particle occurrence, and strong supersaturation). Above the cold point, under such conditions, neither ice particle occurrence, nor enhanced molar mixing ratios of water vapour (above about 6 ppm) are observed.

Models for the calculation of Lagrangian particle dispersion in the atmosphere or the ocean are indispensable tools for understanding natural and anthropogenic processes. These processes range from volcanic ash clouds, through cloud microphysics to the study of the ozone layer on climate scales. With exascale machines at our disposal, such calculations can now be performed at significantly higher resolutions, both in terms of the driving wind field and particle number density.Massive-Parallel Trajectory Calculations (MPTRAC) is a library designed to enable Lagrangian particle dispersion analysis for atmospheric transport processes in the free troposphere and stratosphere. It is optimized for modern high-performance computing infrastructure. MPTRAC was developed with contemporary high-performance computing (HPC) systems in mind, ensuring high scalability across GPU and CPU clusters through an MPI-OpenMP/ACC hybrid parallelization approach. Its data structures are tailored to the multi-layered cache systems of modern compute nodes. MPTRAC is routinely executed on the JUWELS-Booster supercomputer and is planned for deployment on the JUPITER exascale machine.This contribution outlines ongoing developments in MPTRAC. A central aspect of the presented work is the implementation of domain decomposition, which partitions wind field data and associated tracer particles across distributed subdomains. This methodology promises to enhance computational efficiency and scalability, particularly in the context of large-scale atmospheric transport simulations. Furthermore, we detail the integration of MPTRAC with the ICON modeling framework through its community interface. This extension enables the direct application of particle-based transport methods within ICON, supporting high-resolution climate and weather simulations.The described developments are conducted within the scope of the WarmWorld Project, which aims to enable high-resolution calculations using ICON.MPTRAC is available under an open-source licence: https://github.com/slcs-jsc/mptrac