Abstract
Over the past decades, various experimental and numerical model studies have indicated cooling trend in the mesosphere and lower thermosphere (MLT), while the magnitude of the trend varies noticeably. Previous studies using the lidar observations derived the temperature trends and solar responses solely from the traditional nocturnal measurements. While these archived results are more or less in agreement with modeling studies, one of the main uncertainties in these studies is the potential biases induced by the trends of the diurnal tide forced in the lower atmosphere, and that of the in situ exothermal reactions involving the photolysis. In the MLT, the diurnal tide has significant seasonal variations, considerable amplitude and is one of the dominant dynamic sources. However, its potential effects in the trend studies have rarely been discussed. In this paper, we present and compare the long-term temperature trends in the upper mesosphere utilizing the daily mean and nightly mean temperature profiles measured by a Sodium (Na) Doppler lidar at midlatitude. The system was operating routinely in full diurnal cycles between 2002 and 2017, obtaining a unique multi-year temperature data set. A customized multi-linear regression (MLR) model is applied to determine the linear trends and the other fitting parameters, such as ENSO and solar F10.7 responses in the upper mesosphere. This study indicates the daily mean cooling trend between 84 and 98 km is larger than that of nightly mean trend by ∼−1 K/decade, while differences in the solar response are within the fitting uncertainties.
Abstract
We use measurements of trace gases from the Microwave Limb Sounder and polar stratospheric clouds (PSCs) from the Cloud-Aerosol Lidar with Orthogonal Polarization to investigate how the extraordinary stratospheric water vapor enhancement from the 2022 Hunga eruption affected polar processing during the 2023 Antarctic winter. Although the dynamical characteristics of the vortex itself were generally unexceptional, the excess moisture initially raised PSC formation threshold temperatures above typical values. Cold conditions, especially in early July, prompted ice PSC formation and unusually severe irreversible dehydration at higher levels (500–700 K), while atypical hydration occurred at lower levels (380–460 K). Heterogeneous chemical processing was more extensive, both vertically (up to 750–800 K) and temporally (earlier in the season), than in prior Antarctic winters. The resultant HCl depletion and ClO enhancement redefined their previously observed ranges at and above 600 K. Albeit unmatched in the satellite record, the early-winter upper-level chlorine activation was insufficient to induce substantial ozone loss. Chlorine activation, denitrification, and dehydration processes ran to completion by July/August, with trace gas evolution mostly following the climatological mean thereafter, but with chlorine deactivation starting slightly later than usual. While cumulative ozone losses at 410–550 K were relatively large, probably because of the delayed chlorine deactivation, they were not unprecedented. Thus, ozone depletion was unremarkable throughout the lower stratosphere. Although Hunga enhanced PSC formation and chemical processing in early winter, saturation of lower stratospheric denitrification, dehydration, and chlorine activation (as is typical in the Antarctic) prevented an exceptionally severe ozone hole in 2023.
Abstract
Hydration of the stratosphere by tropopause-overshooting convection has received increasing interest due to the extreme concentrations of water vapor that can result and, ultimately, the climate warming potential such hydration provides. Previous work has recognized the importance of numerous dynamic and physical processes that control stratospheric water vapor delivery by convection. This study leverages recent comprehensive observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign to determine the frequency at which each process operates during real events. Specifically, a well-established analysis technique known as tracer-tracer correlation is applied to DCOTSS observations of ozone, water vapor, and potential temperature to identify the occurrence of known processes. It is found that approximately half of convectively-driven stratospheric hydration samples show no indication of significant air mass transport and mixing, emphasizing the importance of ice sublimation to stratospheric water vapor delivery. Furthermore, the temperature of the upper troposphere and lower stratosphere environment and/or overshoot appears to be a commonly active constraint, since the approximate maximum possible water vapor concentration that can be reached in an air mass is limited to the saturation mixing ratio when ice is present. Finally, little evidence of relationships between dynamic and physical processes and their spatial distribution was found, implying that stratospheric water vapor delivery by convection is likely facilitated by a complex collection of processes in each overshooting event.
Abstract
Methane (CH4) is the second most abundant greenhouse gas and affects the Earth's radiative balance. In some regions, the methane burden and budget are still not well understood due to the lack of in situ observations, especially vertical profile observations. Here, we present the first high-resolution aircraft-based tropospheric vertical profiles of CH4 across the Indian subcontinent. Observations show significant variability, with the largest variability seen in the Indo-Gangetic Plain (IGP) during post-monsoon (September). The IGP also shows the highest concentrations and a peak in the boundary layer. By contrast, observations over western India show lower variability, especially during the Asian Summer Monsoon (ASM) (July). During ASM, when CH4 emissions peak, the vertical updraft of CH4 and other tracers is observed, leading to a peak between 4 and 5 km. During winter, the peak occurs in the boundary layer, and a decrease with altitude is observed. Model simulations slightly overestimate CH4 at the surface during some seasons but underestimate it at higher altitudes during all seasons. Integrated over the observed column, model simulations slightly underpredict CH4 (0.5%–3.1%) during all seasons. Calculations made using the observed CO/CH4 enhancement ratios show that in addition to anthropogenic fossil fuel emissions, other sources, such as rice cultivation and wetlands, need to be considered to reproduce the observed CH4 concentrations.
The MESSy DWARF (based on MESSy v2.55.2)
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-117,2024
Preprint under review for GMD (discussion: open, 0 comments)
This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
FINAM – is not a model (v1.0): a new Python-based model coupling framework
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-144,2024
Preprint under review for GMD (discussion: open, 1 comment)
This study presents FINAM ("FINAM Is Not A Model"), a new coupling framework written in Python to dynamically link independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-142,2024
Preprint under review for GMD (discussion: open, 0 comments)
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the north and south Atlantic (south Pacific and Indian Ocean).
Number- and size-controlled rainfall regimes in the Netherlands: physical reality or statistical mirage?
Marc Schleiss
Atmos. Meas. Tech., 17, 4789–4802, https://doi.org/10.5194/amt-17-4789-2024, 2024
Research is conducted to identify special rainfall patterns in the Netherlands using multiple types of rainfall sensors. A total of eight potentially unique events are analyzed, considering both the number and size of raindrops. However, no clear evidence supporting the existence of a special rainfall regime could be found. The results highlight the challenges in experimentally confirming well-established theoretical ideas in the field of precipitation sciences.
A new portable sampler of atmospheric methane for radiocarbon measurements
Giulia Zazzeri, Lukas Wacker, Negar Haghipour, Philip Gautchi, Thomas Laemmel, Sönke Szidat, and Heather Graven
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-123,2024
Preprint under review for AMT (discussion: open, 1 comment)
Radiocarbon (14C) is an optimal tracer of methane (CH4) emissions, as 14C measurements enable distinguishing fossil from biogenic methane. However, these measurements are particularly challenging, mainly due to technical difficulties in the sampling procedure. With this work we made the sample extraction much simpler and time efficient, providing a new technology that can be used by any research group, with the goal of expanding 14C measurements for an improved understanding of methane sources.
Abstract
Typhoons, fueled by warm sea surface waters, heighten concern as they increasingly interact with frequent Marine Heatwaves (MHWs) in a changing climate. Typhoon Hinnamnor (2022) weakened and re-intensified as it approached the Korean Strait, interacting with an underlying MHW in the northern East China Sea (nECS). In-situ observations and reanalysis products revealed a significant increase in latent heat loss from the nECS during the MHW period, contributing to the typhoon re-intensification. Strong sea surface wind forcing with the typhoon enhanced vertical mixing and upwelling, resulting in a pronounced (0.90°C) sea surface cooling after the typhoon passage, facilitating MHW disappearance with reduced thermal stratification. During MHWs, increased background stratification increases temperature oscillations associated with semidiurnal internal tides. Furthermore, post-typhoon changes in stratification weakened semidiurnal internal tides due to unfavorable conditions for generation from a nearby source. These findings highlight the importance of continuous time-series observations to monitor interactions among climatic extremes.
Abstract
Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial-aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river-sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar-aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes.
Abstract
Deep low-frequency earthquakes (DLFE) are observed beneath volcanoes worldwide but are limited to island arc volcanoes, hotspot volcanoes, and rift zones. Here we show DLFEs in the Tengchong Volcano Field, southeast Tibet, located ∼300 km from the Indo-Burma volcanic arc, by analyzing a 12-year continuous seismic data set. The earthquakes were at a depth of ∼12 km, near the sidewall of the magma body detected by the magnetotelluric survey. The features of isotropic focal mechanism, episodic occurrence, and possible non-power-law scaling, with no detectable geodetic deformation, as well as the petrological signatures of the Holocene eruption product, suggest that the earthquakes were likely associated with the weak intermittent magma flows near the magma body. This finding may demonstrate the existence of unsteady magmatic processes in the margin of the Indo-Eurasia collision zone, which could indicate unneglectable volcanic hazards, underestimated geothermal resources, and mineralization processes in similar regions.
Invited perspectives: Fostering interoperability of data, models, communication and governance for disaster resilience through transdisciplinary knowledge co-production
Kai Schröter, Pia-Johanna Schweizer, Benedikt Gräler, Lydia Cumiskey, Sukaina Bharwani, Janne Parviainen, Chahan Kropf, Viktor Wattin Hakansson, Martin Drews, Tracy Irvine, Clarissa Dondi, Heiko Apel, Jana Löhrlein, Stefan Hochrainer-Stigler, Stefano Bagli, Levente Huszti, Christopher Genillard, Silvia Unguendoli, and Max Steinhausen
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-135,2024
Preprint under review for NHESS (discussion: open, 0 comments)
With the increasing negative impacts of extreme weather events globally, it's crucial to align efforts to manage disasters with measures to adapt to climate change. We identify challenges in systems and organizations working together. We suggest that collaboration across various fields is essential and propose an approach to improve collaboration, including a framework for better stakeholder engagement and an open-source data system that helps gather and connect important information.
Abstract
From 1979 to 2022, the summer monsoon precipitation has increased by a substantial 40% over Northwest India compared to the 1980s. This wetting trend aligns with the future projections of the Coupled Model Intercomparison Project 6 (CMIP6). The observationally constrained reanalysis data indicates that significant sea surface warming in the western equatorial Indian Ocean and the Arabian Sea is likely driving this increase in rainfall by enhancing the cross-equatorial monsoonal flow and associated evaporation. We demonstrate that the strengthening of the cross-equatorial monsoon winds is due to the rapid warming of the Indian Ocean and the enhanced Pacific Ocean trade winds, which result from the poleward shift and expansion of the Hadley cell. These strengthened winds boost the latent heat flux (evaporation), leading to increased moisture transport to Northwest India.
Abstract
Earlier proxy-observational studies, and a sole modeling study, suggest that the Indian Ocean Dipole (IOD), an important global climate driver, exhibited multi-scale temporal variability during the Last Millennium (LM; CE 0851–1849, with relatively high number of strong positive IOD events during the Little Ice Age (LIA; CE 1550–1749), and strong negative IOD events during the Medieval Warm Period (MWP; CE 1000–1199). Using nine model simulations from the PMIP3, we study the IOD variability during the LM after due validation of the simulated current day (CE 1850–2005) IOD variability. Majority of the models simulate relatively higher number of positive IOD events during the MWP, and negative IOD events in the LIA, commensurate with simulated background conditions. However, higher number of strong positive IOD events are simulated relative to the negative IODs during the LIA, in agreement with proxy-observations, apparently owing to increased coupled feedback during positive IODs.
Abstract
We investigate the role of the subpolar North Atlantic (SPNA) for downstream predictability, using two decadal climate prediction systems. We use the subpolar extreme cold and fresh anomaly event developing in winter 2013/2014 as initial conditions and evaluate ensemble predictions of the two systems in the following decade. In addition, we perform ensemble pacemaker experiments where the models are forced toward observed ocean temperature and salinity anomalies in the SPNA from November 2014 through December 2019. The pacemaker experiments show improved skill along the Atlantic Water pathway, compared with the standard decadal predictions, and we therefore conclude that the correct description of the ocean in the SPNA is the key. The enhanced skill is most prominent in subsurface salinity in the form of propagating anomalies.
The role of time-varying external factors in the intensification of tropical cyclones
Samuel Watson and Courtney Quinn
Nonlin. Processes Geophys., 31, 381–394, https://doi.org/10.5194/npg-31-381-2024, 2024
The intensification of tropical cyclones (TCs) is explored through a conceptual model derived from geophysical principals. Focus is put on the behaviour of the model with parameters which change in time. The rates of change cause the model to either tip to an alternative stable state or recover the original state. This represents intensification, dissipation, or eyewall replacement cycles (ERCs). A case study which emulates the rapid intensification events of Hurricane Irma (2017) is explored.
Iterative Placement of Decoupling Capacitors using Optimization Algorithms and Machine Learning
Zouhair Nezhi, Nima Ghafarian Shoaee, and Marcus Stiemer
Adv. Radio Sci., 21, 123–132, https://doi.org/10.5194/ars-21-123-2024, 2024
An optimum placement and dimensioning of decaps on a printed circuit board is determined by a Genetic Algorithm (GA). The use of an artificial neural network as surrogate model to compute fitness values for the GA significantly reduces computation time. With the optimization framework at hand, the risk of a redesign that would take several weeks can be significantly reduced by a computation that just needs a few minutes.
Abstract
Specification and forecast of ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionospheric research. Neural networks (NNs) emerge as a powerful modeling tool for Ne prediction. However, heavy manual adjustments are time consuming to determine the optimal NN structures. In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to mitigate this problem. NAS aims to find the optimal network structure through the alternate optimization of the hyperparameters and the corresponding network parameters within a pre-defined hyperparameter search space. A total of 16-year data from Millstone Hill incoherent scatter radar (ISR) are used for the NN models. One single-layer NN (SLNN) model and one deep NN (DNN) model are both trained with NAS, namely SLNN-NAS and DNN-NAS, for Ne prediction and compared with their manually tuned counterparts (SLNN and DNN) based on previous studies. Our results show that SLNN-NAS and DNN-NAS outperformed SLNN and DNN, respectively. These NN predictions of Ne daily variation patterns reveal a 27-day mid-latitude topside Ne variation, which cannot be reasonably represented by traditional empirical models developed using monthly averages. DNN-NAS yields the best prediction accuracy measured by quantitative metrics and rankings of daily pattern prediction, especially with an improvement in mean absolute error more than 10% compared to the SLNN model. The limited improvement of NAS is likely due to the network complexity and the limitation of fully connected NN without the time histories of input parameters.
Abstract
To gain deeper insights into radiation belt loss into the atmosphere, a statistical study of MeV electron precipitation during radiation belt dropout events is undertaken. During these events, electron intensities often drop by an order of magnitude or more within just a few hours. For this study, dropouts are defined as a decrease by at least a factor of five in less than 8 hours. Van Allen probe measurements are employed to identify dropouts across various parameters, complemented by precipitation data from the CALorimetric Electron Telescope instrument on the International Space Station. A temporal analysis unveils a notable increase in precipitation occurrence and intensity during dropout onset, correlating with the decline of SYM-H, the north-south component of the interplanetary magnetic field, and the peak of the solar wind dynamic pressure. Moreover, dropout occurrences show correlations with the solar cycle, exhibiting maxima at the spring and autumn equinoxes. This increase during equinoxes reflects the correlation between equinoxes and the SYM-H index, which itself exhibits a correlation with precipitation during dropouts. Spatial analysis reveals that dropouts with precipitation penetrate into lower L-star regions, mostly reaching L-star <4, while most dropouts without precipitation don't penetrate deeper than L-star 5. This is consistent with the larger average dimensions of dropouts associated with precipitation. During dropouts, precipitation is predominantly observed in the dusk-midnight sector, coinciding with the most intense precipitation events. The results of this study provide insight into the contribution of precipitation to radiation belt dropouts by deciphering when and where precipitation occurred.
