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.
Abstract
The aurora and associated high energy particles and currents pose a space weather hazard to communication networks and ground-based infrastructure. Forecasting the location of the auroral oval forms an integral component of daily space weather operations. We evaluate a version of the OVATION-Prime 2013 auroral forecast model that was implemented for operational use at the UK Met Office Space Weather Operations Cent. Building on our earlier studies, we evaluate the ability of the OVATION-Prime 2013 model to predict the location of the auroral oval in all latitude and local time sectors under different levels of geomagnetic activity, defined by Kp. We compare the model predictions against auroral boundaries determined from IMAGE FUV data. Our analysis shows that the model performs well at predicting the equatorward extent of the auroral oval, particularly as the equatorward auroral boundary expands to lower latitudes for increasing Kp levels. The model performance is reduced in the high latitude region near the poleward auroral boundary, particularly in the nightside sectors where the model does not accurately capture the expansion and contraction of the polar cap as the open flux content of the magnetosphere changes. For increasing levels of geomagnetic activity (Kp ≥ 3), the performance of the model decreases, with the poleward edge of the auroral oval typically observed at lower latitudes than forecast. As such, the forecast poleward edge of the auroral oval is less reliable during more active and hazardous intervals.
Abstract
Measurements of Ar and N2 densities at 160–250 km altitude from the Mars Atmosphere and Volatile Evolution (MAVEN) Neutral Gas and Ion Mass Spectrometer (NGIMS) during February 2015–February 2023 are analyzed to provide a comprehensive analysis of their diurnal- and zonal-mean (DZM) structures, and ZM (solar-synchronous) diurnal (DW1) and semidiurnal (SW2) tides. After applying a solar flux trend correction, multi-year binning and averaging with respect to longitude, local solar time (LST), latitude and Ls at each height results in the first full global picture of these components of the ZM thermosphere for a single climatological Mars year. The following new observational insights into Mars thermosphere are obtained: The DZM N2 latitude versus Ls (latvsLs) structures contain a prominent latitudinally-symmetric annual component (∼±25%–35%) due to the eccentricity of Mars orbit around the Sun, and an antisymmetric component (∼±30%–45%) below about 190 km that is seasonally-symmetric and thus consistent with the tilt of Mars rotation axis. Aperiodic deviations from these symmetries increase with height and are tentatively attributed to dissipation of waves originating in the lower atmosphere. DW1 and SW2 maximize around 200–220 km altitude, suggesting existence of an unknown dissipation mechanism at higher altitudes. The DZM, DW1 and SW2 components of Ar generally exceed those of N2 by factors of 1.4–2.5. The scale heights of Ar and N2 between 205 and 245 km are also employed to derive DZM exosphere temperatures, which reflect aperiodic ∼±15K deviations from the annual-mean in the latvsLs frame.
Abstract
Pc5 ULF waves play an important role in transporting energy and particles in the coupled magnetospheric and ionospheric system. They are known to be initiated by dynamic pressure fluctuations upstream of the magnetopause, including those induced by hot flow anomalies (HFAs). However, the role of HFAs in generating magnetospheric and ground magnetic Pc5 ULF oscillations has not been investigated statistically yet. Thus, in this paper, we investigate the contribution of HFAs to ground magnetic Pc5 ULF oscillations and analyze how the characteristics of HFAs influence these oscillations, based on the coordinated observations between the THEMIS probes and the ground magnetometers at high latitudes during the years 2008, 2009 and 2019. We find that HFAs can serve as a notable source of ground magnetic Pc5 ULF oscillations, with about 18.9% of Interplanetary Magnetic Field (IMF) discontinuity-induced HFAs associated with discernible enhancements in Pc5 ULF wave power, whereas spontaneous HFAs play a comparatively minor role in generating these oscillations. Furthermore, we observe that the cores of HFAs are likely to contribute more significantly to modulating the induced ground magnetic Pc5 ULF oscillations than their compressed boundaries. More dynamic pressure reductions within HFA cores correspond to stronger ground magnetic Pc5 ULF oscillations. Additionally, HFAs can propagate with the IMF discontinuity along the bow shock, continuously generating ground magnetic Pc5 ULF oscillations during their propagation. This research sheds light on the mechanisms underlying Pc5 ULF wave generation and underscores the role of HFAs in driving magnetospheric-ionospheric interactions.
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, 2024
Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
ShellSet v1.1.0 parallel dynamic neotectonic modelling: a case study using Earth5-049
Jon B. May, Peter Bird, and Michele M. C. Carafa
Geosci. Model Dev., 17, 6153–6171, https://doi.org/10.5194/gmd-17-6153-2024, 2024
ShellSet is a combination of well-known geoscience software packages. It features a simple user interface and is optimised through the addition of a grid search input option (automatically searching for optimal models within a defined N-dimensional parameter space) and the ability to run multiple models in parallel. We show that for each number of models tested there is a performance benefit to parallel running, while two examples demonstrate a use case by improving an existing global model.
A Bayesian method for predicting background radiation at environmental monitoring stations
Jens Peter K. W. Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-137,2024
Preprint under review for GMD (discussion: open, 0 comments)
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known `anomalous’ event.
Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0
Andrea L. Campoverde, Uwe Ehret, Patrick Ludwig, and Joaquim G. Pinto
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-134,2024
Preprint under review for GMD (discussion: open, 0 comments)
We looked at how well the model WRF-Hydro performed during the 2018 drought event in the River Rhine basin, even though it is typically used for floods. We used the meteorological ERA5 reanalysis dataset to simulate River Rhine’s streamflow and adjusted the model using parameters and actual discharge measurements. We focused on Lake Constance, a key part of the basin, but found issues with the model’s lake outflow simulation. By removing the lake module, we obtained more accurate results.
