Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, 2024
In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
A Holocene alpine seismic chronicle from Lake Aiguebelette (NW French Alps)
Mathilde Banjan, Christian Crouzet, Hervé Jomard, Pierre Sabatier, David Marsan, and Erwan Messager
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-83,2024
Preprint under review for NHESS (discussion: open, 0 comments)
This research shows how lake sediments reveal seismic activity history over extended periods, surpassing historical records. Sediment analysis from Lake Aiguebelette in the Western Alps found 32 layers likely caused by earthquakes over the Holocene. Robust dating methods correlated these layers with known historical earthquakes. Results suggest Lake Aiguebelette's sediment records mainly reflect local seismic events, enhancing understanding of earthquake recurrence and regional seismic history.
Can remote sensing combustion phase improve estimates of landscape fire smoke emission rate and composition?
Farrer Owsley-Brown, Martin J. Wooster, Mark J. Grosvenor, and Yanan Liu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-73,2024
Preprint under review for AMT (discussion: open, 0 comments)
Landscape fires produce vast amounts of smoke, affecting the atmosphere locally and globally. Whether a fire is flaming or smoldering strongly impacts the rate at which smoke is produced as well as its composition. This study tested two methods to determine these combustion phases in laboratory fires and compared them to the smoke emitted. One of these methods improved estimates of smoke emission significantly. This suggests potential for improvement in global emission estimates.
Innovative aerosol hygroscopic growth study from Mie–Raman–fluorescence lidar and microwave radiometer synergy
Robin Miri, Olivier Pujol, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii, Thierry Podvin, and Fabrice Ducos
Atmos. Meas. Tech., 17, 3367–3375, https://doi.org/10.5194/amt-17-3367-2024, 2024
This paper focuses on the use of fluorescence to study aerosols with lidar. An innovative method for aerosol hygroscopic growth study using fluorescence is presented. The paper presents case studies to showcase the effectiveness and potential of the proposed approach. These advancements will contribute to better understanding the interactions between aerosols and water vapor, with future work expected to be dedicated to aerosol–cloud interaction.
Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024, 2024
We have created a new method to better identify enhanced features in radar data from winter storms. Unlike the clear-cut features seen in warm-season storms, features in winter storms are often fuzzier with softer edges. Our technique is unique because it uses two adaptive thresholds that change based on the background radar values. It can identify both strong and subtle features in the radar data and takes into account uncertainties in the detection process.
Accounting for the effect of aerosols in GHGSat methane retrieval
Qiurun Yu, Dylan Jervis, and Yi Huang
Atmos. Meas. Tech., 17, 3347–3366, https://doi.org/10.5194/amt-17-3347-2024, 2024
This study estimated the effects of aerosols on GHGSat satellite methane retrieval and investigated the performance of simultaneously retrieving aerosol and methane information using a multi-angle viewing method. Results suggested that the performance of GHGSat methane retrieval improved when aerosols were considered, and the multi-angle viewing method is insensitive to the satellite angle setting. This performance assessment is useful for improving future GHGSat-like instruments.
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-92,2024
Preprint under review for GMD (discussion: open, 0 comments)
We explore a high-level programming model for GPU porting of NWP model codes, based on the Python domain-specific library GT4Py. We present a Python rewrite with GT4Py of the ECMWF cloud microphysics scheme and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive performance and robust execution on diverse CPU and GPU architectures. The additional advantages in terms of maintainability, productivity and readability are also highlighted.
Amending the algorithm of aerosol-radiation interaction in WRF-Chem (v4.4)
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-69,2024
Preprint under review for GMD (discussion: open, 0 comments)
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that the new method significantly changes the estimated aerosols' properties and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol-radiation interactions in models.
A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-79,2024
Preprint under review for GMD (discussion: open, 0 comments)
Earth System Models (ESMs) struggle the uncertainties associated with parameterizing sub-grid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran-Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity and effectiveness.
Abstract
To help meet emission standards, hydrogen sulfide (H2S) from geothermal production may be injected back into the subsurface, where basalt offers, in theory, the capacity to mineralize H2S into pyrite. Ensuring the viability of this pollution mitigation technology requires information on how much H2S is mineralized, at what rate and where. To date, monitoring efforts of field-scale H2S reinjection have mostly occurred via mass balance calculations, typically capturing less than 5% of the injected fluid. While these studies, along with laboratory experiments and geochemical models, conclude effective H2S mineralization, their extrapolation to quantify mineralization and its persistence over time leads to considerable uncertainty. Here, a geophysical methodology, using time-domain induced polarization (TDIP) logging in two of the injection wells (NN3 and NN4), is developed as a complementary tool to follow the fate of H2S re-injected at Nesjavellir geothermal site (Iceland). Results show a strong chargeability increase at +40 days, interpreted as precipitation of up to 2 vol.% based on laboratory relationships. A uniform increase is observed along NN4, whereas it is localized below 450 m in NN3. Changes are more pronounced with larger electrode spacing, indicating that pyrite precipitation takes place away from the wells. Furthermore, a chargeability decrease is observed at later monitoring rounds in both wells, suggesting that pyrite is either passivated or re-dissolved after precipitating. These results highlight that a sequence of overlapping reactive processes (pyrite precipitation, passivation, pore clogging and possibly pyrite re-dissolution) results from H2S injection and that TDIP monitoring is sensitive to this sequence.
Abstract
Vegetation responses to rising atmospheric CO2 levels can significantly affect water availability (defined as precipitation minus evapotranspiration (ET)). While this effect has long been recognized and assessed for the mean state, its influence on interannual variability, which is more closely associated with extreme events, has yet to be comprehensively quantified. In this study, our primary focus is to evaluate the impacts of ET by plant physiology (denoted as ET
Phy
) on the mean and interannual variability of water availability under elevated CO2 using multiple CO2 sensitivity experiments from the coupled model intercomparison project phase 6. We show that the contribution of vegetation physiological effects to the mean water availability varies among regions, while it consistently contributes to variability by about 33%. Considering CO2 physiological effects alone, ET
Phy
exerts a more significant influence on the mean state than on variability, particularly in humid regions. Consequently, ET
Phy
contributes less than 5% to the variability of water availability in humid regions under rising CO2, whereas it accounts for about 20% of the mean state. This distinction could be attributed to the different mechanisms governing the mean and variability of ET
Phy
. Specifically, evaporation from CO2 physiological forcing is the most critical contributor to the variability of ET
Phy
in most regions while showing minimal impacts on the mean state. Our findings identify the divergent effects of ET
Phy
on the mean state and interannual variability of water availability under elevated CO2, as important in future climate projections.
Abstract
We investigated the effects of the propagation path and site amplification of shallow tremors along the Nankai Trough. Using far-field S-wave propagation from intraslab earthquake data, the amplification factors at the DONET stations were 5–40 times against an inland outcrop rock site. Thick (∼5 km) sedimentary layers with V
S
of 0.6–2 km/s beneath DONET stations have been confirmed by seismological studies. To investigate the effects of thick sedimentary layers, we synthesized seismograms of shallow tremors and intraslab earthquakes at seafloor stations. The ratios of the maximum amplitudes from the synthetic intraslab seismograms between models with and without thick sedimentary layers were 1–2. This means that thin lower-velocity (<0.6 km/s) sediments just below the stations primarily control the estimated large amplifications. Conversely, at near-source (≤20 km) distances, 1-order amplifications of seismic energies for a shallow tremor source can occur due to thick sedimentary layers. Multiple S-wave reflections between the seafloor and plate interface are contaminated in tremor envelopes; consequently, seismic energy and duration are overestimated. If a shallow tremor occurs within underthrust sediments, the overestimation becomes stronger because of the invalid rigidity assumptions around the source region. After 1-order corrections of seismic energies of shallow tremors along the Nankai Trough, the scaled energies of seismic slow earthquakes were 10−10–10−9 irrespective of the region and source depth. Hence, the physical mechanisms governing seismic slow earthquakes can be the same, irrespective of the region and source depth.
Abstract
We use neural networks and large climate model ensembles to explore predictability of internal variability in sea surface temperature (SST) anomalies on interannual (1–3 years) and decadal (1–5 and 3–7 years) timescales. We find that neural networks can skillfully predict SST anomalies at these lead times, especially in the North Atlantic, North Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The spatial patterns of SST predictability vary across the nine climate models studied. The neural networks identify “windows of opportunity” where future SST anomalies can be predicted with more certainty. Neural networks trained on climate models also make skillful SST predictions in reconstructed observations, although the skill varies depending on which climate model the network was trained. Our results highlight that neural networks can identify predictable internal variability within existing climate data sets and show important differences in how well patterns of SST predictability in climate models translate to the real world.
Abstract
The electric fields of subauroral polarization streams (SAPS) have been suggested to affect energetic charged particles' dynamics in the inner magnetosphere, though their role on radiation belt electrons has never been properly quantified. A moderate geomagnetic storm on 2015-09-07 caused the deep injection of 10–100s of keV electrons in Earth's inner magnetosphere to low L* (L* < 4). Using a 2-D test particle tracer, we present the effects of electric fields given by the Volland-Stern model, a SAPS (Goldstein et al., 2005, https://doi.org/10.1029/2005ja011135) model, and a modified SAPS model on the energetic electron deep injections. The modified SAPS model reflects the SAPS electric field observations by the Van Allen Probes and is supported by Defense Meteorological Satellite Program observations. Simulations suggest that the SAPS electric field pushes 10–20 MeV/G electrons Earthward to L* ∼ 2.7 in 2.5 hr, much deeper compared to the Volland-Stern electric field.
Abstract
Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. In this study, we show that a snow fence experiment on High-Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo-erosion gully in the landscape. We estimate that 1.1–3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilized aquatic carbon. Our results show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss, highlighting the need for improved model representation.
Abstract
A too-weak eddy feedback in models has been proposed to explain the signal-to-noise paradox in seasonal-to-decadal forecasts of the winter Northern Hemisphere. We show that the “eddy feedback parameter” (EFP) used in previous studies is sensitive to sampling and multidecadal variability. When these uncertainties are accounted for, the EFP diagnosed from CMIP6 historical simulations generally falls within the reanalysis uncertainty. We find the EFP is not independent of the sampled North Atlantic Oscillation (NAO). Within the same dataset, a sample containing larger NAO variability will show a larger EFP, suggesting that the link between eddy feedbacks and the signal-to-noise paradox could be due to sampling effects with the EFP. An alternative measure of eddy feedback, the barotropic energy generation rate, is less sensitive to sampling errors and delineates CMIP6 models that have weak, strong, or unbiased eddy feedbacks, but shows little relation to NAO variability.
Abstract
We use multi-year observations of cross-track winds (u) from the CHAllenging Minisatellite Payload (CHAMP) and the Gravity Field and Steady State Ocean Circulation Explorer (GOCE) to calculate third-order structure functions in the thermosphere as a function of horizontal separation (s). They are computed using the mean (〈δu
3〉) and the median 〈δu3〉med $\left({\langle \delta {u}^{3}\rangle }_{\text{med}}\right)$ and implemented over non-polar satellite paths in both hemispheres. On height averages, 〈δu
3〉 is shown to scale with s
2 for s ≃ 80–1,000 km, in agreement with equivalent estimates in the lower atmosphere from aircraft observations. Conversely, 〈δu3〉med ${\langle \delta {u}^{3}\rangle }_{\text{med}}$ follows an s
3 power law for almost the whole s range, consistent with the two-dimensional turbulence scaling law for a direct enstrophy cascade. These scaling laws appear independent of winds in distinct atmospheric regions. Furthermore, the functions are predominantly positive, indicating a preferential cyclonic motion for the wind.
Abstract
Elevated duct (EleD) is an abnormal atmospheric refraction structure with a suspended trapped layer. The precise and highly resolved elevated duct-height-based data (EleDH) is crucial for radio communication systems, especially in electromagnetic wave path loss prediction and EleDH-producing systems. However, producing high-resolution EleDH is challenging because of the massive details in the EleDH data. Direct and high-time refinement procedures mostly lead to unrealistic outcomes. The study provides a Dense-Linear convolutional neural network (DLCNN)-based EleDH refinement technique based on the development of statistical downscaling and super-resolution technologies. Additionally, the stack approach is used, and the refining order is taken into consideration to ensure precision in high-time refinement and provide reliable outcomes. To demonstrate the strength of DLCNN in capturing complex internal characteristics of EleDH, a new EleD data set is first funded, which only contains the duct height. From this data set, we use the duct height as the core refinement of the EleD's trapped layer and the thickness of the trapped layer to ensure reliable duct height. Seven super-resolution models are utilized for fair comparisons. The experimental results prove that the DLCNN has the highest refinement performance; also, it obtained excellent generalization capacity, where the minimum and maximum obtained Accuracy(20%), MAE, and RMSE were 85.22% ∓ 88.30%, 36.09 ∓ 45.97 and 8.68 ∓ 10.14, respectively. High-resolution EleDH improves path loss prediction, where the minimum and maximum obtained bias were 2.37 ∓ 9.51 dB.
Abstract
We use multiple instruments data to investigate the behavior of the equatorial and low-latitude ionosphere during the geomagnetically active and quiet period of November 1–6, 2021. In this context, total electron content (TEC) data obtained from the Global Positioning System (GPS) receivers in the equatorial and low-latitude regions of Asia, Africa, and America are used to assess variations in plasma density during the storm. The storm-time ionization levels were found to vary significantly in the crests of the Equatorial Ionization Anomaly (EIA) region over the 3 longitudes. The Rate of Change TEC Index (ROTI) derived from GPS receiver measurements, is used to study the equatorial/low-latitude ionospheric plasma irregularities at various longitudes under geomagnetically quiet and disturbed conditions. Observations showed longitudinal variations in the ionospheric irregularities under both quiet and disturbed conditions. Some days exhibit a decrease in the strength of the midnight plasma irregularities toward the East, that is, the irregularities are more pronounced in West America, less common in East America, and almost non-existent in Africa and Asia. Our investigations show this storm prevented the occurrence of plasma irregularities at the equatorial/low-latitude region in the American sector during the night following the main phase. In general, no significant storm effects were observed at the target locations in Africa and Asia. The existence of westward Prompt Penetration Electric Field (PPEF) and the Equatorial Electrojet (EEJ) during the main phase, from midnight to noon, is clearly related with the constriction of plasma diffusion and the consequent suppression of plasma irregularities. Thus, the longitudinal dependence for the generation of midnight plasma irregularities during this storm is mainly influenced by local time occurrence of maximum ring current, and the ionospheric electric fields.
Abstract
The mid-lithosphere discontinuity (MLD), identified by a sharp velocity drop at ∼70–100 km depths within the cratonic lithosphere is key to comprehending the chemical composition and thermal structure of the cratonic lithosphere. The MLD is widely accepted to be caused by composition anomalies, such as hydrous minerals, which show low velocities and high electrical conductivities. However, noticeable high-electrical conductivity anomalies have not been detected in the most cratonic lithosphere. Dolomite has an electrical conductivity similar to olivine and can be originated by carbonatitic melts trapped at ∼80–140 km depths. Here we investigated the elasticity of dolomite under mantle conditions using ab initio calculations and found dolomite exhibits significantly lower velocities than the primary minerals in the lithospheric mantle. Therefore, the dolomite enrichment might provide a good explanation for the observed velocity drop of the MLD in cratonic regions where no high-conductivity anomaly has been detected, such as the northern Slave craton.
