The risk of flooding is on the rise in Delta cities, such as Ho Chi Minh City (HCMC) in Vietnam, with projections indicating further increases due to climate change and urbanization. Flood risk analyses, for which loss modeling is a key component, play a crucial role in decisions on flood risk management and urban development. Probabilistic multi-variable loss models are increasingly being used to improve loss estimation, as they describe loss processes better and inherently provide a quantification of uncertainties. However, such models are often based on input variables that are determined by expert judgment. Thus, we propose the first probabilistic multi-variable flood loss model designed for residential buildings in delta cities such as HCMC (BN-FLEMO∆). BN-FLEMO∆ is built upon new building-level empirical survey data. The model is developed with an automatic machine learning-based (ML) feature selection framework and a systematic learning process to determine the optimal structure of the Bayesian Network. Based on a methods comparison, we demonstrate the following key advantages of BN-FLEMO∆: 1. enhanced, empirically-based description of flood loss processes leading to improved accuracy in loss estimation; 2. provision of a probability distribution of losses and inherent quantification of modeling uncertainty; 3. network structure allows model application even when data for one or more input variables are missing, which is particularly valuable in data-scarce environments. We therefore expect that BN-FLEMO∆ will significantly improve risk analyses in HCMC and similar delta cities and support decision-makers in developing sustainable flood risk management strategies for these dynamic flood-prone regions.

Radiation-topography interaction plays an important role in the surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of radiation-topography interaction over the TP. Land-atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for radiation-topography interaction overall improves E3SM’s performance in simulating surface climate. The winter cold bias in air temperature decreases from -4.48 K to -3.70 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP’s radiation-topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts.

Soil property and litter quality are two key factors that control soil organic matter decomposition. Under climate change, it remains unclear how the changes of soil microbial community and litter quality affect soil organic carbon decomposition, although significant changes of these two factors have been reported intensively. This limits our ability to model the dynamics of terrestrial soil carbon in a changing climate. Using a long-term Free Air CO2 Enrichment facility equipped with warming, we investigated the effect of soil property and litter quality change on the decomposition rate of soil organic matter. Results showed that significant change of litter quality was observed under elevated CO2 and warming. Elevated CO2 decreased the concentration of N of rice and wheat straw, while warming decreased the concentration of N and K in wheat straw. However, these changes in plant litter quality did not lead to a shift in soil organic matter decomposition. The legacy effect of long-term elevated CO2 and warming on soil properties dominated the decomposition rate of soil organic matter. Elevated CO2 suppressed soil organic matter decomposition mainly by increasing phosphorous availability and lowering soil C/N, fungi/bacteria ratio, and N-acetyl-glucosaminidase activity; while warming or elevated CO2 plus warming had no effect on soil organic matter decomposition. Our results demonstrated that the change of soil properties other than litter quality control the decomposition of soil organic carbon; and soil property change should be taken into consideration in model developing when predicting terrestrial soil carbon dynamics under elevated atmospheric CO2 and warming.

We report results of our analysis of a solar wind reconnecting current sheet (RCS) and its solar wind magnetic hole observed on 20 November 2018. In the solar wind, the normal vector to the current sheet plane makes an angle of 32° with the Sun-Earth line. A combination of tilted current sheet plane and foreshock effects cause an asymmetric interaction with the bow shock, such that the structure arrives at the quasi-perpendicular side of the bow shock before the quasi-parallel side. The solar wind flow slowdown and deflection during the bow shock crossing significantly disrupt the reconnection exhausts within the RCS. Unlike localized magnetosheath jets, the solar wind RCS has a global impact on the bow shock and the magnetopause. Plasma flow deflection in the magnetosheath also increases with the passage of the RCS. The magnetic field strength inside the magnetic hole decreases by ~69 percent in the solar wind, with a similar depression rate observed inside the magnetosheath due to this structure. The ion density and temperature both increase within the current sheet to form a roughly pressure balanced structure. Field rotation and change in the dynamic pressure during this event modify the reconnection zones at the magnetopause and cause an inward motion of this boundary.

South America is a large continent situated mostly in the Southern Hemisphere (SH) with complex topography and diverse emissions sources. However, the atmospheric chemistry of this region has been historically understudied. Here, we employ the Multi-Scale Infrastructure for Chemistry and Aerosols, a novel global circulation model with regional refinement capabilities and full chemistry, to explore the sources and distribution of the carbon monoxide (CO) tropospheric column in South America during 2019, and also to assess the effect that South American primary emissions have over the rest of the world. Most of the CO over South America can be explained either by NMVOC secondary chemical production or by biomass burning emissions, with biomass burning as the main explanation for the variability in CO. Biomass burning in Central Africa is a relevant contributor to CO in all of the continent, including the southern tip. Biogenic emissions play a dual role in CO concentrations: they provide volatile organic compounds that contribute to the secondary CO production, but they also destroy OH, which limits the chemical production and destruction of CO. As a net effect, the lifetime of CO is extended to ~120 days on average over the Amazon, while still being in the range of 30-60 days in the rest of South America.

The usefulness of satellite multi-sensor precipitation (SMP) and other satellite-informed precipitation products in water resources modeling can be hindered by substantial errors which vary considerably with spatiotemporal scale. One approach to cope with these errors is by combining SMPs with ensemble generation methods, such that each ensemble member reflects one plausible realization of the true—but unknown—precipitation. This requires replicating the spatiotemporal autocorrelation structure of SMP errors. The climatology of this structure is unknown for most locations due to a lack of ground reference observations, while the unique anisotropy and nonstationarity within any particular precipitation system limit the relevance of this climataology to the depiction of error in individual storm systems. Characterizing and simulating this autocorrelation across spatiotemporal scales has thus been called a grand challenge within the precipitation community. We introduce the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which combines anisotropic and nonstationary SMP spatiotemporal correlation structures with a pixel-scale precipitation error model to stochastically generate ensemble precipitation fields that resemble “ground truth” precipitation. We generate STREAM precipitation ensembles at high resolution (1-hour, 0.1˚) with minimal reliance on ground-reference data, and evaluate these ensembles at multiple scales. STREAM ensembles consistently “bracket” ground-truth observations and replicate the autocorrelation structure of ground-truth precipitation fields. STREAM is compatible with pixel-scale error/uncertainty formulations beyond those presented here, and could be applied globally to other precipitation sources such as numerical weather predictions or “blended” products. In combination with recent work in SMP uncertainty characterization, STREAM could be run without any ground data.

This paper examines the rapid losses and acceleration of trapped relativistic and ultrarelativistic electron populations in the Van Allen radiation belt during the September 7-9, 2017, geomagnetic storm. By analyzing the dynamics of the last closed drift shell (LCDS) and the electron flux and phase space density (PSD), we show that the electron dropouts are consistent with magnetopause shadowing and outward radial diffusion to the compressed LCDS. During the recovery phase, an in-bound pass of Van Allen Probe A shows an apparent local peak in PSD. However, a fortuitous timing of a crossing of the two Van Allen Probes reveals instead how the apparent PSD peak arises from aliasing monotonic PSD profiles which are rapidly increasing due to acceleration from very fast inwards radial diffusion. In the absence of such multi-satellite conjunctions during fast acceleration events, the source might otherwise be attributed to local acceleration processes.

Earth system models are a powerful tool to simulate the response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement tools from control theory, called “controllers”, to determine the quantity of aerosol to inject into the stratosphere to reach or maintain specified global temperature targets, such as limiting global warming to 1.5\textdegree C above pre-industrial temperatures. This work explores how internal (unforced) climate variability can impact controller-determined injection amounts using the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) simulations. Since the ARISE-SAI controller determines injection amounts by comparing global annual-mean surface temperature to predetermined temperature targets, internal variability that impacts temperature can impact the total injection amount as well. Using an offline version of the ARISE-SAI controller and data from CESM2 earth system model simulations, we quantify how internal climate variability and volcanic eruptions impact injection amounts. While idealized, this approach allows for the investigation of a large variety of climate states without additional simulations and can be used to attribute controller sensitivities to specific modes of internal variability.

Marine ecosystem is influenced by multiple factors under the global warming. Increasing temperature raises the matabolic demand for oxygen, however, its supply declines as the concentration of dissolved oxygen decreases. Metabolic index is defined as the oxygen supply to demand ratio for a marine organism, quantitatively combining the effects of ocean warming and deoxygenation. A subset of the earth system models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) are used to calculate the century-scale changes in the metabolic index under three different scenarios. Under the most aggressive warming scenario, metabolic index can decline over 50\% at northern extratropics including the US west coast. Overall magnitude of the change is dependent on the emission scenarios, whereas spatial patterns are model-dependent, in particular at low latitudes due to the large uncertainty in projected oxygen changes.

\cite{Narayanan_2024}Gravity waves (GWs) play an important role in the dynamics and energetics of the mesosphere. Geomagnetic activity is a known source of GWs in the upper atmosphere. However, how deep the effects of geomagnetic activity induced GWs penetrate into the mesosphere remains an open question. We use temperature measurements from the SABER/TIMED instrument between 2002 - 2018 to study the variations of mesospheric GW activity following intense geomagnetic disturbances identified by AE and Dst indices. By considering several case studies, we show for the first time that the GWs forced by geomagnetic activity can propagate down to about 80 km in the high latitude mesosphere. Only regions above 55° latitudes show a clear response. The fraction of cases in which there is an unambiguous enhancement in GW activity following the onset of geomagnetic disturbance is smaller during summer than other seasons. Only about half of the events show an unambiguous increase in GW activity during non-summer periods and about one quarter of the events in summer show an enhancement in GWs. In addition, we also find that the high latitude mesopause is seen to descend in altitude following onset of geomagnetic activity in the non-summer high latitude region.

Previous studies of freshwater lenses in saline aquifers adjoining gaining rivers (“riparian lenses”) have so far considered only rivers that fully penetrate the aquifer, whereas in most cases, rivers are only partially penetrating. This paper presents a new methodology for obtaining the saltwater discharge and the shape of a steady-state, non-disperive riparian lens, where the river is partially penetrating, combining two previous analytical solutions. The resulting analytical solution is compared to numerical modelling results to assess assumptions and the methodology adopted to approximate the “turning effect”, which is the change in groundwater flow direction (horizontal to vertical) near the partially penetrating river. A range of conditions are analysed, constrained by parameters adopted previously for River Murray floodplains (Australia). Consistency between analytical and numerical results highlight the capability of the proposed analytical solution to predict the riparian lens geometry and saltwater discharge into partially penetrating rivers. The sensitivity analysis indicates that larger riparian lenses are produced adjacent to the deeper and wider rivers, as expected. The change in width or depth of the river has more influence on the saltwater discharge and the horizontal extent of the riparian lens (and less effect on the vertical extent of the lens adjacent to the river) for shallower and narrower rivers. This research highlights the utility of the new method and demonstrates that the assumption of a fully penetrating river likely leads to significant overestimation of the saltwater discharge to the river and the riparian lens horizontal extent and vertical depth.

The transport and accumulation of moisture played an essential role in the extremely heavy rainfall of July 2020 in Japan. To better understand this event in terms of moisture sources and transport routes, backward particle trajectory analysis was conducted. We found two major moisture sources: transport from the tropics and uptake from the subtropics. A narrow moisture channel was found along the edge of the western Pacific Subtropical High (WPSH), transporting the moisture to the Baiu front. However, most moisture from the tropics was lost due to precipitation, and their contributions were reduced to about 15%. In contrast, the subtropical regions contributed over 80% moisture via evaporation and lower tropospheric convection. Among those regions, the western Pacific contributed the most (33 %). This study highlights the role of WPSH in moisture transport, and also demonstrated the importance of the moisture uptake during the transport.

The North Anatolian Fault (NAF) has produced numerous major earthquakes. After decades of quiescence, the Mw 6.8 Elazig earthquake (January 24, 2020) has recently reminded us that the East Anatolian Fault (EAF) is also capable of producing significant earthquakes. To better estimate the seismic hazard associated with these two faults, we jointly invert Interferometric Synthetic Aperture Radar (InSAR) and GPS data to image the spatial distribution of interseismic coupling along the eastern part of both the North and East Anatolian Faults. We perform the inversion in a Bayesian framework, enabling to estimate uncertainties on both long-term relative plate motion and coupling. We find that coupling is high and deep (0-20 km) on the NAF and heterogeneous and superficial (0-5 km) on the EAF. Our model predicts that the Elazig earthquake released between 200 and 250 years of accumulated moment, suggesting a bi-centennial recurrence time.

We report on the mountain top observation of three terrestrial gamma-ray flashes (TGFs) that occurred during the summer storm season of 2021. To our knowledge, these are the first TGFs observed in a mountaintop environment and the first published European TGFs observed from the ground. A gamma-ray sensitive detector was located at the base of the Santis Tower in Switzerland and observed three unique TGF events with coincident radio sferic data characteristic of TGFs seen from space. We will show an example of a ‘slow pulse’ radio signature (Cummer et al., 2011; Lu et al.,2011; Pu et al., 2019, 2020), a -EIP (Lyu et al., 2016; 2021a; Cummer et al., 2017; Wada et al., 2020), and a double peak TGF associated with an extraordinarily powerful and complicated positive-polarity sferic, where each TGF peak is possibly preceded by a short burst of stepped leader emission.

A document by Daniel Michael Gilford, PhD. Click on the document to view its contents.

The recently developed Micromagnetic Tomography (MMT) technique combines advances in high resolution scanning magnetometry and micro X-ray computed tomography. This allows precise recovery of magnetic moments of individual magnetic grains in a sample using a least-squares inversion approach. Here we investigate five factors, which are governing the mathematical validity of MMT solutions: grain concentration, thickness of the sample, size of the sample’s surface, noise level in the magnetic scan, and sampling interval of the magnetic scan. To compute the influence of these parameters, we set up series of numerical models in which we assign dipole magnetizations to randomly placed grains. Then we assess how well their magnetizations are resolved as function of these parameters. We expanded the MMT inversion to also produce the covariance and standard deviations of the solutions, and use these to define a statistical uncertainty ratio and signal strength ratio for each solution. We show that the magnetic moments of a majority of grains under the inspected conditions are solved with very small uncertainties. However, increasing the grain density and sample thickness carry major challenges for the MMT inversions, demonstrated by uncertainties larger than 100% for some grains. Fortunately, we can use the signal strength ratio to extract grains with the most accurate solutions, even from these challenging models. Hereby we have developed a quick and objective routine to individually select the most reliable grains from MMT results. This will ultimately enable determining paleodirections and paleointensities from large subsets of grains in a sample using MMT.

Geology differentiation converts physical property models derived from geophysical data to a 3D quasi-geology model. It represents a step change in quantitative interpretation of geophysical data. However, quantifying the uncertainties of the differentiated geological units in a 3D quasi-geology model has been largely unexplored. We have developed an empirical method to construct 3D probabilistic quasi-geology models in the deterministic inversion framework. We used mixed Lp norm joint inversion to recover a large sequence of physical property models based on multiple airborne geophysical data sets. Prior petrophysical measurements were used to determine the acceptance and rejection of these models. We then performed geology differentiation for all these accepted models and obtained a set of 3D quasi-geology models, based on which we constructed probabilistic 3D quasi-geology models. Our work has broad implications for 3D geological model building based on multiple geophysical and/or petrophysical measurements.

A document by Ran Holtzman. Click on the document to view its contents.