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.

Severe convection, responsible for hazards such as tornadoes, flash floods, and hail, is usually preceded by abundant Convective Available Potential Energy (CAPE). In this work, we use a Lagrangian approach to study how anomalously large values of CAPE build up in various regions. Nearly all extreme values of CAPE arise from surface fluxes underneath a capping inversion over several diurnal cycles, but the origin of the capping inversion and the diurnal cycle of surface fluxes differ around the world. In some regions, such as North America and Europe, the air above the boundary layer must be much warmer than usual to form this capping inversion, while in other regions, especially the Middle East and Central Africa, a capping inversion is common. Additionally, high CAPE occurrences that are over land (those in the Americas, Europe, Africa, and East Asia) tend to lose their capping inversions before the time of maximum CAPE due to large diurnal cycles of sensible heating, while those that occur over coastal waters (in the Middle East, Northern Australia, South Asia, and the Mediterranean) usually retain a substantial capping inversion. Uniquely, CAPE in Southeast Australia often builds up due to cooling aloft rather than to boundary layer warming. These results show that one hoping to understand or predict CAPE patterns must understand a variety of mechanisms acting in different regions.

Aerosol particle hygroscopicity is a key indicator in determining the aerosol water content (AWC) of a particle. As sulfate emissions decrease, other particle components such as nitrate and organics constitute a greater proportion of particle mass and become an increasingly important driver for determining particle hygroscopicity. However, there remains large uncertainty and limited field evidence quantifying how nitrate, sulfate, and organics determine particle hygroscopicity and AWC. This study aims to expand on the role that nitrates and organics have in determining particle hygroscopicity and AWC by using field measurements from the Tracking Aerosol Convection Interactions Experiment (TRACER) campaign in Houston, TX, conducted from 2021 to 2022 at two concurrently measured field sites. This field data is supplemented with model output from GEOS-Chem, which is used to inform the thermodynamic models ISORROPIA-II and E-AIM-IV to determine the hygroscopicity parameter (κ) and AWC of aerosol particles and their controlling factors. Nitrate is found to be the largest chemical factor influencing AWC. Organics must be included to accurately calculate κ, and doing so closes a measurement-model gap of 0.2 ± 0.1 that arises from inorganics-only estimations. Differences of less than 0.1 µg m-3 in nitrate concentrations result in differences in the AWC of up to 7 ± 4 µg m-3. These results add evidence to the growing importance of nitrate and organics in determining AWC and κ, with implications for understanding cloud droplet formation and subsequent interactions.

To reduce simulation uncertainty and improve process understanding of hydrological systems, integrating models with observations through data assimilation techniques is paramount. Among these, the ensemble smoother (ES) stands out for its ease of implementation and high computational efficiency. When tackling problems involving non-linear processes and non-Gaussian distributions, leveraging deep learning (DL) within ES, termed the ES(DL) method, proves superior to the Kalman-based counterpart. However, the original ES(DL) method is constrained by the traditional paradigm that builds a mapping from the innovation vector (i.e., the difference between observations and model predictions) to the update vector (i.e., the difference between posterior and prior state/parameters). In this study, we introduce a generalized form of ES(DL), where the traditional ES(DL) approach becomes its special case. We explore the optimal implementation of ES(DL) through surface and subsurface scenarios, spanning various dimensions (low to high) and parameter distributions (Gaussian to non-Gaussian). Notably, certain implementations of ES(DL), which diverge significantly from the traditional approach, can yield similar or even better outcomes, especially under the non-Gaussian condition. While this study’s focus lies on the smoothing approach for parameter estimation, the proposed formulation can be extended to filtering problems, facilitating model state updates.

Erosion can remain active and changing in landscapes long after tectonic drivers have ceased, potentially due to local-geologic controls, climate changes, or mantle dynamics. We present new fluvial incision histories and terrain analyses of the Colorado River system through the central Colorado Plateau to test ideas about what has caused the highly variable erosion across this post-orogenic landscape. Results clarify that a mysterious Middle Pleistocene (~1.5 to 0.3 Ma) erosional hiatus (baselevel high) occurred in parts of the region, followed by ~250 m of rapid river incision in the central Colorado Plateau over the last ~350 kyr. Our results potentially support the hypothesis that the lengthy hiatus records a planated landscape graded to the Colorado River before it propagated a signal of baselevel fall from integration through western Grand Canyon in the Pliocene. The subsequent, upstream-migrating incision has been partitioned into multiple waves across the landscape due to the local geologic controls of lava damming in western Grand Canyon, salt tectonics in Cataract Canyon, and heterogenous and layered bedrock throughout. A response-time model indicates that this transient baselevel signal has taken 2-4 Myr to reach the central Colorado Plateau and that it can only account for a small portion of total denudation in the middle-late Cenozoic. This seminal landscape’s highly variable and transient erosion highlights the complexity of landscape evolution even in the absence of orogenic-scale tectonics and regardless of climatic controls.

Simulating the ocean’s submesoscale is key to understand the mass and energy cycles of the ocean and the global climate system. Contrast to the ocean’s mesoscale, submesoscale processes are usually highly ageostrophic and manifest at the scales within 10km. Ocean general circulation models with kilometer-resolution are capable to resolve key submesoscale processes, hence indispensable for both process and climate studies. We construct a grid hierarchy for the ocean-sea ice model in the High-Resolution Earth System Model on Sunway supercomputer (SW-HRESM), which is based on Community Earth System Model (CESM2) with deep optimizations on the Chinese home-brew supercomputing architecture of Sunway. The highest grid resolution is 0.03o (2.4km globally). In this study we evaluate the ocean-sea ice coupled simulations by SW-HRESM, focusing on the submesoscale and the kinetic energy (KE) cycles. In particular, highly ageostrophic submesoscale turbulence is simulated, dominated by deepened mixed layers (ML) during winter and the ensuing instabilities. KE and its transition between scales are further evaluated for major western boundary current systems. During winter, submesoscale is shown to dominate inverse cascading which energizes large-scale flows, as well as forward cascading to dissipation scales. The mesoscale-submesoscale continuum and the associated inverse KE cascading is further complemented by the forward KE cascading from the large-scale due to flow instabilities. In order to fully resolve the submesoscale spectrum, including frontal processes and wind-wave interactions, models finer than 1km are needed. Besides the model resolution, improvements for both the ocean and the fully coupled model of SW-HRESM are also discussed.

Routine ground-based measurements of total ozone column (TOC), as well as ozone profile soundings started in the late 1960s in Germany. The resulting ozone and temperature records at Hohenpeissenberg and Berlin / Potsdam / Lindenberg show long-term changes similar to other stations in Central Europe, and to the changes seen globally. Following the increase of ozone depleting substances (ODS), stratospheric ozone has declined from the 1960s until the 1990s. Since about 2000, ozone has leveled or slightly increased, consistent with declining amounts of ODS. The stratosphere has been cooling and the troposphere has been warming, in agreement with general expectations due to increasing greenhouse gas concentrations. The clearest signs of recovering ozone are seen around 40 km altitude. Two factors contribute to this increase: the decrease of stratospheric chlorine loading and cooling of the upper stratosphere, which slows gas-phase ozone destruction cycles, and enhances the ter-molecular reaction producing ozone. Tropospheric ozone has increased substantially from the 1960s to the early 1990s. Since then, it has remained more or less constant, on a level higher compared to the 1960s and 1970s. Particularly low tropospheric ozone was observed in 2020, due to reduced precursor emissions during the COVID-19 related lockdowns. The atmospheric concentrations of greenhouse gases will likely continue to rise, while the concentrations of ozone depleting substances are expected to slowly decline. To see how the atmosphere responds, and to help understand future changes, continued monitoring will be required for many years to come, both over Germany and worldwide.

We have carried out a statistical study of neutral atmospheric parameters in the mesosphere lower thermosphere (MLT) region, by utilizing simultaneous measurements from the EISCAT VHF radar and sodium LIDAR collocated at  Tromsø , Norway. This study focuses on the incoherent scatter (IS) spectral width, which is a function of the ion-neutral collision frequency, ion temperature, (equal to neutral temperatures in the D-region), and ion mass. Using the neutral temperatures obtained from LIDAR, and ion mass estimated using a chemistry model, we have measured the ion-neutral collision frequency in the 80-100 km altitudes by fitting the spectral width. The study shows that the current widely used formulae underestimate the ion-neutral collision frequency on average by 1.53\(\pm\)0.24 in comparison to the measurements. Also, the measured collision frequencies showed large temporal variations due to neutral density fluctuations, indicating the presence of atmospheric waves. The amplitudes of these waves are found to be as large as 50% of the background densities. This suggests that individual spectral width measurements are likely influenced by these random neutral density fluctuations, which can have a significant impact on the IS temperature fits. In addition, for altitudes below 85 km, the ion mass increases drastically indicating the presence of heavy cluster ions. The dominance of heavy ions makes it further challenging to extract the temperature values from the spectral width at these altitudes. In light of these observations, the inherent limitations of inferring temperatures from IS spectral width in the MLT altitudes are studied.

The energy cycle of a convectively-organized system, as realized by a convective-scale idealized simulation, is analyzed in wavelet space. In the equilibrium state, most of the available potential energy that is generated by convective heating is immediately converted into kinetic energy by means of buoyancy forcing, consistent with the free-ride principle. In turn, most of the generated convective kinetic energy is manifest as gravity waves propagating away from convective centers. The kinetic energy of these small-scale gravity waves is transferred upscale by their own advective nonlinearities. Finally, a large-scale circulation generated by this "inverse cascade' drives the formation of an organized structure in the precipitation field.

Mesoscale warm-core rings, known as Loop Current rings (LCRs) reshape the Gulf of Mexico water masses by redistributing large amounts of heat and salt laterally. LCRs also transform water masses via diapycnal mixing, but the mechanisms by which this occurs are poorly measured. Here, we present glider-MicroPod turbulence observations that reveal enhanced mixing below the mixed layer, along the eddy edges, driving the LCR’s heat, salt, and oxygen exchanges. Submesoscale stirring at the LCR’s edge yields interleavings of adjacent water masses, which facilitates double-diffusive mixing that transforms Subtropical Underwater into Gulf Common Water. Our findings highlight the need for ocean models to parameterize double-diffusive mixing processes directly resulting from submesoscale tracer stirring, which may be important at basin scale in the presence of LCRs in the Gulf of Mexico.

Representing glacial-interglacial changes in ocean carbon sequestration remains a major challenge for Earth system models (ESMs). Uncertainties in ocean circulation and biological carbon export are essential causes for model-proxy data mismatch. We quantify the impact of these factors by calibrating the Max Planck Institute-ESM. A shallower and weaker glacial Atlantic meridional overturning circulation (AMOC) than the present day, achieved by decreasing background vertical diffusivity, enables capturing the main features in observed δ13C, 14C and CO32-. Additionally, a new prognostic computation of marine aggregate sinking speeds enhances organic matter export efficiency in high latitudes. Together, the shallower AMOC and a comprehensive sinking scheme substantially improve the model-data comparison and carbon storage in the glacial Atlantic, but not in the Pacific and Indian oceans owing to low organic matter remineralisation. Our results yield that representing the glacial-interglacial ocean carbon storage in ESMs requires both constraining ocean circulation and improved biogeochemical processes.

A document by Jingwei ZHANG. Click on the document to view its contents.

A document by Tyler Waterman. Click on the document to view its contents.

• Recent regulations on the sulphur content of ship emissions has accelerated global warming by approximately 2-3 years. • Reduced ship emissions induce responses in cloud properties, top-of-atmosphere radiation, and surface temperatures. • The regulations contribute to the exceptional warming observed in 2023, yet other factors are needed to fully account for it.

Soil microbial nitrous acid (HONO) production has been identified as a significant emission source to the atmosphere and, thus, considerably affects atmospheric oxidizing capacity and formation of secondary air pollutants. Soil HONO emissions are controlled by soil water content, soil temperature, and nitrogen content, which are affected by climatic conditions and fertilizer application rates. However, long-term global soil HONO emissions driven by climate change and increasing fertilizer use have not yet been quantified, hindering the assessment and prediction of the global impact of soil emissions on air quality and vegetation. Here, we derive the first global soil HONO emissions from natural and fertilized lands over the past four decades and evaluate the variation of their impacts on global ozone (O3) and vegetation exposure. Our results show that climate change and the enhanced fertilizer use have increased global soil HONO emissions from 11.0 Tg N in 1980 to 13.4 Tg N in 2016. The rise in fertilizer consumption has substantially intensified soil emissions in the three major grain-producing regions, India, China, and North America with growth rates of 14.9, 9.5, and 0.9 kt N yr-1 , respectively; climate change has predominantly increased soil emissions in Africa and South America with growth rates of 22.3 and 10.9 kt N yr-1 , respectively. Incorporating soil HONO emissions in a global chemistry-climate model improves the model performance in simulating global Page 2 of 22 atmospheric HONO. Simulations with the updated model showed that soil HONO emissions increased global surface O3 concentrations by 2.9% (up to 29%) and the subsequent risk of vegetation exposure to O3, especially in crop-production regions. The enhancement effect of soil HONO emissions on O3 is increased from 1980s to 2016 in regions with low anthropogenic emissions. Therefore, we speculate that with future decreases in anthropogenic emissions, the relative impact of soil HONO emissions on air quality and vegetation is expected to increase. Thus, we recommend the consideration of soil HONO emissions in strategies for mitigating global air pollution.

The scientific publishing landscape is evolving rapidly. This evolution is driven by a confluence of internal and external forces, including the growth of metrics-based evaluation of scientists; an increasing volume of manuscripts combined with expectations for rapid review and publication; an increasing number of journals, including for-profit Open Access publications; and the adoption of preprint servers across a growing range of disciplines. Many of these forces are contributing to personal anxiety and fatigue for authors, reviewers, and editors. Collectively, they are placing substantial stress on scientific publishing, which is a foundational pillar of the scientific enterprise. As editors of American Geophysical Union journals and books, we remain confident in the fundamental foundations of scientific publishing, but we are concerned about the impact of these increasing stressors. By affirming and investing in editorial values, respecting scientific integrity and credibility, and committing to accessibility, transparency, and accountability, we can fortify the foundations of the scientific enterprise during a time of rapid change.

Climate models struggle to produce sea surface temperature (SST) gradient trends in the tropical Pacific comparable to those seen recently in nature. Here, we investigate whether models systematically underestimate internally-generated SST trends by underestimating cloud feedbacks on SST gradients. A heat-budget analysis reveals coupling between cloud-radiative effects, circulation, and SST gradients in driving multi-decadal variability in the Eastern Pacific and suggests that biases in cloud feedbacks may be causing models to underestimate internal SST variability. We find that the magnitude of the cloud-SST feedback in the subtropical Southeast Pacific is correlated across models with the magnitude of Eastern Pacific multi-decadal SST variability. Using this relationship and observed feedback estimates, we estimate that internal Eastern Pacific SST variability is under-predicted in most models. Adjusting for model bias increases the likelihood of generating a cooling trend at least as large as observations in preindustrial control simulations by between ~10% and ~58%.

We estimate depth-dependent azimuthal anisotropy and shear wave velocity structure beneath the Alaska subduction zone by the inversion of a new Rayleigh wave dispersion dataset from 8-85 s period. We present a layered azimuthal anisotropy model from the forearc region offshore to the subduction zone onshore. In the forearc crust, we find a trench-parallel pattern in the Semidi and Kodiak segments, while a trench-oblique pattern is observed in the Shumagin segment. These fast directions agree well with the orientations of local faults. Within the subducted slab, a dichotomous pattern of anisotropy fast axes is observed along the trench, which is consistent with the orientation of fossil anisotropy generated at the mid-ocean ridges of the Pacific-Vancouver and Kula-Pacific plates that is preserved during subduction. Beneath the subducted slab, a trench-parallel pattern is observed near the trench, which may indicate the direction of mantle flow.