We conduct a series of eight 45-day experiments with a global storm-resolving model (GSRM) to test the sensitivity of relative humidity R in the tropics to changes in model resolution and parameterizations. These changes include changes in horizontal and vertical grid spacing as well as in the parameterizations of microphysics and turbulence, and are chosen to capture currently existing differences among GSRMs. To link the R distribution in the tropical free troposphere with processes in the deep convective regions, we adopt a trajectory-based assessment of the last-saturation paradigm. The perturbations we apply to the model result in tropical mean R changes ranging from 0.5% to 8% (absolute) in the mid troposphere. The generated R spread is similar to that in a multi-model ensemble of GSRMs and smaller than the spread across conventional general circulation models, supporting that an explicit representation of deep convection reduces the uncertainty in tropical R. The largest R changes result from changes in parameterizations, suggesting that model physics represent a major source of humidity spread across GSRMs. The R in the moist tropical regions is disproportionately sensitive to vertical mixing processes within the tropics, which impact R through their effect on the last-saturation temperature rather than their effect on the evolution of the humidity since last-saturation. In our analysis the R of the dry tropical regions strongly depends on the exchange with the extra-tropics. The interaction between tropics and extratropics could change with warming and presage changes in the radiatively sensitive dry regions.

Recent observations of faults in the shallow regions of subduction zones have revealed slow slip events that nucleate up-dip of the locked zone. Clay-rich sediments are prevalent at shallow depths and a large body of experimental work has shown that these sediments have a tendency towards velocity-strengthening frictional behavior, although velocity-weakening behavior is observed as well. Models of deeper slow slip, down-dip of the locked zone, generally require velocity-weakening behavior for events to nucleate. Here I show that slow slip events can nucleate and propagate on shallow, velocity-strengthening thrust faults, in a numerical model of a thrust fault dipping in a homogeneous, elastic half-space. This behavior is due to the broken symmetry of the thrust fault geometry, and is similar to behavior previously reported on bi-material, and poro-elastic faults. The interaction of the fault with the free surface (i.e. the sea floor) creates a coupling between normal stress on the fault and fault slip. This coupling allows velocity-strengthening slow slip events to nucleate, and becomes stronger at shallower depths. Here I conduct a parameter analysis, and show how this behavior is limited to certain values of the frictional and elastic parameters on the fault.

Pastoral farming in sylvo-pastoral systems remains a key activity for the local economy and food security in the Sahel. Livestock has a great impact on the carbon cycle and on greenhouse gas (GHG) fluxes. Over the last decade, experimental and modeling studies have addressed the variability of ecosystem GHG fluxes, their drivers and efforts at scaling. Nonetheless, in these Sahelian semi-arid ecosystems, there is still limited information on the underlying processes as well as temporal and spatial variability of GHG fluxes, preventing their quantification from local-to-regional scales. This lack of information is mostly due to the paucity of experimental data, but also modeling studies required at the regional scale. We used STEP (Sahelian Transpiration Evaporation and Productivity model), an ecosystem process model for Sahelian herbaceous savannas to investigate the underlying processes of carbon dioxide (CO2) and nitrous oxide (N2O) emissions in a typical sylvo-pastoral system of the Sahel (Dahra, Senegal). Overall, good agreements have been found between model outputs and validation in-situ data for soil moisture, evapotranspiration, soil mineral and organic N, soil C, and herbaceous mass. Dynamics of the model output of N2O and CO2 fluxes clearly show emission pulses after the first rains as generally observed in dry ecosystems. Following calibration and validation at the local scale, the model will be used to assess GHG emissions from different sites across the Sahel. GHG flux maps, validated by in situ measurements, will then be proposed at the scale of the Sahelian region for sylvo-pastoral ecosystems.

The remediation of chromium (Cr) contaminated sites presents a complex challenge due to the hazardous nature of Cr compounds and their adverse impacts on human health and the environment. In addressing this pressing concern, the present study endeavors to contribute to the understanding of technology suitability for effective Cr-contaminated site remediation through a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Drawing upon an exhaustive review of the existing literature, this research first identifies and evaluates the strengths of various technologies commonly employed in Cr-contaminated site remediation. These strengths encompass the capacity of technologies to efficiently reduce Cr concentrations, promote sustainable remediation practices, and ensure minimal collateral damage to surrounding ecosystems. Furthermore, the analysis delves into the weaknesses of these technologies, highlighting potential limitations that may impede their optimal performance. The study then explores the opportunities that arise from implementing these technologies, including advancements in remediation processes, novel materials, and cutting-edge monitoring and assessment tools. Additionally, the research critically assesses the threats posed by the application of these technologies, encompassing potential economic, technical, and environmental challenges that may hinder their widespread adoption. By synthesizing these key findings, the study aims to provide valuable insights into the selection and deployment of appropriate remediation technologies tailored to Cr-contaminated sites. Furthermore, the SWOT analysis offers a structured framework for decision-makers, environmental practitioners, and policymakers to make informed choices concerning the most suitable technology options for site-specific circumstances. In conclusion, this research illuminates the diverse landscape of technologies available for Cr-contaminated site remediation, accentuating their respective strengths, weaknesses, opportunities, and threats. As such, this SWOT analysis serves as a vital resource in the ongoing efforts to remediate and restore Cr-contaminated sites, thereby safeguarding public health and the environment for future generations. Article in AGU23 Website

Accurate knowledge of the dependence of anthropogenic atmospheric CO2, the excess over preindustrial, on future emissions is essential to developing approaches to limit climate change. At present, the lifetime of excess CO2, as represented in current carbon cycle models, is uncertain by more than an order of magnitude, 70 to more than 700 years (Schwartz, JGR, 2018). Consequently observation-based top-down analysis provides an important alternative approach. The turnover time of excess CO2 (ratio of stock in the atmosphere and the mixed-layer ocean, which are in near equilibrium, to the net leaving flux into the terrestrial biosphere and deep ocean) is determined as 54 ± 10 years. A simple model for excess CO2, consisting of four compartments with three observationally determined global-mean parameters (deposition velocity of CO2 to the surface ocean, piston velocity describing the rate of exchange of water between the mixed-layer and deep ocean, and the transfer coefficient of CO2 kat from the atmosphere a to the terrestrial biosphere t), and one uncertain adjustable parameter kta, accurately reproduces CO2 mixing ratio over the Anthropocene. This model yields the adjustment time (inverse of fractional removal rate in the absence of emissions) as 65 ± 10 years over the first 100 years, depending on kta, over which time excess CO2 would decrease by 65 to 81%, depending on kta, Figure 1. The reduction of global emissions required to stabilize atmospheric CO2 over this time scale is 50 to 60%.

Tephra layers are often used to reconstruct past eruptions. However, accurate interpretation of tephra layers depends on their degree of preservation. We asked: how much volcanological information do terrestrial tephra layers typically retain? We addressed this question with a study of the tephra layer produced by the May 1980 eruption of Mount St Helens, comparing historical records of deposit thickness and grain size distribution with measurements made four decades after the eruption. Using published isopach maps as a guide, we looked for the tephra layer in locations 15 – 600 km from Mount St Helens, selecting sample locations we judged to have high preservation potential. We found that preservation of the 1980 tephra deposit was often extremely good: observed thickness, mass loading and grain size distributions were similar to equivalent measurements made in 1980. However, we also observed high variability in preservation on small spatial scales, and our results indicate that landscape-scale volcanological reconstruction is sensitive to sample number and location, even when many sites display excellent preservation. Tephra preservation is clearly a complex and contingent process influenced by climate, biology, topography, parent material (i.e., grain size, morphology and geochemistry) and time. The relative importance of these factors will vary from place to place. We propose that the only way to tease apart these factors – and to fully understand the information content of tephra layers - is through direct observation of tephra deposits (whether natural or experimental) over extended time periods.

To better understand the role projected land-use changes (LUC) may play in future regional climate projections, we assess the combined effects of greenhouse-gas (GHG)-forced climate change and LUCs in regional climate model (RCM) simulations. To do so, we produced RCM simulations that are complementary to the North-American Coordinated Regional Downscaling Experiment (NA-CORDEX) simulations, but with future LUCs that are consistent with particular Shared Socioeconomic Pathways (SSPs) and related to a specific Representative Concentration Pathway (RCP). We examine the state of the climate at the end of the 21st Century with and without two urban and agricultural LUC scenarios that follow SSP3 and SSP5 using the Weather Research and Forecasting model (WRF) forced by one global climate model, the MPI-ESM, under the RCP8.5 scenario. We find that LUCs following different societal trends under the SSPs can significantly affect climate projections in different ways. In regions of significant cropland expansion over previously forested area, projected annual mean temperature increases are diminished by around 0.5-1.0℃. Across all seasons, where urbanization is high, projected temperature increases are magnified. In particular, summer mean temperature projections are up to 4-5℃ greater and minimum and maximum temperature projections are increased by 2.5-6℃, amounts that are on par with the warming due to GHG-forced climate change. Warming is also enhanced in the urban surroundings. Future urbanization also has a large influence on precipitation projections during summer, increasing storm intensity, event length, and the overall amount over urbanized areas, and decreasing precipitation in surrounding areas.

Volcanogenic tsunami and wave hazard remains less understood than that of other tsunami sources. Volcanoes can generate waves in a multitude of ways, including subaqueous explosions. Recent events, including a highly explosive eruption at Hunga Tonga-Hunga Ha’apai and subsequent tsunami in January 2022, have reinforced the necessity to explore and quantify volcanic tsunami sources. We utilise a non-hydrostatic multilayer numerical method to simulate 20 scenarios of sublacustrine explosive eruptions under Lake Taupō, New Zealand, across five locations and four eruption sizes. Waves propagate around the entire lake within 15 minutes, and there is a minimum explosive size required to generate significant waves (positive amplitudes incident on foreshore of >1 m) from the impulsive displacement of water from the eruption itself. This corresponds to a mass eruption rate of 5.8x10^7 kg s^-1, or VEI 5 equivalent. Inundation is mapped across five built areas and becomes significant near shore when considering only the two largest sizes, above VEI 5, which preferentially impact areas of low-gradient run-up. In addition, novel hydrographic output is produced showing the impact of incident waves on the Waikato river inlet draining the lake, and is potentially useful for future structural impact analysis. Waves generated from these explosive source types are highly dispersive, resulting in hazard rapidly diminishing with distance from the source. With improved computational efficiency, a probabilistic study could be formulated and other, potentially more significant, volcanic source mechanisms should be investigated.

Metal compounds abundant in Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals, for example, are proposed to have served as primitive metalloenzyme cofactors for their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis and astrobiology.

Current machine learning based shear wave velocity (Vs) inversion using surface wave dispersion measurements utilizes synthetic dispersion curves calculated from existing 3-D velocity models as training datasets. It is shown in the previous studies that the performances of the resulting networks are dependent on the diversity of the training data. We present an improved semi-supervised deep learning algorithm-based method that incorporates both observed and synthetic surface wave dispersion curves in the network training process. The algorithm is termed Wasserstein cycle-consistent generative adversarial networks (Wcycle-GAN), which combines the architecture of cycle-consistent GAN with Wasserstein loss metrics in optimization. Different from conventional supervised deep learning approaches, the GAN architecture also extracts structural information from the observed surface wave dispersion data in the training process that may improve generalization of the resulting network. The cycle-consistent loss addresses soft constraints on the trained neural networks to be reversible and thus reduces the variance of the trained networks. The Wasserstein metric provides weaker topology for convergence and improves spatial continuity of the predicted shear velocity (Vs) models. We demonstrate these improvements by applying the Wcycle-GAN to 4066 fundamental mode Rayleigh wave phase and group dispersion curves obtained in Southern California (SC). In general, the 3-D Vs model predicted by the best training Wcycle-GAN is consistent with previous surface wave tomography studies of SC in the overlapping area, but with smaller data misfit, yields better spatial smoothing, and provides improved images of structures near faults and in the top 5 km. Our results indicate that the proposed Wcycle-GAN algorithm has strong training stability and generalization abilities.

Current machine learning methods for discharge prediction often employ aggregated basin-wide hydrometeorological data (lumped modeling) for parametric and non-parametric training. This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating temporal-spatial hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. To test this hypothesis, we designed experiments comparing the performance of identical Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models forced with either lumped or distributed features. We gather meteorological forcing and static attributes for the Mackenzie basin in Canada- a large and unique basin. Importantly, discharge performance is assessed out-of-sample with k-fold replication across gauges. Results reveal a 9.6% improvement in the mean Nash-Sutcliffe Efficiency (NSE) and a 4.6% improvement in mean Kling-Gupta Efficiency (KGE) when LSTMs are trained with distributed information. Notably, the models exhibit consistently unbiased predictions, with a negligible relative bias (RBias ≈ 0.0) across all predictions. These experiments and results demonstrate the importance of integrating topologically guided geomorphologic and hydrologic information (distributed modeling) in data-driven discharge predictions.

On decadal timescales, the greenhouse gas methane (CH4) is ~100 times more potent than carbon dioxide. Its abundance is increasing, many of its sources are temperature dependent. The Arctic is the site of the fastest warming globally. Feed-backs between Arctic temperature and CH4 emissions and concentrations need investigation. Unfortunately, available Arctic in situ data are extremely sparse with no marine observations outside summer. Satellite instruments measuring solar radiation reflected from the surface are ineffective in the Arctic. Thus, we leverage satellite data from AIRS, IASI-1, and IASI-2 Thermal Infrared (TIR) spectrometers, which provide year-round, day/night CH4 observations. Available in situ high latitude NOAA/ESRL surface coastal (50-85°N) flask atmospheric CH4 concentrations were compared with satellite data. We find: 1) remote sensing data revealed 150% (IASI-1, mid-upper troposphere) and 80% (surface data for Arctic stations) increases in atmospheric CH4 concentration growth rates between 2010-2014 and 2014-2017 time spans. Global NOAA/ESRL surface concentration rates increased by 90% for the same period; 2) maximum CH4 seasonal emission from the Arctic land occurs in boreal summer, while that from the Barents Kara Sea (BKS) occurs in boreal winter (Nov–Mar). Total annual Arctic Ocean CH4 emissions are preliminary estimated as ~40% of all land emissions North of 50°N; 3) marine emissions are concentrated in shelf areas within ~100 km of the coasts of major Arctic BKS lands; 4) CH4 anomalies over BKS, defined as surplus over its concentration at the North Atlantic area, grew after 2014; 5) the strongest SST increase was observed every year in the southeast Barents Sea in June due to strengthening of the warm Murman Currents and in the south Kara Sea in Sept. Direct in situ CH4 flux measurements during polar night over sea are necessary to test the satellite results.

A challenge to managing water resources is characterizing the scale-dependent heterogeneity created by the interactions among hydrological, ecological and anthropological processes. It is often difficult to collect sufficient empirical data over the range of scales required to construct mathematical models that facilitate robust bottom-up descriptions or predictions. An alternative is identifying emergent properties of complex systems, whose components self-organize into novel structures or processes via their collective interactions with each other and the environment. A new level of organization and complexity emerges that cannot be predicted from or attributed to the components alone. Emergence offers a number of perspectives from which to interpret, if not predict, the behavior of complex water resource systems. One of these is entropy, which maximizes the options for system components to alter their interactions and, thus, permits variability and adaptability. At the scale of watersheds, increased entropy is pertinent because of its relationship to information (as probability functions), which is transmitted through connected components of a watershed in a manner such that the accrued information gives rise to emergent properties. Hence, analyzing the behaviors of a system according to emergence introduces the possibility of evaluating the information content via its interconnected components. Connectivity then assumes an integral role in a hydrologic system’s response to natural or anthropogenic disturbances (e.g., climate change, land use). Replacing the details of multi-scale heterogeneity and causal mechanisms with the functions that watersheds perform allows processes such as stream flow rate/duration and flood frequency to be construed as emergent spatiotemporal patterns. A reductionist or bottom-up approach to assessing the behavior of aquatic systems shifts to a functional or top-down approach that does not depend upon an understanding of all the physical, chemical or biological mechanisms involved. This latter approach could supplement conventional water resource descriptions and predictions via more comprehensively characterizing watershed or aquatic ecosystem functions.

Superseded by: Sun resonant forcing of Mars, Moon, and Earth seismicityhttps://arxiv.org/abs/2301.10800

Heat and drought events are increasing in frequency and intensity, posing significant risks to natural and agricultural areas with uncertain effects on the net ecosystem CO2 exchange (NEE). We modified the Vegetation Photosynthesis and Respiration Model to include soil moisture impacts on the gross ecosystem exchange (GEE) and respiration (RECO) fluxes and determine the temporal variability of NEE over south-western Europe for 2001-2022. Warming temperatures lengthen growing seasons causing an increase in GEE which is mostly compensated by a similar increment in RECO, resulting in a modest annual increase of net carbon sink of 0.80 gC/m2year but with high spatial and annual variability. The heatwave of 2022 reduced NEE by 78.5 TgC, a 27% decrease from the mean. The interannual variability is more influenced by drought in temperate humid regions than in Mediterranean semi-arid regions. These results emphasize the vulnerability of the net carbon sink as drying trends could revert the NEE trends, as it is happening for croplands in the French Central Massif.

Lessons from the strong global response to the coronavirus disease 2019 (COVID-19) pandemic and a renewed call for “One health” approach to health systems management in “The Lancet” parallel climate change emergencies. The weakened health - climate change nexus, perceived largely within public health need to engage how the Earth system (i.e. relationships between air, land, life and water on earth) in shaping the etiologies, incidences and transmission dynamics of diseases. The question “What are the drivers of the drivers of diseases?” using the context of diarrheal diseases is posed. Subsequently, we need to understand how (i) climatic risks drive biological health hazards, (ii) shifts in disease control services of ecosystems regulate diseases, (iii) climate change within Earth systems modify disease pathogens and species hosts relationships. Hence, safeguarding Earth system-related disease dynamics would inform pluralistic approaches beyond “One health”.

This study aims to understand and document the occurrence and variability of cloud cover in West Africa (WA) essential for assessing feasibility of planned large-scale solar energy projects. Investigations are carried out with a 10-year hourly record of two cloud cover data products: Clouds and the Earth Radiant Energy System passive satellite observations (CERES SYN1deg) and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5). The seasonal evolution of high-level, mid-level and low-level clouds (HCC, MCC and LCC) as well as the total cloud cover (TCC) over the region is examined. The results show that both products agree on the average seasonal and spatial distribution of cloud cover over the region, although the CERES SYN1deg product presents lower cloud fractions than ERA5. This is partly attributed to the inability of the satellite sensor to detect optically thin clouds in the atmosphere. Southern WA is found to be cloudier than other parts of the region in all seasons. During the monsoon season (June, July, August and September) cloudiness increases over southern WA (mean cloud fraction up to 70% in CERES SYN1deg and 80% in ERA5) but also extends northwards and strengthens over the Sahelian region. The intense cloud coverage over southern WA during the monsoon season leads to a large regional mean reflectance of incoming solar radiation of about 55%. In all seasons, the presence of LCC over large areas of the Sahel/Sahara region is highlighted in the CERES SYN1deg product. This could be due to a potential misinterpretation of Saharan dust as low clouds by the satellite sensor which may have overestimated their occurrences and fractions and may lead to errors in the estimation of cloud radiative effects over the region. Northern WA is associated with higher frequencies of no cloud occurrence events unlike the south where cloudless skies are rare. Furthermore, in southern WA, overcast conditions of LCC which significantly reduce incoming solar radiation are observed for a significant number of times (up to 10% of the time in CERES SYN1deg and 20% in ERA5).

Ionospheric Faraday rotation distorts satellite radar observations of the Earth's surface. While its impact on radiometric observables is well understood, the errors in repeat-pass InSAR observations and hence in deformation analysis are largely unknown. Because Faraday rotation cannot rigorously be compensated for in non-quad-pol systems, it is imperative to determine the magnitude and nature of the deformation errors. Focusing on distributed targets at L-band, we assess the errors for a range of land covers using airborne observations with simulated Faraday rotation. We find that the deformation error may reach 2 mm in the co-pol channels over a solar cycle. It can exceed 5 mm for intense solar maxima. The cross-pol channel is more susceptible to severe errors. We identify the leakage of polarimetric phase contributions into the interferometric phase as a dominant error source. The polarimetric scattering characteristics induce a systematic dependence of the Faraday-induced deformation errors on land cover and topography. Also their temporal characteristics, with pronounced seasonal and quasi-decadal variability, predispose these systematic errors to be misinterpreted as deformation. While the relatively small magnitude of 1-2 mm is of limited concern in many applications, the persistence on semi- to multi-annual time scales compels attention when long-term deformation is to be estimated with millimetric accuracy. Phase errors induced by uncompensated Faraday rotation constitute a non-negligible source of bias in interferometric deformation measurements.