In light of climate change, wildfires are concurrently becoming more frequent and devastating worldwide. Though we have a good understanding of fire frequency changes in the past using charcoal analysis, records of the characteristics of these fires, such as fire severity, are lacking. This limits our ability to model how fire severity responds to climate change. Boron isotopes in clay minerals show promise as a novel fire severity proxy, where increased 𝛿11B is correlated with higher fire severity. Through reacting boron leached from experimentally combusted plants with clays, we determine that the observed correlation with fire severity is likely caused by input of isotopically heavier boron from combusted leaves. In contrast, combusted barks lower the 𝛿11B of clays upon reaction. Despite the different results, in both experiments with barks and leaves, similar boron isotope fractionation is observed during boron adsorption onto clays, where the lighter 10B is preferentially adsorbed. Therefore, the different results are likely caused by the different boron isotope composition of leaves and barks, where leaves have a much higher 𝛿11B (~30 ‰) than bark (~9 ‰). Combustion temperature can also affect the 𝛿11B of clays: changes to the 𝛿11B of clays were observed only when reacting with bark combusted at >300 °C, or with leaves combusted at >550 °C. This could be because more boron can be leached into solution from materials combusted at higher temperatures, which in turn results in greater adsorption onto clays during reactions. Clays have higher 𝛿11B in soils affected by high severity fires that consume tree crowns, because these fires combust more leaves that then deposit their isotopically heavier boron content into the soil. This relationship could help complete our fire record and improve our ability to predict future fire characteristics.

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Significant volumes of magma are intruded into the crust during continental break-up, which can influence rift evolution by altering thermo-mechanical structure of the crust and thereby its response to extensional stresses. Rift magmas additionally feed surface volcanic activity and can be globally significant sources of tectonic CO2 emissions. Understanding how magmatism may affect rift development requires knowledge on magma intrusion depths in the crust. Here, using data from olivine-hosted melt inclusions, we investigate magma dynamics for basaltic intrusions in the Main Ethiopian Rift (MER). We find evidence for a spatially focused zone of magma intrusion at the MER upper-lower crustal boundary (10-15 km depth), consistent with geophysical datasets. We propose that ascending melts in the MER are intruded over this depth range as discrete sills, likely creating a mechanically weak mid-crustal layer. Our results have important implications for how magma addition can influence crustal rheology in a maturing continental rift.

Long-term high-resolution temperature data of the Compact Rayleigh Autonomous Lidar (CORAL) is used to evaluate temperature and gravity wave (GW) activity in ECMWF Integrated Forecasting System (IFS) over R\’io Grande (53.79$^{\circ}$S, 67.75$^{\circ}$W), which is a hot spot of stratospheric GWs in winter. Seasonal and altitudinal variations of the temperature differences between the IFS and lidar are studied for 2018 with a uniform IFS version. Moreover, interannual variations are considered taking into account updated IFS versions. We find monthly mean temperature differences $<2$~K at 20-40~km altitude. At 45-55~km, the differences are smaller than 4~K during summer. The largest differences are found during winter (4~K in May 2018 and -10~K in August 2018, July 2019 and 2020). The width of the difference distribution (15th/85th percentiles), the root mean square error, and maximum differences between instantaneous individual profiles are also larger during winter ($>\pm10$~K) and increase with altitude. We relate this seasonal variability to middle atmosphere GW activity. In the upper stratosphere and lower mesosphere, the observed temperature differences result from both GW amplitude and phase differences. The IFS captures the seasonal cycle of GW potential energy ($E_p$) well, but underestimates $E_p$ in the middle atmosphere. Experimental IFS simulations without damping by the model sponge for May and August 2018 show an increase in the monthly mean $E_p$ above 45~km from only $\approx10$~\% of the $E_p$ derived from the lidar measurements to 26~\% and 42~\%, respectively. GWs not resolved in the IFS are likely explaining the remaining underestimation of the $E_p$.

Winter Euro-Atlantic atmospheric blocking events have significant socioeconomical impacts as they cause various types of weather extremes in a range of regions. According to current climate projections, fewer of these blocking events will occur as temperatures rise. However, the timing of such a reduction is currently highly uncertain. Meanwhile, recent studies indicate that using climate models with high enough ocean resolutions to simulate mesoscale eddies improve simulated winter Euro-Atlantic blocking events significantly. In this paper, we show from a large ensemble of climate simulations based on the highest emission scenario that largely prominent and coarsely resolved non-eddying climate models project a noticeable significant decline in blocking frequencies from the 2030s-2040s, whereas blocking statistics in eddy-permitting simulations are noticeably decreasing only from years 2060s. Our result suggests with a strong level of confidence that winter blocking activity over the next several decades will keep being dominated by internal variability.

Though Venus’s atmospheric conditions and composition have been directly measured, the composition of the Venus lower atmosphere near the surface is generally still poorly known. It was extrapolated from observational data at other altitudes by assuming the constancy of elemental composition without condensation (Krasnopolsky 2007). Both in-situ measurement and remote-sensing observations reveals the most abundant components that exceed the mixing ratio of 10-4 to be CO2, N2, and SO2 (Bezard & de Bergh 2007, JGR 112, E04S07). Water and formation of photochemical H2SO4 — and condensation of cloud-forming H2SO4 — is only important at higher altitudes (Krasnopolsky 2012, Icarus 191, 25). In this work, the balancing of chemical-gravitational-thermal diffusive potentials for the ternary mixture of CO2, N2, and SO2, which represent the neutral Venusian lower atmosphere near the surface, is addressed to obtain the composition grading and to evaluate the tendency toward supercritical density-driven separation of CO2 and N2 (Lebonnois & Schubert 2017, Nat. Geosci. 10, 473). Even though dynamic atmospheric systems, including advective mixing, are more realistic, the static cases evaluated in this work provide stationary states where every dynamic process would eventually proceed to. Hence, our modeling is of a limiting case of the systems of interest, which could help explain some indications of compositional grading. The CRYOCHEM equation of state, which has been successfully applied in describing phase equilibria of Titan’s atmosphere and the surface liquid (Tan & Kargel 2018, Fluid Phase Equilib. 458, 153), as well as that involving solid phases on Pluto’s surface (Tan & Kargel 2018, MNRAS, 474, 4254), is used in this work on the supercritical Venus’s lower atmosphere. In the absence of direct measurement of composition of the lower atmosphere, as well as no lab evidence of CO2 and N2 separation under Venusian surface conditions (Lebonnois et al. 2020, Icarus 338, 113550), the results from this study may at least introduce some new concepts that would entail some tendency for molecular fractionation.

Marine heatwaves (MHWs) are extreme oceanic warm water events (above 90th percentile threshold) that significantly impact the marine environment. Several studies have recently explored the genesis and impacts of MHWs though they are least understood in the tropical Indian Ocean. Here we investigate the genesis and trend of MHWs in the Indian Ocean during 1982–2018 and their role in modulating the Indian monsoon. We find that the rapid warming in the Indian Ocean plays a critical role in increasing the number of MHWs. Meanwhile, the El Nino has a prominent influence on the occurrence of MHWs during the summer monsoon. The Indian Ocean warming and the El Nino variability have synergistically resulted in some of the strongest and long-lasting MHWs in the Indian Ocean. The western Indian Ocean (WIO) region experienced the largest increase in MHWs at a rate of 1.2–1.5 events per decade, followed by the north Bay of Bengal at a rate of 0.4–0.5 events per decade. Locally, the MHWs are induced by increased solar radiation, relaxation of winds, and reduced evaporative cooling. In the western Indian Ocean, the decreased winds further restrict the heat transport by ocean currents from the near-equatorial regions towards the north. Our analysis indicates that the MHWs in the western Indian Ocean and the north Bay of Bengal lead to a reduction in monsoon rainfall over the central Indian subcontinent. On the other hand, there is an enhancement of monsoon rainfall over southwest India due to the MHWs in the Bay of Bengal.

An open source computer algorithm, the Surface Energy Balance Algorithm for Land-Improved (SEBALI), was designed to estimate actual evapotranspiration (ET) at a basin level. In this study, we build on later versions of SEBALI/SEBALIGEE to estimate ET at a 30-m resolution for any scale application using advanced machine learning approaches (SEBALIGEE v2). We evaluate the monthly ET estimated from the new algorithm across several fluxnet sites in US, China, Italy, Belgium, Germany, and France, yielding an Absolute Mean Error (AME) of 0.41 mm/day versus 0.48 mm/day in the original SEBALIGEE. Analyses of the ET in the US indicate that the annual wheat ET decreases significantly between 2013 and 2021 (p < 0.05), accompanied by a significant air temperature increase. Net solar radiation is found to be the most influencing factor on ET of corn and soybeans with R2 values of ~0.72.

Current global actions to reduce greenhouse gas emissions are very likely to be insufficient to meet the climate targets outlined under the Paris Agreement. This motivates research on possible methods for intervening in the Earth system to minimize climate risk while decarbonization efforts continue. One such hypothetical climate intervention is stratospheric aerosol injection (SAI), where reflective particles would be released into the stratosphere to cool the planet by reducing solar insolation. The climate response to SAI is not well understood, particularly on short-term time horizons frequently used by decision makers and planning practitioners to assess climate information. This knowledge gap limits informed discussion of SAI outside the scientific community. We demonstrate two framings to explore the climate response in the decade after SAI deployment in modeling experiments with parallel SAI and no-SAI simulations. The first framing, which we call a snapshot around deployment, displays change over time within the SAI scenarios and applies to the question “What happens before and after SAI is deployed in the model?” The second framing, the intervention impact, displays the difference between the SAI and no-SAI simulations, corresponding to the question “What is the impact of a given intervention relative to climate change with no intervention?” We apply these framings to annual mean 2-meter temperature, precipitation, and a precipitation extreme in the first two experiments to use large ensembles of Earth system models that comprehensively represent both the SAI injection process and climate response, and connect these results to implications for other climate variables.

Southern Hemisphere (SH) Stratospheric Warming Events (SWEs) are usually associated with a negative phase of the tropospheric Southern Annular Mode (SAM) during the following summer. In contrast, using ensemble climate model simulations we show that the anomalously high ozone concentrations typically occurring during SWEs can force periods of persistent positive tropospheric SAM in austral spring by increasing lower stratospheric static stability and changing troposphere-to-stratosphere wave propagation. Eventually, the tropospheric SAM switches sign to its negative phase in late spring/early summer, but this ‘downward propagation’ of the stratospheric signal does not occur in simulations without seasonal cycle. We find that the downward propagation is forced both dynamically by adiabatic heating and radiatively by increased shortwave absorption by ozone due to the seasonal cycle. Capturing this ozone forcing mechanism in models requires the inclusion of interactive ozone, which has important implications for the predictive skill of current seasonal forecasting systems.

To explore a possible relationship between low FUV albedo swirl signatures and magnetic anomalies on the Moon, we are examining the modeled at-surface total field and vector components of select anomalies with respect to observations in the ultraviolet. UV wavelengths are extremely sensitive to space weathering and could indicate the presence of uneven weathering even at weak magnetic anomalies. Preliminary results are in agreement with previous work suggesting that swirls may be due to lessened weathering from solar wind deflection by their associated magnetic anomalies, as swirl regions with lower FUV values correlate with at-surface magnetic fields that are potentially capable of standing off some degree of solar wind. However, not all anomalies have magnetic field geometry that support this theory, implying other processes may be at play to create swirl-like morphology in these regions.

Phenotyping plays a crucial role in parameterizing, calibrating, and evaluating process-based plant models, which can be used to understand and predict crop behavior under various conditions. This, in turn, allows for more informed decision-making in agriculture and provides insights into how crops may respond to changing environmental factors. Nevertheless, several potential disparities between current plant models and phenotyping methods exist, which have the potential to undermine the precision and applicability of these models. These discrepancies encompass differences in data resolution and scale (both spatial and temporal) between what is feasibly gathered during phenotyping efforts and what is required by the models. Additionally, issues related to compatibility between the traits measured during phenotyping campaigns and the parameters required for models are prevalent. Furthermore, the representation of genetic and environmental variability in both models and phenotyping data is often limited, resulting in a gap between these two components.To address these discrepancies, we are developing a new “minimum” plant model. This model has been designed with strong consideration of what data can be measured in phenotyping campaigns across multiple genotypes. While the core model remains straightforward, it has the capability to simulate essential processes to represent fundamental plant physiology. Moreover, it has the potential to be integrated into larger, more comprehensive models or parameterized to suit specific genotypes. This approach aims to bridge the gap between phenotyping and crop models, thus enhancing their effectiveness in addressing the challenges that emerge by utilizing them in broader agricultural applications.

This study evaluates a hyperspectral imaging (HSI) technique to identify herbicide-resistant kochia (Bassia scoparia) biotypes to support weed management in cropping systems. The experiment was conducted under controlled-environment where glyphosate was applied to six different kochia populations. For each population (72 cell tray of plants), half of the plants were sprayed with Glyphosate 900 g ae ha-1 , while the other half remained an untreated control. Hyperspectral images were acquired over five time points spanning from glyphosate treatment to 15 days after treatment (DAT) using a proximal HSI system (Specim-IQ) with 204 spectral bands from 397nm to 1003nm. The average reflectances were extracted from plants that were characterized as glyphosate-resistant or-susceptible. We first analyzed the temporal variations of the spectra with and without the application of herbicide. The spectral profile exploits the advantages of temporal features in biotype discrimination. Random forest algorithms were used to classify the glyphosate-resistant and-susceptible populations, by using reflectance at optimal wavelengths (near-infrared) and various vegetation indices with high correlations with visual ratings. Based on the classification accuracy, the most important wavebands and vegetation indices were determined to classify the weed biotypes. Preliminary results show that:  1) For the untreated plants, the reflectance at red-edge to near-infrared reached the highest level on 8 DAT, revealing the highest chlorophyll content in the leaves. Then, the reflectance declined until 15 DAT.  2) In contrast, strong effects of glyphosate were captured on 8 DAT for the three herbicide-susceptible populations. For the three glyphosate-resistant populations, reflectance at red-edge to near-infrared did not increase from 1 to 8 DAT, which was opposite of the controlled plants.

Construction with freeboard – vertical height of a structure above the minimum required – is commonly accepted as a sound investment for flood hazard mitigation. However, determining the optimal height of freeboard poses a major decision problem. This research introduces a life-cycle benefit-cost analysis (LCBCA) approach for optimizing freeboard height for a new, single-family residence, while incorporating uncertainty, and, in the case of insured homes, considering the costs from losses, insurance, and freeboard (if any) to the homeowner and National Flood Insurance Program (NFIP) separately. Using a hypothetical, case study home in Metairie, Louisiana, results show that adding 2 ft. of freeboard at the time of construction might be considered the optimal option given that it yields the highest net benefit, but the highest net benefit-cost ratio occurs for the 1 ft. freeboard. Even if flood loss reduction is not considered when adding freeboard, the savings in annual insurance premiums alone are sufficient to recover the construction costs paid by the homeowner if at least one foot of freeboard is included at construction. Collectively, these results based on conservative assumptions suggest that at the time of construction, even a small amount of freeboard provides a huge savings for the homeowner and (especially) for the financially-strapped NFIP. For community planners, the results suggest that wise planning with reasonable expectations on the front end makes for a more sustainable community.

In various research fields such as hydrogeology, environmental science and energy engineering, geological formations with fractures are frequently encountered. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and the transport of solute and energy within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observed state variables like hydraulic head, concentration, and temperature. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non-Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as PEDL (parameter estimator with deep learning) that harnesses the capabilities of DL to capture nonlinear relationships and extract non-Gaussian features. To evaluate PEDL’s performance, we conduct two numerical case studies with increasing complexity. Our results unequivocally demonstrate that PEDL outperforms three popular DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL-based update (ESDL). Sensitivity analyses confirm PEDL’s validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks.

Stratospheric sulfate aerosols play a key role on atmospheric chemistry and Earth's radiation budget, but their size distribution, a critical parameter in climate models, is generally poorly-known. We address such gap for the 2022 Hunga Tonga-Hunga Ha'apai (HT-HH) volcanic eruption by exhaustively analyzing a set of satellite observations (TROPOMI, IASI, AHI, CALIOP) together with photometric ground observations from the worldwide open-access AERONET network. We document a rapid growth of HT-HH sulfate aerosols in the days following eruption, faster than observed for 1991 Pinatubo eruption, likely due to the exceptional hydration of the stratosphere by this phreatomagmatic eruption. An unusual aerosol fine mode (peak radius in 0.3-0.5 µm) is identified at 20 stations of the southern hemisphere until May 2023 (time of writing). Nevertheless, 1.4 years after eruption, HT-HH sulfate aerosols remain smaller than Pinatubo particles. Smaller aerosols backscatter more efficiently visible light and sediment more slowly than larger particles, implying stronger and longer-lasting negative radiative forcing.

Volcanic flood basalt eruptions have covered 1000s of km2 with basalt deposits up to kilometers thick. The massive size and extended duration result in enormous releases of climactically-relevant gases such as SO2 and CO2. However, it is still unknown precisely how flood basalt eruptions influence climate via eruption rates and cadence, height of the volcanic plumes, and relative degassing abundance of species like SO2. Once eruptions occur, the complex interplay of photochemistry, greenhouse gas warming, changes to the atmospheric circulation, and aerosol-cloud interactions can only be properly simulated with a comprehensive global climate model (GCM). We created an eruption scenario for the Goddard Chemistry Climate Model (GEOSCCM) that emits SO2 in the near-surface atmosphere constantly and four times per year an explosive eruption that emits much more SO2 in the upper troposphere/lower stratosphere. The eruption lasts for 4 years and emits 30 Gt of SO2 total. This corresponds to ~1/10th of what may have been emitted during the Wapshilla Ridge eruption phase of the Columbia River flood basalt eruption 15-17 Ma. We use a pre-industrial atmosphere and otherwise modern initial and boundary conditions. The massive flux of SO2 into the atmosphere is quickly converted to H2SO4 aerosols. Global area-weighted mean visible band sulfate aerosol optical depth reaches 220 near the end of the eruption, comparable to cumulonimbus clouds. This reduces the surface shortwave radiative flux by 85% and top-of-atmosphere outgoing longwave flux by 70%. Contrary to our expectations, we find that the climate warms during and immediately following the eruption after a very brief initial cooling. Global mean surface temperature peaks 3-4 years after the eruption ends with a +6 K anomaly relative to a baseline simulation without the eruption. Post-eruption regional temperatures, particularly near-equatorial continental areas, see drastic rises of summertime temperatures with monthly mean temperatures equaling or exceeding 40°C. These temperature responses are radiative- and circulation-driven. The eruption warms and raises the tropical tropopause, allowing a massive flux of water vapor into the stratosphere. Stratospheric water vapor, usually ~3 parts per million reaches 1-2 parts per thousand.

The system of trade wind cumulus clouds observed during the RICO field project was simulated by an LES model over a 50x50 km2 domain size. Parameters of latent heat release were analyzed with the goal of parameterizing their effects on grids typical for NWP and large-scale models. Over 2000 clouds were examined focusing on relationship between parameters of latent heat release (phase transition rates) and dynamical/microphysical cloud characteristics. The phase transition rates (Tr), which in warm tropical clouds are represented by processes of condensation/evaporation, were analyzed by stratifying the clouds by their size/stage of maturity. The analyzed parameters included, among others, integral mass and buoyancy fluxes, cloud and rain water parameters. In our previous investigation we found that a remarkably strong correlation exists between Tr and upward mass flux (ℳ). The strong dependence of phase transition rates on ℳ, as well as linear relationship between Tr and ℳ, was explained by applying the condensation theory and the concept of “quasi-steady” supersaturation. The LES derived slope of the linear fit agreed with its theoretically predicted value with an error less than 5%. This result implies that supersaturation in clouds, on average, varies within a few percentage points of its quasi-steady value. The theory, as well as LES data, show that the Tr - ℳ linear fit is valid for local variables, and, therefore, may be integrated to obtain horizontal mean parameters. Expanding the Tr - ℳ relationship for vertically dependent horizontal mean variables, may provide the framework for development of sub-grid scale (SGS) latent heat release parameterization. It was also suggested that calculating the slope of the linear fit from concurrent measurements of temperature and vertical velocity, and comparing it with the theoretical slope based on the quasi-steady supersaturation assumption, may offer a method for estimating the supersaturation in clouds.