Boreal wildfires impact surface climates with consequences for plant physiology, permafrost thaw, and carbon fluxes. Post-fire temperatures vary over decades due to successional changes in vegetation structure and composition. Yet, the underlying biophysical drivers remain uncertain. Here, we quantify surface climate changes following fire disturbances in the North American boreal forest and identify its dominant biophysical drivers. To do so, we analyse multi-year land-atmosphere energy exchange and satellite observations from across Canada and Alaska. We find post-fire daytime land surface temperatures to be substantially warmer for about five decades while winter temperatures are slightly cooler. Post-fire decadal changes are characterised by a decrease in leaf area index during the first decade, a sharp increase in snow cover period surface albedo, and a decrease in the efficiency of heat transfer for about 2-3 decades. Evaporative fraction increases in the first three decades before returning to lower values again. We find that warming is mainly explained by a decrease in the efficiency of heat transfer while cooling is additionally explained by increasing surface albedo. We estimate that current daytime surface temperatures of the boreal biome of Canada are 0.18 °C warmer in the summer and 0.04 °C cooler during the winter due to fire. For a scenario with a strong increase in burned area, we estimate doubled warming from fire until 2050. Our study highlights the potential for accelerated surface warming in the boreal biome with increasing wildfire activity and disentangles the biophysical drivers of fire-related surface climate impacts.

This study employs Coupled Model Intercomparison Project Phase 6 Earth System Models under Shared Socioeconomic Pathways (SSPs) scenarios SSP2-4.5, SSP3-7.0, and SSP5-8.5 to investigate the future projection of biogeochemical conditions in the southern South China Sea (SCS) from 2015 to 2100. We focus on projected changes in Sea Surface Temperature and seven important biogeochemical variables such as Chlorophyll, Phytoplankton, integrated Primary Production, Oxygen, Nitrate, Phosphate and pH. Our results consistently indicate substantial warming of +1 to +3 °C at the end of 21st century across all scenarios in southern SCS region, potentially leading to cascading consequences for marine ecosystems and fisheries. Projected increases in surface temperatures are expected to induce declining dissolved Oxygen levels of -3 to -8 mmol m-3 and ocean acidification of -0.1 to -0.4 units, posing a multifaceted threat to this crucial fishing ground. Furthermore, our analysis suggests a significant decrease in Phosphate concentrations ranging -1.4 to -2 mmol m-3/year across all scenarios. This decline in nutrient availability could potentially limit productivity and the entire marine food web in this region. These projections provide valuable scientific insights for policymakers and stakeholders to formulate strategies aimed at mitigating the adverse effects of climate change on the southern SCS and ensuring the long-term sustainability of its fishery resources.

The quasi-biennial oscillation (QBO) is the strongest mode of variability in the tropical stratosphere and plays an important role in stratospheric dynamics and chemistry. The QBO is notably deficient in many climate models, including the Energy Exascale Earth System Model (E3SM) developed by the US Department of Energy. In this work, we utilize vertical grid refinement to reduce the lower stratospheric grid spacing from roughly 1 km to 500 m to facilitate more realistic synoptic wave activity in lower stratosphere in E3SM version 2. The refinement results in a simulated QBO with a reasonable amplitude and easterly-westerly transition, but still has a longer period than observed, slower easterly downward propagation speed, and shallower vertical depth. Similar refinement in the multi-scale modeling framework (MMF) configuration of E3SM yields similar improvements. By analyzing the forcing contributions from different wave types, we find that most of the QBO forcing still comes from parameterized gravity wave drag from convection. The improved QBO forcing contributions from resolved waves, especially equatorial Kelvin waves and resolved small scale gravity waves can be attributed to the grid refinement.

Previous studies highlighted the distinct impact of anthropogenic aerosols from the Western and Eastern Hemispheres on the past multi-decadal changes in tropical cyclone frequency of occurrence (TCF). However, the detailed effect of subregional aerosol changes on TCF changes remained unclear. Using idealized simulations with a dynamical climate model, this study reveals that reduced aerosol emissions from Europe and the U.S. since 1980 may have equally contributed to increased TCF over the North Atlantic, with Europe playing a major role in decreased TCF in the Southern Hemisphere. Additionally, increased aerosol emissions from India since 1980 may have played a major role in decreasing TCF over the western North Pacific compared to increased emissions from China. TCFs are projected to decrease for most global tropics toward the end of this century due to the dominant effect of increasing greenhouse gases.

Cloud phase has important impacts on Arctic surface temperatures, and there is substantial circumstantial evidence that dust aerosols have strong impacts on cloud phase over the Arctic on a regional scale. We used seven years of satellite observations and model and reanalysis products to control for co-varying meteorology, and to assess how dust and other aerosols impact cloud phase over the summertime sea ice. We focus on clouds at 3 km, where dust modeling is most accurate. Dust aerosols caused about 4.5% of clouds below -15 °C to change phase, with smaller effects at higher temperatures. Sulfate has a smaller impact on cloud phase. Dust is associated with cloud-mediated surface cooling of up to a 6.3 W m-2 below single-layer clouds at ~3 km in June. Lastly, we discuss the optimal meteorological conditions for future in situ studies to maximize insight into the mechanisms driving the observed dust effects.

Marine heatwaves (MHWs) have devastating effects on ecosystems and impact regional air-sea CO2 exchange. Yet a global assessment of these regional impacts of MHWs on the air-sea CO2 exchange is missing. Here, we analyze thirty global observation-based air-sea CO2 flux datasets from 1990 to 2019. We observe minimal reduction in global oceanic CO2 uptake during MHWs. Regional variations are evident with the equatorial Pacific experiencing a 31% (spread across datasets: 3-49%) reduction in carbon release, suggesting that MHWs are the dominant drivers of strong air-sea CO2 flux anomalies in this region. In low-to-mid latitudes, MHWs cause a 29% (19-37%) decrease in air-sea CO2 uptake. Reduced dissolved inorganic carbon in the tropics weakens outgassing, while high ocean temperatures diminish uptake in the low-to-mid latitudes. In the North Pacific and Southern Ocean, enhanced carbon uptake occurs during MHWs, but uncertainties in pCO2 datasets limit a comprehensive assessment in these regions.

Nitrogen stable isotopes (δ15N) of ice core nitrate of nitrate (NO3-) are often subject to various interpretations, associated with source, atmospheric, and/or post-depositional changes. Here, an analysis of a Mont-Blanc (French Alps) ice core was conducted to investigate δ15N(NO3-) records over the last 1000 years. We find that the glacially archived δ15N(NO3-) signal reliably preserves a record of NOx emission sources with limited post-emission isotopic changes. We use the δ15N record to reconstruct a history of NOx emissions and compare it with existing estimated NOx inventories for Western Europe. Notably, our ice-derived record suggests that inventories for the early 20th-century may have underestimated NOx emissions resulting from agriculture. During the 20th century, the δ15N value substantially decreased, which we attribute to the increasing emissions from oil combustion. Lastly, the ice core signal highlights the success of mitigation policies in reducing fossil fuel-induced NOx emissions, albeit 20 years later than anticipated.

Active collection methods such as ChemComb Speciation Cartridge samplers (CCSCs) are considered more accurate for determining the isotopic composition of ammonia (NH3) and particulate ammonium (NH4+) (i.e., δ15N(NH3) and δ15N(NH4+)), especially in ambient air. However, the discrepancy in δ15N(NH4+) values are unknown between speciated particle samples collected using lab-verified CCSCs with different filter types (Teflon, cellulose, and glass fiber) and particles samples collected separately without gas-particle speciation and a backup/breakthrough filter. In this study, we deployed two CCSCs to collect speciated NH4+, and an EPA-approved Spiral Ambient Speciated Sampler (SASS) side-by-side to collect ambient NH4+ and to compare their δ15N(NH4+) values in urban Beijing during summer 2019. The initial δ15N(NH3) values were derived from δ15N(NH4+) values for speciated NH4+ based on theoretical estimation of isotopic fractionation that could occur during NH3 gas-to-particle conversion, or field-based isotopic fractionation, measured δ15N(NH3) values and observed NH4+ partitioning during NH3 and NH4+ speciation. The results show that the mean δ15N(NH4+) values retained in Teflon filters were 10.2 ± 8.4‰ (n=7) and 12.4 ± 4.1‰ (n=7) for CCSCs A and CCSCs B, respectively. In contrast, the measured δ15N(NH4+) values in SASS quartz filters were 15.9 ± 2.5‰ (n=7). The higher δ15N(NH4+) values observed in particle samples collected using SASS can be attributed to a positive bias resulting from NH4+ volatilization. Additionally, this study revealed that the initial δ15N(NH3) values, derived using theoretically derived isotopic fractionation, were 5.6‰ and 10.1‰ lower compared to the initial δ15N(NH3) values derived using field-based isotopic fractionation in CCSCs A and B, respectively. These findings underscore the significance of using acid-coated backup filters in active sampling systems to effectively capture volatilized NH3. Additionally, they highlight the necessity of utilizing field-based isotopic fractionation to accurately predict initial δ15N(NH3) values, thereby reducing uncertainties in source apportionment of NH4+ in ambient air. This approach is essential for obtaining reliable isotopic data for understanding the sources and processes contributing to NH4+ in the atmosphere.

Numerous submarine landslides have been reported in the last two decades due to Gas hydrate dissociation and fluid migration. In various basins across the globe, the dissociation of hydrates into free gas and water has been observed due to a rise in sea surface temperature over the course of the last century. To have an insight into the process, we have studied the effect of temperature variations and the associated impact on gas hydrate dissociation in two gas hydrate provinces in the East Indian Margin of India, namely the Krishna-Godavari (KG) and Cauvery basins. The 3D seismic data of the Krishna Godavari and Cauvery basin has shown occurrences of Bottom Simulating Reflectors (BSR) at ~160 meters below seafloor (mbsf) and ~110 mbsf, respectively. In the Last Glacial Maximum (LGM), the sea surface temperature increased by 3 ºC in the Bay of Bengal and the Indian Ocean. The modelling of the base of the hydrate stability zone (BHSZ) at ~1000m water depth has shown that the BSR is shifted by ~80m in the KG and ~60 m in the Cauvery basin Post LGM. The difference in the shift is due to the Lithology, high sea bottom temperature, Low Geothermal Gradients and less sedimentation rates in the Cauvery basin compared to the KG. The slope angle in both study areas is less than 2 degrees however, we have identified a large slump of ~21sq. km in the KG that occurred due to the gas hydrate dissociation and fluid migration. It appears that there has been no observed slumping in the Cauvery region, possibly due to lower sedimentation rates and lithology. Another reason is that the seismic data is available in limited area i.e., ~20 sq.km in the Cauvery region. However, significant Mass Transport Deposits and mud diapers were observed which might have formed due to the hydrate dissociation beneath the BSR. Hydrate dissociation due to an increase in the bottom water temperatures is also recorded in the carbon isotopes of benthic (Miliolida) foraminifera in the Indian margin. The first ever detected gas flares in the multibeam echo sounder data of the Cauvery basin lends support to our argument that the gas hydrate dissociation is actively occurring in the East Indian margin.

• A dilation-based algorithm adds sub-resolution porosity, quantified by differential imaging, as microlinks to an extracted pore network. • Empirical models are used to characterize the flow properties of microlinks. • The tuned multiscale network, using permeability, formation factor, and drainage capillary pressure, matches experimental relative permeability.

Paleosecular variation analysis is a primary tool for characterizing ancient geomagnetic behavior and its evolution through time. This study presents a new high-quality directional dataset, paleosecular variation of the Paleogene (PSVP), with and without correction for serial correlation (SC), compiled from 1,667 sites from 45 different localities from the Paleogene and late Cretaceous (84 – 23 Ma). The dataset is used to study the variability, structure, and latitude dependence of the geomagnetic field during that period by varying selection criteria and PSV models. Modeled values for the equatorial virtual geomagnetic pole (VGP) dispersion have over-lapping uncertainty intervals within their uncertainty bounds between 8.3° and 18.6° 30 for the past 250 Ma. We investigate the suitability of two descriptive models of PSV, Model G-style quadratic fits and covariant GGP models, and find that both styles of model fail to satisfactorily reproduce the latitude dependent morphology of PSV,  but suggest that estimates of the equatorial VGP dispersion may still robustly characterize aspects of Earth’s long-term field morphology. During this time where the PSV behavior has not changed substantially, the reversal frequency has varied widely. The lack of a clear relationship between PSV behavior and reversal frequency is not trivially explained in the context of published findings regarding numerical geodynamo simulations.This manuscript is the version accepted for publication in G-Cubed on 16th May, 2024.

We explore deep learning for the Next Generation Water Resources Modeling Framework (NextGen). We present results from Random forest-based multi-model ensembles, Long Short-Term Memory (LSTM) and differentiable parameter learning hydrological models (δ conceptual models) and attribute sensitivity for ungauged basins.This poster was presented at the CIROH Developers Conference May 29 – June 1, 2024, at the University of Utah in Salt Lake City.

Mountain ecosystems are distributed across all continents, covering about 27% of the world's land surface. These regions support 1.1 billion people worldwide, about 15% of the global population, with 91% in developing countries. Mountains are social-ecological systems characterized by vertical gradients of climate and cultural and natural diversity. Rapid shifts in topography and climatic regimes make mountain ecosystems susceptible to climate change. People in these regions use rangelands for extensive livestock production. However, many rangelands previously used by local communities are currently under protection with different use levels. Although it is anticipated that the effects of climate change will be significant in the tropical Andes, the extent of these impacts is still not well understood. This research explores climate change's potential ecological and land management impacts on two agropastoral communities neighboring Huascarán National Park in the Peruvian Andes. For this, we created the Agent-based model of laNd management Dynamics and Ecosystem Services (ANDES) linked to L-Range, an ecosystem-process model. L-Range simulates biogeochemical processes and vegetation dynamics, e.g., monthly primary production, N and C cycling, and plant population dynamics for rangeland ecosystems. Implementing L-Range required parametrizing 54 climatic, edaphic, and plant variables and using landscape and climatic information, e.g., fractional vegetation cover, topographic features, soil properties, temperature, and precipitation. The ANDES model is a spatially explicit model of livestock grazing behavior, land and livestock management, and households' economies. The assessment of the model will involve using a pattern-oriented approach, such as comparing simulated responses of the daily fraction of biomass consumed by livestock and daily distance traveled with information from reports and peerreview journals. The validation of the L-Range model results also will require comparing the model outputs, such as net primary production and vegetation cover, to historic remotely sensed data. This research is in the phase of model validation and data analysis. Thus, we present the methodology for building the coupled model and preliminary findings.

As wildfires in California, USA continue to cause a peril to the population residing there, a clear insight into the spatial patterns and distribution of wildfires is essential. This study aims to explore the high-risk clusters and low risk clusters for wildfire hazard utilizing the GIS tools and techniques along with Statistical modelling tool. By using the up-to-date dataset, the neighborhood effect and spatial relation among them from this study can be helpful for fire mitigation measures and land use planning and building resilience of the community. Also, the result from this study can contribute to strengthening wildfire management strategies.  This study considers the Local Indicator of Spatial Autocorrelation and Hotspot, cold spot analysis methods to identify the neighboring effects and the clustering of the wildfire risks. An integrated approach using GIS spatial analysis and Statistical analysis tools and techniques are utilized. Data is collected from United States Department of Agriculture (Scott et. al. 2020) research data set in the form of raster data.  The findings of the study show that regions with high-risk of burning due to wildfire tend to cluster together, whereas regions with low risk of burning exhibit a similar pattern of spatial correlation. This study can be useful for identifying localized contributing factors for wildfire as well as the hotspot for the hazard in California. Also, spatial autocorrelation is essential for prioritizing risk areas and resource allocation thus effective wildfire management together with better community resilience.  

• We present a two-dimensional Discrete Element Method model that simulates host rock fracturing during the inflation of a magmatic laccolith. • The host rock toughness and stiffness along with source depth control spatial fracture distribution between two end-members. • At a constant injection rate, the vertical displacement maximum and lateral extent show sudden changes in response to host rock fracturing.

A document by Ron baiman. Click on the document to view its contents.

Key Points 1. Regional propensity of flood tail behavior is shown across a diverse geographical range based on the analysis of common streamflow dynamics. 2. Temporal variability in catchment storage, driven by high evapotranspiration and dry soils, is a key mechanism for heavy-tailed floods. 3. Examining flood generation processes aids in unraveling the connections between flood tail behavior and seasons or catchment sizes.

Annually, ~ 3.6 million abandoned oil and gas wells in the U.S. emit a combined ~ 3.2 Tg methane (CH4), adversely affecting climate and regional air quality. However, these estimates depend on emission factors derived from inventorying sub-populations of wells, but which vary by orders of magnitude due to inadequate sampling numbers. This problem is exacerbated by regional differences requiring independent emission inventories and the recent identification of poorly characterized super-emitters that skew the distribution. Currently, U.S. funding to remediate orphaned wells lacks standardized quantification methods needed to both prioritize plugging and account for emission reductions. Sensitive, reliable, affordable, and scalable CH4 flux quantification methods are needed. We evaluate a simple Gaussian plume method constrained by in situ ground measurements of CH4 concentrations and winds to estimate the leak rate from an orphan well in the Permian basin. We derive a flux of 10.53 ± 1.16 kg CH4 h-1 during a venting procedure that agrees with the directly measured volumetric flow rate of 9.0 ± 0.25 kg CH4 h-1. This is 71% greater than the flux measured 7-months prior which induces a bias between bottom-up and top-down estimates. Additionally, we discovered a secondary leak through the surface-casing inferred as 0.43-0.67 kg CH4 h-1 by both our ground Gaussian analysis and by transecting the plume with an uncrewed aerial system (UAS). Our technique addresses operational needs by reducing sampling time of leak detection and quantification and good sensitivity to characterize wells emitting below the detection limit of satellites.