The increase in computational power and richness of Earth system data has allowed new methods for simulating natural processes with higher precision and accuracy than previously imagined. Older methods to increase skill of computer model simulations include parameter inference, where the parameters of a forward simulation model are optimized to better represent reality and allow the model to capture dynamics seen in the observed data. However, these methods are limited by our physical understanding of the underlying system, making it impossible to capture certain dynamics when the model is under-represented. Machine learning methods have emerged as a potential tool to bypass the limitations of our physical understanding, and they can create simulations with much higher skill than previous methods. This work investigates and compares the skill of photosynthesis simulations from various model formulations including those with optimized parameters and those from machine learning.

Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C-only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit vs. implicit representation of soil microbial activity, as represented in the MIcrobial-MIneral Carbon Stabilization (MIMICS) and Carnegie–Ames–Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS-CN model, which also increases the inferred SOM turnover time. Over the historical period, however, higher rates of N mineralization were fueled by warming-induced acceleration of SOM decomposition over high latitude ecosystems in the MIMICS-CN simulation reduce this N limitation, resulting in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS-CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.

Since 2007, the National Academy for Sciences Engineering and Medicine (NASEM) has recommended priorities for Earth Science research and investment every ten years. The Decadal Survey balances the continuation of essential climate variable time series against unmet measurement needs and new Earth Observations made possible by technological breakthroughs. The next survey (2027-2028, DS28) must anticipate the observational needs of the 2030s-2040s, a world increasingly dominated by climate extremes and a rapidly changing Earth system. Here, we identify the critical Earth Observation needs for a hotter, more extreme world where expect challenges in maintaining a safe operating space.

A document by Raj Pandya. Click on the document to view its contents.

The stable carbon isotope composition (δ13C) of plant components such as plant wax biomarkers is an important tool for reconstructing past vegetation. Plant wax δ13C is mainly controlled by photosynthetic pathways, allowing for the differentiation of C4 tropical grasses and C3 forests. Proxy interpretations are however complicated by additional factors such as aridity, vegetation density, elevation, and the considerable δ13C variability found among C3 plant species. Moreover, studies on plant wax δ13C in tropical soils and plants have focused on Africa, while structurally different South American savannas, shrublands and forests remain understudied. Here, we analyze the δ13C composition of long-chain n-alkanes and fatty acids from tropical South American soils and leaf litter to assess the isotopic variability in each vegetation type and to investigate the influence of climatic features on δ13C. Rainforests and open vegetation types show distinct values, with rainforests having a narrow range of low δ13C values (n-C29 n-alkane: -34.5 +0.9/-0.6 ‰ ; Suess-effect corrected) allowing for the detection of even minor incursions of savanna into rainforests (13C-enriched). While Cerrado savannas and semi-arid Caatinga shrublands grow under distinctly different climates, they can yield indistinct δ13C values for most compounds. Cerrado soils and litter show pronounced isotopic spreads between the n-C33 and n-C29 alkanes, while Caatinga shrublands show consistent values across the two homologues, thereby enabling the differentiation of these vegetation types. The same multi-homologue isotope analysis can be extended to differentiate African shrublands from savannas.

In this study, we employed random forest machine learning classification to assess the current state of glaciers in the western United States using Sentinel-2A satellite imagery. By analyzing Sentinel-2A imagery from September 2020 and comparing it to the RGI inventory, the study determined the current conditions of the glaciers. Our findings unveiled a significant reduction in both glacier area and volume in the western United States since the mid-20th century. Currently, the region hosts 4091 glaciers spanning seven states, covering a total area of 432.01 km2 with a corresponding volume of 9.02 km3. During the study period, a loss of 237.07 km2 in glacier area was observed, representing a 35.43% decrease when contrasted with the RGI boundaries. The volume lost during this period amounted to 4.9 km3, roughly equivalent to 4.7 gigatons of water. Among the states, Washington experienced the most significant glacier area reduction, with a loss of 130.06 km2. Notably, glaciers in the North Cascade Range of Washington, such as those in Mt. Baker and Mt. Shuksan, now cover, on average, only 85% of their original glacier boundaries with ice and snow at the conclusion of the 2020 hydrological year. Major glaciers, including the White River glacier, West Nooksack glacier, and White Chuck glacier, have lost more than 50 percent of their original area.

Reflecting recent advances in our understanding of soil organic carbon (SOC) turnover and persistence, a new generation of models increasingly makes the distinction between the more labile soil particulate organic matter (POM) and the more persistent mineral-associated organic matter (MAOM). Unlike the typically poorly defined conceptual pools of traditional SOC models, the POM and MAOM pools can be directly measured for their carbon content and isotopic composition, allowing for pool-specific data assimilation. However, the new-generation models’ predictions of POM and MAOM dynamics have not yet been validated with pool-specific carbon and 14C observations. In this study, we evaluate 5 influential and actively developed new-generation models (CORPSE, Millennial, MEND, MIMICS, SOMic) with pool-specific and bulk soil 14C measurements of 77 mineral topsoil profiles in the International Soil Radiocarbon Database (ISRaD). We find that all 5 models consistently overestimate the 14C content (Δ14C) of POM by 67‰ on average, and 3 out of the 5 models also strongly overestimate the Δ14C of MAOM by 74‰ on average, indicating that the models generally overestimate the turnover rates of SOC and do not adequately represent the long-term stabilization of carbon in soils. These results call for more widespread usage of pool-specific carbon and 14C measurements for parameter calibration, and may even suggest that some new-generation models might need to restructure their simulated pools (e.g. by adding inert pools to POM and MAOM) in order to accurately reproduce SOC dynamics.

A document by Emily Senerth. Click on the document to view its contents.

Tree crown architecture, which we define as the 3-D arrangement and orientation of leaves within a tree crown, influences the rates of photosynthesis, evapotranspiration, and spectral reflectance that affect tree and forest responses to climate change. As part of their adaptive and acclimation strategies for responding to environmental variability, trees are likely to differ in their tree crown architectures, but these differences remain poorly described. We use measurements from 11 deciduous forest locations within the National Ecological Observatory Network (NEON) to quantify traits that can define key dimensions of variability in crown architecture. Specifically, we: (1) measure seasonal trends in mean leaf angle (MLA) from tower-based time-lapse photography, (2) quantify traits describing the density and distribution of leaves in tree crowns from NEON Airborne Observation Platform (AOP) LiDAR data, and (3) infer crown functioning from multi-scale data on near-infrared reflectance of vegetation (NIRv), as obtained from phenocams, the NEON AOP imaging spectrometer, and Harmonized Landsat Sentinel-2 (HLS). From these data, we test for trait covariations (e.g., among MLA, the vertical distribution of plant area index, and the seasonal peak of NIRv) that can suggest fundamental tradeoffs governing how each species arranges and orients leaves in their crowns. In describing how these crown architectural traits covary across the diverse tree species and wide environmental gradients within 11 NEON sites, we highlight implications for tree ecophysiology and remote sensing-based studies on the interactions of trees, forests and climate change.

A document by Dr. Anupam Sharma. Click on the document to view its contents.

Icy moons in the outer Solar System contain rocky, chondritic interiors, but this material is rarely studied under confining pressure. The contribution of rocky interiors to deformation and heat generation is therefore poorly constrained. We deformed LL6 chondrites at confining pressures ≤ 100 MPa and quasistatic strain rates, and recorded acoustic emissions (AEs) using ultrasound probes. We defined a failure envelope, measured ultrasonic velocities, and retrieved elastic moduli for the experimental conditions. Chondritic material stiffened with increasing confining pressure, and reached its peak strength at 50 MPa confining pressure. Microcracking events occurred at low stresses, during nominally “elastic” deformation, indicating that dissipative processes are possible in rocky interiors. These events were most energetic at lower differential stresses, and occurred more frequently at lower confining pressures. We suggest that chondritic interiors of icy moons are therefore stronger, less compliant, and less dissipative with increasing pressure and size.

The global carbon dioxide (CO2) concentration has shown a consistent and substantial increase over the years, representing the dominant component of greenhouse gases (GHGs). Hence, there is an urgent demand to accurately quantify a broad spectrum of CO2 concentration at a fine-scale level to aid policymakers in making informed decisions. Consequently, we present a novel method aimed at addressing the scarcity of ground-based data, enabling the generation of a globally large-scale, continuous CO2 concentration data product.In consideration of the requirements for temporal and spatial coverage of remote sensing imagery, we opt for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, which provides daily surface reflectance of MODIS bands 1 to 7 at resolutions of 500m and 1km.Carbon satellites have developed rapidly and performed well in retrieving the vertically integrated atmospheric column CO2(XCO2) concentrations, which can provide independent top-down CO2 concentration evaluations. Here, the new generated Orbiting Carbon Observatory 3 (OCO-3) with 1.6 km×2.2 km (across × along track) resolution is added three Near Infrared (NIR) wavelength bands, which guarantees a higher accuracy of XCO2 than OCO-2.In this study, we propose a regression model-based method that leverages MODIS data and OCO-3 XCO2 data for training regression models and predicting CO2 concentrations. The proposed method enables rapid establishment of the relationship between MODIS surface reflectivity and CO2 concentration, facilitating the generation of continuous CO2 concentration maps over a large geographical area. Moreover, it offers reliable information for regions lacking ground-based CO2 measurements, such as suburban areas.Additionally, to validate the accuracy of the generated XCO2 data product, we utilize the Total Carbon Column Observing Network (TCCON) as an essential validation source. Upon evaluation, it was observed that the relative errors for each month of the year 2020 at the respective TCCON  sites consistently remained below 2%.  This finding suggests that the proposed method possesses the potential for expansion to additional geographical regions and temporal spans, whilst sustaining a high level of precision.

Vertical motions associated with mesoscale ocean eddies modulate the light and nutrient environment, stimulating anomalies in phytoplankton biomass and chlorophyll. Phytoplankton populations can be subsequently trapped by the horizontal circulation or laterally diluted. In a time-varying flow, Lagrangian methods can be used to quantify eddy trapping, also known as Lagrangian coherency. From two decades of remote sensing observations in the North Pacific Subtropical Gyre, we compared coincident Eulerian and Lagrangian eddy atlases to assess the impact of eddy trapping on chlorophyll concentration. We found higher chlorophyll within Lagrangian coherent boundaries than in Eulerianeddies and outside-eddy waters. Yet, there are differences regionally and seasonally. For example, chlorophyll is most enriched within coherent boundaries of the Hawaiian Lee eddies and to the south of 23N in fall and winter. Our results suggest that by not accounting for lateral dilution, Eulerian analyses may underestimate the role of mesoscale eddies in enhancing chlorophyll.

A document by Sushel Unninayar. Click on the document to view its contents.

This study examines how greenhouse gas (GHG) production and organic matter (OM) transformations in coastal wetland soils vary with the availability of oxygen and other terminal electron acceptors. We also evaluated how OM and redox-sensitive species varied across different size fractions: particulates (0.45-1μm), fine colloids (0.1-0.45μm), and nano particulates plus truly soluble (<0.1μm; NP+S) during 21-day aerobic and anaerobic slurry incubations. Soils were collected from the center of a freshwater coastal wetland (FW-C) in Lake Erie, the upland-wetland edge of the same wetland (FW-E), and the center of a saline coastal wetland (SW-C) in Washington state. Anaerobic methane production for FW-E soils were 47 and 27,537 times greater than FW-C and SW-C soils, respectively. High particulate Fe2+ and dissolved sulfate concentrations in FW-C and SW-C soils suggest that iron and/or sulfate reduction inhibited methanogenesis. Aerobic CO2 production was highest for both freshwater soils, which had a higher proportion of OM in the NP+S fraction (64±28% and 70±10% for FW-C and FW-E, respectively) and C:N ratios reflective of microbial detritus (1.7±0.2 and 1.4±0.3 for FW-E and FW-C, respectively) compared to SW-C, which had a higher fraction of particulate (58±9%) and fine colloidal (19±7%) OM and C:N ratios reflective of vegetation detritus (11.2 ± 0.5). The variability in GHG production and shifts in OM size fractionation and composition observed across freshwater and saline soils collected within individual and across different sites reinforce the high spatial variability in the processes controlling OM stability, mobility, and bioavailability in coastal wetland soils.

In this study, we measure velocity variations during two cycles of crustal inflation and deflation in 2020 on the Reykjanes peninsula (SW Iceland) by applying coda wave interferometry to ambient noise recorded by distributed dynamic strain sensing (also called DAS). We present a new workflow based on spatial stacking of raw data prior to cross-correlation which substantially improves the spatial coherency and the time resolution of measurements. Using this approach, a strong correlation between velocity changes and ground deformation (in the vertical and horizontal direction) is revealed. Our findings may be related to the infiltration of volcanic fluids at shallow depths, even though the concurrent presence of various processes complicates the reliable attribution of observations to specific geological phenomena. Our work demonstrates how the spatial resolution of DAS can be exploited to enhance existing methodologies and overcome limitations inherent in conventional seismological datasets.

Record high temperatures were documented in the McMurdo Dry Valleys, Antarctica, on March 18, 2022, exceeding average temperatures for that day by nearly 30°C. Satellite imagery and stream gage measurements indicate that surface wetting coincided with this warming more than two months after peak summer thaw and likely exceeded thresholds for rehydration and activation of resident organisms that typically survive the cold and dry conditions of the polar fall in a freeze-dried state. Such events may be a harbinger of future climate conditions characterized by warmer temperatures and greater thaw in this region of Antarctica, which could influence the distribution, activity, and abundance of sentinel taxa. Here we describe the ecological responses to this weather anomaly reporting on meteorological and hydrological measurements across the region and on biological observations from Canada Stream, one of the most diverse and productive ecosystems within the McMurdo Dry Valleys.

An attempt has been made to quantitatively analyze different degrees of environmental drought events, given the limited scientific understanding of environmental droughts, which hinders practical assessment efforts. This study thus aims to rigorously develop and assess the applicability of a novel heuristic method in conjunction with creating an Environmental Drought Index \cite{Srivastava_2023}. The heuristic method evaluates the combined influences of drought duration and water shortage levels, providing crucial insights into the environmental flow requirements amidst climate change. The Minimum in-stream Flow Requirements (MFR) is first defined as the threshold value essential for sustaining the river basin's ecological functions, aligning with Tennant’s environmental flow concept. Establishing MFR enables a balance between water resource utilization and ecological preservation, fostering sustainable water management. To comprehensively assess the eco-status, the study defined the High Flow Season (HFS) and the Low Flow Season (LFS). Drought status is then determined by comparing MFR with observed streamflow rate, quantifying negative differences as environmental droughts. Drought Duration Length (DDL) and Water Shortage Level (WSL) are introduced as functions of environmental drought. DDL categorizes consecutive months into four classes: DDL 1 (1-3 months), DDL 2 (4-6 months), DDL 3 (7-12 months), and DDL 4 (>12 months). WSL is determined by the most significant water deficit observed during DDL, classified into four categories: WSL 1 (<40%), WSL 2 (40-60%), WSL 3 (60-80%), and WSL 4 (>80%). Integrating DDL and WSL yields an index classifying environmental drought events into slight, moderate, severe, and extreme levels. The index value is obtained by comparing DDL and WSL values and selecting the maximum. The study enhances the scientific rigor of environmental drought identification and analysis, contributing to understanding drought impacts and effective mitigation strategies.  

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which usually shows directional artefacts and can be strongly impacted by viewing and illumination geometries and shadowing effects. To overcome this challenge, Solar Zenith Angle (SZA) serves as an ideal physical variable to quantify systematic differences between platforms. Here we presented an SZA-based Calibration (SZAC) method to operationally calibrate the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline). SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different products. The SZAC coefficient was spatiotemporally optimised against highest-quality assured (error < 1 K) pixels from the MODerate-resolution Imaging Spectroradiometer (MODIS) daytime LST between 01/Jan/2016 and 31/Dec/2020. We evaluated the calibrated LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparisons. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and -0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the overall mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. Furthermore, it is possible to extend SZAC for LST estimation by using data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas, as this could improve our understanding of the error characteristics of overlapped geostationary imageries, allowing for targeted refinements and calibrations to further enhance applicability.KeywordsLand surface temperature; Geostationary; Himawari-8; Diurnal temperature cycle; Calibration; Solar zenith angle; MODIS; VIIRS

Black carbon (BC) has several direct, indirect, semi-direct, and microphysical effects on the Earth’s climate system. Analyses of the decade-long measurement of BC aerosols at Varanasi (from 2009 to 2021) was done to understand its impact on radiative balance. General studies suggest that the daily BC mass concentration (mean of 9.18±6.53 µg m–3) ranges from 0.07 to 46.23 µg m–3 and show a strong interannual and intra-annual variation over the 13-year study period. Trend analyses suggest that the interannual variability of BC shows significant decreasing trend (-0.47 µg m–3 yr-1) over the station. The decreasing trend is maximum during the post-monsoon (-1.86 µg m–3 yr-1) and minimum during the pre-monsoon season (-0.31 µg m–3 yr-1). The radiative forcing caused specifically by BC (BC-ARF) at the top of the atmosphere (TOA), surface (SUR), and within the atmosphere (ATM) is found to be 10.3 ± 6.4, -30.1 ± 18.9, and 40.5 ± 25.2 Wm−2, respectively. BC-ARF shows strong interannual variability with a decreasing trend at the TOA (–0.47 Wm–2 yr-1) and ATM ((–1.94 Wm–2 yr-1) forcing, while it showed an increasing trend at the SUR (1.33 Wm–2 yr-1). To identify the potential source sectors and the transport pathways of BC aerosols, concentrated weighted trajectories (CWT) and potential source contribution function (PSCF) analyses have been conducted over the station. These analyses revealed that the primary source of pollution at Varanasi originate from the upper IGP, lower IGP, and central India.

The Chinese Loess Plateau (CLP) in northern China serves one of the most prominent loess records in the world. The CLP is an extensive record of changes in past aeolian dust activity in East Asia; however, the interpretation of the loess records is hampered by ambiguity regarding the origin of loess-forming dust and an incomplete understanding of the circulation forcing dust accumulation. In this study, we used a novel modeling approach combining a dust emission model FLEXDUST with simulated back trajectories from FLEXPART to trace the dust back to where it was emitted. Over 21 years (1999-2019), we modeled back trajectories for fine (~ 2mu) and super-coarse (~ 20mu) dust particles at six CLP sites during the peak dust storm season from March to May. The source receptor relationship from FLEXPART is combined with the dust emission inventory from FLEXDUST to create site-dependent high-resolution maps of the source contribution of deposited dust. The nearby dust-emission areas dominate the source contribution at all sites. Wet deposition is important for dust deposition at all sites, regardless of dust size. Non-negligible amounts of dust from distant emission regions could be wet deposited on the CLP following high-level tropospheric transport, with the super-coarse dust preferentially from emission areas upwind of sloping topography. On an interannual scale, the phase of the Arctic Oscillation (AO) in winter was found to have a strong impact on the deposition rate on the CLP, while the strength of the East Asian Winter Monsoon was less influential.

The role that continental margin sediments play in the global carbon cycle and the mitigation of climate change is currently not well understood. Recent research has indicated that these sediments might store large amounts of organic carbon; however, Blue Carbon research continues to focus on vegetated coastal ecosystems as actionable Blue Carbon. Marine sediments are considered emerging Blue Carbon ecosystems, but to decide whether they are actionable requires better quantifications of organic carbon stocks, accumulation rates, and the mitigation potential from avoided emissions. To close some of these knowledge gaps, we spatially predicted organic carbon content, dry bulk density and sediment accumulation rates across the Norwegian margin. The resulting predictions were used to estimate organic carbon stocks in surface sediments and their accumulation rates. We found that organic carbon stocks are two orders of magnitude higher than those of vegetated coastal ecosystems and comparable to terrestrial ecosystems in Norway. Accumulation rates of organic carbon are spatially highly variable and linked to geomorphology and associated sedimentary processes. We identify shelf valleys with a glacial origin as hotspots of organic carbon accumulation with a potentially global role due to their widespread occurrence on formerly glaciated continental margins. The complex and heterogenous nature of continental margins regarding organic carbon accumulation means that to close existing knowledge gaps requires detailed spatial predictions that account for those complexities. Only in this way will it be possible to evaluate whether margin sediments might be actionable Blue Carbon ecosystems.

Optical and acoustic sensors have been widely used in laboratory experiments and field studies to investigate suspended particulate matter concentration and particle size over the last four decades. Both methods face a serious challenge as laboratory and in-situ calibrations are usually required. Furthermore, in coastal and estuarine environments, the coexistence of mud and sand often results in multimodal particle size distributions, amplifying erroneous measurements. This paper proposes a new approach of combining a pair of optical-acoustic signals to estimate the total concentration and sediment composition of a mud/sand mixture in an efficient way without an extensive calibration. More specifically, we first carried out a set of 54 bimodal size regime experiments to derive empirical functions of optical-acoustic signals, concentrations, and mud/sand fractions. The functionalities of these relationships were then tested and validated using more complex multimodal size regime experiments over 30 optical-acoustic pairs of 5 wavelengths (420, 532, 620, 700, 852 nm) and 6 frequencies (0.5, 1, 2, 4, 6, 8 MHz). In the range of our data, without prior knowledge of particle size distribution, combinations between optical wavelengths 620-700 nm and acoustic frequencies 4-6 MHz predict mud/sand fraction and total concentration with the variation < 10% for the former and < 15% for the later. This approach therefore enables the robust estimation of suspended sediment concentration and composition, which is particularly useful in cases where calibration data is insufficient.

Understanding the dynamics of sulfur dioxide (SO2) degassing is of primary importance to track temporal variations of volcanic activity. We develop here an algorithm to automatically estimate daily SO2 masses flux from space-borne hyperspectral images (such as those provided by Sentinel-5P/TROPOMI) without requiring prior knowledge of plume direction or speed. The method computes a linear regression, as a function of distance, of SO2 mass integrated in a series of nested circular domains centered on the volcano. An additional term proportional to the square of the distance, depending solely on a cutoff on the minimum reliable pixel column amount, allows for estimating pixel noise and posterior uncertainty. A statistical test is introduced to automatically detect occurrences of volcanic degassing, by comparing estimated flux and its associated uncertainty. After inversion, a single multiplication by plume speed suffices to deduce the SO2 mass flux, without requiring to re-run the inversion. This way, a range of plume speed scenarios can be easily explored. The method is suited for weakly degassing sources or high-latitude volcanoes. It is applied to two case-studies, where temporal correlation between degassing and seismic energy is highlighted: (a) a one-year-long period of intense degassing at Etna, Italy (2021), and (b) a two-years-long period including three eruptions at Piton de la Fournaise, La Réunion (2021–2023). The method is open-source, and is implemented as an interactive tool within the VolcPlume web portal, facilitating near-real-time exploitation of the TROPOMI archive for both volcano monitoring and assessment of volcanogenic atmospheric hazards.