We report six years of observations of dayside temperatures of the middle and upper atmosphere of Mars made by the Imaging Ultraviolet Spectrograph instrument aboard the MAVEN spacecraft. Thermospheric temperatures show strong long-term variability associated with Martian season and solar cycle. Temperatures from both the Martian thermosphere and mesosphere show strong short-term variability indicating coupling from the lower atmosphere. The observed local time effect is strong in both upper and middle atmosphere temperatures. The thermosphere tends to be colder in the morning compared to the evening when temperatures are higher. Middle atmospheric temperatures show cooling during the dawn and dusk hours. Our analysis shows strong tidal activity during aphelion, whereas non-migrating tides are suppressed during perihelion, possibly due to increased dust activity. Observations during the deep minimum of solar cycle 24 reveal that thermospheric temperatures are highly variable with respect to local time if solar forcing, Mars-Sun distance, and spatial effects are removed. We will discuss these results in the context of coupling between the lower and upper atmosphere of Mars.

Most of the seismic moment release of the complex earthquake sequence beneath the South Sandwich Islands occurred on the central part of the SS megathrust. Significant aftershock activity indicates that the central and southern megathrust was subsequently activated, i.e., where young South America lithosphere is subducted. Seismic activity thus seems to have been restricted by the lateral termination in the south of the SS Trench.   Relatively little energy release occurred on the northern part of the megathrust. It was hypothesized by Govers and Wortel (2005) that here the South America slab breaks away from the surface part of the plate at the active STEP. Geochemical observations and earthquake P-axes orientations do not seem to agree with the hypothesis and we investigate the cause.   We show results of new physical analog lab models that aim to elucidate what controls the geometry of the lithospheric STEP Fault. We study lithospheric tearing in the process of STEP evolution, which is dynamically driven by the buoyancy of the subducting slab. In our experiments, the lithosphere as well as asthenosphere are viscoelastic media in a free subduction setup. A stress-dependent rheology plays a major role in localization of strain in tearing processes of lithosphere such as slab break-off. The results show that the highly curved northern plate boundary is a STEP Fault following from lithospheric tearing at a depth of ~100km. This is a modification of the original STEP model of Govers and Wortel (2005). This is consistent with available observations along the northern Sandwich plate boundary, and likely exists in other STEP regions. The region’s largest recorded event, the 1929 Mw 8.3 earthquake, may reflect horizontal extension perpendicular to the STEP fault, which is also expected based on our experiments.

The high energetic plasma and the embedded magnetic field of coronal mass ejections interact with planetary magnetospheres giving rise to transient perturbations such as geomagnetic storms. Predicting the geomagnetic impact of such interplanetary coronal mass ejections (ICME) is of utmost importance for the protection of our technological infrastructure that is affected by space weather. We use 3D compressible magnetohydrodynamic simulation of a star-planet system to model and study an ICME-Earth interaction event of 20th November 2003. In the modelled interaction, we observe a change in magnetopause shape and stand-off distance on ICME impact, day and night side reconnections and induction of high currents in the magnetosphere. We also notice the formation of a ring of strong equatorial current around the Earth, leading to a reduction of the geomagnetic field. We calculate the simulated reduction in the magnetic field and compare that to the observed geomagnetic indices in order to establish a predictive approach for geomagnetic storms. These simulations are expected to illuminate the physical processes that result in space weather impacts of stellar magnetic storms in planetary and exoplanetary systems.

Saline endorheic lakes play a crucial role in ecological regulation and biodiversity conservation in semi-arid regions. They provide a wide range of ecosystem services such as habitat for aquatic life and migratory birds, water supply, mineral extraction, and recreation. Although saline lakes are critical to ecological health and human wellbeing, they are being shrunk at alarming rates in recent decades, representing some of the most threatened ecosystems in the world. Factors contributing to lake degradation are directly linked to the expansion and intensification of agriculture in the 20th century and its intertwined effect with global warming. However, quantifying the relative contribution of climate variability and anthropogenic stressors in water budget dynamics of the lakes is challenging. Hydrologic processes of highly managed agricultural landscapes are complex and the lack of spatial data in endorheic basins with transboundary water issues further contribute to this problem. In this study, we developed a modeling framework that integrates the Soil and Water Assessment Tool (SWAT) simulations with seasonal trend decomposition by LOESS (STL), residual analysis for anomaly detection, and time series clustering, to identify the major drivers of lake trend and seasonal shrinking patterns. This modeling framework was applied to the Salton Sea Basin (SSB), host of the largest inland lake in California. Salton Sea’s water level has declined by 32% over the last 25 years, causing a massive bird and fish die-off, and a regional asthma crisis due to the spreading of toxic dust from the exposed playa. Results suggest that decreases in Colorado River allocation are causing the lake to shrink, not changes in the irrigation efficiency or the climate regime as commonly believed. Our results are expected to assist decision-makers with a robust modeling tool to evaluate the environmental tradeoffs in formulating and implementing timely adaptation and mitigation strategies across the SSB while minimizing their economic consequences.

The National Research Council published a report on Enhancing the Effectiveness of Team Science in 2015. This report identified 7 fundamental challenges that large research teams, such as the NASA DRIVE Science Centers (DSC), might face including: high diversity of membership, deep knowledge integration, large size, goal misalignment, permeable boundaries, geographic dispersion, and high task interdependence. In Phase I, the COFFIES DSC formed a Center Effectiveness Team (CET) to identify and help overcome these and other unique challenges, including those introduced by the COVID-19 pandemic. CET members have focused on finding and exploring novel ways to align and direct the Science Teams with the goal of enabling breakthrough science. We will present the CET initiatives and implementations, and review the lessons learned for future large-scale science collaborations.

The frequency of major flooding events continues to increase, fueling the already growing concern in numerous fields about quantifying the inequitable distribution of flood hazards. Our previous work of overlaying high resolution flood exposure data with social vulnerability information has already begun to highlight how different communities experience varying levels of risk. However, this fails to capture the complex nature by which flooding affects interconnected infrastructure and service networks which further have an impact on an individual and community’s risk. Our goal is to quantitatively define an individual’s vulnerability to flooding, encompassing how both pluvial and fluvial inundation impacts an individual’s place of residence and disrupts their access to critical resources, including flood, gas, healthcare, and emergency services, while still considering an individual’s socioeconomic standing. With the goal of estimating household level disruption of access to critical resources in near real time, our approach relies on a multilayer network of social vulnerability, transportation infrastructure, essential resources, and emergency services. To estimate inundation in near real time, we utilize the Heigh Above Nearest Drainage (HAND) method and a topographic depression hierarchy algorithm to estimate fluvial and pluvial flooding. Using a minimum cost flow algorithm, we determine an individual’s relative cost to access resources before, during, and after a major flooding event. Combining technical and social information leads to the identification of communities that are more vulnerable to the physical, economical, and social components of floods. This model will be useful in future descriptive and prescriptive analytical frameworks by identifying critical nodes across networks and providing actionable knowledge on at risk communities. Our model will inform agencies involved in flood management, urban planning, and emergency response on where they can best apply resources to increase the resiliency of communities and the infrastructure they rely on.

In this study, we present evaluation of version 3 ozone products derived from the DSCOVR EPIC instrument. EPIC’s total and tropospheric ozone columns have been compared with correlative satellite and ground-based measurements at time scales from daily averages to monthly means. We found that the agreement improves if we only accept retrievals derived from the EPIC 317 nm triplet and limit solar zenith and satellite looking angles to 70°. With such filtering in place, the comparisons of EPIC total columns with correlative satellite and ground-based data show mean differences within ±5-7 DU (or 1.5-2.5%). The biases with OMI and OMPS NM tend to be mostly negative in the Southern Hemisphere (SH), while there are no clear latitudinal patterns in ground-based comparisons. Evaluation of the EPIC ozone time series at different ground-based stations with the correlative ground-based Brewer and Pandora instruments and ozonesondes demonstrated good consistency in capturing ozone variations at daily, weekly and monthly scales with a persistently high correlation (r2>0.9) for total and tropospheric columns. We examined the quality of EPIC tropospheric ozone columns by comparing with ozonesondes at 12 stations and found that differences in tropospheric column ozone are within ±2.5 DU (or ~±10%) after removing a constant 3 DU offset at all stations between EPIC and sondes. The analysis of the time series of zonally averaged EPIC tropospheric ozone revealed a statistically significant drop of ~2-4 DU (~5-10%) over the entire NH in spring and summer of 2020, which is partially related to the unprecedented Arctic stratospheric ozone losses in winter-spring 2019/2020 and reductions in ozone precursor pollutants due to the COVID-19 pandemic.

​​We believe that the future of Earth Observation (EO) is in fusion, harmonization, and interoperability of satellite imagery. Intensified monitoring leads to better understanding of land use and reduction of maintenance costs for all Land Cover products. RapidAI4EO is an initiative that aims to establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. The goal is to provide intensified monitoring of Land Use (LU), Land Cover (LC) changes at a much higher spatial resolution and temporal cadence than is possible today. Key objectives are to explore, evaluate, and quantify state of the art deep learning algorithms and methodologies that leverage three meter, daily time series, in conjunction with higher spectral resolution Sentinel-2 imagery.  Consortium Partners: The RapidAI4EO projects brings together Planet Labs PBC, the operator of the world’s largest fleet of Earth-imaging satellites and the recognized leader of the CubeSat revolution, VITO, the main production center of the Copernicus Global Land Service, Vision Impulse, a recent spin-off of German Research Center for Artificial Intelligence (DFKI, the largest research center for Artificial Intelligence in the world and one of the two European NVIDIA AI Labs), the International Institute for Applied Systems Analysis (IIASA) whose Center for Earth Observation and Citizen Science (EOCS) devises new approaches and technologies to collect data on land cover and land use, and Serco Italia, a worldwide service provider to governments, international agencies and industries, and operator of the ONDA DIAS platform. The objectives of RapidAI4EO are: 1) the creation and release of the most comprehensive spatiotemporal EO training sets ever produced for machine learning;  2) the development and implementation of novel AI solutions for continuous change detection that leverage these data sets; 3)the ability to drive frequent temporal updates of the Corine Land Cover (CLC) product; and 4)to demonstrate improved LULC mapping using harmonized Sentinel-2 and very high resolution, high cadence data streams.

Hydroclimate extreme events, especially precipitation and streamflow, pose serious threats to life, livelihoods, and infrastructure. However, the extremes exhibit significant space-time variability and in conjunction with societal vulnerability and resiliency, resulting in varying levels of damage. Regardless, robust understanding and modeling of these extremes is crucial for effective hazard mitigation strategies. For this study, we focus on the Krishna River Basin in south India, which experiences flooding each year due to monsoon rains and impacts urban and rural communities along its network covering three States. We implement a Bayesian hierarchical model to capture the spatio-temporal variability of streamflow extremes on this river network. In this model, the extremes (3-day maximum seasonal flow) at each station are assumed to follow a Generalized Extreme Value (GEV) distribution with non-stationary parameters. The parameters are modeled as a linear function of suitable covariates. In addition, the spatial dependence of the streamflow extremes is modeled via a Gaussian copula. With suitable priors on the parameters, posterior distribution of the parameters and the predictive posterior distribution of streamflow (i.e., ensembles) at each location. Consequently, various return levels can also be obtained from these ensembles. We developed and tested the model on the monsoon seasonal 3-day max flow at 10-gauge stations for the period 1973 -2015. To find the covariates, we perform analysis to identify relationships between large-scale climate variables such as Sea Surface Temperatures, 850 mb winds, Sea Level Pressure, etc. Statistical learning methods will be employed for this analysis and as a result, obtain potential covariates that best relate to streamflow extremes in the basin. This modeling approach can be adapted to the seasonal and multidecadal projection of extremes, which will greatly help disaster mitigation planning efforts.

The study of lunar plasma environment’s response to the magnetotail lobe condition is the main subject of our investigation in this report. Photoionization and charge exchange of protons with the lunar exosphere arethe ionization processes included in our model. The computational model includes the dynamics of heavy Na+ pickup and ambient magnetospheric ions. The electrons are considered as a fluid.The lunar interior is considered as a weakly conducting body. In this report we consider for the first time a formation of lunar plasma structures, wakes, and a generation of low-frequency electromagnetic waves by using a self-consistent hybrid kinetic modeling. The input parameters were taken from the ARTEMIS observations. At an early stage the Moon with exosphere and conducting core excites whistler waves in case of Sub-Alfvenic/sonic interaction. At a later stage an excitation of the Alfven wave is observed. The topology of the Alfven waves is approximately similar to the Alfven wing near the planetary moons (Io, Europa etc.). The physics of the Moon-magnetotail lobe interaction is also close to the physics of the interaction between plasma clouds (expanding and not expanding) and ambient magnetospheric plasma. The heavy pickup ions create a large structured halo with space scale of more than 10 R_{E} in the direction of the background field. The modeling also shows an excitation of the compressional waves due to expansion of heavy exospheric pickup ions. The lunar model with weaker interior conductivity excites lower levels of the wave activity. This work was supported by NASA Award (80NSSC20K0146) from Solar System Workings Program (NNH18ZDA001N-C.3-SSW2018). Computational resources were provided by the NASA High-End SupercomputingFacilities (Aitken-Ames, Project HEC SMD-20-02357875).

The enormous groundwater demand globally have possessed a threat to groundwater security. Rajasthan being one of the water deficit states of India is pivotal to determine its groundwater potential zone for sustainability. Diverse thematic layers such as drainage density, landuse, rainfall, geology, geomorphology was prepared. The geomorphology, landuse, drainage and soil map was prepared using IRS-1D LISSІІІ satellite image. Through, appointing the relative weightage to the thematic maps the final groundwater potential map was prepared. A comparative analysis for the groundwater potential map and the groundwater level data was done for confirmation. The groundwater potential zone can be classified as high, medium or low. The positions of various existing wells along with the contour map of groundwater table can be draped with groundwater potential map. The groundwater table usually found flat over the regions of higher groundwater potential zones. Thus, with the increasing demography along with rising demand the wells should be laid over the area with higher potential. The remote sensing and GIS can be efficiently and smoothly used to determine the groundwater potential zones with suitable sites for well construction. Keywords: Groundwater sustainability, Remote Sensing, Geographic Information System, Thematic layers, Groundwater potential map.

Creeping faults are typically not associated with large earthquakes. However, new K/Ar dating and biomarker maturity data on the San Andreas Fault Observatory at Depth (SAFOD) present evidence that large paleoearthquakes have occurred in the creeping section of the San Andreas Fault, California. K/Ar ages of bulk samples with evidence of coseismic heating range from 3.3 to 15.8 Ma, and argon diffusion experiments suggest that these ages are only partially reset and the actual event ages may be even younger. Thus, questions remain as to how we can refine such dates to reveal the precise age and location of these earthquakes. To refine the ages and more accurately assess seismic hazard, we date size separates of eight samples from different sections of the SAFOD core. Following Stokes’ Law, we split each sample into five size fractions using hydrodynamic settling: <0.2, 0.2-0.5, 0.5-0.8, 0.8-1.4, and 1.4-2 micrometers. The finest size fractions contain the most authigenic illite, which form during fault slip. We determined chemical composition and separated illite polytypes using x-ray diffraction, and also measured K/Ar ages on each sample. Preliminary results from two scaly black fault rock samples, previously shown to have hosted earthquakes, (3,193.69 m and 3,193.96 m along the core) support that the finest size fractions contain the greatest ratio of authigenic illite. With a York regression between age and detrital illite abundance, we place the authigenic illite ages at 1.08 ± 2.40 Ma and 0.88 ± 5.08 Ma for these two samples, and observe that the detrital illite matches the late Cretaceous age for the country rock. This new age estimate for the authigenic illite means that large earthquakes must have propagated into the creeping section within the last million years. Not only is it significantly younger than the bulk sample age, it is recent enough that translation of faulted material from the locked southern San Andreas fault into the creeping section cannot explain the record. Moving forward, we will expand our procedure to include isotope dilution for measuring K concentration and analyze the other samples previously measured for biomarker maturity and bulk K/Ar age. Resulting insights into the fault rock composition and the timing of past earthquakes will be crucial in assessing the region’s seismic hazard.

The Brazilian Amazon provides important hydrological cycle functions, including precipitation regimes that bring water tothe people and environment and are critical to moisture recycling and transport, and represents an important variable forclimate models to simulate accurately. This paper evaluates the performance of 13 Coupled Model Intercomparison Projectphase 6 (CMIP6) models. This is done by discussing results from spatial pattern mapping, Taylor diagram analysis and Taylorskill score, annual climatology comparison, and Empirical Orthogonal Function (EOF) analysis. Precipitation analysis shows1) This region displays a more uniform spatial distribution of precipitation with higher rainfall in the north-northwest anddrier conditions in the south. Models tend to underestimate northern values or overestimate the central to northwest averages.2) Southern Amazon has a more defined dry season (June, July, and August) and wet season (December, January, andFebruary) and models are able to simulate this well. Northern Amazon dry season tends to occur in August, September, andOctober and the wet season occurs in March, April, and May, and models are not able to capture the climatology as well.Models tend to produce too much rainfall at the start of the wet season and tend to either over- or under-estimate the dryseason, although ensemble means typically display the overall pattern more precisely. 3) EOF analysis of models are able tocapture the dominant mode of variability, which was the annual cycle or SAMS. 4) When all evaluation metrics are taken intoaccount the models that perform best are CESM2, MIROC6, MRIESM20, SAM0UNICON, and the ensemble mean. Thispaper supports research in determining the most up to date CMIP6 model performance of precipitation regime for 1981-2014for the Brazilian Amazon. Results will aid in understanding future projections of precipitation for the selected subset ofglobal climate models and allow scientists to construct reliable model ensembles, as precipitation plays a role in many sectorsof the economy, including the ecosystem, agriculture, energy, and water security.

Development is well-advanced for the next version of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG), labeled Version 07. IMERG is a key output of the U.S. GPM Science Team, and V07 will be the second generation in which data from both the Tropical Rainfall Measuring Mission (TRMM) and GPM projects are combined into a single, uniformly processed record, currently starting in June 2000. This presentation will show several examples of successes and challenges in V06, and use these to illuminate the upgrades that have been pursued for V07. For example, the V06 IMERG near-real-time products (Early and Late Runs) show regional biases because they do not have climatological calibration (despite the documentation), and this will be done in V07. As well, the time series of precipitation-rate histograms shows a seam in the transition from TRMM calibration to GPM Core Observatory calibration at the start of June 2014. V07 will benefit from better continuity in the input calibration datasets across that boundary. A third issue is that the Kalman filter used in IMERG a) introduces a variable amount of smoothing, and b) depends on relatively simple measures of input data quality. Both of these are revisited in V07. We will report the status of IMERG Version 07 processing as of the conference time, and introduce some topics that are being considered for the future, including improved uncertainty estimates, addition of sub-monthly gauge information, and strategies for incorporating precipitation estimates from multiple, relatively short-lived small satellites.

We analyzed correlations between solar, interplanetary-medium parameters, and geomagnetic-activity proxies in 27-day averages (a Bartels rotation) for the 2009 – 2016 time interval. We considered two new proxies: I) Bzs GSM (Geocentric Solar Magnetic), calculated as the daily percentage of the IMF southward component along the GSM z-axis and then averaged every 27 days; ii) four magnetospheric indices (T-indices), calculated from the local north-south (X) contributions of the magnetosphere’s cross-tail (TAIL), the symmetric ring current (SRC), the partial ring current (PRC), and the Birkeland current (FAC), derived from the Tsyganenko and Sitnov (J. Geophys. Res. 110, A03208, 2005: TS05) semi-empirical magnetospheric model. Our results suggest, among the parameters tested here, solar facular areas, interplanetary-magnetic field intensity and new proxies derived here could be taken into account in an empirical model, with a 27-day resolution, to explain geomagnetic activity felt on the Earth’s surface in terms of solar surface features and the IMF condition. We further retrieve a clear annual oscillation in series of 27-day-mean values of toward/away asymmetries of geomagnetic-activity indices, which can be interpreted in the light of the Russell–McPherron hypothesis for the semiannual variation of geomagnetic activity (Russell, C.T., and R. L. McPherron (1973), J. Geophys. Res., 78, 92 – 108).

Ice shelves control the stability of ice sheets and regulate ice sheet contribution to sea level rise by buttressing ice �ow. Most of Greenland’s ice shelves have already been lost, and many ice shelves around Antarctica are thinning and retreating. Ice shelves are increasingly vulnerable to thinning and destabilization due to surface and basal melting, and these processes may be exacerbated by the presence of basal channels, which are deep grooves that entrain meltwater at the base of ice shelves. Basal channels have been observed alongside spatial and temporal changes in grounding line geometry, strain rates and stress transfer, and the incidence and advection of other surface and basal features. The relationships between these processes, and their implications for ice shelf stability, remain largely unknown due to the lack of observations of su�ciently high spatial and temporal resolution. Our methodology employs high temporal and spatial resolution digital elevation models (DEMs) from REMA and ArcticDEM, laser altimetry from ICESat-2, radar sounding and laser altimetry from Operation IceBridge, and velocity data derived from interferometry, enabling us to constrain the morphology and evolution of channels and other ice shelf features at the fringes of both ice sheets. We intend to investigate how the relationships between channels, grounding line processes, and rifts and crevasses impact the persistence of ice shelf area necessary to maintain a “safety band”, or su�cient buttressing force, against grounded ice. Where time-evolving grounding line position data are sparse, we use the DEMs to track the boundary of hydrostatic equilibrium, which we use as a proxy for changes in grounding line position in order to investigate changes in ice shelf geometry. We have completed analysis of three ice shelves and plan to observe at least twelve more in order to develop an inventory of at- risk ice shelves. Based on our preliminary results, we hypothesize that rapidly evolving basal channels are associated with high rates of change in the grounding zone. This work is integral to assessing past and future ice shelf stability, and it will help the glacier dynamics community more accurately account for small-scale ice shelf processes in computational models which predict ice sheet contribution to sea level rise.

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the ‘signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. Although this procedure originated in LiDAR-based estimation of mean tree and canopy height, ground noise filtering has remained prevalent in LiDAR pre-processing, even as modelers have shifted focus to forest aboveground biomass (AGB) and related characteristics for which ground returns may actually contain useful information about stand density and openness. In particular, ground returns may be helpful for making accurate biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. We applied several ground noise filtering thresholds while mapping two regions within New York State, one a forest-dominated area and the other a mixed-use landscape. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling. By fitting random forest models to each of these predictor sets, we found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds. The relative inferiority of AGB models based on filtered LiDAR returns was much greater for the mixed land-cover study area than for the contiguously forested study area. Our results suggest that ground filtering should be avoided when mapping biomass, particularly when mapping heterogeneous and highly patchy landscapes, as ground returns are more likely to represent useful ‘signal’ than extraneous ‘noise’ in these cases.

Abiotic efflux of CO2 from soil is often attributed to dissolution of carbonates, and therefore not expected to occur in soils with a low pH. However, another abiotic source of CO2, less constrained by pH, may arise from reactions that oxidize natural soil organic matter and reduce metal oxides. Studies of redox reactions between phenolic compounds and Fe and Mn oxides in soil have been focused mainly on the environmental fate of both oxidants and reductants and formation of organic matter. We measured CO2 formed during 3-hour, room temperature (22±2 oC), incubations of samples of archived soils and from an ongoing crop diversity study. Subsamples (8 g. ODE) of each soil, were treated (5 ml) with water, or solutions of glucose (0.029 M), or gallic acid (0.025 M). For each soil, subsamples amended with H2O or with the glucose solution produced little CO2 and were nearly identical to each other, while CO2 quickly formed after treatment with gallic acid regardless of pH. The net increase in CO2 due to gallic acid, observed from the 18 archived soils, ranged from less than 0.5 to more than 80 mg CO2-C kg-1 soil. Significant treatment effects were observed in samples from the crop diversity study with more (Tukey’s P≤0.05) net CO2 from a small grain-fallow treatment compared a 5-year rotation treatment, 19.04 and 15.77 mg CO2-C kg-1 soil, respectively. This study suggests abiotic reactions capable of rapidly producing a burst of CO2 can occur in a wide range of soils following inputs of simple phenolic compounds and be impacted by management regimes. We suggest these are redox reactions in soil linked to Mn or Fe metal oxides and when considered together with fluctuations of carbon inputs to soil and redox cycling, might be a larger contributor to C emissions than previously accounted for.