Airborne dust, including Dust storms and weaker dust traces, can have deleterious and hazardous effects on human health, agriculture, solar power generation, and aviation. Although earth observing satellites are extremely useful in monitoring dust using visible and infrared imagery, dust is often difficult to visually identify in single band imagery due to its similarities to clouds, smoke, and underlying surfaces. Furthermore, night-time dust detection is a particularly difficult problem, since radiative properties of dust mimic those of the cooling, underlying surface. The creation of false-color red-green-blue (RGB) composite imagery, specifically the EUMETSAT Dust RGB, was designed to enhance dust detection through the combination of single bands and band differences into a single composite image. However, dust is still often difficult to identify in night-time imagery even by experts. We developed a Deep Learning, UNET image segmentation model to identify airborne dust at night leveraging six GOES-16 infrared bands, with a focus on infrared and water vapor bands.The UNET model architecture is an encoder-decoder Convolutional Neural Network that does not require large amounts of training data, localizes and contextualized image data for precise segmentation, and provides fast training time for high accuracy pixel level prediction. This presentation highlights collection of the training database, development of the model, and preliminary model validation. With further model development, validation, and testing in a real-time context, probability-based dust prediction could alert weather forecasters, emergency managers, and citizens to the location and extent of impending dust storms.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has collected global surface elevation measurements for over three years. ICESat-2 carries the Advanced Topographic Laser Altimeter (ATLAS) instrument, which emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat-2 acquisitions during the Northern Hemisphere melting season. In this work, we performed two case studies over the Greenland Ice Sheet to quantify volumetric scattering in ICESat-2 signals over snow. Elevation data from ICESat-2 was compared to Airborne Topographic Mapper (ATM) data to quantify bias. We used snow optical grain sizes derived from ATM and the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to attribute altimetry bias to snowpack properties. For the first case study, the mean optical grain sizes were 340±65 µm (AVIRIS-NG) and 670±420 µm (ATM), which corresponded with a mean altimetry bias of 4.81±1.76 cm in ATM. We observed larger grain sizes for the second case study, with a mean grain size of 910±381 µm and biases of 6.42±1.77 cm (ICESat-2) and 9.82±0.97 cm (ATM). Although these altimetry biases are within the accuracy requirements of the ICESat-2 mission, we cannot rule out more significant errors over coarse-grained snow, particularly during the Northern Hemisphere melting season.

Methane fluxes are often studied using eddy covariance flux towers or chambers placed on the soil surface. These measurement techniques have improved our understanding of methane emissions from wetlands. However, there are limitations with each measurement method. For example, chambers are fixed in place and have high maintenance costs, limiting spatial coverage and characterization of heterogeneity. Measurements taken in Interior Alaskan wetlands suggest that heterogeneity in methane fluxes from this region may increase during the fall and early winter, when the soils begin to freeze. Unfortunately, off-grid power limitations and freezing conditions complicate chamber operation during this time. Towers share similar demands with respect to maintenance and cost of operation, and, therefore, are not often replicated within a landscape. Moreover, towers provide an integrated measurement which masks any spatial heterogeneity in fluxes within the tower footprint. Therefore, although chamber and flux towers provide important insights into the carbon exchange between terrestrial and atmospheric pools, these methods have limitations, particularly when characterizing spatial heterogeneity. We tested a new technology that may be able to be counteract some of these limitations, thereby providing additional insights into methane emissions from wetlands. We outfitted a small-unmanned aerial system (sUAS, or drone), that can fly extremely close (<2 m) to the wetland’s surface, with a miniature open-path laser spectrometer methane sensor, LIDAR, and a miniature anemometer. We then tested this system in several bogs near Fairbanks, Alaska. We tested if this system could detect spatial and/or temporal variability of methane emissions within a bog. We also compared methane fluxes calculated using this system to values obtained from tower and chamber measurements. Results of these missions will be presented and we will discuss the ability of this new technology to provide additional information regarding methane emissions from wetlands.

In-situ magnetic field measurements are critical to our understanding of a variety of space physics phenomena including field-aligned currents and plasma waves. Unfortunately, high-fidelity magnetometer measurements are often degraded by stray magnetic fields from the host spacecraft, its subsystems, and other instruments. One dominant source of magnetic interference on many missions are reaction wheels - spinning platters of varying rates used to control spacecraft attitude. This manuscript presents a novel approach to the mitigation of reaction wheel interference on magnetometer measurements aboard spacecraft where multiple magnetometer sensors are deployed. Specifically, multichannel singular spectrum analysis is employed to decompose multiple time series simultaneously. A technique for automatic component selection is proposed that classifies the decomposed signals into common geophysical signals and disparate locally generated signals enabling the robust estimation and removal of the local interference without requiring any assumptions about its characteristics or source. The utility of this proposed method is demonstrated empirically using in-situ data from the CASSIOPE/Swarm-Echo mission, and a data interval with near-constant background field was shown to have its local reaction wheel interference reduced from 1.90 nT RMS, for the uncorrected outboard sensor, to 0.21 nT RMS (an 89.0\% reduction). This technique can be generalized to arrays of more than two sensors, and should apply to additional types of magnetic interference.

A supervised neural network algorithm is used to categorize near-global satellite retrievals into three mesoscale cellular convective (MCC) cloud morphology patterns. At constant cloud amount, morphology patterns differ in brightness associated with the amount of optically-thin cloud features. Environmentally-driven transitions from closed MCC to other morphology patterns, typically accompanied by more optically-thin cloud features, are used as a framework to quantify the morphology contribution to the optical depth component of the shortwave cloud feedback. A marine heat wave is used as an out-of-sample test of closed MCC occurrence predictions. Morphology shifts in optical depth between 65°S - 65°N under projected environmental changes (i.e., from an abrupt quadrupling of CO2) assuming constant cloud cover contributes between 0.04-0.07 W/m2/K (aggregate of 0.06) to the global mean cloud feedback.

Grid-based spatially distributed hydrological modeling became feasible along with advances in watershed routing scheme, remote sensing technology, and computing resources. Such modeling is expected to be common in routine hydrological analysis and watershed management planning as it can maximize the use of spatial information and provide the detailed picture of transport processes. However, the heavy computational requirement and resulting long running time are still barriers that prevent the spatially detailed modeling practices from being employed widely, particularly in a fine-resolution large-scale study. Parallelizing computational tasks has been successful in mitigating the difficulty. We propose a noble way to improve the simulation efficiency of direct runoff transport processes by carefully grouping watershed areas based on the time-area routing scheme. The proposed parallelization method was applied to simulating the runoff routing processes of three watersheds draining the areas of 3.79 km2, 133.59 km2, and 2,800 km2 respectively at a 30-m resolution. Results demonstrated that the new method could substantially improve the computational efficiency of the time-area routing method with common computing resources. The efficiency of the parallelization scheme was not limited by the hierarchical relationship between upstream and downstream catchments along flow paths, which could be possible with the Lagrangian flow tracking strategy of the time-area routing method.

Understanding surface temperature is important for habitability. Recent work on Mars has found that the dependence of surface temperature on elevation (surface lapse rate) converges to zero in the limit of a thin $\mathrm{CO_2}$ atmosphere. However, the mechanisms that control the surface lapse rate are still not fully understood. It remains unclear how the surface lapse rate depends on both greenhouse effect and surface pressure. Here, we use climate models to study when and why “mountaintops are cold”. We find the tropical surface lapse rate increases with the greenhouse effect and with surface pressure. The greenhouse effect dominates the surface lapse rate transition and is robust across latitudes. The pressure effect is important at low latitudes in moderately opaque ($\tau \sim 0.1$) atmospheres. A simple model provides insights into the mechanisms of the transition. Our results suggest that topographic cold-trapping may be important for the climate of arid planets.

Sea salt is the largest source of natural aerosol in the atmosphere by mass. Formed when ocean waves break and bubbles burst, sea salt aerosols (SSA) influence Earth’s climate via direct and indirect processes. Models participating in the sixth Coupled Model Intercomparison project (CMIP6) demonstrate a negative effective radiative forcing when SSA emissions are doubled. However, the magnitude of the effective radiative forcing ranges widely from -0.35 +/- 0.04 W/m^2 to -2.28 +/- 0.07 W/m^2, with the largest difference over the Southern Ocean. Differences in the response to doubled SSA emissions arise from model uncertainty (e.g. individual model physics, aerosol size distribution) and parameterization uncertainty (e.g. how SSA is produced in the model). Here, we perform single-model experiments with UKESM1-AMIP incorporating all of the SSA parameterizations used by the current generation of CMIP6 Earth system models. Using a fixed SSA size distribution, our experiments show that the parameterization uncertainty causes large inter-model diversity in SSA emissions in the models, particularly over the tropics and the Southern Ocean. The choice of parameterization influences the ambient aerosol size distribution, cloud condensation nuclei and cloud droplet number concentrations, and therefore direct and indirect radiative forcing. We recommend that modelling groups evaluate their SSA parameterizations and update them where necessary in preparation for future model intercomparison activities

Biogeochemical cycles in the Arctic Ocean are sensitive to the transport of materials from continental shelves into central basins by sea ice. However, it is difficult to assess the net effect of this supply mechanism due to the spatial heterogeneity of sea ice content. Manganese (Mn) is a micronutrient and tracer which integrates source fluctuations in space and time. The Arctic Ocean surface Mn maximum is attributed to freshwater, but studies struggle to distinguish sea ice and river contributions. Informed by observations from 2009 IPY and 2015 Canadian GEOTRACES cruises, we developed a three-dimensional dissolved Mn model within a 1/12 degree coupled ocean-ice model centered on the Canada Basin and the Canadian Arctic Archipelago (CAA). Simulations from 2002-2019 indicate that annually, 87-93% of Mn contributed to the Canada Basin upper ocean is released by sea ice, while rivers, although locally significant, contribute only 2.2-8.5%. Downstream, sea ice provides 34% of Mn transported from Parry Channel into Baffin Bay. While rivers are often considered the main source of Mn, our findings suggest that in the Canada Basin they are less important than sea ice. However, within the shelf-dominated CAA, both rivers and sediment resuspension are important. Climate induced disruption of the transpolar drift may reduce the Canada Basin Mn maximum and supply downstream. Other micronutrients found in sediments, such as Fe, may be similarly affected. These results highlight the vulnerability of the biogeochemical supply mechanisms in the Arctic Ocean and the subpolar seas to climatic changes.

Over the past 15 years climate tipping points (CTPs) – which are reached when change in a climate subsystem (the ‘tipping element’) becomes self-perpetuating independent of the original forcing and results in a regime shift to a new subsystem state – have emerged as a source of scientific and public concern. Some CTPs are estimated to be reachable within the 1.5-2oC Paris Agreement range, with many more accessible by the ~3-4oC of warming possible on current policy trajectories. Recent work has also hypothesised that CTPs could ‘cascade’ – with the impacts of triggering one tipping element sufficient to trigger the next and so on – resulting in an emergent global tipping point. However, much discussion relies on a tipping element characterisation that is now over a decade old, which itself was based on an expert elicitation exercise in 2005. Since then there have been substantial advances in our understanding of CTP dynamics based on results from coupled and offline models, observations, and palaeoclimate studies. The tipping cascade hypothesis has also not yet been rigorously tested, with the suggestion of 2oC as a global tipping point remaining speculative. Furthermore, CTP definitions are often inconsistent, with some purported globally-impactful CTPs more accurately represented as either localised CTPs or even threshold-free feedbacks. Here we undertake an updated review of CTPs based on a wide range of recent literature. We estimate ranges for each proposed element’s tipping threshold, timescales, and impact on global and regional warming, as well as if evidence exists for self-perpetuation, rate-dependence, or hysteresis. Each proposed element is then catalogued with reference to a clear tipping point definition, separating global ‘core’ and regional ‘impact’ tipping elements from threshold-free feedbacks. Our estimates confirm that current global warming (~1.2oC) already lies within the lower end of some CTP threshold ranges, and several CTPs become likely or possible within the 1.5-2oC Paris range. In further work we use these estimates to test the potential for and impact of tipping cascades in response to global warming scenarios using a stylised model.

Since its adoption, the Paris Agreement sets and defines the global climate ambition. The overall scope of this ambition is expressed in its long-term temperature goal in Article 2 as well as the ‘net zero’ mitigation goal in Article 4. To provide guidance to climate policy, the scientific community has explored the characteristics of greenhouse gas (GHG) emission reduction pathways that can meet the Paris Agreement goals. However, when categorizing and presenting such pathways including in reports of the Intergovernmental Panel on Climate Change (IPCC), the focus has been put on the temperature outcome and not on the emission reduction criteria set out in Article 4.1. Here we propose a pathway classification approach that aims to comprehensively reflect all climate criteria set out in the Paris Agreement. We show how such an approach allows for an internally consistent interpretation of the Paris Agreement in terms of emission reduction pathways. For pathways that simultaneously are very likely to hold warming to below 2°C, pursue efforts to limit warming to 1.5°C and achieve the provisions outlined in Article 4.1, we report 2030 global Kyoto-GHG emissions of between 20-26 Gt CO2eq (interquartile range), net zero CO2 emissions around 2050 and net zero GHG emissions around 2060. We further illustrate how prevalent pathway classifications focusing, for example, on the temperature outcome in 2100 result in additional criteria being applied that are not rooted in the Paris Agreement. We outline the consequences of such approaches including for the deployment of carbon dioxide removal (CDR) in such pathways. We find that across pathways classified as ‘no or low overshoot’ pathways in previous IPCC reports, such non-Paris related, additional criteria for end-of-century outcomes may lead to about 20% higher CDR deployment compared to purely achieving the Paris Agreement objectives in mitigation pathways.

The expansion in spatial coverage and data volume of Interferometric Synthetic Aperture Radar (InSAR) is prompting the need for automated InSAR processing. To be useable by stakeholders, deformation maps derived from InSAR must come with estimates of reliability. In this study, we develop a new computer vision algorithm for automatic detection of surface deformation features in InSAR deformation maps. We estimate the atmospheric noise power spectrum directly from interferograms, which we use to generate realistic synthetic noise instances. This allows us to calculate a likelihood that features in a real deformation map came from atmospheric artifacts. Because the procedure only focuses on the probability of false alarm for candidate features, it does not require any geophysical model for the signals of interest. Our method is agnostic to the computer vision algorithm used, and it can be embedded within InSAR processing frameworks to quantify the uncertainty of machine learning detection results. We demonstrate our algorithm using 80 Sentinel-1 SAR images covering 80,000 km2 of the Permian Basin in West Texas, where oil and gas production activities have led to a rise in the number of low magnitude earthquakes. Our algorithm reliably detects millimeter-to-centimeter deformation features related with oil and gas production, groundwater pumping, wastewater injection, and the M5.0 earthquake west of Mentone, Texas. Our method provides guidance on the minimum number of Sentinel-1 acquisitions needed for interferogram stacking to confidently detect the subtle deformation. A decrease in uncertainty can be achieved by detecting and removing SAR images corrupted by tropospheric noise, which reduces the number of required acquisitions for mitigating tropospheric noise.

Previous studies of wind-blown sand have considered either fully erodible or non-erodible soils, but the transport over sparsely sand-covered soils is still poorly understood. The quantitative modeling of this transport is important for the parametrization of Aeolian processes under low sand availability. Here we show, by means of particle-based numerical simulations, that the Aeolian sand transport rate Q scales with the wind shear velocity u∗ as Q = a.[1 + b . (u∗/u∗t − 1)] .√(d/g) . ρf. (u∗² − u∗t²), where u∗t is the minimal threshold u∗ for sustained transport, d is particle size, g is gravity and ρf is air density, while u∗t and the empirical parameters a and b depend on the sand cover thickness. Our model explains the transition from the quadratic to cubic scaling of Q with u∗ as soil conditions change from fully erodible to rigid and provides constraints for modeling Aeolian transport under low sand availability.

The SW Portuguese margin has been intensively studied, particularly for rifting, tectonic inversion and tectonic reactivation of the Atlantic passive margin. In this work we bring new data on the continental shelf, usually not acquired by the heavier geophysical methods (e.g. airborne or low resolution deep seismic-magnetic surveys). These new data allow casting a clear light bridging between the geological structures onshore and offshore. The geology of this margin went through the Variscan orogeny of Paleozoic age, the North Atlantic rifting, the Late Cretaceous alkaline magmatism (intrusive and extrusive), the Alpine tectonic inversion and the Quaternary reactivation of the passive margin. We present results from the compilation of a series of marine magnetic surveys conducted along the Portuguese nearshore from 2014 to 2019. Magnetic data were acquired with 1 nautic mile line separation, resulting in near full coverage of the nearshore along a 120 km long margin segment, from Sintra to Odeceixe. For a large part of the surveyed area, ultra-high resolution seismics and multibeam bathymetry were simultaneously acquired. Magnetic data were processed to produce high resolution mapping of magnetic anomalies, and also to enhance both shallow and deep structures, using several derivative and filtering techniques. We combine the interpretation of high-resolution magnetic mapping with the interpretation of ultra-high resolution and vintage deep penetration seismic data to infer the local and regional expression of tectonic structures and magmatic bodies. Our results allow: identifying the offshore extension of important faults, e.g. the Grândola, Pinhal Novo and Messejana faults; resolving previously blurry-imaged magmatic structures, e.g. Sines and Cabo Raso anomalies; identifying faults recycled from the Paleozoic through Present; constraining the relation between magmatic intrusions and faults; and bringing constraints to the discussion of magmatic emplacement.

Previous work has found that as the surface warms the large-scale tropical circulations weaken, convective anvil cloud fraction decreases, and atmospheric static stability increases. Circulation changes inevitably lead to changes in the humidity and cloud fields which influence the surface energetics. The exchange of mass between the boundary layer and the midtroposphere has also been shown to weaken in global climate models. What has remained less clear is how robust these changes in the circulation are to different representations of convection, clouds, and microphysics in numerical models. We use simulations from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) to investigate the interaction between overturning circulations, surface temperature, and atmospheric moisture. We analyze the underlying mechanisms of these relationships using a 21-member model ensemble that includes both general circulation models and cloud resolving models. We find a large spread in the change of intensity of the overturning circulation. Both the range of the circulation intensity, and its change with warming can be explained by the range of the mean upward vertical velocity. There is also a consistent decrease in the exchange of mass between the boundary layer and the midtroposphere. However, the magnitude of the decrease varies substantially due to the range of responses in both mean precipitation and mean precipitable water. This work implies that despite well understood thermodynamic constraints, there is still a considerable ability for the cloud fields and the precipitation efficiency to drive a substantial range of tropical convective responses to warming.

The transition from subduction to transform motion along horizontal terminations of trenches is associated with tearing of the subducting slab and strike-slip tectonics in the overriding plate. One prominent example is the northern Tonga subduction zone, where abundant strike-slip faulting in the NE Lau back-arc basin is associated with transform motion along the northern plate boundary and asymmetric slab rollback. Here, we address the fundamental question: how does this subduction-transform motion influence the structural and magmatic evolution of the back-arc region? To answer this, we undertake the first comprehensive study of the geology and geodynamics of this region through analyses of morphotectonics (remote-predictive geologic mapping) and fault kinematics interpreted from ship-based multibeam bathymetry and Centroid-Moment Tensor data. Our results highlight two unique features of the NE Lau Basin: (1) the occurrence of widely distributed off-axis volcanism, in contrast to typical ridge-centered back-arc volcanism, and (2) fault kinematics dominated by shallow-crustal strike slip-faulting (rather than normal faulting) extending over ~120 km from the transform boundary. The orientations of these strike-slip faults are consistent with reactivation of earlier-formed normal faults in a sinistral megashear zone. Notably, two distinct sets of Riedel megashears are identified, indicating a recent counter-clockwise rotation of part of the stress field in the back-arc region closest to the arc. Importantly, these structures directly control the development of complex volcanic-compositional provinces, which are characterized by variably-oriented spreading centers, off-axis volcanic ridges, extensive lava flows, and point-source rear-arc volcanoes that sample a heterogenous mantle wedge, with sharp gradients and contrasts in composition and magmatic affinity. This study adds to our understanding of the geologic and structural evolution of modern backarc systems, including the association between subduction-transform motions and the siting and style of seafloor volcanism.

The leaf ecophysiological traits are expected to change with the leaf age, and tree age. Leaf phenology and tree age (seedling, sapling, and tree stages) was a stronger driver of changes in ecophysiological traits. In the present study, we measured effect of leaf phenophases (initiation stage, expansion stage, and senescence stages) and tree age (seedling, sapling, and tree stages) on the leaf physiological and morphological traits of nitrogen-fixing Alnus nepalensis (D. Don), a pioneer tree species in the central Himalaya. In fully expanded leaf and seedling stage demonstrate ecophysiological traits consistent with an acquisitive resource-use strategy. Results revealed that net photosynthetic capacity (Aarea and Amass), leaf stomata conductance (gswarea and gswmass), transpiration rate (Earea and Emass), specific leaf area (SLA), pre-dawn and mid-day water potential (Ψ), leaf total chlorophyll concentration, photosynthetic N-, and P-use efficiency (PNUE and PPUE) were highest in seedling stage and sapling than trees. Seedling stage and sapling had significantly higher transpiration rates (Earea and E mass) and stomatal conductance (gswarea and gswmass), therefore showing significantly lower water use efficiency (WUE) and intrinsic water use efficiency (WUEi). Mass-based net photosynthetic capacity (Amass) were positively correlated with PNUE, PPUE, transpiration rate, stomatal conductance, SLA, and chlorophyll concentrations while negatively correlated with WUE and WUEi. However mass-based leaf nitrogen (N), and phosphorus (P) concentrations were higher in fully expended leaf; they did not vary significantly, despite N concentration negatively correlated with SLA. Collectively, our results indicated that seedling  A. nepalensis displayed characteristic values associated with a more acquisitive resource-use strategy. Consequently, this may explain their survival and replacement strategies during secondary succession and should be considered for the vegetation restoration model of degraded forest in the central Himalaya.

As soon as the outer plasmasphere gets eroded during geomagnetic storms, the greatly depleted plasmasphere is replenished by cold, dense plasma from the ionosphere. A strong correlation has been revealed between plasmaspheric refilling rates and ambient densities in the topside ionosphere and exosphere, particularly that of atomic hydrogen (H). Although measurements of H airglow emission at plasmaspheric altitudes exhibit storm-time response, temporally static distributions have typically been assumed in the H density in plasmasphere modeling. In this presentation, we evaluate the impact of a realistic distribution of the dynamic H density on the plasmaspheric refilling rate during the geomagnetic storm on March 17, 2013. The temporal and spatial evolution of the plasmaspheric density is calculated by using the Ionosphere-Plasmasphere Electrodynamics (IPE) model, which is driven by a global, 3-D, and time-dependent H density distribution reconstructed from the exospheric remote sensing measurements by NASA’s TWINS and TIMED missions. We quantify the spatial and temporal scales of the refilling rate and its correlation with H densities.