Pressure-to-depth conversion is a crucial step towards geodynamic reconstruction but remains strongly debated. Here, we derive pressure-to-depth conversion models using either one or two pressure data points in conjunction. In the two-point method, we assume that both peak and retrograde pressure are recorded at the same depth. This method reduces the depth estimate uncertainty dramatically. We apply the proposed pressure-to-depth conversions to a large set of $P$ data from (ultra)high-pressure metamorphic rocks. We explore different cases to explain the transition from peak to retrograde pressure by varying the direction and magnitude of stress components. Our results show that (1) even small deviatoric stresses have a significant impact on depth estimates, (2) the second principal stress component $\sigma_2$ plays an essential role, (3) several models can explain the $P$ evolution of the data but lead to different depth estimates, and (4) strain data offer a means to falsify two-point models. The most commonly used pressure-to-depth conversion method uses one pressure point and the assumption that pressure is lithostatic. Then, the transition from peak to retrograde pressure is interpreted as the result of deep subduction ($>100$ km), followed by fast exhumation to mid-crustal depth. We show that alternative models where a change in the stress state at a constant depth triggers the pressure transition explain the data equally well. The predicted depth is then shallower than the crustal root Moho ($<75$ km) for all data points.

An NSF EarthCube-funded project supported a field-based workshop designed to evaluate and refine the sedimentology/stratigraphy portion of the StraboSpot digital data management system. Eleven academics attended the workshop, representing a spectrum of career levels and specialties. The participants teach classes in sedimentology and conduct sedimentary research, but had not used any previous digital mobile apps in the field. The field component focused on learning the basic functionality of the StraboSpot app as a method of collecting digital data in the field. On the first day, teams of 2-3 participants measured a stratigraphic section in a highly visited locality of the well-studied Book Cliffs of central Utah. Teams saw how the vocabulary and spot functionality worked to collect sedimentary field data and to generate stratigraphic columns. The second day was spent measuring a more complex mixed carbonate-clastic sequence in the San Rafael Swell (Utah). Half of the third day was spent in discussion on major issues with workflow/vocabulary and getting feedback on how to simplify and streamline descriptive data collection functions (stratal attributes), and reviewing the more challenging interpretation functions (processes, depositional environments, and architecture). A major discussion point was how best to handle data collection and stratigraphic plotting of ‘interbedded’ intervals. As a result of the workshop, we streamlined workflow options and refined portions of the vocabulary. This field testing followed up on two previous workshops that solicited expert advice to develop the program categories and basic vocabulary for the sedimentary community. Overall, workshop participants were enthusiastic about the potential of digital data systems, and the ability to link annotated photographs and sketches to georeferenced localities. All participants indicated they were inclined to use StraboSpot in both teaching and research, particularly with versatile and customizable options.

Recent explorations of the state-dependence of Earth’s equilibrium climate sensitivity (ECS) have revealed a pronounced peak in ECS at a surface temperature of approximately 310 K. This ECS peak has been observed in models spanning the model hierarchy, suggesting a robust physical source. Here we propose an explanation for this ECS peak using a novel spectrally-resolved decomposition of clear-sky longwave feedbacks. We show that the interplay between spectral feedbacks in H2O- and CO2-dominated portions of the longwave spectrum, along with moist-adiabatic amplification of upper-tropospheric warming, conspire to produce a minimum in the feedback parameter, and a corresponding peak in ECS, at a surface temperature of 310 K. Mechanism denial tests highlight three key ingredients for the ECS peak: 1) H2O continuum absorption to quickly close spectral windows at high surface temperature; 2) moist-adiabatic tropospheric temperatures to enhance upper-tropospheric warming; and 3) energetically-consistent increases of CO2 with surface temperature.

Calcareous nannoplankton population underwent a major reorganization across the early and middle Eocene. This reorganization was mainly explored from an open ocean perspective, but marginal settings are usually underexplored. Here we analyzed a Tethyan pelagic record from Avdat, southern Israel in the Levant margin. The calcareous nannofossil assembledge was then compared to a number of Deep Sea and Ocean Drilling sites through paleoecological techniques. From calcareous nannofossil zone NP12 until zone NP15-16 (undiff.), dominance shifted from to discoasters in the warm early Eocene, and then to responding to cooling in the middle Eocene. Markers for middle Eocene oceanic destratification and mixing, e.g., the acme, peak and the first occurrence of , are delayed by ~1.0 Myr at Avdat with respect to oceanic sites at comparable paleolatitudes, and more so when compared to higher latitude sites. The Avdat section is punctuated by peaks of , diversity of spp., and that were not reported in deep oceanic sites. The disparities at Avdat vs. other oceanic localities can be ascribed to its position in a Tethyan remnant ocean surrounded by land masses and orogenic terrains. The punctuational mode and delay in water destratification in the Levant ocean are attributed to connection to warm and saline water off the Afro-Arabian shelf affected by pulses of cooler waters from Boreotropical marine connections, and sea level fall late in the Middle Eocene, increasing proximity to shelves.

The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June-October 2019. The purpose of the study is to examine how the atmospheric boundary layer responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model-data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10×10 km domain of a heterogeneous forest ecosystem in the Chequamegon-Nicolet National Forest in northern Wisconsin USA, centered on the existing Park Falls 447-m tower that anchors an Ameriflux/NOAA supersite (US-PFa / WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft, maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology, and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large eddy simulation and scaling experiments to better understand sub-mesoscale processes and improve formulations of sub-grid scale processes in numerical weather and climate models.

Energetic electron precipitation is a source of both ionospheric and geomagnetic disturbances. The resultant increased ionization in the auroral oval leads to the absorption of high frequency radio waves in the auroral zone, or auroral absorption. Auroral absorption is typically characterized by global geomagnetic activity indices, such as the Kp index. In this paper the hourly range of the magnetic field (HR) is examined as an alternative to the 3-hour Kp index for describing the dynamic and localized features of auroral absorption represented by the hourly range of absorption (HRA). Kp, magnetometer, and riometer data were examined for a 3-year period for stations spread across typical auroral latitudes. A general linear relationship was shown to exist between Kp and LOG(HRA) for Kp<4; for Kp≥4 the correlation was weaker. A stronger linear correlation was demonstrated between LOG(HRA) and LOG(HR) for HR >50 nT, characterized by a correlation coefficient of R=0.63. Increased variability in the relationship between HRA and Kp was attributed to the following factors: the variability of the magnetic field within the 3-hour window characterized by the Kp-index which was better represented by a 1-hour HR; the dependence of the Kp index on sub-auroral magnetic data which is not subject to the energetic electron precipitation experienced within the auroral region; and reduced statistics for Kp>6.

The resolution of climate models is limited by computational cost. Therefore, we must rely on parameterizations to represent processes occurring below the scale resolved by the models. Here, we focus on parameterizations of ocean mesoscale eddies and employ machine learning (ML), namely relevance vector machines (RVM) and convolutional neural networks (CNN), to derive computationally efficient parameterizations from data, which are interpretable and/or encapsulate physics. In particular, we demonstrate the usefulness of the RVM algorithm to reveal closed-form equations for eddy parameterizations with embedded conservation laws. When implemented in an idealized ocean model, all parameterizations improve the statistics of the coarse-resolution simulation. The CNN is more stable than the RVM such that its skill in reproducing the high-resolution simulation is higher than the other schemes; however, the RVM scheme is interpretable. This work shows the potential for new physics-constrained interpretable ML turbulence parameterizations for use in ocean climate models.

Most studies of land-atmosphere coupling have focused on bivariate linear statistics like correlation. However, more complex dependencies exist, including nonlinear relationships between components of land-atmosphere coupling and the transmutability of relationships between soil moisture and surface heat fluxes under different environmental conditions. In this study, a technique called multivariate mutual information, based on information theory, is used to quantify how surface heat fluxes depend on both surface energy and wetness conditions, i.e. net radiation and soil moisture, across the globe by season using reanalysis data. Such interdependency is then decomposed into linear and nonlinear contributions, which are further decomposed as different components explainable as the unique contribution from individual land surface conditions, redundant contributions shared by both land surface conditions, and the synergistic contribution from the coaction of net radiation and soil moisture. The dependency linearly contributed from soil moisture bears a similar global pattern to previously identified hot spots of coupling. The linear unique contributions of net radiation and soil moisture are mainly nonoverlapping, which suggests two separate regimes are governed by either energy or water limitations. These patterns persist when the nonlinearity is superimposed, thus reinforcing the validity of the land-atmospheric coupling hot spot paradigm and the spatial division of energy-limited as well as water-limited regions. Nevertheless, strong nonlinear relationships are detected, particularly over subtropical regions. Synergistic components are found across the globe, implying widespread multidimensional physical relationships among net radiation, soil moisture, and surface heat fluxes that previously had only been inferred locally.

The 2018 drought and heatwave over Europe was exceptional over northern Europe, with unprecedented forest fires in Sweden, searing heat in Germany and water restrictions in England. Monthly, daily and hourly data from ERA5, verified with soil moisture and surface flux measurements over Britain, are examined to investigate the subseasonal-to-seasonal progression of the event and the diurnal evolution of tropospheric profiles to quantify the anomalous land surface contribution to heat and drought. Data suggest the region entered a rare condition of becoming a “hot spot” for land-atmosphere coupling, which exacerbated the heatwave across much of northern Europe. Land-atmosphere feedbacks were prompted by unusually low soil moisture over wide areas, which generated moisture limitations on surface latent heat fluxes, suppressing cloud formation, increasing surface net radiation and driving temperatures higher during several multi-week episodes of extreme heat. We find consistent evidence in field data and reanalysis of a breakpoint threshold of soil moisture at most locations, below which surface fluxes and daily maximum temperatures become hypersensitive to declining soil moisture. Similar recent heatwaves over various parts of Europe in 2003, 2010 and 2019, combined with dire climate change projections, suggest such events could be on the increase. Land-atmosphere feedbacks may play an increasingly important role in exacerbating extremes, but could also contribute to their predictability on subseasonal time scales.

The 2000 European Union Water Framework Directive (WFD) states that ‘Member States shall ensure the necessary protection for the bodies of water identified with the aim of avoiding deterioration in their quality in order to reduce the level of purification treatment required in the production of drinking water’. However, it does not specify how to evaluate or quantify this level of purification treatment. The scientific literature contains several different Water Quality Indices (WQIs), but none are suited for this purpose. Therefore, we propose a novel WQI that we specifically designed to quantify the level of purification required to prepare drinking water from source water. It is based on the WQI of the Canadian Council of Ministers of the Environment (CCME WQI), which was chosen because it is widely accepted, can be used with any number of input parameters, does not require expert judgement and has been applied to assess source water quality before. We compare measured contaminant concentrations in source water to drinking water guidelines and additionally incorporate the resilience of contaminants to treatment processes in the index (which is not possible in the CCME WQI). Furthermore, we accommodate for varying sampling frequencies that are characteristic of the ongoing monitoring programme. These changes make our index more robust and sensitive to relevant changes in source water quality. We calculated index scores for source water from the Rhine and the Meuse rivers to monitor the effect of implementation of the WFD on the effort required to produce of drinking water.

Measurements from the Magnetospheric Multiscale (MMS) mission indicate that the density gradients associated with nonlinear compressional structures (shocklets) in a quasi-parallel bow shock trigger sequentially two instabilities that heat ions and electrons. The Lower-Hybrid-Drift (LHD) instability, triggered by the diamagnetic drift of ions, produces electric fields and ExB drift of electrons that triggers the Electron-Cyclotron-Drift (ECD) instability. Both instabilities create large amplitude electric fields $\sim$20–200 mV/m at wavelengths comparable to the electron gyroradius. Strong gradients of the electric field lead to stochastic heating of both ions and electrons, controlled by a dimensionless function $\chi = m_iq_i^{-1} B^{-2}\mathrm{div}(\mathbf{E}_\perp)$, which represents a universal, non-resonant heating mechanism for particles species with mass $m_i$ and charge $ q_i$, independent of the type of waves and instabilities.

The MAVEN/EUVM solar soft x-ray (SXR) and Lyman-α measurements are compared with analogous measurements made from Earth to characterize the typical error introduced when phase-shifting solar EUV irradiance measurements made from Earth to other points in the solar system according to the 27.27 day synodic solar rotation period. The phase-shifting error, ε, measured at SXR and Lyman-α are extrapolated to the full EUV spectrum by assuming it is proportional to the variability that occurs over the 27-day timescale of solar rotation. Values for ε as a function of wavelength are reported and used to find the typical error for estimates of photoionization frequencies of some major species found in planetary upper atmospheres derived by phase-shifted EUV irradiance. This study finds that the typical extrapolation error for the CO photoionization frequency is 5.7% of the solar cycle mean value, and 87% of the typical 27-day variability.

Spatially-explicit knowledge of the origins of water resources for ecosystems and rivers is challenging when using tracer data alone. We use simulations from a spatially-distributed model calibrated by extensive ecohydrological datasets in a small, energy-limited catchment, where hillslope-riparian dynamics are broadly representative of humid boreal headwater catchments that are experiencing rapid environmental transition. We hypothesize that in addition to wetness status, landscape heterogeneity modulates the water pathways that sustain ecosystem function and streamflows. Simulations show that catchment storage inversely controls streamwater ages year-round, but only during the drier seasons for transpiration and soil evaporation. The ages of these evaporative outputs depend much less on wetness status in the oft-saturated riparian soils than on the freely-draining hillslopes that subsidize them. This work highlights the need to consider local dynamics and time-changing lateral heterogeneities when interpreting the ages, and thus the vulnerability, of water resources feeding streams and ecosystems in landscapes.

Digital cameras on the surface are frequently used for monitoring atmospheric conditions. Several methods were developed to use the images for synoptic observations, cloud assessments, short term forecasting and so on. However, there are some restrictions not considered by these methods, especially when a linear camera is used to observe logarithmic ranges of atmospheric luminance. Cameras accommodate the scene to a linear scale causing distortions on pattern distributions by pixel value saturation (PVS) and drifts from its original hues. This brings on some simplifying practices commonly found in the literature to overcome these problems. But those practices result in loss of data, misinterpretation of valid pixels and restriction on the use of computer vision algorithms. The present work begins by illustrating these problems performing supervised learning for two reasons: all observation systems seek out automation of human synoptic observation in order to provide a sound mathematical modeling of the observed patterns. A new modeling paradigm is proposed to map the sky patterns to represent the existent physical atmospheric phenomena not considered by the literature. We validate the proposed method, and compared the results using 1630 images against two well-established methods. A hypothesis test showed that results are compatible with currently used binary approach with advantages. Differences were due to PVS and other restrictions not considered by the methods existent on literature. Finally, the present work concludes that the new paradigm presents more meaningful results of sky patterns interpretation, allows extended daylight observation periods and uses a higher dimensional space.

The Naval Research Laboratory (NRL) Sami3 is Also a Model of the Ionosphere (SAMI3) ionosphere/plasmasphere code is used to examine the effect of ring current heating during a storm. With a ring current heating function added to SAMI3, a cold thermal ($< 1$ eV) oxygen ion outflow is produced, with O$^+$ density and location similar to observations of the so-called “oxygen torus.’ The ring current heating function is based on a Comprehensive Inner Magnetosphere-Ionosphere (CIMI) model simulation of the 2015 October 7 storm. We find that the ring current can heat plasmasphere electrons, subsequently heating plasmasphere H$^+$, and ionosphere O$^+$. The resulting O$^+$ outflows resemble observed O$^+$ enhancements in the inner magnetosphere.

During the COVID-19 lockdown in China low air pollution levels were reported as a consequence of the reduced economic and social activities. Quantification of the pollution reduction is not straightforward due to effects of transport, meteorology, and chemistry. Here we have analysed the NO emission reductions calculated with an inverse algorithm applied to daily NO observations from the TROPOMI instrument onboard the Copernicus Sentinel-5P satellite. This method allows quantification of emission reductions per city, and the analysis of emissions of maritime transport and of the energy sector separately. The reductions we found are 20 to 50% for cities, about 40% for power plants and 15 to 40% for maritime transport depending on the region. The reduction in both emissions and concentrations shows a similar timeline consisting of a sharp reduction around the Spring festival and a slow recovery from mid-February to mid-March.

We conducted a detailed investigation of an earthquake cluster distributed from the lower crust to the upper crust beneath Hakodate, Hokkaido, which included both low-frequency earthquakes (LFEs) and regular earthquakes. Relocated hypocentres clearly show that both the LFEs and regular earthquakes occurred close to each other in the brittle upper crust of this non-volcanic area, while only LFEs occurred in the lower crust. This indicates that LFEs can occur not only in the ductile lower crust, but also in the brittle upper crust, which suggests that LFEs can occur in an environment similar to that of regular earthquakes. Regular earthquakes that occur in close vicinity of LFEs have very similar waveforms and nearly overlapping source regions, which indicate that they reflect the repeated rupture of the same asperity patch on a fault. Temporally, the intervals between events in the repeating earthquake sequence were very short, thus suggesting that they were caused by a sudden increase in pore pressure. The cluster of LFEs and repeating earthquakes, which has a rod-like distribution extending from the bottom of the crust to the surface and tilted slightly eastward, might represent a pathway of aqueous fluid movement sourced from the subducting slab.

The Mars Science Laboratory (MSL) Curiosity rover has explored over 400 meters of vertical stratigraphy within Gale crater to date. These fluvio-deltaic, lacustrine, and aeolian strata have been well-documented by Curiosity’s in-situ and remote science instruments, including the Mast Camera (Mastcam) pair of multispectral imagers. Mastcam visible to near-infrared (VNIR) spectra can broadly distinguish between iron phases and oxidation states, and in combination with chemical data from other instruments, Mastcam spectra can help constrain mineralogy, depositional origin, and diagenesis. However, no traverse-scale analysis of Mastcam multispectral data has yet been performed. We compiled a database of Mastcam spectra from >600 multispectral observations and 1 quantified spectral variations across Curiosity’s traverse through Vera Rubin ridge (sols 0-2302). From principal component analysis and an examination of spectral parameters, we identified 9 rock spectral classes and 5 soil spectral classes. Rock classes are dominated by spectral differences attributed to hematite and other oxides (due to variations in grain size, composition, and abundance) and are mostly confined to specific stratigraphic members. Soil classes fall along a mixing line between soil spectra dominated by fine-grained Fe-oxides and those dominated by olivine-bearing sands. By comparing trends in soil vs. rock spectra, we find that locally derived sediments are not significantly contributing to the spectra of soils. Rather, varying contributions of dark, mafic sands from the active Bagnold Dune field is the primary spectral characteristic of soils. These spectral classes and their trends with stratigraphy provide a basis for comparison in Curiosity’s ongoing exploration of Gale crater.