The growing scale of Earth and space science challenges dictate new modes of discovery–discovery that embraces cross-disciplinary interactions and links between communities, between data, between technologies. Nowhere is the challenge more pressing than in the field of Heliophysics where solar energy is generated, propagated through interplanetary space, interacts with the Earth’s space environment, and poses immediate threat to our technological infrastructure and human-natural systems (i.e., space weather). We will present a new project within the National Science Foundation Convergence Accelerator program that represents this new mode of discovery “The Convergence Hub for the Exploration of Space Science (CHESS).” Our approach is to semantically link Heliophysics data through a Knowedge Graph/Network (KG). The presentation and discussion will focus on: - What is a knowledge graph (KG)? - In what ways are KGs poised to transform Earth and space science? - The Convergence Hub for the Exploration of Space Science (CHESS) project and bridging to metadata and knowledge architecture efforts in Heliophysics We will highlight linkages to the NSF EarthCube program and ongoing efforts in the geoinformatics and data science communities across e.g., NSF, NOAA, and NASA.

Large-scale field investigations of 7 legacy organochlorine pesticides (OCPs) in the surface seawater and atmosphere from East Asia to the high Arctic Ocean were completed in 2017 and 2016. Seawater OCPs (SOCPs) concentrations displayed rapid and significant seasonal variations during mid-late summer. The ocean is the most significant atmospheric-OCP source region for the Arctic, and feedback affects the mid or low latitude regions of the Northern Hemisphere. The legacy OCP concentrations in the Arctic Ocean were shown to gradually decrease with distance from water masses originating in the Bering Sea and the landmass of Alaska. This study is the first updated investigation of OCPs along the North Pacific Ocean to the Arctic Ocean transect in the past decade. It provides updated field data on the global fate of OCPs in a region undergoing rapid climate change.

The ionosphere of Titan hosts a complex ion chemistry leading to the formation of organic dust below 1200 km. Current models cannot explain the observed electron temperature in this dusty environment. To get new clues, we re-analyzed the data taken in the ionosphere of Titan by the Cassini Langmuir probe (LP), part of the Radio and Plasma Wave Science (RPWS) package. A first paper (Chatain et al., n.d.) introduces the new analysis method and discusses the detection of 4 different electron populations. In this second paper, we present a statistical study of the whole LP dataset below 1200 km and gives clues on the origin of the 4 populations. One small population is attributed to photo- or secondary electrons emitted from the surface of the probe boom. A second population is systematically observed, at a globally constant density (~500 cm), and is attributed to background thermalized electrons. The two last populations increase in density with pressure, solar illumination and extreme UV flux. The third population is observed with varying densities at all altitudes (at least up to 1400 km) and solar zenith angles except on the far nightside (SZA > ~140°), with a maximum density of 2700 cm. It is therefore certainly related to photo-ionization and its subsequent active ion chemistry. Finally, a fourth population detected only on the dayside and below 1200 km reaching up to 2000 cm could be photo- or thermo-emitted from dust grains.

Process-scale development, evaluation and calibration of physically-based parameterizations are key to improve weather and climate models. Cloud–radiation interactions are a central issue because of their major role in global energy balance and climate sensitivity. In a series of papers, we propose papers a strategy for process-based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of single-column versions of climate models with explicit simulations of boundary-layer clouds (LES). Parts I and II apply this framework to the calibration of boundary layer parameters targeting first boundary layer characteristics and then global radiation balance at the top of the atmosphere. This third part focuses on the calibration of cloud geometry parameters that appear in the parameterization of radiation. The solar component of a radiative transfer scheme (ecRad) is run in offline single-column mode on input cloud profiles synthesized from an ensemble of LES outputs. A recent version of ecRad that includes explicit representation of the effects of cloud geometry and horizontal transport is evaluated and calibrated by comparing radiative metrics to reference values provided by Monte Carlo 3D radiative transfer computations. Errors on TOA, surface and absorbed fluxes estimated by ecRad are computed for an ensemble of cumulus fields. The average root-mean-square error can be less than 5 Wm$^{-2}$ provided that 3D effects are represented and that cloud geometry parameters are well calibrated. A key result is that configurations using calibrated parameters yield better predictions than those using parameter values diagnosed in the LES fields.

In late January 2019, a severe cold air outbreak brought the coldest temperatures in over 20 years to Midwestern United States and Eastern Canada. With a newly developed functional analysis tool namely multiscale window transform (MWT), and the MWT-based theory of canonical transfer, it is found, based on the data from National Center for Environmental Prediction, that the cold surge, though initialized by the southward migration of the Arctic air mass, is mainly caused by a synchronization of perturbation temperature and perturbation winds, which leads to a very strong baroclinic instability, and hence an explosive growth of available potential energy on the cold surge scale. The cold event is actually a part of a localized stationary wave train, sandwiched between two warming centers, respectively over western North America and over Atlantic. The synchronization can serve as a precursor for this extremely severe cold surge.

In this study, we proposed a volume-to-point framework for quantitative precipitation estimation (QPE) based on the QPESUMS (Quantitative Precipitation Estimation and Segregation Using Multiple Sensor) Mosaic Radar dataset. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location-specific precipitations. In contrast to QPE methods based on the Z-R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013 ~ 2016. In comparison to the operational QPE scheme used by the Central Weather Bureau (CWB), the volume-to-point framework performed comparably well in general cases and excelled in detecting heavy-rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the QPF in extreme precipitation scenarios.

Glacial hydrology describes the way water moves over, through and under glaciers. Meltwater flows every summer over the surface of glaciers and ice sheets, creating pathways down to below the surface, eventually reaching the glacier bed and thereby influencing ice motion. Glacier and ice sheet models, trying to predict their future sea-level rise contribution, need to therefore be able to properly describe glacial hydrological processes. However, the current knowledge in the field is still limited due to the lack of measurement technology for subsurface in situ flow observations. Here we present a measurement method that allows to reconstruct planar subsurface water flow paths and spatially reference water pressures therein. The approach uses inertial measurements from submersible sensing drifters and reconstructs the flow path from given start and end coordinates. Validation cases show an average error of 3.90 m compared to GNSS reference. We showcase this method by reconstructing the flow path and the spatial water pressure distribution of an englacial channel on Austre Brøggerbreen (Svalbard). The average error of the reconstruction is thereby 12.1 m and the average pressure error 3.4 mbar (0.3%). Our method will allow to study en- and subglacial flow paths and the pressure distribution therein, thereby allowing for model validation and activation. Further on, our method also allows to reconstruct other subsurface fluid flow paths, when a global spatial reference (e.g. GNSS) is not available.

Projections of the sea level contribution from the Greenland and Antarctic ice sheets rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous CMIP5 effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

Over the past several decades, scientists have focused on numerical sensitivity analysis to explain the relative importance of tropical cyclone (TC) forward speed on storm surge and wave height predictions. These past studies performed numerical analyses, but the results have not been sufficiently compared with long term observations. In this study, 42 years of tidal records and landfall TC best tracks in Japan were analyzed, demonstrating that TC pre-landfall forward speed is significantly correlated with both maximum storm surge and significant wave height. Coastal horizontal morphology was the determining factor among these correlations. Fast-moving TCs tended to amplify the storm surge along open coastlines (Pearson correlation coefficient, R = 0.62), but reduce it in semi-enclosed bays (R = -0.52). A similar tendency has also been observed for the case of wave height (open coast, R = 0.62; bay, R = -0.52). The negative correlation contrasts with the general perception that the coincidence of TC wind speed and forward speed vectors generates a larger storm surge. The influence of coastal morphology was most prominent for TCs with a central pressure lower than 956 hPa. Tropical cyclone operational forecasts are continuously improving; however, there is still scope to improve the precision of storm surge - wave predictions. These findings may be beneficial in two main areas. Firstly, considering TC transitional speed and coastal geometry (open coastline or bays) - meteorologists and oceanographers could provide more comprehensive surge-wave forecasts, and emergency management personnel could use pre-landfall forward speed for more precise early warning. Secondly, coastal areas at risk with no access to advanced weather forecasting could use these empirical findings along with other TC intensity related information (e.g., wind speed, central pressure, radius) for improvement of early-warning activities.

Headwater blanket-peat restoration activities, in particular revegetation and gully-blocking, are observed to deliver significant Natural Flood Management (NFM) benefits. A recent Before-After-Control-Intervention (BACI) experiment showed that these interventions reduce flood-peaks and increase lag-times, but the processes controlling these effects remain unclear. We seek to identify these processes at the same BACI sites by inverting the TOPMODEL rainfall-runoff model and linking the response-to-intervention in each catchment to model parameters through rigorous calibration. Through numerical experiments, we infer processes most likely to be driving the BACI observations. Our findings confirm the NFM benefits of these restoration-focused interventions. Independent of storm size/intervention, the increased lag is almost entirely due to surface roughness reducing the floodwave speed. We conceptualise this as a ‘mobile’ surface storage. In flood-relevant storms, at least 90\% of the peak reduction in both interventions is delivered by mobile storage. The additional increase in the mobile storage due to gully-blocking is very significant and comparable to that of revegetation alone. The impact of interventions on ‘immobile’ storage (interception+ponding+evapotranspiration) becomes important for smaller storms, in which revegetation reduces peak discharge by increasing evapotranspiration but the not interception storage. Gully blocking however, increases ponding but reduces evaporation, such that there is no net gain in catchment immobile storage relative to revegetation alone. Although interventions always increase lag-times, they can be less effective in reducing peak magnitude in long duration frontal rainfalls. We propose two approaches to further increase catchment’s surface storage, while adhering to the restoration requirement to keep the water tables high.

The worldwide network of neutron monitors (NMs) is the primary instrument to study cosmic-ray variability on time scales of up to 70 years. Since the 1950s, 147 NMs with publicly available data have been in operation, and their records are archived in and distributed through different repositories and data sources. A comprehensive analysis of all available NM datasets (300 datasets from 147 NMs) is performed here to check the quality and consistency of the data. The data sources include World Data Center for Cosmic Rays (WDCCR), the Neutron Monitor Database (NMDB), the Pushkov Institute of Terrestrial Magnetism, Ionosphere and Radiowave Propagation (IZMIRAN) and individual station/institution databases. It was found that The data from the same NM can be non-identical and of different quality in different sources. We give and tabulate here a recommendation for the optimal data source of each NM. We also present here a list of 29 ‘prime’ stations with the longest and most reliable data. Verified datasets for these prime stations are provided as supplementary information.

Key Points:  SPI and temperature showed negative correlations with large spatial differences.  The probability of compound dry and hot extremes increased with a stronger dependence between SPI and temperature.  Dryness-temperature feedback had great impact on the likelihood of compound dry and hot extremes.

The main events of the 2016 Kumamoto earthquake in Kyushu were a foreshock (Mw 6.2) on 14 April and the main shock (Mw 7.0) on 16 April 2016, both of which were caused by fault ruptures near the intersection of the Futagawa and Hinagu fault zones. However, not all sections of the two fault zones were ruptured during the earthquake. In particular, the northernmost (Takano-Shirahata) section of the Hinagu fault zone ruptured, but the rupture did not propagate to southern sections of the fault zone. We examined fault geometry and paleoseismological data of the fault zones, and furthermore used numerical analyses to investigate rupture conditions around the source faults of the earthquake, which together allowed us to consider the potential for future earthquakes in the region. Fault geometry, slip tendencies, and fault rupture history indicated that the rupture potential on the source faults was high before the earthquake. Seismicity and rupture propagation during the earthquake sequence were probably controlled by geological heterogeneities of the fault zones. Coulomb stress change analysis indicated that stress transfer from the source faults to the Hinagu section during the earthquakes was smooth, and that most of that section received a positive stress change. This stress change and the high slip tendencies we calculated for the Hinagu section indicate that it has high potential for a future rupture. These results provide crucial information for preparation for disaster mitigation of future earthquakes around the rupture zone of the 2016 Kumamoto earthquake.

The ocean surface and subsurface biophysical responses and their feedbacks to super typhoon Nida (2009) are investigated. Nida experienced two Category 5 stages: a rapid intensification stage that was fast moving along a straight-line track, and a rapid weakening stage that was slow moving along a sharp-left sudden-turning track. In the first Category 5 stage, Nida caused an average sea surface temperature (SST) cooling of −1.44 ℃, a sea surface height anomalies (SSHAs) decrease of −5.00 cm and a chlorophyll-a (chl-a) concentration increase of 0.03 mg m−3. During the second Category 5 stage, Nida induced a strong cold cyclonic eddy (SSHA < −60 cm) by strong upwelling due to the slow speed of the sudden-turning track, which caused the maximum SST cooling of 6.68 ℃, a sea surface salinity increase of 0.6 psu, a long-lasting chl-a bloom that exceeded 0.6 mg m−3 and the Kuroshio Current strengthening of 0.25 Sv, resulting in substantial impacts on the ocean ecological environment. Furthermore, the enhanced ocean cold wake and the longer air-sea interaction in turn decreased the average inner-core SST of −4 {degree sign}C and the corresponding enthalpy flux of −780 W m−2, which induced a notable negative feedback to the typhoon intensity by weakening it from Category 5 to Category 2. Our findings provide positive evidence that enhanced oceanic environmental responses and feedbacks can occur under sudden-turning and/or lingering tracks, providing insight to ocean-typhoon interactions.

The sulfate-rich sandstones of the Burns formation investigated by the Opportunity rover on Meridiani Planum, Mars, are directly underlain by the fine-grained sedimentary rocks of the Grasberg formation. It was recently shown that, except for differing amounts of MgO and SO3, the Burns and Grasberg rocks have nearly identical chemical compositions, suggesting both units are genetically related. Here, quantitative models demonstrate that the chemical composition of the Burns formation can be accurately reproduced by addition of MgO and SO3 to a Grasberg-like precursor, along with small amounts of Ni. Based on this result, a new scenario is proposed for the origin of the Burns formation: (1) deposition of fine-grained airfall deposits with composition similar to the Grasberg rocks; (2) induration of the deposits, resulting in materials that closely resemble the current Grasberg formation in composition and texture; (3) erosion into sand-sized particles, which are then reworked by eolian and fluvial processes to form the bedding features observed in the Burns rocks; (4) infiltration of the deposits by one or more generations of groundwater, resulting in diagenetic alteration that included local element redistribution, dissolution/recrystallization of minerals, cementation, and formation of hematite spherules; (5) evaporation of groundwater at some point during diagenesis enriched the Burns sediments in MgO and SO3, with greater amounts accumulated closer to the surface. This scenario fully accounts for the chemical composition of the Burns sandstones as well as their sedimentary and diagenetic features, while invoking only a known sediment source and sedimentary/diagenetic processes widely observed on Mars.

We present a new time series PU approach to improve the unwrapping accuracy in this article. The rationale behind is to first improve the sparse unwrapping by mitigating the phase gradient in a 2D network and then correcting the unwrapping errors in time based on the triplet phase closure. Rather than commonly-used Delaunay network, we employ All-Pairs-Shortest-Path (APSP) algorithm in graph theory to maximize the temporal coherence of all edges and to approach the phase continuity assumption in the 2D spatial domain. Next, we formulate the PU error correction in the 1D temporal domain as a compressed sensing (CS) problem, according to the sparsity rule of remaining phase ambiguity cycles. We finally estimate phase ambiguity cycles by means of Integer Linear Programming. The comprehensive comparisons on synthetic and real Sentinel-1 data covering Lost Hills, California confirm the validity of the proposed 2D+1D unwrapping approach.

Metal compounds abundant in Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals, for example, are proposed to have served as primitive metalloenzyme cofactors for their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis and astrobiology.

Microphysics methods for climate models typically track one, two, or three moments of a droplet size distribution for various categories of liquid, ice, and aerosol. Such methods rely on conversion parameters between these categories, which introduces uncertainty into predictions. While higher-resolution options such as bin and Lagrangian schemes exist, they require too many degrees of freedom for climate modeling applications and introduce numerical challenges. Here we introduce a flexible spectral microphysics method based on collocation of basis functions. This method generalizes to a linear bulk scheme at low resolution and a smoothed bin scheme at high resolution. Tested in an idealized box setting, the method improves spectral accuracy for droplet collision-coalescence and improves precipitation predictions relative to bulk methods; furthermore, it generalizes well to multimodal distributions with less complexity than a bin method. The potential to extend this collocation representation to multiple hydrometeor classes suggests a path forward to unify liquid, ice, and aerosol microphysics in a single, flexible, computational framework for climate modeling.