Ocean Worlds in our Solar System are attractive candidates in the search for extra-terrestrial life. The best chances for detecting biosignatures and biology on these bodies lie in in situ investigations of sub-ice oceans in contact with rocky interiors. The actual conditions that will confront an ice-penetrating vehicle (“cryobot”) performing such investigations are largely unknown. However, any Ocean World cryobot must be able to, at a minimum, successfully negotiate five different operating regimes to have a chance of reaching a subsurface ocean: starting at the surface in vacuum at cryogenic temperatures; brittle/cold ice transit; ductile/warm ice transit; negotiating or penetrating salt or sediment layers, and other obstacles; and detecting and transiting ice-water transitions such as voids and the final ocean entry. PROMETHEUS (nuclear-Powered RObotic MEchanism Technology for Hot-water Exploration of Under-ice Space) represents a full cryobot concept and set of key technology demonstrations that advance the capability to perform such investigations. The PROMETHEUS concept is targeted for deployment on Europa, and consists of a fully-instrumented science vehicle able to actively control descent through the ice shell and into the subsurface ocean. The concept employs closed-cycle hot water drilling (CCHWD) technology as the primary means of penetrating ice, and making forward and turning progress. A “passive” (purely conductive) heat transfer system enables penetration starting on the surface where liquid water cannot exist until hole closure is achieved and the system proceeds inside a melt water “bubble”. PROMETHEUS is compatible with a small fission reactor (the NASA Kilopower design) and employs a vertical motion control system using a trailing tether frozen into the ice to guard against falling through voids and enabling controlled entry into the sub-ice ocean. The design is capable of achieving a 20 km descent through a Europan ice profile in under a year and under 500 kg vehicle mass, including reactor mass.

The NASA Terra and Aqua satellites have been successfully operating for over two decades, exceeding their original 5-year design life. However, the era of NASA’s Earth Observing System (EOS) may be coming to a close as early as 2023. Similarities between the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard Aqua and Terra, and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard the Suomi NPP, NOAA-20 and NOAA-21 satellites enable potential continuity of long-term earth observational records in the VIIRS era. We conducted a comprehensive calibration and validation of the MODIS MOD17 product, which provided the first global, continuous, weekly estimates of ecosystem gross primary productivity (GPP) and annual estimates of net primary productivity (NPP). Using Bayesian model-data fusion, we combined an 18-year record of tower fluxes with prior data on plant traits and hundreds of field measurements of NPP to benchmark MOD17 and to develop the first terrestrial productivity estimates from VIIRS. The updated mean global GPP (NPP) flux from MOD17 and the new VNP17 for 2012-2018 is 127 ±2.8 Pg C year-1 (58 ±1.1 Pg C year-1), which compares well with independent top-down and bottom-up estimates. Both MOD17 and VNP17 depict upward productivity trends over recent decades, with 2000-2018 MOD17 GPP (NPP) rising by 0.47 (0.25) Pg C year-2 but slowing to 0.35-0.44 (0.11-0.13) Pg C year-2 over 2012-2021, with a greater reduction in the NPP growth rate. The new VIIRS VNP17 product has the potential to extend these continuous estimates of global, terrestrial primary productivity beyond 2030.

High-resolution topographic data reveal that meandering river floodplains can contain complex floodplain-channel networks. However, the influence of this topography on flood initiation and progression is unknown. In this study, we investigate how floodplain-channel networks influence flooding processes in low-gradient river systems. To accomplish this, we conducted a series of numerical modeling experiments using a two-dimensional (2-D) model built in Hydrologic Engineering Center’s River Analysis System (HEC-RAS). First, we simulated floods using 2-D HEC-RAS on the East Fork White River near Seymour, IN, USA, including the current, dense network of floodplain channels. Next, we simulated synthetic versions of this river system where we varied the connectivity among floodplain channels, and also between the floodplain channels and river channel. We found distinct differences in flooding patterns and flood hydraulics among model simulations. Numerical modeling experiments showed that increasing floodplain channel connectivity caused increased spatial variability of wet and dry surfaces across the floodplain, increased residence time of water on the floodplain, and increased floodwave attenuation. Results from this study indicate that topographic connectivity on the floodplain challenges the classic notions of flooding initiation, and potentially increases a floodplains ability to store matter.

Reproducibility and replicability in analyzing data is one of the main requirements for the advancement of scientific fields that rely heavily on computational data analysis, such as atmospheric science. However, there are very few research activities that field in Indonesia that emphasize the principle of transparency of codes and data in the dissemination of the results. This issue is a major challenge for the Indonesian scientific community to verify the output of research activities from their peers. One common obstacle to the reproducibility of data-driven research is the portability issue of the computing environment used to reproduce the results. Therefore, in this article, we would like to offer a solution through Debian-based dockerized Jupyter Notebook that have been installed with several Python libraries that are often used in atmospheric science research. Through this containerized computing environment, we expect to overcome the portability and dependency constraints that often faced by atmospheric scientists and also to encourage the growth of research ecosystem in Indonesia through an open and replicable environment.

End Users of Climate data have nowadays to struggle with accessing the data they need for their research because of the rapid increase in data volumes. The whole climate data archive is expected to reach a volume of 30 Pb in 2018 and up to 2000 Pb in 2022 (estimated). On-demand data processing solutions as close as possible to the data storage are emerging, thanks to newly developed standards, provenance and infrastructures. In Europe several initiatives are taking place to support scientific on-demand data analytics at the European scale. They offer the huge potential of interoperability, as for example the DARE e-science platform (http://project-dare.eu), designed for efficient and traceable development of complex experiments and domain-specific services on the Cloud. Also, the IS-ENES (https://is.enes.org) consortium has developed a platform to ease access to climate data for the climate impact community (C4I: https://climate4impact.eu). The platform is based on existing standards (ISO and OGC), such as WPS (Web Processing Service). DARE will integrate services from the EUDAT CDI, enabling generic access and cross-domain interoperability, as well as providing compliance and integration with the future EOSC platform. The DARE platform will use containerization technologies, so that it can be easily deployed on heterogeneous architectures. A scientific pilot has been designed within the DARE project for the ENES community (climate domain). The objectives are to enable delegation of on-demand computational-intensive calculations to the DARE platform, from the IS-ENES C4I interface, seamlessly. The DARE architecture and the solutions being implemented will be presented, along with the generic and agile approach taken to implement the pilot.

As the problems humanity faces become ever more obvious and dangerous, the need for interdisciplinary, cross-disciplinary and trans-disciplinary research and solutions becomes ever more apparent. The problems themselves are often intertwined in complex ways - for example the impact of climate change on human health, food & water security, disasters, and so on and how all of these are exacerbated by human population growth and a general lack of recognition that humanity is part of the ecosystem upon which Earthly life depends. Underpinning our ability to understand and solve these complex problems are data of all kinds, more importantly the information that has and continues to be garnered through obtaining and analyzing these and most importantly their inclusion in the larger body of knowledge and ways of knowing that hopefully leads to the wisdom needed to directly address the root causes of each problem. Over the past decades, through involvement in projects on science topics as diverse as quasars and stellar pediatrics, solar physics, social science, and a wide variety of Earth Science topics; where informatics topics such as data management and curation, systems and framework development; and tools and methodologies such as Use Cases, Natural Language Processing, Deep Learning, Knowledge Graphs, Information Models, etc. were included; a number of principles and lessons learned which unite the technical underpinnings of these projects have come to the fore. These will be discussed in this presentation.

Pore fluids are ubiquitous throughout the lithosphere and are commonly cited as the cause of slow-slip and complex modes of tectonic faulting. We investigate the role of fluids for slow-slip and the frictional stability transition and find that the mode of fault slip is mainly unaffected by pore pressures. We shear samples at effective normal stress (σ’n) of 20 MPa and pore pressures Pp from 1 to 4 MPa. The lab fault zones are 3 mm thick and composed of quartz powder with median grain size of 10 µm. Fault permeability evolves from 10-17 to 10-19 m2 over shear strains up to 26. Under these conditions, dilatancy strengthening is minimal. Slow slip may arise from dilatancy strengthening at higher fluid pressures but for the conditions of our experiments slip rate-dependent changes in the critical rate of frictional weakening are sufficient to explain slow-slip and the stability transition to dynamic rupture.

ORCiD: [https://orcid.org/0000-0003-0655-2343] Keywords: Root imaging, root-system architecture (RSA), soybean, 2D-phenotyping & 3D-phenotyping Roots are a major part of plant systems and are essential to obtaining water and nutrients. Despite their importance, roots have not been extensively examined as compared to their aboveground counterparts, due primarily to the difficulties of access and lack of standard methods to quantify root morphology. While there have been several experiments performed under controlled environments, comparatively fewer studies have examined root architectures under field conditions. Here, we apply two imaging techniques to characterize variability in Root System Architecture (RSA) in diverse soybean genotypes under field settings with two contrasting soil conditions. Thus, our objectives are to (1) quantify root system architecture using 2D image techniques (e.g., Winrhizo and Image J) and (2) evaluate a contrasting subset of these samples (n = 30) using a novel 3D phenotyping approach. The research seeks to meet the need for enhanced methods in root system architecture analysis across diverse field conditions potentially leading to more resilient, high-yielding soybean varieties.

Self-propelled motion is an agnostic biosignature that is observed widely, yet motility of microbes in their natural environments is sparsely studied. In this study we use a Digital Holographic Microscope (DHM) for in situ imaging of aquatic samples in extreme environments to investigate motility and morphology as biosignatures. Samples were collected from glaciovolcanic ice caves, glacial runoff, hot springs, and mixed glacial and hot spring samples.The transport and deposition of materials and heat from the volcanic subsurface in glaciovolcanic caves may be similar in some respects to the eruption processes of the plumes of Enceladus. Through different tracking methods, we identified concentrations of organisms, morphologies, swimming patterns, speeds, and turn angles. In every type of sample we looked we were able to identify motile organisms. Methods for distinguishing active swimming from Browian motion and drift are considered. Field work was done over two deployments in collaboration with the Thermal High-voltage Ocean-penetrating Research platform (THOR) science team and EELS robotics team. This work and these collaborations intend to inform future off-world extant life detection missions of the utility of DHM and motility as an investigation tool and biosignature, respectively.

Synoptic-scale cyclonic vortices produce abundant rainfall in South Asia, where these low pressure systems (LPS) are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department has tracked monsoon depressions for over a century, finding a large decline in the number of those storms in recent decades; their tracking methods, however, seem to have changed over time and do not include monsoon lows, which can produce intense rainfall despite their weak winds. This study presents a fast and objective tracking algorithm that can identify monsoon LPS in high-resolution datasets with a variety of grid structures. A sensitivity analysis has been performed to select a set of atmospheric variables and their corresponding thresholds for optimal tracking of LPS. Approximately 250 combinations of variables and thresholds are used to identify LPS over roughly a decade (the training period) in each of four atmospheric reanalyses, and these combinations are ranked using a skill score that compares the reanalyses with each other and with a preexisting track dataset that was compiled by subjective identification of LPS. This procedure finds the streamfunction of the 850 hPa horizontal wind to be the best variable for tracking LPS. The streamfunction is smoother than the vorticity field and represents the complete non-divergent component of the wind even when the flow is not geostrophic, unlike the geopotential height or sea level pressure. Using this tracking algorithm, LPS statistics are then computed in five reanalysis products that each span at least 40 years, with a primary goal being to determine whether the large decrease in monsoon depressions seen in the India Meteorological Department track dataset since the 1970s can be found in any reanalysis. This trend assessment is particularly relevant for the ERA5 reanalysis, which extends back to 1950 and which contains explicit climate forcings. In addition to secular trends, this study assesses the decadal variation of LPS, as well as interannual changes in LPS activity that are associated with the El Niño-Southern Oscillation and the Indian Ocean Dipole.

During the Great Oxygenation Event (GOE) 2.4 - 2.35 Gyrs ago, Earth experienced a notable shift in its oxidation state. This has been widely attributed to cyanobacteria having “invented” oxygenic photosynthesis.  We present a very different view based on the fact that oxygen is released from rocks during weathering. If so, the GOE must have been caused by an influx of abiogenic O2 into Earth’s surface environment. Minerals forming Earth’s crust contain impurity hydroxyls such as O3SiOH. Pairs of those are known to undergo a common redox conversion, ubiquitously forming peroxy defects plus H2: O3SiOH-HOSiO3 ⇌ O3Si/OO\SiO3 + H2 Being diffusively mobile, most H2 will be lost, leaving behind bound O as peroxy. The peroxy defects release O2 during weathering: O3Si/OO\SiO3 + H2O \(\to\) O3SiOH-HOSiO3 + \(\frac{1}{2}\) O2 We suggest that this abiogenically generated O2 was responsible for the progressive oxidation of the early Earth and that this abiotic process drove the Great Oxygenation Event (GOE).

Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year-to-year increase in atmospheric CO2 through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding events of varying intensity is poorly understood. To improve upon such understanding, the impact of the 2019 US flooding on regional CO2 vegetation fluxes is examined in the context of 2017-2018 years when such precipitation anomalies are not observed. CO2 is simulated with NASA’s Global Earth Observing System (GEOS) combined with the Low-order Flux Inversion (LoFI), where fluxes of CO2 are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power and bias corrected based on in situ observations. Net ecosystem exchange CO2 tracer is separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO2 observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15-25% reduction of net carbon uptake in May-September of 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non-crop vegetation, net carbon uptake is enhanced in May-September of 2019 by about 10-20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.

The Global Warming Potential (GWP) is a widely used single-number metric used to compare the climate change effects of various greenhouse gases. Although GWP has an established role in international climate agreements, it is unphysical, unintuitive, arbitrary, ignores the time dependence of emission sources, and is in some cases misleading. The same doubts have been expressed by the convening lead author of the relevant chapter in the First Assessment Report of the Intergovernmental Panel on Climate Change, in which GWP was introduced. GWP and the related CO2-equivalent methodology have no place in describing the effects of climate change mitigation strategies beyond a 20 year horizon. An important element of sound policy making is the quantitative analysis of policy interventions. This work argues for the broader use of temperature change trajectories in educating policymakers and the public about greenhouse gas control. Modeling tools include general circulation models, related reduced complexity models, and simpler models of global mean temperature change. Examples are presented illustrating the misleading nature of GWP and the CO2-equivalent methodology. These include the greenhouse gas impacts of (1) single year and multiyear emissions of methane, (2) natural gas and coal use in the electric power sector, (3) pipeline and LNG transport of natural gas, (4) natural gas flaring, and (5) livestock herd reduction. Responding to climate change is likely to require global investments measured in trillions of dollars. Clear and accurate communication among and between scientists, policy makers. and the general public is essential. GWP is a crude, inaccurate tool that and the general public is essential. GWP is a crude, inaccurate tool that played a constructive role in the formulation of international agreements. Using better modeling tools can help make climate policy discussions more scientifically rigorous while demystifying the criteria upon which policy choices are made.

2019 was both the hottest and driest year on record for Australia, leading to large forest fires in the southeast from November 2019 to January 2020. However, in early 2020, the fires and hot-dry conditions dissipated with above average rainfall and below average temperatures along Australia’s southeast coast. In this study, we utilize space-based measurements of trace gases (TROPOMI XCO, OCO-2 XCO2) and vegetation function (OCO-2 SIF, MODIS NDVI) to quantify the carbon cycle anomalies resulting from drought and fire in southeast Australia during the 2019/2020 growing season. During the austral spring, we find anomalous reductions in primary productivity and large biomass burning emissions in excess of bottom-up estimates from GFAS. This is then followed by a remarkable recovery and greening during early 2020, coincident with cooler and wetter conditions. We will further discuss different behaviors of recovery over fire-devasted and non-fire regions. This study showcases the capability of combining observations from multiple satellites to monitor the carbon and ecosystem anomalies resulting from extreme events. Finally, we will discuss the remaining challenges in monitoring the carbon cycle from space.

A document by Junjie Chen. Click on the document to view its contents.

This paper is a contribution to the exploration of the parametric Kalman filter (PKF), which is an approximation of the Kalman filter, where the error covariance are approximated by a covariance model. Here we focus on the covariance model parameterized from the variance and the anisotropy of the local correlations, and whose parameters dynamics provides a proxy for the full error-covariance dynamics. For this covariance mode, we aim to provide the boundary condition to specify in the prediction of PKF for bounded domains, focusing on Dirichlet and Neumann conditions when they are prescribed for the physical dynamics. An ensemble validation is proposed for the transport equation and for the heterogeneous diffusion equations over a bounded 1D domain. This ensemble validation requires to specify the auto-correlation time-scale needed to populate boundary perturbation that leads to prescribed uncertainty characteristics. The numerical simulations show that the PKF is able to reproduce the uncertainty diagnosed from the ensemble of forecast appropriately perturbed on the boundaries, which show the ability of the PKF to handle boundaries in the prediction of the uncertainties. It results that Dirichlet condition on the physical dynamics implies Dirichlet condition on the variance and on the anisotropy.

Climate mitigation can bring health co-benefits by improving air quality. Yet, whether mitigation will widen or narrow current health disparities remains unclear. Here we use a coupled climate-energy-health model to assess the effects of a global carbon price on the distribution of ambient fine particulate matter (PM2.5) exposure and associated health risks across an ensemble of nearly 30,000 future scenarios. We find that pricing carbon consistently lowers the PM2.5-attributable death rates in lower-income countries by reducing fossil fuel burning (e.g., China and India). Since these countries are projected to have large ageing populations, the greatest reduction in global average PM2.5-attributable death rate is found in elderly populations, which are more vulnerable to air pollution than the other age groups. In contrast, the health effects in higher-income countries are more complex, because pricing carbon can increase the emissions from bioenergy use and land-use changes, counteracting the mortality decrease from reduced fossil fuel burning. Mitigation technology choices and complex interactions between age structures, energy use, and land use all influence the distribution of health effects. Our results highlight the importance of an improved understanding of regional characteristics and cross-sector dynamics for addressing the interconnected challenges of climate, health, and social inequalities.

PAR (Photosynthetic Active Radiation) vertical profiles that were obtained from casts with the Sea and Sun MSS90 microstructure probe in the southern part of the Gulf of Trieste near buoy Vida were analyzed in the years from 2011 to 2015. PAR fortnightly profiles were explored with the linear fit of decrease with the depth of logarithm of PAR, normalized with its value in the air. The inverse relation between the coefficient of PAR attenuation and the Secchi disk depth was also validated. Also, other relations (e.g. the bi-exponential non-linear decrease of PAR with depth) were explored. Our findings about the attenuation of PAR and the Secchi disk depth are in line with the study conducted decades ago for the north-eastern part of the Gulf of Trieste (Stravisi, 1999). Furthermore, the initial values of PAR profiles measured with the Sea & Sun probe in the air before the cast were validated with the PAR values measured continuously on buoy Vida, about 100 m away from the profiling measurement station.