The conjunction of different spacecraft missions provide the opportunity to investigate the multiple scale of the magnetospheric current system. In our work, we use the data from MMS, Cluster and Swarm to study the characteristics of both the dayside current, including the magnetopause, and other current systems connected by the field aligned currents. Using the MMS four spacecraft data, we investigated the small-scale physical process of the magnetopause current, e.g. current carriers and current sources, which show features beyond the classic Chapman-Ferraro model. We compare MMS crossings to simultaneous crossings at different locations on the MP current layer observed by Cluster. Cluster is an order of magnitude lower than MMS and cannot reveal the detailed current structure due to its large separation. However, we find that the MP at the above two locations can have similar magnetic field structure. This may suggest that the MP has similar current structure across wide region during specific IMF conditions. We also used the conjunction of MMS and Swarm and Cluster and Swarm to investigate the behaviour of field-aligned currents. MMS and Swarm can calculate the current in the dayside inner low latitude boundary and the mapped ionospheric region simultaneously. Comparing the observed current signatures, we can investigate the correlation between them, which may reveal the nature of the magnetopause-ionosphere coupling between the field aligned current systems.

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s core satellite sensors and CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product generated over the continental United States (CONUS). The considered algorithms include: Dual-Frequency Precipitation Radar (DPR) product and its single frequency counterparts (Ka- and Ku-only); the combined DPR and multifrequency microwave imager (CORRA) product; the CloudSat SnowProfile product (2C-SNOW-PROFILE); two passive microwave products i.e. the Goddard PROFiling algorithm (GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The spaceborne and ground-based snowfall products are collocated spatially and temporally and compared at the spatial resolution of spaceborne instruments over the period spanning from January 2016 to March 2020 (4 winters). Detection capabilities of the sensors is assessed in terms of the most commonly used forecast metrices (Probability of Detection, False Alarm Ratio, etc.) whereas precision of the products is quantified by the mean error (ME) and root-mean-square-error (RMSE). 2C-SNOW product agrees with MRMS by far better than any other product. Passive microwave algorithms tend to detect more precipitation events than the DPR and CORRA retrievals, but they also trigger more false alarms. Due to limited sensitivity, DPR detects only approx. 30% of the snow events. All the retrievals underestimate snowfall rates, for the detected snowstorms they produce approximately only a half of the precipitation reported by MRMS. Large discrepancies (RMSE from 0.7 to 2.5 mm/h) between spaceborne and ground-based snowfall rate estimates is the result of limitations of both systems and complex ice scattering properties. The MRMS product is based on a power law relation and it has difficulties in detecting precipitation at far ranges; the DPR system is affected by low sensitivity while the GPM Microwave Imager (GMI) measurements are affected by the confounding effect of the background surface emissivity for snow-covered surfaces and of the emission of supercooled liquid droplet layers.

Radiosonde observations are the gold-standard for quantifying vertical profiles of atmospheric state variables. Knowledge of which is critical for quantifying moisture and instability, two main ingredients for severe weather. Unfortunately, radiosondes are very sparse, averaging just one observation per 500 x 500 km area over CONUS, and most locations have only two observations per day. This creates uncertainty in the representation of short wavelength and rapidly evolving synoptic and mesoscale features in numerical weather prediction (NWP) and provides few points of comparison for human forecasters to interpret NWP in making forecasts. To fill this gap in our knowledge of the atmospheric state, human forecasters make use of satellite imagery to estimate airmass properties for incrementing NWP outputs. Data from geostationary satellites have been especially useful because of its high temporal resolution (5-minutes) and high spatial resolution (2 km). While the Advanced Baseline Imager (ABI) was not designed as a sounding sensor, the three water vapor bands and three infrared window bands do provide some sounding capabilities. Satellite data are particularly useful in assessing position and timing errors, the representation of short waves, and humidity. The key question addressed by this work is can the mental process used by human forecasters be translated into a machine learning (ML) algorithm to provide automated and objective estimates of airmass properties from ABI? Experiments with convolutional neural networks (CNNs) show that ML can indeed be used. Related research efforts, such as NOAA Unique Combined Atmospheric Processing System (NUCAPS) has explored use of dense neural networks (DNNs), which are essentially replacing a radiative transfer model with a ML model. However, we find more skill can be achieved by making use of the spatial information captured with CNNs. This more closely mimics the human imagery interpretation process: it is the spatial patterns in the features (as much as the pixel-wise values themselves) that carry the useful information content. We will present our latest results, focusing especially on relative humidity, compare against radiosondes, and discuss whether skill is enough to potentially make a positive impact on NWP analyses.

We developed a novel Bayesian Hierarchical Network Model (BHNM) for daily streamflow, which uses the spatial dependence induced by the river network topology, and average daily precipitation from the upstream contributing area between station gauges. In this, daily streamflow at each station is assumed to be distributed as Gamma distribution with temporal non-stationary parameters. The mean and standard deviation of the Gamma distribution for each day are modeled as a linear function of suitable covariates. The covariates include daily streamflow from upstream gauges or from the gauge above of the upstream gauges depending on the travel times, and daily, 2-day, or 3-day precipitation from the area between two stations that attempts to reflect the antecedent land conditions. Intercepts and slopes of the mean and standard deviation parameters are modeled as a Multivariate Normal distribution (MVN) to capture their dependence structure. To ensure that the covariance matrix of MVN is positive definite, it is model as an Inverse Wishart distribution. Non-informative priors for each parameter were considered. Using the network structure in incorporating flow information from upstream gauges and precipitation from the immediate contributing area as covariates, enables to capture the spatial correlation of flows simultaneously and parsimoniously. The posterior distribution of the model parameters and, consequently, the predictive posterior Gamma distribution of the daily streamflow at each station and for each day are obtained. The model is demonstrated by its application to daily summer (July-August) streamflow at 4 gauges in the Narmada basin network in central India for the period 1978 – 2014. The skill of the probabilistic forecast is carried out by rank histograms and the Continuous Ranked Probability Score (CRPS). The model validation indicates that the model is highly skillful relative to climatology and relative to a null-model of linear regression. The forecasts present an adequate spread of uncertainty and non-bias. Since flooding is of major concern in this basin, we applied the BHNM in a cross-validated mode on two high flooding years – in that, the model was fitted on other years, and forecasts were made for the dropped-out high flooding year. The skill of the model in forecasting the high flood events was very good across the network – in both the timing and magnitude of the events. The model will be of immense help to policy makers in risk-based flood mitigation. The BHNM framework is general in nature and can be applied to any river network with other covariates as appropriate.

Mentorship experience can be transformational for college and graduate students as they learn how to talk about their science field, articulate their work, learn how to listen, and step away from the focus on their own work. The Lens on Climate Change (LOCC) program engaged secondary students in place-based, environmental science in an informal learning environment. Small groups of students worked together, with the guidance of graduate student science and community college film mentors, to produce a film about climate change, as they perceived it to be relevant to their local communities. The majority of student participants came from historically underserved communities, and the program aimed to provide students with opportunities to engage with science and technology in ways that differ from opportunities available in traditional schooling. The graduate student science mentors and community college film mentors played a critical role in supporting students in their exploration of the topic and learning about local climate change. This presentation explores the impacts on mentors and how the LOCC program influenced their experiences and interests in science education and outreach. Using a qualitative case study approach we examine mentor responses to questions about their mentorship experiences. These responses were collected before and after their experiences in the program, as well as through follow up interviews after the program had ended. Mentors fell into several categories based on how the LOCC program influenced them. Some mentors experienced a transformational impact, where the LOCC program played an influential role in their future career goals. Others felt the program helped them cement their career interests and plans. Several mentors did not experience as much impact on their career trajectory. We examine these relationships in the context of the project to consider how their experiences prior to and through LOCC may have influenced these outcomes.

Intra-urban fine-scale data and models are needed to understand infrastructure interactions that shape equity and health related to extreme heat, cold and precipitation events. Fine-scale data are needed to address spatial equity at the scale of city blocks or block groups where income and race data are available. We conducted nested simulations with the Weather Research and Forecasting (WRF) model that cover parts of the US state of Minnesota — one of the fastest warming states in the contiguous US. The first two nests at 5km and 1km horizontal resolution cover the counties of southern Minnesota, with the outer 5km grid also covering some counties in the neighboring states Iowa and Wisconsin. Within the 1km inner grid, we created two additional nests. The third grid covers the metropolitan region of the Twin Cities Minneapolis and Saint Paul at 200m resolution. Within this grid, we created a fourth nest over a 4x4km neighborhood in downtown Minneapolis that includes the campus of the University of Minnesota at 40m resolution. All model nests have 82 vertical levels. Lateral input data were acquired from the global Coupled Forecast System (CFS) analysis at 30km horizontal resolution. The boundary conditions consist of high-resolution land use data including vegetation types, urban fraction, building heights and shapes. The input data were specifically designed by the Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota and cover the whole Twin Cities Metro Area (TCMA) at 1m horizontal resolution. This dataset was derived from a multi-temporal composite of aerial imagery from the summer of 2015 and fall of 2009-2011, and lidar data of 2011 and 2012. The vertical accuracy of the lidar data meets or exceeds 12.5cm root mean square error (RMSE). The results of our model simulations show remarkable fine-scale climate responses to changes in vegetation cover and albedo that are going to be used for various urban planning projects.

A globally interactive geo-socioeconomic revolution will be necessary in order to sustainably resource our continued our ways of life and be able to expand beyond to the next giant leap of human progression and evolution. Applied comprehensive integrated changes coupled with a global collaboration invested in over-reaching environmental goals incorporating both the public and private sectors are at the crux of properly upgrading infrastructures and societies. Positive non-biased re-engineering of our societies and their governing laws, policies and economies must include two underlying principles- transparency and environmental transformation. These two founding principles are the Great Green Reset’s scaffolding meant to address a lexicon of constantly revolving and evolving socioeconomic, political, and environmental issues. By integrating environmental sustainability and reclamation in conjunction with promoting biodiversity, We, being a socially accepting, fair, safe and healthy Peoples, can progress confidently into the Fourth Industrial Revolution knowing that at this juncture in human history and time, “We actively took a global stance choosing to make the correct decisions for the successful continuation and advancement of our species and its way of life so our future generations may experience the same lush biodiversity on Earth that we so heavily rely on for our existence”. To gain the public trust, transparency and environmental transformation are the core underlying principles that create a scaffolding of sound, logical, common-sense science policies that legislative decisions are built around. Along with developing national and international science policy frameworks centered around a healthier, greener, smarter future, legislators should reap the deep benefits of having a highly qualified transdisciplinary science team dedicated to consulting with indigenous peoples, local communities, and key experts to provide unbiased opportunity to re-imagine and re-engineer science policy promoting fair socioeconomic equitable equality at all levels of interaction on local, national, and international framework scales. All this must be envisioned within a circular economy.

The Antarctic Ice Sheet remains one of the greatest sources of uncertainty for improving predictions of sea level rise, and constraining this uncertainty has long been a difficult challenge within glaciology and climate science. Cryoseismology, paired with the meteoric rise of data science applications within the geosciences, has emerged as a promising field well suited to answering these challenges as the improvement of sampling technology and access have resulted in a proliferation of Antarctic seismic data. Ice flow dynamics in Antarctica are significantly influenced by features and processes at the bed, and basal microseismicity from tremors as ice moves across the bed can yield valuable information for resolving the glacier subsurface. We deployed high-frequency (up to 1000 Hz) geophone arrays at Rutford Ice Stream over the 2018-2019 austral summer to monitor the natural source seismicity from the base of the ice and generate an event catalog. To efficiently process the enormous volumes of cryoseismic data to locate events, we used the Python package QuakeMigrate which utilizes a parallelized waveform stacking algorithm to detect coherent seismic phase arrivals across our network. Over three months of data, we located over 1,700,000 seismic events (majority which were microseismic) within a 4 km x 4 km square grid around our 13-station, ~3.25 km2 area array. The detection and location of icequakes at this resolution provides a unique opportunity to investigate the temporal, location, and size relations between events, and we present the findings from our data mined event catalog and document the QuakeMigrate parameter tuning to optimize event location. The significant amounts of data collected of the region over the past decades mean that the literature and documentation of conditions at Rutford is more complete relative to most of Antarctica, and our work aims to contribute towards a comprehensive survey of an Antarctic region.

The land-sea connection of nutrients via rivers is an essential component in the global nutrient biogeochemistry. Rivers alone transport approximately 80% of the dissolved nutrients from land to ocean and play a vital role in nourishing riverine, coastal and oceanic ecosystems. During the past decades, global scale investigations have estimated the riverine nutrient flux to the oceans along with the significant drivers. However, most of the previous estimations are based on nutrient discharge by a few major rivers. Despite being large in number and high yields, the tropical coastal/mountainous rivers have received less attention. To fill the gap, we have investigated 70 west-flowing coastal rivers, draining the Western Ghats (WG), for their Dissolved Inorganic Nutrient (DIN) transport characteristics and influencing factors. Previous studies in the selected estuaries along the WG coast are combined to update the land-sea fluxes estimation to understand the spatial pattern of DIN over the WG rivers-the Arabian sea coast continuum. Altogether, the DIN fluxes from entire WG (including non-sampled rivers) region to the estuaries are 664, 241 and 6277 Tg yr-1 for Nitrate (N), Phosphate (P) and Silicate (DSi) respectively. The natural factors, such as discharge, cropland, and forest cover each explain 30–85% of the spatial variation in DIN levels at the basin scales. DIN concentrations of rivers reduces to ~ 90% after reaching the respective estuaries. Based on Redfield ratio of C:N:P (106:16:1), annual export of 66.4 Tg of dissolved inorganic N from the WG rivers would support 439.9 Tg of new carbon production in the Arabian Sea coast. The humid tropical climate and high population density (> 300 people per km2 among the bio-diversity hotspots) of the WG region favour the high DIN export, thus, making the Arabian sea coast highly productive among the global coastal regions. Keywords: Dissolved Inorganic Nutrients; Rivers-Coast Continuum; Influencing Factors; Small Mountainous Rivers; Western Ghats.

Significant advancements have been made over the past four decades in understanding and quantifying the stocks and flows of carbon between the reservoirs. However, knowledge of the complex oceanic processes influencing the carbon cycle has been largely compartmentalized into physico-chemical and biological studies. The connections between coastal and open-ocean carbon processes have also been understudied. To fully appreciate the ocean carbon cycle, and its anticipated changes in the future, a holistic and integrated approach to ocean carbon cycle research is needed. In particular, a greater quantitative understanding of how biological processes interact with the physical and chemical drivers in the open ocean and in coastal waters is needed. Moreover, the carbon cycle needs to be understood in the current socio-economic context and large anticipated changes in the next decades. To address these issues, the Integrated Ocean Carbon Research (IOC-R), a formal IOC working group, was formed in 2018. The working group is a response to the UN Decade of Ocean Science for Sustainable Development 2021-2030, “the Decade”. The IOC-R will contribute to the science elements of an overarching Implementation Plan for the Decade. The Implementation Plan is a high-level framework to guide actions by which ocean science can more effectively deliver its contribution to achieving the societal outcomes outlined for the Decade. The IOC-R focusses the ocean carbon cycle component of the Implementation Plan by addressing key issues in ocean carbon research through a combined strategy of research and observational goals. The research will be framed by four key questions that were formulated at the inaugural Expert Workshop on Integrated Ocean Carbon Research at the IOC-UNESCO Headquarters in Paris, France on Oct. 28-30, 2019: 1) Will the ocean uptake of anthropogenic carbon dioxide (CO2) continue as primarily an abiotic process? 2) What is the role of biology in the ocean carbon cycle? 3) What are the exchanges of carbon between the land-ocean continuum and how are they evolving over time? 4) How are humans altering the ocean carbon cycle, and what are the feedbacks?

Brittleness index (BI) has been considered as a key geomechanical parameter in evaluating fracturability of the shale formation for optimal extraction of hydrocarbon. BI is commonly used to quantitatively classify the brittle-ductile zones in thick shale reservoir and is considered as completion quality index. There are different methods for determining BI. In the present study, mineralogy based BI estimation method has been followed to identify brittle zones of Cambay Shale of Jambusar-Broach block, Cambay basin, India. Mineralogical composition is having a very significant role in controlling brittleness of a rock. Advanced geochemical logs like the Elemental Capture Spectroscopy (ECS) / Litho Scanner were utilized to get the continuous estimation of mineralogical composition of the Cambay Shale formation. XRD and SEM-EDS techniques are also used for mineralogical analysis of selected core samples taken from different depths. Analysis from XRD and SEM-EDS techniques reveals that the shale is composed of kaolinite, chlorite, quartz, siderite, pyrite and other minerals. Good match has been observed between the BI estimated using mineralogical composition from geochemical log and XRD analysis of core samples. Along with BI, another parameter namely fracability index (FI) has been also used for hydro-fracture evaluation. FI has been estimated continuously, combining normalized brittleness index and normalized Young’s modulus. In addition to BI and FI, type of clay minerals present has some controlling role during hydro-fracture. Mineralogical analysis reveals that for hydro-fracture operation, Cambay Shale has the advantage of having kaolinite and chlorite as dominant clay minerals which are comparatively less susceptible to swelling and plastic behaviour than other clay minerals like illite. An integrated analysis of BI, FI, mineralogy, petrophysical and geochemical properties will be helpful for identification of potential target zones within thick shale. Acknowledgement The authors would like to thank Oil and Natural Gas Corporation Limited, India for providing the requisite dataset and core samples to carry out the present study.

The total fish production in India is estimated to be 13.42 million metric tons (Mt) of which nearly 3.71 Mt are contributed by marine fisheries. The fisheries sector provides direct livelihood support to approximately 16 million people, of which marine fishers constitute a dominant subset and almost double the number along the value chain. In the past, the fisheries community in India had relied on traditional indicators of fish abundance such as the congregation of birds, colour of sea water, bubble breaking in the sea water and muddy/oily water on the sea surface in their traditionally known fishing grounds. The increased frequency of extreme weather events observed in recent times, have been related to major losses in marine ecosystem services. Further, the livelihoods of Small Scale Fisheries (SSF) communities are endangered not only by their limited understanding of the magnitude of the risks associated with natural hazards but also by the diminishing returns of relying on traditional knowledge for accurate prediction of the fishing stock at their usual locations. Therefore, the Government of India has launched several programs to use geospatial technologies for the benefit of marine pelagic fisheries in India for the sustainable development of the SSF communities. However, the lives of SSF communities are also challenged by complex, inter-linked socio-economic factors. In this paper, we focus on the maritime state of Odisha which is affected by several extreme weather events (cyclones) in the Bay of Bengal. We describe the socio-economic and technological factors which determine the degree of assimilation of the Potential Fishing Zone (PFZ) and Ocean State Forecast (OSF) advisories provided by the Indian National Centre for Ocean Information Services (INCOIS) with a focus on the threads linking natural and anthropocentric factors that produce significant changes in the Catch Per Unit Effort (CPUE) and net profit for pelagic fisheries of Odisha. More importantly, we identify the research and implementation gaps that must be addressed so that science, technology, and societal interactions can be used to enhance the sustainable development of the SSF communities. Keywords: Potential fishing zone, Catch per unit effort, Ocean state forecast, Geospatial technology.

The Upper and Lower Quinn River subbasins (HUC-8 16040201,16040202) comprise one of the largest single drainages in northwest Nevada. The sub-basins cover approximately 1.7 million hectares. The Quinn river originates in the Montana Mountains and Santa Rosa Range and drains to the Black Rock Desert Playa. The elevational gradient ranges from 2000 m to 1100 m. Sagebrush and perennial grasses dominate the higher elevations, with the lowlands being dominated by greasewood, saltbrush, and desert scrub. This presentation examines the human habitation in the sub-basins over the past 13,000 years in context to the palaeoclimatic records from the sub-basins and adjoining areas. Over 1100 prehistoric sites exist in the study area, with 19 sites having been excavated and yielding 108 radiocarbon dates. Palaeoclimate proxies from the study area include the Mud Meadows and Summit Lake pollen cores, and the Jackson Mountain tree ring widths. While the radiocarbon record shows numerous gaps between 13 kBP and 5 kBP, the summed probability distribution of calibrated radiocarbon dates suggests continuous occupation from 5 kBP to 1 kBP with exponential growth. Based on the abundance of Chenopodiaceae pollen in the Summit Lake core, the study area is drier than today during the Late Holocene Drought (LHD, ca. 2.5 to 1.9kBP). During the LHD, sites south of 41N are abandoned or see no growth; northwards, sees site growth. Despite contrasting climatic conditions, the Late Antique Little Ice Age (LALIA) and the Medieval Climatic Anomaly (MCA) are both periods of site occupation and growth across the sub-basins. Occupied sites are found throughout the sub-basins during the LALIA, and during the MCA site occupation is focused at Trego hot springs and at higher elevations. As evidenced by the three palaeoclimate proxies from the sub-basins, the Little Ice Age is a period of cooler temperatures and increased moisture. Despite the ameliorated conditions, the number of dated sites significantly decreases. This may reflect a more dispersed settlement pattern with lower numbers of residents during the Terminal Prehistoric period.

Understanding the complex interplay between erosional and depositional processes, and their relative roles in shaping landscape morphology is a question at the heart of geomorphology. A unified framework for examining this question can be developed by simultaneously considering terrain elevation statistics over multiple scales. We show how a long-standing tool for landscape analysis, the elevation-area or hypsometry, can be complemented by an analysis of the elevation scalings to produce a more sensitive tool for studying the interplay between processes, and their impact on morphology. We then use this method, as well as well-known geomorphic techniques (slope-area scaling relations, the number of basins and basin size as a function of channel order) to demonstrate how the complexity of an experimental landscape evolves through time. Our primary result is that the complexity increases once a flux equilibrium is established as a consequence of the role of diffusive processes acting at intermediate elevations. We gauge landscape complexity by comparing results between the experimental landscape surfaces and those produced from a new algorithm that fixes in place the elevation scaling statistics, but randomizes the elevations with respect to these scalings. We constrain the degree of randomization systematically and use the amount of constraint as a measure of complexity. The starting point for the method is illustrated in the figure, which shows the original landscape (top-left) and three synthetic variants generated with no constraints to the randomization. The value quoted in these panels is the root-mean-squared difference in the elevation values for the synthetic cases relative to the original terrain. This value is greatest where the original ridge becomes a valley. All these landscapes contain the same elevation values (i.e. the same probability distribution functions), and the same elevation scalings at a point. The differences emerge because the elevations themselves are distributed randomly across the surface.

Climate change is impacting polar regions at an accelerated rate, causing rapid changes in land cover and biodiversity. One case is the “greening up” of the Western Antarctic Peninsula (WAP), the result of receding glacial fronts exposing substrate for plants and soil development, together with higher temperatures and potential increases in cloud-free conditions conducive to plant growth. The 2019/2020 austral summer was the warmest on record for the WAP, yet the controls on land surface temperature (LST) here are not well understood. We investigated the relationships between land cover type, solar radiation, and LST for several vegetated coastal outcrops (0.3 to 0.5 ha) distributed from 64 to 65°S along the WAP. Remote sensing data was collected in February/March 2020 using a consumer-grade unmanned aerial vehicle (UAV), additionally equipped with near-IR and thermal-IR sensors. Digital surface models produced from the UAV imagery were used to calculate surface solar radiation. NDVI was used to identify four land cover classes: healthy vegetation, unhealthy vegetation, loose substrate, bedrock. Thermal-IR data provided sub-decimeter LST mapping. LST ranges varied depending on atmospheric conditions. A site surveyed under cloud-free conditions and air temperature of 6.6°C showed a 37.2°C range in LST, while a nearby site surveyed the next day under overcast conditions and air temperature of 2°C showed a 10.4°C range in LST. Vegetation at these two sites reached maximum temperatures of 27°C and 11.6°C, respectively. We found little within-site difference in either mean or range of LST among the land cover classes. Using linear regression, solar radiation explained less than 50% of the observed LST. Healthy vegetation showed the strongest relationship between solar radiation and LST. It was determined that LST in the WAP was strongly affected by factors other than solar radiation, implying latent heat effects. As the abundance of healthy vegetation increases in these areas, LST may show a stronger relationship with solar radiation, thus effecting local feedbacks to warming. This study presents the first application of UAV-derived thermal-IR data for the analysis of LST controls in Antarctica, highlighting the capability of UAV as a data collection platform for use in remote and relatively data-poor environments.

The objective of the study is to assess the familiarity and the use of preprint servers by researchers to share their work. Data collection was conducted by Wiley through an online survey in May and June 2020. 44% of survey respondents (out of a total of 1,905) indicated they are not at all familiar with preprint servers. Most of the researchers surveyed indicated multiple reasons to upload their research on a preprint server. The most popular reasons were to get feedback on their work (46%) and for faster dissemination of their research (45%). 55% of respondents indicated that they have never uploaded any research outputs on preprint servers. This study and our key findings show that there is a need to share the benefits of preprints and to explain to researchers how servers work.

A common goal of next-generation Global Circulation Models (GCMs) is that they should be “scale-aware”, which typically implies that such models should not be excessively sensitive to grid spacings, and that they should in some sense converge monotonically towards a result as grid spacings decrease. While both horizontal and vertical resolution have been treated in this manner, time resolution is typically viewed differently. Specifically, a decrease in time step size is often viewed as a “necessary evil”, being decreased only in cases where spatial resolution is also decreased, requiring a change to the time resolution to satisfy a CFL condition. Our experiments with the E3SM Atmosphere Model suggest that cloud physics and precipitation in GCMs is in fact quite sensitive to process coupling time step size, and that the biases affected by time integration error are independent from (and of comparable size to) biases due to other common sources of error, such as grid spacing and choice of sub-grid-scale physics parameterizations. This suggests that process coupling frequency is a key feature that should be adjusted for future models.

Known as black bread mold, R. stolonifer is a fungus commonly found in contaminated food products such as meat, preserved, and/or baked goods. Potassium sorbate is a common food preservative used to prevent fungi, mold, and mycotoxin growth by damaging the cell membrane or altering proteins in the cell. Based on background research on potassium sorbate growth prevention, it was hypothesized that a 25% concentration of potassium sorbate will most efficiently prevent R. stolonifer growth compared to lower concentrations. R. stolonifer was grown on potato dextrose agar with eight levels of potassium sorbate concentrations. During a two week period, fungal growth was observed, photographed, and the height and width of each sample were recorded. At the end of two weeks, the fungi were stained using lactophenol cotton blue dye and observed under a microscope. Qualitative observation at the cellular level showed healthy R. stolonifer solely in the control group (see Figure 1). When comparing the fungi samples across the 5% and 30% concentration groups, large areas of dead mass dominated their cellular makeup. At the end of two weeks, the 1% concentration sample group had an observed increase of 1.383 cm2 in total area, a significant decrease compared to the control group. Between the 5% and 30% concentration sample groups, there were minor changes in total area with no change exceeding 0.125 cm2. Between the 1% and 5% experimental groups, there was a significant decrease in fungal area growth, and a similarly large decrease between the 10% and 15% concentration fungi samples. Thus, it can be concluded that it is unnecessary to exceed a 1% concentration of potassium sorbate to prevent fungal growth.