During the Arctic summer season, snow atop the sea ice melts and pools into low-lying areas on the surface. These melt ponds reduce surface albedo and increase solar absorption in the Arctic Ocean. Throughout the summer, melt ponds grow, drain, and connect, through a complex drainage system. Current melt pond schemes in sea ice models, such as the level-ice scheme in the Los Alamos Sea Ice Model (CICE), rely on a linear relationship between pond depth and fraction to predict the evolution of pond growth as the snow and sea ice melt. Although the inclusion of melt ponds in models has been shown to improve forecasts of end-of-summer sea ice extent, observations of melt pond depth and fraction guiding these models are from SHEBA, a spatially-limited field campaign which occurred over 20 years ago. Until recently, melt ponds characteristics have been difficult to resolve from spaceborne platforms due to their small size (10s - 100s m in diameter), and indistinguishable radiometric similarity to open water. Here we show that new, high-resolution laser altimetry measurements from ICESat-2 (IS2), combined with coincident high-resolution satellite imagery, provides a three-dimensional view of the melting sea ice cover. IS2, launched in September 2018, has now observed two summer melt seasons in the Arctic. IS2 operates at 532 nm, a wavelength that penetrates low turbidity water, and can therefore be used to capture the bathymetry of shallow water features. Building on previous work, we demonstrate IS2’s ability to detect and measure melt ponds on multiyear sea ice. We validate the existence of melt ponds with high resolution (10 m) visible imagery from the Sentinel-2 (S2) MultiSpectral Instrument. We apply the “density dimension algorithm – bifurcate” (DDA-bifurcate), an auto-adaptive algorithm utilizing data aggregation with the ability to track two surfaces, as well as a second algorithm that tracks melt pond surface and bottom, to derive melt pond depth for dozens of melt ponds in 2019 and 2020. Applying a sea ice surface classification algorithm to S2 imagery, we are able to determine melt pond fraction. We compare our findings of coincident melt pond fraction and depth with the melt pond parameterization used in the level-ice scheme in CICE. We discuss our results in the context of the existing literature on pond depth and volume.

The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies. Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR). Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data. This presentation introduces international community guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product. Supportive case studies demonstrate the applicability of the proposed guidelines.

Solar, auroral, and radiation belt electrons enter the atmosphere at polar regions leading to ionization and affecting its chemistry. Climate models usually parametrize this ionization and the related changes in chemistry based on satellite particle measurements. Precise measurements of the particle and energy influx into the upper atmosphere are difficult because they vary substantially in location and time. Widely used particle data are derived from the POES and GOES satellite measurements which provide electron and proton spectra. We present the electron energy and flux measurements from the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) instruments on board the Defense Meteorological Satellite Program (DMSP) satellites. This formation of now three operating satellites observes the auroral zone in the UV from which electron energies and fluxes are inferred in the range from 2 keV to 20 keV. We use these observed electron energies and fluxes to calculate ionization rates and electron densities in the upper mesosphere and lower thermosphere (≈ 80–200 km). We present our validation study of the SSUSI-derived electron densities to those measured by the ground-based EISCAT radar stations. We find that with the current standard parametrizations, the SSUSI-derived auroral electron densities (90–150 km) agree well with EISCAT measurements, with differences between +/- 20% for F18, and +/- 50 % for F17. The largest differences are at the lower end of the altitude range because there the electron densities decline very rapidly.

The solar polar fields play a crucial role in sustaining the solar dynamo mechanism. Observations and computational modelling indicates that the surface dynamics of active regions mediated via plasma flows, which leads to the polar field build-up, is a fundamental aspect of the solar dynamo mechanism; in fact, this surface mediated flux dynamics also known as the Babcock-Leighton mechanism, is now implicated to be the leading contributor to the Sun’s polar field, and thus also provides a window to the future solar cycle. This emergent understanding is one of the major drivers of novel mission concepts aiming to image the solar high latitudes from out of the ecliptic locations. Based on numerical simulations of the near-surface magnetic field dynamics and the Sun’s polar landscape, here we discuss what new insights may be provided by out-of-the ecliptic solar physics missions.

Seaports in Africa are expanding rapidly to meet an increasing demand for imported goods in Africa and for natural resources and manufactured goods from the continent. Emissions from shipping and anthropogenic activities at seaports, in particular nitrogen oxides (NOx), are challenging to estimate. This impacts our ability to determine the impacts of shipping activities on ozone air pollution for a continent that is NOx limited. Here we develop an approach to oversample tropospheric column observations of nitrogen dioxide (NO2) from the recently launched high spatial resolution TROPOMI instrument to determine NOx emissions from shipping activities at major seaports along the African coastline from August 2019 to July 2020. We use these to evaluate state-of-science emission inventories and determine temporal variability in these emissions for improved implementation in global and regional models.

Waterways such as the Hudson River play an integral role in agriculture, health, transportation, recreation, energy, and sustaining biodiversity. Although water pollution in New York Harbor has been extensively studied, the reduction of millions of commuters during the COVID-19 lockdown presents an unprecedented opportunity to study human impact on water quality. We used remote sensing data to assess how the COVID-19 lockdown impacted water quality in New York Harbor, particularly in areas near Combined Sewer Outfalls (CSOs). This technique has previously been used to measure water quality in the Hudson River. We used ACOLITE to process Landsat-8 and Sentinel-2 images from 2015-2020. The algorithms “t_nechad”, “spm_nechad”, and “kdpar_qaasw” were used to measure turbidity, and “chl_oc2”, “chl_oc3”, “chl_re_moses3b”, “chl_re_moses740”, and “chl_re_mishra” to measure chlorophyll concentration. After uploading processed images into SeaDAS, we extracted values from pixels corresponding to Department of Environmental Protection (DEP) field sites. At these sites, the DEP measures Total Suspended Solids and Chlorophyll A Concentration using optical turbidity sensors and fluorometers, respectively. By comparing pixel values with DEP data we determined that the chlorophyll algorithms did not produce accurate readings of chlorophyll concentration in New York Harbor. We focused on analyzing turbidity at five DEP sites, four of which were located around wastewater treatment plants, to assess any CSO-induced changes in water quality. The frequency of usable satellite data from 2020 was severely limited by cloudiness, so we combined Landsat-8 and Sentinel-2 turbidity measurements (R = 0.8685) to form time series for each site. We expected to see a decrease in turbidity during the lockdown period, due to a decrease in sewage from office buildings. However, turbidity strongly fluctuated throughout all years with no discernable temporal pattern, and we could not distinguish between 2020 measurements and seasonal patterns. Thus, preliminary analysis shows that there was no significant variation in water turbidity due to the COVID-19 lockdown.

Detecting and locating microearthquakes from continuous waveform records is the fundamental step in microseismic processing. Dense local networks and arrays have introduced the possibility to detect large numbers of far weaker events, but when viewed on seismic records from individual stations their waveforms are often obscured by noise. Furthermore, areas of interest for microseismic monitoring often feature extremely high event rates, highlighting the limitations of traditional techniques based on phase picking and association. In order to maximise the new insights gained, we require fully automated techniques which can exploit modern recordings to produce highly complete earthquake catalogues containing few artefacts. QuakeMigrate is a new modular, open-source python package providing a framework to efficiently, automatically and robustly detect and locate microseismicity. The user inputs continuous seismic data, a velocity model or pre-calculated look-up table and list of station locations. Instead of reducing the raw waveforms to discrete time picks, they are transformed (by amplitude, frequency and/or polarisation analysis) to continuous functions representing the probability of a particular phase arrival through time. These ‘onset functions’ from stations across the network are then migrated according to a travel-time look-up table and stacked to perform a grid-search for coherent sources of energy in the subsurface. This enables detection of earthquakes at close to or below the signal-to-noise ratio at individual stations, and implicitly associates phase arrivals even at very small inter-event times. We demonstrate the flexibility and power of this approach with examples of basal icequakes detected at the Rutford Ice Stream, Antarctica, dike- and caldera-collapse induced seismicity at Bárðarbunga central volcano, Iceland, and the aftershock sequence from a M5 earthquake at Mt. Kinabalu, North Borneo. The modular nature of the workflow and wide range of automatic plotting options makes parameter choice straightforward, and robust event location uncertainty statistics facilitate filtering to produce a robust catalogue. QuakeMigrate also outputs phase picks and local magnitude estimates, with an architecture designed to promote further community-driven extension in future.

The visual representation of data is at the heart of science. From weather forecasts, to hazard maps, to the topography of planets, the choice of colors is critical to conveying information. Yet, largely due to historical usage, default software options, and an apparent attraction to multiple bright colors, color maps such as rainbow-like “jet” are still widely used. These color maps are problematic from both a scientific and societal perspective. For instance, they can distort data because they use uneven color gradients, which lose meaning when printed in black and white, and color combinations are often applied that are unintuitive to the data they are trying to represent. From an inclusivity standpoint, such rainbow maps are also unreadable for the population with some form of color-vision deficiency. Here, we present the work that has been accomplished by the scientific (inc. visualization) community, as well as the readily available solution - “Scientific Colour Maps” (Crameri 2018, Zenodo; Crameri et al. (2020; Nature Coms); www.fabiocrameri.ch/colourmaps). This initiative features freely available, citable color map downloads for an extensive suite of software programs, and handy how-to guide, and discussion around data types and coloring options. There is a pot of scientific gold at the end of every rainbow. Crameri, F. (2018). Scientific colour-maps. Zenodo. http://doi.org/10.5281/zenodo.1243862 Crameri, F., Shephard, G.E. Heron, P.J. The misuse of colour in science. (2020 v11; Nature Communications) https://doi.org/10.1038/s41467-020-19160-7

To assess spatial correlation between field-surveyed, discontinuous occurrences of surface water during base-flow conditions on Santa Rosa Island, Channel Islands National Park with geologic substrate, we compared mapped geology to the corresponding permeability at GPS-located sites. We used a portable, handheld air permeameter (NER TinyPerm II) to measure in situ rock permeability on outcrops, under ambient moisture conditions, at the core scale. Sites represent a range of surface water conditions, from wet to dry, and mapped geologic features such as rock type or structure, including unit contacts and Quaternary faults. Along the island-spanning Santa Rosa Island fault and other subsidiary faults, we measured permeability within fault cores and damage zones, and in adjacent rock protolith. Permeability estimates at 31 sites (>600 measurements) reveal four trends associated with rock type and geologic structure consistent with presence or absence of surface-water occurrence: i) Volcanic rocks and intact, indurated sandstones and shales express lower and intermediate permeabilities (~10^-1 – 10^2 mD), correlating with more continuous surface water presence. ii) Similar rocks, but with secondary fractures induced by tectonic or topographic stresses, express intermediate to higher permeabilities (~10^3 – 10^6 mD) and little correlation with surface water leading to generally dry valley floors. iii) Faults may act as both conduits and barriers to flow with clay-rich cores exhibiting low permeability (~10^-1 – 10^2 mD) whereas adjacent damage zones contain broken rock with intermediate to higher permeabilities (~10^2 – 10^7 mD). Outside the damage zones, non-fractured rock protoliths generally express lower permeability (~10^0 – 10^3 mD). Laterally continuous fault-core gouge, and juxtaposition of rock types with contrasting permeabilities, likely act as effective barriers to cross-fault flow, forcing groundwater to the surface. iv) Bedding, parting, and fracture planes appear to impart local permeability anisotropy as reflected in observed reduced permeability in directions perpendicular to such planes. This research could benefit vegetation restoration strategies in water-limited landscapes where ecosystems are reliant on surface water and where mapped geology can be used to infer permeability.

Morphodynamic evolution of coastal barriers and the way it is recorded in the internal structure are still revealing many uncertainties. Coastal spits have a high potential of preservation due to their fast elongation under littoral drift, and are thus suitable for this kind of study. Compared to sandy spits, gravel and mixed spits are understudied. Yet these coarse-grained coastal barriers are sensitive sedimentary bodies due to the high energy conditions they are usually associated with and to often scarce sediment supply. The present study focus on the relationships between multi-decadal morphological evolution of a mixed sand and gravel spit in the bay of Somme (Northern France) and its sedimentary architecture. A geophysical survey using a 400 MHz GPR GSSI antenna have been performed on the Molliere spit, a fast-growing secondary spit developing along the main 15 km-long Somme barrier spit. The development of this sedimentary body starts in the 1940s and allows a morpho-stratigraphical approach, thanks to the cross analysis of aerial photographs and GPR profiles. The spit is 5 km long and is composed of several ridges, sometimes with hooks at the tip. A synthetic depositional and stratigraphical model specific to gravel spits is proposed, based on radar profiles and aerial photographs analysis, differentiating three contrasting morpho-sedimentary units. The Unit 1, at the spit root, is characterized by mainly progradating structures that can be assimilated to a beach ridge. The Unit 2, in the central part of the spit, is characterized by individual ridges with a core structure reflecting a longshore development, topped by an aggradating unit associated to a cross-shore-dominated dynamics. The third unit, at the spit terminus, is also mainly characterized by longshore dynamics, but interfering with sand bank dynamics due to the proximity of the Somme estuary and the development of the spit platform. Larger-scale GPR prospecting, especially in older coast sections, will be necessary to understand how this fast secondary spit development (decadal to centennial time scale) is integrated into the longer-term (millennial) construction dynamics of the main Somme spit.

When will a particular city on the Eastern Seaboard have a 70% chance of flooding at least one hour on at least 70 days of the year? This jam-packed question drives this study, as coastal flooding is becoming increasingly frequent in many East Coast U.S. cities. The frequency of floods that were once categorized as high tide or “nuisance” flooding is rapidly increasing, and many events are escalating to the moderate and major/severe flooding thresholds, putting life and property at risk. This study focuses on flooding events prone to causing property damage and hazardous conditions to make improvements for the subseasonal and seasonal outlooks that are most impactful. A number of East Coast cities are employed as case studies, such as Key West, FL, Charleston, SC, and Annapolis, MD, to analyze current and projected flooding rates. The spatial extent of flooding is considered by using high-resolution topographical data in combination with National Ocean Service tidal datums. NOAA sea level rise scenarios are used to determine the probable time period at which a portion of each city will be experiencing flooding for at least one hour per day frequently throughout the year. Additionally, tide gauge data from the case study cities is decomposed to understand which physical components are contributing to the regional coastal flooding on timescales from subseasonal to seasonal and beyond.

Depressions are topographic areas that have no outward flow and occur in portions of landscapes with little to no elevation change, or areas with negative relief in relation to surrounding areas. While these depressions are an important part of the hydrological system, they have historically been filled in or ignored during flow routing and other hydrological processing calculations. With the increased prevalence of high-resolution topography data, understanding and evaluating how topographic depressions can impact overland flow is vital for improving hydrological analyses, specifically in the context of flood inundation mapping. Flooding caused from the filling of depressions (pluvial flooding) can have compounding effects when simultaneously occurring with river (fluvial) or coastal flooding. Our goal is to consider both pluvial and fluvial flooding in flat urban environments to identify areas that are significantly more vulnerable to inundation as compared to flood mapping from only one particular source. Our approach relies on a multiplex network that utilizes the Height Above Nearest Drainage (HAND) method as well as a hydraulic head equalization algorithm to estimate inundation patterns. Social vulnerability data are integrated in this framework to identify urban hot spots, defined as areas with a lower relative socioeconomic status in conjunction with a higher probability of inundation. Combining technical and social information leads to the identification of communities that are more vulnerable to the physical, economical, and social components of floods. This approach can help urban flood planners associate social disparities in relationship to flood preparedness and response.

An ENSO event can transition from one event to another in complex ways. An El Niño (La Niña) event can be preceded by a La Niña (El Niño) event to become a cyclic ENSO, by a neutral event to become an episodic ENSO, or by another El Niño (La Niña) event to become a multi-year ENSO. We find that the observed El Niño and La Niña exhibit different complexities in their event-to-event transition patterns. The El Niño is dominated in order by episodic, cyclic, and multi-year transitions, but the reversed order is found in the La Niña. This difference in the transition complexity arises from a subtropical Pacific forcing mechanism that triggers ENSO events. Using observational analyses and forced atmospheric model experiments, we show that a preceding ENSO event can activate a subtropical Pacific forcing mechanism to trigger another ENSO event during the following year. These tropical-subtropical Pacific interactions result in a cyclic ENSO transition if the two ENSO events are of opposite signs or a multi-year ENSO transition if they are of the same sign. The preceding ENSO event should excite deep convections in the tropical Pacific in order to activate the subtropical Pacific mechanism. This requirement enables mean temperatures in the cold tongue and warm pool to respectively control how easily the cyclic and multi-year transitions can occur. This mean state control enables the subtropical Pacific forcing mechanism to result in more multi-year transitions for La Niña than El Niño and more episodic transitions for El Niño than La Niña. Furthermore, a future warmer tropical Pacific is projected to decrease the frequency of occurrence of multi-year ENSO transitions but increase the occurrence of cyclic ENSO transitions.. We find that the CMIP5 and CMIP6 models can reproduce the transition complexity for El Niño but not for La Niña. The models tend to produce too many episodic La Niña events and too few multi-year La Niña events. We are able to link the former deficiency to a weaker than observed subtropical Pacific forcing mechanism in the CMIP5/6 models and the latter to a cold bias in mean state SSTs in the equatorial Pacific in the CMIP5/6 models. To achieve better simulations of ENSO transition complexity, further efforts are to improve the model deficiencies in simulating the SP-onset mechanism and mean SSTs in the equatorial Pacific.

As noted in the session description any research object produced should be made ’FAIR’; but ‘FAIR’ objects require attribution. What does that mean for the vast variety of research objects? What roles need to be acknowledged, how and where? These are questions the Federation of Earth Science Data Partners (ESIP) Research Object Citation cluster have been addressing. In particular, we have been applying the lens of the CREDiT taxonomy roles in these discussions. Here our conclusions to date will be described.

A large number of General Circulation Models (GCMs) are currently available for modelling the atmospheric conditions over the Earth. However, there is a large variability in the future climate predicted by the available set of climate models. Hence, the climate data introduces the most amount of uncertainty in the climate change impact assessment. Regional-scale climate change impact studies based on these models may produce a wide range of possible impacts that becomes unusable for policymakers. A robust GCM selection procedure is introduced in the current study to bring the uncertainty to a realistic range. The proposed approach takes into account the process representation in the climate models by checking teleconnections in data along with their ability to predict the regional climate in spatial and temporal scale. The interdependence between the climate models are also accounted for in the proposed approach to avoid underestimation of uncertainty. The procedure is validated in the Bharathapuzha River Basin, Kerala, India. The study considers 22 GCMs that participated in the Coupled Model Inter-comparison Project-5 and 6 Regional Climate Models (RCMs) that are recommended for the Indian subcontinent. The climate models BNU-ESM, CMCC-CM, GFDL-ESM2G, GFDL-ESM2M and MPI-ESM-MR are found to be performing well for the prediction of both precipitation and temperature. The proposed climate model selection procedure can bring down the band width of uncertainty from 376 mm to 162 mm in monthly rainfall prediction with a containing ratio of 44%. The downscaling of the climate predictions can further increase the containing ratio by removing the systematic error. The bandwidth of uncertainty has reduced from 10.82 K to 3.83 K in the prediction of minimum temperature and from 8.35 K to 4.52 K for maximum temperature. The proposed GCM selection procedure provides more confidence in the predicted future climate since regionally significant correlations between climate variables are preserved in the selected models. The model selection procedure is validated for the period 2006-2018 with the observed climatic variables, and the selected models are found to be performing well.

The Anomalous Transport Experiment (ATREX) comprised seven quasi-simultaneous observations of lower thermospheric winds, horizontally separated by up to 600 km, on 27 March 2012, 0900 UT. Zonal and meridional wind profiles were obtained with five sounding rockets launched from Wallops Island (38 N, 75.5 W) releasing seven trimethyl aluminum trails which were observed from three cameral sites. The wind profiles exhibit deep coherent wave structures between 85 and 140 km, dominated by very strong diurnal and semidiurnal tides below 120 km. The total wind amplitude exceeds 100 m/s, three times the value from the Global Scale Wave Model. In addition, a large scale, vertically propagating and growing, inertia-gravity wave, most visible above 120 km, has been isolated by empirical orthogonal function analysis. The region between 100 and 110 km shows the largest mesoscale variability of winds and wind shears, often exceeding 40 m/s/km. We will present an estimation of the structure function for wind fluctuations and possible evidence for stratified mesoscale turbulence.

In Washington State, climate change will reshape the Puget Sound marine ecosystem through bottom-up and, top-down processes, directly affecting species at all trophic levels. We applied analytical approaches to better understand future climate change effects on temperature and salinity in Puget Sound. We used empirical downscaling techniques to derive high resolution time series of future sea surface temperature and salinity, based on scenario outputs of two coarse resolution General Circulation Models, GFDL-CM4 and CNRM-CM6-1-HR, which were created as part of the CMIP6 - Coupled Model Intercomparison Project Phase 6. We calculated long-term averages for historical simulations, calculated anomalies for future years, and applied a delta-downscaling approach to a Regional Ocean Modeling System (ROMS) time series, yielding short (2020–2050) and long-term (2070–2100) forecasts. Downscaled output for Puget Sound showed temperature and salinity variability between scenarios and models, but overall there was strong model agreement. Model variability and uncertainty was higher for long-term projections. Spatially, we found regional differences for both temperature and salinity: including higher temperatures in the South Basin and higher salinity in the North Basin. Caveats to our methodology include the assumption that variable relationships are static and cannot represent interactions between large scale and local change, but this study is a first step to translating CMIP6 outputs to higher resolution predictions of future conditions in Puget Sound. The climate projections for Puget Sound oceanography will be used to drive the Atlantis ecosystem model for Puget Sound, an end-to-end ecosystem modeling approach that represents all trophic levels and evaluates the species-level impacts of climate change. This project is part of a Washington State Sea Grant funded project, “Evaluating the effects of Southern Resident orcas recovery actions and external threats in the marine ecosystem of Puget Sound.”

Around the world there is a consensus of the importance of knowladge the ice thickness distribution and total ice volume for estimating future glacier change for both glaciological and hydrological application, especially the Polar Regions for being important regulators of the global climate system and response rapidly to climate change. This work aimed to estimate the ice thickness distribution and total ice volume of Znosko glacier (King George Island, Antarctic Peninsula) using Glacier Bed Topography (Glabtop) model and ground data. Ground penetrating radar (GPR) measurements were collected in two field campaigns. GPR measurements were used to validate, calibrate (shape factor) and improve the accuracy of the ice thickness distribution model. The ice thickness (GPR) distribution is not uniform over the glacier, which varies from 0 to 155 m. The total stored ice volumen of Znosko glacier in 2020 is estimated to be 0.11 km3. Additionally, the model bounded areas in the bedrock topography below sea level, as a sign of future lake formation. Sensitivity analysis reveal that Glabtop model with a shape factor (f) = 0.5 gave best results when compared with ground penetrating radar (GPR) data measured on Znosko glacier and has an uncertainty range of ± 20.5%.