Large-scale electrical resistivity investigations of the Antarctic crust and upper mantle utilizing the magnetotelluric method (MT) are limited in number compared to temperate regions, but provide physical insights difficult to obtain with other techniques. Key to the method’s success in polar environments are instrumentation advances that allow micro-volt level measurements of the MT electric field in the face of mega-ohm contact resistances. In this presentation, we analyze MT data from four campaigns over the Antarctic interior using modern 3D non-linear inversion analysis and offer additional geophysical conclusions and context beyond the original studies. A profile of MT soundings over transitional Ellsworth-Whitmore block in central West Antarctica implies near-cratonic lithospheric geothermal conditions with interpreted graphite-sulphide horizons deformed along margins of high-grade silicate lithologic blocks. Data across South Pole soundings confirms large-scale low resistivity spanning Moho depths that is consistent with limited seismic tomography and elevated crustal thermal regime inferences. Upper mantle under a presumed adiabatic thermal gradient below the Ross Ice Shelf region of West Antarctica appears to be of a moderately hydrated state but not sufficient so as to induce melting. The degree of hydration there is comparable to that below the north-central Great Basin extensional province of the western U.S. Comprehensive 3D coverage of Mount Erebus and Ross Island reveals unprecedented views of the magmatic plumbing feeding the phonolitic volcanic system. This includes a lower middle crustal staging area for episodic magmatic replenishment of the upper middle crustal chamber hosting phonolitic differentiation, which in turn is fed from an upper mantle source region of the parental basanite.

Micro-scale models are important to assess processes in complex domains, for example cities. The most common data standard for atmospheric model output data are the CF-conventions, a data standard for netCDF files, but this standard is not adapted to the model output of micro-scale models. As a part of the project AtMoDat (Atmospheric Model Data) we want to develop a model data standard for obstacle resolving models (ORM), including the additional variables (i.e. building structures, wall temperatures) used by these models. In order to involve the micro-scale modeller community in this process, a web based survey was developed and distributed in the modeller community via conferences and email. With this survey we want to find out which micro-scale ORMs are currently in use, their model specifics (e.g. used grid, coordinate system), and the handling of the model result data. Furthermore, the survey provides the opportunity to include suggestions and ideas, what we should consider in the development of the standard. Between September 2019 and July 2020, the survey was accessed 29 times, but only 12 surveys were completed. The finished surveys refer to eight different models and their corresponding model information. Results show that these different models use different output formats and processing tools, which results in different model result handling routines. The participants suggested to use the netCDF data format and to provide information on model initialization, model settings and model input along with the model output data. This would enable an easier intercomparison between different models and repetition of model simulations. Standardized model output and variable names would also enhance the development of shared routines for the analysis of micro-scale model data and a better findability of the data with search engines. This survey will remain open with regular assessments of contents (i.e. November 2020, May 2021; https://uhh.de/orm-survey).

Though spurious geomagnetic activity lead to terrestrial storms, which in turn impacts space weather as a result of interaction between Solar wind (SW) plasma carried by Solar wind moving towards Earth due to Coronal Mass Ejection (CME) and interplanetary magnetic field, the interplay linking the Solar plasma to the Earth’s magnetic field is yet to be understood thoroughly, and hence modelling the space weather is still perceived as a challenge. To address this issue, we resort to geometrical analysis of the phenomena using state-of-the-art Multi Fractal Analysis (MFA) techniques as well as Wavelet Transforms of SW and Earth’s magnetic field parameters during the period starting from few days before up to few days after the last Solar Eclipse on June 21, 2020, in order to predict abnormal behaviour in terrestrial environment, may or may not be leading to geomagnetic storms, well before the occurrence of the event.

Coastal cities are exposed to multiple flood drivers such as extreme coastal high tide, storm surge, and extreme river discharge. The interaction among these flood drivers may cause compound flooding events, which could exacerbate social and economic consequences. Climate change can put greater pressure on these areas by increasing the frequency and intensity of coastal and riverine flooding. In this study, a bivariate compound flooding risk assessment method is developed to incorporate sea level rise (SLR) and nonstationary river discharge conditions. Extreme sea water level (SWL) and river stage are identified using the peak-over-threshold method, and subsequently, pairs of extremes are selected when both SWL and river stage exceed their defined thresholds within ±1 day from each other. A copula-based approach is then used to estimate the joint distribution and return period of compound coastal riverine flooding by incorporating nonstationarity into the marginal distributions of extreme SWL and river stage. The future flood risk is assessed using the notation of failure probability, which here refers to (1) the probability of occurrence of at least one major coastal or riverine flooding for a given design life (i.e., total flood risk); and (2) the probability of occurrence of at least one compound major coastal riverine flooding for a given design life (i.e., compound flood risk). Compound flood risk assessment is conducted at 26 paired NOAA-USGS stations along the Contiguous United States coast with long‐term observed data and defined flood thresholds. The results indicate that in some regions the joint return period of coastal/riverine flooding are substantially lower when considering the projected future hydroclimate conditions and SLR. The importance impact of future SLR and hydroclimate conditions is discussed regionally in terms of changes in the frequency of compound major coastal riverine flooding events.

The COVID-19 pandemic resulted in reduced carbon dioxide (CO2) emissions in 2020 in large parts of the world. We have analysed an ensemble of satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2, to find out if the COVID-19 related regional-scale reduction of anthropogenic CO2 emissions can be detected from space. We focus on East China and analysed a set of latest version XCO2 data products from the satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse gases Observing SATellite (GOSAT). We use a data-driven approach, which is based on the computation of XCO2 anomalies using a method called DAM. Via DAM, trends and seasonal variations are largely filtered out and resulting positive values of the XCO2 anomalies correlate with the location of major CO2 source regions such as East China after spatio-temporal averaging. We analysed satellite data between January 2015 to May 2020 and compared monthly XCO2 anomalies in the time period January to May 2020 with corresponding monthly XCO2 anomalies from previous years. In order to link the satellite-derived XCO2 anomalies to East China fossil fuel (FF) emissions, we used target region XCO2 and corresponding FF emissions from a model.

Declining Arctic sea ice thickness and extent over the past several decades has resulted in extensive regions of thin ice vulnerable to melting. We use the ERA-5 reanalysis along with a fully coupled atmosphere-ocean-ice model (Navy ESPC) to explore the role of Arctic cyclones in summertime sea-ice change. The ERA-5 reanalysis is used to statistically analyze how surface energy fluxes and wind forcing from Arctic cyclones in the marginal ice zone between May and August (1999-2018) influence sea-ice extent on 1-10 day timescales. In May and June, cyclones decelerate the local seasonal loss of sea-ice extent due to a reduction in the incoming solar radiation. In late summer, cyclones no longer decelerate the seasonal loss of sea-ice extent, despite still reducing the net surface energy flux. Surface wind forcing across the ice edge explains only a small fraction of the short-term changes in local sea-ice extent, which suggests other processes not accounted for in this analysis, such as bottom melt, become important later in the melt season. In order to gain a more detailed understanding of how cyclones affect sea ice we then utilize the coupled Navy ESPC to examine the intense “Great Arctic Cyclone” of August 2012. Two mechanisms of cyclone-induced melting are identified: turbulent mixing of a warm layer located at 15- to 35-m depth increases bottom melting and warm air advection by the strong surface winds increases surface melting. The sea ice melt rate is substantially enhanced by the cyclone, however this effect is confined to a relatively small region and only lasts for a few days. Lastly, we note that despite the marked trend in the reduction of sea ice extent over the past several decades, reanalysis indicates little trend in the Arctic storminess as diagnosed by kinetic energy as a proxy for Arctic cyclones.

The occurrence of Jovian decametric emission (DAM) is sporadic as observed from ground-based instruments. When the timing intervals of observed occurrences of Jovian DAM are compared to all periods when Jupiter was observable, a set of Jovian DAM emission occurrence probabilities can be created. These probabilities are usefully plotted as a function of Jovian system III (magnetospheric) central meridian longitude (CML-III) and Io’s phase measured from superior geocentric conjunction (SGC), producing a CML-Io phase plane. It has been known since 1964 that Jovian DAM tends to have higher occurrence probabilities in different regions of the CML-Io phase plane, leading to the identification of different Io-related and non-Io-related DAM components. AJ4CO Observatory, located in High Springs, Florida, USA, has been observing Jupiter when it is within ~4.5 hours of transit since October, 2013. The primary instrument used for observing Jovian DAM is a swept-frequency (16 to 32 MHz) dual polarization spectrograph fed by an eight-element phased array of terminated folded dipoles. A high-speed digital spectrograph with a tunable 2 MHz bandwidth was also used from 2013 to 2016 to observe emission at higher time resolution. We analyze the dynamic spectra of Jovian DAM observed at AJ4CO Observatory from 2013 through 2020 to measure emission timing intervals and classify the emission into four types: L (for wideband L bursts), S (for wideband S bursts), N (for narrowband continuous emission), and T (for narrowband trains of S bursts). For this presentation, we show CML-Io phase plane probabilities categorized by radio frequency, polarization, emission type, and emission arc shape. We show how the various high-probability DAM regions within the phase plane change with each parameter and with various combinations of parameters. We present updated definitions of the DAM component phase plane boundaries and discuss how the DAM components appearing in various parts of the CML-Io phase plane may differ from one another.

Marine microplastics (< 5 mm) pollution is a growing problem affecting coastal communities, marine ecosystems, marine life and human health. Microplastics have been detected throughout the entire global ocean, and it is hard to escape news of microplastics debris in our oceans. Despite the growing awareness, the management of marine microplastics data, from large-scale surveys along the coast and open ocean, to effects of microplastics on planktonic communities, lags far behind the needs of the scientific, education, and decision maker communities. The lack of large-scale, long-term comprehensive data on microplastics makes it difficult to completely understand the sources, distribution and impacts of microplastics, as well as forecasting their trajectory and aggregation under current and future climates. The National Oceanic and Atmospheric Administration’s (NOAA) National Centers for Environmental Information (NCEI), which is regarded as the nation’s environmental data steward, is spearheading efforts to create a global database on microplastics. As a first step, we are currently collating microplastics data from across the world into a primary marine debris data warehouse. The ultimate goal is to develop a one-stop repository where data on all types of microplastics are aggregated, archived, and served to the community in a consistent and reliable manner. Together with other NCEI databases, such as the Global Ocean Current Database, World Ocean Database, and the Surface Underway Marine Database, stakeholders will be able to access and assess data for their needs, thereby enhancing our capacity to understand and manage the problem of microplastics pollution. The microplastics database will be freely accessible and maintained with newly received data from global stakeholders.

Ethiopia’s socioeconomic development is strongly dependent on both its natural resources and hydroclimatic dynamics. Current and projected effects of climate change and variability in the Horn of Africa pose an enormous challenge to the country’s water resources management. We modeled multi-basin runoff scenarios in the country by calibrating statistical models first followed by extrapolation of the regressed functions into a future data domain under assumptions of stationarity. Precipitation and average near-surface air temperature predictors were used to calibrate Generalized Linear Models (GLM) to project 2011-2070 monthly runoff in a high-emission scenario (RCP 8.5) for selected General Circulation Models (GCM). Gridded fields of downscaled and bias-corrected precipitation, Tmax and Tmin for 10 CMIP5 GCMs were obtained from the NASA NEX-GDDP database. Hydrologic simulations from the NASA Land Data Assimilation System (LDAS) were used as proxies of observational basin response. Noah-MP’s climate forcings (CHIRPS precipitation and MERRA temperatures) were used to perform additional bias-correction over basin-averaged predictors extracted from the NEX-GDDP ensemble models. Monthly mean estimates for precipitation/temperature projections showed wetter/warmer conditions than the baseline for almost all regions. 2011-2040 July temperature climatology in most GCMs exhibited the strongest warming (> 1.5C o) in Central Ethiopia and it gradually decreased northwards and southwards. Correlation analysis showed that precipitation variations explain most of runoff variability during the rainy seasons. Future GLM runoff estimates suggest a generalized national increase of mean annual water supply when compared with historical LDAS, although spatio-temporal differences were observed across the country. The mentioned hydrological gains are driven by spatially distributed changes in precipitation with the biggest positive trends in the southeastern region followed by moderate precipitation increases in the Central Highlands and neutral changes in the Northwest. Few GCMs (e.g., GFDL-CM3) project drier conditions in the rainy seasons and a slight decrease in the mean annual runoff for most basins. The wettest model in the Abay basin, IPSL-CM5A-LR, predicts 15% increase in annual runoff when compared to historical averages.

Endogeneity or reverse causality can lead to biased estimation of effects and to inaccurate interpretations of cause and effect water-use relationships. However, biased estimations of the effects of designed interventions on water–related behaviour are rarely discussed. This study investigates the endogeneity of psychological factors in water-related behaviour using an instrument variable (IV) approach. Data from eight household water treatment (HWT) studies in Asia, Africa, and South America are utilized. A combination of several socio-economic characteristics, such as education and accessibility, is used as a control variable and three psychological factors, i.e., perception of risk, attitude, and social norms, are used as predictors of the adoption of HWT. Variables related to country level institutional quality are used as the IVs to remove the effects of reverse causality of households having adopted HWT on the psychological factors. Results show that psychological factors and HWT adoption indeed co-evolve, resulting in the emergence of reverse causalities, indicating that endogeneity exists in the water-related behaviour. Institutional quality indicators are found to be valid instruments for psychological factors such as attitude and norms towards HWT, but not households’ perception of risk in context of HWT. This suggests that institutional performance “heavily” influences households’ attitude and norms regarding water-related behaviour. The effects of households’ attitude and norms on HWT behaviour are underestimated by 59 and 40%, respectively, if the feedback effects of adoption behaviour on psychological factors are not considered. Further, households’ experience of using water-related technologies and external nudges, such as from policy makers, are more important drivers of the behaviour compared to risk perception. Despite the challenge of finding valid instruments for psychological factors, the study recommends that the effects of reverse causality addressed here need to be considered in future water-related behavioural studies. The conventional regression analysis approaches that ignore such effects are no longer suggested for analysing water-related behaviour.

Diverse algorithms have been developed for efficient earthquake signal processing and characterization. These algorithms are becoming increasingly important as seismologists strive to extract as much insight as possible from exponentially increasing volumes of continuous seismic data. Deep neural networks have been shown to be promising tools for this. We have developed a number of deep learning tools for more efficient processing and characterizing of earthquake signals. In my presentation, I demonstrate the performance of some of these tools applied to seismic data. AI-based techniques have the potential to improve our monitoring ability and as a result understanding of earthquake processes and hazards.

Why would hundreds of scientists from around the world freeze a ship in Arctic sea ice for an entire year, braving subzero temperatures and months of polar darkness? This may sound like a fictional adventure movie plot, but from September 2019 through October 2020, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) Arctic research expedition did just this. Currently, the Arctic is warming twice as fast as the global average (a phenomenon known as Arctic amplification) and due to a lack of observations, there is considerable uncertainty in climate models projecting the Arctic climate of the future. The MOSAiC expedition aims to better understand the changing Arctic climate system by gathering data from ground zero over a full seasonal cycle to augment satellite observation data. Using the expedition as an engagement hook, scientists and curriculum developers developed a high school earth science curriculum anchored by the phenomenon that climate scientists are actively trying to explain: Arctic amplification. The curriculum follows the model-based inquiry instructional framework where each lesson provides students with learning experiences (e.g., virtual reality tours of MOSAiC field sites, analyzing authentic Arctic satellite datasets) that relate back to the phenomena. Focusing on explaining natural phenomena provides an authentic context for students to learn and apply scientific understanding, which research shows can help engage students in NGSS scientific practices. Here we present an overview of the learning sequence using refinement of mental models throughout the unit and present preliminary results from pre-post assessments from two educator workshops (~100 teachers) that show that participants’ understanding of Earth’s climate system improved significantly after engaging with the curriculum. Based on these results, we expect this curriculum to be an important tool in engaging students in Earth’s systems thinking.

Scientific expeditions can be used to engage the public in science learning within an exciting and compelling context, giving insight into the often messy and serendipitous nature of science and the humanness of scientists. The 2019-2020 MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) research expedition allowed scientists from around the world to study all aspects of the Arctic climate system at ground zero for a full seasonal cycle. The German icebreaker Polarstern was intentionally frozen in Arctic sea ice and drifted almost continuously across the Arctic Ocean for a year. The Polarstern served as the central research observatory for MOSAiC, allowing scientists to study Arctic sea ice, atmospheric processes, and more. Here we present our U.S.-based MOSAiC expedition outreach efforts and the role of MOSAiC scientists in them. By harnessing the public’s fascination with the Arctic and the excitement of the expedition, coordinated MOSAiC communication, outreach, and education efforts promoted a broad understanding of the changing Arctic and the societal implications of these changes, hopefully inspiring a future generation of potential scientists. Many MOSAiC scientists were directly engaged in MOSAiC outreach efforts with students, teachers, and the public, and outreach materials were developed that could be accessed and distributed virtually. The expedition was brought to life for learners of all ages by providing them with immersive experiences like VR Google Expeditions and 360-degree videos from the field, opportunities to connect directly with scientists through video calls and an #askmosaic question submission campaign, and insight into what day-to-day life on an icebreaker in the remote Arctic is like. Our outreach efforts helped us better understand the importance of providing scientists with diverse outreach opportunities that are fulfilling to them and the power in using scientific expeditions to engage the public.

During recent droughts in North Carolina, various audiences have articulated needs for information that explains current or anticipated impacts, droughts’ geographic extent and timing, and how the State monitors drought. This is despite there being a regular process in place to evaluate statewide conditions and seemingly abundant information available through federal, state, and local agency websites; media outlets; and other channels. This presentation provides findings from a research project designed to improve the availability, understandability, and usability of drought communications products for North Carolina audiences, focusing on the US Drought Monitor map of North Carolina as an example. The North Carolina Drought Management Advisory Council (DMAC) technical committee has met weekly to assess drought conditions since the 1990s and has recommended the state’s drought designations to the US Drought Monitor since 2000. The DMAC recommendations typically align with the weekly USDM map. Through surveys, focus groups, usability studies, and other engagement methods, we collected information from groups such as extension agents and water utility staff about 1) their communications preferences - resources that are concise, easily readable, and readily shareable through email, listservs, and social media - and 2) infographic prototypes created to address those preferences. User feedback on the prototypes informed iterative refinements to their content and design and provided information about their potential use for communications and management decisions. Ultimately, understanding the monitoring process and how drought designations are made was a key factor affecting the extent to which extension and other communication professionals apply, share, and value the information produced by monitoring groups and scientific agencies. This research suggests that addressing transparency questions can support efforts to communicate complex problems, such as drought.

Accurate photoionization rates are vital for the study and understanding of planetary ionospheres. Previous model calculations of terrestrial photoionization rates lack sufficient spectral resolution to account for highly structured photoionization cross sections as well as the solar spectral irradiance. We present new photoionization rate calculations from CPI’s Atmospheric Ultraviolet Radiance Integrated Code [AURIC; Strickland et al., 1999] using high-resolution (0.01 Å) solar spectra and high-resolution (0.01 Å) atomic oxygen (O) and molecular nitrogen (N2) photoionization cross sections. Theoretical photoionization cross sections of O are determined utilizing the R-matrix plus pseudo-states (RMPS) approximation whereas N2 cross sections are determined using the R-matrix approximation. We include 34 high-resolution partial O state photoionization cross sections and 3 high-resolution partial N2 state photoionization cross sections with supplemental Conway [1988] tabulations for molecular oxygen and the remaining N2 states. We find that photoionization rates computed at 0.01 Å resolution differ substantially from rates computed using low-resolution cross sections, especially in the lower thermosphere below 200 km. Specifically, we find that ionization production rate ratios exhibit variations in altitude of more than ±40% between the high- and low-resolution cases. Past low-resolution calculations at various low spectral resolutions do not sufficiently account for or preserve the highly structured auto-ionization lines in the photoionization cross sections [Meier et al., 2007]. These features, in combination with high-resolution solar spectra, allow photons to penetrate deeper into the Earth’s atmosphere producing larger total ionization rates. These higher ionization rates may finally resolve data-model discrepancies in altitude profiles of electron densities due to the use of low-resolution photoionization cross sections in current E-region models.

This study identifies the role of mid-latitude teleconnection in determining the interannual variability of the Indian summer monsoon in the CFSv2 model. Since CFSv2 has been identified as a potential forecast model for the Indian summer monsoon, it is important to understand the factors that determine its prediction skills at seasonal timescales. ENSO is one of the most important factors driving Indian monsoon variability at seasonal timescales. It is represented realistically in CFSv2. The model, however, misses associated mid-latitude teleconnections. We show that the inadequate strength of mid-latitude teleconnections, especially from the North Atlantic and North-western Pacific can be the primary reasons for the weaker monsoon variability, despite strong ENSO-Monsoon relationship in the model.

As soon as the outer plasmasphere gets eroded during geomagnetic storms, the greatly depleted plasmasphere is replenished by cold, dense plasma from the ionosphere. A strong correlation has been revealed between plasmaspheric refilling rates and ambient densities in the topside ionosphere and exosphere, particularly that of atomic hydrogen (H). Although measurements of H airglow emission at plasmaspheric altitudes exhibit storm-time response, temporally static distributions have typically been assumed in the H density in plasmasphere modeling. In this presentation, we evaluate the impact of a realistic distribution of the dynamic H density on the plasmaspheric refilling rate during the geomagnetic storm on March 17, 2013. The temporal and spatial evolution of the plasmaspheric density is calculated by using the Ionosphere-Plasmasphere Electrodynamics (IPE) model, which is driven by a global, 3-D, and time-dependent H density distribution reconstructed from the exospheric remote sensing measurements by NASA’s TWINS and TIMED missions. We quantify the spatial and temporal scales of the refilling rate and its correlation with H densities.

Earth’s thermosphere is driven by a combination of meteorological, magnetospheric, and solar forcing that exhibits significant variation from day-to-day. The relative importance of these drivers and their combined affects in determining daily thermospheric variability on global and local scales is an important science question particularly under solar minimum conditions. Far-ultraviolet, satellite-based airglow observations are a valuable tool to probe the thermosphere and can provide the spatial coverage and temporal resolution required to improve our understanding of thermospheric day-to-day variability in response to driver variability. This paper presents first results from principal component analysis and ensemble sensitivity analysis to quantify the major modes of dayglow variability in both OI 135.6 nm emissions and N2 Lyman-Birge-Hopfield emissions and the sensitivity of these modes to geomagnetic and lower atmosphere drivers. The ensemble simulations are performed with NOAA’s Whole Atmosphere Model that extends from Earth’s surface to the exobase and NCAR’s Global Airglow Model for a recent period with low-to-moderate levels of geomagnetic activity and low solar activity. The ensemble simulations are compared to thermospheric observations over the same period by the NASA Global-scale Observations of the Limb and Disk (GOLD) mission.