Extratropical cyclones are weather phenomena with significant transfer of energy between the surface (over the ocean or on land) and the atmosphere. Recurrently, reanalysis data are used to understand the behavior of cyclonic tracks and to study extreme events, with constant updates and validations with the observational base in the Northern Hemisphere. However, studies using cyclone tracking in the Southwestern Atlantic, has proven more difficult. This disagreement seems to be in function of the structure and intensity of the forcing factors that influence both cyclogenesis and the displacement to the South Atlantic, when compared to the Northern Hemisphere. In this work, synoptic pressure charts at sea level, manually made and processed by the Brazilian Navy every 12 hours between the years 2010 and 2020, as a product resulting from a consensus among Navy meteorologists, were used to study the cyclonic pathways in the Southwestern Atlantic (METAREA V). Data obtained for all cyclones identified in the charts, based on their position and displacement, formed a database with 10737 cyclones, containing speed, dimensions, and pressure gradient. The cyclones identified have a higher radius frequency between 200/400 km and a faster-moving center shift. In addition, about 60% of cyclones associated with cold fronts have a life cycle ranging from 3 to 4 days. There is also a expressive cyclogenesis between latitudes 23ºS and 43ºS where, in austral autumn winter, increases its frequency over the ocean and close to the southern Brazilian coast. During spring, the greater cyclogenesis frequency occurs over the continent, close to Chaco area in Argentina and Uruguay. The impacts of these statistical figures on the south and southeastern Brazilian coast, mainly the continental insertion point of the cold fronts and cyclonic displacement that influence rough seas and storm surges, are discussed in this work. Keywords: EXTRATROPICAL CYCLONES, CYCLONE TRACK, SYNOPTIC CHARTS, SOUTHWESTERN ATLANTIC

The usage of the term Knowledge Graph (KG) has gained significant popularity since 2012, when Google introduced its own knowledge graph, and how they used it to enhance their searches and question answering systems. While various definitions and interpretations for knowledge graphs have been presented, what remains consistent is that knowledge graphs are commonly used with reasonsers to make inferences about data, based on assertions and axioms written by human experts. But knowledge graphs, which store complex, multi-dimensional data contain hidden patterns and trends that cannot be explored simply using reasoners. In such a case it becomes necessary to extract parts of the knowledge graph (focusing on the instances related to one property at a time) and analyze them individually in order to conduct a focused but tractable exploration of the domain. In this presentation, we present one way to gain insights from knowledge graphs, using network science. To achieve this goal, we have formalised the partitioning of knowledge graphs to unipartite knowledge networks, and present various ways to explore and analyse such knowledge networks to form scientific hypotheses, gain scientific insights and make discoveries.

China offers the Global Energy Interconnections as a planetary project to deliver sustainable development and climate action through large-scale energy transition. What does this mean for Southeast Asian countries’ regional quest for greater energy connectivity? Although China-Southeast Asia interconnections promise a durable regional energy connectivity, bringing distant renewable energy to demand centers using GEI technologies, these future energy systems are not free of challenges: technical and political. A closer look at this future interconnection further reveals other chokepoints: politically powerful domestic energy regimes; levels of trust between China and these countries; aging and absent transmission infrastructures; heterogeneous national energy markets and regulations; and differing receptions towards China’s externalization of its surplus industrial capacity.

The isotopic composition of water vapor (e.g. its Deuterium content) evolves along the water cycle as phase changes are associated with isotopic fractionation. In the tropics, it is especially sensitive to convective processes. Consequently the isotopic composition of precipitation recorded in paleoclimate archives has significantly contributed to the reconstruction of past hydrological changes. It has also been suggested that observed isotopic composition of water vapor could help better understand convective processes and evaluate their representation in climate models. Yet, water isotopes remain rarely used beyond the isotopic community to answer today’s pressing climate questions. A prerequisite to better assess the strengths and weaknesses of the isotopic tool is to better understand what controls spatio-temporal variations in water vapor isotopic composition through the tropical atmosphere. A first step towards this better understanding is to understand what controls the isotopic composition of the sub-cloud layer water vapor over the ocean. Isotopic measurements show that the water vapor is the most enriched in trade-wind regions, and becomes more depleted as precipitation increases. To understand this pattern, we use global simulations with the isotope-enabled general circulation model LMDZ, large-eddy simulation in radiative-convective equilibrium and with large-scale ascent or descent, with the isotope-enabled model SAM and simple analytical models. We show that increased precipitation rate is associated with increased isotopic depletion if it is associated with stronger large-scale ascent, but with decreased isotopic depletion if it is associated with warmer surface temperature. As large-scale ascent increases, the isotopic vertical gradient in the lower troposphere is steeper, which makes downdrafts and updrafts more efficient in depleting the sub-cloud layer water vapor. The steeper gradient is caused mainly by the larger quantity of snow falling down to the melting level, forming rain whose evaporation depletes the water vapor.

Inland waters are recognized as a significant source of CO2 to the atmosphere; however, the global magnitude of this flux remains uncertain. In particular, CO2 concentrations and fluxes in stream systems are extremely variable at scales of 10’s to 100’s of meters, complicating monitoring and prediction efforts. Thus, models of pCO2 that capture these scales of spatial variability are necessary for the accurate prediction and monitoring of stream CO2 fluxes. Despite a strong conceptual framework for the hydrologic processes that control stream CO2, predictive models to date have been empirical, based on Strahler stream order and regressions between observed pCO2 and landscape variables. We hypothesize that models incorporating well-described hydrologic processes may lead to new insights into the magnitude of various CO2 sources and improve predictions. Here, we develop and apply a process-based stream network model of CO2 based on NHDplus flowlines and driven by groundwater inputs, hyporheic exchange, water-column metabolism, advective transport, and atmospheric exchange. Model output is compared with 151 measurements of pCO2 (424 - 9718 ppm) collected in August, 2019 across the upper East River watershed in Gothic, CO, a mountainous, high-elevation headwaters system within the Colorado River basin. We find that modeled pCO2 captures observed spatial patterns and predicts measured values with a RMSE of ~250 ppm and R2 of 0.47 (p<10-15). Additionally, our process-based model performs significantly better than a multiple linear regression model between observations and a geomorphic variables (r2=0.35, p<10-7). Estimates from an optimized stream network model give additional insight into CO2 sources, suggesting that groundwater accounts for 70-80% of evasion fluxes, hyporheic processes for 20-30%, and water-column metabolism for ~1% across the East River watershed. The ability of our model to predict pCO2 at the spatial scales of variability may provide an important next step in estimating global CO2 fluxes, and future research will test the predictive power of process-based models at regional and global scales.

Over the last century, runoff from farms and cities, along with land cover and land use changes, have drastically altered the mass balance of nutrients in aquatic systems, affecting both their ecological functioning and the living communities they support. Here we present the results of a multi-year, long-term study designed to assess the control of land-use and hydrology on nutrient fate and transport within a mixed land-use watershed in north-central Ohio. A total of 64 streams (with a mix of urban, cropland, pasture, and forest catchments) have been sampled periodically since the summer of 2008. Hydrological conditions during the study period exhibited marked seasonality, with usually dry winter seasons (average ppt: 23.5±7.4 cm) and wet spring seasons (average ppt: 34.5±8.1 cm). Runoff generation in response to precipitation events is faster in streams draining developed catchments and slowest in forested streams, where runoff is generated only by events > 10 mm/day. Hydrologic connectivity in the watershed appear to be limited, since only about 25% of precipitation inputs were translated into quick flow. There is a significant, positive correlation between runoff and nutrient concentrations (R2 values are: 0.40 for streams draining urban landscapes, 0.34 for forested streams, 0.30 for cropland, and 0.28 for pastureland). We also observed significant inter-annual and seasonal variations on both DIN (p = 0.02) and PO4 concentrations (p < 0.01). Compared to dry years, nutrient fluxes during wetter years are, on average, 16% higher in urban catchments and 47% higher in forested catchments, but 32% lower in pasture-dominated catchments. Baseflow is responsible for only between 20-30% of the annual nutrient export from the watershed.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.