80 years after aerial photography revealed thousands of aligned oval depressions on the USA’s Atlantic Coastal Plain, the geomorphology of the “Carolina bays” remains enigmatic. Geologists and astronomers alike hold that invoking a cosmic impact for their genesis is indefensible. Rather, the bays are commonly attributed to gradualistic fluvial, marine and/or aeolian processes operating during the Pleistocene era. The major axis orientations of Carolina bays are noted for varying statistically by latitude, suggesting that, should there be any merit to a cosmic hypothesis, a highly accurate triangulation network and suborbital analysis would yield a locus and allow for identification of a putative impact site. Digital elevation maps using LiDAR technology offer the precision necessary to measure their exquisitely-carved circumferential rims and orientations reliably. To support a comprehensive geospatial survey of Carolina bay landforms (Survey) we generated about a million km2 of false-color hsv-shaded bare-earth topographic maps as KML-JPEG tile sets for visualization on virtual globes. Considering the evidence contained in the Survey, we maintain that interdisciplinary research into a possible cosmic origin should be encouraged. Consensus opinion does hold a cosmic impact accountable for an enigmatic Pleistocene event - the Australasian tektite strewn field - despite the failure of a 60-year search to locate the causal astroblem. Ironically, a cosmic link to the Carolina bays is considered soundly falsified by the identical lack of a causal impact structure. Our conjecture suggests both these events are coeval with a cosmic impact into the Great Lakes area during the Mid-Pleistocene Transition, at 786 ka ± 5 k. All Survey data and imagery produced for the Survey are available on the Internet to support independent research. A table of metrics for 50,000 bays examined for the Survey is available from an on-line Google Fusion Table: https://goo.gl/XTHKC4 . Each bay is also geospatially referenceable through a map containing clickable placemarks that provide information windows displaying that bay’s measurements as well as further links which allows visualization of the associated LiDAR imagery and the bay’s planform measurement overlay within the Google Earth virtual globe: https://goo.gl/EHR4Lf .
The Inter-Tropical Convergence Zone (ITCZ) is a persistent band of organized convection in the tropics that arises due to the surface convergence of the Hadley cells. The location and intensity of the ITCZ is heavily influenced by sea surface temperature and low-level latent heat transport. The ITCZ undergoes an annual march across the equator, and during the summer moves north over India and the Bay of Bengal, affecting the Indian summer monsoon. Occasionally a second parallel band of convection forms to the south, referred to as a double-ITCZ. Double-ITCZs in the tropical east Pacific have been heavily studied, and their development is understood to be linked to seasonal changes in sea-surface temperature. The existence of double ITCZs over the tropical Indian Ocean is well documented, but the underlying mechanism is poorly understood. We develop an algorithm to identify this phenomenon in NOAA outgoing longwave radiation data, and create a thirty-year record of double-ITCZ occurrence. We then use this record to investigate linkages between summer-time double-ITCZ occurrence and intra-seasonal variability in the Indian summer monsoon, and discuss possible physical mechanisms.
Holistic approaches are needed to investigate the capacity of current water resource operations and infrastructure to sustain water supply and critical ecosystem health under projected drought conditions. Drought vulnerability is complex, dynamic, and challenging to assess, requiring simultaneous consideration of changing water demand, use and management, hydrologic system response, and water quality. We are bringing together a community of scientists from the U.S. Geological Survey, National Center for Atmospheric Research, Department of Energy, and Cornell University to create an integrated human-hydro-terrestrial modeling framework, linking pre-existing models, that can explore and synthesize system response and vulnerability to drought in the Delaware River Basin (DRB). The DRB provides drinking water to over 15 million people in New York, New Jersey, Pennsylvania, and Delaware. Critical water management decisions within the system are coordinated through the Delaware River Basin Commission and must meet requirements set by prior litigation. New York City has rights to divert water from the upper basin for water supply but must manage reservoir releases to meet downstream flow and temperature targets. The Office of the Delaware River Master administers provisions of the Flexible Flow Management Program designed to manage reservoir releases to meet water supply demands, habitat, and specified downstream minimum flows to repel upstream movement of saltwater in the estuary that threatens Philadelphia public water supply and other infrastructure. The DRB weathered a major drought in the 1960s, but water resource managers do not know if current operations and water demands can be sustained during a future drought of comparable magnitude. The integrated human-hydro-terrestrial modeling framework will be used to identify water supply and ecosystem vulnerabilities to drought and will characterize system function and evolution during and after periods of drought stress. Models will be forced with consistent input data sets representing scenarios of past, present, and future conditions. The approaches used to unify and harmonize diverse data sets and open-source models will provide a roadmap for the broader community to replicate and extend to other water resource issues and regions.
Evidence based on sparse tree-ring data suggests a severe sustained drought occurred in the 2nd century CE that could have rivaled medieval period droughts in the Colorado River basin (Gangopadhyay et al. 2022). Most of these tree-ring data have been used in gridded drought reconstructions (Cook et al., 2010) which extend back to 1 CE over an area that includes the intermountain western US. However, the 2nd century drought has not been highlighted in prior studies given the sparseness of the data available for this time period. A new reconstruction of Colorado River flow based on these data documents a notably severe and sustained drought over much of the 2nd century (Gangopadhyay et al. 2022). While this reconstruction suggests that the drought exceeds the severity and duration of any drought in the past 2000 years, a complete assessment of the 2nd century drought is challenging due to the sparseness of data. In this poster presentation, we describe the tree-ring data available, along with other proxy data that provide evidence for the 2nd century drought and support its severity. In our conclusions, we discuss outstanding questions and thoughts for further work.
Moisture recycling via evapotranspiration (ET) is often invoked as a mechanism for the high deuterium excess signals observed in continental precipitation (dP). However, a global-scale analysis of precipitation monitoring station isotope data shows that metrics of ET contributions to precipitation (van der Ent et al., 2014) explain little dp variability on seasonal timescales. This occurs despite the fact that ET contributions increase by ~50% in continental locations such as the Eurasian interior from wet to dry seasons. To explain this apparent paradox, we hypothesize that the effects of ET on dP are dampened during dry seasons due to contributions from isotopically-evolved residual water storage that act to lower the d-excess of ET fluxes (dET), in combination with changes in transpiration fraction (T/ET). To test this hypothesis, we develop a parsimonious two-season (wet, dry) model for dET incorporating residual water storage and ET partitioning effects. We find that in environments with limited water storage, such as shallow-rooted grasslands, dry season dET is lower than wet season dET despite lower relative humidity. As global average ratios of annual water storage to precipitation are relatively low (Guntner et al., 2007), these dynamics may be widespread over continents. In environments where water storage is not limiting, such as groundwater-dependent ecosystems, dry season dET is still likely lower; however, this effect arises instead due to higher seasonal T/ET when energy-driven plant water use is enhanced and surface evaporation is relatively limited by water availability. Together, these analyses also indicate multiple mechanisms by which dET may be lower than dp during the same season, challenging the view that moisture recycling feedback increases the dp in continental interiors. This work demonstrates the potential complexity of seasonal dp dynamics and cautions against simple interpretations of dP as a process tracer for moisture recycling. References: Guntner et al., 2007. Water Resour. Res., 43, W05416. van der Ent et al., 2014. Earth Syst. Dynam., 5, 471–489.
Though Venus’s atmospheric conditions and composition have been directly measured, the composition of the Venus lower atmosphere near the surface is generally still poorly known. It was extrapolated from observational data at other altitudes by assuming the constancy of elemental composition without condensation (Krasnopolsky 2007). Both in-situ measurement and remote-sensing observations reveals the most abundant components that exceed the mixing ratio of 10-4 to be CO2, N2, and SO2 (Bezard & de Bergh 2007, JGR 112, E04S07). Water and formation of photochemical H2SO4 — and condensation of cloud-forming H2SO4 — is only important at higher altitudes (Krasnopolsky 2012, Icarus 191, 25). In this work, the balancing of chemical-gravitational-thermal diffusive potentials for the ternary mixture of CO2, N2, and SO2, which represent the neutral Venusian lower atmosphere near the surface, is addressed to obtain the composition grading and to evaluate the tendency toward supercritical density-driven separation of CO2 and N2 (Lebonnois & Schubert 2017, Nat. Geosci. 10, 473). Even though dynamic atmospheric systems, including advective mixing, are more realistic, the static cases evaluated in this work provide stationary states where every dynamic process would eventually proceed to. Hence, our modeling is of a limiting case of the systems of interest, which could help explain some indications of compositional grading. The CRYOCHEM equation of state, which has been successfully applied in describing phase equilibria of Titan’s atmosphere and the surface liquid (Tan & Kargel 2018, Fluid Phase Equilib. 458, 153), as well as that involving solid phases on Pluto’s surface (Tan & Kargel 2018, MNRAS, 474, 4254), is used in this work on the supercritical Venus’s lower atmosphere. In the absence of direct measurement of composition of the lower atmosphere, as well as no lab evidence of CO2 and N2 separation under Venusian surface conditions (Lebonnois et al. 2020, Icarus 338, 113550), the results from this study may at least introduce some new concepts that would entail some tendency for molecular fractionation.
Solid-vapor phase equilibria describe the volatile ices on Pluto’s surface (Tan & Kargel 2018, MNRAS 474, 4254). A simple model of the atmosphere with three components N2/CH4/CO may have solved the long-standing puzzle of the existence of CH4-rich ice in addition to the expected N2-rich ice. An isobaric treatment using CRYOCHEM equation of state naturally results in one solid phase of either ice, which is in equilibrium with the atmosphere, depending on the local temperature variations of Pluto’s surface. CH4-rich ice forms at higher temperatures, while N2-rich ice forms at lower temperatures. A temperature also exists on Pluto where three phases coexist, including vapor in equilibrium with two ices, and where the ices can switch from one type to the other upon cooling or warming. Our model relies on fundamental physics-based thermodynamics, and it explains New Horizons observations of the distributions of these ices, as presented by Bertrand et al. (Nat. Commun. 2020, 11, 1), without invoking a vertically distributed atmospheric CH4 that has not been verified with observation. As observed by New Horizons, Pluto’s surface has valley networks and channels, perhaps resulting from either fluvial (Moore et al. 2016, Science 351, 1284) or glacial (Howard et al. 2017, Icarus 287, 287; Umurhan et al. 2017, Icarus 287, 301) mechanisms, or both, at the present or in the past. Considering the present freezing condition on the surface, if the mechanisms are still in action, they must occur under the surface. Therefore, it is of great interest to know the phase equilibria involving the ices and liquid at conditions that may exist underground. Similar to the treatment of the surface ices, this work also applies CRYOCHEM to describe the phase equilibria that progress through depth as the temperature and pressure increase. The fate of the ices can be determined by examining the resulting phase diagrams at conditions at different depths, specifically the appearance of a liquid phase.
In this study, we assess pan-Arctic and regional seasonal sea ice forecast skill in versions 1 and 2 of the Canadian Seasonal to Inter-annual Prediction System (CanSIPSv1 and CanSIPSv2) dynamical seasonal prediction systems. Each version applies a multi-model ensemble approach using two coupled general circulation models. CanSIPSv2 features a new model formulation (where one of the underlying models, CanCM3, was replaced with GEM-NEMO) and improved sea ice initialization. We show that the modifications made in the development of CanSIPSv2 substantially enhance forecast skill. For example, the lead time for skillful forecasts of detrended pan-Arctic September sea ice area increases from three months in CanSIPSv1 to seven months in CanSIPSv2. We also show that forecasts of detrended winter sea ice area are improved, with CanSIPSv2 producing skillful forecasts for all considered lead times (up to 11 months) for December, January, and February. We find that improvements in pan-Arctic forecast skill are due primarily to improved initialization methods.Further, a potential predictability experiment is conducted for one of the two CANSIPSv2 models, CanCM4, in order to establish – in conjunction with similar studies – the potential to further increase forecast skill with improved models, observations and initialization methods.
Concerns about water security often inform climate risk-related decisions made by environmentally focused investors (Porritt, 2001; Stern, 2006). Yet, potential liabilities for damage caused by extreme flood and drought events linked to global warming present risks that are not always reflected in share prices (Krosinsky et al., 2012). Considering the highly destructive nature of such events, we query whether companies, or specific sectors, could and should be held at least partially liable for their emission-releasing business activities. Recent articles (Rayer & Millar, 2018; Rayer et al., 2020) estimate that under a hypothetical climate liability regime, North Atlantic hurricane seasons might increasingly generate 1-2% losses on market capitalizations (or share prices) for the top seven carbon-emitting, publicly listed companies. In this paper, we extend the concept of the climate liability regime to estimate the impact of global flood- and drought-related damages on the share prices of nine fossil-fuel firms (including the seven mentioned by Rayer et al. (2020)). Following Rayer et al. (2020), we use incremental climate impacts and historical corporate emissions to estimate that climate change-related global flood and drought damages for the period of 2012 to 2016 amount to approximately 2-3% of the top nine carbon-emitting companies’ market capitalizations. We also include a discussion of moral responsibility and the proportion of obligations between producers and users. Quantifying impacts from extreme weather events increases salience and serves as an example of how science can identify and address the important business questions, pertinent to both investors and companies, that arise from a changing climate. References Krosinsky, C., Robins, N., & Viederman, S. (2012). Evolutions in sustainable investing. John Wiley & Sons. Porritt, J. (2001). The world in context. HRH The Prince of Wales’ Business and the Environment Programme, Cambridge. Rayer, Q. G., & Millar, R. J. (2018). Investing in Extreme Weather Conditions. Citywire Wealth Manager®, (429) 36. Rayer, Q., Pfleiderer, P., & Haustein, K. (2020). Global Warming and Extreme Weather Investment Risks. Palgrave Macmillan. https://doi.org/10.1007/978-3-030-38858-4_3 Stern, N. (2006). Stern Review executive summary. London.
Through the PolarTREC program that pairs US educators with field researchers in polar regions, our team has been collaborating on K-12 and undergraduate curriculum development and outreach activities on Arctic amplification of climate change. We have created new lesson plans and activities focused on how organic carbon from thawing permafrost in the Arctic is turned into carbon dioxide, a greenhouse gas that amplifies climate change. This presentation will cover our collaboration to bring this knowledge and experience to high school science students through classroom activities and projects. The focus will be laboratory activities designed for the chemistry classroom: use of spectrophotometry to assess degree of photobleaching in organic samples and evaluation of data from high resolution mass spectrometry to characterize complex organic mixtures. We will also review lessons learned from our efforts to promote enthusiasm for polar science within the general public and discuss the benefits of the PolarTREC program to researchers, educators, students, and the public.
For science to reliably support new discoveries, its results must be reproducible. This has proven to be a challenge in many fields including fields that rely on computational methods as a means for supporting new discoveries. Reproducibility in these studies is particularly difficult because they require open, documented sharing of data and models and careful control of underlying hardware and software dependencies so that computational procedures executed by the original researcher are portable and can be run on different hardware or software and produce consistent results. Despite recent advances in making scientific work more findable, accessible, interoperable and reusable (FAIR), fundamental questions in the conduct of reproducible computational studies remain: Can published results be repeated in different computing environments? If yes, how similar are they to previous results? Can we further verify and build on the results by using additional data or changing computational methods? Can these changes be automatically and systematically tracked? This presentation will describe our EarthCube project to advance computational reproducibility and make it easier and more efficient for geoscientists to preserve, share, repeat and replicate scientific computations. Our approach is based on Sciunit software developed by prior EarthCube projects which encapsulates application dependencies composed of system binaries, code, data, environment and application provenance so that the resulting computational research object can be shared and re-executed on different platforms. We have deployed Sciunit within the HydroShare JupyterHub platform operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) for the hydrology research community and will present use cases that demonstrate how to preserve, share, repeat and replicate scientific results from the field of hydrologic modeling. While illustrated in the context of hydrology, the methods and tools developed as part of this project have the potential to be extended to other geoscience domains. They also have the potential to inform the reproducibility evaluation process as currently undertaken by journals and publishers.
Deformation bands are the main structural element of fault damage zones within sandstone reservoirs. The prediction of band occurrence and their petrophysical impacts is based largely on the understanding that the yield and deformation mechanism of sandstones is primarily controlled by porosity and mean grain size. Whilst this is supported by field observations within aeolian successions, where bands are predictably favoured within coarse-grained, high-porosity sandstones, the prediction of deformation bands within texturally complex mixed aeolian-fluvial reservoirs on the basis of porosity and grain size alone, may be unreliable. The effect of grain sorting on the mechanical behaviour of sandstones is not well understood, although it is generally regarded that deformation band formation is inhibited in texturally immature sandstones with a poor level of sorting. We examine the effect of sorting on both the inelastic yield of sandstones, the dominant deformation mechanism by which yield occurs, and the textural and microstructural changes with deformation, using a series of triaxial experiments on unconsolidated quartz sands. Hydrostatic experiments were conducted on over-consolidated samples of very well- to moderately-sorted sands with a range of mean grain sizes from 128-700µm. We report accurate prediction of P* using porosity x grain radius, with P* reduced with decreased sorting. Constant displacement rate triaxial experiments are performed at up to 10% axial strain to explore yield behaviour in both the brittle dilatant regime and shear-enhanced compactive regime. Experiments were repeated with systematically varied grain sorting whilst mean grain size and porosity was maintained. The textural and petrophysical changes are observed and quantified using pore volumometry, back scattered electron microscopy, digital image analysis and point counting. Results show that in well-sorted sands, localised cataclasis and deformation band formation is the dominant deformation mechanism. In poorly-sorted sands deformation occurs through a combination of grain boundary sliding and randomly distributed pockets of cataclasis. Using grain size analysis we identify greater levels of cataclasis and production of fines in well-sorted sands, resulting in permeability reduction up to one order of magnitude more than that of poorly-sorted sands deformed at the same conditions. We hypothesise that band formation within poorly sorted sandstones may be promoted by the formation and propagation of bands in adjacent well sorted sandstones where band formation is favoured. These results give insight into the deformation, textural changes, and permeability impact of both unconsolidated and consolidated siliciclastic reservoirs.
The Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) and the Global Precipitation Measuring (GPM) Microwave Imager (GMI) have been used as the radiometric transfer standard one after another for the GPM constellation radiometers, during the past nearly two decades. Given that GMI and TMI share only a 13-month common operational period, for the time there is no overlap in between, WindSat can serve as the calibration bridge to provide additional intercalibration for the realization of a consistent multi-decadal oceanic brightness temperature (Tb) product. Thus, we conducted the intercalibration of TMI/GMI for 13-month period, TMI/WindSat for >9 years’ overlap period, and WindSat/GMI XCAL for one year, to assess the Tb bias of one to another. A multi-decadal oceanic Tb dataset was thereafter achieved to ensure a consistent long-term precipitation record that covers TRMM and GPM eras. Moreover, a generic uncertainty quantification model (UQM) was developed by taking various sources of uncertainties into account rigorously and orderly. This UQM model was then applied to quantify the uncertainty estimates associated with these Tb biases. This allows the unified high-sampling-frequency and globally-covered Tb product with associated boundary uncertainties to be much improved for scientific utilization as compared to existing Tb products that are with ad-hoc uncertainties estimates. Moreover, based upon the results of uncertainty quantification process, it is recognized that there is room for improvement in the intercalibration for the water vapor sensitive channels. Further analysis indicates that the issue may be associated with the atmospheric water vapor profile input to the radiative transfer model. Suggestions are subsequently made to use water vapor profile retrieved from millimeter radiometer sounders’ measurements (rather than numerical weather predictions) to determine the impact on the Tb biases of these problematic channels.
It’s very difficult to understand the mechanism producing solar magnetic fields, as it mingled with various activities, it also hindered by gaseous model of the sun; an alternative view is suggested based on characteristics of electrons exhibited in electric current; in 1820 Ørsted discovered both the relation between electricity and magnesium and the Circular Magnetic Field (CMF) produced by electric current, later discovered its produced by electrons in motion; thus the bulky rotation of charged particles (electrons, protons and ions) in tornado mode, produced intense CMF, designated as Plasma Pillar Intense Magnetic Field (PPIMF) with magnitude exceeds millions Tesla; and since EUV images in F-A, illustrates subsurface intense Magnetic Lines of Force (MLF), it also shows activities of Solar Flare (SF), both are suggested as due to PPIMF, which accounted for most solar activities, the Active Region (AR) as in F-B suggested to represent the PPIMF, where AR near surface are in circle, while AR at deep depth in squares; at deep depths the influence of PPIMF on photosphere during quiet sun resulted in pairs of negative and positive magnetic fields represented by magnetogram in F-C; during active sun, PPIMF raise nearer photosphere, it’s negative and positive fields interacted with the photosphere’s state, resulted in pairs of sunspots in F-D, look like iron filings, but formed by plasma, their shapes determined by proximity to PPIMF; as charged particles gyrate around the pillar, any increase in field’s intensity reduced radius of gyration, hence the adjacent distances between ions, thus at critical distance Solar Flare (SF) is triggered producing great energy, radiations and plasma including heavy ions; this knowledge will unlock dynamics of the sun, it’s internal structures and related mechanisms, it will help attained the alternative renewable energy, avert negative consequences of climate change, improve prediction of solar activity and space weather among others.
The spatiotemporal patterns of precipitation are critical for understanding the underlying mechanism of many hydrological and climate phenomena. Over the last decade, applications of the complex network theory as a data-driven technique has contributed significantly to study the intricate relationship between many variable in a compact way. In our work, we conduct a study to compare an extreme precipitation pattern in Ganga River Basin, by constructing the networks using two nonlinear methods - event synchronization (ES) and edit distance (ED). Event synchronization has been frequently used to measure the synchronicity between the climate extremes like extreme precipitation by calculating the number of synchronized events between two events like time series. Edit distance measures the similarity/dissimilarity between the events by reducing the number of operations required to convert one segment to another, that consider the events’ occurrence and amplitude. Here, we compare the extreme precipitation patterns obtained from both network construction methods based on different network’s characteristics. We used degree to understand network topology and identify important nodes in the networks. We also attempted to quantify the impact of precipitation seasonality and topography on extreme events. The study outcomes suggested that the degree is decreased in the southwest to the northwest direction and the timing of peak precipitation influences it. We also found an inverse relationship between elevation and timing of peak precipitation exists and the lower elevation greatly influences the connectivity of the stations. The study highlights that Edit distance better captures the network’s topology without getting affected by artificial boundaries.
An accurate estimation of the shale permeability is essential to understand heterogeneous organic-rich shale reservoir rocks and predict the complexity of pore fluid transport in the rocks. However, predicting the matrix permeability by traditional models is still challenging because they require information often measured from core measurements. First, Kozeny’s equation (Kozeny, 1927) uses porosity and specific surface area of solid grains. However, it is difficult to characterize the specific surface area values or grain sizes from the logs. Second, Herron’s method (Herron, 1987) has been used for predicting permeability based on the mineral contents provided by well log data in conventional sandstone reservoirs. However, the predictive accuracy is low due to the different pore network structures of the shales. In this study, we estimate shale matrix permeability by a combined exploratory data analysis (EDA) and nonlinear regression estimation from the wireline logs. First, we conduct a bivariate correlation analysis for permeability and rock properties in core measurements. According to the correlation and Shapley value sensitivity test, we find that permeability change has a significant effect on the variation in porosity. Also, we investigate a nonlinear behavior between porosity and permeability. Second, we derive a nonlinear polylogarithmic estimation function of porosity to permeability, comparing it to the multivariate linear regression of porosity and clay volume fraction. As a result, a cubic logarithmic function of porosity significantly improves the fitting performance of the permeability values, better than the traditional methods. Moreover, we generate the permeability logs from the calibrated porosity logs, and they imply better shale permeability prediction as well. Since we can invert the porosity distribution from seismic data, this approach can provide a more accurate permeability estimation and reliable fluid flow modeling for shale and mudrock.
The tandem rise in satellite-based observations and computing power has changed the way we (can) see rivers across the Earth’s surface. Global datasets of river and river network characteristics at unprecedented resolutions are becoming common enough that the sheer amount of available information presents problems itself. Fully exploiting this new knowledge requires linking these geospatial datasets to each other within the context of a river network. In order to cope with this wealth of information, we are developing Veins of the Earth (VotE), a flexible system designed to synthesize knowledge about rivers and their networks into an adaptable and readily-usable form. VotE is not itself a dataset, but rather a database of relationships linking existing datasets that allows for rapid comparison and exports of river networks at arbitrary resolutions. VotE’s underlying river network (and drainage basins) is extracted from MERIT-Hydro. We link within VotE a newly-compiled dam dataset, streamflow gages from the GRDC, and published global river network datasets characterizing river widths, slopes, and intermittency. We highlight VotE’s utility with a demonstration of how vector-based river networks can be exported at any requested resolution, a global comparison of river widths from three independent datasets, and an example of computing watershed characteristics by coupling VotE to Google Earth Engine. Future efforts will focus on including real-time datasets such as SWOT river discharges and ReaLSAT reservoir areas.
Concurrent temperature and precipitation extremes during Indian summer monsoon generally have signicant effects on agriculture, society and ecosystems. Due to climate change, frequency and spatial extent of concurrent extremes have changed, and there is a need to advance our understanding in this domain. Quantication of individual extremes (temperature and precipitation) during the summer monsoon season and its teleconnections to climate indices have been studied comprehensively. But, less attention is devoted to the quantication of concurrent extremes and its teleconnections to climate indices. In this study, concurrent extremes (dry/hot and wet/cold) based on mean monthly temperature and total monthly precipitation during the Indian summer season from 1951 to 2019 over the Indian mainland are investigated. Next, the study uses wavelet coherence analysis to unravel the teleconnections of the spatial extent of concurrent extremes to climate indices (Nino 3.4, WEIO SST and SEEIO SST). Results show that the frequency of wet/hot concurrent extremes has increased signicantly, while the frequency of wet/cold concurrent has decreased for the time window 1985 to 2019 relative to 1951-1984. Also, a statistically signicant increase (decrease) in the spatial extent exists in concurrent dry/hot (wet/cold) extremes during the July, August and September months. The ndings of this study could advance our understanding of changes in concurrent extremes during the Indian summer monsoon due to climate change.