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# Dataset: Subglacial freshwater driven speedup of East Antarctic outlet glacier retreat [https://doi.org/10.5061/dryad.1vhhmgr0b](https://doi.org/10.5061/dryad.1vhhmgr0b) Journal: Journal of Geophysical Research: Earth Surface Principle Investigator: * Tyler Pelle, Scripps Institution of Oceanography, University of California San Diego, [tpelle@ucsd.edu](mailto:tpelle@ucsd.edu) Co-Authors: * Dr. Jamin Greenbaum, Scripps Institution of Oceanography, University of California San Diego * Dr. Shivani Ehrenfeucht, Department of Geography and Environmental Management, University of Waterloo * Prof. Christine Dow, Department of Geography and Environmental Management, University of Waterloo * Dr. Felicity S. McCormack, Securing Antarctica's Environmental Future, School of Earth, Atmosphere, & Environment, Monash University Created on October 4, 2023 ## Description of the data and file structure ### File description: 1. runme.m - MATLAB script used to run coupled ISSM-GlaDS SSP5-8.5_{F,M} simulation - includes melt rate parameterization. 2. ssp585.mat – Yearly ice sheet model output from 2017-2100 for SSP5-8.5 simulation. 3. ssp585_F.mat – Yearly ice sheet model output from 2017-2100 for SSP5-8.5_{F} simulation. 4. ssp585_M.mat – Yearly ice sheet model output from 2017-2100 for SSP5-8.5_{M} simulation. 5. ssp585_FM.mat – Yearly ice sheet model output from 2017-2100 for SSP5-8.5_{F,M} simulation. 6. ssp126.mat – Yearly ice sheet model output from 2017-2100 for SSP1-2.6 simulation. 7. ssp126_F.mat – Yearly ice sheet model output from 2017-2100 for SSP1-2.6_{F} simulation. 8. ssp126_M.mat – Yearly ice sheet model output from 2017-2100 for SSP1-2.6_{M} simulation. 9. ssp126_FM.mat – Yearly ice sheet model output from 2017-2100 for SSP1-2.6_{F,M} simulation. 10. ssp585_Totten_T.mat - Bi-weekly ocean temperature (Ta) for Totten Glacier from January 1, 2017 to December 31, 2099 (high emission). 11. ssp585_Moscow_T.mat - Bi-weekly ocean temperature (Ta) for Moscow University Glacier from January 1, 2017 to December 31, 2099 (high emission). 12. ssp585_Vander_T.mat - Bi-weekly ocean temperature (Ta) for Vander Glacier from January 1, 2017 to December 31, 2099 (high emission). 13. ssp585_Totten_S.mat - Bi-weekly ocean salinity (Sa) for Totten Glacier from January 1, 2017 to December 31, 2099 (high emission). 14. ssp585_Moscow_S.mat - Bi-weekly ocean salinity (Sa) for Moscow University Glacier from January 1, 2017 to December 31, 2099 (high emission). 15. ssp585_Vander_S.mat - Bi-weekly ocean salinity (Sa) for Vander Glacier from January 1, 2017 to December 31, 2099 (high emission). 16. ssp126_Totten_T.mat - Bi-weekly ocean temperature (Ta) for Totten Glacier from January 1, 2017 to December 31, 2099 (low emission). 17. ssp126_Moscow_T.mat - Bi-weekly ocean temperature (Ta) for Moscow University Glacier from January 1, 2017 to December 31, 2099 (low emission). 18. ssp126_Vander_T.mat - Bi-weekly ocean temperature (Ta) for Vander Glacier from January 1, 2017 to December 31, 2099 (low emission). 19. ssp126_Totten_S.mat - Bi-weekly ocean salinity (Sa) for Totten Glacier from January 1, 2017 to December 31, 2099 (low emission). 20. ssp126_Moscow_S.mat - Bi-weekly ocean salinity (Sa) for Moscow University Glacier from January 1, 2017 to December 31, 2099 (low emission). 21. ssp126_Vander_S.mat - Bi-weekly ocean salinity (Sa) for Vander Glacier from January 1, 2017 to December 31, 2099 (low emission). 22. TotBasin.exp - Polygon that contains Totten Glacier over which Totten's ocean temperature is applied. 23. MuisBasin.exp - Polygon that contains Moscow University Glacier over which Totten's ocean temperature is applied. 24. VandBasin.exp - Polygon that contains Vanderford Glacier over which Totten's ocean temperature is applied. ### File specific information: **ASB_IceHydroModel.mat**: All data associated with the ice sheet and subglacial hydrology model initial state is held in ASB_IceHydroModel.mat, which contains a MATLAB ‘model’ object (for more information, see [https://issm.jpl.nasa.gov/documentation/modelclass/](https://issm.jpl.nasa.gov/documentation/modelclass/). In MATLAB, the model can be loaded and displayed by running load(‘ASB_IceHydroModel.mat’), which will load in the model variable ‘md’. Of particular interest will be the following data contained in md: md.mesh (mesh information), md.geometry (initial ice sheet geometry, ice shelf geometry, and bed topography), md.hydrology (initial hydrology model fields), md.initialization (model initialization fields) and md.mask (ice mask and grounded ice mask). Note that all fields are defined on the mesh nodes, and one can plot a given field in MATLAB using the ISSM tool ‘plotmodel’ (e.g., plotmodel(md,'data',md.geometry.bed) will plot the model bed topography). For more information on plotting, please see [https://issm.jpl.nasa.gov/documentation/plotmatlab/](https://issm.jpl.nasa.gov/documentation/plotmatlab/). **Model output files (e.g. ssp585_FM.mat)**: Yearly ice sheet model results between 2017-2100 for all model simulations described in the paper. Fields appended with '*' are included in results with changing subglacial hydrology (ssp126_F, ssp126_M, ssp126_FM, ssp585_F, ssp585_M, ssp585_FM). Fields appended with '**' are included in results where ice shelf melt is enhanced by subglacial discharge (ssp126_M, ssp126_FM, ssp585_M, ssp585_FM). These files contain a MATLAB variable that is the same as the file name, which is a model object of size 1x83 that contains the following yearly variables: * \* Vel (velocity norm, m/yr) * \* Thickness (ice sheet thickness, m) * \* Surface (ice sheet surface elevation, m) * \* Base (ice sheet base elevation, m) * \* BasalforcingsFloatingiceMeltingRate (ice shelf basal melting rate field, m/yr) * \* MaskOceanLevelset (ground ice mask, grounded ice if > 0, grounding line position if = 0, floating ice if < 0) * \* IceVolume (total ice volume in the model domain, t) * \* IceVolumeAboveFloatation (total ice volume in the model domain that is above hydrostatic equilibrium, t) * \* TotalFloatingBmb (Total floating basal mass balance, Gt) * \* \\*ChannelDischarge\\_Node (GlaDS-computed channel discharge interpolated onto model node, m3/s) * \* \\*ChannelDiameter\\_Node (GlaDS-computed channel diameter interpolated onto model node, m) * \* \\*ChannelArea (GlaDS-computed channel area defined on model edges, m2) * \* \\*ChannelDischarge (GlaDS\\_computed channel discharge defined on model edges, m3/s) * \* \\*EffectivePressure (GlaDS-computed ice sheet effective pressure, Pa) * \* \\*HydraulicPotential (GlaDS computed hydraulic potential, - * \* \\*HydrologySheetThickness (GlaDS-computed after sheet thickness, m) * \* \\*GroundedIceMeltingRate (Grounded ice melting rate defined on all grounded nodes, m/yr) * \* \\*\\*melt\\_nodis (ice shelf basal melting rate computed when discharge is set to zero, m/yr) * \* \\*\\*zgl (grounding line height field, m) * \* \\*\\*glfw (grounding line fresh water flux field, m2/s) * \* \\*\\*chan\\_wid (Domain average subglacial discharge channel width, m) * \* \\*\\*maxdist (5L' length scale used in melt computation, m) * \* \\*\\*maxis (maximum discharge at each subglacial outflow location, m2/s) * \**\\*\\_T.mat**: Bi-weekly ocean temperature extracted from an East Antarctic configuration of the MITgcm (Pelle et al., 2021), where '\\*' ssp126 (low emission) or ssp585 (high emission). Ocean temperature was averaged adjacent to each target ice front in both depth and in the contours shown in figure 1b. * \**\\*\\_S.mat**: Same as above, but for salinity in units on the Practical Salinity Scale (PSU). * \***.exp**: Exp files that contain coordinates that outline a polygon for the drainage basins of each major glacier in this study (Vanderford Glacier contains the drainage basins for Adams, Bond, and Underwood Glaciers as well). Recent studies have revealed the presence of a complex freshwater system underlying the Aurora Subglacial Basin (ASB), a region of East Antarctica that contains ~7 m of global sea level potential in ice mainly grounded below sea level. Yet, the impact that subglacial freshwater has on driving the evolution of the dynamic outlet glaciers that drain this basin has yet to be tested in a coupled ice sheet-subglacial hydrology numerical modeling framework. Here, we project the evolution of the primary outlet glaciers draining the ASB (Moscow University Ice Shelf, Totten, Vanderford, and Adams Glaciers) in response to an evolving subglacial hydrology system and to ocean forcing through 2100, following low and high CMIP6 emission scenarios. By 2100, ice-hydrology feedbacks enhance the ASB’s 2100 sea level contribution by ~30% (7.50 mm to 9.80 mm) in high emission scenarios and accelerate retreat of Totten Glacier’s main ice stream by 25 years. Ice-hydrology feedbacks are particularly influential in the retreat of the Vanderford and Adams Glaciers, driving an additional 10 km of retreat in fully-coupled simulations relative to uncoupled simulations. Hydrology-driven ice shelf melt enhancements are the primary cause of domain-wide mass loss in low emission scenarios, but are secondary to ice sheet frictional feedbacks under high emission scenarios. The results presented here demonstrate that ice-subglacial hydrology interactions can significantly accelerate retreat of dynamic Antarctic glaciers and that future Antarctic sea level assessments that do not take these interactions into account might be severely underestimating Antarctic Ice Sheet mass loss. In this data publication, we present the model output and results associated with the following manuscript recently submitted to the Journal of Geophysical Research: Earth Surface: “Subglacial discharge accelerates ocean driven retreat of Aurora Subglacial Basin outlet glaciers over the 21st century”. We include yearly ice sheet model output between 2017-2100 for eight numerical ice-subglacial hydrology model runs. We also include the ice sheet and subglacial hydrology model initial states. In addition, we include all ocean forcing time-series (temperature and salinity for the low emission and high emission climate forcing scenarios for three glacial regions), which are used as input into the melt parameterization. Lastly, we include a MATLAB script that contains the code used to couple the ice-subglacial hydrology models as well as a "readme" file with further information on all data in this publication. Ice sheet model results: Direct results taken from the Ice-sheet and Sea-level System Model (ISSM, Larour et al. 2012) with no processing applied, provided yearly as *.mat files. Ice sheet and subglacial hydrology model initial states: Initial state of the ice sheet model (ice geometry, mesh information, inversion results, etc.) and subglacial hydrology model (steady-state water column thickness, effective pressure, channelized discharge, etc.) containing Aurora Subglacial Basin outlet glaciers with no processing applied, provided as a *.mat file. The contents of the *.mat file is a MATLAB variable of class "model", which is compatible with ISSM. Model coupling script: Documented MATLAB script ready to run with the provided data sets. Ocean temperature and salinity timeseries: Bottom ocean temperature (°C) and salinity (PSU) timeseries (January 1st, 2017 through December 31, 2099) extracted from an East Antarctic configuration of the ocean component of the MITgcm (Pelle et al., 2021). Temperature and salinity are provided bi-weekly and averged both in depth and along the ice fronts of Moscow University, Totten, and Vanderford Glaciers (see white dashed contour in figure 1b of the main manuscript text). Data are provided as *.mat files. Polygons that provide locaion to apply ocean temperature and salinity: Polygons provided as a list of x/y coordinates (meters) are provided in three *.exp files that cover the drainage basins of Moscow University, Totten, and Vanderford Glaciers (the polygon for Vanderford also includes the drainage basins of Adams, Bond, and Underwood Glaciers). 

Proso millet (Panicum miliaceum L.) is a drought tolerant underutilised crop cultivated in rainfed subsistence agricultural systems. Proso millet yields were simulated using a calibrated Agricultural Production Systems Simulator (APSIM) model for 95 locations in Sri Lanka. The yield maps were generated according to the Inverse Distance Weighting (IDW) model using ArcMap 10.7.1. The database contains Proso millet yield maps for current climate and yield change under 5 hypothetical climate change scenarios; 1oC, 1.5 oC and 2 oC temperature increments, 25% rainfall increment, and 25% rainfall reduction compared to the baseline (1980-2009) climate.

In order to achieve paramount economy, hybrid renewable energy sources are gaining importance, as renewable sources are costless. Over the past few years wind energy incorporation drew more consideration in the electricity market, as wind power took an affirmative role in energy saving as well as sinking emission pollutants. Recently developed Grey wolf optimizer (GWO) algorithm has conspicuous behavior for verdicting global optima, without getting ensnared in premature convergence. In the proposed research the exploitation phase of the grey wolf optimizer has been further improved using random exploratory search algorithm, which uses perturbed solutions vectors along with previously generated solution vectors. The paper presents a hybrid version of Grey Wolf Optimizer algorithm combined with random exploratory search algorithm (hGWO-RES) for the solution of combinatorial scheduling and dispatch problem of electric power systems. To validate the feasibility of the algorithm, the proposed algorithm has been tested on 23 benchmark problems. To verify the feasibility and efficacy of operation of proposed algorithm on generation scheduling and dispatch of electric power systems, small and medium scale power systems consisting of 7-, 10-, 19-, 20- and 40-generating units systems taken into consideration. Commitment and scheduling pattern has been evaluated with and without wind integration and it has been experimentally founded that proposed hybrid algorithm provides superior solution as compared to other recently reported meta-heuristics search algorithms.

With the extensive growth in technology, healthcare sector has benefitted a lot recently. Looking into the academic research and validation in the area of medical image processing and visualization, many platforms and the open-source resources are available. Insight toolkit (ITK) and visualization toolkit (VTK) are extensively used for medical image processing and 3D visualization respectively. Resources used to develop an application using ITK-VTK and same resources be used to deliver it to the users such as, clinicians, doctors etc. This can be achieved by using respective hardware and the infrastructure. In the proposed article, the infrastructure and resource used to build and deploy the application and remote access given to the users are elucidated.

Siderophores are low molecular weight secondary metabolites produced by microorganisms under low iron stress as a specific iron chelator. In the present study, a rhizospheric bacterium was isolated from the rhizosphere of sesame plants from Salem district, Tamil Nadu, India and later identified as Bacillus subtilis LSBS2. It exhibited multiple plant-growth-promoting (PGP) traits such as hydrogen cyanide (HCN), ammonia, and indole acetic acid (IAA), and solubilized phosphate. The chrome azurol sulphonate (CAS) agar plate assay was used to screen the siderophore production of LSBS2 and quantitatively the isolate produced 296 mg/L of siderophores in succinic acid medium. Further characterization of the siderophore revealed that the isolate produced catecholate siderophore bacillibactin. A pot culture experiment was used to explore the effect of LSBS2 and its siderophore in promoting iron absorption and plant growth of Sesamum indicum L. Data from the present study revealed that the multifarious Bacillus sp. LSBS2 could be exploited as a potential bioinoculant for growth and yield improvement in S. indicum.

Production of biodiesel from yellow grease (waste cooking oil and waste animal fats) is fast emerging as a promising alternative to address the twin challenges before the biodiesel industry today-fluctuation in prices of vegetable oil and the food versus fuel debate. Yellow grease has a high percentage of free fatty acids (FFA) and proves to be an unsuitable feedstock for biodiesel production from commercially viable alkali-catalyzed production systems due to saponification problems.“Green” methodologies based on heterogeneous solid acid catalyzed reactions have the potential to simultaneously promote esterification and transesterification reactions of yellow grease to produce biodiesel without soap formation and offer easy catalyst separation without generation of toxic streams. This paper presents kinetic studies for the conversion of model yellow grease feeds to biodiesel using a heteropolyacid supported on alumina (HSiW/Al2O3) using a batch autoclave. Three model yellow grease feeds were prepared using canola oil with added FFA such as palmitic, oleic and linoleic acid. A pseudo homogeneous kinetic model for the parallel esterification and transesterification was developed. The rate constants and activation parameters for esterification and transesterification reactions for the model yellow grease feeds were determined. The rate constants for esterification are higher than the transesterification rate constants. The kinetic model was validated using the experimental biodiesel data obtained from processing a commercial yellow grease feed. The kinetic model could be used to design novel processes to convert various low-value waste oils, fats and non-food grade oils to sustainable biodiesel. Keywords: Yellow grease, Canola oil, Free fatty acids, Heteropolyacid, Kinetics for esterification and transesterification

Second-generation bioethanol can be produced from various lignocellulosic biomasses such as wood, agricultural or forest residues. Lignocellulosic biomass is inexpensive, renewable and abundant source for bioethanol production. The conversion of lignocellulosic biomass to bioethanol could be a promising technology though the process has several challenges and limitations such as biomass transport and handling, and efficient pretreatment methods for total delignification of lignocellulosics. Proper pretreatment methods can increase concentrations of fermentable sugars after enzymatic saccharification, thereby improving the efficiency of the whole process. Conversion of glucose as well as xylose to bioethanol needs some new fermentation technologies to make the whole process inexpensive. The main goal of pretreatment is to increase the digestibility of maximum available sugars. Each pretreatment process has a specific effect on the cellulose, hemicellulose and lignin fraction; thus, different pretreatment methods and conditions should be chosen according to the process configuration selected for the subsequent hydrolysis and fermentation steps. The cost of ethanol production from lignocellulosic biomass in current technologies is relatively high. Additionally, low yield still remains as one of the main challenges. This paper reviews the various technologies for maximum conversion of cellulose and hemicelluloses fraction to ethanol, and it point outs several key properties that should be targeted for low cost and maximum yield.

Abstract In this study, crude cellulose derived from cornstalk, after bleaching, was used as raw material for the synthesis of sodium carboxymethyl cellulose (CMC) by reacting with the cellulose with NaOH and chloroacetic acid at 75 °C for 1.5 h. Effects of alkali dosage, concentration of chloroacetic acid on the physical and chemical properties of the CMC products were investigated. It was revealed that the reactants alkali reagent/chloroacetic acid/cellulose at the molar ratio of 4.6:2.8:1and 4:2.5:1, or at the molar ratio of NaOH/ClCH 2 COOH ≈1.6–1.64, resulted in CMC products of relatively high water solubility. The viscosity-average molecular weight M v of these two CMC products obtained at molar ratios of 4.0:2.5:1 and 4.6:2.8:1 is in the range of 1.94 × 10 4 –2.48 × 10 4 g mol −1 , and the average DS of the two products are 0.57 and 0.85, respectively. As the solute concentration is above 2 wt%, the viscosity of the CMC-water solution exhibits nonlinear (exponential) increasing with increasing the solute concentration (typical of non-Newton fluids).