• Energy Research
• 2021-2025close
• CA close

# 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). 

Incident solar irradiance was measured using an ACC (Advanced-Cosine-Collector) RAMSES hyper-spectral radiometer (TriOS) installed on-board the ship during the ARTofMELT2023 expedition in May and June 2023. All times are given in Universal Coordinated Time (UTC).

Logging is the main human disturbance impacting biodiversity in forest ecosystems. However, the impact of forest harvesting on biodiversity is modulated by abiotic conditions through complex relationships that remain poorly documented. Therefore, the interplay between forest management and climate change can no longer be ignored. Our aim was to study the expected long-term variations in the assemblage of bird and beetle communities following modifications in forest management under different climate change scenarios. We developed species distribution models to predict the occurrence of 88 species of birds and beetles in eastern Canadian boreal forests over the next century. We simulated three climate scenarios (baseline, RCP4.5 and RCP8.5) under which we varied the level of harvesting. We also analyzed the regional assemblage dissimilarity by decomposing it into balanced variations in species occupancy and occupancy gradient. We predict that forest harvesting will alter the diversity by increasing assemblage dissimilarity under all the studied climate scenarios, mainly due to species turnover. Species turnover intensity was greater for ground-dwelling beetles, probably because they have lower dispersal capacity than flying beetles or birds. A good dispersal capacity allows species to travel more easily between ecosystems across the landscape when they search for suitable habitats after a disturbance. Regionally, an overall increase in the probability of occupancy is projected for bird species, whereas a decrease is predicted for beetles, a variation that could reflect differences in ecological traits between taxa. Our results further predict a decrease in the number of species that increase their occupancy after harvest under the most severe climatic scenario for both taxa. We anticipate that under severe climate change, increasing forest disturbance will be detrimental to beetles associated with old forests but also with young forests after disturbances.

Incident solar irradiance was measured using an ACC (Advanced-Cosine-Collector) RAMSES hyper-spectral radiometer (TriOS) installed on-board the ship during the ARTofMELT2023 expedition in May and June 2023. All times are given in Universal Coordinated Time (UTC).

The objective of this study is to determine the effects of ocean acidification (OA) on the survival, development and swimming behaviour of embryos of the deep-sea coral Desmophyllum pertusum (syn. Lophelia pertusa). Upon spawning, fertilized embryos were collected and exposed to two pCO2 treatments corresponding to present pCO2 conditions (400 ppm) and future pCO2 conditions predicted under scenario IPCC RCP8.5 for the end of the century (1000 ppm). We monitored survival daily and we measured swimming velocity on day 9 after spawning. Temperature and pH were measured every 24h, salinity was measured every other day, and water samples were collected during the first and last day of the experiment to determine total alkalinity (TA). This dataset includes data on the effects of ocean acidification on swimming velocity of larvae of the deep-sea coral Desmophyllum pertusum. Embryos were exposed to two acidification (pCO2) treatments: present pCO2 conditions (400 ppm) and future pCO2 conditions predicted under RCP8.5 for the end of the century (1000 ppm). After rearing the embryos in the respective treatments for nine days, we recorded the swimming behaviour of larvae with a video camera. Videos were analyzed with manual particle tracking, and here we report the swimming velocity and total traveled distance of larvae in each experimental treatment. Experimental treatment, CTR: present pCO2 400 ppm; AC: end-of-century pCO2 1000 ppm

Premise: Seed recruitment niches along estuarine elevation gradients are seldom experimentally field-tested under tidal regimes of the Pacific Northwest of North America. Addressing this knowledge gap is important to better understand estuary restoration and plant community response to sea level rise. Methods: Germination was tested in marsh organ mesocosms across an elevation gradient (0.5–1.7 m above mean sea level). Seeds were sown on sterile peat moss, and the tops of pipes were secured with horticultural “frost cloth” to ensure no experimental seeds were washed out and no new seeds were introduced. The trials tested artificial and overwinter chilling regimes, as well as the presence and/or absence of a near-neighbor transplant. Results: Carex lyngbyei had significant elevation-driven germination after overwinter and artificial chilling. Schoenoplectus tabernaemontani had near-significant germination across elevation after overwinter chilling, and germination in the absence of competition was significantly greater than with a near-neighbor transplant. Discussion: Carex lyngbyei had the highest germination rate at higher elevations, which suggests restricted seed recruitment potential, and required clonal expansion to extend into lower marsh elevations. Identifying species-specific recruitment niches provides insight for restoration opportunities or invasive species monitoring, as well as for estuary migration under sea level rise. Datasets contain temperature data automatically recorded by iButton data loggers (iButtonLink, LLC, Whitewater, WI, USA. Ibuttonlink.com), or manually observed/recorded percent seed germination in experimental mesocosm (marsh organ). Please see ReadMe, then metadata files for each data file. R code is annotated with notes about analysis process, and often links to resources used to produce analyses.

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. The historical and future global warming thresholds and their corresponding time periods are as follows: Global Warming Scenario Time Period Historical 1991-2021 Global Warming 0.5ºC 2003-2033 Global Warming 1.0ºC 2014-2044 Global Warming 1.5ºC 2024-2054 Global Warming 2.0ºC 2034-2064 Global Warming 2.5ºC 2042-2072 Global Warming 3.0ºC 2051-2081 Global Warming 3.5ºC 2064-2094 The following variables are included in the files: Variable Description RUN Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data YEAR Year associated with the record MONTH Month associated with the record DAY Day of the month associated with the record HOUR Hour associated with the record YDAY Day of the year associated with the record DRI_kJPerM2 Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated) DHI_kJperM2 Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated) DNI_kJperM2 Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated) GHI_kJperM2 Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated) TCC_Percent Instantaneous total cloud cover at the HOUR in % (range: 0-100) RAIN_Mm Total rainfall in mm (total from previous HOUR to the HOUR indicated) WDIR_ClockwiseDegFromNorth Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North) WSP_MPerSec Instantaneous wind speed at the HOUR in meters/sec RHUM_Percent Instantaneous relative humidity at the HOUR in % TEMP_K Instantaneous temperature at the HOUR in Kelvin ATMPR_Pa Instantaneous atmospheric pressure at the HOUR in Pascal SnowC_Yes1No0 Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow) SNWD_Cm Instantaneous snow depth at the HOUR in cm

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells.

This contains the monthly heating rate kernel based on ERA5 and data for "Diagnosing atmospheric heating rate changes using radiative kernels". Please find more details in README file.