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

Aim Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We study how species community composition would change following anthropogenic and natural disturbances. We determine the main drivers of assemblage dissimilarity for bird and beetle communities. Location Côte-Nord, Québec, Canada. Methods We quantify two climate-induced pathways based on direct and indirect effects on species occurrence under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through habitat-based climate change. We develop empirical models to predict the distribution of more than 100 species over the next century. We analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into 'balanced variation in species occupancy and occurrence' and 'occupancy and occurrence gradient'. Results Both pathways increased dissimilarity in species assemblage. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Main conclusions The two pathways are complementary and alter biodiversity, mainly caused by species turnover. Yet, responses are much larger in magnitude under mixed climate effects. Therefore, the inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Moreover, we expect differences in occupancy between the two studied taxa. This could indicate the potential range of change in boreal species concerning novel environmental conditions.

Les paysages arctiques se caractérisent par la présence de nombreux lacs et étangs qui possèdent des propriétés physico-chimiques et biologiques distinctes. Depuis 2018, nous étudions les communautés zooplanctoniques de plus de 22 lacs et 13 étangs d'eau douce situés au sud de l'Île Victoria à Cambridge Bay, Nunavut (69 ° N, 105 ° O). Pour chacun des lacs et étangs échantillonnés les communautés de zooplancton ont été récoltées et les spécimens ont été identifiés jusqu'à l'espèce. Au total, plus de 77 espèces différentes ont été identifiées incluant 56 rotifères, 6 copépodes, 11 cladocères, 2 crevettes arctiques, une espèce appartenant à la famille des Mysidacea et une crevette têtard. Arctic landscapes are characterized by the presence of many lakes and ponds that exhibit distinct physico-chemical and biological properties. Since 2018, we have been studying the zooplankton communities of more than 22 lakes and 13 freshwater ponds located on southern Victoria Island, Cambridge Bay, Nunavut (69°N, 105°W). For each of the lakes and ponds sampled, zooplankton communities were collected and specimens were identified to species. In total, more than 77 different species were found, including 56 rotifers, 6 copepods, 11 cladocerans, 2 fairy shrimps, a mysid and a tadpole shrimp.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.NUIST.NESM3.amip' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The NUIST ESM v3 climate model, released in 2016, includes the following components: atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa), land: JSBACH v3.1, ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4.1. The model was run by the Nanjing University of Information Science and Technology, Nanjing, 210044, China (NUIST) in native nominal resolutions: atmos: 250 km, land: 2.5 km, ocean: 100 km, seaIce: 100 km.

Files for the manuscript “Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Soils and Fast Cycling of a Boreal Forest Ecosystem” 1. “Raw_Data” folder contains the files in .xlsx: - Lab_Atmospheric_Samples: D14C results from ambient air at the sampled heights. - Lab_Soil_Respiration: D14C results with date and integration time for the FFSR sampling campaign. - Lab_Solid_Samples: D14C and TOC results for soil, vegetation, roots, fungi and incubation samples.

Climate change affects species' spatio-temporal distribution deeply. However, how climate affects the spatio-temporal distribution pattern of related species on the large scale remains largely unclear. Here, we selected two closely related species in the Taxus genus, Taxus chinensis and Taxus mairei, to explore their distribution pattern. Four environmental variables were employed to simulate the distribution patterns using the optimized Maxent model. The results showed that the highly suitable area of T. chinensis and T. mairei in the current period was 1.616 × 105 km2 and 3.093 × 105 km2, respectively. The distribution area of T. chinensis was smaller than that of T. mairei in different periods. Comparison of different periods shows that the distribution area of the two species was almost in stasis from LIG to the future periods. Temperature and precipitation were the main climate factors that determined the potential distribution of the two species. The centroids of T. chinensis and T. mairei were in Sichuan and Hunan provinces in current period, respectively. In the future, the centroid migration direction of the two species would shift towards the northeast. Our results revealed that the average elevation distribution of T. chinensis was higher than that of T. mairei. This study sheds new insights into the habitat preference and limiting environmental factors of the two related species and provides a valuable reference for the conservation of these two threatened species.

This dataset includes data from two experiments.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.GMMIP.CAS.FGOALS-f3-L' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

# 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).