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28 results for “Firn”
Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan
<p><strong>README Grigoriev data</strong></p> <p><strong>Overview</strong></p> <p>The Grigoriev data collection consists of the following files, which are briefly explained further below.<br>From a relatively large number of files and for clarity, we provide mainly those files which have been directly<br>used in the generation of figures contained in Machguth et al. (2024). While the use in figure<br>creation was the main selection criteria, the files have not been truncated to data shown in the figures. <br>The files contain more information than shown in the figures. A few files have been added for completeness although<br>not used to create figures (see below).</p> <p>The data sets provided in this repository are listed in the following. Most of these tables contain relatively raw data. <br>The suggested citations are added in brackets. Please also check Table 1 in Machguth et al. (2024) for potential further references.</p> <p>- 1990_GRG_90_H1-BETA.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H1-CHM.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H1-STRAT.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H2-CHM.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H2-STRAT.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_H1-H2_2018_Grigoriev_MI-decadal.xlsx (Arkhipov et al., 1996; Thompson et al., 1997; Machguth et al., 2024)<br>- 2001_GRG_01_S1-EE.xlsx (Arkhipov et al., 2004; Mikhalenko et al., 2005)<br>- 2001_metals.pdf (Usubaliev, 2003)<br>- 2003_GRG03-S1-EE_001.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_GRG03-S2-EE 001.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_pits.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_temperature_density.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_temperature_logger_data.xls (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2018_density_stratigraphy_field_and_PSI_by_centimeter.xlsx (Machguth et al., 2024)<br>- 2018_PSI_dating_20230517.xlsx (Eichler et al., 2020; Machguth et al., 2024)</p> <p><br><strong>Detailed Information</strong></p> <p>1990_GRG_90_H1-BETA.xlsx: refers to Core 1 1990 (labelled H1 probably for "Hole 1"). Unknown to what the 1991 data refer, likely a repeat measurement.</p> <p>1990_GRG_90_H1-CHM.xlsx: Chemistry Core 1 1990.</p> <p>1990_GRG_90_H1-STRAT.xlsx: Stratigraphic information Core 1 1990</p> <p>1990_GRG_90_H2-CHM.xlsx: Chemistry Core 2 1990</p> <p>1990_GRG_90_H2-STRAT.xlsx: Stratigraphic information Core 2 1990</p> <p>1990_H1-H2_2018_Grigoriev_MI-decadal.xlsx: This table we calculated from the 1990 tables as well as the 2018 data for the purpose of visualizing<br> decadal means in MIs (Fig. 7). Decadal dating of the 1990 cores was done based on the bomb horizon of 1963 (Thompson et al., 1993, 1997), <br> decadal picks from Thompson et al. (1993) and personal communication by Lonnie Thompson (email 19 June 2023).</p> <p>2001_GRG_01_S1-EE.xlsx: 2001 core, stable water isotope ratios, firn temperatures, percentage of infiltration ice, stratigraphy</p> <p>2003_GRG03-S1-EE_001.xlsx: 2003 51m and 22.6m cores, 51m core was drilled thermally, 22.6m core mechanically. For the latter similar data as for 2001</p> <p>2003_GRG03-S2-EE 001.xlsx: 2003 21.3m core. Reduced amount of measured parameters compared to e.g. 2001 core. </p> <p>2003_pits.xlsx: Stratigraphy and density measured in a series of snow pits in 2003.</p> <p>2003_temperature_density.xlsx: Density and temperature measured in 2003 22.6m core. Comparison of T_ice at 4440 m a.s.l. to 1962 core (Dikikh, 1965)</p> <p>2003_temperature_logger_data.xls: Firn temperatures measured through a thermistor chain during 3 days in June 2003. Data from 14 June have been used for Fig. 5.</p> <p>2018_density_stratigraphy_field_and_PSI_by_centimeter.xlsx: 2018 core stratigraphy and density. This is a somwhat outdated file which shows the data per centimetre.<br> The file compares the two measurements of density (only the one from the laboratory was used in Machguth et al., 2024). <br> Also contains visually observed dust layers (not shown in Machguth et al., 2024)</p> <p>2018_PSI_dating_20230517.xlsx: Complete data from the analysis of the 2018 core.</p> <p><br><strong>Bibliography</strong></p> <p>Arkhipov, S. M., Mikhalenko, V. N., & Thompson, L. (1996). Struktura i stratigrafiya deyatel’nogo sloya lednika Grigor’eva na Tyan’-Shanye (Structure and stratigraphy of the active layer <br>of the Griroriev glacier in the Tjan-Shan). Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 80, 68–83.</p> <p>Arkhipov, S. M., Mikhalenko, V. N., Kunakhovich, M. G., Dikikh, A. N., and Nagornov, O. V.: Termicheskiy reshim, uslovija l’doobrazovanija i akkumulatsija na lednike Grigor’eva (Tyan’-<br>Shan), v 1962–2001 gg. (Thermal regime, types of ice formation and accumulation on the Grigoriev glacier (Tien Shan), 1962–2001), Materialy Glyatsiologicheskikh Issledovaniy (Data<br>of Glaciological Studies), 96, 77–83, 2004.</p> <p>Eichler, A., Kronenberg, M., Brütsch, S., Rüthi, M., Heule, M., Schwikowski, M., et al. (2020). Chernobyl horizon in a Central Asian ice core. <br>Annual Report 2019 - Laboratory of Environmental Chemistry - PSI, 31.</p> <p>Kutuzov, S. S.: Prostranstvennie izmenenija i stroenie lednikov vnutrennogo Tyan’-Shanya za poslednie 150 let (Spatial changes and structure of the glaciers of the inner Tien Shan over the last 150<br>years), Master’s thesis, Lomonossov State University, Moskva, 2005.</p> <p>Machguth, H., Eichler, A., Schwikowski, M., Brütsch, S., Mattea, E., Kutuzov, S., et al. (2024). Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan. <br>The Cryosphere, 18(4), 1633–1646. https://doi.org/10.5194/tc-18-1633-2024</p> <p>Mikhalenko, V. N., Kutuzov, S. S., Fayzrakhmanov, F. F., Nagornov, . B., Thompson, L. G., Kunakhovich, M. G., Arkhipov, S. M., Dikikh, A. N., and Usubaliev, R.: Sokrashhenie oledenenija<br>Tyan’-Shanja v XIX – nachale XXI vv.: rezul’taty kernovoro burenija i izmerenija temperatury v skvazhinakh (Glacier recession in the Tien Shan from the XIX to the beginning of the XXI century:<br>results from ice core drilling and borehole temperature measurements), Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 98, 175–182, 2005.</p> <p>Thompson, L. G., Mosley-Thompson, E., Davis, M., Lin, P. N., Yao, T., Dyurgerov, M., & Dal, J. (1993). “Recent warming” ice core evidence from tropical ice cores with emphasis <br>on Central Asia. Global Planet. Change, 7(1–3), 145–156. https://doi.org/10.1016/0921-8181(93)90046-Q</p> <p>Thompson, L. G., Mikhalenko, V., Mosley-Thompson, E., Durgerov, M., Lin, P. N., Moskalevsky, M., et al. (1997). Ice core records of recent climatic variability: Grigoriev and It-Tish ice caps <br>in Central Tien Shan, Central Asia. Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 81, 100–109.</p> <p>Usubaliev, R. A. (2003). Khimitcheskoe zagryaznenie lednikov Tyan’-Shanya (na primere lednika Grigor’eva) (Chemical pollution of Tien Shan glaciers (on the example of Grigoriev Glacier)). <br>Izvestija Natsional’noy Akademii Nauk Kirgizskoy Respubliki (News of the National Academy of Sciences of the Kyrgyz Republic), 4, 154–160.</p>
Abramov glacier firn data
<p>Historical and recent firn data for Abramov glacier, Pamir Alay compiled and measured in the context of the PhD Thesis 'Changing glacier firn in Central Asia and its impact on glacier mass balance' by Marlene Kronenberg (2022).</p> <p>This repository contains firn data measured in 2018 consisting of GPR measurements (zip folder), subsurface temperature measurements (zip folder) and firn core data from three drill sites as well as historical firn profile data digitized from Kislov (1982) and Suslov and Krenke (1980). Refer to abra_firn_cores_overview.xlsx for an overview of the core data.</p> <p>The data are described in Kronenberg et al. (2021) and Kronenberg (2022). Please refer to these studies when using the data.</p> <p>References:</p> <p>Kislov BV (1982) Formation and regime of the firn-ice stratum of a mountain glacier [in Russian] (Ph.D. thesis). SARNIGMI Tashkent.</p> <p>Kronenberg M, Machguth H, Eichler A, Schwikowski M, Hoelzle M (2021). Comparison of historical and recent accumulation rates on Abramov Glacier, Pamir Alay. Journal of Glaciology 67(262), 253–268. https://doi.org/10.1017/jog.2020.103</p> <p>Kronenberg Marlene. (2022). Changing glacier firn in Central Asia and its impact on glacier mass balance. PhD thesis, University of Fribourg.</p> <p>Suslov VF and Krenke AN (1980) Abaramov Glacier (Alay Range) [in Russian]. St. Petersburg: Gidrometeoizdat.</p>
Modelled and Sentinel-1 detected firn aquifers areas in the Antarctic Peninsula
<p>FDM results: This dataset contains firn aquifer extent output from IMAU-FDM (Firn Densification Model), version v1.2A, for the Antarctic Peninsula on a 5.5 km grid. The dataset consists of maps of the extent of simulated seasonal aquifers in at least one year (2017-2020), perennial aquifers in at least on year (2017-2020), and perennial aquifers in all years (2018-2020). Further details are described in Buth et al. (2022).<br> Model adjustments and run were performed by Sanne B. M. Velduijsen.</p> <p>S1 detection results: The GeoTIFF image is the result of the Sentinel-1 firn aquifer detection routine which makes use of the typical delayed increase of SAR backscatter after the peak melt season in case of an aquifer. The image has two bands per year (2017-2020), one containing the DOY80 parameter for the whole Antarctic Peninsula (excluding masked areas, see Buth et al, 2022), the other containing DOY80 only for detected aquifer areas, where it exceeds the threshold of DOY80=105. DOY80 here stands for the day of the year at which 80% of the September Sentinel-1 HH backscatter is reached. Further details are described in Buth et al. (2022).<br> S1 aquifer detection was performed by Lena G. Buth, using the Python API of the Google Earth Engine. The associated code is available as a GitLab project: https://gitlab.awi.de/lenbuth/tc-aquifers</p>
Greenland Ice Sheet modeled firn properties from SNOWPACK and the Community Firn Model (1980-2020)
<p>This dataset contains model output from the physics-based SNOWPACK firn model and the semi-empirical Community Firn Model (CFM) over the Greenland Ice Sheet from 1980 through 2020. Included are individual density profiles for locations with firn density observations as well as firn air content (FAC) calculated over different depth intervals. Data for both models are supplied. These data are used in a manuscript to be submitted to The Cryosphere journal (see Thompson-Munson et al., in review).</p>
SNOWISO model snow- and firn core simulations for the EastGRIP drilling site in Greenland
<p>This dataset (.csv) includes four SNOWISO v2 snowpack simulations of the stable water isotopes (δ<sup>18</sup>O, δD, d-excess) and is the result of snowpack simulations in:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters</em>, <a href="https://doi.org/10.1029/2023GL104249">http</a><a href="https://doi.org/10.1029/2023GL104249">s://doi.org/10.1029/2023GL104249</a></p> <p>The SNOWISO model is a 1-D isotope-enabled snowpack and surface exchange model. The model accumulates snowfall (input) and applies water vapor exchange (input) at the snow surface with subsequent isotopic fractionation of the surface snow. In addition, diffusion of water isotopes in the accumulated snowpack is applied. This dataset is simulated in a 1 cm vertical layer resolution.</p> <p>The scientific theory of the SNOWISO model is described in:<br><em>Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529.</em></p> <p>The documentation of the SNOWISO model v2 operational set-up is given in:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, </em><a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a></p> <p>This model dataset consists of simulations for two model configurations each, with (control) and without (no_frac) fractionation during vapor exchange: </p> <ol> <li>daily average isotopes in the <strong>surface snow</strong> (top 2 cm) for the periods 11/05/2018-5/8/2018 and 17/5/2019-31/7/2019 <ul> <li>surface_snow_simulation_2018-2019_control.csv</li> <li>surface_snow_simulation_2018-2019_no_frac.csv</li> </ul> </li> <li>three 1-m long <strong>snow cores </strong>ending in 2017, 2018, and 2019, respectively <ul> <li>snowpack_core_simulation_2017_control.csv</li> <li>snowpack_core_simulation_2018_control.csv</li> <li>snowpack_core_simulation_2019_control.csv</li> <li>snowpack_core_simulation_2017_no_frac.csv</li> <li>snowpack_core_simulation_2018_no_frac.csv</li> <li>snowpack_core_simulation_2019_no_frac.csv</li> </ul> </li> <li>one <strong>firn core </strong>simulation in the period 1990-2011 (~6 m) <ul> <li>snowiso_model_1990-2012_control.csv</li> <li>snowiso_model_1990-2012_no_frac.csv</li> </ul> </li> <li>one <strong>firn core </strong>simulation in the period 1990-2020 (~8.5 m) <ul> <li>snowiso_model_1990-2020_control.csv</li> <li>snowiso_model_1990-2020_no_frac.csv</li> </ul> </li> </ol> <p>Model input:</p> <ul> <li>6-hourly precipitation rate, vapor, and precipitation water stable isotopes from ECHAM6-wiso simulation nudged to the ERA-5 reanalysis (https://zenodo.org/record/8341390)</li> <li>hourly latent heat flux, near-surface meteorological variables, and snowpack variables from MARv3.12 simulation driven by the ERA-5 reanalysis (https://zenodo.org/record/8335402)</li> </ul> <p>Please be encouraged to contact me (Laura.Dietrich@uib.no) if you have any questions or ideas regarding these SNOWISO model simulations.<br><br><strong>Data usage notice:</strong></p> <p>When using the <strong>SNOWISO model</strong>, you should refer to:<br><em>Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529.</em></p> <p>If you use <strong>any of these simulations</strong>, you should refer to:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a></em></p> <p> </p>
Reconstruction of atmospheric H2 from Greenland and Antarctic firn air
<p>This data set contains:</p> <ol> <li>firn air depth profiles of H<sub>2</sub> from 4 polar sites (South Pole and Megadunes in Antarctica) and NEEM and Summit in Greenland used to by Patterson et al., in press to reconstruct the history of atmospheric H<sub>2</sub> </li> <li>Matlab code for the UCI firn air model and data used in the reconstructions </li> </ol> <p>Reference: Patterson, J. D., Aydin, M., Crotwell, A. M., Pétron, G., Severinghaus, J. P., Krummel, P. B., Langenfelds, R. L., Petrenko, V. V., and Saltzman, E. S.: Reconstructing atmospheric H<sub>2</sub> over the past century from bi-polar firn air records, Clim. Past, https://doi.org/10.5194/cp-2023-27, in press.</p> <p>Note: The firn air depth profiles archived here are processed to remove outliers and average replicates. For raw data, please contact the laboratories at which the measurements were made (G. Petron, GML/NOAA; P. Krummel; CSIRO). </p>
Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica
<p>This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.</p> <p><br> 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator.</p> <p>2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data.</p> <p>3. Refraction P wave travel time from a geophone array refraction survey.</p>
Gregoriev firn data
<p>Firn core and thermistor data measured in 2018 within the PhD Thesis 'Changing glacier firn in Central Asia and its impact on glacier mass balance' by Marlene Kronenberg (2022).</p> <p>Kronenberg Marlene. (2022). Changing glacier firn in Central Asia and its impact on glacier mass balance. PhD thesis, University of Fribourg.</p>
DATASET: In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet
<p>This repository contains all the data and code used to analyse these data related to the paper "In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet" by Clerx et al. (2022), published in "The Cryosphere".</p>
Runoff from Greenland's Firn Area - Why do MODIS, RCMs and a Firn Model disagree? - Code and Data
<h1>README – Overview code and data for obtaining MODIS runoff limits and comparison to and between MAR and RACMO / IMAU-FDM</h1> <p> </p> <h2>Basic workflow</h2> <p>Table 1 lists the projects, the pieces of code in the projects and the key output of each piece of code. This serves at illustrating the basic workflow. More detailed information on required input is provided below. The README files of the various projects provide details on how to use the code.</p> <p> </p> <p><strong><em>Table 1:</em></strong><em> This <strong>table might not display properly, please refer to the file _README_overview.pdf.</strong> General project overview. The projects and code are shown in the order they are intended to be used. In italic are parts of code that were not used for Machguth et al. (in review) but instead for Machguth et al. (2022). They might not be fully compatible anymore with runoff limits Y<sub>r</sub> calculated along flowline-polygons.</em></p> <table> <tbody> <tr> <td> <p><strong>Project</strong></p> </td> <td> <p><strong>Code</strong></p> </td> <td> <p><strong>Output</strong></p> </td> </tr> <tr> <td> <p>flowlines</p> </td> <td> <p>crop_gdalwarp.py</p> </td> <td> <p>DEM, cropped and reprojected to required size and grid (identical to the MODIS files)</p> </td> </tr> <tr> <td> <p>flowline_seedpoints.py</p> </td> <td> <p>Seedpoints to calculate flowlines</p> </td> </tr> <tr> <td> <p>flowline.py</p> </td> <td> <p>Flowlines and flowline- polygons</p> </td> </tr> <tr> <td> <p>MODIS_Greenland</p> </td> <td> <p>MODIS_array_filter.py</p> </td> <td> <p>Daily MODIS grids filtered for outliers</p> </td> </tr> <tr> <td> <p>MODIS_stddev_spatial.py</p> </td> <td> <p>Daily grids of MODIS spatial standard deviation</p> </td> </tr> <tr> <td> <p>MODIS_mean_stddev.py</p> </td> <td> <p>Greenland-wide map of background MODIS spatial standard deviation</p> </td> </tr> <tr> <td> <p>MODIS_NDWI_tiff_to_nc.py</p> </td> <td> <p>MODIS NDWI converted from tiff to netCDF</p> </td> </tr> <tr> <td> <p>MODIS_find_slush_limit.py</p> </td> <td> <p>Table of all detected daily <em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>Greenland_RCM_analysis_prep</p> </td> <td> <p>modis_vs_mar.py</p> </td> <td> <p>Tables of daily MAR <em>Y<sub>r</sub></em> for all flowline-polygons</p> </td> </tr> <tr> <td> <p>modis_vs_racmo.py</p> </td> <td> <p>Table of daily RACMO <em>Y<sub>r</sub></em> for all flowline-polygons</p> </td> </tr> <tr> <td> <p>MODIS_Greenland_analysis</p> </td> <td> <p>MODIS_A_SLmax.py</p> </td> <td> <p>Calculates annual maxima of the Yr, written into table. C<em>reates plots visualizing MODIS Y<sub>r</sub> spatial and temporal distribution – only used in Machguth et al. (2022)</em></p> </td> </tr> <tr> <td> <p><em>MODIS_A_SLanalyis.py</em></p> </td> <td> <p><em>Plots that visualize progression and forcing behind MODIS Y<sub>r</sub> – only used in Machguth et al. (2022)</em></p> </td> </tr> <tr> <td> <p>MODIS_comp_RCM_maxYs.py</p> </td> <td> <p>For all of Greenland: Plots that compare RCM and MODIS max<em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>MODIS_comp_RCM_Ys.py</p> </td> <td> <p>For all of Greenland: Plots that compare RCM and MODIS daily <em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>Greenland_RCM_analysis</p> </td> <td> <p>RCM_analysis_comp_Ktransect.py</p> </td> <td> <p>For the K-transect: Plots/tables that compare RACMO/FDM and MAR Yr as well as various RCM parameters</p> </td> </tr> <tr> <td> <p>Jupyter notebooks</p> </td> <td> <p>K-transect_MAR_Greenland-wide_to_K-transect.ipynb</p> </td> <td> <p>Writes MAR .nc files that contain data only for the K-Transect</p> </td> </tr> <tr> <td> <p>K-transect_RACMO-MAR_depth-data-plot.ipynb</p> </td> <td> <p>For the K-transect: Plots to compare depth information of MAR and RACMO/FDM</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <h2>Input and output data</h2> <p>Unless mentioned otherwise, all input data required is provided in this repository. Input files for one piece of code are at the same time the output of a piece of code that needs to be run before.</p> <p><em>Note 1:</em> due to space constraints only the so called “test” data set for runoff limit detection is provided. The full input data to Greenland-wide runoff limit detection are too large in volume but can be obtained from the authors.</p> <p>Note 2: While we only provide the test data for the MODIS runoff limit detection, we provide the full output of the Greenland-wide runoff limit detection (these data are small in volume).</p> <h3>Input to flowlines/crop_gdalwarp.py</h3> <p>arcticdem_mosaic_100m_v30_greenland_icesheet_geoidCorr.tif</p> <h3> </h3> <h3>Input to <em>flowlines/flowline_seedpoints.py</em></h3> <p>seedline_v2.1.shp</p> <h3> </h3> <h3>Input to <em>flowlines/flowline.py</em></h3> <p>è In the case of using the test data, no seed file exists. Do not provide one, seed points will be calculated along a north-south line as specified.</p> <p>greenland_vel_mosaic200_2015-2018_vx_v02-composite-crop.tif</p> <p>greenland_vel_mosaic200_2015-2018_vy_v02-composite-crop.tif</p> <p>dem_test_gapfilled.tif</p> <p><em>or for Greenland-wide:</em></p> <p>greenland_vel_mosaic500_2015-2018_vx_v02-composite-crop.tif</p> <p>greenland_vel_mosaic500_2015-2018_vy_v02-composite-crop.tif</p> <p>arcticdem_mosaic_500m_v30_greenland_icesheet_geoidCorr_GapFilled.tif</p> <p>seedpoints_v3.4.shp</p> <h3> </h3> <h3>Input to <em>MODIS_Greenland/MODIS_array_filter.py</em></h3> <p>/sat_modis_proc_GR_l1test/*</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_stddev_spatial.py</em></h3> <p>/sat_modis_proc_test_l2/*</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_mean_stddev.py</em></h3> <p>/sat_modis_proc_test_l3/*</p> <p>dem_test_gapfilled.tif</p> <p> </p> <h3>Input to <em>MODIS_Greenland/ MODIS_NDWI_tiff_to_nc.py</em></h3> <p>è Input data were computed directly on Google Earth Engine, they have not been preserved, only output data exist</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_find_slush_limit.py</em></h3> <p>/sat_modis_proc_test_l3/*</p> <p>/sat_modis_proc_test_l2_NDWI/*</p> <p>mask_greenland_icesheet/dem_test_gapfilled.tif</p> <p>Ys_polygons__test_W20km.shp</p> <p>flowlines__test_W20km.shp</p> <p>test_MOD10A1.l3.v4_yrs2000-2021_doy126-136_stddev_median.tif</p> <p> </p> <h3>Input to <em>Greenland_RCM_analysis_prep/modis_vs_mar.py</em></h3> <p>/flash/tedstona/MARv.HorstRCMStudy_20240624/*</p> <p>what is the DEM, probably the same as used elsewhere?</p> <p>flowline Polygons</p> <p>/flash/tedstona/_list_PolyIDs.xlsx</p> <p>Why output still to 'MAR-v3.12.1-rlim-slush.nc'? Probably simply not changed without any effect?</p> <h3> </h3> <h3>Input to <em>Greenland_RCM_analysis_prep/modis_vs_racmo.py</em></h3> <p>/flash/tedstona/RACMO/1km/runoff/*</p> <p>arcticdem_mosaic_500m_v30_greenland_icesheet_GeoidCorr_GapFilled_RACMO1km.tif</p> <p>racmo_polys.nc</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_A_SLmax.py</em></h3> <p>_test_slush-limit_output_table.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>_GR_slush-limit_output_table.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_A_SLanalyis.py</em></h3> <p>KAN_U_hourly_v3_fewer_columns.xlsx</p> <p>KAN_M_hourly_v3_fewer_columns.xlsx</p> <p>(selected_SL_years_and_stripes_20km.xlsx: <em>not found and was also only used in Machguth et al., 2022)</em></p> <p>__test_table_complete_annual_max_SL.xlsx</p> <p>__test_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p>__GR_test_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_comp_RCM_maxYs.py</em></h3> <p>è This file has not been tested whether it also works with the “test” data</p> <p>RACMO2.3p2_ERA5_3h_FGRN055.1km-rlim-RUa1mm.xlsx</p> <p>MAR-v.20240624-rlim-RUa1mm.xlsx</p> <p>_stripes_with_aquifers.xlsx</p> <p>__test_table_simple_annual_max_SL.xlsx</p> <p>__test_table_complete_annual_max_SL.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>__GR_table_simple_annual_max_SL.xlsx</p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_comp_RCM_Ys.py</em></h3> <p>è This file has not been tested whether it also works with the “test” data</p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p>flowlines_daily_rlims_RACMO_1mmEvents_10mmAnnual_2000_2021.xlsx</p> <p>flowlines_daily_rlims_MAR-v.20240624_1mmEvents_10mmAnnual_2000_2021.xlsx</p> <p> </p> <h3>Input to <em>Greenland_RCM_analysis/RCM_analysis_comp_Ktransect.py</em></h3> <p>è Certain data sets (indicated below) are not provided as they would exceed the available space in the repository. They can be obtained at no conditions from the authors</p> <p>è This file has not been tested whether it would also works with the “test” data</p> <p><em>Too large: /FDM_Greenland-wide/* </em></p> <p><em>Too large: /RACMO-FDM_K-Transect_updated/* </em></p> <p><em>Too large: /RACMO_Greenland-wide/* </em></p> <p><em>Too large: /MARv3.14_K-transect_continuous/*</em></p> <p><em>Too large: FGRN055_Masks.nc</em></p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p>__GR_table_simple_annual_max_SL.xlsx</p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p> </p> <h3>Input to <em>_notebooks\K-transect_MAR_Greenland-wide_to_K-transect.ipynb</em></h3> <p> </p> <h3>Input to <em>_notebooks\K-transect_RACMO-MAR_depth-data-plot.ipynb</em></h3> <p> </p> <h2>Output data</h2> <p>The basic output files of the runoff limit detection for all of Greenland are:</p> <p>_GR_slush-limit_output_table.xlsx (written by MODIS_Greenland/MODIS_find_slush_limit.py)</p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__GR_table_simple_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__GR_table_complete_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p> </p> <p>The basic output files of the runoff limit detection for the “test” data set are:</p> <p>_test_slush-limit_output_table.xlsx (written by MODIS_Greenland/MODIS_find_slush_limit.py)</p> <p>__test_slush-limit_output_table_OnlyValidEntries.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__test_table_simple_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__test_table_complete_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p> </p> <p> </p> <p> </p>
Ground-penetrating radar and shallow firn cores from Devon Ice Cap, Canadian Arctic
<p>GPR data and firn cores were collected over Devon Ice Cap, Canadian Arctic in May 2015.</p> <p>-----------------------------------------------------------------</p> <p><strong>Firn cores</strong></p> <p>Six ~11 m long firn cores were drilled using a Kovacs drill (9 cm diameter) along the GPR profiles. Pictures were taken of the firn cores, which were subsequently used to log the firn facies. From each core, three sections at different depths that did not include ice layers were weighted with a digital scale and used to calculate the firn density. At each firn core location, the snow depth was recorded, as well as at an additional location where a snow pit was dug (SPB1).</p> <p><em>DIC_firn_cores_2015_density.xlsx</em>: Firn core density measurements. Three measurements were taken from each core, using ice-free sections. </p> <p><em>DIC_firn_cores_2015_stratigraphy.xlsx</em>: Firn stratigraphy for each core location, derived from the firn core pictures. F stands for firn, I for ice layer, and P for percolation pipe/feature (ice in the firn core that does not present as an ice layer throughout the core diameter).</p> <p><em>DIC_snowdepth_2015.xlsx</em>: Snow depth measurements at each core location.</p> <p><em>Firn_core_pictures.zip</em>: Pictures of the firn cores taken with infrared and visible light cameras.</p> <p>-----------------------------------------------------------------</p> <p><strong>GPR data</strong></p> <p>GPR data were collected with a PulseEKKO Noggin radar (Sensors & Software Inc.) with 500 MHz center frequency antennae (i.e., 0.6 m wavelength). The antennae were mounted on a plastic sled towed by snowmobile, generating a data set sampled every ~0.4 m along track. Positioning was obtained with a Leica Geosystems GPS system providing a 25 cm RMS accuracy.</p> <p>Processing of the GPR data was performed in Matlab and included dewow filtering, time-zero shift, background removal, Butterworth band-pass filtering and the application of a gain function.</p> <p><em>PulseEkko_RawData</em>: Folder containing the raw PulseEKKO GPR and GPS files.</p> <p><em>PulseEkko_ProcessedData</em>: Contains the processed GPR data as .mat files. Description of the data files can be found in <em>ProcessedData_readme.txt</em>.</p> <p> </p>
Data for The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica
<p>This is the data used in the paper "The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica"</p>
Reconstruction of atmospheric H2 from Greenland and Antarctic firn air
Open the record for dataset details and reuse information.
NASA GSFC Firn Densification Model version 1.2.1 (GSFC-FDMv1.2.1) for the Greenland and Antarctic Ice Sheets: Jan 1980 - Jul 2024
<p><strong>Overview</strong></p> <p>The NASA GSFC-FDM v1.2.1 provides the evolution of firn air content (FAC), surface mass balance (SMB) (and its individual components), and total firn height change over the Greenland and Antarctic Ice Sheets from January 1, 1980 to July 30, 2024 at 5-day temporal resolution. The model uses atmospheric forcing from NASA GMAO's Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global atmospheric reanalysis, combined with a higher resolution replay (see Medley et al., 2022) as input into the Community Firn Model (CFMv1.1.6) to simulate the evolution of firn properties across the ice sheets. The GSFC-FDMv1.2.1 is provided on a 12.5 km x 12.5 km North/South Polar Stereographic Grid, depending on the ice sheet.</p> <p>For a thorough description of how the GSFC-FDMv1.2.1 was generated see Medley et al. (2022) in <em>The Cryosphere</em>. Release 2 contains model output up through June 30, 2022, whereas the initial release only extended through September 30, 2021. Release 3 contains model output up through July 31, 2024 and uses CFMv2.3.1. The model set up is identical between releases.</p> <h3>*** The spatial grids are incorrect in this version, so we have restricted access to these files. Please use Version 4. ***</h3>
Modeled Greenland firn properties in warming and cooling experiments
<p>This dataset contains processed output from the SNOWPACK firn model forced by idealized MERRA-2 data. Please see the readme for a detailed description of each file.</p>
Data and code for firn air content emulator
<p>This data corresponds to the paper: "Future (2015-2100) Antarctic-wide ice-shelf firn air depletion from a statistical firn emulator".</p> <p>In this new version we share the data and code used in this manuscript.</p> <p>Emulators.zip contains the models used to calculate FAC. The ‘weights’ files are used to standardize data based on the training dataset.</p> <p>fac_tables.zip contains csv files of CMIP6 model min, median, and max FAC for each scenario for each ice shelf.</p> <p>masks.zip contains ice shelf masks and other useful files used in the analysis</p> <p>figures.zip contains code and data used to make figures</p> <p>code.zip contains code used to create and run emulator</p> <p>SNOWPACK_FAC.zip contains FAC results from the SNOWPACK model as netcdf files for each emission scenario</p> <p>ERA5.zip contains the ERA5 climate output used for this analysis</p> <p>ssp***<em>_fac_*</em>.nc.zip are the FAC emulator results using the TP1 ice lens thickness-permeability relationship</p> <p>ssp***_allCMIP6.nc.zip is the CMIP6 climate output from each of the 34 models used in this study. </p>
Peak refreezing in the Greenland firn layer under future warming scenarios
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "Peak refreezing in the Greenland firn layer under future warming scenarios". The data consist of:</p> <p>1. Time series of annual Greenland ice sheet (GrIS) integrated <strong>SMB components</strong> (Gigatons or Gt per year). These time series are available in ASCII format for all simulations presented in the manuscript.</p> <p><strong>RACMO2.3</strong><strong>p2-ERA: </strong>1 member</p> <ul> <li><strong>SMB-components_RACMO2.3p2-ERA_1958-2020_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the ERA-forced RACMO2.3p2 simulation at 5.5 km, statistically downscaled to 1 km spatial resolution (1958-2020).</li> </ul> <p><strong>RACMO2.3</strong><strong>p2</strong><strong>-CESM2</strong>: 3 members</p> <ul> <li><strong>SMB-components_RACMO2.3p2-CESM2-HIST-12_1950</strong><strong>-</strong><strong>2014_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 historical reconstruction (HIST) at 11 km, statistically downscaled to 1 km spatial resolution (1950-2014). RACMO2.3p2 was forced by member 12 of the CESM2-HIST ensemble (HIST-12, see <strong>CESM2-HIST</strong> below).</li> <li><strong>SMB-components_RACMO2.3p2-CESM2-SSP126-4_2015-2099_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126) at 11 km, statistically downscaled to 1 km spatial resolution (2015-2099). RACMO2.3p2 was forced by member 4 of the CESM2-SSP126 ensemble (SSP126-4, see <strong>CESM2-SSP126</strong> below).</li> <li><strong>SMB-components_RACMO2.3p2-CESM2-SSP</strong><strong>585</strong><strong>-</strong><strong>3</strong><strong>_2015-2099_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585) at 11 km, statistically downscaled to 1 km spatial resolution (2015-2099). RACMO2.3p2 was forced by member 3 of the CESM2-SSP585 ensemble (SSP585-3, see <strong>CESM2-SSP585</strong> below).</li> </ul> <p><strong>CESM2</strong><strong>-IND: </strong>11 members</p> <ul> <li><strong>SMB-components_CESM2-IND-</strong><strong>X</strong><strong>_1850-1949_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 pre-industrial reconstructions (IND) at ~111 km, statistically downscaled to 1 km spatial resolution (1850-1949). The term “X” in the filename above represents the CESM2 member ranging from 1 to 11.</li> </ul> <p><strong>CESM2</strong><strong>-HIST: </strong>12 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>HIST</strong><strong>-</strong><strong>X</strong><strong>_1</strong><strong>9</strong><strong>50-</strong><strong>2014</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 historical reconstructions (HIST) at ~111 km, statistically downscaled to 1 km spatial resolution (1950-2014). The term “X” in the filename above represents the CESM2 member ranging from 1 to 12.</li> </ul> <p><strong>CESM2</strong><strong>-SSP126: </strong>6 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP126</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP1-2.6 projections (SSP126) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 5.</li> <li><strong>SMB-components_CESM2-</strong><strong>SSP126</strong><strong>-</strong><strong>6</strong><strong>_</strong><strong>2100</strong><strong>-</strong><strong>2299</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2 SSP1-2.6 projection (SSP126) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299).</li> </ul> <p><strong>CESM2</strong><strong>-SSP245: </strong>6 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP245</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP2-4.5 projections (SSP245) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 6.</li> </ul> <p><strong>CESM2</strong><strong>-SSP370: </strong>5 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP370</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP3-7.0 projections (SSP370) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 5.</li> </ul> <p><strong>CESM2</strong><strong>-SSP585: </strong>8 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP585</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP5-8.5 projections (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 6.</li> <li><strong>SMB-components_CESM2-</strong><strong>SSP585</strong><strong>-</strong><strong>7</strong><strong>_</strong><strong>2100</strong><strong>-</strong><strong>2299</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2 SSP5-8.5 projection (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299). This run (member 7) does not include ice dynamics.</li> <li><strong>SMB-components_CESM2-CISM2-SSP585-8_2100-2299_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2-CISM2 SSP5-8.5 projection (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299). This run (member 8) includes ice dynamics.</li> </ul> <p>2. Time series of annual GrIS-wide <strong>runoff line altitude</strong> in meters above sea-level (m a.s.l.). These time series are available in ASCII format for the RACMO2.3p2-ERA simulation (ERA), and as an ensemble mean for all pre-industrial (IND) and historical reconstructions (HIST), and projections (SSP) including both native CESM2 and RACMO2.3p2-CESM2, statistically downscaled to 1 km.</p> <ul> <li><strong>Runoff-line-altitude_ERA_1958-2020_GrIS_1km.txt</strong>: time series of GrIS-wide runoff line altitude (m a.s.l.) derived from the ERA-forced RACMO2.3p2 simulation at 1 km (1958-2020).</li> <li><strong>Runoff-line-altitude_IND-EnsembleMean_1850-1949_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all pre-industrial reconstructions at 1 km (1850-1949, 11 IND members).</li> <li><strong>Runoff-line-altitude_HIST-EnsembleMean_1950-2014_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all historical reconstructions at 1 km (1950-2014, 13 HIST members).</li> <li><strong>Runoff-line-altitude_SSP126-EnsembleMean_2015-2299_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP1-2.6 projections at 1 km (2015-2299, 7 SSP126 members).</li> <li><strong>Runoff-line-altitude_SSP245-EnsembleMean_2015-2099_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP2-4.5 projections at 1 km (2015-2099, 6 SSP245 members).</li> <li><strong>Runoff-line-altitude_SSP370-EnsembleMean_2015-2099_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP3-7.0 projections at 1 km (2015-2099, 5 SSP245 members).</li> <li><strong>Runoff-line-altitude_SSP585-EnsembleMean_2015-2299_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP5-8.5 projections at 1 km (2015-2299, 9 SSP585 members).</li> </ul> <p>3. Time series of annual <strong>500hPa global temperature anomalies</strong> (ºC). These time series are available in ASCII format for the ERA5 reanalysis (ERA5-reanalysis), and as an ensemble mean for all pre-industrial (IND) and historical reconstructions (HIST), and projections (SSP) from the native CESM2 model at ~111 km spatial resolution.</p> <ul> <li><strong>Tglobal-500hPa-anomaly_ERA5-reanalysis_1950-2020.txt</strong>: time series of annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from the ERA5 climate reanalysis (1950-2020). Anomalies are estimated relative to the reference period 1950-1990.</li> <li><strong>Tglobal-500hPa-anomaly_IND-EnsembleMean_1850-1949.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 pre-industrial reconstructions (1850-1949, 11 IND members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_HIST-EnsembleMean_1950-2014.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 historical reconstructions (1950-2014, 12 HIST members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP126-EnsembleMean_2015-2299.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 short/long term SSP1-2.6 projections (2015-2299, 6 SSP126 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP245-EnsembleMean_2015-2099.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 SSP2-4.5 projections (2015-2099, 6 SSP245 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP370-EnsembleMean_2015-2099.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 SSP3-7.0 projections (2015-2099, 5 SSP370 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP585-EnsembleMean_2015-2299.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 short/long term SSP5-8.5 projections (2015-2299, 8 SSP585 members). Anomalies are estimated relative to the reference period 1850-1949.</li> </ul> <p>The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6 and high-end SSP5-8.5 warming scenario, as well as gridded, monthly downscaled SMB data sets from native CESM2 under pre-industrial (IND), historical (HIST), and short/long term climate projections (SSPs) are freely available from the authors upon request and without conditions (contact: b.p.y.noel@uu.nl). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention and total sublimation (surface and drifting snow) at 1 km horizontal resolution. </p> <p><strong>Abstract</strong>: Firn (compressed snow) covers approximately 90% of the Greenland ice sheet (GrIS) and currently retains about half of rain and meltwater through refreezing, reducing runoff and subsequent mass loss. The loss of firn could mark a tipping point for sustained GrIS mass loss, since decades to centuries of cold summers would be required to rebuild the firn buffer. Here we estimate the warming required for GrIS firn to reach peak refreezing, using 51 climate simulations statistically downscaled to 1 km resolution, that project the long-term firn layer evolution under multiple emission scenarios (1850–2300). We predict that refreezing stabilises under low warming scenarios, whereas under extreme warming, refreezing could peak and permanently decline starting in southwest Greenland by 2100, and further expanding GrIS-wide in the early 22<sup>nd</sup> century. After passing this peak, the GrIS contribution to global sea level rise would increase over twenty-fold compared to the last three decades.</p> <p> </p>
Southeast Greenland firn core FC-23-A - Depth, density, and oxygen isotope data
<p><br><br></p>
Data perennial firn aquifer emulator
<div> <p>These NetCDF files contains perennial firn aquifer output from an XGBoost firn emulator for Antarctica. The datasets are time series of annual perennial liquid water content (if present), and otherwise the days per year without the presence of liquid water within the firn (times -1). The dataset covers the period 2015-2100 for several RCM and GCM combinations. Please feel free to send me a message if you have any questions (s.b.m.veldhuijsen@uu.nl). These data are used in a manuscript to be submitted to <strong><em>The Cryosphere</em></strong>.</p> <p> </p> </div>
H2 in firn air from Megadunes, Antarctica
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