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15 results for “Sea ice loss”
Supporting Data for: McKenna et al. (2018), Arctic sea-ice loss in different regions leads to contrasting Northern Hemisphere impacts
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: </p> <p>McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. <em>Geophysical Research Letters</em>, 45, 945-954. <a href="https://doi.org/10.1002/2017GL076433">https://doi.org/10.1002/2017GL076433</a></p> <p> </p> <p>Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'.</p> <p>All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are:</p> <ul> <li>ua: zonal winds</li> <li>zg: geopotential height</li> <li>ts: surface temperature</li> <li>hfls, hfss, rlds, rlus: surface heatfluxes</li> <li>Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February)</li> </ul> <p>The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. </p> <p>Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files. </p>
Model data from GRL paper: "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone"
<p>This folder includes monthly model data (experiments using SC-WACCM4 and E3SMv1) of temperature (TEMP) and sea level pressure (SLP) that were used in the Geophysical Research Letters paper "<strong>Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone</strong>", # 2020GL088583. See also for additional information/data: <a href="https://zenodo.org/record/3066448">https://zenodo.org/record/3066448</a></p> <p>Labe, Z., Peings, Y., & Magnusdottir, G. (2020). Warm Arctic , cold Siberia pattern : role of full Arctic amplification versus sea ice loss alone. <em>Geophysical Research Letters</em>, 1–26. <a href="https://doi.org/10.1029/2020GL088583">https://doi.org/10.1029/2020GL088583</a></p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088583">[Paper]</a><a href="https://sites.uci.edu/zlabe/arctic-amplification/">[Plain Language Summary]</a><a href="https://github.com/zmlabe/AA">[GitHub]</a></p>
Processed model output and observational products used in `Observed winds crucial for September Arctic sea ice loss'
<p>Processed model output from wind-nudging experiments used to investigate Arctic sea ice loss. Also includes processed observational data shown in the manuscript.</p> <p> </p> <p>For further details, see </p> <p>Roach, L. A and Blanchard-Wrigglesworth E. (2022). Observed winds crucial for September Arctic sea ice loss. Accepted at Geophysical Research Letters</p>
Datasets for "Arctic sea ice loss weakens Northern Hemisphere summertime storminess due to ocean coupling"
<p>The datasets contain post-processed model outputs and reanalysis data for creating figures in the paper. The data are npz files, which can be easily accessed using Python 3 and numpy package.</p>
Data supporting 'The Response of Midlatitude Surface Temperature Persistence to Arctic Sea-Ice Loss' by Neil T Lewis, William J M Seviour, Hannah E Roberts-Straw, and James A Screen.
<p>Data supporting Lewis et al., 2023. The Response of Midlatitude Surface Temperature Persistence to Arctic Sea-Ice Loss. Submitted to Geophysical Research Letters.</p><p>All model output is contained within the folder data/. All data is in NetCDF format.</p><p>The folder data/PAMIP/ contains output from coupled AOGCMs that contributed piArcSIC and futArcSIC timeslice runs to PAMIP. The AOGCMS present are: HadGEM3-GC31-MM, IPSL-CM6A-LR, CESM2-WACCM6, and CESM-WACCM-SC. For each model + run, two data files are included. One contains the autocorrelation of surface temperature, at 5, 10, and 15 day lags. The second contains the frequency and duration of persistent extremes (as defined in Lewis et al., 2023).</p><p>Additional output is included in data/PAMIP/ from extended pdSIC-ext and futArcSIC-ext experiments run using CNRM-CM6-1. For each run, a file containing the autocorrelation of surface temperature (as above) is included.</p><p>The folder data/CMIP/ contains output from CMIP6 historical/SSP585 runs using three of the models listed above: HadGEM3-GC31-MM, IPSL-CM6A-LR, and CESM2-WACCM6. For each model, 'pre-industrial' and 'future' output is available. Output in these files was computed from 30-year time-periods, subsampled from the historical/SSP585 runs, selected so that the 30-year average sea-ice area matched that in the corresponding PAMIP runs above. For each model and time period, two data files are included. One contains the autocorrelation of surface temperature, at 5, 10, and 15 day lags. The second contains the frequency and duration of persistent extremes (as defined in Lewis et al., 2023).</p><p>Output is also included in data/CMIP/ from CNRM-CM6-1 'present day' and 'future' time periods, selected to match the sea-ice area in the CNRM -ext PAMIP runs. For this model, output files contain the autocorrelation of surface temperaure. </p>
Post-2000 faster ENSO phase transitions amplify autumn sea ice loss in the Laptev–East-Siberian Sea
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Data from: Range contraction and increasing isolation of a polar bear subpopulation in an era of sea-ice loss
Climate change is expected to result in range shifts and habitat fragmentation for many species. In the Arctic, loss of sea ice will reduce barriers to dispersal or eliminate movement corridors, resulting in increased connectivity or geographic isolation with sweeping implications for conservation. We used satellite telemetry, data from individually marked animals (research and harvest), and microsatellite genetic data to examine changes in geographic range, emigration, and interpopulation connectivity of the Baffin Bay (BB) polar bear (Ursus maritimus) subpopulation over a 25-year period of sea-ice loss. Satellite telemetry collected from n = 43 (1991–1995) and 38 (2009–2015) adult females revealed a significant contraction in subpopulation range size (95% bivariate normal kernel range) in most months and seasons, with the most marked reduction being a 70% decline in summer from 716,000 km2 (SE 58,000) to 211,000 km2 (SE 23,000) (p < .001). Between the 1990s and 2000s, there was a significant shift northward during the on-ice seasons (2.6° shift in winter median latitude, 1.1° shift in spring median latitude) and a significant range contraction in the ice-free summers. Bears in the 2000s were less likely to leave BB, with significant reductions in the numbers of bears moving into Davis Strait (DS) in winter and Lancaster Sound (LS) in summer. Harvest recoveries suggested both short and long-term fidelity to BB remained high over both periods (83–99% of marked bears remained in BB). Genetic analyses using eight polymorphic microsatellites confirmed a previously documented differentiation between BB, DS, and LS; yet weakly differentiated BB from Kane Basin (KB) for the first time. Our results provide the first multiple lines of evidence for an increasingly geographically and functionally isolated subpopulation of polar bears in the context of long-term sea-ice loss. This may be indicative of future patterns for other polar bear subpopulations under climate change.
Data from: Loss of connectivity among island-dwelling Peary caribou following sea ice decline
Global warming threatens to reduce population connectivity for terrestrial wildlife through significant and rapid changes to sea ice. Using genetic fingerprinting, we contrasted extant connectivity in island-dwelling Peary caribou in northern Canada with continental-migratory caribou. We next examined if sea-ice contractions in the last decades modulated population connectivity and explored the possible impact of future climate change on long-term connectivity among island caribou. We found a strong correlation between genetic and geodesic distances for both continental and Peary caribou, even after accounting for the possible effect of sea surface. Sea ice has thus been an effective corridor for Peary caribou, promoting inter-island connectivity and population mixing. Using a time series of remote sensing sea-ice data, we show that landscape resistance in the Canadian Arctic Archipelago has increased by approximately 15% since 1979 and may further increase by 20–77% by 2086 under a high-emission scenario (RCP8.5). Under the persistent increase in greenhouse gas concentrations, reduced connectivity may isolate island-dwelling caribou with potentially significant consequences for population viability.
Data supporting 'Assessing the Spurious Impacts of Ice-Constraining Methods on the Climate Response to Sea-Ice Loss using an Idealized Aquaplanet GCM' by Neil T Lewis et al.
<p>Data supporting Lewis et al., 2024. Assessing the Spurious Impacts of Ice-Constraining Methods on the Climate Response to Sea-Ice Loss using an Idealised Aquaplanet GCM. Submitted to Journal of Climate. </p> <p>All data is in NetCDF format. </p> <p>Output from each experiment is contained in its own folder (e.g., 'ALB.1'). For a description of each experiment, see the accompanying paper. </p> <p>Data files contain the following outputs: </p> <p>dyn_vars_daily_clim.nc contains day of year- and zonally-averaged atmospheric fields (u, v, T, etc). </p> <p>eddy_products.nc constains day of year- and zonally-averaged products of atmospheric fields (e.g., u'v'). </p> <p>ice_temp_daily.nc contains daily-averaged surface temperature and sea-ice thickness. </p> <p>toa_fluxes_clim.nc contains day of year-averaged top of atmosphere radiative fluxes (e.g., OLR). </p> <p>For the experiments NDG1.05, NDG1.1, and NDG1.2, nudge_daily_clim.nc is also included, and contains the day of year-averaged nudging heat flux applied to melt the ice. </p>
Data for: Global coupled climate response to polar sea ice loss: evaluating the effectiveness of different ice-constraining approaches
<p>This is the data for the paper titled "Global coupled climate response to polar sea ice loss: Evaluating the effectiveness of different ice-constraining approaches".</p>
Data and Code for "Comparing the Impacts of Ozone-Depleting Substances and Carbon Dioxide on Arctic Sea Ice Loss"
<p>This upload contains data and code related to the submitted manuscript "Comparing the Impacts of Ozone-Depleting Substances and Carbon Dioxide on Arctic Sea Ice Loss" by Bushuk, Polvani, and England. See README.txt for a description of the datasets and code.</p>
Data from: Range contraction and increasing isolation of a polar bear subpopulation in an era of sea-ice loss
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Data from: Loss of connectivity among island-dwelling Peary caribou following sea ice decline
Open the record for dataset details and reuse information.
Data and code for the paper titled "Tibetan Plateau Warming Amplification Boosts Arctic Sea-ice Loss"
<p>This is the data and code for the paper titled "Tibetan Plateau Warming Amplification Boosts Arctic Sea-ice Loss", which is to be submitted for peer review.</p>
Accelerating ice mass loss across Arctic Russia in response to Atlantification of the Eurasian Arctic Shelf Seas
<p>This file contains three datasets of geophysical data compiled from satellite observations made over the archipelagos of Novaya Zemlya and Severnaya Zemlya in the Russian Arctic between 2010 and 2018:</p> <p>- maps of surface elevation change (dh) over the entire glaciated area of the two regions, and rasterised ice masks (source RGI 6.0) of both land- and marine-terminating glaciers and ice caps; </p> <p>- time series of surface elevation change (dh) at 90-day time steps over single glacier and ice cap basins, and over larger areas (control domains) defined as follows: B1N, B2N, B3N, B4N, K1N, K2N, K3N (Novaya Zemlya), and K1S, K2S, L1S, L2S, L3S (Severnaya Zemlya);</p> <p>- climate forcing (T2m, SST, SIC and SOTF1-3) averaged over each of the 12 aforementioned control areas. The climate data is presented as time series, means and longer term trends of seasonal (90-days) anomalies with respect to a pre-defined baseline period.</p> <p>A more complete description of these datasets will be provided in the publication with the same name (currently in review).</p>
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