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126 results for “Arctic sea ice”
GFDL-FLOR Large Ensemble Arctic Sea Ice Data
<p>This upload contains Arctic sea ice data from the GFDL-FLOR Large Ensemble and related data analysis code, as published in Bushuk et al. (2020). See readme.txt for a description of the datasets and code.</p> <p>Reference: Bushuk, M., M. Winton, D. Bonan, E. Blanchard-Wrigglesworth, T. Delworth, 2020: A mechanism for the Arctic sea ice spring predictability barrier, Geophysical Research Letters, in press.</p>
Data from: Divergence of Arctic shrub growth associated with sea ice decline
<p><span>Arctic sea ice extent (SIE) is declining at an accelerating rate with a wide range of ecological consequences. However, determining sea ice effects on tundra vegetation remains a challenge. In this study, we examined the universality or lack thereof in tundra shrub growth responses to changes in SIE and summer climate across the Pan-Arctic, taking advantage of 23 tundra shrub-ring chronologies from 19 widely distributed sites (56⁰-83⁰N). </span></p>
Data from: Effects of sea ice cover on satellite-detected primary production in the Arctic Ocean
The influence of decreasing Arctic sea ice on net primary production (NPP) in the Arctic Ocean has been considered in multiple publications but is not well constrained owing to the potentially large errors in satellite algorithms. In particular, the Arctic Ocean is rich in coloured dissolved organic matter (CDOM) that interferes in the detection of chlorophyll a concentration of the standard algorithm, which is the primary input to NPP models. We used the quasi-analytic algorithm (Lee et al. 2002 Appl. Opti. 41, 5755−5772. (doi:10.1364/AO.41.005755)) that separates absorption by phytoplankton from absorption by CDOM and detrital matter. We merged satellite data from multiple satellite sensors and created a 19 year time series (1997–2015) of NPP. During this period, both the estimated annual total and the summer monthly maximum pan-Arctic NPP increased by about 47%. Positive monthly anomalies in NPP are highly correlated with positive anomalies in open water area during the summer months. Following the earlier ice retreat, the start of the high-productivity season has become earlier, e.g. at a mean rate of −3.0 d yr−1 in the northern Barents Sea, and the length of the high-productivity period has increased from 15 days in 1998 to 62 days in 2015. While in some areas, the termination of the productive season has been extended, owing to delayed ice formation, the termination has also become earlier in other areas, likely owing to limited nutrients.
Data from: Variable sea-ice conditions influence trophic dynamics in an Arctic community of marine top predators
Sea‐ice coverage is a key abiotic driver of annual environmental conditions in Arctic marine ecosystems and could be a major factor affecting seabird trophic dynamics. Using stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) in eggs of thick‐billed murres (Uria lomvia), northern fulmars (Fulmarus glacialis), glaucous gulls (Larus hyperboreus), and black‐legged kittiwakes (Rissa tridactyla), we investigated the trophic ecology of prebreeding seabirds nesting at Prince Leopold Island, Nunavut, and its relationship with sea‐ice conditions. The seabird community of Prince Leopold Island had a broader isotopic niche during lower sea‐ice conditions, thus having a more divergent diet, while the opposite was observed during years with more extensive sea‐ice conditions. Species' trophic position was influenced by sea ice; in years of lower sea‐ice concentration, gulls and kittiwakes foraged at higher trophic levels while the opposite was observed for murres and fulmars. For murres and fulmars over a longer time series, there was no evidence of the effect of sea‐ice concentration on species' isotopic niche. Results suggest a high degree of adaptation in populations of high Arctic species that cope with harsh and unpredictable conditions. Such different responses of the community isotopic niche also show that the effect of variable sea‐ice conditions, despite being subtle at the species level, might have larger implications when considering the trophic ecology of the larger seabird community. Species‐specific responses in foraging patterns, in particular trophic position in relation to sea ice, are critical to understanding effects of ecosystem change predicted for a changing climate.
Arctic Atmoopheric Rivers and Sea Ice Data based on CESM2 Large Ensemble
Open the record for dataset details and reuse information.
Arctic Sea Ice Type Products Based on Active and Passive Microwave Remote Sensing (2002-2021)
<p>Based on FY-3 MWRI and other microwave radiometer and scatterometer data, this dataset explores the distribution of Arctic sea ice types from 2002 to 2021 using the K-means clustering method. Spatial and temporal resolutions are 12.5 km and 1 day, respectively. The dataset is of integer type, where 0 represents open water, 1 represents first-year ice, 2 represents multi-year ice, 3 represents land, and 4 represents data gaps. The data is stored in .tiff format.</p>
Record low Arctic sea ice extent in 2012 linked to two-year La Niña-driven sea surface temperature pattern
<p class="Abstract"><span>Arctic summer sea ice decline accelerated from the mid-2000s to 2012, with the 2012 record low remaining unbroken. While frequent La Niña events during this period have been suggested as a driver of this trend acceleration, no convincing evidence has been presented. Here, using a climate model nudged to observed pan-tropical sea surface temperatures (SST), we show that the back-to-back La Niña events during 2010–2011, followed by a North Pacific cooling and a marginal El Niño, were a key contribution to the 2012 record low. Specifically, the La Niña events in 2010–2011 warmed the Arctic Pacific sector, whereas tropical SST anomalies in 2012 strengthened the Greenland high pressure, leading to an Arctic dipole-like pressure pattern and strengthening of transpolar ice drift. These Arctic temperature and circulation anomalies led to the record low sea ice extent in 2012, highlighting the strong influence of tropical SSTs on Arctic climate.</span></p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 1)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 3)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 2)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 2a)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Vortex and MetaModel Manager input files -- Exploring impacts of declining sea-ice on ice-dependent species in the Arctic
<p>Input files for Vortex PVA models and for MetaModel Manager used in publication:</p> <p><span>Lacy, Robert C., Kit M. Kovacs, Christian Lydersen, and Jon Aars</span></p> <p><span>Linking PVA models into metamodels to explore impacts of declining sea ice on ice-dependent species in the Arctic: the ringed seal, bearded seal, polar bear complex</span></p> <p> </p>
Supporting Data for Hahn et al. J. Climate: Seasonality in Arctic Warming Driven By Sea Ice Effective Heat Capacity
<p>This dataset includes CESM model experiment output for Hahn et al.: “Seasonality in Arctic Warming Driven By Sea Ice Effective Heat Capacity” submitted to Journal of Climate. Here we provide monthly climatologies averaged over the last thirty years for the Ice, No ice, and No ice, set albedo experiments with preindustrial and doubled CO<sub>2</sub> forcing. The variables hyam, hybm, and P0, useful for interpolating to pressure levels, are included in the FlatSOM1850.Q.nc file.</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>
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
A numerical study on melt water feedback in the coupled Arctic Sea ice-ocean system
<p>This dataset contains one-dimensional model code and input files for studying the effects of melt water on upper ocean stratification and sea ice melt and growth. The model used in this study is the Massachusetts Institute of Technology general circulation model (MITgcm). Original source code is the MITgcm_c66m.</p> <p>code_1frw: contains control run configuration. </p> <p>code_08frw: contains sensitivity experiment (MWP-80% run) configuration. </p> <p>code_06frw: contains sensitivity experiment (MWP-60% run) configuration. </p> <p>code_04frw: contains sensitivity experiment (MWP-40% run) configuration. </p> <p>code_02frw: contains sensitivity experiment (MWP-20% run) configuration. </p> <p>code_0frw: contains sensitivity experiment (MWP-0% run) configuration. </p> <p>input: contains parameter settings and input files for all experiment.</p> <p>model results: the model results used in this paper.</p>
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"
<p>The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"</p> <p>Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations.</p> <p>In case information is missing, do not hesitate to contact chengsukun@hotmail.com</p> <p>We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)</p>
data for the paper "Seasonal Prediction of Regional Arctic Sea Ice Using the High-Resolution Climate Prediction System CMA-CPSv3"
<p>CMA-CPSv3 data for the paper "Seasonal Prediction of Regional Arctic Sea Ice Using the High-Resolution Climate Prediction System CMA-CPSv3"</p>
Data from: Effects of sea ice cover on satellite-detected primary production in the Arctic Ocean
Open the record for dataset details and reuse information.
Data from: Variable sea-ice conditions influence trophic dynamics in an Arctic community of marine top predators
Open the record for dataset details and reuse information.
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