CMAD: Advancing Understanding of Anomalous Melt Events over the Antarctic Sea Ice

28 Oct 2024 | Contributor(s): Maloy Kumar Devnath, Sudip Chakraborty, Vandana Janeja

Antarctic Sea ice extent reached a new record low of 1.965 million km2 on 23rd February 2023 (~ 32% below climatological values). We need to understand how the melting occurred during the August 2022 to February 2023. Did anomalous melting occur over there? Where did...

Deep Learning for Antarctic Sea Ice Anomaly Detection and Prediction: A Two-Module Framework

28 Oct 2024 | Contributor(s): Maloy Kumar Devnath, Sudip Chakraborty, Vandana Janeja

The Antarctic sea ice cover plays a crucial role in regulating global climate and sea level rise. The recent retreat of the Antarctic Sea Ice Extent and the accelerated melting of ice sheets (which causes sea level rise) raise concerns about the impact of climate change. Understanding the spatial...

CMAD: Advancing Understanding of Geospatial Clusters of Anomalous Melt Events in Sea Ice Extent

28 Oct 2024 | Contributor(s): Maloy Kumar Devnath, Sudip Chakraborty, Vandana Janeja

Traditional statistical analyses do not reveal the spatial locations and the temporal occurrences of clusters of anomalous events that are responsible for a significant loss of sea ice extent. To address this problem, we present a novel method named Convolution Matrix Anomaly Detection (CMAD)....

Accelerating Subglacial Bed Topography Prediction in Greenland: A Performance Evaluation of Spark-Optimized Machine Learning Models

24 Oct 2024 | Contributor(s): Mostafa Cham, Tartela Tabassum, Ehsan Shakeri, Jianwu Wang

Accurate estimation of subglacial bed topography is crucial for understanding ice sheet dynamics and their responses to climate change. In this study, we employ machine learning models, enhanced with Spark parallelization, to predict subglacial bed elevation using surface attributes such as ice...

Pegasus WMS Tutorial and Templates

10 Oct 2024 | Contributor(s): Renette Jones-Ivey, Joseph P Tulenko (contributor), Sophie Goliber (contributor)

Pegasus WMS Tutorial and Templates

Ghub Jupyter Book Template

10 Oct 2024 | Contributor(s): Renette Jones-Ivey, Joseph P Tulenko (contributor), Sophie Goliber (contributor)

Template and guideline for creating a Jupyter Book tool on Ghub.

Greenland paleo sea level indicators and proxies derived from the GAPSLIP database

02 Oct 2024 | Contributor(s): Evan James Gowan

This is a spreadsheet that contains the paleo sea level indicators and proxies that were compiled and described by Gowan (2023) in the GAPSLIP paleo sea level database for Greenland. Included are the datasets in ODS and tab delimited formats. The dataset includes 1019 data points, with 647 marine...

Insolation and Ice Volume Explorer

17 Sep 2024 | Contributor(s): Kristin Poinar

Explore the volume of ice on Earth, its rate of change, and insolation at 65°N over the last 4 ice ages

Greenland ICESHEET Simulator

12 Sep 2024 | Contributor(s): Evan James Gowan, Renette Jones-Ivey, Sophie Goliber (contributor), Joseph P Tulenko (contributor), Sophie Nowicki (contributor), Jason Briner (contributor)

A Jupyter Notebook tool which implements an easy-to-use setup to run the ICESHEET program with the Greenland Ice Sheet.

MEaSUREs Greenland Ice Mapping Project (GIMP) Land Ice and Ocean Classification Masks as Shape Files

11 Sep 2024 | Contributor(s): Ivan Parmuzin, Beata Maria Csatho

The MEaSUREs Greenland Ice Mapping Project (GIMP) Land Ice and Ocean Classification Mask, Version 1 is now available in GIS friendly vector format (shape files) on Ghub! Shape files were converted from 30 m resolution raster masks and provide in WGS 84/UTM N24 (EPSG: 32624) and ...

Demonstration Code for "Comprehensive Assessment of Stress Calculations for Crevasse Depths"

14 Aug 2024 | Contributor(s): Benjamin Reynolds, Sophie Nowicki, Kristin Poinar

This tool make plots of crevasse penetration with six resistive stress calculations found in literature for the Larsen B remnant and Pine Island Glacier ice shelves.

JupyterLab 4.2.4 / Anaconda3.2024.6.1 / Debian 10

07 Aug 2024 | Contributor(s): David Benham

JupyterLab 4.2.4 on Debian 10 container

Estimating Causal Effects of Greenland Blocking on Arctic Sea Ice Melt using Deep Learning Technique

23 Jul 2024 | Contributor(s): Sahara Ali, Omar Faruque, Yiyi Huang, Md Osman Gani, Aneesh Subramanian, Nicole Schlegel, Jianwu Wang, Josephine Namayanja (contributor)

Over the recent decades, Earth scientists have noted a more pronounced shift in climate patterns near the polar regions, specifically the Arctic, in comparison to the rest of the Earth. The increased warming is largely attributed to the diminishing ice cover in the Arctic, which causes solar...

Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference

23 Jul 2024 | Contributor(s): Sahara Ali, Omar Faruque, Yiyi Huang, Aneesh Subramanian, Nicole-Jienne Shchlegel, Md Osman Gani, Jianwu Wang, Josephine Namayanja (contributor)

The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are still unknown. Inferring the causal effects of atmospheric processes on sea ice melt using fixed treatment effect...

Benchmarking probabilistic machine learning models for arctic sea ice forecasting

23 Jul 2024 | Contributor(s): Sahara Ali, Seraj Mostafa, Xingyan Li, Sara Khanjani, Jianwu Wang, James Foulds, Vandana Janeja

The Arctic is a region with unique climate features, motivating new AI methodologies to study it. Unfortunately, Arctic sea ice has seen a continuous decline since 1979. This not only poses a significant threat to Arctic wildlife and surrounding coastal communities but is also adversely affecting...

MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting

23 Jul 2024 | Contributor(s): Sahara Ali, Jianwu Wang, Josephine Namayanja (contributor)

Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades. The essential part of Arctic amplification is the unprecedented sea ice loss as demonstrated by satellite observations....

Incorporating Causality with Deep Learning in Predicting Short-Term and Seasonal Sea Ice

16 Jul 2024 | Contributor(s): Emam Hossain, Sahara Ali, Yiyi Huang, Nicole Schlegel, Jianwu Wang, Aneesh Subramanian, Md Osman Gani

Abstract: Arctic sea ice (ASI) is playing a pivotal role in keeping global warming under control. However, the recently amplified decreasing sea ice trend has become a major concern. Since satellites started monitoring the ASI in 1979, every decade the Arctic has lost 13.1% of sea ice and the...

A Survey on Causal Discovery Methods for IID and Time Series Data

16 Jul 2024 | Contributor(s): Uzma Hasan, Emam Hossain, Md Osman Gani

Abstract: The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data with certain assumptions. Over the...

Visualizing Glacier/Ocean Change

21 May 2024 | Contributor(s): Sophie Goliber, John Erich Christian

A simple 1-D glacier model to teach students about glacier/ocean changes.

Evaluating Machine Learning and Statistical Models for Greenland Bed Topography

15 May 2024 | Contributor(s): Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan Li, Mathieu Morlighem, Jianwu Wang

Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bed topography in Greenland using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for understanding ice sheet...