ILCE's BLOG

1. ACIA Overview:
   • The ACIA was initiated in response to a request from the Ministers of the Arctic Council. It aimed to synthesize knowledge on climate variability, change, and increased ultraviolet radiation in the Arctic.
   • The assessment involved over 250 scientists and six circumpolar indigenous peoples’ organizations.
   • Its objective was to assess the environmental, human health, social, cultural, and economic impacts of climate change in the Arctic, providing valuable policy recommendations.
2. Key Findings:
   • The ACIA highlighted several critical impacts:
     • Rising temperatures: The Arctic was experiencing warming at an alarming rate.
     • Loss of sea ice: The extent and thickness of Arctic sea ice were diminishing.
     • Greenland ice sheet melting: Unprecedented melting was observed.
     • Ecosystem changes: Arctic ecosystems, animals, and people faced significant consequences.
3. Social Science Integration:
   • The ACIA was a milestone because it was the first Arctic Council assessment to comprehensively include social science components. It assessed the impacts of climate change on socio-economic conditions in the Arctic.
   • By integrating natural science and social science, the ACIA provided a holistic understanding of climate change effects.
4. IPCC Connection:
   • The ACIA results were fed into the IPCC fourth assessment process. They played a crucial role in raising the profile of Arctic climate change issues in international forums such as the UNFCCC and subsequent IPCC work.
   • While the ACIA focused specifically on the Arctic, its findings contributed to global climate change discussions and informed broader policy decisions

Now, let’s explore the IPCC’s work on polar regions:

1. IPCC Reports on Polar Regions:
   • The Intergovernmental Panel on Climate Change (IPCC) has assessed polar regions (both Arctic and Antarctic) in various reports.
   • The Fourth Assessment Report (AR4) in 2007 included a chapter on polar regions, discussing impacts, adaptation, and vulnerability. It covered both the Arctic and Antarctic
   • The Sixth Assessment Report (AR6) also addresses polar regions, emphasizing the physical, biological, and human dimensions. It integrates emerging understanding to assess climate change holistically
2. Different Focus:
   • While the ACIA specifically targeted the Arctic, the IPCC’s assessments encompass both polar regions.
   • The IPCC’s work extends beyond the Arctic to include the Antarctic as well, recognizing the interconnectedness of polar systems and their global implications.

In summary, the ACIA laid the groundwork for understanding Arctic climate assesment, but has gradually been substituted for reasons of lack of manpower, administartive reorganisation and delegation from CPH to Nuuk, the progresss in technologies – satelittes and remote sensing – and the greater interest in the IPCC’s broader assessments of polar regions and their relevance to global climate dynamics. Both efforts contribute vital information for informed decision-making and climate action.

What about glaciers ?

1. World Glacier Monitoring Service (WGMS):
   • The WGMS has been at the forefront of compiling and disseminating standardized data on glacier fluctuations for over a century. It operates under the auspices of organizations such as the International Council for Science (ICSU), UNEP, UNESCO, and WMO.
   • The WGMS collaborates with a scientific network spanning more than 30 countries. Annually, it collects glacier data, assessing parameters like mass balance (snow accumulation and melt) to evaluate glacier health¹.
   • In close collaboration with the U.S. National Snow and Ice Data Center (NSIDC) and the Global Land Ice Measurements from Space (GLIMS) initiative, the WGMS runs the Global Terrestrial Network for Glaciers (GTN-G). This network supports the United Nations Framework Convention on Climate Change (UNFCCC)¹.

2. Greenland:
   • In Greenland, glacier surveillance involves a combination of field measurements, satellite observations, and modeling.
   • The PROMICE (Programme for Monitoring of the Greenland Ice Sheet) project monitors ice sheet mass balance, ice velocity, and surface melt using automatic weather stations, GPS, and remote sensing data.
   • Satellite missions like Sentinel-1 provide radar images to track glacier movement and detect changes in ice flow and crevasses.

3. Himalayas:
   • The Himalayan region faces significant glacier-related challenges due to climate change.
   • ESA’s Sentinel-1 satellite mission plays a crucial role in monitoring Himalayan glaciers. Its all-weather radar allows scientists to track ice movement and peer through snow cover.
   • Researchers assess glacier health by measuring mass balance, studying crevasses, and understanding ice flow dynamics.

4. Role of AI in Glacier Monitoring:
   • AI has revolutionized glacier surveillance:
     • Crevasse Detection: Scientists have developed AI algorithms to identify crevasses in radar images. For instance, the Thwaites Glacier Ice Tongue in West Antarctica is monitored using AI techniques.
     • Change Detection: Machine learning helps analyze radar images over time, identifying glacier speed changes and fracture formation.
     • Predictive Modeling: AI aids in predicting glacier behavior and assessing risks to coastal communities.
     • Data Fusion: AI combines satellite data, field measurements, and climate models for comprehensive glacier assessments.

In summary, global efforts like the WGMS, combined with AI advancements, enhance our understanding of glacier dynamics. These initiatives are crucial for informed climate action and sustainable water resource management.

Shippers and sailors navigating the Greenlandic waters may also benfit from Artificial Intelligence (AI) in weather forecasting. Let’s explore how AI can enhance weather predictions and whether decentralization is underway:

1. GraphCast: AI for Global Weather Forecasting:
   • GraphCast, developed by Google DeepMind, is a state-of-the-art AI model for medium-range weather forecasts.
   • Key features:
     • Accuracy: GraphCast predicts weather conditions up to 10 days in advance with unprecedented accuracy.
     • Speed: It delivers these forecasts in under one minute.
     • Applications: GraphCast identifies extreme weather events, predicts cyclone tracks, atmospheric rivers, and extreme temperatures.
     • Open Source: The model code is open-sourced, benefiting scientists and forecasters worldwide
2. Greenland Waters and Sea Ice:
   • Operational Forecast System:
     • The Danish Meteorological Institute (DMI) provides an improved operational forecast system for Arctic sea ice, with a focus on Greenlandic waters.
     • This system integrates remotely sensed-based sea ice services and conducts inter-comparisons.
   • Observations:
     • DMI collects weather observations from Greenland since World War II.
     • These observations cover areas off West Greenland and contribute to understanding ocean circulation and climate fluctuations
3. Decentralization:
   • While centralized systems like the European Centre for Medium-Range Weather Forecasts (ECMWF) provide global forecasts, there’s a trend toward decentralization:
     • Regional Models: AI models like MetNet-3 offer regional weather forecasts with high accuracy.
     • Nearcasting: AI-based nowcasting models provide short-term forecasts (up to 90 minutes ahead).
     • Local Expertise: Decentralized systems can incorporate local knowledge, improving predictions in specific regions.
     • Operational Services: Establishing operational oceanographic services can enhance safety and resource management

In summary, AI-driven weather models like GraphCast and localized efforts by institutions like DMI contribute to more accurate and efficient forecasts. Decentralization allows tailored predictions for specific regions, benefiting shippers, sailors, and communities in Greenlandic water

https://www.ipcc.ch/report/ar6/wg2/chapter/ccp6/

https://acia.amap.no/

https://wgms.ch/

https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1/Sentinel-1_and_AI_uncover_glacier_crevasses

https://www.esa.int/Applications/Observing_the_Earth/Revealing_invisible_Himalaya_glacier_loss

https://www.frontiersin.org/articles/10.3389/fmars.2023.979782/full

https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/