Dartmouth Study on Polar Jet Stream Variability and Climate Change

Introduction

A recent Dartmouth study challenges the prevailing assumption that climate change is the primary driver behind the erratic wintertime behavior of the polar jet stream, a major Arctic air current influencing weather across the Northern Hemisphere.

Background

• Since the 1990s, large waves in the jet stream have caused abnormally cold temperatures and severe winter storms in regions such as the southern United States.
• Previous scientific consensus suggested that a warming atmosphere due to climate change was responsible for these wild undulations, bringing Arctic air southward.

Study Overview

1. The study, published in AGU Advances, was led by Jacob Chalif ’21 and Associate Professor Erich Osterberg from Dartmouth’s Earth Sciences department.
2. It utilized machine learning to analyze climate records dating back to 1901, extending far beyond the typical satellite data starting in 1979.
3. Findings indicate that the jet stream has experienced natural, sporadic periods of “waviness” for over a century, some more pronounced than current patterns.

Key Findings

• The current period of jet stream waviness is not unprecedented and occurred before climate change effects were significant.
• Climate change, while amplifying extreme winter weather, likely does not increase jet stream waviness but intensifies storms through other mechanisms such as increased atmospheric moisture.
• The study suggests a need to shift scientific focus toward direct links between global warming and severe weather events.

Implications for Sustainable Development Goals (SDGs)

• SDG 13: Climate Action — The research informs climate action strategies by refining understanding of climate change impacts on weather systems, emphasizing the importance of addressing extreme weather through multiple pathways.
• SDG 11: Sustainable Cities and Communities — Improved knowledge of jet stream behavior aids in preparing urban and rural communities for severe winter storms, enhancing resilience and disaster risk reduction.
• SDG 9: Industry, Innovation, and Infrastructure — The application of machine learning in climate data analysis exemplifies innovation critical to advancing climate science and infrastructure planning.

Detailed Insights

The polar jet stream modulates weather across Europe, Asia, and North America, flowing near the U.S.-Canada border. Large waves cause Arctic air to penetrate subtropical regions, leading to cold snaps and severe winter storms where warm and cold air masses collide. The polar vortex, a mass of subzero air around the North Pole, can also be pushed southward by these waves.

Contrary to prior studies linking jet stream waviness directly to climate change, the Dartmouth team found that the last major wavy period peaked around 1979, coinciding with the start of satellite observations. This timing created an impression of abnormality in recent decades.

Historical Context: The “Warming Hole” Phenomenon

• From the 1960s to the 1980s, a strong wavy jet stream period contributed to the “warming hole,” a 30-year phase of unusually cool winters in the southeastern United States.
• This period saw average winter temperatures drop by more than 2°F (1.3°C) starting around 1958, persisting until the late 1980s.
• The study confirms that jet stream variability accounted for two-thirds of the cooling during this time, linking jet stream patterns to regional climate anomalies before significant climate change influence.

Conclusions and Future Directions

Jacob Chalif emphasizes the complexity of climate systems, noting the role of “climate chaos” in influencing weather variability on daily and yearly scales. The 125-year record developed by the study provides a broader context for understanding jet stream behavior beyond recent decades.

Associate Professor Osterberg highlights that these findings fundamentally change the approach to studying the relationship between climate change and extreme weather. The focus should shift towards alternative explanations such as increased atmospheric moisture driving storm intensity.

Relevance to Sustainable Development Goals

• SDG 13: Climate Action — Enhances scientific understanding necessary for effective mitigation and adaptation policies.
• SDG 3: Good Health and Well-being — By improving prediction and understanding of extreme weather, the study supports efforts to protect populations from climate-related health risks.
• SDG 17: Partnerships for the Goals — Demonstrates the importance of interdisciplinary research and international data sharing in addressing global climate challenges.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 13: Climate Action
   • The article discusses climate change and its impact on weather patterns, specifically the polar jet stream and extreme winter weather events.
2. SDG 3: Good Health and Well-being
   • Extreme winter storms and cold snaps affect human health and safety, which relates to well-being.
3. SDG 11: Sustainable Cities and Communities
   • Severe weather events impact communities, infrastructure, and urban resilience.
4. SDG 9: Industry, Innovation, and Infrastructure
   • The use of machine learning and satellite data for climate analysis reflects innovation and infrastructure for climate monitoring.

2. Specific Targets Under Those SDGs Identified

1. SDG 13: Climate Action
   • Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters.
   • Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning.
2. SDG 3: Good Health and Well-being
   • Target 3.d: Strengthen the capacity of all countries for early warning, risk reduction and management of national and global health risks.
3. SDG 11: Sustainable Cities and Communities
   • Target 11.5: Reduce the number of deaths and the number of people affected by disasters, including water-related disasters, with a focus on protecting the poor and vulnerable.
4. SDG 9: Industry, Innovation, and Infrastructure
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors, including climate monitoring technologies.

3. Indicators Mentioned or Implied to Measure Progress

1. Indicators Related to SDG 13
   • Frequency and intensity of extreme weather events (e.g., winter storms, cold snaps).
   • Concentration of greenhouse gases in the atmosphere.
   • Variability and waviness of the polar jet stream over time (125-year record).
   • Decline of Arctic sea ice extent.
2. Indicators Related to SDG 3
   • Number of health incidents related to extreme cold weather events.
   • Effectiveness of early warning systems for severe weather.
3. Indicators Related to SDG 11
   • Number of people affected by extreme winter storms.
   • Damage to infrastructure caused by severe weather events.
4. Indicators Related to SDG 9
   • Use and advancement of machine learning and satellite data in climate monitoring.
   • Development of long-term climate variability records.

4. Table of SDGs, Targets, and Indicators

Source: home.dartmouth.edu