A New Year-Round Weather Regime Classification for North America

Intro

Salient’s Advisory Board member Dr. Jason Furtado is joining our team for a few months to work on extreme weather predictions at subseasonal-to-seasonal (S2S) timescales. Together, we’re improving the tails of the probabilistic distributions to produce S2S forecasts that accurately and reliably detect extreme events. Our work is informed, in part, by new research out of 美国哥伦比亚大学 on weather regimes.

Columbia University's Simon H. Lee , Michael Tippett , and Lorenzo M. Polvani ’s recent study, A New Year-Round Weather Regime Classification for North America, could transform and improve how we predict and understand weather patterns across the continent. With a consistent framework for identifying weather regimes year-round, forecasters like Salient can improve the accuracy of predictions for weeks to months ahead, especially for extreme weather events.?

Salient’s R&D team is on top of the latest research, but our customers often don’t have time to read academic papers. Below we unpack this recent research in <1,000 words.?

S2S Research: “A New Year-Round Weather Regime Classification for North America”

Why do we need year-round regimes??

Existing weather regimes, which categorize anomalous regional circulation patterns to help anticipate general weather patterns (such as prolonged periods of wet or dry conditions), are seasonal and thus limited in their applicability to forecasting. In fact, most regimes focus on winter, since atmospheric patterns tend to be more well-defined and persistent compared to other seasons. Examples of seasonal regimes include: North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and El Ni?o-Southern Oscillation (ENSO).?

The annual regimes presented in this study categorize unusual weather conditions throughout an entire year, instead of just seasonally, allowing us to understand daily weather variations as part of a greater trend.?

What are year-round weather regimes, exactly??

To define the year-round weather regimes for North America, the researchers at Columbia smoothed out the seasonal changes in weather data, identified patterns and trends, then clustered similar patterns together to form the new, year-round regimes. From most-to-least frequent (from 1979-2022), the four regimes are classified as:

1. Pacific Trough (PT, occurring on 25% of days)
2. Pacific Ridge (PR, 22%)
3. Alaskan Ridge (AKR, 20%)
4. Greenland High (GH, 19%)

• No Regime (the remaining 14% of days, conditions close to climatology)

Key findings?

1. Persistence: The weather regimes identified by the researchers are generally persistent. Each of the four weather regimes typically lasts between 4 to 10 days, with a median duration of 7 days. The "No Regime" state, which is when the weather doesn't fit any of the four patterns, has a median duration of 2 days. Sometimes, a weather regime can last much longer (more than 3 weeks). For example, one Pacific Ridge lasted 44 days in 1994.?

2. Patterns: Each regime is associated with distinct temperature and precipitation patterns.

• PT: Tends to bring warmer temperatures to western Canada and increased precipitation along the west coast, from Oregon to Alaska.
• PR: Increases likelihood of colder temperatures along the West Coast and warmer conditions in the eastern U.S. and Canada.
• AKR: Often causes warmer conditions in Alaska and the Pacific Northwest and cooler temperatures in the central and eastern U.S.
• GH: Tends to result in colder temperatures across much of the U.S. and southern Canada during winter, with drier conditions along the coast of British Columbia.

3. Trends:?

Historical data reveals significant trends since 1979:

• PT: Decrease in annual frequency.
• GH: Increase in annual frequency, especially during the summer months.
• These trends suggest changes in atmospheric circulation patterns, likely influenced by broader climate changes, and highlight the importance of continuous monitoring and analysis.

Although these regimes are annual, they demonstrate seasonal variability:

• PT and AKR: More frequent in the winter, less frequent in the summer
• GH: More frequent in the late spring and early summer, and less frequent in the winter
• PR: Does not demonstrate seasonality (generally constant throughout the year)
• No Regime: More frequent in the summer

Relationships with Existing Seasonal Regimes

• ENSO: El Ni?o tends to increase the frequency of the Pacific Trough, while La Ni?a boosts the Pacific Ridge.
• NAO: A negative NAO is strongly associated with the Greenland High regime.
• PNA: A positive PNA favors the Pacific Trough, while a negative PNA is linked to the Pacific Ridge.

Implications for weather prediction & beyond?

Enhanced Forecasting Accuracy

• S2S: By providing a consistent framework for identifying weather regimes year-round, forecasters like Salient can improve the accuracy of predictions for weeks to months ahead.?
• Extreme Weather Events: The persistence of weather regimes allows for better prediction of extreme weather events.?

Climate Model Validation & Improvement?

• Model Testing: The classification system can be used to validate climate models and correct biases by comparing the frequency and characteristics of weather regimes generated by models with those observed in the real world.

Resource Management and Planning

• Agriculture and Water Resources: Predicting weather regimes can aid in agricultural planning, allowing farmers to prepare for droughts or heavy rainfall. Water resource managers can also use this information to manage reservoirs and supply.
• Energy Sector: The energy sector can benefit from accurate weather regime predictions for optimizing heating and cooling demands, as well as managing renewable energy sources like wind and solar power.
• Disaster Preparedness: Governments and organizations can use these regime predictions as indications/early warnings for extreme events (like prolonged heat waves) to enhance disaster preparedness and response.

Go Deeper

A New Year-RoundWeatherRegime Classification for North America [link ] authored by Simon H. Lee, Michael K. Tippett, and Lorenzo M. Polvani.