Winter predictions for the northeast
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Winter Predictions for the Northeast: Insights from Recent Research
Winter Surface Air Temperature Predictions in Northeast China
Recent studies have developed models to predict winter surface air temperature (SAT) in Northeast China. One approach involves forecasting the year-to-year increment in SAT using multi-linear regression models. These models have shown a high correlation coefficient (0.73) between simulated and observed annual increments in winter SAT from 1965 to 2002, with a relative root mean square error of -7.9%1. This method has proven effective in capturing the increasing trend of winter SAT in Northeast China, suggesting its relevance for operational seasonal forecasts.
Winter Weather Regimes in the Northeast United States
In the Northeast United States, k-means cluster analysis has identified five distinct winter weather patterns based on daily 850-hPa winds. These patterns vary in terms of precipitation anomalies and storm tracks. For instance, Weather Type (WT) 1 is associated with positive precipitation anomalies extending to the Great Lakes, while WT5 features negative precipitation anomalies along the entire U.S. East Coast2. Understanding these patterns helps in predicting the duration and progression of winter weather events in the region.
Temperature and Precipitation Extremes
An analysis of temperature and precipitation extremes in the Northeast U.S. from 1926 to 2000 reveals an increase in both extremes, particularly over the past four decades. Variability in these extremes is linked to climate teleconnections such as the Arctic Oscillation (AO), El Niño–Southern Oscillation (ENSO), and the Pacific–North American (PNA) pattern. The AO, in particular, is a strong predictor of winter warm nights, while ENSO better predicts consecutive dry days3. These findings highlight the importance of large-scale atmospheric patterns in influencing regional climate extremes.
Future Winter Trends in Northeastern North America
Global climate models project continued warming and snow loss in northeastern North America under both lower and higher climate-warming scenarios. This warming trend will result in more days above freezing, fewer nights below freezing, and shorter durations of deep snowpacks. These changes will impact ecosystems and communities, necessitating adaptation and mitigation strategies, especially in winter-recreation and forest-management sectors5.
Heavy Snow Activity Predictions in Northeast China
For predicting heavy snow activity in Northeast China, a model using four key predictors—soil moisture, sea-air interactions, the Mascarene High, and low-level thermal conditions—has shown high accuracy. Cross-validation tests reveal a high correlation coefficient (0.86) between predicted and observed snow days, indicating the model's effectiveness in operational settings4.
Stratospheric Polar Vortex and Arctic Oscillation
The winter of 2019-2020 was marked by an extremely strong stratospheric polar vortex and a positive Arctic Oscillation (AO). Forecasts from multiple prediction systems showed high skill in predicting these conditions, demonstrating the interdependence between stratospheric vortex strength and tropospheric states. Accurate predictions of these phenomena are crucial for understanding and forecasting winter weather patterns7.
Synoptic and Climatic Conditions of Extreme Snowstorms
An extreme snowstorm in Northeast China in November 2020 was driven by specific synoptic and climatic conditions, including an anomalous anticyclone over Japan and a rapidly enhanced Siberian high. The positive North Pacific Oscillation (NPO) and North Atlantic Oscillation (NAO) played significant roles in accumulating water vapor, leading to the snowstorm. However, the La Niña condition in 2020 was not a key factor8.
Model Errors and Physics Parameterization
Evaluations of different physics parameterization schemes in winter forecasts for the Northeast U.S. reveal persistent biases. For example, a near-surface cold bias is attributed to the NOAH-MP land surface model's simulation of lower soil water content, leading to lower thermal conductivity and surface temperatures. Identifying and addressing these biases is essential for improving forecast accuracy9.
Conclusion
The research highlights the complexity and variability of winter weather patterns and predictions in the Northeast. From temperature and precipitation extremes to the impacts of large-scale atmospheric patterns and climate change, understanding these factors is crucial for accurate forecasting and effective adaptation strategies. Continued advancements in modeling and analysis will enhance our ability to predict and respond to winter weather challenges in the region.
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Most relevant research papers on this topic
Predicting Winter Surface Air Temperature in Northeast China
The prediction model for winter surface air temperature in Northeast China shows high correlation with observed data and can capture the increasing trend from 1965-2008.
Winter Weather Regimes in the Northeast United States
Five distinct winter weather patterns in the Northeast United States are identified, with WT5 and WT1 being the most persistent, and each pattern affecting precipitation anomalies differently.
Variations of Twentieth-Century Temperature and Precipitation Extreme Indicators in the Northeast United States
The Arctic Oscillation is a better predictor of winter warm nights than the El Nio–Southern Oscillation for the northeastern United States, with the latter being a better predictor of consecutive dry days.
Highly CitedPrediction of wintertime heavy snow activity in Northeast China
This study develops an effective climate prediction model for Northeast China's heavy snow activities, using soil moisture, sea-air interactions, the preceding summer Mascarene High, and low-level thermal conditions from the previous year.
Future of Winter in Northeastern North America: Climate Indicators Portray Warming and Snow Loss That Will Impact Ecosystems and Communities
Winters in northeastern North America are projected to continue warming, causing increased days above freezing, decreased snow cover, and fewer nights below freezing, affecting ecosystems and communities.
A hybrid downscaling model for winter temperature over northeast China
The hybrid downscaling model, using winter sea level pressure, sea surface temperature, and sea-ice concentration, significantly improves winter temperature forecasting in northeast China compared to real-time CFSv2 forecasting.
Seasonal Forecasts of the Exceptional Northern Hemisphere Winter of 2020
Most seasonal forecast systems performed extremely well for the 2019-2020 winter, with strong interdependence between stratospheric vortex and Arctic Oscillation forecasts.
Synoptic and Climatic Conditions of an Extreme Snowstorm Event Over Northeast China and Its Climate Predictability
The extreme snowstorm in Northeast China in November 2020 was triggered by an anomalous anticyclone over Japan, an anomalous cyclone in South China, and the rapidly enhanced Siberian high, with limited predictability.
Diagnosing Near-Surface Model Errors with Candidate Physics Parameterization Schemes for Multi-Physics Rapid Refresh Forecast System (RRFS) Ensemble during Winter over the Northeastern US and Southern Great Plains
Near-surface model errors in the Northeast US and Southern Great Plains can be identified and addressed through changes in land surface models and planetary boundary layer parameterizations.
Model and Observational Analysis of the Northeast U.S. Regional Climate and Its Relationship to the PNA and NAO Patterns during Early Winter
A positive PNA pattern leads to cold, dry conditions in the Northeast, while increased cyclone activity leads to below-normal wintertime precipitation.
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