The development and application of a new global-scale air mass classification

Authors: Cameron Lee*, Kent State University
Topics: Climatology and Meteorology, Environmental Science, Global Change
Keywords: climate science, climate change, air masses, classification
Session Type: Virtual Paper
Day: 4/10/2021
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 24
Presentation File: No File Uploaded


This research describes updates to – and applications of – the gridded weather typing classification (GWTC) system, the first such classification to be available at a global scale. This new global GWTC (the GWTC-2) utilizes data at a 0.5-degree spatial resolution from the Climate Forecast System to classify every location and every day into one of 11 geographically- and seasonally-relative air masses (AMs). Compared to the original, the extreme GWTC-2 AMs have more-extreme meteorological characteristics – a goal of the updated classification. These air masses are also used to develop two global-scale indicators of multivariate climate change: a Warm/Cool Index (WCI) and a Global Extremes Index (GEI). Most existing climate indicators are based upon a single variable (often temperature or precipitation) and/or are regionally defined; however, significant changes are occurring in a variety of different atmospheric variables across the globe. The WCI and GEI exhibit moderate (GEI) to strong (WCI) relationships with the global mean temperature record and most other temperature-based indicators. In addition, the significant changes in cloud cover, dew points, and meridional winds in the tropical Pacific Ocean were well-captured by these two new indices. As the GWTC-2 is the first weather-typing classification available over the oceans or over the tropics, it may pave the way for many new frontiers in synoptic climatological applications in these areas. Nearly 4 billion location-days have been classified already, with about 8 million more added each month, and daily forecasts of the GWTC-2 out to 60 days are currently available in real-time.

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