The Multi‐Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation

• Published in: Earth's future 2023-11, Vol.11 (11), p.n/a
• Main Authors: Prein, Andreas F., Mooney, Priscilla A., Done, James M.
• Format: Article
• Language: English
• Subjects: Algorithms; Anticyclones; Atmospheric models; Atmospheric processes; Classification; Climate change; Climate models; Climatic extremes; Coastal zone; Cut-off lows; Cyclones; Datasets; Equatorial waves; Extratropical cyclones; extreme precipitation; Extreme weather; feature tracking; Frontal zones; Gravity waves; Latitude; Machine learning; Mesoscale convective systems; Missing data; Precipitation; Precipitation estimation; Radiation; Satellites; scale interactions; Tropical cyclones; Tropical environments; Variables
• ISSN: 2328-4277; 2328-4277
• DOI: 10.1029/2023EF003534

Climate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi‐Object Analysis of Atmospheric Phenomenon algorithm that uses a set of 12 common atmospheric variables to identify and track tropical and extra‐tropical cyclones, cut‐off lows, frontal zones, anticyclones, atmospheric rivers (ARs), jets, mesoscale convective systems (MCSs), and equatorial waves. We apply the algorithm to global historical data between 2001–2020 and associate phenomena with hourly and daily satellite‐derived extreme precipitation estimates in major climate regions. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid‐latitude land regions, while extreme precipitation in mid and high‐latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with phenomena interacting across scales that intensify precipitation. These interactions are a function of the intensity (i.e., rarity) of extreme events. The presented methodology and results could have wide‐ranging applications including training of machine learning methods, Lagrangian‐based evaluation of climate models, and process‐based understanding of extreme precipitation in a changing climate. Plain Language Summary Increases in intense precipitation and faster onsets of droughts are just two of many precipitation‐related extreme events that worsen under progressive climate change. Surprisingly little is known about the weather systems that are driving these changes in many regions around the world. In order to better predict and prepare for these events, scientists need an improved understanding of the causes of the involved atmospheric processes and their interactions. A new algorithm called the Multi‐Object Analysis of Atmospheric Phenomenon has been developed to identify and track different types of weather systems, such as tropical and extra‐tropical cyclones, that can lead to extreme precipitation. The algorithm was applied to global weather data from 2001 to 2020. The results