Propagation pathways of Indo-Pacific rainfall extremes are modulated by Pacific sea surface temperatures
Year: 2023
Type: article
Abstract: Intraseasonal variation of rainfall extremes within boreal summer in the Indo-Pacific region is driven by the Boreal Summer Intraseasonal Oscillation (BSISO), a quasi-periodic north-eastward movement of convective precipitation from the Indian Ocean to the Western Pacific. Predicting the spatiotemporal location of the BSISO is essential for subseasonal prediction of rainfall extremes but still remains a major challenge due to insufficient understanding of its propagation pathway. Here, using unsupervised machine learning, we characterize how rainfall extremes travel within the region and reveal three distinct propagation modes: north-eastward, eastward-blocked, and quasi-stationary. We show that Pacific sea surface temperatures modulate BSISO propagation - with El Niño-like (La Niña-like) conditions favoring quasi-stationary (eastward-blocked) modes-by changing the background moist static energy via local overturning circulations. Finally, we demonstrate the potential for early warning of rainfall extremes in the region up to four weeks in advance.
Source: Nature Communications
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $6,290
Funders: International Max Planck Research School for Environmental, Cellular and Molecular Microbiology, European Commission, Deutsche Forschungsgemeinschaft, Volkswagen Foundation, Eberhard Karls Universität Tübingen, Bundesministerium für Bildung und Forschung, International Max Planck Research School for Advanced Methods in Process and Systems Engineering
Awards: G1562512020, G1999011743, G2059623525, Multiscales and Critical Transitions in the Earth System, G233922387, G434337789, Förderung von drei explorativen Investitionsbereichen im Kontext der strategischen Neuausrichtung des Potsdam-Instituts für Klimafolgenforschung, EXC 2064: Maschinelles Lernen: Neue Perspektiven für die Wissenschaft, Tipping Points in the Earth System, G8483472199
OpenAlex ID: W4386782305
