TY  - JOUR
A1  - Koul, Vimal
A1  - Brune, Sebastian
A1  - Akimova, Anna
A1  - Düsterhus, André
A1  - Pieper, Patrick
A1  - Hövel, Laura
A1  - Parekh, Anant
A1  - Schrum, Corinna
A1  - Baehr, Johanna
T1  - Seasonal Prediction of Arabian Sea Marine Heatwaves
Y1  - 2023-09-19
VL  - 50
IS  - 18
SP  - 
EP  - 
JF  - Geophysical Research Letters
DO  - 10.1029/2023GL103975
PB  - 
N2  - <title xmlns:mml="http://www.w3.org/1998/Math/MathML">Abstract</title><p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en">Marine heatwaves are known to have a detrimental impact on marine ecosystems, yet predicting when and where they will occur remains a challenge. Here, using a large ensemble of initialized predictions from an Earth System Model, we demonstrate skill in predictions of summer marine heatwaves over large marine ecosystems in the Arabian Sea seven months ahead. Retrospective forecasts of summer (June to August) marine heatwaves initialized in the preceding winter (November) outperform predictions based on observed frequencies. These predictions benefit from initialization during winters of medium to strong El Niño conditions, which have an impact on marine heatwave characteristics in the Arabian Sea. Our probabilistic predictions target spatial characteristics of marine heatwaves that are specifically useful for fisheries management, as we demonstrate using an example of Indian oil sardine (<italic>Sardinella longiceps</italic>).</p>
N2  - Plain Language Summary: Marine heatwaves (MHWs) are prolonged extreme events associated with exceptionally high ocean water temperatures. Such events impose heat stress on marine life, and thus predicting such events is beneficial for management applications. In this work we show that the occurrence of MHWs in summer in the Arabian Sea can be skilfully predicted seven month in advance. Our prediction system benefits from the information of sea surface temperature anomalies in the eastern Pacific Ocean in the preceding winter, among other aspects. Our predictions suggest potential for using climate information in fisheries management in this region.</p>
N2  - Key Points: <list list-type="bullet"> <list-item> <p xml:lang="en">Summer marine heatwaves in the Arabian Sea are predictable seven months in advance</p></list-item> <list-item> <p xml:lang="en">The prediction skill in summer is mainly associated with a preceding El Niño event in winter</p></list-item> <list-item> <p xml:lang="en">Probabilistic predictions of Arabian Sea area under heatwave can be tailored to benefit fisheries</p></list-item> </list> </p>
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/11376
ER  -