AUTHOR=Taylor John , Feng Ming 

TITLE=A deep learning model for forecasting global monthly mean sea surface temperature anomalies

JOURNAL=Frontiers in Climate

VOLUME=Volume 4 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2022.932932

DOI=10.3389/fclim.2022.932932

ISSN=2624-9553

ABSTRACT=Sea surface temperature (SST) variability plays a key role in the global weather and climate
system, with phenomena such as El Nin ̃o-Southern Oscillation (ENSO) regarded as a major
source of interannual climate variability at the global scale. The ability to make long-range
forecasts of sea surface temperature variations and extreme marine heatwave events has
potentially significant economic and societal benefits, especially in a warming climate. We have
developed a deep learning time series prediction model (Unet-LSTM), based on more than 70
years (1950-2021) of ECMWF ERA5 monthly mean sea surface temperature and 2-metre air
temperature data, to predict global 2-dimensional sea surface temperatures up to a 24-month lead.
Model prediction skills are high in the equatorial and subtropical Pacific. We have assessed the
ability of the model to predict sea surface temperature anomalies in the Nin ̃o3.4 region, an ENSO
index in the equatorial Pacific, and the Blob marine heatwave events in the northeast Pacific in
detail. Model hindcasts of the Nin ̃o3.4 index capture the strong 2010-11 La Nin ̃a and 2009-10 El
Nino well. An assessment of the predictions of the 2019-20 El Nin ̃o and the 2016-17 and 2017-18
La Nin ̃a show that the model has skill up to 18 months in advance. The prediction of the 2015-16
extreme El Nin ̃o is less satisfactory, which suggests that subsurface ocean information may be
crucial for the evolution of this event. Note that the model makes predictions of the 2-d monthly SST field and Nino 3.4 is just one region embedded in the global field. The model also shows long lead prediction skills for the northeast Pacific marine heatwave, the Blob. However, the prediction of the marine heatwaves in the southeast Indian Ocean, the Ningaloo Nin ̃o, shows a short lead prediction. These results indicate the significant potential of data-driven methods to yield long-range predictions of sea surface temperature anomalies.