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Physics-guided deep learning for skillful wind-wave modeling.
Sci Adv
December 2024
College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.
Modeling sea surface wind-waves is crucial for both scientific research and engineering applications. Nowadays, the most accurate wave models are based on numerical methods, which primarily concern the wave spectrum evolution by solving wave action balance partial differential equations. These methods are computationally expensive and limited by incomplete physical representations of wave spectral evolution.
View Article and Find Full Text PDFAccurate and efficient AI-assisted paradigm for adding granularity to ERA5 precipitation reanalysis.
Sci Rep
October 2024
Department of Civil, DICCA, Chemical and Environmental Engineering, Via Montallegro 1, 16145, Genova, Italy.
Scientific inquiry has long relied on deterministic algorithms for systematic problem-solving and predictability. However, the rise of artificial intelligence (AI) has revolutionized data analysis, allowing us to uncover complex patterns in large datasets. In this study, we combine these two approaches by using AI to improve the reconstruction of past precipitation events, which is crucial for understanding climate change.
View Article and Find Full Text PDFForecasting national CO emissions worldwide.
Sci Rep
September 2024
DIATI, Politecnico di Torino, Turin, 10129, Italy.
Urgent climate action, especially carbon emissions reduction, is required to achieve sustainable goals. Therefore, understanding the drivers of and predicting [Formula: see text] emissions is a compelling matter. We present two global modeling frameworks-a multivariate regression and a Random Forest Regressor (RFR)-to hindcast (until 2021) and forecast (up to 2035) [Formula: see text] emissions across 117 countries as driven by 12 socioeconomic indicators regarding carbon emissions, economic well-being, green and complexity economics, energy use and consumption.
View Article and Find Full Text PDFModeling future cliff-front waves during sea level rise and implications for coastal cliff retreat rates.
Sci Rep
April 2024
Scripps Institution of Oceanography, University of California San Diego, San Diego, USA.
It is often assumed that future coastal cliff retreat rates will accelerate as global sea level rises, but few studies have investigated how SLR (sea level rise) might change cliff-front wave dynamics. Using a new simple numerical model, this study simulates the number and type (breaking, broken, or unbroken) of cliff-front waves under future SLR scenarios. Previous research shows breaking waves deliver more energy to cliffs than broken waves, and unbroken waves generate minimal impact.
View Article and Find Full Text PDFMulti-temporal variability forecast of particulate organic carbon in the Indonesian seas.
Environ Monit Assess
February 2023
Research Center for Oceanography, National Research and Innovation Agency of the Republic of Indonesia, Pasir Putih Raya 1, Ancol Timur, Jakarta, 14430, Indonesia.
The current global condition characterized by high levels of CO is altering the carbon cycle and elemental biogeochemistry, resulting in subsequent global warming, climate change, ocean acidification, and the indirect response of deoxygenation. The features of Indonesia's coastal ecosystems and continental shelf waters also contribute to spatio-temporal ocean carbon variability. For instance, the level of particulate organic carbon (POC) will change annually, and thus, over a decadal period, ocean dynamics may affect the temporal variability of POC.
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