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The fate of Greenland Ice Sheet supraglacial lakes in a warm and cool year

Supraglacial lakes form on the surface of the Greenland Ice Sheet during the summer months and can directly impact ice sheet mass balance by removing mass via drainage and runoff …

aneesh-subramanian

New Climate Change Center of Saudi Arabia: Advancing Understanding and Prediction for the Arabian Peninsula Climate

The desert climate of the Arabian Peninsula (AP), marked by sparse rainfall, extreme temperatures, and frequent dust events, significantly impacts its 80‐million population, …

ibrahim-hoteit

Koopman operator theory for enhanced Pacific SST forecasting

El Niño-Southern Oscillation (ENSO) is a complex climatic phenomenon with significant impacts on global weather patterns and ecosystems. Improving ENSO predictability is therefore …

paula-lorenzo-sanchez

Engaging K-12 Learners in Data Annotation for AI Climate Models

Due to the climate crisis, summers in Greenland have been rapidly getting warmer, causing increasing rates of ice melt on the Greenland ice sheet and speeding up sea-level rise. …

michael-macferrin

Earth, Wind, and Fire: Are Boulder’s Extreme Downslope Winds Changing?

Abstract A Denver newspaper in 2016 reported that a new Colorado all-time record peak wind gust of 148 mph was recorded on 18 February 2016, on Monarch Pass in the Colorado Rockies …

gerald-a-meehl

Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction

Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, …

emam-hossain

Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction

Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, …

emam-hossain

Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift

Causal modeling offers a principled foundation for uncovering stable, invariant relationships in time-series data, thereby improving robustness and generalization under …

emam-hossain

Building Machine Learning Challenges for Anomaly Detection in Science

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not …

elizabeth-g-campolongo