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David Neelin

Professor

UCLA

United States

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About

David Neelin is a Professor at UCLA in the United States. His research primarily focuses on climate science, with recent articles addressing issues such as the increasing duration of heatwaves due to global warming, the application of machine learning in weather forecasting, and the impacts of regional climate interventions. He also explores atmospheric cloud radiative effects and their relationship to deep convection, as well as the climatic influences of geographical features like the Rocky Mountains and Tibetan Plateau.

Articles (16)

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2025

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2023

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2022

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2021

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2021

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2021

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2020

Collaborators (2)

yongkang xue

Professor

UNITED STATES

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Mengxi Wu

Research Assistant Professor

The Hong Kong University of Science and Technology

CHINA

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David Neelin

Research Interests

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Articles (16)

Accelerating increase in the duration of heatwaves under global warming

A data-to-forecast machine learning system for global weather

A Framework for Minimizing Remote Effects of Regional Climate Interventions: Cooling the Great Barrier Reef Without Teleconnections

Observed Links Between Atmospheric Cloud Radiative Effects and Mesoscale Organization of Deep Convection

Effect of Rocky Mountains and Tibetan Plateau 1998 Spring Land Temperature on N. American and East Asian Summer Precipitation Anomalies

Author Correction: Multi-objective observational constraint of tropical Atlantic and Pacific low-cloud variability narrows uncertainty in cloud feedback

Multi-objective observational constraint of tropical Atlantic and Pacific low-cloud variability narrows uncertainty in cloud feedback

Let’s celebrate Earth Day as the Age of Open Science

Short Warm Distribution Tails Accelerate the Increase of Humid‐Heat Extremes Under Global Warming

Identifying the Deep‐Inflow Mixing Features in Orographically‐Locked Diurnal Convection

Extreme Tropical Precipitation Clusters Show Strong Increases in Frequency Under Global Warming in CMIP6 Models

Precipitation Extremes and Water Vapor

A Process‐Oriented Diagnostic to Assess Precipitation‐Thermodynamic Relations and Application to CMIP6 Models

Diagnosing Non‐Gaussian Temperature Distribution Tails Using Back‐Trajectory Analysis

Climate models capture key features of extreme precipitation probabilities across regions

Observed Tightening of Tropical Ascent in Recent Decades and Linkage to Regional Precipitation Changes

Collaborators (2)

yongkang xue

Mengxi Wu

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