Results 51 to 60 of about 430 (169)

Understanding aerosol–cloud interactions using a single-column model for a cold-air outbreak case during the ACTIVATE campaign [PDF]

Atmospheric Chemistry and Physics
Marine boundary layer clouds play a critical role in Earth's energy balance. Their microphysical and radiative properties are highly impacted by ambient aerosols and dynamic forcings.
S. Tang, S. Tang, H. Wang, X.-Y. Li, J. Chen, J. Chen, A. Sorooshian, A. Sorooshian, X. Zeng, E. Crosbie, E. Crosbie, K. L. Thornhill, L. D. Ziemba, C. Voigt, C. Voigt   +14 more
doaj   +1 more source

The Role of Tropical Cyclone—Ocean Interactions in Future Changes in Hurricane Katrina

Geophysical Research Letters, Volume 53, Issue 9, 16 May 2026.
Abstract Tropical cyclone (TC) intensity and precipitation are projected to increase in the future. However, some projections are based on atmosphere‐only models in which sea surface temperatures are prescribed, whereas projections based on global atmosphere‐ocean coupled models can be subject to long‐term ocean biases.
Dakota C. Forbis, Christina M. Patricola‐DiRosario, I.‐I. Lin, Ping Chang   +3 more
wiley   +1 more source

Enhancing Great Plains Nocturnal Precipitation and Low‐Level Jets in AM4 With an Extended CLUBB Closure

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract In this study, we extend the Cloud Layers Unified by Binormals (CLUBB) turbulence scheme within the GFDL atmospheric model (AM4) by implementing direct momentum‐flux prognosis and a multiscale turbulent lengthscale, to improve the simulation of nocturnal precipitation and associated Low‐Level Jets (LLJs) over the Great Plains (GP). Toward this
Emanuele Silvio Gentile, Vincent E. Larson, Ming Zhao, Colin Zarzycki, Gunilla Svensson, Leo Donner   +5 more
wiley   +1 more source

Exploring Ways to Reduce Biases in a Hybrid Global Climate Model With Machine‐Learned Moist Physics

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract In a previous study (Han et al., 2023, https://doi.org/10.1029/2022ms003508), we implemented a deep convolutional residual neural network for moist physics into the 3‐D real‐geography CAM5 and carried out a stable multi‐year integration successfully. However, the simulation has large temperature and moisture biases in high latitude troposphere
Yilun Han, Guang J. Zhang, Yong Wang
wiley   +1 more source

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA‐Based Surrogate for Spatial Fields

Journal of Advances in Modeling Earth Systems
Global Climate Model tuning (calibration) is a tedious and time‐consuming process, with high‐dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand‐picked values of tuning parameters.
Drew Yarger, Benjamin Moore Wagman, Kenny Chowdhary, Lyndsay Shand   +3 more
doaj   +1 more source

Beyond the Training Data: Confidence‐Guided Mixing of Parameterizations in a Hybrid AI‐Climate Model

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract Persistent systematic errors in Earth system models (ESMs) arise from difficulties in representing the full diversity of subgrid, multiscale atmospheric convection and turbulence. Machine learning (ML) parameterizations trained on short high‐resolution simulations show strong potential to reduce these errors.
Helge Heuer, Tom Beucler, Mierk Schwabe, Julien Savre, Manuel Schlund, Veronika Eyring   +5 more
wiley   +1 more source

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $50,000 Kaggle Competition

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract Subgrid machine‐learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher‐resolution physics without incurring the prohibitive computational cost associated with more explicit physics‐based simulations.
Jerry Lin, Zeyuan Hu, Tom Beucler, Katherine Frields, Hannah Christensen, Walter Hannah, Helge Heuer, Peter Ukkonen, Laura A. Mansfield, Tian Zheng, Liran Peng, Ritwik Gupta, Pierre Gentine, Yusef Al‐Naher, Mingjiang Duan, Kyo Hattori, Weiliang Ji, Chunhan Li, Kippei Matsuda, Naoki Murakami, Shlomo Ron, Marec Serlin, Hongjian Song, Yuma Tanabe, Daisuke Yamamoto, Jianyao Zhou, Mike Pritchard   +26 more
wiley   +1 more source

An Integrated Modeling Framework for Sediment Dynamics During Urban Flooding: Application to Hurricane Harvey in Houston

Water Resources Research, Volume 62, Issue 5, May 2026.
Abstract Floodwater can mobilize and redistribute large volumes of sediment from upland to downstream urban areas, threatening infrastructure, water quality, and ecosystem health. However, existing modeling approaches often fail to capture sediment dynamics in urban floodplains due to the lack of integration between upland hydrological processes and ...
Dongyu Feng, Zeli Tan, Donghui Xu, Lingcheng Li, Gautam Bisht   +4 more
wiley   +1 more source

Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model [PDF]

Geoscientific Model Development
Emissions of anthropogenic aerosol and their precursors are often prescribed in global aerosol models. Most of these emissions are spatially heterogeneous at model grid scales.
T. Hassan, K. Zhang, J. Li, B. Singh, S. Zhang, H. Wang, P.-L. Ma   +6 more
doaj   +1 more source

The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning

Geophysical Research Letters, Volume 53, Issue 8, 28 April 2026.
Abstract Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines.
Vinh Ngoc Tran, Donghui Xu, Phong Le, Jongho Kim, Tam Van Nguyen, Giang Tien Nguyen, Pedro J. Restrepo, Valeriy Y. Ivanov   +7 more
wiley   +1 more source