Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3619-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-3619-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Sep 2025
Research article |
| 26 Sep 2025
| 26 Sep 2025
Disentangling atmospheric, hydrological, and coupling uncertainties in compound flood modeling within a coupled Earth system model
Dongyu Feng
CORRESPONDING AUTHOR
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Darren Engwirda
T-3 Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Jonathan D. Wolfe
T-3 Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Donghui Xu
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Chang Liao
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Gautam Bisht
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
James J. Benedict
T-3 Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Tian Zhou
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Mithun Deb
Marine and Coastal Research Laboratory, Pacific Northwest National Laboratory, Sequim, WA 98382, USA
Hong-Yi Li
Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77204, USA
L. Ruby Leung
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
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Dylan Schlichting, Katherine Smith, Mark Petersen, Robert Hetland, Kyle Hinson, and Darren Engwirda
EGUsphere, https://doi.org/10.5194/egusphere-2026-1567, https://doi.org/10.5194/egusphere-2026-1567, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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River plumes can strongly influence coastal dynamics but are poorly resolved in global ocean models. Building on a companion study, we perform high-resolution simulations of the Mississippi–Atchafalaya (M-A) plume with the Model for Prediction Across Scales–Ocean (MPAS-O), a global model, and compare with a limited-domain Regional Ocean Modeling System configuration. MPAS-O qualitatively captures the M-A plume seasonality but fails to generate realistic eddies on the Texas–Louisiana shelf.
Fang Li, David M. Lawrence, Brendan M. Rogers, Chantelle Burton, Huilin Huang, Yiquan Jiang, Johannes W. Kaiser, Matthew Kasoar, Hanna Lee, Ruby Leung, Lars Nieradzik, Aihui Wang, Daniel S. Ward, Ligeer Ce, Yangchun Li, Zhongda Lin, Apostolos Voulgarakis, and Yongkang Xue
Geosci. Model Dev., 19, 3989–4007, https://doi.org/10.5194/gmd-19-3989-2026, https://doi.org/10.5194/gmd-19-3989-2026, 2026
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Extreme fire events are increasing, and fires are projected to rise across most regions, posing growing risks to ecosystems and society. As a key Earth system process, fire is now modeled in most Earth System Models (ESMs). FireMIP (Fire Modeling Intercomparison Project) within CMIP7 will evaluate fire simulations in state‑of‑the‑art ESMs, project future fire changes, and provide quantitative, process‑based understanding of fire's role in the Earth system.
Kyle E. Hinson, Dylan R. Schlichting, Robert D. Hetland, Darren Engwirda, Katherine M. Smith, Mark R. Petersen, and Kaila Uyeda
EGUsphere, https://doi.org/10.5194/egusphere-2026-1566, https://doi.org/10.5194/egusphere-2026-1566, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Physical dynamics of ocean flows at fine spatial scales are challenging for regional and global scale ocean models due to high computational effort associated with smaller grid cell sizes. We compared two ocean models: a regional model (ROMS; Regional Ocean Modeling System) and global model (MPAS-O; Model for Prediction Across Scales – Ocean) across a range of resolutions (10 km to 100 m) in an idealized domain, and found that their similarities may hold promise for future coupling between them.
Koichi Sakaguchi, Seth A. McGinnis, L. Ruby Leung, Melissa S. Bukovsky, Rachel R. McCrary, Ziming Chen, Chuan-Chieh Chang, and Yanjie Li
Geosci. Model Dev., 19, 3643–3688, https://doi.org/10.5194/gmd-19-3643-2026, https://doi.org/10.5194/gmd-19-3643-2026, 2026
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We evaluated Rossby waves in dynamical downscaling simulations over North America, and their connections to surface air temperature variability and heatwaves. Simulated Rossby wave propagation is distorted by flow discontinuities at lateral boundaries and by biased mean wind patterns, thereby breaking the region-specific connections between Rossby waves and surface temperature. Adjusting simulated large-scale winds to match the forcing data can reduce these biases.
Tian Zhou, Yun Qian, and L. Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2026-2237, https://doi.org/10.5194/egusphere-2026-2237, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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AI can now write scientific analysis code that looks correct but quietly produces wrong results. We tested whether asking the assistant to assemble analyses from a library of validated building blocks, rather than write code from scratch, makes it more reliable. Across six language models and seven Earth science tasks, free code writing succeeded only about five percent of the time while the constrained approach exceeded eighty percent, suggesting trust needs guardrails.
Shixuan Zhang, L. Ruby Leung, Bryce E. Harrop, Aniruddha Bora, George Karniadakis, Khemraj Shukla, and Kai Zhang
Geosci. Model Dev., 19, 1937–1964, https://doi.org/10.5194/gmd-19-1937-2026, https://doi.org/10.5194/gmd-19-1937-2026, 2026
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We developed a new method to guide the simulated atmosphere in an Earth system model so it better reflects real-world weather. By adjusting temperature and humidity, it reduces unwanted side effects and improves the realism of rainfall, energy flows, land–surface conditions, and extreme storms such as cyclones and atmospheric rivers. This makes the model more useful for testing its performance, understanding high-impact weather events, and creating reliable training data for machine learning.
Pengfei Shi, L. Ruby Leung, Zhaoxia Pu, Samson Hagos, and Karthik Balaguru
EGUsphere, https://doi.org/10.5194/egusphere-2025-4910, https://doi.org/10.5194/egusphere-2025-4910, 2025
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Reliable climate prediction requires accurate initialization of the ocean state. We developed a new data assimilation system that incorporates ocean temperature and salinity observations into a fully coupled climate model. This system improves simulations of Earth system variability from years to decades, and enhances skills in simulating winter temperature and precipitation variability over the United States. The results advance more reliable and skillful climate predictions.
Gokhan Danabasoglu, Frederic S. Castruccio, Burcu Boza, Alice M. Barthel, Arne Biastoch, Adam Blaker, Alexandra Bozec, Diego Bruciaferri, Frank O. Bryan, Eric P. Chassignet, Yao Fu, Ian Grooms, Catherine Guiavarc'h, Hakase Hayashida, Andrew McC. Hogg, Ryan M. Holmes, Doroteaciro Iovino, Andrew E. Kiss, M. Susan Lozier, Gustavo Marques, Alex Megann, Franziska U. Schwarzkopf, Dave Storkey, Luke van Roekel, Jon Wolfe, Xiaobiao Xu, and Rong Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-5406, https://doi.org/10.5194/egusphere-2025-5406, 2025
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A comparison of simulated and observed overturning transports across the Overturning in the Subpolar North Atlantic Program sections for the 2014–2022 period is presented. Eighteen ocean simulations participate in the study. The simulated transports are in general agreement with observations. Analyzing overturning circulations in both depth and density space together provides a more complete picture of the overturning properties. The study serves as a benchmark for evaluation of ocean models.
Naser Mahfouz, Hassan Beydoun, Johannes Mülmenstädt, Noel Keen, Adam C. Varble, Luca Bertagna, Peter Bogenschutz, Andrew Bradley, Matthew W. Christensen, T. Conrad Clevenger, Aaron Donahue, Jerome Fast, James Foucar, Jean-Christophe Golaz, Oksana Guba, Walter Hannah, Benjamin Hillman, Robert Jacob, Wuyin Lin, Po-Lun Ma, Yun Qian, Balwinder Singh, Christopher Terai, Hailong Wang, Mingxuan Wu, Kai Zhang, Andrew Gettelman, Mark Taylor, L. Ruby Leung, Peter Caldwell, and Susannah Burrows
Atmos. Chem. Phys., 25, 15105–15120, https://doi.org/10.5194/acp-25-15105-2025, https://doi.org/10.5194/acp-25-15105-2025, 2025
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Our study assesses the aerosol effective radiative forcing in a global cloud-resolving atmosphere model at ultra-high resolution. We demonstrate that global aerosol forcing signal can be robustly reproduced across resolutions when aerosol activation processes are carefully parameterized. Further, we argue that simplified prescribed aerosol schemes will open the door for further process/mechanism studies under controlled conditions.
Yuanqi Hong, Guta Abeshu, Hong-Yi Li, Dingbao Wang, Mengqi Zhao, Thomas Wild, Günter Blöschl, and Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2025-5039, https://doi.org/10.5194/egusphere-2025-5039, 2025
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Rain runs off in two main ways: saturation excess when soil is full, and infiltration excess when rain exceeds infiltration. They often occur simultaneously in a watershed, but most models treat them separately. We introduce a new theory that captures both processes within a single framework, tracking their shift over space and time. Tested over 181 U.S. watersheds, the theory well predicts streamflow in most watersheds and improves understanding of runoff under diverse climates.
Manon Maisonnier, Maoyuan Feng, David Bastviken, Sandra Arndt, Ronny Lauerwald, Aidin Jabbari, Goulven Gildas Laruelle, Murray D. MacKay, Zeli Tan, Wim Thiery, and Pierre Regnier
Earth Syst. Dynam., 16, 1779–1808, https://doi.org/10.5194/esd-16-1779-2025, https://doi.org/10.5194/esd-16-1779-2025, 2025
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A new process-based modelling framework, FLaMe-v1.0 (Fluxes of Lake Methane version 1.0), is developed to simulate methane (CH4) emissions from lakes at large scales. FLaMe couples the dynamics of organic carbon, oxygen and methane in lakes and rests on an innovative, computationally efficient lake clustering approach for the simulation of CH4 emissions across a large number of lakes. The model evaluation suggests that FLaMe captures the sub-annual and spatial variability of CH4 emissions well.
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 3833–3852, https://doi.org/10.5194/hess-29-3833-2025, https://doi.org/10.5194/hess-29-3833-2025, 2025
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Flow depth and velocity determine various river functions, but their high-resolution simulations are expensive. Here, we developed a downscaling approach that can provide fast and accurate estimation of high-resolution river hydrodynamics. The 84-fold acceleration achieved by the method makes reliable flood risk analysis that needs hundreds or thousands of model runs feasible. More importantly, it provides an opportunity to couple large-scale hydrodynamics with local processes in river models.
Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui
Earth Syst. Sci. Data, 17, 3721–3740, https://doi.org/10.5194/essd-17-3721-2025, https://doi.org/10.5194/essd-17-3721-2025, 2025
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We developed a high-resolution (4 km and hourly) observational derecho dataset over the United States east of the Rocky Mountains from 2004 to 2021 by using a mesoscale convective system dataset, bow echoes detected by a machine learning method, hourly gust speeds, and physically based identification criteria.
Chang Liao, L. Ruby Leung, Yilin Fang, Teklu Tesfa, and Robinson Negron-Juarez
Geosci. Model Dev., 18, 4601–4624, https://doi.org/10.5194/gmd-18-4601-2025, https://doi.org/10.5194/gmd-18-4601-2025, 2025
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Understanding horizontal groundwater flow is important for understanding how water moves through the ground. Current climate models often simplify this process because they do not have information about the land surface that is detailed enough. Our study developed a new model that divides the land surface into hillslopes to better represent how groundwater flows. This model can help improve predictions of water availability and how it affects ecosystems.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data, 17, 2713–2733, https://doi.org/10.5194/essd-17-2713-2025, https://doi.org/10.5194/essd-17-2713-2025, 2025
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We have developed new maps that reveal how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2500 gauges and a wealth of climate and environmental information. The maps are a critical step in understanding and predicting how carbon moves through our environment, hence making them a useful tool for tackling climate challenges.
Chang Liao, Darren Engwirda, Matthew G. Cooper, Mingke Li, and Yilin Fang
Earth Syst. Sci. Data, 17, 2035–2062, https://doi.org/10.5194/essd-17-2035-2025, https://doi.org/10.5194/essd-17-2035-2025, 2025
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Discrete global grid systems, or DGGS, are digital frameworks that help us organize information about our planet. Although scientists have used DGGS in areas like weather and nature, using them in the water cycle has been challenging because some core datasets are missing. We created a way to generate these datasets. We then developed the datasets in the Amazon and Yukon basins, which play important roles in our planet's climate. These datasets may help us improve our water cycle models.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Guta Wakbulcho Abeshu, Hong-Yi Li, Mingjie Shi, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 1847–1864, https://doi.org/10.5194/hess-29-1847-2025, https://doi.org/10.5194/hess-29-1847-2025, 2025
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This study examined how water availability, climate dryness, and plant productivity interact at the catchment scale. Using various indices and statistical methods, we found a 0–2-month lag in these interactions. Strong correlations during peak-productivity months were observed, with a notable hysteresis effect in vegetation response to changes in water availability and climate dryness. The findings help better understand catchment responses to climate variability.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Irena Vaňková, Xylar Asay-Davis, Carolyn Branecky Begeman, Darin Comeau, Alexander Hager, Matthew Hoffman, Stephen F. Price, and Jonathan Wolfe
The Cryosphere, 19, 507–523, https://doi.org/10.5194/tc-19-507-2025, https://doi.org/10.5194/tc-19-507-2025, 2025
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We study the effect of subglacial discharge on basal melting for Antarctic ice shelves. We find that the results from previous studies of vertical ice fronts and two-dimensional ice tongues do not translate to the rotating ice-shelf framework. The melt rate dependence on discharge is stronger in the rotating framework. Further, there is a substantial melt-rate sensitivity to the location of the discharge along the grounding line relative to the directionality of the Coriolis force.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024, https://doi.org/10.5194/acp-24-13633-2024, 2024
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Stratocumulus clouds play a large role in Earth's climate by reflecting incoming solar energy back to space. Turbulence at stratocumulus cloud top mixes in dry, warm air, which can lead to cloud dissipation. This process is challenging for coarse-resolution global models to represent. We show that global models nevertheless agree well with our process understanding. Global models also think the process is less important for the climate than other lines of evidence have led us to conclude.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
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Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Mengqi Zhao, Thomas B. Wild, Neal T. Graham, Son H. Kim, Matthew Binsted, A. F. M. Kamal Chowdhury, Siwa Msangi, Pralit L. Patel, Chris R. Vernon, Hassan Niazi, Hong-Yi Li, and Guta W. Abeshu
Geosci. Model Dev., 17, 5587–5617, https://doi.org/10.5194/gmd-17-5587-2024, https://doi.org/10.5194/gmd-17-5587-2024, 2024
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The Global Change Analysis Model (GCAM) simulates the world’s climate–land–energy–water system interactions , but its reservoir representation is limited. We developed the GLObal Reservoir Yield (GLORY) model to provide GCAM with information on the cost of supplying water based on reservoir construction costs, climate and demand conditions, and reservoir expansion potential. GLORY enhances our understanding of future reservoir capacity needs to meet human demands in a changing climate.
Mithun Deb, James J. Benedict, Ning Sun, Zhaoqing Yang, Robert D. Hetland, David Judi, and Taiping Wang
Nat. Hazards Earth Syst. Sci., 24, 2461–2479, https://doi.org/10.5194/nhess-24-2461-2024, https://doi.org/10.5194/nhess-24-2461-2024, 2024
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We coupled earth system, hydrology, and hydrodynamic models to generate plausible and physically consistent ensembles of hurricane events and their associated water levels from the open coast to tidal rivers of Delaware Bay and River. Our results show that the hurricane landfall locations and the estuarine wind can significantly amplify the extreme surge in a shallow and converging system, especially when the wind direction aligns with the surge propagation direction.
Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024, https://doi.org/10.5194/acp-24-7331-2024, 2024
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Human activities release copious amounts of small particles called aerosols into the atmosphere. These particles change how much sunlight clouds reflect to space, an important human perturbation of the climate, whose magnitude is highly uncertain. We found that the latest climate models show a negative correlation but a positive causal relationship between aerosols and cloud water. This means we need to be very careful when we interpret observational studies that can only see correlation.
Matthew J. Hoffman, Carolyn Branecky Begeman, Xylar S. Asay-Davis, Darin Comeau, Alice Barthel, Stephen F. Price, and Jonathan D. Wolfe
The Cryosphere, 18, 2917–2937, https://doi.org/10.5194/tc-18-2917-2024, https://doi.org/10.5194/tc-18-2917-2024, 2024
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The Filchner–Ronne Ice Shelf in Antarctica is susceptible to the intrusion of deep, warm ocean water that could increase the melting at the ice-shelf base by a factor of 10. We show that representing this potential melt regime switch in a low-resolution climate model requires careful treatment of iceberg melting and ocean mixing. We also demonstrate a possible ice-shelf melt domino effect where increased melting of nearby ice shelves can lead to the melt regime switch at Filchner–Ronne.
Tapio Schneider, L. Ruby Leung, and Robert C. J. Wills
Atmos. Chem. Phys., 24, 7041–7062, https://doi.org/10.5194/acp-24-7041-2024, https://doi.org/10.5194/acp-24-7041-2024, 2024
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Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
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This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
John T. Fasullo, Jean-Christophe Golaz, Julie M. Caron, Nan Rosenbloom, Gerald A. Meehl, Warren Strand, Sasha Glanville, Samantha Stevenson, Maria Molina, Christine A. Shields, Chengzhu Zhang, James Benedict, Hailong Wang, and Tony Bartoletti
Earth Syst. Dynam., 15, 367–386, https://doi.org/10.5194/esd-15-367-2024, https://doi.org/10.5194/esd-15-367-2024, 2024
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Climate model large ensembles provide a unique and invaluable means for estimating the climate response to external forcing agents and quantify contrasts in model structure. Here, an overview of the Energy Exascale Earth System Model (E3SM) version 2 large ensemble is given along with comparisons to large ensembles from E3SM version 1 and versions 1 and 2 of the Community Earth System Model. The paper provides broad and important context for users of these ensembles.
Yawen Liu, Yun Qian, Philip J. Rasch, Kai Zhang, Lai-yung Ruby Leung, Yuhang Wang, Minghuai Wang, Hailong Wang, Xin Huang, and Xiu-Qun Yang
Atmos. Chem. Phys., 24, 3115–3128, https://doi.org/10.5194/acp-24-3115-2024, https://doi.org/10.5194/acp-24-3115-2024, 2024
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Fire management has long been a challenge. Here we report that spring-peak fire activity over southern Mexico and Central America (SMCA) has a distinct quasi-biennial signal by measuring multiple fire metrics. This signal is initially driven by quasi-biennial variability in precipitation and is further amplified by positive feedback of fire–precipitation interaction at short timescales. This work highlights the importance of fire–climate interactions in shaping fires on an interannual scale.
Sara Calandrini, Darren Engwirda, and Luke Van Roekel
EGUsphere, https://doi.org/10.5194/egusphere-2024-472, https://doi.org/10.5194/egusphere-2024-472, 2024
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Most modern ocean circulation models only consider the hydrostatic pressure, but for coastal phenomena nonhydrostatic effects become important, creating the need to include the nonhydrostatic pressure. In this work, we present a nonhydrostatic formulation for MPAS-Ocean (MPAS-O NH) and show its correctness on idealized benchmark test cases. MPAS-O NH is the first global nonhydrostatic model at variable resolution and is the first nonhydrostatic ocean model to be fully coupled in a climate model.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Calvin Howes, Pablo E. Saide, Hugh Coe, Amie Dobracki, Steffen Freitag, Jim M. Haywood, Steven G. Howell, Siddhant Gupta, Janek Uin, Mary Kacarab, Chongai Kuang, L. Ruby Leung, Athanasios Nenes, Greg M. McFarquhar, James Podolske, Jens Redemann, Arthur J. Sedlacek, Kenneth L. Thornhill, Jenny P. S. Wong, Robert Wood, Huihui Wu, Yang Zhang, Jianhao Zhang, and Paquita Zuidema
Atmos. Chem. Phys., 23, 13911–13940, https://doi.org/10.5194/acp-23-13911-2023, https://doi.org/10.5194/acp-23-13911-2023, 2023
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To better understand smoke properties and its interactions with clouds, we compare the WRF-CAM5 model with observations from ORACLES, CLARIFY, and LASIC field campaigns in the southeastern Atlantic in August 2017. The model transports and mixes smoke well but does not fully capture some important processes. These include smoke chemical and physical aging over 4–12 days, smoke removal by rain, sulfate particle formation, aerosol activation into cloud droplets, and boundary layer turbulence.
Dongyu Feng, Zeli Tan, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 27, 3911–3934, https://doi.org/10.5194/hess-27-3911-2023, https://doi.org/10.5194/hess-27-3911-2023, 2023
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This study assesses the flood risks concurrently induced by river flooding and coastal storm surge along the coast of the contiguous United States using statistical and numerical models. We reveal a few hotspots of such risks, the critical spatial variabilities within a river basin and over the whole US coast, and the uncertainties of the risk assessment. We highlight the importance of weighing different risk measures to avoid underestimating or exaggerating the compound flood impacts.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
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We find that E3SM-HR reproduces the main features of the Antarctic coastal polynyas. Despite the high amount of coastal sea ice production, the densest water masses are formed in the open ocean. Biases related to the lack of dense water formation are associated with overly strong atmospheric polar easterlies. Our results indicate that the large-scale polar atmospheric circulation must be accurately simulated in models to properly reproduce Antarctic dense water formation.
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
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As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
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We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Nairita Pal, Kristin N. Barton, Mark R. Petersen, Steven R. Brus, Darren Engwirda, Brian K. Arbic, Andrew F. Roberts, Joannes J. Westerink, and Damrongsak Wirasaet
Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, https://doi.org/10.5194/gmd-16-1297-2023, 2023
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Understanding tides is essential to accurately predict ocean currents. Over the next several decades coastal processes such as flooding and erosion will be severely impacted due to climate change. Tides affect currents along the coastal regions the most. In this paper we show the results of implementing tides in a global ocean model known as MPAS–Ocean. We also show how Antarctic ice shelf cavities affect global tides. Our work points towards future research with tide–ice interactions.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
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We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Chandan Sarangi, Yun Qian, L. Ruby Leung, Yang Zhang, Yufei Zou, and Yuhang Wang
Atmos. Chem. Phys., 23, 1769–1783, https://doi.org/10.5194/acp-23-1769-2023, https://doi.org/10.5194/acp-23-1769-2023, 2023
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We show that for air quality, the densely populated eastern US may see even larger impacts of wildfires due to long-distance smoke transport and associated positive climatic impacts, partially compensating the improvements from regulations on anthropogenic emissions. This study highlights the tension between natural and anthropogenic contributions and the non-local nature of air pollution that complicate regulatory strategies for improving future regional air quality for human health.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
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Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
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Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
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We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
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The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Taher Chegini and Hong-Yi Li
Hydrol. Earth Syst. Sci., 26, 4279–4300, https://doi.org/10.5194/hess-26-4279-2022, https://doi.org/10.5194/hess-26-4279-2022, 2022
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Belowground urban stormwater networks (BUSNs) play a critical and irreplaceable role in preventing or mitigating urban floods. However, they are often not available for urban flood modeling at regional or larger scales. We develop a novel algorithm to estimate existing BUSNs using ubiquitously available aboveground data at large scales based on graph theory. The algorithm has been validated in different urban areas; thus, it is well transferable.
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, https://doi.org/10.5194/gmd-15-5489-2022, 2022
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Land surface heterogeneity plays a critical role in the terrestrial water, energy, and biogeochemical cycles. Our study systematically quantified the effects of four dominant heterogeneity sources on water and energy partitioning via Sobol' indices. We found that atmospheric forcing and land use land cover are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning. Our findings can help prioritize the future development of land surface models.
Sol Kim, L. Ruby Leung, Bin Guan, and John C. H. Chiang
Geosci. Model Dev., 15, 5461–5480, https://doi.org/10.5194/gmd-15-5461-2022, https://doi.org/10.5194/gmd-15-5461-2022, 2022
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The Energy Exascale Earth System Model (E3SM) project is a state-of-the-science Earth system model developed by the US Department of Energy (DOE). Understanding how the water cycle behaves in this model is of particular importance to the DOE’s mission. Atmospheric rivers (ARs) – which are crucial to the global water cycle – move vast amounts of water vapor through the sky and produce rain and snow. We find that this model reliably represents atmospheric rivers around the world.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
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Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Donghui Xu, Gautam Bisht, Khachik Sargsyan, Chang Liao, and L. Ruby Leung
Geosci. Model Dev., 15, 5021–5043, https://doi.org/10.5194/gmd-15-5021-2022, https://doi.org/10.5194/gmd-15-5021-2022, 2022
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The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be constrained by parameter calibration. In this work, we used a surrogate-assisted Bayesian framework to efficiently calibrate the runoff-generation processes in the Energy Exascale Earth System Model v1 at a global scale. The model performance was improved compared to the default parameter after calibration, and the associated parametric uncertainty was significantly constrained.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
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How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Xue Zheng, Qing Li, Tian Zhou, Qi Tang, Luke P. Van Roekel, Jean-Christophe Golaz, Hailong Wang, and Philip Cameron-Smith
Geosci. Model Dev., 15, 3941–3967, https://doi.org/10.5194/gmd-15-3941-2022, https://doi.org/10.5194/gmd-15-3941-2022, 2022
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We document the model experiments for the future climate projection by E3SMv1.0. At the highest future emission scenario, E3SMv1.0 projects a strong surface warming with rapid changes in the atmosphere, ocean, sea ice, and land runoff. Specifically, we detect a significant polar amplification and accelerated warming linked to the unmasking of the aerosol effects. The impact of greenhouse gas forcing is examined in different climate components.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
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We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Milena Veneziani, Wieslaw Maslowski, Younjoo J. Lee, Gennaro D'Angelo, Robert Osinski, Mark R. Petersen, Wilbert Weijer, Anthony P. Craig, John D. Wolfe, Darin Comeau, and Adrian K. Turner
Geosci. Model Dev., 15, 3133–3160, https://doi.org/10.5194/gmd-15-3133-2022, https://doi.org/10.5194/gmd-15-3133-2022, 2022
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We present an Earth system model (ESM) simulation, E3SM-Arctic-OSI, with a refined grid to better resolve the Arctic ocean and sea-ice system and low spatial resolution elsewhere. The configuration satisfactorily represents many aspects of the Arctic system and its interactions with the sub-Arctic, while keeping computational costs at a fraction of those necessary for global high-resolution ESMs. E3SM-Arctic can thus be an efficient tool to study Arctic processes on climate-relevant timescales.
Pinya Wang, Yang Yang, Huimin Li, Lei Chen, Ruijun Dang, Daokai Xue, Baojie Li, Jianping Tang, L. Ruby Leung, and Hong Liao
Atmos. Chem. Phys., 22, 4705–4719, https://doi.org/10.5194/acp-22-4705-2022, https://doi.org/10.5194/acp-22-4705-2022, 2022
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China is now suffering from both severe ozone (O3) pollution and heat events. We highlight that North China Plain is the hot spot of the co-occurrences of extremes in O3 and high temperatures in China. Such coupled extremes exhibit an increasing trend during 2014–2019 and will continue to increase until the middle of this century. And the coupled extremes impose more severe health impacts to human than O3 pollution occurring alone because of elevated O3 levels and temperatures.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Sally S.-C. Wang, Yun Qian, L. Ruby Leung, and Yang Zhang
Atmos. Chem. Phys., 22, 3445–3468, https://doi.org/10.5194/acp-22-3445-2022, https://doi.org/10.5194/acp-22-3445-2022, 2022
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This study develops an interpretable machine learning (ML) model predicting monthly PM2.5 fire emission over the contiguous US at 0.25° resolution and compares the prediction skills of the ML and process-based models. The comparison facilitates attributions of model biases and better understanding of the strengths and uncertainties in the two types of models at regional scales, for informing future model development and their applications in fire emission projection.
Jinshi Jian, Xuan Du, Juying Jiao, Xiaohua Ren, Karl Auerswald, Ryan Stewart, Zeli Tan, Jianlin Zhao, Daniel L. Evans, Guangju Zhao, Nufang Fang, Wenyi Sun, Chao Yue, and Ben Bond-Lamberty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-87, https://doi.org/10.5194/essd-2022-87, 2022
Manuscript not accepted for further review
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Field soil loss and sediment yield due to surface runoff observations were compiled into a database named AWESOME: Archive for Water Erosion and Sediment Outflow MEasurements. Annual soil erosion data from 1985 geographic sites and 75 countries have been compiled into AWESOME. This database aims to be an open framework for the scientific community to share field-based annual soil erosion measurements, enabling better understanding of the spatial and temporal variability of annual soil erosion.
Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
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Existing riverbed sediment particle size data are sparsely available at individual sites. We develop a continuous map of median riverbed sediment particle size over the contiguous US corresponding to millions of river segments based on the existing observations and machine learning methods. This map is useful for research in large-scale river sediment using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
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We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Claudia Tebaldi, Kalyn Dorheim, Michael Wehner, and Ruby Leung
Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, https://doi.org/10.5194/esd-12-1427-2021, 2021
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We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
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Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
David A. Griffin, Mike Herzfeld, Mark Hemer, and Darren Engwirda
Geosci. Model Dev., 14, 5561–5582, https://doi.org/10.5194/gmd-14-5561-2021, https://doi.org/10.5194/gmd-14-5561-2021, 2021
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In support of the developing ocean renewable energy sector, and indeed all mariners, we have developed a new tidal model for Australian waters and thoroughly evaluated it using a new compilation of tide gauge and current meter data. We show that while there is certainly room for improvement, the model provides useful predictions of tidal currents for about 80 % (by area) of Australian shelf waters. So we intend to commence publishing tidal current predictions for those regions soon.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Cited articles
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: Tensorflow: A system for large-scale machine learning, in: 12th USENIX symposium on operating systems design and implementation (OSDI 16), ISBN 978-1-931971-33-1, 2016.
Abbaszadeh, P., Munoz, D. F., Moftakhari, H., Jafarzadegan, K., and Moradkhani, H.: Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting, Iscience, 25, https://doi.org/10.1016/j.isci.2022.105201, 2022.
Andreadis, K. M., Schumann, G. J. P., and Pavelsky, T.: A simple global river bankfull width and depth database, Water Resour. Res., 49, 7164–7168, https://doi.org/10.1002/wrcr.20440, 2013.
Bakhtyar, R., Maitaria, K., Velissariou, P., Trimble, B., Mashriqui, H., Moghimi, S., Abdolali, A., Van der Westhuysen, A., Ma, Z., and Clark, E.: A new 1D/2D coupled modeling approach for a riverine-estuarine system under storm events: Application to Delaware river basin, J. Geophys. Res.-Oceans, 125, e2019JC015822, https://doi.org/10.1029/2019JC015822, 2020.
Bao, D., Xue, Z. G., Warner, J. C., Moulton, M., Yin, D., Hegermiller, C. A., Zambon, J. B., and He, R.: A numerical investigation of Hurricane Florence-induced compound flooding in the Cape Fear Estuary using a dynamically coupled hydrological-ocean model, J. Adv. Model. Earth Sy., 14, e2022MS003131, https://doi.org/10.1029/2022MS003131, 2022.
Bao, D., Xue, Z. G., and Warner, J. C.: Quantifying compound and nonlinear effects of hurricane-induced flooding using a dynamically coupled hydrological-ocean model, Water Resour. Res., 60, e2023WR036455, https://doi.org/10.1029/2023WR036455, 2024.
Barton, K. N., Pal, N., Brus, S. R., Petersen, M. R., Arbic, B. K., Engwirda, D., Roberts, A. F., Westerink, J. J., Wirasaet, D., and Schindelegger, M.: Global Barotropic Tide Modeling Using Inline Self-Attraction and Loading in MPAS-Ocean, J. Adv. Model. Earth Sy., 14, e2022MS003207, https://doi.org/10.1029/2022MS003207, 2022.
Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., Schumann, G., Giustarini, L., Coxon, G., Porter, J. R., Amodeo, M. F., Chu, Z., Lewis-Gruss, S., Freeman, N. B., Houser, T., Delgado, M., Hamidi, A., Bolliger, I., E. McCusker, K., Emanuel, K., Ferreira, C. M., Khalid, A., Haigh, I. D., Couasnon, A., E. Kopp, R., Hsiang, S., and Krajewski, W. F.: Combined modelling of US fluvial, pluvial, and coastal flood hazard under current and future climates, Water Resour. Res., 57, e2020WR028673, https://doi.org/10.1029/2020WR028673, 2021.
Bennett, W. G., Karunarathna, H., Xuan, Y., Kusuma, M. S., Farid, M., Kuntoro, A. A., Rahayu, H. P., Kombaitan, B., Septiadi, D., and Kesuma, T. N.: Modelling compound flooding: a case study from Jakarta, Indonesia, Nat. Hazards, 118, 277–305, https://doi.org/10.1007/s11069-023-06001-1, 2023.
Bermúdez, M., Farfán, J., Willems, P., and Cea, L.: Assessing the effects of climate change on compound flooding in coastal river areas, Water Resour. Res., 57, e2020WR029321, https://doi.org/10.1029/2020WR029321, 2021.
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J. W.: The relative importance of different flood-generating mechanisms across Europe, Water Resour. Res., 55, 4582–4593, https://doi.org/10.1029/2019WR024841, 2019.
Bevacqua, E., Vousdoukas, M. I., Zappa, G., Hodges, K., Shepherd, T. G., Maraun, D., Mentaschi, L., and Feyen, L.: More meteorological events that drive compound coastal flooding are projected under climate change, Communications Earth and Environment, 1, 47, https://doi.org/10.1038/s43247-020-00044-z, 2020.
Beven, K. and Binley, A.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Beven, K. J., Almeida, S., Aspinall, W. P., Bates, P. D., Blazkova, S., Borgomeo, E., Freer, J., Goda, K., Hall, J. W., Phillips, J. C., Simpson, M., Smith, P. J., Stephenson, D. B., Wagener, T., Watson, M., and Wilkins, K. L.: Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas, Nat. Hazards Earth Syst. Sci., 18, 2741–2768, https://doi.org/10.5194/nhess-18-2741-2018, 2018.
Bilskie, M. and Hagen, S.: Defining flood zone transitions in low-gradient coastal regions, Geophys. Res. Lett., 45, 2761–2770, https://doi.org/10.1002/2018GL077524, 2018.
Bilskie, M. V., Zhao, H., Resio, D., Atkinson, J., Cobell, Z., and Hagen, S. C.: Enhancing flood hazard assessments in coastal Louisiana through coupled hydrologic and surge processes, Frontiers in Water, 3, 609231, https://doi.org/10.3389/frwa.2021.609231, 2021.
Blanton, B., Dresback, K., Colle, B., Kolar, R., Vergara, H., Hong, Y., Leonardo, N., Davidson, R., Nozick, L., and Wachtendorf, T.: An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 2 – Hazard Modeling, Risk Anal., 40, 117– 133, https://doi.org/10.1111/risa.13004, 2020.
Blöschl, G.: Three hypotheses on changing river flood hazards, Hydrol. Earth Syst. Sci., 26, 5015–5033, https://doi.org/10.5194/hess-26-5015-2022, 2022.
Camacho, R. A., Martin, J. L., McAnally, W., Díaz-Ramirez, J., Rodriguez, H., Sucsy, P., and Zhang, S.: A comparison of Bayesian methods for uncertainty analysis in hydraulic and hydrodynamic modeling, J. Am. Water Resour. As., 51, 1372–1393, https://doi.org/10.1111/1752-1688.12319, 2015.
Capodaglio, G. and Petersen, M.: Local time stepping for the shallow water equations in MPAS, J. Comput. Phys., 449, 110818, https://doi.org/10.1016/j.jcp.2021.110818, 2022.
Costabile, P., Costanzo, C., Kalogiros, J., and Bellos, V.: Toward street-level nowcasting of flash floods impacts based on HPC hydrodynamic modeling at the watershed scale and high-resolution weather radar data, Water Resour. Res., 59, e2023WR034599, https://doi.org/10.1029/2023WR034599, 2023.
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009.
Craig, A. P., Vertenstein, M., and Jacob, R.: A new flexible coupler for earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform. C., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2012.
Deb, M., Sun, N., Yang, Z., Wang, T., Judi, D., Xiao, Z., and Wigmosta, M. S.: Interacting effects of watershed and coastal processes on the evolution of compound flooding during Hurricane Irene, Earths Future, 11, e2022EF002947, https://doi.org/10.1029/2022EF002947, 2023.
Deb, M., Benedict, J. J., Sun, N., Yang, Z., Hetland, R. D., Judi, D., and Wang, T.: Estuarine hurricane wind can intensify surge-dominated extreme water level in shallow and converging coastal systems, Nat. Hazards Earth Syst. Sci., 24, 2461–2479, https://doi.org/10.5194/nhess-24-2461-2024, 2024.
Du, X., García-Berthou, E., Wang, Q., Liu, J., Zhang, T., and Li, Z.: Analyzing the importance of top-down and bottom-up controls in food webs of Chinese lakes through structural equation modeling, Aquat. Ecol., 49, 199–210, https://doi.org/10.1007/s10452-015-9518-3, 2015.
Dullo, T. T., Gangrade, S., Morales-Hernández, M., Sharif, M. B., Kao, S.-C., Kalyanapu, A. J., Ghafoor, S., and Evans, K. J.: Simulation of Hurricane Harvey flood event through coupled hydrologic-hydraulic models: Challenges and next steps, J. Flood Risk Manag., 14, e12716, https://doi.org/10.1111/jfr3.12716, 2021.
Dykstra, S. and Dzwonkowski, B.: The propagation of fluvial flood waves through a backwater-estuarine environment, Water Resour. Res., 56, e2019WR025743, https://doi.org/10.1029/2019WR025743, 2020.
Eilander, D., Couasnon, A., Leijnse, T., Ikeuchi, H., Yamazaki, D., Muis, S., Dullaart, J., Haag, A., Winsemius, H. C., and Ward, P. J.: A globally applicable framework for compound flood hazard modeling, Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, 2023.
ESRI: “Topographic” [basemap], Scale Not Given, “World Topographic Map”, http://www.arcgis.com/home/item.html?id=30e5fe3149c34df1ba922e6f5bbf808f (last access: 23 September 2024), 2012.
Fan, Y., Yu, L., Shi, X., and Duan, Q.: Tracing Uncertainty Contributors in the Multi-Hazard Risk Anal. for Compound Extremes, Earths Future, 9, e2021EF002280, https://doi.org/10.1029/2021EF002280, 2021.
Feng, D.: E3SMv2 branch used for river-ocean two-way coupled ensemble simulations on variable-resolution meshes, Zenodo [code], https://doi.org/10.5281/zenodo.10927690, 2024.
Feng, D., Passalacqua, P., and Hodges, B. R.: Innovative approaches for geometric uncertainty quantification in an operational oil spill modeling system, Journal of Marine Science and Engineering, 7, 259, https://doi.org/10.3390/jmse7080259, 2019.
Feng, D., Tan, Z., Engwirda, D., Liao, C., Xu, D., Bisht, G., Zhou, T., Li, H.-Y., and Leung, L. R.: Investigating coastal backwater effects and flooding in the coastal zone using a global river transport model on an unstructured mesh, Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, 2022.
Feng, D., Tan, Z., and He, Q.: Physics-Informed Neural Networks of the Saint-Venant Equations for Downscaling a Large-Scale River Model, Water Resour. Res., 59, e2022WR033168, https://doi.org/10.1029/2022WR033168, 2023a.
Feng, D., Tan, Z., Xu, D., and Leung, L. R.: Understanding the compound flood risk along the coast of the contiguous United States, Hydrol. Earth Syst. Sci., 27, 3911–3934, https://doi.org/10.5194/hess-27-3911-2023, 2023b.
Feng, D., Tan, Z., Engwirda, D., Wolfe, J. D., Xu, D., Liao, C., Bisht, G., Benedict, J. J., Zhou, T., Li, H.-Y., and Leung, L. R.: Simulation of compound flooding using river-ocean two-way coupled E3SM ensemble on variable-resolution meshes, J. Adv. Model. Earth Sy., 16, e2023MS004054, https://doi.org/10.1029/2023MS004054, 2024.
Fisher, A., Rudin, C., and Dominici, F.: All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously, J. Mach. Learn. Res., 20, 1–81, 2019.
Giuntoli, I., Villarini, G., Prudhomme, C., and Hannah, D. M.: Uncertainties in projected runoff over the conterminous United States, Climatic Change, 150, 149–162, https://doi.org/10.1007/s10584-018-2280-5, 2018.
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., et al.: The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019.
Golaz, J.-C., Van Roekel, L. P., Zheng, X., et al.: The DOE E3SM Model Version 2: overview of the physical model and initial model evaluation, J. Adv. Model. Earth Sy., 14, https://doi.org/10.1029/2022MS003156, 2022.
Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning, MIT press, ISBN 9780262035613, 2016.
Gori, A., Lin, N., and Xi, D.: Tropical cyclone compound flood hazard assessment: From investigating drivers to quantifying extreme water levels, Earths Future, 8, e2020EF001660, https://doi.org/10.1029/2020EF001660, 2020a.
Gori, A., Lin, N., and Smith, J.: Assessing compound flooding from landfalling tropical cyclones on the North Carolina coast, Water Resour. Res., 56, e2019WR026788, https://doi.org/10.1029/2019WR026788, 2020b.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hamill, T. M., Whitaker, J. S., Kleist, D. T., Fiorino, M., and Benjamin, S. G.: Predictions of 2010's tropical cyclones using the GFS and ensemble-based data assimilation methods, Mon. Weather Rev., 139, 3243–3247, https://doi.org/10.1175/MWR-D-11-00079.1, 2011.
Hjelmstad, A., Shrestha, A., Garcia, M., and Mascaro, G.: Propagation of radar rainfall uncertainties into urban pluvial flood modeling during the North American monsoon, Hydrolog. Sci. J., 66, 2232–2248, https://doi.org/10.1080/02626667.2021.1980216, 2021.
Hendry, A., Haigh, I. D., Nicholls, R. J., Winter, H., Neal, R., Wahl, T., Joly-Laugel, A., and Darby, S. E.: Assessing the characteristics and drivers of compound flooding events around the UK coast, Hydrol. Earth Syst. Sci., 23, 3117–3139, https://doi.org/10.5194/hess-23-3117-2019, 2019.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hou, X., Hodges, B. R., Feng, D., and Liu, Q.: Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system, Mar. Pollut. Bull., 116, 420–433, https://doi.org/10.1016/j.marpolbul.2017.01.038, 2017.
Hu, R., Fang, F., Pain, C., and Navon, I.: Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method, J. Hydrol., 575, 911–920, https://doi.org/10.1016/j.jhydrol.2019.05.087, 2019.
Igolkina, A. A. and Meshcheryakov, G.: semopy: A python package for structural equation modeling, Struct. Equ. Modeling, 27, 952–963, https://doi.org/10.1080/10705511.2019.1704289, 2020.
Ikeuchi, H., Hirabayashi, Y., Yamazaki, D., Muis, S., Ward, P. J., Winsemius, H. C., Verlaan, M., and Kanae, S.: Compound simulation of fluvial floods and storm surges in a global coupled river-coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh, J. Adv. Model. Earth Sy., 9, 1847–1862, https://doi.org/10.1002/2017MS000943, 2017.
IOC and IHO: Intergovernmental Oceanographic Commission (IOC) of UNESCO and International Hydrographic Organization (IHO) [data set], General Bathymetric Chart of the Oceans (GEBCO) Digital Atlas, IOC, IHO, https://www.gebco.net/data_and_products/gridded_bathymetry_data/ (last access: 18 September 2025), 2020.
Ivancic, T. J. and Shaw, S. B.: Examining why trends in very heavy precipitation should not be mistaken for trends in very high river discharge, Climatic Change, 133, 681–693, https://doi.org/10.1007/s10584-015-1476-1, 2015.
Ivanov, V. Y., Xu, D., Dwelle, M. C., Sargsyan, K., Wright, D. B., Katopodes, N., Kim, J., Tran, V. N., Warnock, A., and Fatichi, S.: Breaking down the computational barriers to real-time urban flood forecasting, Geophys. Res. Lett., 48, e2021GL093585, https://doi.org/10.1029/2021GL093585, 2021.
Jafarzadegan, K., Moradkhani, H., Pappenberger, F., Moftakhari, H., Bates, P., Abbaszadeh, P., Marsooli, R., Ferreira, C., Cloke, H. L., and Ogden, F.: Recent advances and new frontiers in riverine and coastal flood modeling, Rev. Geophys., 61, e2022RG000788, https://doi.org/10.1029/2022RG000788, 2023.
Jalili Pirani, F. and Najafi, M.: Recent trends in individual and multivariate compound flood drivers in Canada's coasts, Water Resour. Res., 56, e2020WR027785, https://doi.org/10.1029/2020WR027785, 2020.
Joyce, J., Chang, N.-B., Harji, R., Ruppert, T., and Singhofen, P.: Cascade impact of hurricane movement, storm tidal surge, sea level rise and precipitation variability on flood assessment in a coastal urban watershed, Clim. Dynam., 51, 383–409, https://doi.org/10.1007/s00382-017-3930-4, 2018.
Kerns, B. W. and Chen, S. S.: Compound effects of rain, storm surge, and river discharge on coastal flooding during Hurricane Irene and Tropical Storm Lee (2011) in the Mid-Atlantic region: coupled atmosphere-wave-ocean model simulation and observations, Nat. Hazards, 116, 693–726, https://doi.org/10.1007/s11069-022-05694-0, 2023.
Kim, H.: Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1), Data Integration and Analysis System (DIAS) [data set], https://doi.org/10.20783/dias.501, 2017.
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 22 December 2014.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, EOS T. Am. Geophys. Un., 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Leijnse, T., van Ormondt, M., Nederhoff, K., and van Dongeren, A.: Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind-and wave-driven processes, Coast. Eng., 163, 103796, https://doi.org/10.1016/j.coastaleng.2020.103796, 2021.
Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B., McInnes, K., Risbey, J., Schuster, S., Jakob, D., and Stafford-Smith, M.: A compound event framework for understanding extreme impacts, WIRES Clim. Change, 5, 113–128, https://doi.org/10.1002/wcc.252, 2014.
Li, L., Bisht, G., Hao, D., and Leung, L. R.: Global 1 km land surface parameters for kilometer-scale Earth system modeling, Earth System Science Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, 2024.
Li, D., Lettenmaier, D. P., Margulis, S. A., and Andreadis, K.: The role of rain-on-snow in flooding over the conterminous United States, Water Resour. Res., 55, 8492–8513, https://doi.org/10.1029/2019WR024950, 2019.
Liao, C.: MPAS Mesh-Based Flow Routing Datasets for Hydrologic Modeling in Mid-Atlantic Coastal Watersheds, Zenodo [data set], https://doi.org/10.5281/zenodo.14664927, 2025.
Liao, C., Zhou, T., Xu, D., Barnes, R., Bisht, G., Li, H.-Y., Tan, Z., Tesfa, T., Duan, Z., Engwirda, D., and Leung, L. R.: Advances in hexagon mesh-based flow direction modeling, Adv. Water Resour., 160, 104099, https://doi.org/10.1016/j.advwatres.2021.104099, 2022.
Liao, C., Zhou, T., Xu, D., Cooper, M. G., Engwirda, D., Li, H.-Y., and Leung, L. R.: Topological Relationship-Based Flow Direction Modeling: Mesh-Independent River Networks Representation, J. Adv. Model. Earth Sy., 15, e2022MS003089, https://doi.org/10.1029/2022MS003089, 2023a.
Liao, C., Zhou, T., Xu, D., Tan, Z., Bisht, G., Cooper, M. G., Engwirda, D., Li, H.-Y., and Leung, L. R.: Topological Relationship-Based Flow Direction Modeling: Stream Burning and Depression Filling, J. Adv. Model. Earth Sy., 15, e2022MS003487, https://doi.org/10.1029/2022MS003487, 2023b.
Lilly, J. R., Capodaglio, G., Petersen, M. R., Brus, S. R., Engwirda, D., and Higdon, R. L.: Storm Surge Modeling as an Application of Local Time-Stepping in MPAS-Ocean, J. Adv. Model. Earth Sy., 15, e2022MS003327, https://doi.org/10.1029/2022MS003327, 2023.
Lin, Q., Shi, L., Liang, B., Wu, G., Wang, Z., Zhang, X., and Wu, Y.: Characterizing interactive compound flood drivers in the Pearl River Estuary: A case study of Typhoon Hato (2017), Journal of Hydrology, 132270, https://doi.org/10.1016/j.jhydrol.2024.132270, 2024.
Lin, G., Leung, L. R., Lee, J., Harrop, B. E., Baker, I. T., Branson, M. D., Denning, A. S., Jones, C. R., Ovchinnikov, M., and Randall, D. A.: Modeling Land–Atmosphere Coupling at Cloud-Resolving Scale Within the Multiple Atmosphere Multiple Land (MAML) Framework in SP-E3SM, J. Adv. Model. Earth Sy., 15, e2022MS003101, https://doi.org/10.1029/2022MS003101, 2023.
Loveland, M., Kiaghadi, A., Dawson, C. N., Rifai, H. S., Misra, S., Mosser, H., and Parola, A.: Developing a modeling framework to simulate compound flooding: when storm surge interacts with riverine flow, Frontiers in Climate, 2, 609610, https://doi.org/10.3389/fclim.2020.609610, 2021.
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Advances in Neural Information Processing Systems 30, edited by Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., 4765–4774, Curran Associates, Inc., http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf (last access: 18 September 2025), 2017.
Luo, X., Li, H.-Y., Leung, L. R., Tesfa, T. K., Getirana, A., Papa, F., and Hess, L. L.: Modeling surface water dynamics in the Amazon Basin using MOSART-Inundation v1.0: impacts of geomorphological parameters and river flow representation, Geosci. Model Dev., 10, 1233–1259, https://doi.org/10.5194/gmd-10-1233-2017, 2017.
Kulp, S. A. and Strauss, B. H.: New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding, Nat. Commun., 10, 1–12, https://doi.org/10.1038/s41467-019-12808-z, 2019.
Meresa, H., Murphy, C., Fealy, R., and Golian, S.: Uncertainties and their interaction in flood hazard assessment with climate change, Hydrol. Earth Syst. Sci., 25, 5237–5257, https://doi.org/10.5194/hess-25-5237-2021, 2021.
Miguez-Macho, G. and Fan, Y.: The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2012JD017539, 2012.
Moftakhari, H. R., Salvadori, G., AghaKouchak, A., Sanders, B. F., and Matthew, R. A.: Compounding effects of sea level rise and fluvial flooding, P. Natl. Acad. Sci. USA, 114, 9785–9790, https://doi.org/10.1073/pnas.1620325114, 2017.
Muñoz, D. F., Muñoz, P., Moftakhari, H., and Moradkhani, H.: From local to regional compound flood mapping with deep learning and data fusion techniques, Sci. Total Environ., 782, 146927, https://doi.org/10.1016/j.scitotenv.2021.146927, 2021.
Muñoz, D. F., Abbaszadeh, P., Moftakhari, H., and Moradkhani, H.: Accounting for uncertainties in compound flood hazard assessment: The value of data assimilation, Coast. Eng., 171, 104057, https://doi.org/10.1016/j.coastaleng.2021.104057, 2022.
Muñoz, D. F., Moftakhari, H., and Moradkhani, H.: Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models, Hydrol. Earth Syst. Sci., 28, 2531–2553, https://doi.org/10.5194/hess-28-2531-2024, 2024.
Nanditha, J. and Mishra, V.: Multiday precipitation is a prominent driver of floods in Indian river basins, Water Resour. Res., 58, e2022WR032723, https://doi.org/10.1029/2022WR032723, 2022.
Naseri, K. and Hummel, M. A.: A Bayesian copula-based nonstationary framework for compound flood risk assessment along US coastlines, J. Hydrol., 610, 128005, https://doi.org/10.1016/j.jhydrol.2022.128005, 2022.
Nederhoff, K., van Ormondt, M., Veeramony, J., van Dongeren, A., Antolínez, J. A. Á., Leijnse, T., and Roelvink, D.: Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding, Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, 2024.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., and Gulden, L. E.: A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in 515 global climate models, J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2005JD006111, 2005.
Olbert, A. I., Moradian, S., Nash, S., Comer, J., Kazmierczak, B., Falconer, R. A., and Hartnett, M.: Combined statistical and hydrodynamic modelling of compound flooding in coastal areas-Methodology and application, J. Hydrol., 620, 129383, https://doi.org/10.1016/j.jhydrol.2023.129383, 2023.
Pal, N., Barton, K. N., Petersen, M. R., Brus, S. R., Engwirda, D., Arbic, B. K., Roberts, A. F., Westerink, J. J., and Wirasaet, D.: Barotropic tides in MPAS-Ocean (E3SM V2): impact of ice shelf cavities, Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, 2023.
Pappenberger, F., Beven, K. J., Hunter, N. M., Bates, P. D., Gouweleeuw, B. T., Thielen, J., and de Roo, A. P. J.: Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS), Hydrol. Earth Syst. Sci., 9, 381–393, https://doi.org/10.5194/hess-9-381-2005, 2005.
Pettett, A. and Zarzycki, C. M.: The 1996 Mid-Atlantic Winter Flood: Exploring Climate Risk through a Storyline Approach, J. Hydrometeorol., 24, 2259–2280, https://doi.org/10.1175/JHM-D-22-0146.1, 2023.
Pires dos Santos, R., Dean, D., Weaver, J., and Hovanski, Y.: Identifying the relative importance of predictive variables in artificial neural networks based on data produced through a discrete event simulation of a manufacturing environment, International Journal of Modelling and Simulation, 39, 234–245, https://doi.org/10.1080/02286203.2018.1558736, 2019.
Qiu, H., Bisht, G., Li, L., Hao, D., and Xu, D.: Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0), Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, 2024.
Ran, Q., Wang, J., Chen, X., Liu, L., Li, J., and Ye, S.: The relative importance of antecedent soil moisture and precipitation in flood generation in the middle and lower Yangtze River basin, Hydrol. Earth Syst. Sci., 26, 4919–4931, https://doi.org/10.5194/hess-26-4919-2022, 2022.
Santiago-Collazo, F. L., Bilskie, M. V., Bacopoulos, P., and Hagen, S. C.: Compound inundation modeling of a 1-D idealized coastal watershed using a reduced-physics approach, Water Resour. Res., 60, e2023WR035718, https://doi.org/10.1029/2023WR035718, 2024.
Saleh, F., Ramaswamy, V., Wang, Y., Georgas, N., Blumberg, A., and Pullen, J.: A multi-scale ensemble-based framework for forecasting compound coastal-riverine flooding: The Hackensack-Passaic watershed and Newark Bay, Adv. Water Resour., 110, 371–386, https://doi.org/10.1016/j.advwatres.2017.10.026, 2017.
Santoro, S., Lovreglio, R., Totaro, V., Camarda, D., Iacobellis, V., and Fratino, U.: Community risk perception for flood management: A structural equation modelling approach, Int. J. Disast. Risk Re., 97, 104012, https://doi.org/10.1016/j.ijdrr.2023.104012, 2023.
Sarhadi, A., Rousseau-Rizzi, R., Mandli, K., Neal, J., Wiper, M. P., Feldmann, M., and Emanuel, K.: Climate change contributions to increasing compound flooding risk in New York City, B. Am. Meteorol. Soc., 105, E337–E356, https://doi.org/10.1175/BAMS-D-23-0177.1, 2024.
Schrapffer, A., Sörensson, A., Polcher, J., and Fita, L.: Benefits of representing floodplains in a Land Surface Model: Pantanal simulated with ORCHIDEE CMIP6 version, Clim. Dynam., 55, 1303–1323, https://doi.org/10.1007/s00382-020-05324-0, 2020.
Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K. M., Channan, S., and DiMiceli, C.: Global, 30 m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error, Int. J. Digit. Earth, 6, 427–448, https://doi.org/10.1080/17538947.2013.786146, 2013.
Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M., Fenicia, F., Kifer, D., and Li, L.: Differentiable modelling to unify machine learning and physical models for geosciences, Nature Reviews Earth and Environment, 4, 552–567, https://doi.org/10.1038/s43017-023-00450-9, 2023.
Shen, Y., Zhu, Z., Zhou, Q., and Jiang, C.: An improved dynamic bidirectional coupled hydrologic–hydrodynamic model for efficient flood inundation prediction, Nat. Hazards Earth Syst. Sci., 24, 2315–2330, https://doi.org/10.5194/nhess-24-2315-2024, 2024.
Shrikumar, A., Greenside, P., and Kundaje, A.: Learning important features through propagating activation differences, in: International conference on machine learning, PMLR, 3145–3153, https://doi.org/10.48550/arXiv.1704.02685, 2017.
Štrumbelj, E. and Kononenko, I.: Explaining prediction models and individual predictions with feature contributions, Knowledge and information systems, 41, 647–665, https://doi.org/10.1007/s10115-013-0679-x, 2014.
Tanim, A. H. and Goharian, E.: Developing a hybrid modeling and multivariate analysis framework for storm surge and runoff interactions in urban coastal flooding, J. Hydrol., 595, 125670, https://doi.org/10.1016/j.jhydrol.2020.125670, 2021.
Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., and Slayback, D. A.: Satellite imaging 540 reveals increased proportion of population exposed to floods, Nature, 596, 80–86, https://doi.org/10.1038/s41586-021-03695-w, 2021.
Tramblay, Y., Bouvier, C., Martin, C., Didon-Lescot, J.-F., Todorovik, D., and Domergue, J.-M.: Assessment of initial soil moisture conditions for event-based rainfall–runoff modelling, J. Hydrol., 387, 176–187, https://doi.org/10.1016/j.jhydrol.2010.04.006, 2010.
Tran, H., Fang, Y., Tan, Z., Zhou, T., and Leung, L. R.: Quantifying the impacts of land cover change on the hydrologic response to Hurricane Ida in the Lower Mississippi River Basin, J. Hydrometeorol., 25, 899–914, https://doi.org/10.1175/JHM-D-23-0094.1, 2024.
Tsang, M., Cheng, D., and Liu, Y.: Detecting statistical interactions from neural network weights, arXiv [preprint], https://doi.org/10.48550/arXiv.1705.04977, 2017.
Villarini, G., Luitel, B., Vecchi, G. A., and Ghosh, J.: Multi-model ensemble forecasting of North Atlantic tropical cyclone activity, Clim. Dynam., 53, 7461–7477, https://doi.org/10.1007/s00382-016-3369-z, 2019.
Wahl, T., Jain, S., Bender, J., Meyers, S. D., and Luther, M. E.: Increasing risk of compound flooding from storm surge and rainfall for major US cities, Nat. Clim. Change, 5, 1093–1097, https://doi.org/10.1038/nclimate2736, 2015.
Wang, Z., Leung, M., Mukhopadhyay, S., Sunkara, S. V., Steinschneider, S., Herman, J., Abellera, M., Kucharski, J., Nederhoff, K., and Ruggiero, P.: A hybrid statistical–dynamical framework for compound coastal flooding analysis, Environ. Res. Lett., 20, 014005, https://doi.org/10.1088/1748-9326/ad96ce, 2024.
Ward, N. D., Megonigal, J. P., Bond-Lamberty, B., Bailey, V. L., Butman, D., Canuel, E. A., Diefenderfer, H., Ganju, N. K., Goñi, M. A., and Graham, E. B.: Representing the function and sensitivity of coastal interfaces in Earth system models, Nat. Commun., 11, 2458, https://doi.org/10.1038/s41467-020-16236-2, 2020.
Willis, T., Wright, N., and Sleigh, A.: Systematic analysis of uncertainty in 2D flood inundation models, Environ. Modell. Softw., 122, 104520, https://doi.org/10.1016/j.envsoft.2019.104520, 2019.
Wing, O. E., Bates, P. D., Sampson, C. C., Smith, A. M., Johnson, K. A., and Erickson, T. A.: Validation of a 30 m resolution flood hazard model of the conterminous United States, Water Resour. Res., 53, 7968–7986, https://doi.org/10.1002/2017WR020917, 2017.
Wright, S.: Correlation and causation, J. Agric. Res., 20, 557–585, 1921.
Xiao, Z., Yang, Z., Wang, T., Sun, N., Wigmosta, M., and Judi, D.: Characterizing the non-linear interactions between tide, storm surge, and river flow in the delaware bay estuary, United States, Frontiers in Marine Science, 8, 715557, https://doi.org/10.3389/fmars.2021.715557, 2021.
Xu, D., Bisht, G., Sargsyan, K., Liao, C., and Leung, L. R.: Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1, Geosci. Model Dev., 15, 5021–5043, https://doi.org/10.5194/gmd-15-5021-2022, 2022a.
Xu, D., Bisht, G., Zhou, T., Leung, L. R., and Pan, M.: Development of Land-River Two-Way Hydrologic Coupling for Floodplain Inundation in the Energy Exascale Earth System Model, J. Adv. Model. Earth Sy., 14, e2021MS002772, https://doi.org/10.1029/2021MS002772, 2022b.
Xu, D., Bisht, G., Feng, D., Tan, Z., Li, L., Qiu, H., and Leung, L. R.: Impacts of Sea-Level Rise on Coastal Groundwater Table Simulated by an Earth System Model With a Land-Ocean Coupling Scheme, Earth's Future, 12, e2024EF004479, https://doi.org/10.1029/2024EF004479, 2024.
Xu, K., Wang, C., and Bin, L.: Compound flood models in coastal areas: a review of methods and uncertainty analysis, Nat. Hazards, 116, 469–496, https://doi.org/10.1007/s11069-022-05683-3, 2023.
Xu, D., Bisht, G., Engwirda, D., Feng, D., Tan, Z., and Ivanov, V. Y.: Uncertainties in simulating flooding during Hurricane Harvey using 2D shallow water equations, Water Resour. Res., 61, e2024WR038032, https://doi.org/10.1029/2024WR038032, 2025.
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based description of floodplain inundation dynamics in a global river routing model, Water Resour. Res., 47, W04501, https://doi.org/10.1029/2010WR009726, 2011.
Ye, F., Zhang, Y. J., Yu, H., Sun, W., Moghimi, S., Myers, E., Nunez, K., Zhang, R., Wang, H. V., and Roland, A.: Simulating storm surge and compound flooding events with a creek-to-ocean model: Importance of baroclinic effects, Ocean Model., 145, 101526, https://doi.org/10.1016/j.ocemod.2019.101526, 2020.
Zarzycki, C. M., Ascher, B. D., Rhoades, A. M., and McCrary, R. R.: Algorithmically Detected Rain-on-Snow Flood Events in Different Climate Datasets: A Case Study of the Susquehanna River Basin, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-3094, 2024.
Zhang, A. and Yu, X.: Development of A Land-River-Ocean Coupled Model for Compound Floods Jointly Caused by Heavy Rainfalls and Storm Surges in Large River Delta Regions, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3217, 2024.
Zhang, W., Villarini, G., Vecchi, G. A., and Smith, J. A.: Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston, Nature, 563, 384–388, https://doi.org/10.1038/s41586-018-0676-z, 2018.
Zhang, H., Shen, D., Bao, S., and Len, P.: Two-Way Coupling of the National Water Model (NWM) and Semi-Implicit Cross-Scale Hydroscience Integrated System Model (SCHISM) for Enhanced Coastal Discharge Predictions, Hydrology, 11, 145, https://doi.org/10.3390/hydrology11090145, 2024.
Zhong, L., Li, M., and Zhang, D.-L.: How do uncertainties in hurricane model forecasts affect storm surge predictions in a semi-enclosed bay?, Estuar. Coast. Shelf S., 90, 61–72, https://doi.org/10.1016/j.ecss.2010.07.001, 2010.
Zscheischler, J., Westra, S., Van Den Hurk, B. J., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.: Future climate risk from compound events, Nature Clim. Change, 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018.
Short summary
Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river, and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
Our study explores how riverine and coastal flooding during hurricanes is influenced by the...
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