Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-5021-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-5021-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USA
Gautam Bisht
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USA
Khachik Sargsyan
Chemistry, Combustion, and Materials Science Center, Sandia National Laboratories, Livermore, CA, USA
Chang Liao
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USA
L. Ruby Leung
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USA
Related authors
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
Short summary
Short summary
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.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and Lai-Yung Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1879, https://doi.org/10.5194/egusphere-2023-1879, 2023
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled Earth System Model. We found that calibrate only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of 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
EGUsphere, https://doi.org/10.5194/egusphere-2023-375, https://doi.org/10.5194/egusphere-2023-375, 2023
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2023-1555, https://doi.org/10.5194/egusphere-2023-1555, 2023
Short summary
Short summary
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 the cloud-radiative heating, as well as those examining its covariance with circulations. The manuscript 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.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and Lai-Yung Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1879, https://doi.org/10.5194/egusphere-2023-1879, 2023
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled Earth System Model. We found that calibrate only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of 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.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-124, https://doi.org/10.5194/gmd-2023-124, 2023
Preprint under review for GMD
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new land coupled data assimilation (LCDA) 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 U.S. Midwest drought. The LCDA system provides the groundwork for future predictability studies.
Lingcheng Li, Gautam Bisht, Dalei Hao, and Lai-Yung Ruby Leung
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-242, https://doi.org/10.5194/essd-2023-242, 2023
Preprint under review for ESSD
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
EGUsphere, https://doi.org/10.5194/egusphere-2023-375, https://doi.org/10.5194/egusphere-2023-375, 2023
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Jianfeng Li, Zhe Feng, Yun Qian, and L. Ruby Leung
Earth Syst. Sci. Data, 13, 827–856, https://doi.org/10.5194/essd-13-827-2021, https://doi.org/10.5194/essd-13-827-2021, 2021
Short summary
Short summary
Deep convection has different properties at different scales. We develop a 4 km h−1 observational data product of mesoscale convective systems and isolated deep convection in the United States from 2004–2017. We find that both types of convective systems contribute significantly to precipitation east of the Rocky Mountains but with distinct spatiotemporal characteristics. The data product will be useful for observational analyses and model evaluations of convection events at different scales.
Kurt C. Solander, Brent D. Newman, Alessandro Carioca de Araujo, Holly R. Barnard, Z. Carter Berry, Damien Bonal, Mario Bretfeld, Benoit Burban, Luiz Antonio Candido, Rolando Célleri, Jeffery Q. Chambers, Bradley O. Christoffersen, Matteo Detto, Wouter A. Dorigo, Brent E. Ewers, Savio José Filgueiras Ferreira, Alexander Knohl, L. Ruby Leung, Nate G. McDowell, Gretchen R. Miller, Maria Terezinha Ferreira Monteiro, Georgianne W. Moore, Robinson Negron-Juarez, Scott R. Saleska, Christian Stiegler, Javier Tomasella, and Chonggang Xu
Hydrol. Earth Syst. Sci., 24, 2303–2322, https://doi.org/10.5194/hess-24-2303-2020, https://doi.org/10.5194/hess-24-2303-2020, 2020
Short summary
Short summary
We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.
Zhiyuan Hu, Jianping Huang, Chun Zhao, Yuanyuan Ma, Qinjian Jin, Yun Qian, L. Ruby Leung, Jianrong Bi, and Jianmin Ma
Atmos. Chem. Phys., 19, 12709–12730, https://doi.org/10.5194/acp-19-12709-2019, https://doi.org/10.5194/acp-19-12709-2019, 2019
Short summary
Short summary
This study investigates aerosol chemical compositions and relative contributions to total aerosols in the western US. The results show that trans-Pacific aerosols have a maximum concentration in the boreal spring, with the greatest contribution from dust. Over western North America, the trans-Pacific aerosols dominate the column-integrated aerosol mass and number concentration. However, near the surface, aerosols mainly originated from local emissions.
Mingchen Ma, Yang Gao, Yuhang Wang, Shaoqing Zhang, L. Ruby Leung, Cheng Liu, Shuxiao Wang, Bin Zhao, Xing Chang, Hang Su, Tianqi Zhang, Lifang Sheng, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 19, 12195–12207, https://doi.org/10.5194/acp-19-12195-2019, https://doi.org/10.5194/acp-19-12195-2019, 2019
Short summary
Short summary
Ozone pollution has become severe in China, and extremely high ozone episodes occurred in summer 2017 over the North China Plain. While meteorology impacts are clear, we find that enhanced biogenic emissions, previously ignored by the community, driven by high vapor pressure deficit, land cover change and urban landscape contribute substantially to ozone formation. This study has significant implications for ozone pollution control with more frequent heat waves and urbanization growth in future.
Chun Zhao, Mingyue Xu, Yu Wang, Meixin Zhang, Jianping Guo, Zhiyuan Hu, L. Ruby Leung, Michael Duda, and William Skamarock
Geosci. Model Dev., 12, 2707–2726, https://doi.org/10.5194/gmd-12-2707-2019, https://doi.org/10.5194/gmd-12-2707-2019, 2019
Short summary
Short summary
Simulations at global uniform and variable resolutions share similar characteristics of precipitation and wind in the refined region. The experiments reveal the significant impacts of resolution on simulating the distribution and intensity of precipitation and updrafts. This study provides evidence supporting the use of convection-permitting global variable-resolution simulations to study extreme precipitation.
Junxi Zhang, Yang Gao, L. Ruby Leung, Kun Luo, Huan Liu, Jean-Francois Lamarque, Jianren Fan, Xiaohong Yao, Huiwang Gao, and Tatsuya Nagashima
Atmos. Chem. Phys., 19, 887–900, https://doi.org/10.5194/acp-19-887-2019, https://doi.org/10.5194/acp-19-887-2019, 2019
Short summary
Short summary
ACCMIP simulations were used to study NOy deposition over East Asia in the future. Both dry and wet NOy deposition show significant decreases in the 2100s under RCP4.5 and RCP8.5 due to large anthropogenic emission reduction. The changes in climate only significantly affect the wet deposition primarily linked to changes in precipitation. Over the coastal seas of China, weaker transport of NOy from land due to emission reduction infers a larger impact from shipping and lightning emissions.
Ge Zhang, Yang Gao, Wenju Cai, L. Ruby Leung, Shuxiao Wang, Bin Zhao, Minghuai Wang, Huayao Shan, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 19, 565–576, https://doi.org/10.5194/acp-19-565-2019, https://doi.org/10.5194/acp-19-565-2019, 2019
Short summary
Short summary
Based on observed data, this study reveals a distinct seesaw feature of abnormally high and low PM2.5 concentrations in December 2015 and January 2016 over North China. The mechanism of the seesaw pattern was found to be linked to a super El Niño and the Arctic Oscillation (AO). During the mature phase of El Niño in December 2015, the weakened East Asian winter monsoon favors strong haze formation; however, the circulation pattern was reversed in the next month due to the phase change of the AO.
Gautam Bisht, William J. Riley, Glenn E. Hammond, and David M. Lorenzetti
Geosci. Model Dev., 11, 4085–4102, https://doi.org/10.5194/gmd-11-4085-2018, https://doi.org/10.5194/gmd-11-4085-2018, 2018
Short summary
Short summary
Most existing global land surface models used to study impacts of climate change on water resources routinely use different models for near-surface unsaturated soil and the deeper groundwater table. We developed a model that uses a unified treatment of soil hydrologic processes throughout the entire soil column. Using a calibrated drainage parameter, the new model is able to correctly predict deep water table depth as reported in an observationally constrained global dataset.
Junxi Zhang, Yang Gao, Kun Luo, L. Ruby Leung, Yang Zhang, Kai Wang, and Jianren Fan
Atmos. Chem. Phys., 18, 9861–9877, https://doi.org/10.5194/acp-18-9861-2018, https://doi.org/10.5194/acp-18-9861-2018, 2018
Short summary
Short summary
We used a regional model to investigate the impact of atmosphere with high temperature and low wind speed on ozone concentration. When these compound events (heat waves and stagnant weather) occur simultaneously, a striking ozone enhancement is revealed. This type of compound event is projected to increase more dominantly compared to single events in the future over the US, Europe, and China, implying the importance of reducing emissions in order to alleviate the impact from the compound events.
Christine A. Shields, Jonathan J. Rutz, Lai-Yung Leung, F. Martin Ralph, Michael Wehner, Brian Kawzenuk, Juan M. Lora, Elizabeth McClenny, Tashiana Osborne, Ashley E. Payne, Paul Ullrich, Alexander Gershunov, Naomi Goldenson, Bin Guan, Yun Qian, Alexandre M. Ramos, Chandan Sarangi, Scott Sellars, Irina Gorodetskaya, Karthik Kashinath, Vitaliy Kurlin, Kelly Mahoney, Grzegorz Muszynski, Roger Pierce, Aneesh C. Subramanian, Ricardo Tome, Duane Waliser, Daniel Walton, Gary Wick, Anna Wilson, David Lavers, Prabhat, Allison Collow, Harinarayan Krishnan, Gudrun Magnusdottir, and Phu Nguyen
Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018, https://doi.org/10.5194/gmd-11-2455-2018, 2018
Short summary
Short summary
ARTMIP (Atmospheric River Tracking Method Intercomparison Project) is a community effort with the explicit goal of understanding the uncertainties, and the implications of those uncertainties, in atmospheric river science solely due to detection algorithm. ARTMIP strives to quantify these differences and provide guidance on appropriate algorithmic choices for the science question posed. Project goals, experimental design, and preliminary results are provided.
Kai Zhang, Philip J. Rasch, Mark A. Taylor, Hui Wan, Ruby Leung, Po-Lun Ma, Jean-Christophe Golaz, Jon Wolfe, Wuyin Lin, Balwinder Singh, Susannah Burrows, Jin-Ho Yoon, Hailong Wang, Yun Qian, Qi Tang, Peter Caldwell, and Shaocheng Xie
Geosci. Model Dev., 11, 1971–1988, https://doi.org/10.5194/gmd-11-1971-2018, https://doi.org/10.5194/gmd-11-1971-2018, 2018
Short summary
Short summary
The conservation of total water is an important numerical feature for global Earth system models. Even small conservation problems in the water budget can lead to systematic errors in century-long simulations for sea level rise projection. This study quantifies and reduces various sources of water conservation error in the atmosphere component of the Energy Exascale Earth System Model.
Gautam Bisht, William J. Riley, Haruko M. Wainwright, Baptiste Dafflon, Fengming Yuan, and Vladimir E. Romanovsky
Geosci. Model Dev., 11, 61–76, https://doi.org/10.5194/gmd-11-61-2018, https://doi.org/10.5194/gmd-11-61-2018, 2018
Short summary
Short summary
The land model integrated into the Energy Exascale Earth System Model was extended to include snow redistribution (SR) and lateral subsurface hydrologic and thermal processes. Simulation results at a polygonal tundra site near Barrow, Alaska, showed that inclusion of SR resulted in a better agreement with observations. Excluding lateral subsurface processes had a small impact on mean states but caused a large overestimation of spatial variability in soil moisture and temperature.
Gautam Bisht, Maoyi Huang, Tian Zhou, Xingyuan Chen, Heng Dai, Glenn E. Hammond, William J. Riley, Janelle L. Downs, Ying Liu, and John M. Zachara
Geosci. Model Dev., 10, 4539–4562, https://doi.org/10.5194/gmd-10-4539-2017, https://doi.org/10.5194/gmd-10-4539-2017, 2017
Short summary
Short summary
A fully coupled three-dimensional surface and subsurface land model, CP v1.0, was developed to simulate three-way interactions among river water, groundwater, and land surface processes. The coupled model can be used for improving mechanistic understanding of ecosystem functioning and biogeochemical cycling along river corridors under historical and future hydroclimatic changes. The dataset presented in this study can also serve as a good benchmarking case for testing other integrated models.
Randal D. Koster, Alan K. Betts, Paul A. Dirmeyer, Marc Bierkens, Katrina E. Bennett, Stephen J. Déry, Jason P. Evans, Rong Fu, Felipe Hernandez, L. Ruby Leung, Xu Liang, Muhammad Masood, Hubert Savenije, Guiling Wang, and Xing Yuan
Hydrol. Earth Syst. Sci., 21, 3777–3798, https://doi.org/10.5194/hess-21-3777-2017, https://doi.org/10.5194/hess-21-3777-2017, 2017
Short summary
Short summary
Large-scale hydrological variability can affect society in profound ways; floods and droughts, for example, often cause major damage and hardship. A recent gathering of hydrologists at a symposium to honor the career of Professor Eric Wood motivates the present survey of recent research on this variability. The surveyed literature and the illustrative examples provided in the paper show that research into hydrological variability continues to be strong, vibrant, and multifaceted.
Shi Zhong, Yun Qian, Chun Zhao, Ruby Leung, Hailong Wang, Ben Yang, Jiwen Fan, Huiping Yan, Xiu-Qun Yang, and Dongqing Liu
Atmos. Chem. Phys., 17, 5439–5457, https://doi.org/10.5194/acp-17-5439-2017, https://doi.org/10.5194/acp-17-5439-2017, 2017
Short summary
Short summary
An online climate–chemistry coupled model (WRF-Chem) is integrated for 5 years at cloud-permitting scale to quantify the impacts of urbanization-induced changes in land cover and pollutants emission on regional climate in the Yangtze River Delta region in eastern China. Urbanization over this region increases the frequency of extreme precipitation and heat wave in summer. The results could help China government in making policies in mitigating the environmental impact of urbanization.
Xiangyu Luo, Hong-Yi Li, L. Ruby Leung, Teklu K. Tesfa, Augusto Getirana, Fabrice Papa, and Laura L. Hess
Geosci. Model Dev., 10, 1233–1259, https://doi.org/10.5194/gmd-10-1233-2017, https://doi.org/10.5194/gmd-10-1233-2017, 2017
Short summary
Short summary
This study shows that alleviating vegetation-caused biases in DEM data, refining channel cross-sectional geometry and Manning roughness coefficients, as well as accounting for backwater effects can effectively improve the modeling of streamflow, river stages and flood extent in the Amazon Basin. The obtained understanding could be helpful to hydrological modeling in basins with evident inundation, which has important implications for improving land–atmosphere interactions in Earth system models.
Teklu K. Tesfa and Lai-Yung Ruby Leung
Geosci. Model Dev., 10, 873–888, https://doi.org/10.5194/gmd-10-873-2017, https://doi.org/10.5194/gmd-10-873-2017, 2017
Short summary
Short summary
Motivated by the significant topographic influence on land surface processes, this study explored two methods to discretize watersheds into two types of subgrid structures to capture spatial heterogeneity for land surface models. Adopting geomorphologic concepts in watershed discretization yields improved capability in capturing subgrid topographic heterogeneity, which also allowed climatic and land cover variability to be better represented with a nominal increase in computational requirements.
Jiwen Fan, L. Ruby Leung, Daniel Rosenfeld, and Paul J. DeMott
Atmos. Chem. Phys., 17, 1017–1035, https://doi.org/10.5194/acp-17-1017-2017, https://doi.org/10.5194/acp-17-1017-2017, 2017
Short summary
Short summary
How orographic mixed-phase clouds respond to changes in cloud condensation nuclei (CCN) and ice nucleating particles (INPs) is highly uncertain. We conducted this study to improve understanding of these processes. We found a new mechanism through which CCN can invigorate orographic mixed-phase clouds and drastically intensify snow precipitation when CCN concentrations are high. Our findings have very important implications for orographic precipitation in polluted regions.
Reindert J. Haarsma, Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, Susanna Corti, Neven S. Fučkar, Virginie Guemas, Jost von Hardenberg, Wilco Hazeleger, Chihiro Kodama, Torben Koenigk, L. Ruby Leung, Jian Lu, Jing-Jia Luo, Jiafu Mao, Matthew S. Mizielinski, Ryo Mizuta, Paulo Nobre, Masaki Satoh, Enrico Scoccimarro, Tido Semmler, Justin Small, and Jin-Song von Storch
Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, https://doi.org/10.5194/gmd-9-4185-2016, 2016
Short summary
Short summary
Recent progress in computing power has enabled climate models to simulate more processes in detail and on a smaller scale. Here we present a common protocol for these high-resolution runs that will foster the analysis and understanding of the impact of model resolution on the simulated climate. These runs will also serve as a more reliable source for assessing climate risks that are associated with small-scale weather phenomena such as tropical cyclones.
Jitendra Kumar, Nathan Collier, Gautam Bisht, Richard T. Mills, Peter E. Thornton, Colleen M. Iversen, and Vladimir Romanovsky
The Cryosphere, 10, 2241–2274, https://doi.org/10.5194/tc-10-2241-2016, https://doi.org/10.5194/tc-10-2241-2016, 2016
Short summary
Short summary
Microtopography of the low-gradient polygonal tundra plays a critical role in these ecosystem; however, patterns and drivers are poorly understood. A modeling-based approach was developed in this study to characterize and represent the permafrost soils in the model and simulate the thermal dynamics using a mechanistic high-resolution model. Results shows the ability of the model to simulate the patterns and variability of thermal regimes and improve our understanding of polygonal tundra.
Bin Zhao, Kuo-Nan Liou, Yu Gu, Cenlin He, Wee-Liang Lee, Xing Chang, Qinbin Li, Shuxiao Wang, Hsien-Liang R. Tseng, Lai-Yung R. Leung, and Jiming Hao
Atmos. Chem. Phys., 16, 5841–5852, https://doi.org/10.5194/acp-16-5841-2016, https://doi.org/10.5194/acp-16-5841-2016, 2016
Short summary
Short summary
We examine the impact of buildings on surface solar fluxes in Beijing by accounting for their 3-D structures. We find that inclusion of buildings changes surface solar fluxes by within ±1 W m−2, ±1–10 W m−2, and up to ±100 W m−2 at grid resolutions of 4 km, 800 m, and 90 m, respectively. We can resolve pairs of positive-negative flux deviations on different sides of buildings at ≤ 800 m resolutions. We should treat building-effect on solar fluxes differently in models with different resolutions.
Zhiyuan Hu, Chun Zhao, Jianping Huang, L. Ruby Leung, Yun Qian, Hongbin Yu, Lei Huang, and Olga V. Kalashnikova
Geosci. Model Dev., 9, 1725–1746, https://doi.org/10.5194/gmd-9-1725-2016, https://doi.org/10.5194/gmd-9-1725-2016, 2016
Short summary
Short summary
This study conducts the simulation of WRF-Chem with the quasi-global configuration for 2010–2014, and evaluates the simulation with multiple observation datasets for the first time. This study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA to further understand the impact of transported pollutants on the regional air quality and climate.
Guoping Tang, Fengming Yuan, Gautam Bisht, Glenn E. Hammond, Peter C. Lichtner, Jitendra Kumar, Richard T. Mills, Xiaofeng Xu, Ben Andre, Forrest M. Hoffman, Scott L. Painter, and Peter E. Thornton
Geosci. Model Dev., 9, 927–946, https://doi.org/10.5194/gmd-9-927-2016, https://doi.org/10.5194/gmd-9-927-2016, 2016
Short summary
Short summary
We demonstrate that CLM-PFLOTRAN predictions are consistent with CLM4.5 for Arctic, temperate, and tropical sites. A tight relative tolerance may be needed to avoid false convergence when scaling, clipping, or log transformation is used to avoid negative concentration in implicit time stepping and Newton-Raphson methods. The log transformation method is accurate and robust while relaxing relative tolerance or using the clipping or scaling method can result in efficient solutions.
X. Shi, P. E. Thornton, D. M. Ricciuto, P. J. Hanson, J. Mao, S. D. Sebestyen, N. A. Griffiths, and G. Bisht
Biogeosciences, 12, 6463–6477, https://doi.org/10.5194/bg-12-6463-2015, https://doi.org/10.5194/bg-12-6463-2015, 2015
C. He, K.-N. Liou, Y. Takano, R. Zhang, M. Levy Zamora, P. Yang, Q. Li, and L. R. Leung
Atmos. Chem. Phys., 15, 11967–11980, https://doi.org/10.5194/acp-15-11967-2015, https://doi.org/10.5194/acp-15-11967-2015, 2015
W.-L. Lee, Y. Gu, K. N. Liou, L. R. Leung, and H.-H. Hsu
Atmos. Chem. Phys., 15, 5405–5413, https://doi.org/10.5194/acp-15-5405-2015, https://doi.org/10.5194/acp-15-5405-2015, 2015
Short summary
Short summary
This paper investigates 3-D mountain effects on solar flux distributions and their impact on surface hydrology over the western United States, specifically the Rocky Mountains and the Sierra Nevada, using the global CCSM4 (CAM4/CLM4) with a 0.23°×0.31° resolution for simulations over 6 years. We show that deviations in the net surface fluxes are not only affected by 3-D mountains but also influenced by feedbacks of cloud and snow in association with the long-term simulations.
Y. Fang, C. Liu, and L. R. Leung
Geosci. Model Dev., 8, 781–789, https://doi.org/10.5194/gmd-8-781-2015, https://doi.org/10.5194/gmd-8-781-2015, 2015
Short summary
Short summary
1. A gradient projection method was used to reduce the computation time of carbon-nitrogen spin-up processes in CLM4.
2. Point-scale simulations showed that the cyclic stability of total carbon for some cases differs from that of the periodic atmospheric forcing, and some cases even showed instability.
3. The instability issue is resolved after the hydrology scheme in CLM4 is replaced with a flow model for variably saturated porous media.
G. Bisht and W. J. Riley
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-12833-2014, https://doi.org/10.5194/hessd-11-12833-2014, 2014
Revised manuscript has not been submitted
C. Zhao, Z. Hu, Y. Qian, L. Ruby Leung, J. Huang, M. Huang, J. Jin, M. G. Flanner, R. Zhang, H. Wang, H. Yan, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 14, 11475–11491, https://doi.org/10.5194/acp-14-11475-2014, https://doi.org/10.5194/acp-14-11475-2014, 2014
G. S. H. Pau, G. Bisht, and W. J. Riley
Geosci. Model Dev., 7, 2091–2105, https://doi.org/10.5194/gmd-7-2091-2014, https://doi.org/10.5194/gmd-7-2091-2014, 2014
T. K. Tesfa, H.-Y. Li, L. R. Leung, M. Huang, Y. Ke, Y. Sun, and Y. Liu
Geosci. Model Dev., 7, 947–963, https://doi.org/10.5194/gmd-7-947-2014, https://doi.org/10.5194/gmd-7-947-2014, 2014
J. Fan, L. R. Leung, P. J. DeMott, J. M. Comstock, B. Singh, D. Rosenfeld, J. M. Tomlinson, A. White, K. A. Prather, P. Minnis, J. K. Ayers, and Q. Min
Atmos. Chem. Phys., 14, 81–101, https://doi.org/10.5194/acp-14-81-2014, https://doi.org/10.5194/acp-14-81-2014, 2014
Y. Sun, Z. Hou, M. Huang, F. Tian, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 17, 4995–5011, https://doi.org/10.5194/hess-17-4995-2013, https://doi.org/10.5194/hess-17-4995-2013, 2013
K. N. Liou, Y. Gu, L. R. Leung, W. L. Lee, and R. G. Fovell
Atmos. Chem. Phys., 13, 11709–11721, https://doi.org/10.5194/acp-13-11709-2013, https://doi.org/10.5194/acp-13-11709-2013, 2013
N. Voisin, L. Liu, M. Hejazi, T. Tesfa, H. Li, M. Huang, Y. Liu, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 4555–4575, https://doi.org/10.5194/hess-17-4555-2013, https://doi.org/10.5194/hess-17-4555-2013, 2013
Y. Fang, M. Huang, C. Liu, H. Li, and L. R. Leung
Geosci. Model Dev., 6, 1977–1988, https://doi.org/10.5194/gmd-6-1977-2013, https://doi.org/10.5194/gmd-6-1977-2013, 2013
C. Zhao, S. Chen, L. R. Leung, Y. Qian, J. F. Kok, R. A. Zaveri, and J. Huang
Atmos. Chem. Phys., 13, 10733–10753, https://doi.org/10.5194/acp-13-10733-2013, https://doi.org/10.5194/acp-13-10733-2013, 2013
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
Y. Ke, L. R. Leung, M. Huang, and H. Li
Geosci. Model Dev., 6, 1609–1622, https://doi.org/10.5194/gmd-6-1609-2013, https://doi.org/10.5194/gmd-6-1609-2013, 2013
H. Wan, P. J. Rasch, K. Zhang, J. Kazil, and L. R. Leung
Geosci. Model Dev., 6, 861–874, https://doi.org/10.5194/gmd-6-861-2013, https://doi.org/10.5194/gmd-6-861-2013, 2013
Related subject area
Climate and Earth system modeling
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0
Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
URock 2023a: an open-source GIS-based wind model for complex urban settings
DASH: a MATLAB toolbox for paleoclimate data assimilation
Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)
All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Truly Conserving with Conservative Remapping Methods
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
Earth system modeling on Modular Supercomputing Architectures: coupled atmosphere-ocean simulations with ICON 2.6.6-rc
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Understanding Changes in Cloud Simulations from E3SM Version 1 to Version 2
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
WRF (v4.0)-SUEWS (v2018c) Coupled System: Development, Evaluation and Application
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
Resolving the mesoscale at reduced computational cost with FESOM 2.5: efficient modeling approaches applied to the Southern Ocean
Modeling and evaluating the effects of irrigation on land-atmosphere interaction in South-West Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Deep Learning Model based on Multi-scale Feature Fusion for Precipitation Nowcasting
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Xinzhu Yu, Li Liu, Chao Sun, Qingu Jiang, Biao Zhao, Zhiyuan Zhang, Hao Yu, and Bin Wang
Geosci. Model Dev., 16, 6285–6308, https://doi.org/10.5194/gmd-16-6285-2023, https://doi.org/10.5194/gmd-16-6285-2023, 2023
Short summary
Short summary
In this paper we propose a new common, flexible, and efficient parallel I/O framework for earth system modeling based on C-Coupler2.0. CIOFC1.0 can handle data I/O in parallel and provides a configuration file format that enables users to conveniently change the I/O configurations. It can automatically make grid and time interpolation, output data with an aperiodic time series, and accelerate data I/O when the field size is large.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
Short summary
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
Short summary
Short summary
The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
Short summary
Short summary
The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
Short summary
Short summary
Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
Short summary
Short summary
The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
Short summary
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
Short summary
Short summary
Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Karl E. Taylor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-177, https://doi.org/10.5194/gmd-2023-177, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
Short summary
Short summary
A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
Short summary
Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
Short summary
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
EGUsphere, https://doi.org/10.5194/egusphere-2023-1476, https://doi.org/10.5194/egusphere-2023-1476, 2023
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere-ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 59 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
Short summary
Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
Short summary
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
Short summary
Short summary
This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
Short summary
Short summary
Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
Short summary
Short summary
A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1263, https://doi.org/10.5194/egusphere-2023-1263, 2023
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the E3SMv2 to document model performance on clouds and understand what updates in E3SMv2 have caused the changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved primarily due to the re-tuning of cloud macrophysics parameters. This study offers additional insights about clouds simulated in E3SMv2 and will benefit the future E3SM developments.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
Short summary
Short summary
In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
Short summary
Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-117, https://doi.org/10.5194/gmd-2023-117, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
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
Short summary
Short summary
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.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1496, https://doi.org/10.5194/egusphere-2023-1496, 2023
Short summary
Short summary
Ocean models struggle to simulate small-scale ocean flows due to the computational cost of high-resolution simulations. Several cost-reducing strategies are applied to simulations of the Southern Ocean and evaluated with respect to observations and traditional, lower-resolution modelling methods. The high-resolution simulations effectively reproduce small-scale flows seen in satellite data and are largely consistent with traditional model simulations regarding their response to climate change.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
EGUsphere, https://doi.org/10.5194/egusphere-2023-890, https://doi.org/10.5194/egusphere-2023-890, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The caused effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which is simulating the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
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
Short summary
Short summary
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.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
Short summary
Short summary
A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
Short summary
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
Short summary
Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-109, https://doi.org/10.5194/gmd-2023-109, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
1. This study present a deep learning architecture MFF to improve the forecast skills of precipitations especially for heavy precipitations. 2. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. 3. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors, so that heavy precipitations are produced.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
Short summary
Short summary
This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio Bento, and Angelina Bushenkova
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-136, https://doi.org/10.5194/gmd-2023-136, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data, and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
Short summary
Short summary
In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Cited articles
Alkama, R., Decharme, B., Douville, H., and Ribes, A.: Trends in Global and
Basin-Scale Runoff over the Late Twentieth Century: Methodological Issues
and Sources of Uncertainty, J. Climate, 24, 3000–3014,
https://doi.org/10.1175/2010JCLI3921.1, 2011.
Alkama, R., Marchand, L., Ribes, A., and Decharme, B.: Detection of global runoff changes: results from observations and CMIP5 experiments, Hydrol. Earth Syst. Sci., 17, 2967–2979, https://doi.org/10.5194/hess-17-2967-2013, 2013.
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.
Bechtold, B.: Violin Plots for Matlab, Github Project, Zenodo [code],
https://doi.org/10.5281/zenodo.4559847, 2016.
Beck, H. E., van Dijk, A. I. J. M., Miralles, D. G., de Jeu, R. A. M.,
Bruijnzeel, L. A., McVicar, T. R., and Schellekens, J.: Global patterns in
base flow index and recession based on streamflow observations from 3394
catchments, Water Resour. Res., 49, 7843–7863, https://doi.org/10.1002/2013WR013918, 2013.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017.
Bisht, G., Riley, W. J., Hammond, G. E., and Lorenzetti, D. M.: Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) version 1.0, Geosci. Model Dev., 11, 4085–4102, https://doi.org/10.5194/gmd-11-4085-2018, 2018.
Bosmans, J. H. C., van Beek, L. P. H., Sutanudjaja, E. H., and Bierkens, M. F. P.: Hydrological impacts of global land cover change and human water use, Hydrol. Earth Syst. Sci., 21, 5603–5626, https://doi.org/10.5194/hess-21-5603-2017, 2017.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Brunke, M. A., Broxton, P., Pelletier, J., Gochis, D., Hazenberg, P.,
Lawrence, D. M., Leung, L. R., Niu, G.-Y., Troch, P. A., and Zeng, X.:
Implementing and evaluating variable soil thickness in the Community Land
Model, version 4.5 (CLM4. 5), J. Climate, 29, 3441–3461, 2016.
Chegwidden, O. S., Rupp, D. E., and Nijssen, B.: Climate change alters flood
magnitudes and mechanisms in climatically-diverse headwaters across the
northwestern United States, Environ. Res. Lett., 15, 094048,
https://doi.org/10.1088/1748-9326/ab986f, 2020.
Clark, E. A., Sheffield, J., van Vliet, M. T., Nijssen, B., and Lettenmaier,
D. P.: Continental runoff into the oceans (1950–2008), J. Hydrometeorol., 16,
1502–1520, 2015.
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C.
D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model
Benchmarking (ILAMB) System: Design, Theory, and Implementation, J. Adv. Model.
Earth Sy., 10, 2731–2754, https://doi.org/10.1029/2018MS001354,
2018 (code available at: https://doi.org/10.18139/ILAMB.v002.00/1251621).
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A
Statistical Exploration of the Relationships of Soil Moisture
Characteristics to the Physical Properties of Soils, Water Resour. Res., 20,
682–690, https://doi.org/10.1029/WR020i006p00682, 1984.
Dagon, K., Sanderson, B. M., Fisher, R. A., and Lawrence, D. M.: A machine
learning approach to emulation and biophysical parameter estimation with the
Community Land Model, version 5, Adv. Stat. Clim. Meteorol. Oceanogr., 6,
223–244, https://doi.org/10.5194/ascmo-6-223-2020, 2020.
Dai, A.: Increasing drought under global warming in observations and models,
Nat. Clim. Change, 3, 52–58, https://doi.org/10.1038/nclimate1633, 2013.
Dai, A., Qian, T., Trenberth, K. E., and Milliman, J. D.: Changes in
Continental Freshwater Discharge from 1948 to 2004, J. Climate, 22,
2773–2792, https://doi.org/10.1175/2008JCLI2592.1, 2009.
Debusschere, B., Sargsyan, K., Safta, C., and Chowdhary, K.: Uncertainty
Quantification Toolkit (UQTk), in: Handbook of Uncertainty Quantification,
edited by: Ghanem, R., Higdon, D., and Owhadi, H., Springer International
Publishing, Cham, 1–21, https://doi.org/10.1007/978-3-319-11259-6_56-1, 2016.
Debusschere, B. J., Najm, H. N., Pébay, P. P., Knio, O. M., Ghanem, R.
G., and Maître, O. P. L.: Numerical Challenges in the Use of Polynomial
Chaos Representations for Stochastic Processes, SIAM J. Sci.
Comput., 26, 698–719, https://doi.org/10.1137/s1064827503427741, 2004.
Decharme, B., Delire, C., Minvielle, M., Colin, J., Vergnes, J.-P., Alias,
A., Saint-Martin, D., Séférian, R., Sénési, S., and
Voldoire, A.: Recent Changes in the ISBA-CTRIP Land Surface System for Use
in the CNRM-CM6 Climate Model and in Global Off-Line Hydrological
Applications, J. Adv. Model. Earth Sy., 11, 1207–1252, https://doi.org/10.1029/2018MS001545, 2019.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018.
Doocy, S., Daniels, A., Murray, S., and Kirsch, T. D.: The human impact of
floods: a historical review of events 1980–2009 and systematic literature
review, PLoS Curr., 5, https://doi.org/10.1371/currents.dis.f4deb457904936b07c09daa98ee8171a,
2013.
Drewniak, B. A.: Simulating Dynamic Roots in the Energy Exascale Earth
System Land Model, J. Adv. Model. Earth Sy., 11, 338–359, https://doi.org/10.1029/2018MS001334, 2019.
Dwelle, M. C., Kim, J., Sargsyan, K., and Ivanov, V. Y.: Streamflow,
stomata, and soil pits: Sources of inference for complex models with fast,
robust uncertainty quantification, Adv. Water Resour., 125, 13–31, https://doi.org/10.1016/j.advwatres.2019.01.002, 2019.
Ekici, A., Lee, H., Lawrence, D. M., Swenson, S. C., and Prigent, C.: Ground subsidence effects on simulating dynamic high-latitude surface inundation under permafrost thaw using CLM5, Geosci. Model Dev., 12, 5291–5300, https://doi.org/10.5194/gmd-12-5291-2019, 2019.
Fischer, E. M. and Knutti, R.: Observed heavy precipitation increase
confirms theory and early models, Nat. Clim. Change, 6, 986–991,
https://doi.org/10.1038/nclimate3110, 2016.
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using
Multiple Sequences, Stat. Sci., 7, 457–472, https://doi.org/10.1214/ss/1177011136, 1992.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019a.
Ghiggi, G., Gudmundsson, L., and Humphrey, V.: G-RUN: Global Runoff Reconstruction, figshare [data set], https://doi.org/10.6084/m9.figshare.9228176.v2, 2019b.
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., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M.
A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J.
G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J.,
Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W.,
Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W.,
Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A.,
McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch,
P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F.,
Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J.,
Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang,
H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang,
Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang,
K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: 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.
Gong, W., Duan, Q., Li, J., Wang, C., Di, Z., Dai, Y., Ye, A., and Miao, C.: Multi-objective parameter optimization of common land model using adaptive surrogate modeling, Hydrol. Earth Syst. Sci., 19, 2409–2425, https://doi.org/10.5194/hess-19-2409-2015, 2015.
Gosling, S., Müller Schmied, H., Betts, R. A., Chang, J., Ciais, P.,
Dankers, R., Döll, P., Eisner, S., Flörke, M., Gerten, D.,
Grillakis, M., Hanasaki, N., Hagemann, S., Huang, M., Huang, Z., Jerez, S.,
Kim, H., Koutroulis, A., Leng, G., Liu, X., Masaki, Y., Montavez, P.,
Morfopoulos, C., Oki, T., Papadimitriou, L., Pokhrel, Y., Portmann, F. T.,
Orth, R., Ostberg, S., Satoh, Y., Seneviratne, S., Sommer, P., Stacke, T.,
Tang, Q., Tsanis, I., Wada, Y., Zhou, T., Büchner, M., Schewe, J., and
Zhao, F.: ISIMIP2a Simulation Data from Water (global) Sector (V. 1.1), GFZ
Data Services [data set], https://doi.org/10.5880/PIK.2019.003,
2019.
Gosling, S. N. and Arnell, N. W.: Simulating current global river runoff
with a global hydrological model: model revisions, validation, and
sensitivity analysis, Hydrol. Process., 25, 1129–1145, https://doi.org/10.1002/hyp.7727, 2011.
Gudmundsson, L., Do, H. X., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018, 2018.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration
of hydrologic models: Multiple and noncommensurable measures of information,
Water Resour. Res., 34, 751–763, https://doi.org/10.1029/97WR03495, 1998.
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.
Haario, H., Saksman, E., and Tamminen, J.: An adaptive Metropolis algorithm,
Bernoulli, 7, 223–242, 2001.
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.
Haddeland, I., Clark, D. B., Franssen, W., Ludwig, F., Voß, F., Arnell,
N. W., Bertrand, N., Best, M., Folwell, S., and Gerten, D.: Multimodel
estimate of the global terrestrial water balance: setup and first results, J.
Hydrometeorol., 12, 869–884, 2011.
Hall, J. W., Grey, D., Garrick, D., Fung, F., Brown, C., Dadson, S. J., and
Sadoff, C. W.: Coping with the curse of freshwater variability, Science,
346, 429–430, https://doi.org/10.1126/science.1257890, 2014.
Hintze, J. and Nelson, R.: Violin plots: A box plot-density trace
synergism, Am. Stat., 52, 181–184, 1998.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D.,
Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate
change, Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/Nclimate1911, 2013.
Hou, Z., Huang, M., Leung, L. R., Lin, G., and Ricciuto, D. M.: Sensitivity
of surface flux simulations to hydrologic parameters based on an uncertainty
quantification framework applied to the Community Land Model, J.
Geophys. Res.-Atmos., 117, D15108, https://doi.org/10.1029/2012JD017521, 2012.
Huang, M., Hou, Z., Leung, L. R., Ke, Y., Liu, Y., Fang, Z., and Sun, Y.:
Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in
the Community Land Model: Evidence from MOPEX Basins, J. Hydrometeorol., 14,
1754–1772, https://doi.org/10.1175/JHM-D-12-0138.1, 2013.
Huang, M., Ray, J., Hou, Z., Ren, H., Liu, Y., and Swiler, L.: On the
applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion
to the Community Land Model: Case studies at flux tower sites, J.
Geophys. Res.-Atmos., 121, 7548–7563, https://doi.org/10.1002/2015JD024339, 2016.
Ivanov, V. Y., Xu, D., Dwelle, M. C., Sargsyan, K., Wright, D. B.,
Katopodes, N., Kim, J., Tran, V. N., Warnock, A., Fatichi, S., Burlando, P.,
Caporali, E., Restrepo, P., Sanders, B. F., Chaney, M. M., Nunes, A. M. B.,
Nardi, F., Vivoni, E. R., Istanbulluoglu, E., Bisht, G., and Bras, R. L.:
Breaking Down the Computational Barriers to Real-Time Urban Flood
Forecasting, Geophys. Res. Lett., 48, e2021GL093585, https://doi.org/10.1029/2021GL093585, 2021.
Jenicek, M. and Ledvinka, O.: Importance of snowmelt contribution to seasonal runoff and summer low flows in Czechia, Hydrol. Earth Syst. Sci., 24, 3475–3491, https://doi.org/10.5194/hess-24-3475-2020, 2020.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G.,
Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM
ensemble of global land-atmosphere energy fluxes, Scientific Data, 6, 1–14,
2019.
Kim, H., Yeh, P. J. F., Oki, T., and Kanae, S.: Role of rivers in the
seasonal variations of terrestrial water storage over global basins, Geophys.
Res. Lett., 36, L17402, https://doi.org/10.1029/2009GL039006, 2009.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Knutti, R. and Sedláèek, J.: Robustness and uncertainties in the new
CMIP5 climate model projections, Nat. Clim. Change, 3, 369–373,
https://doi.org/10.1038/nclimate1716, 2012.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A.:
Challenges in Combining Projections from Multiple Climate Models, J. Climate,
23, 2739–2758, 2010.
Krysanova, V., Zaherpour, J., Didovets, I., Gosling, S. N., Gerten, D.,
Hanasaki, N., Müller Schmied, H., Pokhrel, Y., Satoh, Y., Tang, Q., and
Wada, Y.: How evaluation of global hydrological models can help to improve
credibility of river discharge projections under climate change, Climatic
Change, 163, 1353–1377, https://doi.org/10.1007/s10584-020-02840-0, 2020.
Laloy, E. and Jacques, D.: Emulation of CPU-demanding reactive transport
models: a comparison of Gaussian processes, polynomial chaos expansion, and
deep neural networks, Comput. Geosci., 23, 1193–1215, 2019.
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S.
C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K., Bonan,
G. B., and Slater, A. G.: Parameterization improvements and functional and
structural advances in Version 4 of the Community Land Model, J. Adv. Model.
Earth Sy., 3, M03001, https://doi.org/10.1029/2011MS00045, 2011.
Lehner, F., Wood, A. W., Vano, J. A., Lawrence, D. M., Clark, M. P., and
Mankin, J. S.: The potential to reduce uncertainty in regional runoff
projections from climate models, Nat. Clim. Change, 9, 926–933,
https://doi.org/10.1038/s41558-019-0639-x, 2019.
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020.
Leung, L. R., Bader, D. C., Taylor, M. A., and McCoy, R. B.: An Introduction
to the E3SM Special Collection: Goals, Science Drivers, Development, and
Analysis, J. Adv. Model. Earth Sy., 12, e2019MS001821, https://doi.org/10.1029/2019MS001821, 2020.
Li, H.-Y., Leung, L. R., Getirana, A., Huang, M., Wu, H., Xu, Y., Guo, J.,
and Voisin, N.: Evaluating Global Streamflow Simulations by a Physically
Based Routing Model Coupled with the Community Land Model, J. Hydrometeorol.,
16, 948–971, https://doi.org/10.1175/JHM-D-14-0079.1, 2015.
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.
Lin, G. and Karniadakis, G. E.: Sensitivity analysis and stochastic
simulations of non-equilibrium plasma flow, Int. J.
Numer. Meth. Eng., 80, 738–766, https://doi.org/10.1002/nme.2582, 2009.
Lu, D., Ricciuto, D., Stoyanov, M., and Gu, L.: Calibration of the E3SM Land
Model Using Surrogate-Based Global Optimization, J. Adv. Model. Earth Sy., 10,
1337–1356, https://doi.org/10.1002/2017MS001134, 2018.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Milliman, J. D., Farnsworth, K. L., Jones, P. D., Xu, K. H., and Smith, L.
C.: Climatic and anthropogenic factors affecting river discharge to the
global ocean, 1951–2000, Global Planet. Change, 62, 187–194,
https://doi.org/10.1016/j.gloplacha.2008.03.001, 2008.
Milly, P. C. D., Wetherald, R. T., Dunne, K. A., and Delworth, T. L.:
Increasing risk of great floods in a changing climate, Nature, 415, 514–517,
https://doi.org/10.1038/415514a, 2002.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz,
Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity Is Dead:
Whither Water Management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915,
2008.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391,
202–216, https://doi.org/10.1016/j.jhydrol.2010.07.012, 2010.
Mortatti, J., Moraes, J., Rodrigues, J., Victoria, R., and Martinelli, L.:
Hydrograph separation of the Amazon River using 18O as an isotopic tracer,
Sci. Agr., 54, 167–173, 1997.
Müller, J., Paudel, R., Shoemaker, C. A., Woodbury, J., Wang, Y., and Mahowald, N.: CH4 parameter estimation in CLM4.5bgc using surrogate global optimization, Geosci. Model Dev., 8, 3285–3310, https://doi.org/10.5194/gmd-8-3285-2015, 2015.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., and Gulden, L. E.: A simple
TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate
models, J. Geophys. Res.-Atmos., 110, D21106, https://doi.org/10.1029/2005JD006111, 2005.
Oleson, K., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven,
C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S., Thornton,
P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J.-F.,
Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S. P., Ricciuto, D.
M., Sacks, W. J., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description
of version 4.5 of the Community Land Model (CLM) (No. NCAR/TN-503+STR), UCAR, https://doi.org/10.5065/D6RR1W7M, 2013.
Olson, R., Fan, Y. A., and Evans, J. P.: A simple method for Bayesian model
averaging of regional climate model projections: Application to southeast
Australian temperatures, Geophys. Res. Lett., 43, 7661–7669,
https://doi.org/10.1002/2016gl069704, 2016.
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540,
418–422, https://doi.org/10.1038/nature20584, 2016.
Pilgrim, D. H., Chapman, T. G., and Doran, D. G.: Problems of
rainfall-runoff modelling in arid and semiarid regions, Hydrolog.
Sci. J., 33, 379–400, https://doi.org/10.1080/02626668809491261, 1988.
Ray, J., Hou, Z., Huang, M., Sargsyan, K., and Swiler, L.: Bayesian
Calibration of the Community Land Model Using Surrogates, SIAM/ASA Journal
on Uncertainty Quantification, 3, 199–233, https://doi.org/10.1137/140957998, 2015.
Razavi, S., Tolson, B. A., and Burn, D. H.: Review of surrogate modeling in
water resources, Water Resour. Res., 48, W07401, https://doi.org/10.1029/2011WR011527, 2012.
Ricciuto, D., Sargsyan, K., and Thornton, P.: The Impact of Parametric
Uncertainties on Biogeochemistry in the E3SM Land Model, J. Adv. Model. Earth
Sy., 10, 297–319, https://doi.org/10.1002/2017ms000962, 2018.
Rodell, M., Beaudoing, H. K., L'Ecuyer, T., Olson, W. S., Famiglietti, J.
S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., and
Chambers, D.: The observed state of the water cycle in the early
twenty-first century, J. Climate, 28, 8289–8318, 2015.
Sargsyan, K., Safta, C., Najm, H. N., Debusschere, B. J., Ricciuto, D., and
Thornton, P.: Dimensionality Reduction for Complex Models Via Bayesian
Compressive Sensing, Int. J. Uncertain. Quan., 4, 63–93, 2014.
Sargsyan, K., Najm, H. N., and Ghanem, R.: On the Statistical Calibration of
Physical Models, Int. J. Chem. Kinet., 47, 246–276,
https://doi.org/10.1002/kin.20906, 2015.
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, 2017.
Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D.
B., Dankers, R., Eisner, S., Fekete, B. M., Colón-González, F. J.,
Gosling, S. N., Kim, H., Liu, X., Masaki, Y., Portmann, F. T., Satoh, Y.,
Stacke, T., Tang, Q., Wada, Y., Wisser, D., Albrecht, T., Frieler, K.,
Piontek, F., Warszawski, L., and Kabat, P.: Multimodel assessment of water
scarcity under climate change, P. Natl. Acad.
Sci. USA, 111, 3245–3250, https://doi.org/10.1073/pnas.1222460110, 2014.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau,
J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968.
Seyoum, W. M., Kwon, D., and Milewski, A. M.: Downscaling GRACE TWSA Data
into High-Resolution Groundwater Level Anomaly Using Machine Learning-Based
Models in a Glacial Aquifer System, Remote Sens., 11, 824, https://doi.org/10.3390/rs11070824, 2019.
Sheng, M., Lei, H., Jiao, Y., and Yang, D.: Evaluation of the Runoff and
River Routing Schemes in the Community Land Model of the Yellow River Basin,
J. Adv. Model. Earth Sy., 9, 2993–3018, https://doi.org/10.1002/2017MS001026, 2017.
Sobol', I. M.: Global sensitivity indices for nonlinear mathematical models
and their Monte Carlo estimates, Math. Comput. Simulat.,
55, 271–280, https://doi.org/10.1016/S0378-4754(00)00270-6,
2001.
Sun, Y., Hou, Z., Huang, M., Tian, F., and Ruby Leung, L.: Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model, Hydrol. Earth Syst. Sci., 17, 4995–5011, https://doi.org/10.5194/hess-17-4995-2013, 2013.
Swenson, S. C., Lawrence, D. M., and Lee, H.: Improved simulation of the
terrestrial hydrological cycle in permafrost regions by the Community Land
Model, J. Adv. Model. Earth Sy., 4, M08002, https://doi.org/10.1029/2012MS000165, 2012.
Swenson, S. C., Clark, M., Fan, Y., Lawrence, D. M., and Perket, J.:
Representing Intrahillslope Lateral Subsurface Flow in the Community Land
Model, J. Adv. Model. Earth Sy., 11, 4044–4065, https://doi.org/10.1029/2019MS001833, 2019.
Tan, Z., Leung, L. R., Li, H.-Y., Tesfa, T., Zhu, Q., and Huang, M.: A
substantial role of soil erosion in the land carbon sink and its future
changes, Glob. Change Biol., 26, 2642–2655, https://doi.org/10.1111/gcb.14982, 2020.
Tebaldi, C., Smith, R. L., Nychka, D., and Mearns, L. O.: Quantifying
uncertainty in projections of regional climate change: A Bayesian approach
to the analysis of multimodel ensembles, J. Climate, 18, 1524–1540, 2005.
Tesfa, T. K., Leung, L. R., and Ghan, S. J.: Exploring Topography-Based
Methods for Downscaling Subgrid Precipitation for Use in Earth System
Models, J. Geophys. Res.-Atmos., 125, e2019JD031456,
https://doi.org/10.1029/2019JD031456, 2020.
Toure, A. M., Luojus, K., Rodell, M., Beaudoing, H., and Getirana, A.:
Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model
Using Satellite-Based Products and Streamflow Observations, J. Adv. Model.
Earth Sy., 10, 2933–2951, https://doi.org/10.1029/2018MS001389,
2018.
Trenberth, K. E.: Changes in precipitation with climate change, Clim. Res.,
47, 123–138, 2011.
Troy, T. J., Wood, E. F., and Sheffield, J.: An efficient calibration method
for continental-scale land surface modeling, Water Resour. Res., 44, W09411,
https://doi.org/10.1029/2007WR006513, 2008.
Tsai, W.-P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., and
Shen, C.: From calibration to parameter learning: Harnessing the scaling
effects of big data in geoscientific modeling, Nat. Commun., 12,
5988, https://doi.org/10.1038/s41467-021-26107-z, 2021.
Vörösmarty, C. J., Green, P., Salisbury, J., and Lammers, R. B.:
Global Water Resources: Vulnerability from Climate Change and Population
Growth, Science, 289, 284–288, https://doi.org/10.1126/science.289.5477.284, 2000.
Wang, C., Duan, Q., Gong, W., Ye, A., Di, Z., and Miao, C.: An evaluation of
adaptive surrogate modeling based optimization with two benchmark problems,
Environ. Modell. Softw., 60, 167–179, https://doi.org/10.1016/j.envsoft.2014.05.026, 2014.
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and
Schewe, J.: The Inter-Sectoral Impact Model Intercomparison Project
(ISI–MIP): Project framework, P. Natl. Acad.
Sci. USA, 111, 3228–3232, https://doi.org/10.1073/pnas.1312330110, 2014.
Wu, H., Kimball, J. S., Mantua, N., and Stanford, J.: Automated upscaling of
river networks for macroscale hydrological modeling, Water Resour. Res., 47, W03517,
https://doi.org/10.1029/2009WR008871, 2011.
Xie, Z., Yuan, F., Duan, Q., Zheng, J., Liang, M., and Chen, F.: Regional
Parameter Estimation of the VIC Land Surface Model: Methodology and
Application to River Basins in China, J. Hydrometeorol., 8, 447–468,
https://doi.org/10.1175/JHM568.1, 2007.
Xiu, D. and Karniadakis, G. E.: The Wiener–Askey Polynomial Chaos for
Stochastic Differential Equations, SIAM J. Sci. Comput., 24,
619–644, https://doi.org/10.1137/S1064827501387826, 2002.
Xu, D.: Code for “Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1”, Zenodo [code], https://doi.org/10.5281/zenodo.5815500, 2022a.
Xu, D.: Data for “Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1”, Zenodo [data set], https://doi.org/10.5281/zenodo.5815730, 2022b.
Xu, D., Ivanov, V. Y., Kim, J., and Fatichi, S.: On the use of observations
in assessment of multi-model climate ensemble, Stoch. Env.
Res. Risk A., 33, 1923–1937, https://doi.org/10.1007/s00477-018-1621-2,
2019.
Xu, D., Ivanov, V. Y., Li, X., and Troy, T. J.: Peak Runoff Timing is Linked
to Global Warming Trajectories, Earths Future, 9, e2021EF002083,
https://doi.org/10.1029/2021EF002083, 2021a.
Xu, D., Bisht, G., Zhou, T., Leung, L. R., and Pan, M.: Development of
Land-River Two-Way Coupling in the Energy Exascale Earth System Model, Earth
and Space Science Open Archive [preprint], https://doi.org/10.1002/essoar.10507802.2, 2021b.
Yang, H., Zhou, F., Piao, S. L., Huang, M. T., Chen, A. P., Ciais, P., Li,
Y., Lian, X., Peng, S. S., and Zeng, Z. Z.: Regional patterns of future
runoff changes from Earth system models constrained by observation, Geophys.
Res. Lett., 44, 5540–5549, https://doi.org/10.1002/2017gl073454, 2017.
Yang, S. L., Xu, K. H., Milliman, J. D., Yang, H. F., and Wu, C. S.: Decline
of Yangtze River water and sediment discharge: Impact from natural and
anthropogenic changes, Sci. Rep.-UK, 5, 12581, https://doi.org/10.1038/srep12581,
2015.
Zhang, Y., Zheng, H., Chiew, F. H. S., Arancibia, J. P. A., and Zhou, X.:
Evaluating Regional and Global Hydrological Models against Streamflow and
Evapotranspiration Measurements, J. Hydrometeorol., 17, 995–1010,
https://doi.org/10.1175/JHM-D-15-0107.1, 2016.
Zhou, T., Leung, L. R., Leng, G., Voisin, N., Li, H.-Y., Craig, A. P.,
Tesfa, T., and Mao, Y.: Global Irrigation Characteristics and Effects
Simulated by Fully Coupled Land Surface, River, and Water Management Models
in E3SM, J. Adv. Model. Earth Sy., 12, e2020MS002069, https://doi.org/10.1029/2020MS002069, 2020.
Short summary
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.
The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be...