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
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Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
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Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
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Fire management has long been a challenge. Here we report that spring-peak fire activity over southern Mexico and Central America (SMCA) has a distinct quasi-biennial signal by measuring multiple fire metrics. This signal is initially driven by quasi-biennial variability in precipitation and is further amplified by positive feedback of fire–precipitation interaction at short timescales. This work highlights the importance of fire–climate interactions in shaping fires on an interannual scale.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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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
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Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
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We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
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We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Chandan Sarangi, Yun Qian, L. Ruby Leung, Yang Zhang, Yufei Zou, and Yuhang Wang
Atmos. Chem. Phys., 23, 1769–1783, https://doi.org/10.5194/acp-23-1769-2023, https://doi.org/10.5194/acp-23-1769-2023, 2023
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We show that for air quality, the densely populated eastern US may see even larger impacts of wildfires due to long-distance smoke transport and associated positive climatic impacts, partially compensating the improvements from regulations on anthropogenic emissions. This study highlights the tension between natural and anthropogenic contributions and the non-local nature of air pollution that complicate regulatory strategies for improving future regional air quality for human health.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
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Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
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We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
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The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Sol Kim, L. Ruby Leung, Bin Guan, and John C. H. Chiang
Geosci. Model Dev., 15, 5461–5480, https://doi.org/10.5194/gmd-15-5461-2022, https://doi.org/10.5194/gmd-15-5461-2022, 2022
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The Energy Exascale Earth System Model (E3SM) project is a state-of-the-science Earth system model developed by the US Department of Energy (DOE). Understanding how the water cycle behaves in this model is of particular importance to the DOE’s mission. Atmospheric rivers (ARs) – which are crucial to the global water cycle – move vast amounts of water vapor through the sky and produce rain and snow. We find that this model reliably represents atmospheric rivers around the world.
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, https://doi.org/10.5194/gmd-15-5489-2022, 2022
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Land surface heterogeneity plays a critical role in the terrestrial water, energy, and biogeochemical cycles. Our study systematically quantified the effects of four dominant heterogeneity sources on water and energy partitioning via Sobol' indices. We found that atmospheric forcing and land use land cover are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning. Our findings can help prioritize the future development of land surface models.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
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Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
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How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Pinya Wang, Yang Yang, Huimin Li, Lei Chen, Ruijun Dang, Daokai Xue, Baojie Li, Jianping Tang, L. Ruby Leung, and Hong Liao
Atmos. Chem. Phys., 22, 4705–4719, https://doi.org/10.5194/acp-22-4705-2022, https://doi.org/10.5194/acp-22-4705-2022, 2022
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China is now suffering from both severe ozone (O3) pollution and heat events. We highlight that North China Plain is the hot spot of the co-occurrences of extremes in O3 and high temperatures in China. Such coupled extremes exhibit an increasing trend during 2014–2019 and will continue to increase until the middle of this century. And the coupled extremes impose more severe health impacts to human than O3 pollution occurring alone because of elevated O3 levels and temperatures.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Sally S.-C. Wang, Yun Qian, L. Ruby Leung, and Yang Zhang
Atmos. Chem. Phys., 22, 3445–3468, https://doi.org/10.5194/acp-22-3445-2022, https://doi.org/10.5194/acp-22-3445-2022, 2022
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This study develops an interpretable machine learning (ML) model predicting monthly PM2.5 fire emission over the contiguous US at 0.25° resolution and compares the prediction skills of the ML and process-based models. The comparison facilitates attributions of model biases and better understanding of the strengths and uncertainties in the two types of models at regional scales, for informing future model development and their applications in fire emission projection.
Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
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Existing riverbed sediment particle size data are sparsely available at individual sites. We develop a continuous map of median riverbed sediment particle size over the contiguous US corresponding to millions of river segments based on the existing observations and machine learning methods. This map is useful for research in large-scale river sediment using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
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We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Claudia Tebaldi, Kalyn Dorheim, Michael Wehner, and Ruby Leung
Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, https://doi.org/10.5194/esd-12-1427-2021, 2021
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We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
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Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
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
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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.
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...