Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-2119-2019
© Author(s) 2019. 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-12-2119-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of the WRF lake module (v1.0) and its improvements at a deep reservoir
Fushan Wang
Department of Hydraulic Engineering, Tsinghua University, Beijing,
China
Earth and Environmental Sciences Area, Lawrence Berkeley National Lab,
Berkeley, CA, USA
Guangheng Ni
Department of Hydraulic Engineering, Tsinghua University, Beijing,
China
William J. Riley
Earth and Environmental Sciences Area, Lawrence Berkeley National Lab,
Berkeley, CA, USA
Jinyun Tang
Earth and Environmental Sciences Area, Lawrence Berkeley National Lab,
Berkeley, CA, USA
Dejun Zhu
Department of Hydraulic Engineering, Tsinghua University, Beijing,
China
Department of Meteorology, University of Reading, Reading, United
Kingdom
Related authors
No articles found.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data, 17, 2713–2733, https://doi.org/10.5194/essd-17-2713-2025, https://doi.org/10.5194/essd-17-2713-2025, 2025
Short summary
Short summary
We have developed new maps that reveal how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2500 gauges and a wealth of climate and environmental information. The maps are a critical step in understanding and predicting how carbon moves through our environment, hence making them a useful tool for tackling climate challenges.
Shuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-1485, https://doi.org/10.5194/egusphere-2025-1485, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
Short summary
Short summary
Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Elsa Abs, Christoph Keuschnig, Pierre Amato, Chris Bowler, Eric Capo, Alexander Chase, Luciana Chavez Rodriguez, Abraham Dabengwa, Thomas Dussarrat, Thomas Guzman, Linnea Honeker, Jenni Hultman, Kirsten Küsel, Zhen Li, Anna Mankowski, William Riley, Scott Saleska, and Lisa Wingate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1716, https://doi.org/10.5194/egusphere-2025-1716, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Meta-omics technologies offer new tools to understand how microbial and plant functional diversity shape biogeochemical cycles across ecosystems. This perspective explores how integrating omics data with ecological and modeling approaches can improve our understanding of greenhouse gas fluxes and nutrient dynamics, from soils to clouds, and from the past to the future. We highlight challenges and opportunities for scaling omics insights from local processes to Earth system models.
Jinyun Tang and William J. Riley
Biogeosciences, 22, 1809–1819, https://doi.org/10.5194/bg-22-1809-2025, https://doi.org/10.5194/bg-22-1809-2025, 2025
Short summary
Short summary
A new mathematical formulation of the dynamic energy budget model is presented for the growth of biological organisms. This new formulation combines mass conservation law and chemical kinetics theory and is computationally faster than the standard formulation of dynamic energy budget models. In simulating the growth of Thalassiosira weissflogii in a nitrogen-limiting chemostat, the new model is as good as the standard dynamic energy budget model using almost the same parameter values.
Ashley Brereton, Zelalem Mekonnen, Bhavna Arora, William Riley, Kunxiaojia Yuan, Yi Xu, Yu Zhang, Qing Zhu, Tyler Anthony, and Adina Paytan
EGUsphere, https://doi.org/10.5194/egusphere-2025-361, https://doi.org/10.5194/egusphere-2025-361, 2025
Short summary
Short summary
Wetlands absorb carbon dioxide (CO2), helping slow climate change, but they also release methane, a potent warming gas. We developed a collection of AI-based models to estimate magnitudes of CO2 and methane exchanged between the land and the atmosphere, for wetlands on a regional scale. This approach helps to inform land-use planning, restoration, and greenhouse gas accounting, while also creating a foundation for future advancements in prediction accuracy.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
Short summary
Short summary
This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Ruidong Li, Jiapei Liu, Ting Sun, Shao Jian, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3780, https://doi.org/10.5194/egusphere-2024-3780, 2025
Short summary
Short summary
This work presents a new approach to simulate sewer drainage effects for urban flooding with key missing information like flow directions and nodal depths estimated from incomplete information. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.
Zewei Ma, Kaiyu Guan, Bin Peng, Wang Zhou, Robert Grant, Jinyun Tang, Murugesu Sivapalan, Ming Pan, Li Li, and Zhenong Jin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-340, https://doi.org/10.5194/hess-2024-340, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We explore tile drainage’ impacts on the integrated hydrology-biogeochemistry-plant system, using ecosys with soil oxygen and microbe dynamics. We found that tile drainage lowers soil water content and improves soil oxygen levels, which helps crops grow better, especially during wet springs, and the developed root system also helps mitigate drought stress on dry summers. Overall, tile drainage increases crop resilience to climate change, making it a valuable future agricultural practice.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Biogeosciences, 21, 5173–5183, https://doi.org/10.5194/bg-21-5173-2024, https://doi.org/10.5194/bg-21-5173-2024, 2024
Short summary
Short summary
Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Using machine learning, we develop and compare predictive relationships in observations (Obs) and ESMs. We find different relationships between environmental factors and SOC stocks in Obs and ESMs. SOC prediction in ESMs may be improved by representing the functional relationships of environmental controllers in a way consistent with observations.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Jinyun Tang and William J. Riley
Biogeosciences, 21, 1061–1070, https://doi.org/10.5194/bg-21-1061-2024, https://doi.org/10.5194/bg-21-1061-2024, 2024
Short summary
Short summary
A chemical kinetics theory is proposed to explain the non-monotonic relationship between temperature and biochemical rates. It incorporates the observed thermally reversible enzyme denaturation that is ensured by the ceaseless thermal motion of molecules and ions in an enzyme solution and three well-established theories: (1) law of mass action, (2) diffusion-limited chemical reaction theory, and (3) transition state theory.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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.
Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson
Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, https://doi.org/10.5194/gmd-16-869-2023, 2023
Short summary
Short summary
We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022, https://doi.org/10.5194/gmd-15-2839-2022, 2022
Short summary
Short summary
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson
Geosci. Model Dev., 15, 1899–1911, https://doi.org/10.5194/gmd-15-1899-2022, https://doi.org/10.5194/gmd-15-1899-2022, 2022
Short summary
Short summary
Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
Jinyun Tang, William J. Riley, and Qing Zhu
Geosci. Model Dev., 15, 1619–1632, https://doi.org/10.5194/gmd-15-1619-2022, https://doi.org/10.5194/gmd-15-1619-2022, 2022
Short summary
Short summary
We here describe version 2 of BeTR, a reactive transport model created to help ease the development of biogeochemical capability in Earth system models that are used for quantifying ecosystem–climate feedbacks. We then coupled BeTR-v2 to the Energy Exascale Earth System Model to quantify how different numerical couplings of plants and soils affect simulated ecosystem biogeochemistry. We found that different couplings lead to significant uncertainty that is not correctable by tuning parameters.
Jing Tao, Qing Zhu, William J. Riley, and Rebecca B. Neumann
The Cryosphere, 15, 5281–5307, https://doi.org/10.5194/tc-15-5281-2021, https://doi.org/10.5194/tc-15-5281-2021, 2021
Short summary
Short summary
We improved the DOE's E3SM land model (ELMv1-ECA) simulations of soil temperature, zero-curtain period durations, cold-season CH4, and CO2 emissions at several Alaskan Arctic tundra sites. We demonstrated that simulated CH4 emissions during zero-curtain periods accounted for more than 50 % of total emissions throughout the entire cold season (Sep to May). We also found that cold-season CO2 emissions largely offset warm-season net uptake currently and showed increasing trends from 1950 to 2017.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
Short summary
Short summary
Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Robinson I. Negrón-Juárez, Jennifer A. Holm, Boris Faybishenko, Daniel Magnabosco-Marra, Rosie A. Fisher, Jacquelyn K. Shuman, Alessandro C. de Araujo, William J. Riley, and Jeffrey Q. Chambers
Biogeosciences, 17, 6185–6205, https://doi.org/10.5194/bg-17-6185-2020, https://doi.org/10.5194/bg-17-6185-2020, 2020
Short summary
Short summary
The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics of forest regrowth after disturbances in Central Amazon. This variability was represented by the dynamics of forest regrowth after disturbances were properly represented by the ELM-FATES model (Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in the Energy Exascale Earth System Model (E3SM) Land Model (ELM)).
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, and Scott R. Saleska
Biogeosciences, 17, 5849–5860, https://doi.org/10.5194/bg-17-5849-2020, https://doi.org/10.5194/bg-17-5849-2020, 2020
Short summary
Short summary
Methane (CH4) is a strong greenhouse gas that can accelerate climate change and offset mitigation efforts. A key assumption embedded in many large-scale climate models is that ecosystem CH4 emissions can be estimated by fixed temperature relations. Here, we demonstrate that CH4 emissions cannot be parameterized by emergent temperature response alone due to variability driven by microbial and abiotic interactions. We also provide mechanistic understanding for observed CH4 emission hysteresis.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
Short summary
Short summary
It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, https://doi.org/10.5194/gmd-13-4891-2020, 2020
Short summary
Short summary
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, and socio-economic factors and in response to environmental conditions.
Cited articles
Ahmad, A., El-Shafie, A., Razali, S. F. M., and Mohamad, Z. S.: Reservoir
Optimization in Water Resources: a Review, Water Resour. Manag., 28,
3391–3405, https://doi.org/10.1007/s11269-014-0700-5, 2014.
Anohin, V. V., Imberger, J., Romero, J. R., and Ivey, G. N.: Effect of long
internal waves on the quality of water withdrawn from a stratified reservoir,
J. Hydraul. Eng.-ASCE, 132, 1134–1145, 2006.
Ataktürk, S. S. and Katsaros, K. B.: Wind Stress and Surface Waves
Observed on Lake Washington, J. Phys. Oceanogr., 29, 633–650, 1999.
Bates, G. T., Giorgi, F., and Hostetler, S. W.: Toward the simulation of the
effects of the Great Lakes on regional climate, Mon. Weather Rev., 121,
1373–1387, 1993.
Bates, G. T., Hostetler, S. W., and Giorgi, F.: Two-Year Simulation of the
Great Lakes Region with a Coupled Modeling System, Mon. Weather Rev., 123,
1505–1522, 1995.
Bonan, G. B.: Sensitivity of a GCM simulation to inclusion of inland water
surfaces, J. Chem. Ecol., 8, 2691—2704, 1995.
Burchard, H., Bolding, K., and Villarreal, M. R.: GOTM, a General Ocean
Turbulence Model: theory, implementation and test cases, Space Applications
Institute, Ispra, Italy, 1999.
Çalışkan, A. and Elçi, S. : Effects of selective withdrawal on
hydrodynamics of a stratified reservoir, Water Resour. Manag., 23,
1257–1273, 2009.
Charnock, H.: Wind stress over a water surface, Q. J. Roy. Meteor. Soc., 81,
639–640, 1955.
Chen, Y., Yang, K., He, J., Qin, J., Shi, J., Du, J., and He, Q.: Improving
land surface temperature modeling for dry land of China, J. Geophys. Res.,
116, D20104, https://doi.org/10.1029/2011jd015921, 2011.
Cristofor, S., Vadineanu, A., Ignat, G., and Ciubuc, C.: Factors affecting
light penetration in shallow lakes, Hydrobiologia, 275–276, 493–498, 1994.
Deng, B., Liu, S., Xiao, W., Wang, W., Jin, J., and Lee, X.: Evaluation of
the CLM4 lake model at a large and shallow freshwater lake,
J. Hydrometeorol., 14, 636–649, 2013.
Dutra, E., Stepanenko, V. M., Balsamo, G., Viterbo, P., Miranda, P. M. A.,
Mironov, D., and Schär, C.: An offline study of the impact of lakes on
the performance of the ECMWF surface scheme, Boreal Environ. Res., 15,
100–112, 2010.
Ellis, C. R.: Water temperature dynamics and heat transfer beneath the ice
cover of a lake, Limnol. Oceanogr., 36, 324–334, 1991.
Engineering Sciences Data Unit (ESDU): Characteristics of Wind Speed in the
Lower Layers of the Atmosphere Near the Ground: Strong Winds (Neutral
Atmosphere), ESDU Data Item No. 72026, ESDU, Regent Street, London, UK, 1972.
Fairall, C. W., Bradley, E. F., Rogers, D. P., Edson, J. B., and Young, G.
S.: Bulk parameterization of air-sea fluxes for Tropical Ocean-Global
Atmosphere Coupled-Ocean Atmosphere Response Experiment, J. Geophys. Res.,
101, 3747–3764, 1996.
Fang, X. and Stefan, H. G.: Long-term lake water temperature and ice cover
simulations/measurements, Cold Reg. Sci. Technol., 24, 289–304, 1996.
Goudsmit, G.-H., Burchard, H., Peeters, F., and Wüest, A.: Application of
k–ϵ turbulence models to enclosed basins: The role of internal
seiches, J. Geophys. Res., 107, 3230, https://doi.org/10.1029/2001JC000954, 2002.
Gu, H., Jin, J., Wu, Y., Ek, M. B., and Subin, Z. M.: Calibration and
validation of lake surface temperature simulations with the coupled WRF-lake
model, Climatic Change, 129, 471–483, https://doi.org/10.1007/s10584-013-0978-y, 2015.
Gu, H., Ma, Z., and Li, M.: Effect of a large and very shallow lake on local
summer precipitation over the Lake Taihu basin in China, J. Geophys.
Res.-Atmos., 121, 8832–8848, https://doi.org/10.1002/2015jd024098, 2016.
Gula, J. and Peltier, W. R.: Dynamical downscaling over the Great Lakes basin
of North America using the WRF regional climate model: the impact of the
Great Lakes system on regional greenhouse warming, J. Climate, 25,
7723–7742, 2012.
Håkanson, L.: Models to predict Secchi depth in small glacial lakes,
Aquat. Sci., 57, 31–53, 1995.
Harris, L. M., Lin, S.-J., and Tu, C.: High-Resolution Climate Simulations
Using GFDL HiRAM with a Stretched Global Grid, J. Climate, 29, 4293–4314,
https://doi.org/10.1175/jcli-d-15-0389.1, 2016.
Henderson-Sellers, B.: New formulation of eddy diffusion thermocline models,
Appl. Math. Model., 9, 441–446, 1985.
Henderson-Sellers, B., Mccormick, M. J., and Scavia, D.: A comparison of the
formulation for eddy diffusion in two one-dimensional stratification models,
Appl. Math. Model., 7, 212–215, 1983.
Hocking, G. C. and Straškraba, M.: The effect of light extinction on
thermal stratification in reservoirs and lakes, Int. Rev. Hydrobiol., 84,
535–556, 2015.
Hondzo, M., and Stefan, H. G.: Lake Water Temperature Simulation Model, J.
Hydraul. Eng.-ASCE, 119, 1251–1273, 1993.
Hostetler, S. W. and Bartlein, P. J.: Simulation of lake evaporation with
application to modeling lake level variations of Harney-Malheur Lake, Oregon,
Water Resour. Res., 26, 2603–2612, 1990.
Hostetler, S. W. and Giorgi, F.: Effects of a 2×CO2, climate
on two large lake systems: Pyramid Lake, Nevada, and Yellowstone Lake,
Wyoming, Global Planet. Change, 10, 43–54, 1995.
Hostetler, S. W., Giorgi, F., Bates, G. T., and Bartlein, P. J.:
Lake-atmosphere feedbacks associated with paleolakes Bonneville and Lahontan,
Science, 263, 665–668, 1994.
Hutchinson, G. E.: A treatise on limnology v.1, John Wiley, John Wiley &
Sons, Inc., Hoboken, United States, 1957.
Imberger, J., Patterson, J., Hebbert, B., and Loh, I.: Dynamics of reservoir
of medium size, J. Hydr. Div.-ASCE, 104, 725–743, 1978.
Jain, S. K. and Singh, V. P.: Water resources systems planning and
management, Elsevier, Amsterdam, 2003.
Jassby, A. and Powell, T.: Vertical patterns of eddy diffusion during
stratification in Castle Lake, California1, Limnol. Oceanogr., 20, 530–543,
1975.
Jerlov, N. G.: Marine optics, Elsevier Scientific Publishing Company,
Amsterdam, The Netherlands, 231 pp., 1976.
Karagounis, I., Trösch, J., and Zamboni, F.: A coupled
physical-biochemical lake model for forecasting water quality, Aquat. Sci.,
55, 87–102, 1993.
Kraus, E. B. and Turner, J. S.: A one-dimensional model of the seasonal
thermocline II. The general theory and its consequences, Tellus, 19, 98–106,
1967.
Krinner, G.: Impact of lakes and wetlands on boreal climate, J. Geophys.
Res., 108, 4520, https://doi.org/10.1029/2002JD002597, 2003.
Kullenberg, G., Murthy, C., and Westerbuy, H.: An experimental study of
diffusion characteristics in the thermocline and hypolimnion regions of Lake
Ontario, IEEE, 7, 739–751, 1973.
Kullenberg, G.: An experimental and theoretical investigation of the
turbulent diffusion in the upper layer of the sea, J. Mod. Greek Stud., 18,
151–160, 1974.
Lerman, A. and Chou, L.: Physics and chemistry of lakes with 48 tables,
Springer, Berlin, 2013.
Li, Y. H.: Vertical eddy diffusion coefficient in Lake Zürich, Schweiz.
Z. Hydrol., 35, 1–7, 1973.
Li, Z., Lyu, S., Zhao, L., Wen, L., Ao, Y., and Wang, S.: Turbulent transfer
coefficient and roughness length in a high-altitude lake, Tibetan Plateau,
Theor. Appl. Climatol., 124, 723–735, https://doi.org/10.1007/s00704-015-1440-z, 2015.
Lofgren, B. M.: Simulated effects of idealized Laurentian great lakes on
regional and large-scale climate, J. Climate, 10, 2847–2858, 1997.
Long, Z., Perrie, W., Gyakum, J., Caya, D., and Laprise, R.: Northern lake
impacts on local seasonal climate, J. Hydrometeorol., 8, 881–896, 2007.
MacKay, M. D., Neale, P. J., Arp, C. D., De Senerpont Domis, L. N., Fang, X.,
Gal, G., Jöhnk, K. D., Kirillin, G., Lenters, J. D., Litchman, E.,
MacIntyre, S., Marsh, P., Melack, J., Mooij, W. M., Peeters, F., Quesada, A.,
Schladow, S. G., Schmid, M., Spence, C., and Stokesr, S. L.: Modeling lakes
and reservoirs in the climate system, Limnol. Oceanogr., 54, 2315–2329,
2009.
Mallard, M. S., Nolte, C. G., Spero, T. L., Bullock, O. R., Alapaty, K.,
Herwehe, J. A., Gula, J., and Bowden, J. H.: Technical challenges and
solutions in representing lakes when using WRF in downscaling applications,
Geosci. Model Dev., 8, 1085–1096, https://doi.org/10.5194/gmd-8-1085-2015, 2015.
Mallard, M. S., Nolte, C. G., Bullock, O. R., Spero, T. L., and Gula, J.:
Using a coupled lake model with WRF for dynamical downscaling, J. Geophys.
Res., 119, 7193–7208, https://doi.org/10.1002/2014JD021785, 2014.
Martynov, A., Sushama, L., and Laprise, R.: Simulation of temperate freezing
lakes by one-dimensional lake models: Performance assessment for interactive
coupling with regional climate models, Boreal Environ. Res., 15, 143–164,
2010.
Mironov, D., Kourzeneva, E., Ritter, B., and Schneider, N.: Implementation of
the lake parameterisation scheme Flake into numerical weather prediction
model COSMO, Boreal Environ. Res., 15, 77–95, 2010.
Niziol, T. A., Snyder, W. R., and Waldstreicher, J.S.: Winter weather
forecasting throughout the eastern United States. IV: lake effect snow,
Weather Forecast., 10, 61–77, 1995.
Notaro, M., Holman, K., Zarrin, A., Fluck, E., Vavrus, S. J., and Bennington,
V.: Influence of the Laurentian Great Lakes on regional climate, J. Climate,
26, 789–804, 2013.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E.,
Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A., Decker,
M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque, J.-F.,
Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi,
K., Slater, A., Stöckli, R., Wang, A., Yang, Z.-L., Zeng, X., and Zeng,
X.: Technical description of version 4.0 of the Community Land Model (CLM),
NCAR Technical Note NCAR/TN-478+STR, National Center for Atmospheric
Research, Boulder, CO, 257 pp., 2010.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven,
C. D., Levis, S., Li, F., W. J., Riley, Subin, Z. M., Swenson, S. C.,
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.,
and Ricciuto, D. M.: Technical Description of version 4.5 of the Community
Land Model (CLM), NCAR Technical Note NCAR/TN-503+STR, National Center for
Atmospheric Research, Boulder, CO, 422 pp., 2013.
Paulson, C. A. and Simpson, J. J.: The temperature difference across the cool
skin of the ocean, J. Geophys. Res., 86, 11044–11054,
https://doi.org/10.1029/jc086ic11p11044, 1981.
Peeters, F., Livingstone, D. M., Goudsmit, G., Kipfer, R., and Forster, R.:
Modeling 50 years of historical temperature profiles in a large central
European lake, Limnol. Oceanogr., 47, 186–197, 2002.
Perroud, M., Goyette, S., Martynov, A., Beniston, M., and Anneville, O.:
Simulation of multiannual thermal profiles in deep Lake Geneva: a comparison
of one-dimensional lake models, Limnol. Oceanogr., 54, 1574–1594, 2009.
Quay, P. D., Broecker, W. S., Hesslein, R. H., and Schindler, D. W.: Vertical
diffusion rates determined by tritium tracer experiments in the thermocline
and hypolimnion of two lakes, Limnol. Oceanogr., 25, 201–218, 1980.
Riley, M. J. and Stefan, H. G.: Minlake: A dynamic lake water quality
simulation model, Ecol. Model., 43, 155–182, 1988.
Samuelsson, P., Kourzeneva, E., and Mironov, D.: The impact of lakes on the
European climate as simulated by a regional climate model, Boreal Environ.
Res., 15, 113–129, 2010.
Sarmiento, J. L., Feely, H. W., Moore, W. S., Bainbridge, A. E., and
Broecker, W. S.: The relationship between vertical eddy diffusion and
buoyancy gradient in the deep sea, Earth Planet. Sc. Lett., 32, 357–370,
1976.
Scott, R. W. and Huff, F. A.: Impacts of the great lakes on regional climate
conditions, J. Great Lakes Res., 22, 845–863, 1996.
Shen, C.: Accumulation impact on water temperature in a south-north dammed
river – A case study in the middle and lower reaches of Lancang river,
Report, Tsinghua University, Beijing, 2017 (in Chinese).
Skamarock, W. C., Duda, M. G., Ha, S., and Park, S.-H.: Limited-Area
Atmospheric Modeling Using an Unstructured Mesh, Mon. Weather Rev., 146,
3445–3460, https://doi.org/10.1175/mwr-d-18-0155.1, 2018.
Small, E. E., Sloan, L. C., Hostetler, S., and Giorgi, F.: Simulating the
water balance of the Aral Sea with a coupled regional climate-lake model, J.
Geophys. Res., 104, 6583–6602, 1999.
Smith, I. R.: Hydraulic conditions in isothermal lakes, Freshwater Biol., 9,
119–145, 1979.
Smith, S. D.: Coefficients for sea surface wind stress, heat flux, and wind
profiles as a function of wind speed and temperature, J. Geophys. Res., 93,
15467–15472, 1988.
Stepanenko, V. M. and Lykossov, V. N.: Numerical modeling of heat and
moisture transfer processes in a system lake–soil, Russ. J. Meteorol.
Hydrol., 3, 95–104, 2005.
Stepanenko, V. M., Goyette, S., Martynov, A., Perroud, M., Fang, X., and
Mironov, D.: First steps of a lake model intercomparison project: lakemip,
Boreal Environ. Res., 15, 191–202, 2010.
Stepanenko, V. M., Machul'Skaya, E. E., Glagolev, M. V., and Lykossov, V. N.:
Numerical modeling of methane emissions from lakes in the permafrost zone,
Izv. Atmos. Ocean. Phy., 47, 252–264, 2011.
Stepanenko, V. M., Martynov, A., Jöhnk, K. D., Subin, Z. M., Perroud, M.,
Fang, X., Beyrich, F., Mironov, D., and Goyette, S.: A one-dimensional model
intercomparison study of thermal regime of a shallow, turbid midlatitude
lake, Geosci. Model Dev., 6, 1337–1352, https://doi.org/10.5194/gmd-6-1337-2013, 2013.
Subin, Z. M., Riley, W. J., and Mironov, D.: An improved lake model for
climate simulations: model structure, evaluation, and sensitivity analyses in
CESM1, J. Adv. Model. Earth Sy., 4, M02001, https://doi.org/10.1029/2011MS000072,
2012a.
Subin, Z. M., Murphy, L. N., Li, F., Bonfils, C., and Riley, W. J.: Boreal
lakes moderate seasonal and diurnal temperature variation and perturb
atmospheric circulation: analyses in the Community Earth System Model 1
(CESM1), Tellus A, 64, 53–66, 2012b.
Subin, Z. M., Koven, C. D., Riley, W. J., Torn, M. S., Lawrence, D. M., and
Swenson, S. C.: Effects of soil moisture on the responses of soil
temperatures to climate change in cold regions, J. Climate, 26, 3139–3158,
2013.
Svensson, U.: A mathematical model of the seasonal thermocline, Phd thesis,
Lund Institute of Technology, 1978.
Ulrich, M.: Modeling of chemicals in lakes – development and application of
user-friendly simulation software (MASAS & CHEMSEE) on personal computers,
PhD thesis, ETH Zürich, No. 9632, https://doi.org/10.3929/ethz-a-000626302, 1991.
Wang, F., Ni, G., Riley, W. J., Tang, J., Zhu, D., and Sun, T.: Code and
sample data for GMD paper (https://doi.org/10.5194/gmd-2018-168), Zenodo,
https://doi.org/10.5281/zenodo.2624892, 2019.
Wright, D. M., Posselt, D. J., and Steiner, A. L.: Sensitivity of lake-effect
snowfall to lake ice cover and temperature in the Great Lakes region, Mon.
Weather Rev., 141, 670–689, 2013.
Xiao, C., Lofgren, B. M., Wang, J., and Chu, P. Y.: Improving the lake scheme
within a coupled WRF-lake model in the Laurentian great lakes, J. Adv. Model.
Earth Sy., 8, 1969–1985, 2016.
Yang, K., Jie, H., Tang, W., Qin, J., and Cheng, C. C. K.: On downward
shortwave and longwave radiations over high altitude regions: observation and
modeling in the Tibetan Plateau, Agr. Forest Meteorol., 150, 38–46, 2010.
Zamboni, F., Barbieri, A., Polli, B., Salvadè, G., and Simona, M.: The
dynamic model SEEMOD applied to the southern basin of lake Lugano, Aquat.
Sci., 54, 367–380, 1992.
Zhao, L., Jin, J., Wang, S., and Ek, M. B.: Integration of remote-sensing
data with WRF to improve lake-effect precipitation simulations over the Great
Lakes region, J. Geophys. Res., 117, D09102, https://doi.org/10.1029/2011JD016979,
2012.
Short summary
The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
The current lake model in the Weather Research and Forecasting system was reported to be...