Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5131-2023
© Author(s) 2023. 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-16-5131-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Sep 2023
Model description paper |
| 08 Sep 2023
| 08 Sep 2023
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
National Center for Atmospheric Research (NCAR), Boulder, Colorado,
USA
Prasanth Valayamkunnath
National Center for Atmospheric Research (NCAR), Boulder, Colorado,
USA
Indian Institute of Science Education and Research, Thiruvananthapuram,
India
Michael Barlage
NOAA Environmental Modeling Center (EMC), College Park, Maryland, USA
Fei Chen
National Center for Atmospheric Research (NCAR), Boulder, Colorado,
USA
David Gochis
National Center for Atmospheric Research (NCAR), Boulder, Colorado,
USA
Ryan Cabell
National Center for Atmospheric Research (NCAR), Boulder, Colorado,
USA
Tim Schneider
National Center for Atmospheric Research (NCAR), Boulder, Colorado,
USA
Roy Rasmussen
National Center for Atmospheric Research (NCAR), Boulder, Colorado,
USA
Guo-Yue Niu
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona, USA
Zong-Liang Yang
Department of Earth and Planetary Sciences, University of Texas Austin, Austin, Texas, USA
Dev Niyogi
Department of Earth and Planetary Sciences, University of Texas Austin, Austin, Texas, USA
Michael Ek
National Center for Atmospheric Research (NCAR), Boulder, Colorado,
USA
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Mohammad A. Farmani, Andrew Bennet, Sadaf Moghisi, Hoshin Gupta, Muhammad Jawad, Ali Behrangi, Ahmad A. Tavakoly, and Guo-Yue Niu
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Short summary
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Short summary
We present a novel data-driven approach to understand how pollution and weather processes interact to influence snowmelt in Asian glaciers and how these interactions are represented in three climate models. Our findings show where models need improvement in predicting snowmelt, particularly dust and its transport. This method can support future model development for reliable predictions in climate-vulnerable regions.
Yong-He Liu and Zong-Liang Yang
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Short summary
Short summary
We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS). The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county-level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.
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Short summary
Short summary
This study evaluates how different methods of simulating runoff impact river flow predictions globally. By comparing seven approaches within the Noah-Multi-parameterisation (Noah-MP) land surface model, we found significant differences in accuracy, with some methods underestimating or overestimating runoff. The results are crucial for improving water resource management and flood prediction. Our work highlights the need for precise modelling to better prepare for climate-related challenges.
Max Berkelhammer, Gerald F. M. Page, Frank Zurek, Christopher Still, Mariah S. Carbone, William Talavera, Laura Hildebrand, James Byron, Kyle Inthabandith, Angellica Kucinski, Melissa Carlson, Kelsey Foss, Wendy Brown, Rosemary W. H. Carroll, Austin Simonpietri, Marshall Worsham, Ian Breckheimer, Anna Ryken, Reed Maxwell, David Gochis, Mark S. Raleigh, Eric Small, and Kenneth H. Williams
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Soil moisture memory (SMM) shows how long soil stays moist after rain, impacting climate and ecosystems. Current models often overestimate SMM, causing inaccuracies in evaporation predictions. We enhanced a land model, Noah-MP, to include better water flow and ponding processes, and we tested it against satellite and field data. This improved model reduced overestimations and enhanced short-term predictions, helping create more accurate climate and weather forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Arezoo Rafieeinasab, Amir Mazrooei, Thomas Enzminger, Ishita Srivastava, Aubrey Dugger, David Gochis, Nina Omani, Joe Grim, Kevin Sampson, Yongxin Zhang, Jacob LaFontaine, Roland Viger, Yuqiong Liu, and Tim Schneider
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-262, https://doi.org/10.5194/hess-2024-262, 2024
Manuscript not accepted for further review
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Integrated Water Availability Assessments is a national initiative to characterize water availability in the U.S. The WRF-Hydro model is used to generate an estimate of hydrological fluxes and storage across the conterminous United States. The streamflow performance is reasonable especially in the eastern and western U.S. Model performance in estimating snow, evapotranspiration and soil moisture is also reasonable with some differences against the verification datasets in certain areas.
Liqing Peng, Justin Sheffield, Zhongwang Wei, Michael Ek, and Eric F. Wood
Earth Syst. Dynam., 15, 1277–1300, https://doi.org/10.5194/esd-15-1277-2024, https://doi.org/10.5194/esd-15-1277-2024, 2024
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Integrating evaporative demand into drought indicators is effective, but the choice of method and the effectiveness of surface features remain undocumented. We evaluate various methods and surface features for predicting soil moisture dynamics. Using minimal ancillary information alongside meteorological and vegetation data, we develop a simple land-cover-based method that improves soil moisture drought predictions, especially in forests, showing promise for better real-time drought forecasting.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Hui Zheng, Wenli Fei, Zong-Liang Yang, Jiangfeng Wei, Long Zhao, Lingcheng Li, and Shu Wang
Earth Syst. Sci. Data, 15, 2755–2780, https://doi.org/10.5194/essd-15-2755-2023, https://doi.org/10.5194/essd-15-2755-2023, 2023
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An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP land surface model by perturbing model parameterization schemes. The data could be beneficial for monitoring and understanding the variability of water resources. Model developers could also gain insights by intercomparing the ensemble members.
Wenfu Tang, Simone Tilmes, David M. Lawrence, Fang Li, Cenlin He, Louisa K. Emmons, Rebecca R. Buchholz, and Lili Xia
Atmos. Chem. Phys., 23, 5467–5486, https://doi.org/10.5194/acp-23-5467-2023, https://doi.org/10.5194/acp-23-5467-2023, 2023
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Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. Geoengineering reduces fire by decreasing surface temperature and wind speed and increasing relative humidity and soil water. However, geoengineering also yields reductions in precipitation, which offset some of the fire reduction.
Erin Towler, Sydney S. Foks, Aubrey L. Dugger, Jesse E. Dickinson, Hedeff I. Essaid, David Gochis, Roland J. Viger, and Yongxin Zhang
Hydrol. Earth Syst. Sci., 27, 1809–1825, https://doi.org/10.5194/hess-27-1809-2023, https://doi.org/10.5194/hess-27-1809-2023, 2023
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Hydrologic models developed to assess water availability need to be systematically evaluated. This study evaluates the long-term performance of two high-resolution hydrologic models that simulate streamflow across the contiguous United States. Both models show similar performance overall and regionally, with better performance in minimally disturbed basins than in those impacted by human activity. At about 80 % of the sites, both models outperform the seasonal climatological benchmark.
Sisi Chen, Lulin Xue, Sarah Tessendorf, Kyoko Ikeda, Courtney Weeks, Roy Rasmussen, Melvin Kunkel, Derek Blestrud, Shaun Parkinson, Melinda Meadows, and Nick Dawson
Atmos. Chem. Phys., 23, 5217–5231, https://doi.org/10.5194/acp-23-5217-2023, https://doi.org/10.5194/acp-23-5217-2023, 2023
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The possible mechanism of effective ice growth in the cloud-top generating cells in winter orographic clouds is explored using a newly developed ultra-high-resolution cloud microphysics model. Simulations demonstrate that a high availability of moisture and liquid water is critical for producing large ice particles. Fluctuations in temperature and moisture down to millimeter scales due to cloud turbulence can substantially affect the growth history of the individual cloud particles.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
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The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023, https://doi.org/10.5194/gmd-16-2215-2023, 2023
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This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Xinlei He, Yanping Li, Shaomin Liu, Tongren Xu, Fei Chen, Zhenhua Li, Zhe Zhang, Rui Liu, Lisheng Song, Ziwei Xu, Zhixing Peng, and Chen Zheng
Hydrol. Earth Syst. Sci., 27, 1583–1606, https://doi.org/10.5194/hess-27-1583-2023, https://doi.org/10.5194/hess-27-1583-2023, 2023
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This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface atmospheric conditions, and land–atmosphere interactions by implementing detailed land characterization information in basins with complex underlying surfaces.
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.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
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Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
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High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
Atmos. Chem. Phys., 22, 8725–8737, https://doi.org/10.5194/acp-22-8725-2022, https://doi.org/10.5194/acp-22-8725-2022, 2022
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This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
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This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Istvan Geresdi, Lulin Xue, Sisi Chen, Youssef Wehbe, Roelof Bruintjes, Jared A. Lee, Roy M. Rasmussen, Wojciech W. Grabowski, Noemi Sarkadi, and Sarah A. Tessendorf
Atmos. Chem. Phys., 21, 16143–16159, https://doi.org/10.5194/acp-21-16143-2021, https://doi.org/10.5194/acp-21-16143-2021, 2021
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By releasing soluble aerosols into the convective clouds, cloud seeding potentially enhances rainfall. The seeding impacts are hard to quantify with observations only. Numerical models that represent the detailed physics of aerosols, cloud and rain formation are used to investigate the seeding impacts on rain enhancement under different natural aerosol backgrounds and using different seeding materials. Our results indicate that seeding may enhance rainfall under certain conditions.
Cited articles
Abolafia-Rosenzweig, R., He, C., Burns, S. P., and Chen, F.: Implementation
and Evaluation of a Unified Turbulence Parameterization Throughout the
Canopy and Roughness Sublayer in Noah-MP Snow Simulations, J. Adv. Model
Earth Sy., 13, e2021MS002665, https://doi.org/10.1029/2021MS002665, 2021.
Abolafia-Rosenzweig, R., He, C., and Chen, F.: Winter and spring climate
explains a large portion of interannual variability and trend in western
U.S. summer fire burned area, Environ. Res. Lett., 17, 054030,
https://doi.org/10.1088/1748-9326/ac6886, 2022a.
Abolafia-Rosenzweig, R., He, C., McKenzie Skiles, S., Chen, F., and Gochis,
D.: Evaluation and Optimization of Snow Albedo Scheme in Noah-MP Land
Surface Model Using In Situ Spectral Observations in the Colorado Rockies,
J. Adv. Model Earth Sy., 14, e2022MS003141m https://doi.org/10.1029/2022MS003141,
2022b.
Abolafia-Rosenzweig, R., He, C., Chen, F., Ikeda, K., Schneider, T., and
Rasmussen, R.: High resolution forecasting of summer drought in the western
United States, Water Resour. Res., 59, e2022WR033734,
https://doi.org/10.1029/2022WR033734, 2023a.
Abolafia-Rosenzweig, R., He, C., Chen, F., Zhang, Y., Dugger, A., Livneh,
B., and Gochis, D.: Evaluating Noah-MP simulated runoff and snowpack in
heavily burned Pacific-Northwest snow-dominated catchments, J. Geophys.
Res.-Atmos., in review, 2023b.
Anderson, E. A.: A point energy and mass balance model of a snow cover, NOAA
Tech. Rep. NWS 19, Off. of Hydrol., Natl. Weather Serv., Silver
Spring, Md., 150 pp., https://repository.library.noaa.gov/view/noaa/6392 (last access: 4 September 2023), 1976.
Arsenault, K. R., Shukla, S., Hazra, A., Getirana, A., McNally, A., Kumar,
S. V., Koster, R. D., Peters-Lidard, C. D., Zaitchik, B. F., Badr, H., Jung,
H. C., Narapusetty, B., Navari, M., Wang, S., Mocko, D. M., Funk, C.,
Harrison, L., Husak, G. J., Adoum, A., Galu, G., Magadzire, T., Roningen,
J., Shaw, M., Eylander, J., Bergaoui, K., McDonnell, R. A., and Verdin, J.
P.: Better Advance Warnings of Drought, B. Am. Meteorol. Soc., 101,
899–903, 2020.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal
conductance and its contribution to the control of photosynthesis under
different environmental conditions, in: Process in Photosyn. Res., Vol. 1,
edited by: Biggins, J., Martinus Nijhoff, Dordrecht,
Netherlands, 221–234, 1987.
Barlage, M., Tewari, M., Chen, F., Miguez-Macho, G., Yang, Z. L., and Niu,
G. Y.: The effect of groundwater interaction in North American regional
climate simulations with WRF/Noah-MP, Climatic Change, 129, 485–498, https://doi.org/10.1007/s10584-014-1308-8, 2015.
Barlage, M., Chen, F., Rasmussen, R., Zhang, Z., and Miguez-Macho, G.: The
importance of scale-dependent groundwater processes in land-atmosphere
interactions over the central United States, Geophys. Res. Lett., 48,
e2020GL092171, https://doi.org/10.1029/2020GL092171, 2021.
Blyth, E. M., Arora, V. K., Clark, D. B., Dadson, S. J., De Kauwe, M. G.,
Lawrence, D. M., Melton, J. R., Pongratz, J., Turton, R. H., Yoshimura, K.,
and Yuan, H.: Advances in land surface modelling, Curr. Clim. Change
Rep., 7, 45–71, https://doi.org/10.1007/s40641-021-00171-5, 2021.
Bonan, G. B.: A land surface model (LSM version 1.0) for ecological,
hydrological, and atmospheric studies: Technical description and user's
guide, NCAR Tech. Note, NCAR/TN-417+STR, Natl. Cent. for Atmos.
Res., Boulder, Colorado, 150 pp., https://doi.org/10.5065/D6DF6P5X, 1996.
Bonan, G. B. and Doney, S. C.: Climate, ecosystems, and planetary futures:
The challenge to predict life in Earth system models, Science, 359,
eaam8328, https://doi.org/10.1126/science.aam8328, 2018.
Brunsell, N. A., de Oliveira, G., Barlage, M., Shimabukuro, Y., Moraes, E.,
and Aragao, L.: Examination of seasonal water and carbon dynamics in eastern
Amazonia: a comparison of Noah-MP and MODIS, Theor. Appl. Climatol., 143,
571–586, https://doi.org/10.1007/s00704-020-03435-6, 2021.
Brutsaert, W.: Evaporation into the Atmosphere: Theory, History, and
Applications, Springer, Dordrecht,
https://doi.org/10.1007/978-94-017-1497-6, 1982.
Cai, X., Yang, Z. L., David, C. H., Niu, G. Y., and Rodell, M.: Hydrological
evaluation of the Noah-MP land surface model for the Mississippi River
Basin, J. Geophys. Res.-Atmos., 119, 23–38, https://doi.org/10.1002/2013JD020792, 2014.
Cai, X., Yang, Z.-L., Fisher, J. B., Zhang, X., Barlage, M., and Chen, F.: Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions, Geosci. Model Dev., 9, 1–15, https://doi.org/10.5194/gmd-9-1-2016, 2016.
Chang, M., Cao, J., Zhang, Q., Chen, W., Wu, G., Wu, L., Wang, W., and Wang, X.: Improvement of stomatal resistance and photosynthesis mechanism of Noah-MP-WDDM (v1.42) in simulation of NO2 dry deposition velocity in forests, Geosci. Model Dev., 15, 787–801, https://doi.org/10.5194/gmd-15-787-2022, 2022.
Chen, F. and Dudhia, J.: Coupling an advanced land surface–hydrology model
with the Penn State–NCAR MM5 Modeling System. Part I: Model implementation
and sensitivity, Mon. Weather Rev., 129, 17, https://doi.org/10.1175/1520-0493(2001)129<0569:caalsh>2.0.co;2,
2001.
Chen, F. and Zhang, Y.: On the coupling strength between the land surface
and the atmosphere: From viewpoint of surface exchange
coefficients, Geophys. Res. Lett., 36, L10404, https://doi.org/10.1029/2009GL037980, 2009.
Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H. L., Koren, V., Duan,
Q. Y., Ek, M. and Betts, A.: Modeling of land surface evaporation by four
schemes and comparison with FIFE observations, J. Geophys. Res.-Atmos., 101,
7251–7268, https://doi.org/10.1029/95JD02165, 1996.
Chen, F., Janjicì, Z., and Mitchell, K.: Impact of atmospheric surface-layer
parameterizations in the new land-surface scheme of the NCEP Mesoscale Eta
Model, Bound.-Lay. Meteorol., 85, 391–421,
https://doi.org/10.1023/A:1000531001463, 1997.
Chen, L., Li, Y., Chen, F., Barr, A., Barlage, M., and Wan, B.: The incorporation of an organic soil layer in the Noah-MP land surface model and its evaluation over a boreal aspen forest, Atmos. Chem. Phys., 16, 8375–8387, https://doi.org/10.5194/acp-16-8375-2016, 2016.
Dickinson, R. E.: Land surface processes and climate-surface albedos and
energy balance, in: Adv. Geophys., vol. 25, edited by: Saltzman, B.,
Academic, San Diego, Calif., 305–353, https://doi.org/10.1016/S0065-2687(08)60176-4, 1983.
Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P. J.:
Biosphere-Atmosphere Transfer Scheme (BATS) version 1e as coupled to the
NCAR Community Climate Model, NCAR Tech. Note, NCAR/TN- 387+STR, 80 pp.,
Natl. Cent. for Atmos. Res., Boulder, Colo., https://doi.org/10.5065/D67W6959, 1993.
Dickinson, R. E., Shaikh, M., Bryant, R., and Graumlich, L.: Interactive
canopies for a climate model, J. Climate, 11, 2823–2836, https://doi.org/10.1175/1520-0442(1998)011<2823:ICFACM>2.0.CO;2, 1998.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G. and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851,
https://doi.org/10.1029/2002JD003296, 2003.
Fan, Y., Miguez-Macho, G., Weaver, C. P., Walko, R., and Robock, A.:
Incorporating water table dynamics in climate modeling: 1. Water table
observations and equilibrium water table simulations, J. Geophys.
Res.-Atmos., 112, D10125, https://doi.org/10.1029/2006JD008111, 2007.
Gao, Y., Xiao, L., Chen, D., Chen, F., Xu, J., and Xu, Y.: Quantification of
the relative role of land-surface processes and large-scale forcing in
dynamic downscaling over the Tibetan Plateau, Clim. Dynam., 48, 1705–1721, https://doi.org/10.1007/s00382-016-3168-6,
2017.
Hazra, A., McNally, A., Slinski, K., Arsenault, K. R., Shukla, S., Getirana,
A., Jacob, J. P., Sarmiento, D. P., Peters-Lidard, C., Kumar, S. V., and
Koster, R. D.: NASA's NMME-based S2S hydrologic forecast system for food
insecurity early warning in southern Africa, J. Hydrol., 617, 129005, https://doi.org/10.1016/j.jhydrol.2022.129005,
2023.
He, C., Chen, F., Barlage, M., Liu, C., Newman, A., Tang, W., Ikeda, K., and
Rasmussen, R.: Can convection-permitting modeling provide decent
precipitation for offline high-resolution snowpack simulations over
mountains, J. Geophys. Res.-Atmos.,
124, 12631–12654, https://doi.org/10.1029/2019JD030823, 2019.
He, C., Chen, F., Abolafia-Rosenzweig, R., Ikeda, K., Liu, C. and Rasmussen,
R.: What causes the unobserved early-spring snowpack ablation in
convection-permitting WRF modeling over Utah mountains?, J. Geophys.
Res.-Atmos, 126, e2021JD035284, https://doi.org/10.1029/2021JD035284, 2021.
He, C., Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., Cabell, R.,
Schneider, T., Rasmussen, R., Niu, G. Y., Yang, Z. L., Niyogi, D., and Ek,
M.: The Community Noah-MP Land Surface Modeling System Technical Description
Version 5.0, NCAR Tech. Note, No. NCAR/TN-575+STR,
https://doi.org/10.5065/ew8g-yr95, 2023a.
He, C., Barlage, M., Valayamkunnath, P., Gill, D., Mocko, D., and Chen. F.: NCAR/noahmp: Release of v5.0.0 (v5.0.0), Zenodo [code]. https://doi.org/10.5281/zenodo.7901855, 2023b.
He, C., Barlage, M., Zhang, Z., xutr-bnu, Mocko, D., and Chen, F.: NCAR/hrldas: Release of v5.0.0 (v5.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7901868, 2023c.
Ingwersen, J., Högy, P., Wizemann, H. D., Warrach-Sagi, K., and Streck,
T.: Coupling the land surface model Noah-MP with the generic crop growth
model Gecros: Model description, calibration and validation, Agr. Forest
Meteorol., 262, 322–339, https://doi.org/10.1016/j.agrformet.2018.06.023, 2018.
Jarvis, P. G.: The interpretation of the variations in leaf water potential
and stomatal conductance found in canopies in the field, Philos. T. R.
Soc. B, 273, 593–610, https://doi.org/10.1098/rstb.1976.0035, 1976.
Jayawardena, A. W. and Zhou, M. C.: A modified spatial soil moisture storage
capacity distribution curve for the Xinanjiang model, J. Hydrol., 227,
93–113, https://doi.org/10.1016/S0022-1694(99)00173-0, 2000.
Jiang, Y., Chen, F., Gao, Y., He, C., Barlage, M., and Huang, W.: Assessment
of uncertainty sources in snow cover simulation in the Tibetan Plateau, J.
Geophys. Res.-Atmos., 125, e2020JD032674, https://doi.org/10.1029/2020JD032674, 2020.
Jiang, Y., Gao, Y., He, C., Liu, B., Pan, Y., and Li, X.: Spatiotemporal
distribution and variation of wind erosion over the Tibetan Plateau based on
a coupled land-surface wind-erosion model, Aeolian Res., 50, 100699, https://doi.org/10.1016/j.aeolia.2021.100699, 2021.
Jordan, R.: A one-dimensional temperature model for a snow cover, Spec. Rep.
91–16, Cold Reg. Res. and Eng. Lab., U.S. Army Corps. of Eng., Hanover, N.
H., 1991.
Ju, C., Li, H., Li, M., Liu, Z., Ma, Y., Mamtimin, A., Sun, M., and Song,
Y.: Comparison of the Forecast Performance of WRF Using Noah and Noah-MP
Land Surface Schemes in Central Asia Arid Region, Atmosphere, 13, 927, https://doi.org/10.3390/atmos13060927,
2022.
Koren, V., Schaake, J. C., Mitchell, K. E., Duan, Q.-Y., Chen, F., and
Baker, J. M.: A parameterization of snowpack and frozen ground intended for
NCEP weather and climate models, J. Geophys. Res., 104, 19569–19585,
https://doi.org/10.1029/1999JD900232, 1999.
Kumar, S. V., Mocko, D. M., Wang, S., Peters-Lidard, C. D., and Borak, J.:
Assimilation of remotely sensed leaf area index into the Noah-MP land
surface model: Impacts on water and carbon fluxes and states over the
continental United States, J. Hydrometeorol., 20, 1359–1377, 2019.
Kumar, S. V., Holmes, T., Andela, N., Dharssi, I., Vinodkumar, Hain, C.,
Peters-Lidard, C., Mahanama, S. P., Arsenault, K. R., Nie, W., and Getirana,
A.: The 2019–2020 Australian drought and bushfires altered the partitioning
of hydrological fluxes, Geophys. Res. Lett., 48, e2020GL091411, https://doi.org/10.1029/2020GL091411, 2021.
Li, J., Chen, F., Lu, X., Gong, W., Zhang, G., and Gan, Y.: Quantifying
contributions of uncertainties in physical parameterization schemes and
model parameters to overall errors in Noah-MP dynamic vegetation modeling,
J. Adv. Model. Earth Sy., 12, e2019MS001914, https://doi.org/10.1029/2019MS001914, 2020.
Li, L., Yang, Z. L., Matheny, A. M., Zheng, H., Swenson, S. C., Lawrence, D.
M., Barlage, M., Yan, B., McDowell, N. G., and Leung, L. R.: Representation
of plant hydraulics in the Noah-MP land surface model: Model development and
multiscale evaluation, J. Adv. Model. Earth Sy., 13, e2020MS002214, https://doi.org/10.1029/2020MS002214,
2021.
Li, M., Wu, P., Ma, Z., Lv, M., Yang, Q., and Duan, Y.: The decline in the
groundwater table depth over the past four decades in China simulated by the
Noah-MP land model, J. Hydrol., 607, 127551, https://doi.org/10.1016/j.jhydrol.2022.127551, 2022.
Li, X., Wu, T., Zhu, X., Jiang, Y., Hu, G., Hao, J., Ni, J., Li, R., Qiao,
Y., Yang, C., Ma, W., Wen, A., and Ying, X.: Improving the Noah-MP model for
simulating hydrothermal regime of the active layer in the permafrost regions
of the Qinghai-Tibet Plateau, J. Geophys. Res.-Atmos., 125,
e2020JD032588, https://doi.org/10.1029/2020JD032588, 2020.
Liang, J., Yang, Z., and Lin, P.: Systematic hydrological evaluation of the
Noah-MP land surface model over China, Adv. Atmos. Sci., 36, 1171–1187, https://doi.org/10.1007/s00376-019-9016-y,
2019.
Liang, X. and Xie, Z.: Important factors in land–atmosphere interactions:
surface runoff generations and interactions between surface and groundwater,
Global Planet. Change, 38, 101–114, https://doi.org/10.1016/S0921-8181(03)00012-2, 2003.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94JD00483,
1994.
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F.,
Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T., Gochis,
D., Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.:
Continental-scale convection-permitting modeling of the current and future
climate of North America, Clim. Dynam., 49, 71–95, https://doi.org/10.1007/s00382-016-3327-9, 2017.
Liu, X., Chen, F., Barlage, M., Zhou, G., and Niyogi, D.: Noah-MP-Crop:
Introducing dynamic crop growth in the Noah-MP land surface model, J.
Geophys. Res.-Atmos., 121, 13953–13972,
https://doi.org/10.1002/2016JD025597, 2016.
McDaniel, R., Liu, Y., Valayamkunnath, P., Barlage, M., Gochis, D.,
Cosgrove, B. A., and Flowers, T.: Moisture condition impact and seasonality
of National Water Model performance under different runoff-infiltration
partitioning schemes, in: AGU Fall Meeting Abstracts, Vol. 2020, 2020AGUFMH111.0028M,
2020.
Miguez-Macho, G., Fan, Y., Weaver, C. P., Walko, R., and Robock, A.:
Incorporating water table dynamics in climate modeling: 2. Formulation,
validation, and soil moisture simulation, J. Geophys. Res.-Atmos., 112, D13108,
https://doi.org/10.1029/2006JD008112,
2007.
Nie, W., Kumar, S. V., Arsenault, K. R., Peters-Lidard, C. D., Mladenova, I. E., Bergaoui, K., Hazra, A., Zaitchik, B. F., Mahanama, S. P., McDonnell, R., Mocko, D. M., and Navari, M.: Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco, Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, 2022.
Niu, G.-Y. and Yang, Z.-L.: The effects of canopy processes on snow surface
energy and mass balances, J. Geophys. Res.-Atmos., 109, D23111,
https://doi.org/10.1029/2004JD004884, 2004.
Niu, G.-Y. and Yang, Z.-L.: Effects of frozen soil on snowmelt runoff and
soil water storage at a continental scale, J. Hydrometeorol., 7, 937–952,
https://doi.org/10.1175/JHM538.1, 2006.
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.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E., and Su, H.:
Development of a simple groundwater model for use in climate models and
evaluation with Gravity Recovery and Climate Experiment data, J. Geophys.
Res.-Atmos., 112, D07103, https://doi.org/10.1029/2006JD007522, 2007.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res.-Atmos., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011.
Niu, G. Y., Fang, Y. H., Chang, L. L., Jin, J., Yuan, H., and Zeng, X.:
Enhancing the Noah-MP ecosystem response to droughts with an explicit
representation of plant water storage supplied by dynamic root water
uptake, J. Adv. Model. Earth Sy., 12, e2020MS002062, https://doi.org/10.1029/2020MS002062, 2020.
Oleson, K., Dai, Y., Bonan, B., Bosilovichm, M., Dickinson, R., Dirmeyer,
P., Hoffman, F., Houser, P., Levis, S., Niu, G. Y., Thornton, P.,
Vertenstein, M., Yang, Z. L., and Zeng, X.: Technical description of the
Community Land Model (CLM), NCAR Tech. Note, NCAR/TN-461+STR,
Natl. Cent. for Atmos. Res., Boulder, Colo., 174 pp., https://doi.org/10.5065/D6N877R0, 2004.
Patel, P., Jamshidi, S., Nadimpalli, R., Aliaga, D. G., Mills, G., Chen, F.,
Demuzere, M., and Niyogi, D.: Modeling Large-Scale Heatwave by Incorporating
Enhanced Urban Representation, J. Geophys. Res.-Atmos., 127,
e2021JD035316, https://doi.org/10.1029/2021JD035316, 2022.
Rasmussen, R., Chen, F., Liu, C., Ikeda, K., Prein, A., Kim, J.-H., Schneider, T., Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C., Harrold, M., Xue, L., Chen, S., Newman, A., Dougherty, E., Abolafia-Rosenzweig, R., Lybarger, N., Viger, R., Lesmes, D. P., Skalak, K., Brakebill, J. W., Clline, D., Dunne, K., Rasmussen, K., and Miguez-Macho, G.: CONUS404: The NCAR-USGS 4-km long-term regional hydroclimate reanalysis over the CONUS, B. Am. Meteorol. Soc., E1382–E1408, https://doi.org/10.1175/BAMS-D-21-0326.1, 2023.
Sakaguchi, K. and Zeng, X.: Effects of soil wetness, plant litter, and
under-canopy atmospheric stability on ground evaporation in the Community
Land Model (CLM3. 5), J. Geophys. Res.-Atmos., 114, D01107, https://doi.org/10.1029/2008JD010834, 2009.
Salamanca, F., Zhang, Y., Barlage, M., Chen, F., Mahalov, A., and Miao, S.:
Evaluation of the WRF-urban modeling system coupled to Noah and Noah-MP land
surface models over a semiarid urban environment, J. Geophys.
Res.-Atmos., 123, 2387–2408, https://doi.org/10.1002/2018JD028377, 2018.
Saxton, K. E. and Rawls, W. J.: Soil water characteristic estimates by
texture and organic matter for hydrologic solutions, Soil Sci. Soc. Am.
J., 70, 1569–1578, https://doi.org/10.2136/sssaj2005.0117, 2006.
Sellers, P. J.: Canopy reflectance, photosynthesis and transpiration, Int.
J. Remote Sens., 6, 1335–1372, https://doi.org/10.1080/01431168508948283, 1985.
Sellers, P. J., Heiser, M. D., and Hall, F. G.: Relations between surface
conductance and spectral vegetation indices at intermediate (100 m2 to 15
km2) length scales, J. Geophys. Res.-Atmos., 97, 19033–19059,
https://doi.org/10.1029/92JD01096, 1992.
Schaake, J. C., Koren, V. I., Duan, Q. Y., Mitchell, K., and Chen, F.: Simple
water balance model for estimating runoff at different spatial and temporal
scales, J. Geophys. Res.-Atmos., 101, 7461–7475, https://doi.org/10.1029/95JD02892,
1996.
Shu, Z., Zhang, B., Tian, L., and Zhao, X.: Improving Dynamic Vegetation
Modeling in Noah-MP by Parameter Optimization and Data Assimilation Over
China's Loess Plateau, J. Geophys. Res.-Atmos., 127, e2022JD036703, https://doi.org/10.1029/2022JD036703,
2022.
Smith, B. J.: Campaign Storage file system, https://arc.ucar.edu/knowledge_base/70549621 (last access: 4 September 2023), 2023.
Suzuki, K. and Zupanski, M.: Uncertainty in solid precipitation and snow
depth prediction for Siberia using the Noah and Noah-MP land surface
models, Front. Earth Sci., 12, 672–682, https://doi.org/10.1007/s11707-018-0691-2, 2018.
Valayamkunnath, P., Chen, F., Barlage, M. J., Gochis, D. J., Franz, K. J.,
and Cosgrove, B. A.: Impact of Agriculture Management Practices on the
National Water Model Simulated Streamflow, in: 101st Am. Meteorol. Soc.
Annual Meeting, https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/383317 (last access: 4 September 2023), 2021.
Valayamkunnath, P., Gochis, D. J., Chen, F., Barlage, M., and Franz, K. J.:
Modeling the hydrologic influence of subsurface tile drainage using the
National Water Model, Water Resour. Res., 58, e2021WR031242, https://doi.org/10.1029/2021WR031242, 2022.
Verseghy, D. L.: CLASS-A Canadian land surface scheme for GCMS: I. Soil
model, Int. J. Climatol., 11, 111–133, https://doi.org/10.1002/joc.3370110202, 1991.
Wang, P., Niu, G. Y., Fang, Y. H., Wu, R. J., Yu, J. J., Yuan, G. F.,
Pozdniakov, S. P., and Scott, R. L.: Implementing dynamic root optimization
in Noah-MP for simulating phreatophytic root water uptake, Water Resour.
Res., 54, 1560–1575, https://doi.org/10.1002/2017WR021061, 2018.
Wang, W., Yang, K., Zhao, L., Zheng, Z., Lu, H., Mamtimin, A., Ding, B., Li,
X., Zhao, L., Li, H., Che, T., and Moore, J. C.: Characterizing surface
albedo of shallow fresh snow and its importance for snow ablation on the
interior of the Tibetan Plateau, J. Hydrometeorol., 21, 815–827, https://doi.org/10.1175/JHM-D-19-0193.1, 2020.
Wang, W., He, C., Moore, J., Wang, G., and Niu, G. Y.: Physics-Based
Narrowband Optical Parameters for Snow Albedo Simulation in Climate
Models, J. Adv. Model. Earth Syst., 14, e2020MS002431, https://doi.org/10.1029/2020MS002431, 2022.
Wang, Y. H., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., and
Niu, G. Y.: A wet-bulb temperature-based rain-snow partitioning scheme
improves snowpack prediction over the drier western United States, Geophys.
Res. Lett., 46, 13825–13835, https://doi.org/10.1029/2019GL085722, 2019.
Warrach-Sagi, K., Ingwersen, J., Schwitalla, T., Troost, C., Aurbacher, J.,
Jach, L., Berger, T., Streck, T., and Wulfmeyer, V.: Noah-MP with the generic
crop growth model Gecros in the WRF model: Effects of dynamic crop growth on
land-atmosphere interaction, J. Geophys. Res.-Atmos., 127, e2022JD036518,
https://doi.org/10.1029/2022JD036518, 2022.
Wrzesien, M. L., Pavelsky, T. M., Kapnick, S. B., Durand, M. T., and
Painter, T. H.: Evaluation of snow cover fraction for regional climate
simulations in the Sierra Nevada, Int. J. Climatol., 35, 2472–2484, https://doi.org/10.1002/joc.4136, 2015.
Wu, W. Y., Yang, Z. L., and Barlage, M.: The Impact of Noah-MP Physical
Parameterizations on Modeling Water Availability during Droughts in the
Texas–Gulf Region, J. Hydrometeorol., 22, 1221–1233, https://doi.org/10.1175/JHM-D-20-0189.1, 2021.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo,
L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V.,
Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System project phase 2 (NLDAS-2): 1. Intercomparison and application of
model products, J. Geophys. Res.-Atmos., 117, D03109,
https://doi.org/10.1029/2011JD016048, 2012.
Xu, T., Chen, F., He, X., Barlage, M., Zhang, Z., Liu, S., and He, X.:
Improve the performance of the noah-MP-crop model by jointly assimilating
soil moisture and vegetation phenology data, J. Adv. Model. Earth
Sy., 13, e2020MS002394, https://doi.org/10.1029/2020MS002394, 2021.
Xu, X., Chen, F., Shen, S., Miao, S., Barlage, M., Guo, W., and Mahalov, A.:
Using WRF-urban to assess summertime air conditioning electric loads and
their impacts on urban weather in Beijing, J. Geophys. Res.-Atmos., 123,
2475–2490, https://doi.org/10.1002/ 2017JD028168, 2018.
Xue, Y., Sellers, P. J., Kinter, J. L., and Shukla, J.: A simplified
biosphere model for global climate studies, J. Climate, 4, 345–364,
https://doi.org/10.1175/1520-0442(1991)004<0345:ASBMFG>2.0.CO;2,
1991.
Yang, Z.-L. and Dickinson, R. E.: Description of the Biosphere- Atmosphere
Transfer Scheme (BATS) for the soil moisture workshop and evaluation of its
performance, Global Planet. Change, 13, 117–134,
https://doi.org/10.1016/0921-8181(95)00041-0, 1996.
Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 2. Evaluation over global river basins, J. Geophys.
Res.-Atmos., 116, D12110,
https://doi.org/10.1029/2010JD015140, 2011.
Yen, Y. C.: Effective thermal conductivity and water vapor diffusivity of
naturally compacted snow, J. Geophys. Res.-Atmos., 70, 1821–1825, https://doi.org/10.1029/JZ070i008p01821, 1965.
Yen, Y. C.: Review of thermal properties of snow, ice, and sea ice, Vol. 81,
No. 10, US Army Corps of Engineers, Cold Regions Research and Engineering
Laboratory, 1981.
Zhang, G., Chen, F., and Gan, Y.: Assessing uncertainties in the Noah-MP
ensemble simulations of a cropland site during the Tibet Joint International
Cooperation program field campaign, J. Geophys. Res.-Atmos., 121,
9576–9596, https://doi.org/10.1002/2016JD024928, 2016.
Zhang, X., Xie, Z., Ma, Z., Barron-Gafford, G. A., Scott, R. L., and Niu, G.
Y.: A Microbial-Explicit Soil Organic Carbon Decomposition Model (MESDM):
Development and Testing at a Semiarid Grassland Site, J. Adv. Model. Earth
Sy., 14, e2021MS002485, https://doi.org/10.1029/2021MS002485, 2022.
Zhang, X. Y., Jin, J., Zeng, X., Hawkins, C. P., Neto, A. A., and Niu, G.
Y.: The compensatory CO2 fertilization and stomatal closure effects on
runoff projection from 2016–2099 in the western United States, Water
Resour. Res., 58, e2021WR030046, https://doi.org/10.1029/2021WR030046, 2022.
Zhang, Z., Barlage, M., Chen, F., Li, Y., Helgason, W., Xu, X., Liu, X., and
Li, Z.: Joint modeling of crop and irrigation in the central United States
using the Noah-MP land surface model, J. Adv. Model. Earth Sy., 12,
e2020MS002159, https://doi.org/10.1029/2020MS002159, 2020.
Zhang, Z., Chen, F., Barlage, M., Bortolotti, L. E., Famiglietti, J., Li,
Z., Ma, X. and Li, Y.: Cooling Effects Revealed by Modeling of Wetlands and
Land-Atmosphere Interactions, Water Resour. Res., 58, e2021WR030573, https://doi.org/10.1029/2021WR030573,
2022.
Zhang, Z., Li, Y., Chen, F., Harder, P., Helgason, W., Famiglietti, J., Valayamkunnath, P., He, C., and Li, Z.: Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress , Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, 2023.
Zhuo, L., Dai, Q., Han, D., Chen, N., and Zhao, B.: Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application, Hydrol. Earth Syst. Sci., 23, 4199–4218, https://doi.org/10.5194/hess-23-4199-2019, 2019.
Zonato, A., Martilli, A., Gutierrez, E., Chen, F., He, C., Barlage, M.,
Zardi, D., and Giovannini, L.: Exploring the effects of rooftop mitigation
strategies on urban temperatures and energy consumption, J. Geophys.
Res.-Atmos., 126, e2021JD035002, https://doi.org/10.1029/2021JD035002, 2021.
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Noah-MP is one of the most widely used open-source community land surface models in the world,...
