Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-2197-2026
© Author(s) 2026. 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-19-2197-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
17 Mar 2026
Model evaluation paper |
| 17 Mar 2026
| 17 Mar 2026
Assessing and enhancing Noah-MP land surface modeling over tropical forests using machine learning techniques
Asian School of the Environment, Nanyang Technological University, Singapore, Singapore
Centre for Climate Research Singapore, Singapore, Singapore
Yaomin Wang
Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore
Kalli Furtado
Centre for Climate Research Singapore, Singapore, Singapore
Cenlin He
NSF National Center for Atmospheric Research, Boulder, Colorado, USA
Fei Chen
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong SAR, China
Alan D. Ziegler
Andaman Coastal Research Station for Development, Faculty of Fisheries, Kasetsart University, Ranong, Thailand
Song Chen
Centre for Climate Research Singapore, Singapore, Singapore
Matteo Detto
Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
Smithsonian Tropical Research Institute, Panama City, Panama
Yuna Mao
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Baoxiang Pan
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Yoshiko Kosugi
Laboratory of Forest Hydrology, Division of Forest and Biomaterials Science, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
Marryanna Lion
Forest Research Institute Malaysia, Kepong, Selangor, Malaysia
Shoji Noguchi
Tohoku Research Center, Forestry and Forest Products Research Institute, Iwate, Japan
Satoru Takanashi
Kansai Research Center, Forestry and Forest Products Research Institute, Kyoto, Japan
Lulie Melling
UN Sustainable Development Solutions Network, Asia Headquarters, Sunway University, Bandar Sunway, Selangor, Malaysia
Baoqing Zhang
Key Laboratory of West China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, China
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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.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
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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.
Bo Qu, Alexandre Roy, Joe R. Melton, Jennifer L. Baltzer, Youngryel Ryu, Matteo Detto, and Oliver Sonnentag
EGUsphere, https://doi.org/10.5194/egusphere-2023-1167, https://doi.org/10.5194/egusphere-2023-1167, 2023
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Accurately simulating photosynthesis and evapotranspiration challenges terrestrial biosphere models across North America’s boreal biome, in part due to uncertain representation of the maximum rate of photosynthetic carboxylation (Vcmax). This study used forest stand scale observations in an optimization framework to improve Vcmax values for representative vegetation types. Several stand characteristics well explained spatial Vcmax variability and were useful to improve boreal forest modelling.
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.
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.
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.
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.
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.
Lei Tian, Baoqing Zhang, and Pute Wu
Earth Syst. Sci. Data, 14, 2259–2278, https://doi.org/10.5194/essd-14-2259-2022, https://doi.org/10.5194/essd-14-2259-2022, 2022
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We propose a global monthly drought dataset with a resolution of 0.25° from 1948 to 2010 based on a multitype and multiscalar drought index, the standardized moisture anomaly index adding snow processes (SZIsnow). The consideration of snow processes improved its capability, and the improvement is prominent over snow-covered high-latitude and high-altitude areas. This new dataset is well suited to monitoring, assessing, and characterizing drought and is a valuable resource for drought studies.
Jie Tian, Baoqing Zhang, Xuejin Wang, and Chansheng He
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-21, https://doi.org/10.5194/essd-2022-21, 2022
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Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 soil moisture (SM) stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we compiled a soil hydraulic properties (SHP) and SM dataset for a mountainous area, Northeastern QTP. The dataset provides information about SHP variability and SM trends at a large-scale mountainous area, thus offers opportunity for further understanding of water and energy processes over the QTP.
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.
Eva Sinha, Kate Calvin, Ben Bond-Lamberty, Beth Drewniak, Dan Ricciuto, Khachik Sargsyan, Yanyan Cheng, Carl Bernacchi, and Caitlin Moore
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-244, https://doi.org/10.5194/gmd-2021-244, 2021
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Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Model (E3SM) Land Model (ELM) to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion.
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Short summary
Tropical land surface processes shape the Earth's climate, but models often lack accuracy in the tropics due to limited data for validation. We improved the Noah with Multi-Parameterizations (Noah-MP) land surface model for the tropics using data from forests in Panama and Malaysia. Calibration enhanced simulations of energy and water fluxes, and revealed key vegetation and soil parameters, as well as future directions for model improvement in tropical regions.
Tropical land surface processes shape the Earth's climate, but models often lack accuracy in the...
