Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3755-2025
© Author(s) 2025. 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-18-3755-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Jun 2025
Development and technical paper |
| 23 Jun 2025
| 23 Jun 2025
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Carolina A. Bieri
Department of Climate, Meteorology, and Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Francina Dominguez
CORRESPONDING AUTHOR
Department of Climate, Meteorology, and Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Gonzalo Miguez-Macho
Nonlinear Physics Group, Faculty of Physics, Universidade de Santiago de Compostela, Santiago de Compostela, Galicia, Spain
Ying Fan
Department of Earth and Planetary Sciences, Rutgers University, New Brunswick, New Jersey, USA
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Marc Lemus-Canovas, Sergi Gonzalez-Herrero, Laura Trapero, Anna Albalat, Damian Insua-Costa, Martin Senande-Rivera, and Gonzalo Miguez-Macho
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-192, https://doi.org/10.5194/nhess-2024-192, 2024
Revised manuscript accepted for NHESS
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This study explores the 2022 heatwaves in the Pyrenees, examining the factors that contributed to their intensity and distribution. The June event was driven by strong winds that created uneven temperature patterns, while the July heatwave featured calmer conditions and more uniform temperatures. Human-driven climate change has made these heatwaves more severe compared to the past. This research helps us better understand how climate change affects extreme weather in mountainous regions.
Alfredo Crespo-Otero, Damián Insua-Costa, Emilio Hernández-García, Cristóbal López, and Gonzalo Míguez-Macho
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-18, https://doi.org/10.5194/esd-2024-18, 2024
Revised manuscript under review for ESD
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We evaluated two Lagrangian moisture tracking tools, WaterSip and UTrack, and compared them against the WRF model with Water Vapor Tracers. Our results show that UTrack, which relies on evaporation and precipitable water data, has a slightly better agreement with WRF-WVTs than WaterSip, based on specific humidity data. Implementing simple physics-based changes substantially improved both methodologies, reducing discrepancies by about 50 % and narrowing the the disparities among all approaches.
Xavier Fonseca, Gonzalo Miguez-Macho, José A. Cortes-Vazquez, and Antonio Vaamonde
Geosci. Commun., 5, 177–188, https://doi.org/10.5194/gc-5-177-2022, https://doi.org/10.5194/gc-5-177-2022, 2022
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In this paper, we discuss the instrumental role of the press in informing and educating the public on the subject of climate science and climate change. We illustrate this using an example of a dissemination format called Weather Stories, published daily in one of the most read newspapers in Spain. The particularities of this journalistic format are described using a practical example of a relatively complex physical concept: the jet stream.
Sara Cloux, Daniel Garaboa-Paz, Damián Insua-Costa, Gonzalo Miguez-Macho, and Vicente Pérez-Muñuzuri
Hydrol. Earth Syst. Sci., 25, 6465–6477, https://doi.org/10.5194/hess-25-6465-2021, https://doi.org/10.5194/hess-25-6465-2021, 2021
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We examine the performance of a widely used Lagrangian method for moisture tracking by comparing it with a highly accurate Eulerian tool, both operating on the same WRF atmospheric model fields. Although the Lagrangian approach is very useful for a qualitative analysis of moisture sources, it has important limitations in quantifying the contribution of individual sources to precipitation. These drawbacks should be considered by other authors in the future so as to not draw erroneous conclusions.
Breogán Gómez and Gonzalo Miguez-Macho
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2020-71, https://doi.org/10.5194/esd-2020-71, 2020
Publication in ESD not foreseen
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Spectral nudging imposes the large scale fields from a global model into a regional model. We study which are the best scales on a tropical setting and how long is needed to run the model before it is in balance with the nudging force. Optimal results are obtained when nudging is applied in the Rossby Radius scales for at least 72 h to 96 h. We also propose a new method where a different scale is used for each nudged variable, which bests other configurations when applied in 4 hurricanes cases.
Caspar T. J. Roebroek, Lieke A. Melsen, Anne J. Hoek van Dijke, Ying Fan, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 24, 4625–4639, https://doi.org/10.5194/hess-24-4625-2020, https://doi.org/10.5194/hess-24-4625-2020, 2020
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Vegetation is a principal component in the Earth system models that are used for weather, climate and other environmental predictions. Water is one of the main drivers of vegetation; however, the global distribution of how water influences vegetation is not well understood. This study looks at spatial patterns of photosynthesis and water sources (rain and groundwater) to obtain a first understanding of water access and limitations for the growth of global forests (proxy for natural vegetation).
Elham Rouholahnejad Freund, Ying Fan, and James W. Kirchner
Hydrol. Earth Syst. Sci., 24, 1927–1938, https://doi.org/10.5194/hess-24-1927-2020, https://doi.org/10.5194/hess-24-1927-2020, 2020
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Evapotranspiration (ET) rates and properties that regulate them are spatially heterogeneous. Averaging over spatial heterogeneity in precipitation (P) and potential evapotranspiration (PET) as the main drivers of ET may lead to biased estimates of energy and water fluxes from the land to the atmosphere. We show that this bias is largest in mountainous terrains, in regions with temperate climates and dry summers, and in landscapes where spatial variations in P and PET are inversely correlated.
Zhe Zhang, Yanping Li, Michael Barlage, Fei Chen, Gonzalo Miguez-Macho, Andrew Ireson, and Zhenhua Li
Hydrol. Earth Syst. Sci., 24, 655–672, https://doi.org/10.5194/hess-24-655-2020, https://doi.org/10.5194/hess-24-655-2020, 2020
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The groundwater regime in cold regions is strongly impacted by the soil freeze–thaw processes and semiarid climatic conditions. In this paper, we incorporate groundwater dynamics in the Noah-MP land surface model to simulate the water exchange between the unsaturated soil zone and an unconfined aquifer in the Prairie Pothole Region. The water table dynamics are reasonably simulated. The water budget of groundwater aquifer under current and future climate are also investigated.
Alberto Martínez-de la Torre and Gonzalo Miguez-Macho
Hydrol. Earth Syst. Sci., 23, 4909–4932, https://doi.org/10.5194/hess-23-4909-2019, https://doi.org/10.5194/hess-23-4909-2019, 2019
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Over semi-arid regions, it is essential to have a correct representation of the groundwater processes in climate modelling. We present a land surface and groundwater model that incorporates groundwater–soil interactions, groundwater–rivers flow and lateral transport at the subsurface. We study the groundwater influence on soil moisture distribution and memory, and on evapotranspiration in the Iberian Peninsula. Shallow water table regions persist and provide water to the surface during droughts.
Damián Insua-Costa, Gonzalo Miguez-Macho, and María Carmen Llasat
Hydrol. Earth Syst. Sci., 23, 3885–3900, https://doi.org/10.5194/hess-23-3885-2019, https://doi.org/10.5194/hess-23-3885-2019, 2019
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Here, we study the main moisture sources of the two famous western Mediterranean flood events of autumn 1982 (October and November). Results confirm the hypothesis that a large amount of precipitable water was involved, which was to a great extent advected from the tropics and subtropics. This remote moisture transport occurred at medium levels of the atmosphere via moisture plumes or atmospheric rivers. During the October event the contribution of local sources was also important.
Miguel A. Prósper, Ian Sosa Tinoco, Carlos Otero-Casal, and Gonzalo Miguez-Macho
Earth Syst. Dynam., 10, 485–499, https://doi.org/10.5194/esd-10-485-2019, https://doi.org/10.5194/esd-10-485-2019, 2019
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We study the fine-scale structure of Tehuano winds in the Isthmus of Tehuantepec, focusing on the flow beyond the well-known strong gap wind jet. We use high-resolution WRF model simulations to show that different downslope windstorm conditions and hydraulic jumps with rotor circulations develop in the mountains east of Chivela Pass depending on crest height and thermodynamic conditions of the air mass. The intense turbulent flows can have a large impact on the existent wind farms in the region.
Suyog Chaudhari, Yadu Pokhrel, Emilio Moran, and Gonzalo Miguez-Macho
Hydrol. Earth Syst. Sci., 23, 2841–2862, https://doi.org/10.5194/hess-23-2841-2019, https://doi.org/10.5194/hess-23-2841-2019, 2019
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Comprehensive characterization of extreme drought events in the Amazon is provided with respect to their cause, type, spatial extent, and impact on different water stores. Basin-averaged trends in water storage indicate that the Amazon is getting wetter; however its southern and southeastern portions are getting drier. Water deficit is found to be 3-fold higher than the total water supplied during some drought years. Water deficit due to low precipitation events is absorbed by the groundwater.
Iago Algarra, Jorge Eiras-Barca, Gonzalo Miguez-Macho, Raquel Nieto, and Luis Gimeno
Earth Syst. Dynam., 10, 107–119, https://doi.org/10.5194/esd-10-107-2019, https://doi.org/10.5194/esd-10-107-2019, 2019
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We analyse moisture transport triggered by the Great Plains low-level jet (GPLLJ), a maximum in wind speed fields located within the first kilometre of the US Great Plain's troposphere, through the innovative Eulerian Weather Research and Forecasting Model tracer tool. Much moisture associated with this low-level jet has been found in northern regions located in a vast extension of the continent, highlighting the key role played by the GPLLJ in North America's advective transport of moisture.
Rogier Westerhoff, Paul White, and Gonzalo Miguez-Macho
Hydrol. Earth Syst. Sci., 22, 6449–6472, https://doi.org/10.5194/hess-22-6449-2018, https://doi.org/10.5194/hess-22-6449-2018, 2018
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Our study improved a global-scale groundwater model to build the first nationwide estimate of the water table surface in New Zealand. By identifying the main alluvial aquifers with high spatial detail, we showed that this model can help better delineate aquifer boundaries. In catchment studies we demonstrated excellent correlation with ground observations and provided water table estimates where data were sparse and across regions, which could help solve trans-boundary issues between catchments.
Jorge Eiras-Barca, Nieves Lorenzo, Juan Taboada, Alba Robles, and Gonzalo Miguez-Macho
Nat. Hazards Earth Syst. Sci., 18, 1633–1645, https://doi.org/10.5194/nhess-18-1633-2018, https://doi.org/10.5194/nhess-18-1633-2018, 2018
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This paper analyzes the connection between the so-called atmospheric rivers (ARs, long and narrow structures of anomalously high water vapor flux located in the warm sector of extratropical cyclones) and floods in the northwestern region of the Iberian Peninsula through the use of the
weather typesclassification adopting the subjective procedure of Lamb.
Francina Dominguez, Sandy Dall'erba, Shuyi Huang, Andre Avelino, Ali Mehran, Huancui Hu, Arthur Schmidt, Lawrence Schick, and Dennis Lettenmaier
Earth Syst. Dynam., 9, 249–266, https://doi.org/10.5194/esd-9-249-2018, https://doi.org/10.5194/esd-9-249-2018, 2018
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Atmospheric rivers (ARs) account for most of the extreme flooding events on the northwestern coast of the US. In a warmer climate, ARs in this region are projected to become more frequent and intense. We present an integrated modeling system to quantify atmospheric–hydrologic–hydraulic and economic impacts of an AR event in western Washington. Our integrated modeling tool provides communities in the region with a range of possible future physical and economic impacts associated with AR flooding.
Damián Insua-Costa and Gonzalo Miguez-Macho
Earth Syst. Dynam., 9, 167–185, https://doi.org/10.5194/esd-9-167-2018, https://doi.org/10.5194/esd-9-167-2018, 2018
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We present here a newly implemented water vapor tracer tool into the WRF meteorological model (WRF-WVT). A detailed validation shows high accuracy, with an error of much less than 1 % in moisture traceability. As an example application, we show that for the 2014 Great Lake-effect snowstorm, above 30 % of precipitation in the regions immediately downwind originated from lake evaporation, with contributions exceeding 50 % in the areas with highest snowfall accumulations.
Jorge Eiras-Barca, Alexandre M. Ramos, Joaquim G. Pinto, Ricardo M. Trigo, Margarida L. R. Liberato, and Gonzalo Miguez-Macho
Earth Syst. Dynam., 9, 91–102, https://doi.org/10.5194/esd-9-91-2018, https://doi.org/10.5194/esd-9-91-2018, 2018
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This paper analyses the potential role of atmospheric rivers in the explosive cyclone deepening. Using ERA-Interim reanalysis data for 1979–2011, we analyse the concurrence of atmospheric rivers and explosive cyclogenesis over the North Atlantic and North Pacific basins for the extended winter months (ONDJFM).
Jorge Eiras-Barca, Francina Dominguez, Huancui Hu, Daniel Garaboa-Paz, and Gonzalo Miguez-Macho
Earth Syst. Dynam., 8, 1247–1261, https://doi.org/10.5194/esd-8-1247-2017, https://doi.org/10.5194/esd-8-1247-2017, 2017
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This paper analyzes the origin of the moisture in two extremely important atmospheric river (and extreme precipitation) events. The distribution of the moisture with regard to the low-level jet is analyzed as well, and the classic association of the atmospheric river to the former is discussed.
Pere Quintana-Seguí, Marco Turco, Sixto Herrera, and Gonzalo Miguez-Macho
Hydrol. Earth Syst. Sci., 21, 2187–2201, https://doi.org/10.5194/hess-21-2187-2017, https://doi.org/10.5194/hess-21-2187-2017, 2017
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The quality of two high-resolution precipitation datasets for Spain at the daily time scale is reported: the new SAFRAN-based dataset and Spain02. ERA-Interim is also included. The precipitation products are compared with observations. SAFRAN and Spain02 have very similar scores, and they perform better than ERA-Interim. The high-resolution gridded products overestimate the number of precipitation days. Both SAFRAN and Spain02 underestimate high precipitation events.
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Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
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This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
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Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
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We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
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In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
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Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
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We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
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To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Hui Wan, Abhishek Yenpure, Berk Geveci, Richard C. Easter, Philip J. Rasch, Kai Zhang, and Xubin Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2024-4020, https://doi.org/10.5194/egusphere-2024-4020, 2025
Short summary
Short summary
In E3SMv2 and many other global climate models, the simulated anthropogenic aerosol influence on the Earth's energy balance is sensitive to the presence of clouds with very low droplet number concentrations. Numerical experiments conducted in this study suggest that mid- and high-latitude low-level stratus occurring under weak turbulence is an important cloud regime for understanding the causes of very low cloud droplet number concentrations in global climate simulations.
Cited articles
AmazonBasinLimits: GitHub [data set], https://github.com/gamamo/AmazonBasinLimits (last access: 20 July 2024), 2017. a
Amenu, G. G., Kumar, P., and Liang, X.-Z.: Interannual variability of deep-layer hydrologic memory and mechanisms of its influence on surface energy fluxes, J. Climate, 18, 5024–5045, https://doi.org/10.1175/JCLI3590.1, 2005. a
Baker, I. T., Prihodko, L., Denning, A. S., Goulden, M., Miller, S., and da Rocha, H. R.: Seasonal drought stress in the amazon: Reconciling models and observations, J. Geophys. Res.-Biogeo., 113, G00B01, https://doi.org/10.1029/2007JG000644, 2009. a, b
Baker, J. C., Garcia-Carreras, L., Gloor, M., Marsham, J. H., Buermann, W., da Rocha, H. R., Nobre, A. D., de Araujo, A. C., and Spracklen, D. V.: Evapotranspiration in the Amazon: spatial patterns, seasonality, and recent trends in observations, reanalysis, and climate models, Hydrol. Earth Syst. Sci., 25, 2279–2300, https://doi.org/10.5194/hess-25-2279-2021, 2021. a, b
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: Progress in photosynthesis research: Volume 4 Proceedings of the VIIth International Congress on Photosynthesis, Providence, 10–15 August 1986, Rhode Island, USA, Springer, 221–224, https://doi.org/10.1007/978-94-017-0519-6_48, 1987. a
Balsamo, G., Pappenberger, F., Dutra, E., Viterbo, P., and Van den Hurk, B.: A revised land hydrology in the ECMWF model: A step towards daily water flux prediction in a fully-closed water cycle, Hydrol. Process., 25, 1046–1054, https://doi.org/10.1002/hyp.7808, 2011. a
Bao, J., Stevens, B., Kluft, L., and Muller, C.: Intensification of daily tropical precipitation extremes from more organized convection, Sci. Adv., 10, eadj6801, https://doi.org/10.1126/sciadv.adj6801, 2024. a
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. a, b
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. a, b, c
Batelis, S.-C., Rahman, M., Kollet, S., Woods, R., and Rosolem, R.: Towards the representation of groundwater in the Joint UK Land Environment Simulator, Hydrol. Process., 34, 2843–2863, https://doi.org/10.1002/hyp.13767, 2020. a
Beaudoing, H., Rodell, M., and NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 3 hourly 0.25×0.25 degree, Version 2.1, Goddard Earth Sciences Data and Information Services Center [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020. a, b, c
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bieri, C. A., Dominguez, F., Miguez-Macho, G., and Fan, Y.: Model configuration files and forcing data for Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration, Zenodo [data set], https://doi.org/10.5281/zenodo.13061969, 2024. a
Bieri, C. A., Dominguez, F., Miguez-Macho, G., and Fan, Y.: DynaRoot Noah-MP, Zenodo [code], https://doi.org/10.5281/zenodo.13137184, 2025a. a
Bieri, C. A., Dominguez, F., Miguez-Macho, G., and Fan, Y.: Analysis scripts for Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration, Zenodo [code], https://doi.org/10.5281/zenodo.13137807, 2025b. a
Bieri, C. A., Dominguez, F., Miguez-Macho, G., and Fan, Y.: Data for Implementing Deep Soil and Dynamic Root Uptake in Noah-MP (v4.5): Impact on Amazon Dry-Season Transpiration, University of Illinois Urbana-Champaign [data set], https://doi.org/10.13012/B2IDB-8777292_V1, 2025c. a
Bisht, G., Riley, W. J., Hammond, G. E., and Lorenzetti, D. M.: Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) version 1.0, Geosci. Model Dev., 11, 4085–4102, https://doi.org/10.5194/gmd-11-4085-2018, 2018. a
Bruno, R. D., da Rocha, H. R., De Freitas, H. C., Goulden, M. L., and Miller, S. D.: Soil moisture dynamics in an eastern Amazonian tropical forest, Hydrol. Process., 20, 2477–2489, https://doi.org/10.1002/hyp.6211, 2006. a, b, c, d
Caldwell, P. M., Terai, C. R., Hillman, B., Keen, N. D., Bogenschutz, P., Lin, W., Beydoun, H., Taylor, M., Bertagna, L., Bradley, A. M., Clevenger, T. C., Donahue, A. S., Eldred, C., Foucar, J., Golaz, J.-C., Guba, O., Jacob, R., Johnson, J., Krishna, J., Liu, W., Pressel, K., Salinger, A. G., Singh, B., Steyer, A., Ullrich, P., Wu, D., Yuan, X., Shpund, J., Ma, H.-Y., and Zender, C. S.: Convection-permitting simulations with the E3SM global atmosphere model, J. Adv. Model. Earth Syst., 13, e2021MS002544, https://doi.org/10.1029/2021MS002544, 2021. a, b
Canadell, J., Jackson, R. B., Ehleringer, J., Mooney, H. A., Sala, O. E., and Schulze, E.-D.: Maximum rooting depth of vegetation types at the global scale, Oecologia, 108, 583–595, https://doi.org/10.1007/BF00329030, 1996. a
Chen, F., Manning, K. W., Lemone, M. A., Trier, S. B., Alfieri, J. G., Roberts, R., Tewari, M., Niyogi, D., Horst, T. W., Oncley, S. P., Basara, J. B., and Blanken, P. D.: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system, J. Appl. Meteorol. Clim., 46, 694–713, https://doi.org/10.1175/JAM2463.1, 2007. a, b
Chen, S., Stark, S. C., Nobre, A. D., Cuartas, L. A., de Jesus Amore, D., Restrepo-Coupe, N., Smith, M. N., Chitra-Tarak, R. and Ko, H., Nelson, B. W., and Saleska, S. R.: Amazon forest biogeography predicts resilience and vulnerability to drought, Nature, 631, 111–117, https://doi.org/10.1038/s41586-024-07568-w, 2024. a, b
Christoffersen, B. O., Restrepo-Coupe, N., Arain, M. A., Baker, I. T., Cestaro, B. P., Ciais, P., Fisher, J. B., Galbraith, D., Guan, X., Gulden, L., van den Hurk, B., Ichii, K., Imbuzeiro, H., Jain, A., Levine, N., Miguez-Macho, G., Poulter, B., Roberti, D. R., Sakaguchi, K., Sahoo, A., Schaefer, K., Shi, M., Verbeeck, H., Yang, Z. L., Araújo, A. C., Kruijt, B., Manzi, A. O., da Rocha, H. R., von Randow, C., Muza, M. N., Borak, J., Costa, M. H., de Gonçalves, L. G. G., Zeng, X., and Saleska, S. R.: Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado, Agr. Forest Meteorol., 191, 33–50, https://doi.org/10.1016/j.agrformet.2014.02.008, 2014. a
Clapp, R. B. and Hornberger, G. M.: Empirical equations for some soil hydraulic properties, Water Resour. Res., 14, 601–604, https://doi.org/10.1029/WR014i004p00601, 1978. a, b
Computational and Information Systems Laboratory: Derecho: HPE Cray EX System (University Community Computing), https://doi.org/10.5065/qx9a-pg09, 2024. a
Cusack, D. F., Christoffersen, B., Smith-Martin, C. M., Andersen, K. M., Cordeiro, A. L., Fleischer, K., Wright, S. J., Guerrero-Ramírez, N. R., Lugli, L. F., McCulloch, L. A., Sanchez-Julia, M., Batterman, S. A., Dallstream, C., Fortunel, C., Toro, L., Fuchslueger, L., Wong, M. Y., Yaffar, D., Fisher, J. B., Arnaud, M., Dietterich, L. H., Addo-Danso, S. D., Valverde-Barrantes, O. J., Weemstra, M., Ng, J. C., and Norby, R. J.: Toward a coordinated understanding of hydro-biogeochemical root functions in tropical forests for application in vegetation models, New Phytol., 242, 351–371, https://doi.org/10.1111/nph.19561, 2024. a
Davidson, E., Lefebvre, P. A., Brando, P. M., Ray, D. M., Trumbore, S. E., Solorzano, L. A., Ferreira, J. N., Bustamante, M. M. d. C., and Nepstad, D. C.: Carbon inputs and water uptake in deep soils of an eastern Amazon forest, Forest Sci., 57, 51–58, https://doi.org/10.1093/forestscience/57.1.51, 2011. a, b, c, d
Dominguez, F., Eiras-Barca, J., Yang, Z., Bock, D., Nieto, R., and Gimeno, L.: Amazonian Moisture Recycling Revisited Using WRF With Water Vapor Tracers, J. Geophys. Res.-Atmos., 127, e2021JD035259, https://doi.org/10.1029/2021JD035259, 2022. a
Dominguez, F., Rasmussen, R., Liu, C., Ikeda, K., Prein, A., Varble, A., Arias, P. A., Bacmeister, J., Bettolli, M. L., Callaghan, P., Carvalho, L. M. V., Castro, C. L., Chen, F., Chug, D., Chun, K. P., Dai, A., Danaila, L., da Rocha, R. P., Nascimento, E. D. L., Dougherty, E., Dudhia, J., Eidhammer, T., Feng, Z., Fita, L., Fu, R., Giles, J., Gilmour, H., Halladay, K., Huang, Y., Iza Wong, A. M., Lagos-Zúñiga, M. Á., Jones, C., Llamocca, J., Llopart, M., Martinez, J. A., Martinez, J. C., Minder, J. R., Morrison, M., Moon, Z. L., Mu, Y., Neale, R. B., Núñez Ocasio, K. M., Pal, S., Potter, E., Poveda, G., Puhales, F., Rasmussen, K. L., Rehbein, A., Rios-Berrios, R., Risanto, C. B., Rosales, A., Scaff, L., Seimon, A., Somos-Valenzuela, M., Tian, Y., Van Oevelen, P., Veloso-Aguila, D., Xue, L., and Schneider, T.: Advancing South American Water and Climate Science through Multidecadal Convection-Permitting Modeling, B. Am. Meteorol. Soc., 105, E32–E44, https://doi.org/10.1175/BAMS-D-22-0226.1, 2024. a, b
Drewniak, B. A.: Simulating dynamic roots in the energy exascale earth system land model, J. Adv. Model. Earth Syst., 11, 338–359, https://doi.org/10.1029/2018MS001334, 2019. a
ECMWF: IFS Documentation CY48R1 – Part IV: Physical Processes, ECMWF, https://doi.org/10.21957/02054f0fbf, 2023. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
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. a, b, c, d
Fan, Y., Li, H., and Miguez-Macho, G.: Global patterns of groundwater table depth, Science, 339, 940–943, 2013. a
FAO/UNESCO: The FAO-UNESCO Soil Map of the World, https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ (last access: 20 July 2024), 1971. a
Feddes, R. A., Hoff, H., Bruen, M., Dawson, T., de Rosnay, P., Dirmeyer, P., Jackson, R. B., Kabat, P., Kleidon, A., Lilly, A., and Pitman, A. J.: Modeling root water uptake in hydrological and climate models, B. Am. Meteorol. Soc., 82, 2797–2810, https://doi.org/10.1175/1520-0477(2001)082<2797:MRWUIH>2.3.CO;2, 2001. a
Feng, Z., Leung, L. R., Hardin, J., Terai, C. R., Song, F., and Caldwell, P.: Mesoscale convective systems in DYAMOND global convection-permitting simulations, Geophys. Res. Lett., 50, e2022GL102603, https://doi.org/10.1029/2022GL102603, 2023. a
Fersch, B., Senatore, A., Adler, B., Arnault, J., Mauder, M., Schneider, K., Völksch, I., and Kunstmann, H.: High-resolution fully coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation, Hydrol. Earth Syst. Sci., 24, 2457–2481, https://doi.org/10.5194/hess-24-2457-2020, 2020. a
Gash, J. and Nobre, C.: Climatic effects of Amazonian deforestation: Some results from ABRACOS, B. Am. Meteorol. Soc., 78, 823–830, https://doi.org/10.1175/1520-0477(1997)078<0823:CEOADS>2.0.CO;2, 1997. a
Gayler, S., Wöhling, T., Grzeschik, M., Ingwersen, J., Wizemann, H.-D., Warrach-Sagi, K., Högy, P., Attinger, S., Streck, T., and Wulfmeyer, V.: Incorporating dynamic root growth enhances the performance of Noah-MP at two contrasting winter wheat field sites, Water Resour. Res., 50, 1337–1356, https://doi.org/10.1002/2013WR014634, 2014. a, b
Gilbert, R. O.: Statistical methods for environmental pollution monitoring, John Wiley & Sons, ISBN 978-0-471-28878-7, 1987. a
Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P.-L., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N. and Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Di Vittorio, A. V., Dang, C., Conlon, L. M., Chen, C.-C.-J., Brunke, M. A., Bisht, G., Benedict, J. J., Asay-Davis, X. S., Zhang, Y., Zhang, M., Zeng, X., Xie, S., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Reeves Eyre, J. E. J., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM Model Version 2: Overview of the physical model and initial model evaluation, J. Adv. Model. Earth Syst., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022. a
Guimberteau, M., Ducharne, A., Ciais, P., Boisier, J. P., Peng, S., De Weirdt, M., and Verbeeck, H.: Testing conceptual and physically based soil hydrology schemes against observations for the Amazon Basin, Geosci. Model Dev., 7, 1115–1136, https://doi.org/10.5194/gmd-7-1115-2014, 2014. a
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.: Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability, Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, 2023. a
Honey, M.: Mapping Tree and Shrub Cover on Pastures in Rondônia, Brazil, MS thesis, San Diego State University, https://www.proquest.com/dissertations-theses/mapping-tree-shrub-cover-on-pastures-rondonia/docview/2819930021/se-2 (last access: 15 January 2025), 2023. a
Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree × 0.1 degree V07, Goddard Earth Sciences Data and Information Services Center [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/07, 2023. a, b, c
Ivanov, V. Y., Hutyra, L. R., Wofsy, S. C., Munger, J. W., Saleska, S. R., Oliveira, R. C. D., and Camargo, P. B. D.: Root niche separation can explain avoidance of seasonal drought stress and vulnerability of overstory trees to extended drought in a mature Amazonian forest, Water Resour. Res., 48, W12507, https://doi.org/10.1029/2012WR011972, 2012. a, b
Jain, A., Yang, X., Kheshgi, H., McGuire, A. D., Post, W., and Kicklighter, D.: Nitrogen attenuation of terrestrial carbon cycle response to global environmental factors, Global Biogeochem. Cy., 23, GB4028, https://doi.org/10.1029/2009GB003519, 2009. a
Jipp, P. H., Nepstad, D. C., Cassel, D., and Reis de Carvalho, C.: Deep soil moisture storage and transpiration in forests and pastures of seasonally-dry Amazonia, Climatic Change, 39, 395–412, https://doi.org/10.1023/A:1005308930871, 1998. a, b
Joetzjer, E., Douville, H., Delire, C., and Ciais, P.: Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3, Clim. Dynam., 41, 2921–2936, https://doi.org/10.1007/s00382-012-1644-1, 2013. a
Joetzjer, E., Maignan, F., Chave, J., Goll, D., Poulter, B., Barichivich, J., Maréchaux, I., Luyssaert, S., Guimberteau, M., Naudts, K., Bonal, D., and Ciais, P.: Effect of tree demography and flexible root water uptake for modeling the carbon and water cycles of Amazonia, Ecol. Model., 469, 109969, https://doi.org/10.1016/j.ecolmodel.2022.109969, 2022. a
Jucker, M., Lane, T., Vincent, C., Webster, S., Wales, S., and Louf, V.: Locally forced convection in subkilometre-scale simulations with the Unified Model and WRF, Q. J. Roy. Meteorol. Soc., 146, 3450–3465, https://doi.org/10.1002/qj.3855, 2020. a
Kendall, M. G.: Rank correlation methods, Charles Griffin & Company, London, England, 1948. a
Kendon, E., Prein, A., Senior, C., and Stirling, A.: Challenges and outlook for convection-permitting climate modelling, Philos. T. Roy. Soc. A, 379, 20190547, https://doi.org/10.1098/rsta.2019.0547, 2021. a
Kennedy, D., Swenson, S., Oleson, K. W., Lawrence, D. M., Fisher, R., Lola da Costa, A. C., and Gentine, P.: Implementing plant hydraulics in the community land model, version 5, J. Adv. Model. Earth Syst., 11, 485–513, https://doi.org/10.1029/2018MS001500, 2019. a
Kim, Y., Knox, R. G., Longo, M., Medvigy, D., Hutyra, L. R., Pyle, E. H., Wofsy, S. C., Bras, R. L., and Moorcroft, P. R.: Seasonal carbon dynamics and water fluxes in an Amazon rainforest, Global Change Biol., 18, 1322–1334, https://doi.org/10.1111/j.1365-2486.2011.02629.x, 2012. a
Kleidon, A. and Heimann, M.: Assessing the role of deep rooted vegetation in the climate system with model simulations: mechanism, comparison to observations and implications for Amazonian deforestation, Clim. Dynam., 16, 183–199, https://doi.org/10.1007/s003820050012, 2000. a, b
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005. a
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G. Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty, J. Adv. Model. Earth Syst., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a, b
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 Syst., 13, e2020MS002214, https://doi.org/10.1029/2020MS002214, 2021. a, b, c
Lin, T.-S.: Modeling the effects of environmental and management variables on crop productivity, PhD thesis, University of Illinois at Urbana-Champaign, https://hdl.handle.net/2142/117585 (last access: 20 July 2024), 2022. a
Lin, T.-S., Kheshgi, H. S., Song, Y., Vörösmarty, C. J., and Jain, A. K.: Which crop has the highest bioethanol yield in the United States?, Front. Energ. Res., 11, 1070186, https://doi.org/10.3389/fenrg.2023.1070186, 2023. a
Liu, T., Chen, D., Yang, L., Meng, J., Wang, Z., Ludescher, J., Fan, J., Yang, S., Chen, D., Kurths, J., Chen, X., Havlin, S., and Schellnhuber, H. J.: Teleconnections among tipping elements in the Earth system, Nat. Clim. Change, 13, 67–74, https://doi.org/10.1038/s41558-022-01558-4, 2023. a
Liu, X., Chen, F., Barlage, M., and Niyogi, D.: Implementing dynamic rooting depth for improved simulation of soil moisture and land surface feedbacks in Noah-MP-Crop, J. Adv. Model. Earth Syst., 12, e2019MS001786, https://doi.org/10.1029/2019MS001786, 2020. a, b
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945. a
Markewitz, D., Devine, S., Davidson, E. A., Brando, P., and Nepstad, D. C.: Soil moisture depletion under simulated drought in the Amazon: Impacts on deep root uptake, New Phytol., 187, 592–607, https://doi.org/10.1111/j.1469-8137.2010.03391.x, 2010. a, b, c, d
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a, b, c, d
Martinez, J. A., Dominguez, F., and Miguez-Macho, G.: Effects of a groundwater scheme on the simulation of soil moisture and evapotranspiration over southern South America, J. Hydrometeorol., 17, 2941–2957, https://doi.org/10.1175/JHM-D-16-0051.1, 2016a. a, b, c, d
Martinez, J. A., Dominguez, F., and Miguez-Macho, G.: Impacts of a groundwater scheme on hydroclimatological conditions over southern South America, J Hydrometeorol., 17, 2959–2978, https://doi.org/10.1175/JHM-D-16-0052.1, 2016b. a, b, c
Miguez-Macho, G. and Fan, Y.: The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands, J. Geophys. Res.-Atmos., 117, D15113, https://doi.org/10.1029/2012JD017539, 2012. a, b
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. a, b, c, d
Milly, P. C., Malyshev, S. L., Shevliakova, E., Dunne, K. A., Findell, K. L., Gleeson, T., Liang, Z., Phillipps, P., Stouffer, R. J., and Swenson, S.: An enhanced model of land water and energy for global hydrologic and earth-system studies, J. Hydrometeorol., 15, 1739–1761, https://doi.org/10.1175/JHM-D-13-0162.1, 2014. a, b
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011. a, b
Morton, D. C., Nagol, J., Carabajal, C. C., Rosette, J., Palace, M., Cook, B. D., Vermote, E. F., Harding, D. J., and North, P. R.: Amazon forests maintain consistent canopy structure and greenness during the dry season, Nature, 506, 221–224, https://doi.org/10.1038/nature13006, 2014. a
Naudts, K., Ryder, J., McGrath, M. J., Otto, J., Chen, Y., Valade, A., Bellasen, V., Berhongaray, G., Bönisch, G., Campioli, M., Ghattas, J., De Groote, T., Haverd, V., Kattge, J., MacBean, N., Maignan, F., Merilä, P., Penuelas, J., Peylin, P., Pinty, B., Pretzsch, H., Schulze, E. D., Solyga, D., Vuichard, N., Yan, Y., and Luyssaert, S.: A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes, Geosci. Model Dev., 8, 2035–2065, https://doi.org/10.5194/gmd-8-2035-2015, 2015. a
Nepstad, D. C.: The effects of partial throughfall exclusion on canopy processes, aboveground production, and biogeochemistry of an Amazon forest, J. Geophys. Res., 107, 8085, https://doi.org/10.1029/2001JD000360, 2002. a
Nepstad, D. C., de Carvalho, C. R., Davidson, E. A., Jipp, P. H., Lefebvre, P. A., Negreiros, G. H., da Silva, E. D., Stone, T. A., Trumbore, S. E., and Vieira, S.: The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures, Nature, 372, 666–669, https://doi.org/10.1890/06-1046.1, 1994. a, b, c, d, e, f, g
Nepstad, D. C., Tohver, I. M., David, R., Moutinho, P., and Cardinot, G.: Mortality of large trees and lianas following experimental drought in an amazon forest, Ecology, 88, 2259–2269, 2007. a
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. a, b, c, d, e, f
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 Syst., 12, e2020MS002062, https://doi.org/10.1029/2020MS002062, 2020. a, b, c, d
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, P., Bozbiyik, A., Fisher, R. A., Heald, C., Kluzek, E., Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S., Ricciuto, D. M., Sacks, W., Sun Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), Technical Note No. NCAR/TN-503+STR, National Center for Atmospheric Research Earth System Laboratory, Boulder, CO, https://doi.org/10.5065/D6RR1W7M, 2013. a
Oliveira, R., Bezerra, L., Davidson, E., Pinto, F., Klink, C., Nepstad, D., and Moreira, A.: Deep root function in soil water dynamics in cerrado savannas of central Brazil, Funct. Ecol., 19, 574–581, https://doi.org/10.1111/j.1365-2435.2005.01003.x, 2005a. a
Oliveira, R. S., Dawson, T. E., Burgess, S. S., and Nepstad, D. C.: Hydraulic redistribution in three Amazonian trees, Oecologia, 145, 354–363, https://doi.org/10.1007/s00442-005-0108-2, 2005b. a
Piontekowski, V. J., Ribeiro, F. P., Matricardi, E. A. T., Lustosa Junior, I. M., Bussinguer, A. P., and Gatto, A.: Modeling deforestation in the state of Rondônia, Floresta Ambient., 26, e20180441, https://doi.org/10.1590/2179-8087.044118, 2019. a
Pitman, A.: The evolution of, and revolution in, land surface schemes designed for climate models, Int. J. Climatol., 23, 479–510, https://doi.org/10.1002/joc.893, 2003. a
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Numerical recipes in FORTRAN : the art of scientific computing, Cambridge University Press, ISBN 9780521430647, 1992. a
Qiu, H., Bisht, G., Li, L., Hao, D., and Xu, D.: Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0), Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, 2024. a
Rehbein, A., Ambrizzi, T., and Mechoso, C. R.: Mesoscale convective systems over the Amazon basin. Part I: climatological aspects, Int. J. Climatol., 38, 215–229, https://doi.org/10.1002/joc.5171, 2018. a
Reick, C. H., Gayler, V., Goll, D., Hagemann, S., Heidkamp, M., Nabel, J. E. M. S., Raddatz, T., Roeckner, E., Scnhur, R., and Wilkenskjeld, S.: JSBACH 3 – The land component of the MPI Earth System Model: documentation of version 3.2, Berichte zur Erdsystemforschung, 240, https://doi.org/10.17617/2.3279802, 2021. a
Restom, T. G. and Nepstad, D. C.: Seedling growth dynamics of a deeply rooting liana in a secondary forest in eastern Amazonia, Forest Ecol. Manage., 190, 109–118, https://doi.org/10.1016/j.foreco.2003.10.010, 2004. a
Restrepo-Coupe, N., da Rocha, H., Hutyra, L., de Araujo, A., Borma, L., Christoffersen, B., Cabral, O., de Camargo, P., Cardoso, F., Costa, A., Fitzjarrald, D., Goulden, M., Kruijt, B., Maia, J., Malhi, Y., Manzi, A., Miller, S., Nobre, A., von Randow, C., Abreu Safaj, L., Sakai, R., Tota, J., Wofsy, S., Zanchi, F., and Saleska, S.: LBA-ECO CD-32 Flux Tower Network Data Compilation, Brazilian Amazon: 1999–2006, V2, ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/1842, 2021. a
Roberts, D., Numata, I., Holmes, K., Batista, G., Krug, T., Monteiro, A., Powell, B., and Chadwick, O.: Large area mapping of land-cover change in Rondônia using multitemporal spectral mixture analysis and decision tree classifiers, J. Geophys. Res.-Atmos., 107, 8073, https://doi.org/10.1029/2001JD000374, 2002. a
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data assimilation system, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a, b
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. a
Sakschewski, B., Von Bloh, W., Boit, A., Poorter, L., Peña-Claros, M., Heinke, J., Joshi, J., and Thonicke, K.: Resilience of Amazon forests emerges from plant trait diversity, Nat. Clim. Change, 6, 1032–1036, https://doi.org/10.1038/nclimate3109, 2016. a
Saleska, S. R., Didan, K., Huete, A. R., and Rocha, H. R. D.: Amazon forests green-up during 2005 drought, Science, 318, 612, https://doi.org/10.1126/science.1146663, 2007. a
Satoh, M., Stevens, B., Judt, F., Khairoutdinov, M., Lin, S.-J., Putman, W. M., and Düben, P.: Global cloud-resolving models, Curr. Clim. Change Rep., 5, 172–184, https://doi.org/10.1007/s40641-019-00131-0, 2019. a
Schneck, R., Gayler, V., Nabel, J. E. M. S., Raddatz, T., Reick, C. H., and Schnur, R.: Assessment of JSBACHv4.30 as a land component of ICON-ESM-V1 in comparison to its predecessor JSBACHv3.2 of MPI-ESM1.2, Geosci. Model Dev., 15, 8581–8611, https://doi.org/10.5194/gmd-15-8581-2022, 2022. a
Schwitalla, T., Warrach-Sagi, K., Wulfmeyer, V., and Resch, M.: Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1, Geosci. Model Dev., 13, 1959–1974, https://doi.org/10.5194/gmd-13-1959-2020, 2020. a
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau, J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968. a, b
Shahi, N. K., Polcher, J., Bastin, S., Pennel, R., and Fita, L.: Assessment of the spatio-temporal variability of the added value on precipitation of convection-permitting simulation over the Iberian Peninsula using the RegIPSL regional earth system model, Clim. Dynam., 59, 471–498, https://doi.org/10.1007/s00382-022-06138-y, 2022. a
Shevliakova, E., Malyshev, S., Martinez-Cano, I., Milly, P. C. D., Pacala, S. W., Ginoux, P., Dunne, K. A., Dunne, J. P., Dupuis, C., Findell, K. L., Ghannam, K., Horowitz, L. W., Knutson, T. R., Krasting, J. P., Naik, V., Phillipps, P., Zadeh, N., Yu, Y., Zeng, F., and Zeng, Y.: The land component LM4.1 of the GFDL Earth System Model ESM4.1: Model description and characteristics of land surface climate and carbon cycling in the historical simulation, J. Adv. Model. Earth Syst., 16, e2023MS003922, https://doi.org/10.1029/2023MS003922, 2024. a, b
Singh, C., Wang-Erlandsson, L., Fetzer, I., Rockström, J., and Van Der Ent, R.: Rootzone storage capacity reveals drought coping strategies along rainforest-savanna transitions, Environ. Res. Lett., 15, 124021, https://doi.org/10.1088/1748-9326/abc377, 2020. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Version 4, NCAR Tech. Note NCAR/TN-556+STR, NCAR [data set], https://doi.org/10.5065/1dfh-6p97, 2019. a, b, c, d
Smith, T. and Boers, N.: Global vegetation resilience linked to water availability and variability, Nat. Commun., 14, 498, https://doi.org/10.1038/s41467-023-36207-7, 2023. a, b
Song, Y., Jain, A. K., and McIsaac, G. F.: Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation, Biogeosciences, 10, 8039–8066, https://doi.org/10.5194/bg-10-8039-2013, 2013. a
Song, Y., Cervarich, M., Jain, A. K., Kheshgi, H. S., Landuyt, W., and Cai, X.: The interplay between bioenergy grass production and water resources in the United States of America, Environ. Sci. Technol., 50, 3010–3019, https://doi.org/10.1021/acs.est.5b05239, 2016. a
Spera, S. A., Winter, J. M., and Chipman, J. W.: Evaluation of agricultural land cover representations on regional climate model simulations in the Brazilian Cerrado, J. Geophys. Res.-Atmos., 123, 5163–5176, https://doi.org/10.1029/2017JD027989, 2018. a
Theil, H.: A Rank-Invariant Method of Linear and Polynomial Regression Analysis, in: Henri Theil's Contributions to Economics and Econometrics, Advanced Studies in Theoretical and Applied Econometrics, vol. 23, edited by: Raj, B. and Koerts, J., Springer, 345–381, https://doi.org/10.1007/978-94-011-2546-8_20, 1992. a, b
van Oorschot, F., van der Ent, R. J., Hrachowitz, M., and Alessandri, A.: Climate-controlled root zone parameters show potential to improve water flux simulations by land surface models, Earth Syst. Dynam., 12, 725–743, https://doi.org/10.5194/esd-12-725-2021, 2021. a
Verbeeck, H., Peylin, P., Bacour, C., Bonal, D., Steppe, K., and Ciais, P.: Seasonal patterns of CO2 fluxes in Amazon forests: Fusion of eddy covariance data and the ORCHIDEE model, J. Geophys. Res.-Biogeo., 116, G02018, https://doi.org/10.1029/2010JG001544, 2011. a
von Randow, C., Manzi, A. O., Kruijt, B., de Oliveira, P. J., Zanchi, F. B., Silva, R. L., Hodnett, M. G., Gash, J. H., Elbers, J. A., Waterloo, M. J., Cardoso, F. L., and Kabat, P.: Comparative measurements and seasonal variations in energy and carbon exchange over forest and pasture in South West Amazonia, Theor. Appl. Climatol., 78, 5–26, https://doi.org/10.1007/s00704-004-0041-z, 2004. a, b
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. a, b
Wei, Z., Yoshimura, K., Wang, L., Miralles, D. G., Jasechko, S., and Lee, X.: Revisiting the contribution of transpiration to global terrestrial evapotranspiration, Geophys. Res. Lett., 44, 2792–2801, https://doi.org/10.1002/2016GL072235, 2017. a
Werth, D. and Avissar, R.: The local and global effects of Amazon deforestation, J. Geophys Res.-Atmos., 107, 8087, https://doi.org/10.1029/2001JD000717, 2002. a
Wiltshire, A. J., Duran Rojas, M. C., Edwards, J. M., Gedney, N., Harper, A. B., Hartley, A. J., Hendry, M. A., Robertson, E., and Smout-Day, K.: JULES-GL7: the Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2, Geosci. Model Dev., 13, 483–505, https://doi.org/10.5194/gmd-13-483-2020, 2020. a
Yao, Y., Joetzjer, E., Ciais, P., Viovy, N., Cresto Aleina, F., Chave, J., Sack, L., Bartlett, M., Meir, P., Fisher, R., and Luyssaert, S.: Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment, Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, 2022. a
Zanin, P. R.: Soil Water Uptake by Amazonian Trees and Simulation of Impacts on Energy Fluxes and Soil Moisture Dynamics at the LBA Flux Towers, Rev. Bras. Meteorol., 36, 441–454, https://doi.org/10.1590/0102-77863630029, 2021. a, b, c, d
Zemp, D. C., Schleussner, C.-F., Barbosa, H. M. J., van der Ent, R. J., Donges, J. F., Heinke, J., Sampaio, G., and Rammig, A.: On the importance of cascading moisture recycling in South America, Atmos. Chem. Phys., 14, 13337–13359, https://doi.org/10.5194/acp-14-13337-2014, 2014. a
Zilli, M. T., Lemes, M. R., Hart, N. C. G., Halladay, K., Kahana, R., Fisch, G., Prein, A., Ikeda, K., and Liu, C.: The added value of using convective-permitting regional climate model simulations to represent cloud band events over South America, Clim. Dynam., 62, 10543–10564, https://doi.org/10.1007/s00382-024-07460-3, 2024. a
Short summary
Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Access to deep moisture below the Earth's surface is important for vegetation in areas of the...
